var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"jsonp\":\"1\",\"host\":\"bibbase.org\"},\n      data: [{\"title\":\"Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment.\",\"type\":\"article\",\"year\":\"2010\",\"keywords\":\"Bayes Theorem,Consumer Product Safety,Food Microbiology,Models, Theoretical,Monte Carlo Method,Risk Assessment\",\"pages\":\"S57-63\",\"volume\":\"139 Suppl \",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0168160509006680\",\"month\":\"5\",\"day\":\"30\",\"id\":\"45a289ef-c69e-3346-9260-b60cad2cd92d\",\"created\":\"2015-04-12T20:17:33.000Z\",\"accessed\":\"2015-04-01\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2017-03-14T14:39:57.651Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"We discuss different aspects of farm-to-fork risk assessment from a modelling perspective. Stochastic simulation models as they are presented today represent a mathematical representation of nature. In food safety risk assessment, a common modelling approach consists of a logic chain beginning at the source of the hazard and ending with the unwanted consequences of interest. This 'farm-to-fork' approach usually begins with the hazard on the farm, sometimes with different compartments presenting different parts of the production chain, and ends with the 'dose' received by the consumer or in case a dose response model is available the number of cases of illness. These models are typically implemented as Monte Carlo simulations, which are unidirectional in nature, and the link between statistics and simulation model is not interactive. A possible solution could be the use of Bayesian belief networks (BBNs) and this paper tries to discuss in an intuitive way the possibilities of using BBNs as an alternative for Monte Carlo modelling. An inventory is made of the strengths and weaknesses of both approaches, and an example is given showing an additional use of BBNs in biotracing problems.\",\"bibtype\":\"article\",\"author\":\"Smid, J H and Verloo, D and Barker, G C and Havelaar, A H\",\"doi\":\"10.1016/j.ijfoodmicro.2009.12.015\",\"journal\":\"International journal of food microbiology\",\"bibtex\":\"@article{\\n title = {Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment.},\\n type = {article},\\n year = {2010},\\n keywords = {Bayes Theorem,Consumer Product Safety,Food Microbiology,Models, Theoretical,Monte Carlo Method,Risk Assessment},\\n pages = {S57-63},\\n volume = {139 Suppl },\\n websites = {http://www.sciencedirect.com/science/article/pii/S0168160509006680},\\n month = {5},\\n day = {30},\\n id = {45a289ef-c69e-3346-9260-b60cad2cd92d},\\n created = {2015-04-12T20:17:33.000Z},\\n accessed = {2015-04-01},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2017-03-14T14:39:57.651Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {We discuss different aspects of farm-to-fork risk assessment from a modelling perspective. Stochastic simulation models as they are presented today represent a mathematical representation of nature. In food safety risk assessment, a common modelling approach consists of a logic chain beginning at the source of the hazard and ending with the unwanted consequences of interest. This 'farm-to-fork' approach usually begins with the hazard on the farm, sometimes with different compartments presenting different parts of the production chain, and ends with the 'dose' received by the consumer or in case a dose response model is available the number of cases of illness. These models are typically implemented as Monte Carlo simulations, which are unidirectional in nature, and the link between statistics and simulation model is not interactive. A possible solution could be the use of Bayesian belief networks (BBNs) and this paper tries to discuss in an intuitive way the possibilities of using BBNs as an alternative for Monte Carlo modelling. An inventory is made of the strengths and weaknesses of both approaches, and an example is given showing an additional use of BBNs in biotracing problems.},\\n bibtype = {article},\\n author = {Smid, J H and Verloo, D and Barker, G C and Havelaar, A H},\\n doi = {10.1016/j.ijfoodmicro.2009.12.015},\\n journal = {International journal of food microbiology}\\n}\",\"author_short\":[\"Smid,  J., H.\",\"Verloo,  D.\",\"Barker,  G., C.\",\"Havelaar,  A., H.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0168160509006680\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"smid-verloo-barker-havelaar-strengthsandweaknessesofmontecarlosimulationmodelsandbayesianbeliefnetworksinmicrobialriskassessment-2010\",\"role\":\"author\",\"keyword\":[\"Bayes Theorem\",\"Consumer Product Safety\",\"Food Microbiology\",\"Models\",\"Theoretical\",\"Monte Carlo Method\",\"Risk Assessment\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Bayesian Networks for enterprise risk assessment\",\"type\":\"article\",\"year\":\"2007\",\"keywords\":\"Bayesian Networks,Enterprise risk assessment,Mutual information\",\"pages\":\"22-28\",\"volume\":\"382\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S037843710700132X\",\"month\":\"8\",\"id\":\"4a3b8b75-5751-3913-8fae-6e8c96544164\",\"created\":\"2015-04-16T00:39:20.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2017-03-14T14:39:57.651Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. Risk, in general, is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover, qualitative data must be converted in numerical values or bounds to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Networks (BNs) are a useful tool to integrate different information and in particular to study the risk's joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a BN in the particular case in which only prior probabilities of node states and marginal correlations between nodes are available, and when the variables have only two states.\",\"bibtype\":\"article\",\"author\":\"Bonafede, C.E. and Giudici, P.\",\"doi\":\"10.1016/j.physa.2007.02.065\",\"journal\":\"Physica A: Statistical Mechanics and its Applications\",\"number\":\"1\",\"bibtex\":\"@article{\\n title = {Bayesian Networks for enterprise risk assessment},\\n type = {article},\\n year = {2007},\\n keywords = {Bayesian Networks,Enterprise risk assessment,Mutual information},\\n pages = {22-28},\\n volume = {382},\\n websites = {http://www.sciencedirect.com/science/article/pii/S037843710700132X},\\n month = {8},\\n id = {4a3b8b75-5751-3913-8fae-6e8c96544164},\\n created = {2015-04-16T00:39:20.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2017-03-14T14:39:57.651Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. Risk, in general, is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover, qualitative data must be converted in numerical values or bounds to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Networks (BNs) are a useful tool to integrate different information and in particular to study the risk's joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a BN in the particular case in which only prior probabilities of node states and marginal correlations between nodes are available, and when the variables have only two states.},\\n bibtype = {article},\\n author = {Bonafede, C.E. and Giudici, P.},\\n doi = {10.1016/j.physa.2007.02.065},\\n journal = {Physica A: Statistical Mechanics and its Applications},\\n number = {1}\\n}\",\"author_short\":[\"Bonafede,  C.\",\"Giudici,  P.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S037843710700132X\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"bonafede-giudici-bayesiannetworksforenterpriseriskassessment-2007\",\"role\":\"author\",\"keyword\":[\"Bayesian Networks\",\"Enterprise risk assessment\",\"Mutual information\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A Bayesian network model to assess the public health risk associated with wet weather sewer overflows discharging into waterways\",\"type\":\"article\",\"year\":\"2012\",\"keywords\":\"Bayesian networks,Risk assessment,Sanitary sewer overflows,Water pollution\",\"pages\":\"4933-4940\",\"volume\":\"46\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0043135412002187\",\"month\":\"10\",\"id\":\"b4ee6e90-27d4-3d22-8405-a99c04f106a3\",\"created\":\"2015-04-16T00:39:20.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2017-03-14T14:39:57.651Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Overflows from sanitary sewers during wet weather, which occur when the hydraulic capacity of the sewer system is exceeded, are considered a potential threat to the ecological and public health of the waterways which receive these overflows. As a result, water retailers in Australia and internationally commit significant resources to manage and abate sewer overflows. However, whilst some studies have contributed to an increased understanding of the impacts and risks associated with these events, they are relatively few in number and there still is a general lack of knowledge in this area. A Bayesian network model to assess the public health risk associated with wet weather sewer overflows is presented in this paper. The Bayesian network approach is shown to provide significant benefits in the assessment of public health risks associated with wet weather sewer overflows. In particular, the ability for the model to account for the uncertainty inherent in sewer overflow events and subsequent impacts through the use of probabilities is a valuable function. In addition, the paper highlights the benefits of the probabilistic inference function of the Bayesian network in prioritising management options to minimise public health risks associated with sewer overflows.\",\"bibtype\":\"article\",\"author\":\"Goulding, R. and Jayasuriya, N. and Horan, E.\",\"doi\":\"10.1016/j.watres.2012.03.044\",\"journal\":\"Water Research\",\"number\":\"16\",\"bibtex\":\"@article{\\n title = {A Bayesian network model to assess the public health risk associated with wet weather sewer overflows discharging into waterways},\\n type = {article},\\n year = {2012},\\n keywords = {Bayesian networks,Risk assessment,Sanitary sewer overflows,Water pollution},\\n pages = {4933-4940},\\n volume = {46},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0043135412002187},\\n month = {10},\\n id = {b4ee6e90-27d4-3d22-8405-a99c04f106a3},\\n created = {2015-04-16T00:39:20.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2017-03-14T14:39:57.651Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Overflows from sanitary sewers during wet weather, which occur when the hydraulic capacity of the sewer system is exceeded, are considered a potential threat to the ecological and public health of the waterways which receive these overflows. As a result, water retailers in Australia and internationally commit significant resources to manage and abate sewer overflows. However, whilst some studies have contributed to an increased understanding of the impacts and risks associated with these events, they are relatively few in number and there still is a general lack of knowledge in this area. A Bayesian network model to assess the public health risk associated with wet weather sewer overflows is presented in this paper. The Bayesian network approach is shown to provide significant benefits in the assessment of public health risks associated with wet weather sewer overflows. In particular, the ability for the model to account for the uncertainty inherent in sewer overflow events and subsequent impacts through the use of probabilities is a valuable function. In addition, the paper highlights the benefits of the probabilistic inference function of the Bayesian network in prioritising management options to minimise public health risks associated with sewer overflows.},\\n bibtype = {article},\\n author = {Goulding, R. and Jayasuriya, N. and Horan, E.},\\n doi = {10.1016/j.watres.2012.03.044},\\n journal = {Water Research},\\n number = {16}\\n}\",\"author_short\":[\"Goulding,  R.\",\"Jayasuriya,  N.\",\"Horan,  E.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0043135412002187\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"goulding-jayasuriya-horan-abayesiannetworkmodeltoassessthepublichealthriskassociatedwithwetweatherseweroverflowsdischargingintowaterways-2012\",\"role\":\"author\",\"keyword\":[\"Bayesian networks\",\"Risk assessment\",\"Sanitary sewer overflows\",\"Water pollution\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Statistical models for operational risk management\",\"type\":\"article\",\"year\":\"2004\",\"keywords\":\"02.50.−r,89.65.Gh,Bayesian networks,Operational risk management,Predictive models,Value at risk\",\"pages\":\"166-172\",\"volume\":\"338\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0378437104002341\",\"month\":\"7\",\"id\":\"665e0f15-0663-37b7-8791-5303b0fdd603\",\"created\":\"2015-04-16T00:39:20.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2017-03-14T14:39:57.651Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"The Basel Committee on Banking Supervision has released, in the last few years, recommendations for the correct determination of the risks to which a banking organization is subject. This concerns, in particular, operational risks, which are all those management events that may determine unexpected losses. It is necessary to develop valid statistical models to measure and, consequently, predict, such operational risks. In the paper we present the possible approaches, including our own proposal, which is based on Bayesian networks.\",\"bibtype\":\"article\",\"author\":\"Cornalba, Chiara and Giudici, Paolo\",\"doi\":\"10.1016/j.physa.2004.02.039\",\"journal\":\"Physica A: Statistical Mechanics and its Applications\",\"number\":\"1-2\",\"bibtex\":\"@article{\\n title = {Statistical models for operational risk management},\\n type = {article},\\n year = {2004},\\n keywords = {02.50.−r,89.65.Gh,Bayesian networks,Operational risk management,Predictive models,Value at risk},\\n pages = {166-172},\\n volume = {338},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0378437104002341},\\n month = {7},\\n id = {665e0f15-0663-37b7-8791-5303b0fdd603},\\n created = {2015-04-16T00:39:20.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2017-03-14T14:39:57.651Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The Basel Committee on Banking Supervision has released, in the last few years, recommendations for the correct determination of the risks to which a banking organization is subject. This concerns, in particular, operational risks, which are all those management events that may determine unexpected losses. It is necessary to develop valid statistical models to measure and, consequently, predict, such operational risks. In the paper we present the possible approaches, including our own proposal, which is based on Bayesian networks.},\\n bibtype = {article},\\n author = {Cornalba, Chiara and Giudici, Paolo},\\n doi = {10.1016/j.physa.2004.02.039},\\n journal = {Physica A: Statistical Mechanics and its Applications},\\n number = {1-2}\\n}\",\"author_short\":[\"Cornalba,  C.\",\"Giudici,  P.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0378437104002341\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"cornalba-giudici-statisticalmodelsforoperationalriskmanagement-2004\",\"role\":\"author\",\"keyword\":[\"02.50.−r\",\"89.65.Gh\",\"Bayesian networks\",\"Operational risk management\",\"Predictive models\",\"Value at risk\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Safety and risk analysis of managed pressure drilling operation using Bayesian network\",\"type\":\"article\",\"year\":\"2015\",\"keywords\":\"BHP,BN,BT,Bayesian Network,Bayesian network analysis,Blowout prevention,Bottom Hole Pressure,Bow-tie approach,CBHP,CCS,COBD,CPT,Conditional Probability Table,Constant Bottom Hole Pressure,Continuous Circulation System,Conventional Over-balanced Drilling,DAG,DAPC,DGD,Directed Acyclic Graph,Dual Gradient Drilling,Dynamic Annular Pressure Control,ECD,ET,Equivalent Circulating Density,FG,FIT,FT,Formation Integrity Test,Fracture Gradient,IADC,ICU,Intelligent Control Unit,International Association of Drilling Contractors,LHS,LOT,Leak off Test,Left Hand Side,MODU,MPD,Managed Pressure Drilling,Managed pressure drilling,Mobile Offshore Drilling Unit,NPT,PMCD,PP,PWD,Pore Pressure,Pressure measurement While Drilling,Pressurized Mud Cap Drilling,QRA,Quantitative Risk Analysis,RCD,RHS,Right Hand Side,Rotating control device,bow-tie,event tree,fault tree,non productive time,rotating control device\",\"pages\":\"133-144\",\"volume\":\"76\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0925753515000119\",\"month\":\"7\",\"id\":\"7370c67c-7dea-38d3-8b76-5fe12c2f5f70\",\"created\":\"2015-04-16T00:39:20.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2017-03-14T14:39:57.651Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"The exploration and development of oil and gas resources located in extreme and harsh offshore environments are characterized with high safety risk and drilling cost. Some of these resources would be either uneconomical if extracted using conventional overbalanced drilling due to increased drilling problems and prolonged non-productive time, or too risky to adopt underbalanced drilling technique. Seeking new ways to reduce drilling cost and minimize risks has led to the development of managed pressure drilling techniques. Managed pressure drilling methods address the drawbacks of conventional overbalanced and underbalanced drilling techniques. As managed pressure drilling techniques are evolving, there are many unanswered questions related to safety and operating pressure regime. This study investigates the safety and operational issues of constant bottom-hole pressure drilling technique which is used in managed pressure drilling compared to conventional overbalanced drilling. The study first uses bow-tie models to map safety challenges and operating pressure regimes in constant bottom-hole pressure drilling technique. Due to the difficulties in modeling dependencies and updating the belief on the operational data, the bow-ties are mapped into Bayesian networks. The Bayesian networks are thoroughly analyzed to assess the safety critical elements of constant bottom-hole pressure drilling techniques and their safe operating pressure regime.\",\"bibtype\":\"article\",\"author\":\"Abimbola, Majeed and Khan, Faisal and Khakzad, Nima and Butt, Stephen\",\"doi\":\"10.1016/j.ssci.2015.01.010\",\"journal\":\"Safety Science\",\"bibtex\":\"@article{\\n title = {Safety and risk analysis of managed pressure drilling operation using Bayesian network},\\n type = {article},\\n year = {2015},\\n keywords = {BHP,BN,BT,Bayesian Network,Bayesian network analysis,Blowout prevention,Bottom Hole Pressure,Bow-tie approach,CBHP,CCS,COBD,CPT,Conditional Probability Table,Constant Bottom Hole Pressure,Continuous Circulation System,Conventional Over-balanced Drilling,DAG,DAPC,DGD,Directed Acyclic Graph,Dual Gradient Drilling,Dynamic Annular Pressure Control,ECD,ET,Equivalent Circulating Density,FG,FIT,FT,Formation Integrity Test,Fracture Gradient,IADC,ICU,Intelligent Control Unit,International Association of Drilling Contractors,LHS,LOT,Leak off Test,Left Hand Side,MODU,MPD,Managed Pressure Drilling,Managed pressure drilling,Mobile Offshore Drilling Unit,NPT,PMCD,PP,PWD,Pore Pressure,Pressure measurement While Drilling,Pressurized Mud Cap Drilling,QRA,Quantitative Risk Analysis,RCD,RHS,Right Hand Side,Rotating control device,bow-tie,event tree,fault tree,non productive time,rotating control device},\\n pages = {133-144},\\n volume = {76},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0925753515000119},\\n month = {7},\\n id = {7370c67c-7dea-38d3-8b76-5fe12c2f5f70},\\n created = {2015-04-16T00:39:20.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2017-03-14T14:39:57.651Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The exploration and development of oil and gas resources located in extreme and harsh offshore environments are characterized with high safety risk and drilling cost. Some of these resources would be either uneconomical if extracted using conventional overbalanced drilling due to increased drilling problems and prolonged non-productive time, or too risky to adopt underbalanced drilling technique. Seeking new ways to reduce drilling cost and minimize risks has led to the development of managed pressure drilling techniques. Managed pressure drilling methods address the drawbacks of conventional overbalanced and underbalanced drilling techniques. As managed pressure drilling techniques are evolving, there are many unanswered questions related to safety and operating pressure regime. This study investigates the safety and operational issues of constant bottom-hole pressure drilling technique which is used in managed pressure drilling compared to conventional overbalanced drilling. The study first uses bow-tie models to map safety challenges and operating pressure regimes in constant bottom-hole pressure drilling technique. Due to the difficulties in modeling dependencies and updating the belief on the operational data, the bow-ties are mapped into Bayesian networks. The Bayesian networks are thoroughly analyzed to assess the safety critical elements of constant bottom-hole pressure drilling techniques and their safe operating pressure regime.},\\n bibtype = {article},\\n author = {Abimbola, Majeed and Khan, Faisal and Khakzad, Nima and Butt, Stephen},\\n doi = {10.1016/j.ssci.2015.01.010},\\n journal = {Safety Science}\\n}\",\"author_short\":[\"Abimbola,  M.\",\"Khan,  F.\",\"Khakzad,  N.\",\"Butt,  S.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0925753515000119\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"abimbola-khan-khakzad-butt-safetyandriskanalysisofmanagedpressuredrillingoperationusingbayesiannetwork-2015\",\"role\":\"author\",\"keyword\":[\"BHP\",\"BN\",\"BT\",\"Bayesian Network\",\"Bayesian network analysis\",\"Blowout prevention\",\"Bottom Hole Pressure\",\"Bow-tie approach\",\"CBHP\",\"CCS\",\"COBD\",\"CPT\",\"Conditional Probability Table\",\"Constant Bottom Hole Pressure\",\"Continuous Circulation System\",\"Conventional Over-balanced Drilling\",\"DAG\",\"DAPC\",\"DGD\",\"Directed Acyclic Graph\",\"Dual Gradient Drilling\",\"Dynamic Annular Pressure Control\",\"ECD\",\"ET\",\"Equivalent Circulating Density\",\"FG\",\"FIT\",\"FT\",\"Formation Integrity Test\",\"Fracture Gradient\",\"IADC\",\"ICU\",\"Intelligent Control Unit\",\"International Association of Drilling Contractors\",\"LHS\",\"LOT\",\"Leak off Test\",\"Left Hand Side\",\"MODU\",\"MPD\",\"Managed Pressure Drilling\",\"Managed pressure drilling\",\"Mobile Offshore Drilling Unit\",\"NPT\",\"PMCD\",\"PP\",\"PWD\",\"Pore Pressure\",\"Pressure measurement While Drilling\",\"Pressurized Mud Cap Drilling\",\"QRA\",\"Quantitative Risk Analysis\",\"RCD\",\"RHS\",\"Right Hand Side\",\"Rotating control device\",\"bow-tie\",\"event tree\",\"fault tree\",\"non productive time\",\"rotating control device\"],\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"title\":\"A Bayesian network to manage risks of maritime piracy against offshore oil fields\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"Bayesian networks,Offshore oil fields,Oil platforms,Pirate attacks,Quantitative and qualitative knowledge\",\"pages\":\"222-230\",\"volume\":\"68\",\"id\":\"bd48a973-ffd3-36a1-b61e-c3c4e76dcd52\",\"created\":\"2015-04-22T21:19:06.000Z\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2017-03-14T14:39:57.651Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"In recent years, pirate attacks against shipping and oil field installations have become more frequent and more serious. This article proposes an innovative solution to the problem of offshore piracy from the perspective of the entire processing chain: from the detection of a potential threat to the implementation of a response. The response to an attack must take into account multiple variables: the characteristics of the threat and the potential target, existing protection tools, environmental constraints, etc. The potential of Bayesian networks is used to manage this large number of parameters and identify appropriate counter-measures. ?? 2014 Elsevier Ltd.\",\"bibtype\":\"article\",\"author\":\"Bouejla, Amal and Chaze, Xavier and Guarnieri, Franck and Napoli, Aldo\",\"doi\":\"10.1016/j.ssci.2014.04.010\",\"journal\":\"Safety Science\",\"bibtex\":\"@article{\\n title = {A Bayesian network to manage risks of maritime piracy against offshore oil fields},\\n type = {article},\\n year = {2014},\\n keywords = {Bayesian networks,Offshore oil fields,Oil platforms,Pirate attacks,Quantitative and qualitative knowledge},\\n pages = {222-230},\\n volume = {68},\\n id = {bd48a973-ffd3-36a1-b61e-c3c4e76dcd52},\\n created = {2015-04-22T21:19:06.000Z},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2017-03-14T14:39:57.651Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {In recent years, pirate attacks against shipping and oil field installations have become more frequent and more serious. This article proposes an innovative solution to the problem of offshore piracy from the perspective of the entire processing chain: from the detection of a potential threat to the implementation of a response. The response to an attack must take into account multiple variables: the characteristics of the threat and the potential target, existing protection tools, environmental constraints, etc. The potential of Bayesian networks is used to manage this large number of parameters and identify appropriate counter-measures. ?? 2014 Elsevier Ltd.},\\n bibtype = {article},\\n author = {Bouejla, Amal and Chaze, Xavier and Guarnieri, Franck and Napoli, Aldo},\\n doi = {10.1016/j.ssci.2014.04.010},\\n journal = {Safety Science}\\n}\",\"author_short\":[\"Bouejla,  A.\",\"Chaze,  X.\",\"Guarnieri,  F.\",\"Napoli,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"bouejla-chaze-guarnieri-napoli-abayesiannetworktomanagerisksofmaritimepiracyagainstoffshoreoilfields-2014\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Bayesian networks\",\"Offshore oil fields\",\"Oil platforms\",\"Pirate attacks\",\"Quantitative and qualitative knowledge\"],\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"title\":\"Mathematical Decision Model for Reverse Supply Chains Inventory Literature review\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"bayesian networks,decision aid,inventory models,reverse supply chains\",\"pages\":\"686-693\",\"volume\":\"9\",\"id\":\"ef05a9de-88df-321b-930e-079ea100bc9e\",\"created\":\"2015-04-23T00:08:23.000Z\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2017-03-14T14:39:57.651Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Duta, L and Zamfirescu, C B and Filip, F G\",\"journal\":\"INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL\",\"number\":\"6\",\"bibtex\":\"@article{\\n title = {Mathematical Decision Model for Reverse Supply Chains Inventory Literature review},\\n type = {article},\\n year = {2014},\\n keywords = {bayesian networks,decision aid,inventory models,reverse supply chains},\\n pages = {686-693},\\n volume = {9},\\n id = {ef05a9de-88df-321b-930e-079ea100bc9e},\\n created = {2015-04-23T00:08:23.000Z},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2017-03-14T14:39:57.651Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Duta, L and Zamfirescu, C B and Filip, F G},\\n journal = {INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL},\\n number = {6}\\n}\",\"author_short\":[\"Duta,  L.\",\"Zamfirescu,  C., B.\",\"Filip,  F., G.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"duta-zamfirescu-filip-mathematicaldecisionmodelforreversesupplychainsinventoryliteraturereview-2014\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"bayesian networks\",\"decision aid\",\"inventory models\",\"reverse supply chains\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Risk analysis of deepwater drilling operations using Bayesian network\",\"type\":\"article\",\"year\":\"2015\",\"pages\":\"11-23\",\"volume\":\"38\",\"websites\":\"http://linkinghub.elsevier.com/retrieve/pii/S0950423015300188\",\"month\":\"11\",\"publisher\":\"Elsevier Ltd\",\"id\":\"24fe5c2c-2a98-36f8-afbc-5f12277dc006\",\"created\":\"2017-06-01T01:02:34.427Z\",\"accessed\":\"2017-05-31\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2017-06-01T01:02:34.427Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Bhandari, Jyoti and Abbassi, Rouzbeh and Garaniya, Vikram and Khan, Faisal\",\"doi\":\"10.1016/j.jlp.2015.08.004\",\"journal\":\"Journal of Loss Prevention in the Process Industries\",\"bibtex\":\"@article{\\n title = {Risk analysis of deepwater drilling operations using Bayesian network},\\n type = {article},\\n year = {2015},\\n pages = {11-23},\\n volume = {38},\\n websites = {http://linkinghub.elsevier.com/retrieve/pii/S0950423015300188},\\n month = {11},\\n publisher = {Elsevier Ltd},\\n id = {24fe5c2c-2a98-36f8-afbc-5f12277dc006},\\n created = {2017-06-01T01:02:34.427Z},\\n accessed = {2017-05-31},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2017-06-01T01:02:34.427Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Bhandari, Jyoti and Abbassi, Rouzbeh and Garaniya, Vikram and Khan, Faisal},\\n doi = {10.1016/j.jlp.2015.08.004},\\n journal = {Journal of Loss Prevention in the Process Industries}\\n}\",\"author_short\":[\"Bhandari,  J.\",\"Abbassi,  R.\",\"Garaniya,  V.\",\"Khan,  F.\"],\"urls\":{\"Website\":\"http://linkinghub.elsevier.com/retrieve/pii/S0950423015300188\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"bhandari-abbassi-garaniya-khan-riskanalysisofdeepwaterdrillingoperationsusingbayesiannetwork-2015\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Probabilistic risk assessment of major accidents: application to offshore blowouts in the Gulf of Mexico\",\"type\":\"article\",\"year\":\"2014\",\"volume\":\"74\",\"id\":\"50bddf4a-e099-3353-9f63-0da05bd9a677\",\"created\":\"2017-08-23T21:32:33.818Z\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2017-08-23T21:34:10.828Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Major accidents are low-frequency, high-consequence accidents which are not well supported by conventional statistical methods due to the scarcity of directly relevant data. Modeling and decomposition techniques such as event tree have been proved as robust alternatives as they facilitate incorporation of partially relevant near accident data-accident precursor data-in probability estimation and risk analysis of major accidents. In this study, we developed a methodology based on event tree and hierarchical Bayesian analysis to establish informative distributions for offshore blowouts using data of near accidents, such as kicks, leaks, and failure of blowout preventers collected from a variety of offshore drilling rigs. These informative distributions can be used as predictive tools to estimate relevant failure probabilities in the future. Further, having a set of near accident data of a drilling rig of interest, the informative distributions can be updated to render case-specific posterior distributions which are of great importance in quantitative risk analysis. To cope with uncertainties, we implemented the methodology in a Markov Chain Monte Carlo framework and applied it to risk assessment of offshore blowouts in the Gulf of Mexico.\",\"bibtype\":\"article\",\"author\":\"Khakzad, Nima and Khakzad, Sina and Khan, Faisal\",\"doi\":\"10.1007/s11069-014-1271-8\",\"journal\":\"Natural Hazards\",\"number\":\"3\",\"bibtex\":\"@article{\\n title = {Probabilistic risk assessment of major accidents: application to offshore blowouts in the Gulf of Mexico},\\n type = {article},\\n year = {2014},\\n volume = {74},\\n id = {50bddf4a-e099-3353-9f63-0da05bd9a677},\\n created = {2017-08-23T21:32:33.818Z},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2017-08-23T21:34:10.828Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Major accidents are low-frequency, high-consequence accidents which are not well supported by conventional statistical methods due to the scarcity of directly relevant data. Modeling and decomposition techniques such as event tree have been proved as robust alternatives as they facilitate incorporation of partially relevant near accident data-accident precursor data-in probability estimation and risk analysis of major accidents. In this study, we developed a methodology based on event tree and hierarchical Bayesian analysis to establish informative distributions for offshore blowouts using data of near accidents, such as kicks, leaks, and failure of blowout preventers collected from a variety of offshore drilling rigs. These informative distributions can be used as predictive tools to estimate relevant failure probabilities in the future. Further, having a set of near accident data of a drilling rig of interest, the informative distributions can be updated to render case-specific posterior distributions which are of great importance in quantitative risk analysis. To cope with uncertainties, we implemented the methodology in a Markov Chain Monte Carlo framework and applied it to risk assessment of offshore blowouts in the Gulf of Mexico.},\\n bibtype = {article},\\n author = {Khakzad, Nima and Khakzad, Sina and Khan, Faisal},\\n doi = {10.1007/s11069-014-1271-8},\\n journal = {Natural Hazards},\\n number = {3}\\n}\",\"author_short\":[\"Khakzad,  N.\",\"Khakzad,  S.\",\"Khan,  F.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"khakzad-khakzad-khan-probabilisticriskassessmentofmajoraccidentsapplicationtooffshoreblowoutsinthegulfofmexico-2014\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Network based approach for predictive accident modelling\",\"type\":\"article\",\"year\":\"2015\",\"volume\":\"80\",\"id\":\"431a0392-b88e-356a-8bd0-ca485c28fe29\",\"created\":\"2017-08-23T21:37:06.090Z\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2017-08-23T21:37:06.090Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Accident modeling methodologies in literature such as the System Hazard Identification, Prediction and Prevention (SHIPP) consider accident precursors and the associated five engineering safety barriers to assess the likelihood of accident occurrence with the help of fault and event trees to model the cause-consequence relationship between the failure of safety barriers and potential adverse events and design preventive, controlling and mitigating measures for improving the industrial process safety. In the SHIPP method, a restrictive assumption is used that the severity of the adverse events progresses only through sequential failures of the five safety barriers considered.In the proposed methodology, shortcomings of the existing accident model are improved in the following ways. Firstly, the above mentioned restrictive sequential progression assumption is mitigated in the SHIPP methodology by allowing non-sequential failure of safety barriers to cause adverse events in any order. Secondly, in the prediction of posterior probabilities of adverse events for real time industrial data, an important mechanical safety barrier 'Damage Control Emergency Management Barrier' has been included. Further, posterior probabilities of the occurrence of the adverse events are calculated using a Bayesian network (BN) approach. The utility of this approach is tested and demonstrated with the data from a liquefied natural gas (LNG) process facility. The method allows for continual updating of occurrence probabilities for adverse events and failure probabilities of safety barriers for successive real time data from industry.\",\"bibtype\":\"article\",\"author\":\"Baksh, Al Amin and Khan, Faisal and Gadag, Veeresh and Ferdous, Refaul\",\"doi\":\"10.1016/j.ssci.2015.08.003\",\"journal\":\"Safety Science\",\"bibtex\":\"@article{\\n title = {Network based approach for predictive accident modelling},\\n type = {article},\\n year = {2015},\\n volume = {80},\\n id = {431a0392-b88e-356a-8bd0-ca485c28fe29},\\n created = {2017-08-23T21:37:06.090Z},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2017-08-23T21:37:06.090Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Accident modeling methodologies in literature such as the System Hazard Identification, Prediction and Prevention (SHIPP) consider accident precursors and the associated five engineering safety barriers to assess the likelihood of accident occurrence with the help of fault and event trees to model the cause-consequence relationship between the failure of safety barriers and potential adverse events and design preventive, controlling and mitigating measures for improving the industrial process safety. In the SHIPP method, a restrictive assumption is used that the severity of the adverse events progresses only through sequential failures of the five safety barriers considered.In the proposed methodology, shortcomings of the existing accident model are improved in the following ways. Firstly, the above mentioned restrictive sequential progression assumption is mitigated in the SHIPP methodology by allowing non-sequential failure of safety barriers to cause adverse events in any order. Secondly, in the prediction of posterior probabilities of adverse events for real time industrial data, an important mechanical safety barrier 'Damage Control Emergency Management Barrier' has been included. Further, posterior probabilities of the occurrence of the adverse events are calculated using a Bayesian network (BN) approach. The utility of this approach is tested and demonstrated with the data from a liquefied natural gas (LNG) process facility. The method allows for continual updating of occurrence probabilities for adverse events and failure probabilities of safety barriers for successive real time data from industry.},\\n bibtype = {article},\\n author = {Baksh, Al Amin and Khan, Faisal and Gadag, Veeresh and Ferdous, Refaul},\\n doi = {10.1016/j.ssci.2015.08.003},\\n journal = {Safety Science}\\n}\",\"author_short\":[\"Baksh,  A., A.\",\"Khan,  F.\",\"Gadag,  V.\",\"Ferdous,  R.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"baksh-khan-gadag-ferdous-networkbasedapproachforpredictiveaccidentmodelling-2015\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Dynamic Bayesian network modeling of reliability of subsea blowout preventer stack in presence of common cause failures\",\"type\":\"article\",\"year\":\"2015\",\"volume\":\"38\",\"id\":\"38063a61-ecb5-369c-a24b-d6016f0a17a3\",\"created\":\"2017-08-23T21:39:19.143Z\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2017-08-23T21:39:19.143Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"A subsea blowout preventer (BOP) stack is used to seal, control and monitor oil and gas wells. It can be regarded as a series-parallel system consisting of several subsystems. This paper develops the dynamic Bayesian network (DBN) of a parallel system with n components, taking account of common cause failures and imperfect coverage. Multiple error shock model is used to model common cause failures. Based on the proposed generic model, DBNs of the two commonly used stack types, namely the conventional BOP and modern BOP are developed. In order to evaluate the effects of the failure rates and coverage factor on the reliability and availability of the stacks, sensitivity analysis is performed.\",\"bibtype\":\"article\",\"author\":\"Liu, Zengkai and Liu, Yonghong and Cai, Baoping and Zhang, Dawei and Zheng, Chao\",\"doi\":\"10.1016/j.jlp.2015.09.001\",\"journal\":\"Journal of Loss Prevention in the Process Industries\",\"bibtex\":\"@article{\\n title = {Dynamic Bayesian network modeling of reliability of subsea blowout preventer stack in presence of common cause failures},\\n type = {article},\\n year = {2015},\\n volume = {38},\\n id = {38063a61-ecb5-369c-a24b-d6016f0a17a3},\\n created = {2017-08-23T21:39:19.143Z},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2017-08-23T21:39:19.143Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {A subsea blowout preventer (BOP) stack is used to seal, control and monitor oil and gas wells. It can be regarded as a series-parallel system consisting of several subsystems. This paper develops the dynamic Bayesian network (DBN) of a parallel system with n components, taking account of common cause failures and imperfect coverage. Multiple error shock model is used to model common cause failures. Based on the proposed generic model, DBNs of the two commonly used stack types, namely the conventional BOP and modern BOP are developed. In order to evaluate the effects of the failure rates and coverage factor on the reliability and availability of the stacks, sensitivity analysis is performed.},\\n bibtype = {article},\\n author = {Liu, Zengkai and Liu, Yonghong and Cai, Baoping and Zhang, Dawei and Zheng, Chao},\\n doi = {10.1016/j.jlp.2015.09.001},\\n journal = {Journal of Loss Prevention in the Process Industries}\\n}\",\"author_short\":[\"Liu,  Z.\",\"Liu,  Y.\",\"Cai,  B.\",\"Zhang,  D.\",\"Zheng,  C.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"liu-liu-cai-zhang-zheng-dynamicbayesiannetworkmodelingofreliabilityofsubseablowoutpreventerstackinpresenceofcommoncausefailures-2015\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study\",\"type\":\"article\",\"year\":\"2016\",\"volume\":\"60\",\"id\":\"3d0130c7-327c-38ae-8805-c5b66b3d8e29\",\"created\":\"2017-08-23T21:40:59.017Z\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2017-08-23T21:40:59.017Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project.\",\"bibtype\":\"article\",\"author\":\"Yet, Barbaros and Constantinou, Anthony and Fenton, Norman and Neil, Martin and Luedeling, Eike and Shepherd, Keith\",\"doi\":\"10.1016/j.eswa.2016.05.005\",\"journal\":\"Expert Systems with Applications\",\"bibtex\":\"@article{\\n title = {A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study},\\n type = {article},\\n year = {2016},\\n volume = {60},\\n id = {3d0130c7-327c-38ae-8805-c5b66b3d8e29},\\n created = {2017-08-23T21:40:59.017Z},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2017-08-23T21:40:59.017Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project.},\\n bibtype = {article},\\n author = {Yet, Barbaros and Constantinou, Anthony and Fenton, Norman and Neil, Martin and Luedeling, Eike and Shepherd, Keith},\\n doi = {10.1016/j.eswa.2016.05.005},\\n journal = {Expert Systems with Applications}\\n}\",\"author_short\":[\"Yet,  B.\",\"Constantinou,  A.\",\"Fenton,  N.\",\"Neil,  M.\",\"Luedeling,  E.\",\"Shepherd,  K.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"yet-constantinou-fenton-neil-luedeling-shepherd-abayesiannetworkframeworkforprojectcostbenefitandriskanalysiswithanagriculturaldevelopmentcasestudy-2016\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches\",\"type\":\"article\",\"year\":\"2017\",\"volume\":\"45\",\"id\":\"703fab28-be7f-3340-b2ca-f27e3b100b6c\",\"created\":\"2018-03-31T13:52:13.308Z\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2018-03-31T13:52:13.308Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Industrial accidents are a major concern for companies and families alike. It is a high priority to all stakeholders that steps be taken to prevent accidents from occurring. In this paper, three approaches to safety are examined: fault trees (FT), Bayesian networks (BN), and the Functional Resonance Analysis Method (FRAM). A case study of a propane feed control system is used to apply these methods. In order to make safety improvements to industrial workplaces high understanding of the systems is required. It is shown that consideration of the chance of failure of the system components, as in the FT and BN approaches, may not provide enough understanding to fully inform safety assessments. The FT and BN methods are top-down approaches that are formed from the perspective of management in workplaces. The FRAM methodology uses a bottom-up approach from the operational perspective to improve the understanding of the industrial workplace. The FRAM approach can provide added insight to the human factor and context and increase the rate at which we learn by considering successes as well as failures. FRAM can be a valuable tool for industrial safety assessment and to consider industrial safety holistically, by providing a framework to examine the operations in detail. However, operations should be considered using both top-down and bottom-up perspectives and all operational experience to make the most informed safety decisions.\",\"bibtype\":\"article\",\"author\":\"Smith, Doug and Veitch, Brian and Khan, Faisal and Taylor, Rocky\",\"doi\":\"10.1016/j.jlp.2016.11.016\",\"journal\":\"Journal of Loss Prevention in the Process Industries\",\"bibtex\":\"@article{\\n title = {Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches},\\n type = {article},\\n year = {2017},\\n volume = {45},\\n id = {703fab28-be7f-3340-b2ca-f27e3b100b6c},\\n created = {2018-03-31T13:52:13.308Z},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2018-03-31T13:52:13.308Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Industrial accidents are a major concern for companies and families alike. It is a high priority to all stakeholders that steps be taken to prevent accidents from occurring. In this paper, three approaches to safety are examined: fault trees (FT), Bayesian networks (BN), and the Functional Resonance Analysis Method (FRAM). A case study of a propane feed control system is used to apply these methods. In order to make safety improvements to industrial workplaces high understanding of the systems is required. It is shown that consideration of the chance of failure of the system components, as in the FT and BN approaches, may not provide enough understanding to fully inform safety assessments. The FT and BN methods are top-down approaches that are formed from the perspective of management in workplaces. The FRAM methodology uses a bottom-up approach from the operational perspective to improve the understanding of the industrial workplace. The FRAM approach can provide added insight to the human factor and context and increase the rate at which we learn by considering successes as well as failures. FRAM can be a valuable tool for industrial safety assessment and to consider industrial safety holistically, by providing a framework to examine the operations in detail. However, operations should be considered using both top-down and bottom-up perspectives and all operational experience to make the most informed safety decisions.},\\n bibtype = {article},\\n author = {Smith, Doug and Veitch, Brian and Khan, Faisal and Taylor, Rocky},\\n doi = {10.1016/j.jlp.2016.11.016},\\n journal = {Journal of Loss Prevention in the Process Industries}\\n}\",\"author_short\":[\"Smith,  D.\",\"Veitch,  B.\",\"Khan,  F.\",\"Taylor,  R.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"smith-veitch-khan-taylor-understandingindustrialsafetycomparingfaulttreebayesiannetworkandframapproaches-2017\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A Bayesian network methodology for optimal security management of critical infrastructures\",\"type\":\"article\",\"year\":\"2018\",\"websites\":\"http://linkinghub.elsevier.com/retrieve/pii/S0951832017311535\",\"month\":\"3\",\"id\":\"f32946ff-ce59-32d6-80db-4fd7062c5445\",\"created\":\"2018-03-31T22:16:37.668Z\",\"accessed\":\"2018-03-31\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2018-03-31T22:16:37.668Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"Security management of critical infrastructures is a complex task as a great variety of technical and socio-political information is needed to realistically predict the risk of intentional malevolent acts. In the present study, a methodology based on Limited Memory Influence Diagram (LIMID) has been developed for the protection of critical infrastructures via cost-effective allocation of security measures. LIMID is an extension of Bayesian network (BN) intended for decision-making, allowing for efficient modeling of complex systems while accounting for interdependencies and interaction of system components. The probability updating feature of BN has been used to investigate the effect of vulnerabilities on adversaries’ preferences when planning attacks. Moreover, the proposed methodology has been shown to be able to identify an optimal defensive strategy given an attack through maximizing defenders’ expected utility. Despite being demonstrated via a chemical facility, the methodology can easily be tailored to a wide variety of critical infrastructures.\",\"bibtype\":\"article\",\"author\":\"Misuri, Alessio and Khakzad, Nima and Reniers, Genserik and Cozzani, Valerio\",\"doi\":\"10.1016/j.ress.2018.03.028\",\"journal\":\"Reliability Engineering & System Safety\",\"bibtex\":\"@article{\\n title = {A Bayesian network methodology for optimal security management of critical infrastructures},\\n type = {article},\\n year = {2018},\\n websites = {http://linkinghub.elsevier.com/retrieve/pii/S0951832017311535},\\n month = {3},\\n id = {f32946ff-ce59-32d6-80db-4fd7062c5445},\\n created = {2018-03-31T22:16:37.668Z},\\n accessed = {2018-03-31},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2018-03-31T22:16:37.668Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Security management of critical infrastructures is a complex task as a great variety of technical and socio-political information is needed to realistically predict the risk of intentional malevolent acts. In the present study, a methodology based on Limited Memory Influence Diagram (LIMID) has been developed for the protection of critical infrastructures via cost-effective allocation of security measures. LIMID is an extension of Bayesian network (BN) intended for decision-making, allowing for efficient modeling of complex systems while accounting for interdependencies and interaction of system components. The probability updating feature of BN has been used to investigate the effect of vulnerabilities on adversaries’ preferences when planning attacks. Moreover, the proposed methodology has been shown to be able to identify an optimal defensive strategy given an attack through maximizing defenders’ expected utility. Despite being demonstrated via a chemical facility, the methodology can easily be tailored to a wide variety of critical infrastructures.},\\n bibtype = {article},\\n author = {Misuri, Alessio and Khakzad, Nima and Reniers, Genserik and Cozzani, Valerio},\\n doi = {10.1016/j.ress.2018.03.028},\\n journal = {Reliability Engineering & System Safety}\\n}\",\"author_short\":[\"Misuri,  A.\",\"Khakzad,  N.\",\"Reniers,  G.\",\"Cozzani,  V.\"],\"urls\":{\"Website\":\"http://linkinghub.elsevier.com/retrieve/pii/S0951832017311535\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"misuri-khakzad-reniers-cozzani-abayesiannetworkmethodologyforoptimalsecuritymanagementofcriticalinfrastructures-2018\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Risk identification of third-party damage on oil and gas pipelines through the Bayesian network\",\"type\":\"article\",\"year\":\"2018\",\"pages\":\"163-178\",\"volume\":\"54\",\"websites\":\"http://linkinghub.elsevier.com/retrieve/pii/S0950423017310719\",\"month\":\"7\",\"id\":\"c01b2f9d-9a0b-3add-b06e-7f4d8d78fe8c\",\"created\":\"2018-03-31T22:27:14.637Z\",\"accessed\":\"2018-03-31\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2018-03-31T22:27:14.637Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"This paper aims to identify the risks influencing oil and gas (O&G) pipeline safety caused by third-party damage (TPD). After comprehensive literature study, we found that the traditional risk identification of TPD suffers from defining binary states of risk only and ignores the risk factors after pipeline failure. To overcome this problem, we investigated incident reports to identify previously unrecognized additional factors. This work also developed a graphic model by using Bayesian theory to cope with the multistate risks arising from third parties and to present the incident evolution process explicitly. Furthermore, this paper included a leakage case study conducted to verify the logicality of this model. The results of case study inspire that the proposed methodology can be used in a dual assurance approach for risk mitigation, namely learning from previous incidents and continuously capturing new risk information for risk prevention.\",\"bibtype\":\"article\",\"author\":\"Guo, Xiaoyan and Zhang, Laibin and Liang, Wei and Haugen, Stein\",\"doi\":\"10.1016/j.jlp.2018.03.012\",\"journal\":\"Journal of Loss Prevention in the Process Industries\",\"bibtex\":\"@article{\\n title = {Risk identification of third-party damage on oil and gas pipelines through the Bayesian network},\\n type = {article},\\n year = {2018},\\n pages = {163-178},\\n volume = {54},\\n websites = {http://linkinghub.elsevier.com/retrieve/pii/S0950423017310719},\\n month = {7},\\n id = {c01b2f9d-9a0b-3add-b06e-7f4d8d78fe8c},\\n created = {2018-03-31T22:27:14.637Z},\\n accessed = {2018-03-31},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2018-03-31T22:27:14.637Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {This paper aims to identify the risks influencing oil and gas (O&G) pipeline safety caused by third-party damage (TPD). After comprehensive literature study, we found that the traditional risk identification of TPD suffers from defining binary states of risk only and ignores the risk factors after pipeline failure. To overcome this problem, we investigated incident reports to identify previously unrecognized additional factors. This work also developed a graphic model by using Bayesian theory to cope with the multistate risks arising from third parties and to present the incident evolution process explicitly. Furthermore, this paper included a leakage case study conducted to verify the logicality of this model. The results of case study inspire that the proposed methodology can be used in a dual assurance approach for risk mitigation, namely learning from previous incidents and continuously capturing new risk information for risk prevention.},\\n bibtype = {article},\\n author = {Guo, Xiaoyan and Zhang, Laibin and Liang, Wei and Haugen, Stein},\\n doi = {10.1016/j.jlp.2018.03.012},\\n journal = {Journal of Loss Prevention in the Process Industries}\\n}\",\"author_short\":[\"Guo,  X.\",\"Zhang,  L.\",\"Liang,  W.\",\"Haugen,  S.\"],\"urls\":{\"Website\":\"http://linkinghub.elsevier.com/retrieve/pii/S0950423017310719\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"guo-zhang-liang-haugen-riskidentificationofthirdpartydamageonoilandgaspipelinesthroughthebayesiannetwork-2018\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Life-cycle Risk Modeling: Alternate Methods Using Bayesian Belief Networks\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"Bayesian Belief Network,Life-Cycle,Supply Chain Risk\",\"pages\":\"320-325\",\"volume\":\"17\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S2212827114002881\",\"id\":\"13cec4f1-25f9-313b-84fe-55538d5bbc6a\",\"created\":\"2018-03-31T22:35:56.457Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2018-03-31T22:35:56.457Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"In recent years natural and man-made disasters have highlighted the need for robust supply chain risk management (SCRM) in manufacturing firms from a life-cycle perspective (pre-manufacturing, manufacturing, use, post-use stages). Bayesian Belief Networks (BBN) provide a means to probabilistically represent risk interdependencies and to proactively identify and manage any existing vulnerabilities. In this work, the BBN method is implemented for a product in the aerospace industry. Risk network maps are developed to identify interdependencies and describe the potential risk propagation behavior during each life-cycle phase and from one phase to another. Due to limited number of respondents and lack of certainty with respect to the post-use phase, enhanced methods of risk likelihood assessment are necessary specifically for the post-use phase assessment. In this paper two alternate techniques are compared for risk modeling using BBN in such situations: Boolean nodes and numeric simulation nodes. Results show that numeric nodes provide a more thorough explanation of the interconnections of the risk items modeled. Further enhancement using an approach that combines both BBN and System Dynamics (SD) for SCRM is discussed and possible variations for linking variables between SD and BBN are also presented.\",\"bibtype\":\"article\",\"author\":\"Amundson, Joseph and Brown, Adam and Grabowski, Matthias and Badurdeen, Fazleena\",\"doi\":\"10.1016/j.procir.2014.02.029\",\"journal\":\"Procedia CIRP\",\"bibtex\":\"@article{\\n title = {Life-cycle Risk Modeling: Alternate Methods Using Bayesian Belief Networks},\\n type = {article},\\n year = {2014},\\n keywords = {Bayesian Belief Network,Life-Cycle,Supply Chain Risk},\\n pages = {320-325},\\n volume = {17},\\n websites = {http://www.sciencedirect.com/science/article/pii/S2212827114002881},\\n id = {13cec4f1-25f9-313b-84fe-55538d5bbc6a},\\n created = {2018-03-31T22:35:56.457Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2018-03-31T22:35:56.457Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {In recent years natural and man-made disasters have highlighted the need for robust supply chain risk management (SCRM) in manufacturing firms from a life-cycle perspective (pre-manufacturing, manufacturing, use, post-use stages). Bayesian Belief Networks (BBN) provide a means to probabilistically represent risk interdependencies and to proactively identify and manage any existing vulnerabilities. In this work, the BBN method is implemented for a product in the aerospace industry. Risk network maps are developed to identify interdependencies and describe the potential risk propagation behavior during each life-cycle phase and from one phase to another. Due to limited number of respondents and lack of certainty with respect to the post-use phase, enhanced methods of risk likelihood assessment are necessary specifically for the post-use phase assessment. In this paper two alternate techniques are compared for risk modeling using BBN in such situations: Boolean nodes and numeric simulation nodes. Results show that numeric nodes provide a more thorough explanation of the interconnections of the risk items modeled. Further enhancement using an approach that combines both BBN and System Dynamics (SD) for SCRM is discussed and possible variations for linking variables between SD and BBN are also presented.},\\n bibtype = {article},\\n author = {Amundson, Joseph and Brown, Adam and Grabowski, Matthias and Badurdeen, Fazleena},\\n doi = {10.1016/j.procir.2014.02.029},\\n journal = {Procedia CIRP}\\n}\",\"author_short\":[\"Amundson,  J.\",\"Brown,  A.\",\"Grabowski,  M.\",\"Badurdeen,  F.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S2212827114002881\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"amundson-brown-grabowski-badurdeen-lifecycleriskmodelingalternatemethodsusingbayesianbeliefnetworks-2014\",\"role\":\"author\",\"keyword\":[\"Bayesian Belief Network\",\"Life-Cycle\",\"Supply Chain Risk\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Evaluating risk of water mains failure using a Bayesian belief network model\",\"type\":\"article\",\"year\":\"2015\",\"keywords\":\"Bayesian Belief Network (BBN),Consequence,Deterioration,Risk analysis,Water distribution network\",\"pages\":\"220-234\",\"volume\":\"240\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0377221714005360\",\"month\":\"1\",\"id\":\"27c1d147-4e5c-325d-959c-65a1f28e89a9\",\"created\":\"2018-03-31T22:35:56.468Z\",\"accessed\":\"2015-03-05\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2018-03-31T22:35:56.468Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"It has been reported that since year 2000, there have been an average 700 water main breaks per day only in Canada and the USA costing more than CAD 10 billions/year. Moreover, water main leaks affect other neighboring infrastructure that may lead to catastrophic failures. For this, municipality authorities or stakeholders are more concerned about preventive actions rather reacting to failure events. This paper presents a Bayesian Belief Network (BBN) model to evaluate the risk of failure of metallic water mains using structural integrity, hydraulic capacity, water quality, and consequence factors. BBN is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. The proposed model is capable of ranking water mains within distribution network that can identify vulnerable and sensitive pipes to justify proper decision action for maintenance/rehabilitation/replacement (M/R/R). To demonstrate the application of proposed model, water distribution network of City of Kelowna has been studied. Result indicates that almost 9% of the total 259 metallic pipes are at high risk in both summer and winter.\",\"bibtype\":\"article\",\"author\":\"Kabir, Golam and Tesfamariam, Solomon and Francisque, Alex and Sadiq, Rehan\",\"doi\":\"10.1016/j.ejor.2014.06.033\",\"journal\":\"European Journal of Operational Research\",\"number\":\"1\",\"bibtex\":\"@article{\\n title = {Evaluating risk of water mains failure using a Bayesian belief network model},\\n type = {article},\\n year = {2015},\\n keywords = {Bayesian Belief Network (BBN),Consequence,Deterioration,Risk analysis,Water distribution network},\\n pages = {220-234},\\n volume = {240},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221714005360},\\n month = {1},\\n id = {27c1d147-4e5c-325d-959c-65a1f28e89a9},\\n created = {2018-03-31T22:35:56.468Z},\\n accessed = {2015-03-05},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2018-03-31T22:35:56.468Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {It has been reported that since year 2000, there have been an average 700 water main breaks per day only in Canada and the USA costing more than CAD 10 billions/year. Moreover, water main leaks affect other neighboring infrastructure that may lead to catastrophic failures. For this, municipality authorities or stakeholders are more concerned about preventive actions rather reacting to failure events. This paper presents a Bayesian Belief Network (BBN) model to evaluate the risk of failure of metallic water mains using structural integrity, hydraulic capacity, water quality, and consequence factors. BBN is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. The proposed model is capable of ranking water mains within distribution network that can identify vulnerable and sensitive pipes to justify proper decision action for maintenance/rehabilitation/replacement (M/R/R). To demonstrate the application of proposed model, water distribution network of City of Kelowna has been studied. Result indicates that almost 9% of the total 259 metallic pipes are at high risk in both summer and winter.},\\n bibtype = {article},\\n author = {Kabir, Golam and Tesfamariam, Solomon and Francisque, Alex and Sadiq, Rehan},\\n doi = {10.1016/j.ejor.2014.06.033},\\n journal = {European Journal of Operational Research},\\n number = {1}\\n}\",\"author_short\":[\"Kabir,  G.\",\"Tesfamariam,  S.\",\"Francisque,  A.\",\"Sadiq,  R.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0377221714005360\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"kabir-tesfamariam-francisque-sadiq-evaluatingriskofwatermainsfailureusingabayesianbeliefnetworkmodel-2015\",\"role\":\"author\",\"keyword\":[\"Bayesian Belief Network (BBN)\",\"Consequence\",\"Deterioration\",\"Risk analysis\",\"Water distribution network\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Low-capacity utilization of process plants: A cost-robust approach to tackle man-made domino effects\",\"type\":\"article\",\"year\":\"2018\",\"websites\":\"http://linkinghub.elsevier.com/retrieve/pii/S0951832017309158\",\"month\":\"3\",\"id\":\"b6b43a9b-8093-3d54-aca0-93d57e805ebb\",\"created\":\"2018-03-31T23:32:39.672Z\",\"accessed\":\"2018-03-31\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2018-03-31T23:32:39.672Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"Process plants can be potential targets to terrorist attacks with the aim of triggering domino effects. Compared to accidental domino effects where the possibility of having multiple primary events is very remote, man-made domino effects are likelier to be initiated from multiple units within the plant in order to increase the knock-on likelihood and thus causing maximum damage. In this regard, identification of critical units that - under attack - may lead to likelier and severer domino effects is crucial both to assess the vulnerability of process plants and subsequently to increase their robustness to such attacks. In the present work, we have applied graph theory and dynamic Bayesian network to identify critical units. Further, low-capacity utilization of process plants (e.g., by keeping some of the storage tanks empty) has been demonstrated as an effective strategy in the case of imminent terrorist attacks. As such, the robustness of the plant against intentional attacks can temporarily be increased while considering the cost incurred because of such a low-capacity utilization.\",\"bibtype\":\"article\",\"author\":\"Khakzad, Nima and Reniers, Genserik\",\"doi\":\"10.1016/j.ress.2018.03.030\",\"journal\":\"Reliability Engineering & System Safety\",\"bibtex\":\"@article{\\n title = {Low-capacity utilization of process plants: A cost-robust approach to tackle man-made domino effects},\\n type = {article},\\n year = {2018},\\n websites = {http://linkinghub.elsevier.com/retrieve/pii/S0951832017309158},\\n month = {3},\\n id = {b6b43a9b-8093-3d54-aca0-93d57e805ebb},\\n created = {2018-03-31T23:32:39.672Z},\\n accessed = {2018-03-31},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2018-03-31T23:32:39.672Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Process plants can be potential targets to terrorist attacks with the aim of triggering domino effects. Compared to accidental domino effects where the possibility of having multiple primary events is very remote, man-made domino effects are likelier to be initiated from multiple units within the plant in order to increase the knock-on likelihood and thus causing maximum damage. In this regard, identification of critical units that - under attack - may lead to likelier and severer domino effects is crucial both to assess the vulnerability of process plants and subsequently to increase their robustness to such attacks. In the present work, we have applied graph theory and dynamic Bayesian network to identify critical units. Further, low-capacity utilization of process plants (e.g., by keeping some of the storage tanks empty) has been demonstrated as an effective strategy in the case of imminent terrorist attacks. As such, the robustness of the plant against intentional attacks can temporarily be increased while considering the cost incurred because of such a low-capacity utilization.},\\n bibtype = {article},\\n author = {Khakzad, Nima and Reniers, Genserik},\\n doi = {10.1016/j.ress.2018.03.030},\\n journal = {Reliability Engineering & System Safety}\\n}\",\"author_short\":[\"Khakzad,  N.\",\"Reniers,  G.\"],\"urls\":{\"Website\":\"http://linkinghub.elsevier.com/retrieve/pii/S0951832017309158\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"khakzad-reniers-lowcapacityutilizationofprocessplantsacostrobustapproachtotacklemanmadedominoeffects-2018\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Risk management in port and maritime logistics\",\"type\":\"article\",\"year\":\"2018\",\"websites\":\"https://www.sciencedirect.com/science/article/abs/pii/S0001457518301416#!\",\"month\":\"4\",\"publisher\":\"Pergamon\",\"day\":\"11\",\"id\":\"d8b52b51-ce3a-327d-a2e3-41160dbf58a3\",\"created\":\"2018-04-28T14:15:44.386Z\",\"accessed\":\"2018-04-24\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2018-04-28T14:15:44.386Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Lam, Jasmine Siu Lee and Lun, Y.H. Venus and Bell, Michael G.H.\",\"doi\":\"10.1016/J.AAP.2018.04.003\",\"journal\":\"Accident Analysis & Prevention\",\"bibtex\":\"@article{\\n title = {Risk management in port and maritime logistics},\\n type = {article},\\n year = {2018},\\n websites = {https://www.sciencedirect.com/science/article/abs/pii/S0001457518301416#!},\\n month = {4},\\n publisher = {Pergamon},\\n day = {11},\\n id = {d8b52b51-ce3a-327d-a2e3-41160dbf58a3},\\n created = {2018-04-28T14:15:44.386Z},\\n accessed = {2018-04-24},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2018-04-28T14:15:44.386Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Lam, Jasmine Siu Lee and Lun, Y.H. Venus and Bell, Michael G.H.},\\n doi = {10.1016/J.AAP.2018.04.003},\\n journal = {Accident Analysis & Prevention}\\n}\",\"author_short\":[\"Lam,  J., S., L.\",\"Lun,  Y., V.\",\"Bell,  M., G.\"],\"urls\":{\"Website\":\"https://www.sciencedirect.com/science/article/abs/pii/S0001457518301416#!\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"lam-lun-bell-riskmanagementinportandmaritimelogistics-2018\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"title\":\"Assessment and Countermeasures for Offshore Wind Farm Risks Based on a Dynamic Bayesian Network\",\"type\":\"article\",\"year\":\"2018\",\"pages\":\"368-384\",\"volume\":\"09\",\"websites\":\"http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jep.2018.94024\",\"month\":\"4\",\"publisher\":\"Scientific Research Publishing\",\"day\":\"17\",\"id\":\"3a4dfcfb-c5d9-3d33-95e0-8b555fc4a9e8\",\"created\":\"2018-05-29T00:32:00.841Z\",\"accessed\":\"2018-05-28\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2018-05-29T00:32:00.841Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"Wind power is a kind of clean energy promising significant social and environmental benefits, and in The Peoples Republic of China, the government supports and encourages the development of wind power as one element in a shift to renewable energy. In recent years however, maritime safety issues have arisen during offshore wind power construction and attendant production processes associated with the rapid promotion and development of offshore wind farms. Therefore, it is necessary to carry out risk assessment for phases in the life cycle of offshore wind farms. This paper reports on a risk assessment model based on a Dynamic Bayesian network that performs offshore wind farms maritime risk assessment. The advantage of this approach is the way in which a Bayesian model expresses uncertainty. Furthermore, such models permit simulations and reenactment of accidents in a virtual environment. There were several goals in this research. Offshore wind power project risk identification and evaluation theories and methods were explored to identify the sources of risk during different phases of the offshore wind farm life cycle. Based on this foundation, a dynamic Bayesian network model with Genie was established, and evaluated, in terms of its effectiveness for analysis of risk during different phases of the offshore wind farm life cycle. Research results show that a dynamic Bayesian network method can perform risk assessments effectively and flexibly, responding to the actual context of offshore wind power construction. Historical data and almost real-time information are combined to analyze the risk of the construction of offshore wind power. Our results inform a discussion of security and risk mitigation measures that when implemented, could improve safety. This work has value as a reference and guide for the safe development of offshore wind power.\",\"bibtype\":\"article\",\"author\":\"Zhou, Chunhui and Liu, Xin and Gan, Langxiong\",\"doi\":\"10.4236/jep.2018.94024\",\"journal\":\"Journal of Environmental Protection\",\"number\":\"04\",\"bibtex\":\"@article{\\n title = {Assessment and Countermeasures for Offshore Wind Farm Risks Based on a Dynamic Bayesian Network},\\n type = {article},\\n year = {2018},\\n pages = {368-384},\\n volume = {09},\\n websites = {http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jep.2018.94024},\\n month = {4},\\n publisher = {Scientific Research Publishing},\\n day = {17},\\n id = {3a4dfcfb-c5d9-3d33-95e0-8b555fc4a9e8},\\n created = {2018-05-29T00:32:00.841Z},\\n accessed = {2018-05-28},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2018-05-29T00:32:00.841Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Wind power is a kind of clean energy promising significant social and environmental benefits, and in The Peoples Republic of China, the government supports and encourages the development of wind power as one element in a shift to renewable energy. In recent years however, maritime safety issues have arisen during offshore wind power construction and attendant production processes associated with the rapid promotion and development of offshore wind farms. Therefore, it is necessary to carry out risk assessment for phases in the life cycle of offshore wind farms. This paper reports on a risk assessment model based on a Dynamic Bayesian network that performs offshore wind farms maritime risk assessment. The advantage of this approach is the way in which a Bayesian model expresses uncertainty. Furthermore, such models permit simulations and reenactment of accidents in a virtual environment. There were several goals in this research. Offshore wind power project risk identification and evaluation theories and methods were explored to identify the sources of risk during different phases of the offshore wind farm life cycle. Based on this foundation, a dynamic Bayesian network model with Genie was established, and evaluated, in terms of its effectiveness for analysis of risk during different phases of the offshore wind farm life cycle. Research results show that a dynamic Bayesian network method can perform risk assessments effectively and flexibly, responding to the actual context of offshore wind power construction. Historical data and almost real-time information are combined to analyze the risk of the construction of offshore wind power. Our results inform a discussion of security and risk mitigation measures that when implemented, could improve safety. This work has value as a reference and guide for the safe development of offshore wind power.},\\n bibtype = {article},\\n author = {Zhou, Chunhui and Liu, Xin and Gan, Langxiong},\\n doi = {10.4236/jep.2018.94024},\\n journal = {Journal of Environmental Protection},\\n number = {04}\\n}\",\"author_short\":[\"Zhou,  C.\",\"Liu,  X.\",\"Gan,  L.\"],\"urls\":{\"Website\":\"http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jep.2018.94024\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"zhou-liu-gan-assessmentandcountermeasuresforoffshorewindfarmrisksbasedonadynamicbayesiannetwork-2018\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A Three-Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property\",\"type\":\"article\",\"year\":\"2018\",\"keywords\":\"Bayesian network,benefit,cost,dwelling fires,human reaction,probability of fatality\",\"websites\":\"http://doi.wiley.com/10.1111/risa.13113\",\"month\":\"5\",\"publisher\":\"Wiley/Blackwell (10.1111)\",\"day\":\"17\",\"id\":\"54568ea5-39f0-3290-9788-d5305e7ed240\",\"created\":\"2018-05-29T00:38:58.160Z\",\"accessed\":\"2018-05-28\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2018-05-29T00:38:58.160Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Matellini, D. B. and Wall, A. D. and Jenkinson, I. D. and Wang, J. and Pritchard, R.\",\"doi\":\"10.1111/risa.13113\",\"journal\":\"Risk Analysis\",\"bibtex\":\"@article{\\n title = {A Three-Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property},\\n type = {article},\\n year = {2018},\\n keywords = {Bayesian network,benefit,cost,dwelling fires,human reaction,probability of fatality},\\n websites = {http://doi.wiley.com/10.1111/risa.13113},\\n month = {5},\\n publisher = {Wiley/Blackwell (10.1111)},\\n day = {17},\\n id = {54568ea5-39f0-3290-9788-d5305e7ed240},\\n created = {2018-05-29T00:38:58.160Z},\\n accessed = {2018-05-28},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2018-05-29T00:38:58.160Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Matellini, D. B. and Wall, A. D. and Jenkinson, I. D. and Wang, J. and Pritchard, R.},\\n doi = {10.1111/risa.13113},\\n journal = {Risk Analysis}\\n}\",\"author_short\":[\"Matellini,  D., B.\",\"Wall,  A., D.\",\"Jenkinson,  I., D.\",\"Wang,  J.\",\"Pritchard,  R.\"],\"urls\":{\"Website\":\"http://doi.wiley.com/10.1111/risa.13113\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"matellini-wall-jenkinson-wang-pritchard-athreepartbayesiannetworkformodelingdwellingfiresandtheirimpactuponpeopleandproperty-2018\",\"role\":\"author\",\"keyword\":[\"Bayesian network\",\"benefit\",\"cost\",\"dwelling fires\",\"human reaction\",\"probability of fatality\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Project Portfolio Risk Identification and Analysis, Considering Project Risk Interactions and Using Bayesian Networks\",\"type\":\"article\",\"year\":\"2018\",\"keywords\":\"Bayesian networks,project portfolio risk,risk analysis,risk identification,risk interactions\",\"pages\":\"1609\",\"volume\":\"10\",\"websites\":\"http://www.mdpi.com/2071-1050/10/5/1609\",\"month\":\"5\",\"publisher\":\"Multidisciplinary Digital Publishing Institute\",\"day\":\"17\",\"id\":\"f794f84b-0d80-3381-a929-3d81f93220c7\",\"created\":\"2018-05-29T16:48:25.361Z\",\"accessed\":\"2018-05-28\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2018-05-29T16:48:25.361Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"An organization&rsquo;s strategic objectives are accomplished through portfolios. However, the materialization of portfolio risks may affect a portfolio&rsquo;s sustainable success and the achievement of those objectives. Moreover, project interdependencies and cause&ndash;effect relationships between risks create complexity for portfolio risk analysis. This paper presents a model using Bayesian network (BN) methodology for modeling and analyzing portfolio risks. To develop this model, first, portfolio-level risks and risks caused by project interdependencies are identified. Then, based on their cause&ndash;effect relationships all portfolio risks are organized in a BN. Conditional probability distributions for this network are specified and the Bayesian networks method is used to estimate the probability of portfolio risk. This model was applied to a portfolio of a construction company located in Iran and proved effective in analyzing portfolio risk probability. Furthermore, the model provided valuable information for selecting a portfolio&rsquo;s projects and making strategic decisions.\",\"bibtype\":\"article\",\"author\":\"Ghasemi, Foroogh and Sari, Mohammad and Yousefi, Vahidreza and Falsafi, Reza and Tamošaitienė, Jolanta\",\"doi\":\"10.3390/su10051609\",\"journal\":\"Sustainability\",\"number\":\"5\",\"bibtex\":\"@article{\\n title = {Project Portfolio Risk Identification and Analysis, Considering Project Risk Interactions and Using Bayesian Networks},\\n type = {article},\\n year = {2018},\\n keywords = {Bayesian networks,project portfolio risk,risk analysis,risk identification,risk interactions},\\n pages = {1609},\\n volume = {10},\\n websites = {http://www.mdpi.com/2071-1050/10/5/1609},\\n month = {5},\\n publisher = {Multidisciplinary Digital Publishing Institute},\\n day = {17},\\n id = {f794f84b-0d80-3381-a929-3d81f93220c7},\\n created = {2018-05-29T16:48:25.361Z},\\n accessed = {2018-05-28},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2018-05-29T16:48:25.361Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {An organization&rsquo;s strategic objectives are accomplished through portfolios. However, the materialization of portfolio risks may affect a portfolio&rsquo;s sustainable success and the achievement of those objectives. Moreover, project interdependencies and cause&ndash;effect relationships between risks create complexity for portfolio risk analysis. This paper presents a model using Bayesian network (BN) methodology for modeling and analyzing portfolio risks. To develop this model, first, portfolio-level risks and risks caused by project interdependencies are identified. Then, based on their cause&ndash;effect relationships all portfolio risks are organized in a BN. Conditional probability distributions for this network are specified and the Bayesian networks method is used to estimate the probability of portfolio risk. This model was applied to a portfolio of a construction company located in Iran and proved effective in analyzing portfolio risk probability. Furthermore, the model provided valuable information for selecting a portfolio&rsquo;s projects and making strategic decisions.},\\n bibtype = {article},\\n author = {Ghasemi, Foroogh and Sari, Mohammad and Yousefi, Vahidreza and Falsafi, Reza and Tamošaitienė, Jolanta},\\n doi = {10.3390/su10051609},\\n journal = {Sustainability},\\n number = {5}\\n}\",\"author_short\":[\"Ghasemi,  F.\",\"Sari,  M.\",\"Yousefi,  V.\",\"Falsafi,  R.\",\"Tamošaitienė,  J.\"],\"urls\":{\"Website\":\"http://www.mdpi.com/2071-1050/10/5/1609\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"ghasemi-sari-yousefi-falsafi-tamoaitien-projectportfolioriskidentificationandanalysisconsideringprojectriskinteractionsandusingbayesiannetworks-2018\",\"role\":\"author\",\"keyword\":[\"Bayesian networks\",\"project portfolio risk\",\"risk analysis\",\"risk identification\",\"risk interactions\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network\",\"type\":\"article\",\"year\":\"2018\",\"websites\":\"https://www.sciencedirect.com/science/article/pii/S1364815218301191\",\"month\":\"10\",\"publisher\":\"Elsevier\",\"day\":\"26\",\"id\":\"0cfa7415-7be3-3872-b2bb-6a249987c9bd\",\"created\":\"2018-11-13T13:56:30.150Z\",\"accessed\":\"2018-11-10\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2018-11-13T13:56:30.150Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"This paper proposes a model for risk analysis of real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. The proposed model consists of three components: Monte Carlo simulations, dynamic Bayesian network establishing, and risk-informed inference for decision making. The Monte Carlo simulations provide basic data inputs for the dynamic Bayesian network establishing using the historical floods and operation models of the multi-reservoir system. The dynamic Bayesian network is built with expert knowledge and the relationships among the uncertainties. The component of risk-informed inference for decision making is to provide risk information about the operation schedules using the trained dynamic Bayesian network. We apply the proposed model to a multi-reservoir system in China. The results show that the proposed method has a capability for bi-directional inferences and can be served as a risk-informed decision-making tool under uncertainties in the real-time flood control operation of a multi-reservoir system.\",\"bibtype\":\"article\",\"author\":\"Chen, Juan and Zhong, Ping-An and An, Ru and Zhu, Feilin and Xu, Bin\",\"doi\":\"10.1016/J.ENVSOFT.2018.10.007\",\"journal\":\"Environmental Modelling & Software\",\"bibtex\":\"@article{\\n title = {Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network},\\n type = {article},\\n year = {2018},\\n websites = {https://www.sciencedirect.com/science/article/pii/S1364815218301191},\\n month = {10},\\n publisher = {Elsevier},\\n day = {26},\\n id = {0cfa7415-7be3-3872-b2bb-6a249987c9bd},\\n created = {2018-11-13T13:56:30.150Z},\\n accessed = {2018-11-10},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2018-11-13T13:56:30.150Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {This paper proposes a model for risk analysis of real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. The proposed model consists of three components: Monte Carlo simulations, dynamic Bayesian network establishing, and risk-informed inference for decision making. The Monte Carlo simulations provide basic data inputs for the dynamic Bayesian network establishing using the historical floods and operation models of the multi-reservoir system. The dynamic Bayesian network is built with expert knowledge and the relationships among the uncertainties. The component of risk-informed inference for decision making is to provide risk information about the operation schedules using the trained dynamic Bayesian network. We apply the proposed model to a multi-reservoir system in China. The results show that the proposed method has a capability for bi-directional inferences and can be served as a risk-informed decision-making tool under uncertainties in the real-time flood control operation of a multi-reservoir system.},\\n bibtype = {article},\\n author = {Chen, Juan and Zhong, Ping-An and An, Ru and Zhu, Feilin and Xu, Bin},\\n doi = {10.1016/J.ENVSOFT.2018.10.007},\\n journal = {Environmental Modelling & Software}\\n}\",\"author_short\":[\"Chen,  J.\",\"Zhong,  P.\",\"An,  R.\",\"Zhu,  F.\",\"Xu,  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SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case- study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed.\",\"bibtype\":\"article\",\"author\":\"Hosseini, Seyedmohsen and Ivanov, Dmitry\",\"doi\":\"10.1007/s10479-019-03350-8\",\"journal\":\"Annals of Operations Research\",\"bibtex\":\"@article{\\n title = {A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach},\\n type = {article},\\n year = {2019},\\n publisher = {Springer US},\\n id = {40c54eef-72a2-37a2-b82e-380dd4e0bb65},\\n created = {2019-11-23T23:40:00.765Z},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2019-11-23T23:40:00.765Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {This is the first study that presents a supply chain (SC) resilience measure with the ripple effect considerations including both disruption and recovery stages. SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case- study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed.},\\n bibtype = {article},\\n author = {Hosseini, Seyedmohsen and Ivanov, Dmitry},\\n doi = {10.1007/s10479-019-03350-8},\\n journal = {Annals of Operations Research}\\n}\",\"author_short\":[\"Hosseini,  S.\",\"Ivanov,  D.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"hosseini-ivanov-anewresiliencemeasureforsupplynetworkswiththerippleeffectconsiderationsabayesiannetworkapproach-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Use of Bayesian Network for Risk-Based Fatigue Integrity Assessment: Application for Topside Piping in an Arctic 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type = {article},\\n year = {2019},\\n pages = {421-428},\\n volume = {29},\\n websites = {http://legacy.isope.org/publications/journals/journalDecember19.htm},\\n month = {12},\\n day = {1},\\n id = {cfa87aad-9ef5-3ba6-861e-bf24be1dd1bf},\\n created = {2019-12-04T14:15:49.112Z},\\n accessed = {2019-12-04},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2019-12-04T14:15:49.181Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Keprate, Arvind and Ratnayake, RM Chandima},\\n doi = {10.17736/ijope.2019.bn19},\\n journal = {International Journal of Offshore and Polar Engineering},\\n number = {4}\\n}\",\"author_short\":[\"Keprate,  A.\",\"Ratnayake,  R., C.\"],\"urls\":{\"Website\":\"http://legacy.isope.org/publications/journals/journalDecember19.htm\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"keprate-ratnayake-useofbayesiannetworkforriskbasedfatigueintegrityassessmentapplicationfortopsidepipinginanarcticenvironment-2019\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Understanding the vulnerability of beef producers in Australia to an FMD outbreak using a Bayesian Network predictive 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outbreak using a Bayesian Network predictive model},\\n type = {article},\\n year = {2019},\\n pages = {104872},\\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0167587719306348},\\n month = {12},\\n id = {dde1afe5-4e27-3ca7-8ebc-7831619b86b3},\\n created = {2019-12-21T18:28:26.312Z},\\n accessed = {2019-12-21},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2019-12-21T18:28:26.312Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Manyweathers, Jennifer and Maru, Yiheyis and Hayes, Lynne and Loechel, Barton and Kruger, Heleen and Mankad, Aditi and Xie, Gang and Woodgate, Rob and Hernandez-Jover, Marta},\\n doi = {10.1016/j.prevetmed.2019.104872},\\n journal = {Preventive Veterinary Medicine}\\n}\",\"author_short\":[\"Manyweathers,  J.\",\"Maru,  Y.\",\"Hayes,  L.\",\"Loechel,  B.\",\"Kruger,  H.\",\"Mankad,  A.\",\"Xie,  G.\",\"Woodgate,  R.\",\"Hernandez-Jover,  M.\"],\"urls\":{\"Website\":\"https://linkinghub.elsevier.com/retrieve/pii/S0167587719306348\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"manyweathers-maru-hayes-loechel-kruger-mankad-xie-woodgate-etal-understandingthevulnerabilityofbeefproducersinaustraliatoanfmdoutbreakusingabayesiannetworkpredictivemodel-2019\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Ship collision on temporary structures Combi-walls under collision loading Cover photo: Construction of the Maasdeltatunnel Photo by Rijkswaterstaat\",\"type\":\"techreport\",\"year\":\"2018\",\"websites\":\"http://repository.tudelft.nl/\",\"id\":\"467b614a-1429-3dd6-a33a-7c1b94463372\",\"created\":\"2019-12-21T18:41:50.531Z\",\"accessed\":\"2019-12-21\",\"file_attached\":\"true\",\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2019-12-21T18:42:31.208Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"techreport\",\"author\":\"Jansen, J\",\"bibtex\":\"@techreport{\\n title = {Ship collision on temporary structures Combi-walls under collision loading Cover photo: Construction of the Maasdeltatunnel Photo by Rijkswaterstaat},\\n type = {techreport},\\n year = {2018},\\n websites = {http://repository.tudelft.nl/},\\n id = {467b614a-1429-3dd6-a33a-7c1b94463372},\\n created = {2019-12-21T18:41:50.531Z},\\n accessed = {2019-12-21},\\n file_attached = {true},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2019-12-21T18:42:31.208Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {techreport},\\n author = {Jansen, J}\\n}\",\"author_short\":[\"Jansen,  J.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/1128f09e-ec1a-512f-0965-652858c68b04/full_text.pdf.pdf\",\"Website\":\"http://repository.tudelft.nl/\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"jansen-shipcollisionontemporarystructurescombiwallsundercollisionloadingcoverphotoconstructionofthemaasdeltatunnelphotobyrijkswaterstaat-2018\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines\",\"type\":\"article\",\"year\":\"2020\",\"pages\":\"178-188\",\"volume\":\"134\",\"websites\":\"https://linkinghub.elsevier.com/retrieve/pii/S0957582019306664\",\"month\":\"2\",\"id\":\"96335c3c-c3eb-39e7-a873-ffffd85cbdb6\",\"created\":\"2019-12-28T14:08:41.182Z\",\"accessed\":\"2019-12-28\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2019-12-28T14:08:41.270Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Cui, Yan and Quddus, Noor and Mashuga, Chad V.\",\"doi\":\"10.1016/j.psep.2019.11.038\",\"journal\":\"Process Safety and Environmental Protection\",\"bibtex\":\"@article{\\n title = {Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines},\\n type = {article},\\n year = {2020},\\n pages = {178-188},\\n volume = {134},\\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0957582019306664},\\n month = {2},\\n id = {96335c3c-c3eb-39e7-a873-ffffd85cbdb6},\\n created = {2019-12-28T14:08:41.182Z},\\n accessed = {2019-12-28},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2019-12-28T14:08:41.270Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Cui, Yan and Quddus, Noor and Mashuga, Chad V.},\\n doi = {10.1016/j.psep.2019.11.038},\\n journal = {Process Safety and Environmental Protection}\\n}\",\"author_short\":[\"Cui,  Y.\",\"Quddus,  N.\",\"Mashuga,  C., V.\"],\"urls\":{\"Website\":\"https://linkinghub.elsevier.com/retrieve/pii/S0957582019306664\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"cui-quddus-mashuga-bayesiannetworkandgametheoryriskassessmentmodelforthirdpartydamagetooilandgaspipelines-2020\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"title\":\"Flood Risk Prediction under Global Vegetated Hydrodynamics: A Bayesian Network\",\"type\":\"misc\",\"year\":\"2019\",\"id\":\"36c9fdda-4fc4-3b96-8f4d-f1f6da7244b0\",\"created\":\"2019-12-29T15:56:48.150Z\",\"accessed\":\"2019-12-29\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2019-12-29T15:56:48.225Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"The increasing frequency and intensity of extreme events due to global warming and climate change is increasing flood risk. To act, rather than react, nature-based solutions (NBS) involving vegetation and wetlands are being explored on top of conventional solutions like dikes. WHY? There was a dire need for global study quantifying the potential of vegetation in reducing flood risk and eventually make a decision support tool which enables quick assessments about flood risk reduction in a vegetated hydrodynamic system. WHAT? The developed tool can predict flood risk anywhere in the world without rigorous modeling through user defined conditionalization of in-situ hydrodynamic or vegetation characteristics. HOW? Multivariate dependence among parameters of schematized system can exhibit characteristics of vegetated hydrodynamics. To ensure global representation of vegetated hydrodynamics a copula-based multivariate stochastic model has been developed which caters global ranges of each parameter, their probability distributions and the inter-parameter dependencies through ranked correlations. Numerical modeling has been carried out through XBeach non-hydrostatic model by resolving full spectrum of high and low frequency waves. A non-parametric Bayesian network-based flood risk prediction tool has been developed from the synthetic dataset developed from the simulations. SO? Bulk results conclude that saltmarshes attenuates waves by 87% and mangroves by 94% as a mean value. Wave attenuation, flood risk reduction and wave run-up manifests maximum dependence on offshore wave height, water depth, drag coefficient, vegetation height, frontal width, and forest length and least on offshore slope and vegetation density. NOW? The flood risk prediction tool would help decision makers in implementing NBS, in making better informed decisions about early warnings and policy making related to flood risk reduction and climate change adaptation by incorporating vegetation. NOVELTY? To the author's knowledge no such study exists which captures natural variability of hydrodynamics and vegetation together in a probabilistic model over global scales. Additionally, no such study exist which applies non-parametric Bayesian networks to predict flood risk. The dependence modeling of global vegetated hydrodynamic environments is also unique which skims out the most critical parameters.\",\"bibtype\":\"misc\",\"author\":\"Niazi, Muhammad Hassan Khan\",\"bibtex\":\"@misc{\\n title = {Flood Risk Prediction under Global Vegetated Hydrodynamics: A Bayesian Network},\\n type = {misc},\\n year = {2019},\\n id = {36c9fdda-4fc4-3b96-8f4d-f1f6da7244b0},\\n created = {2019-12-29T15:56:48.150Z},\\n accessed = {2019-12-29},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2019-12-29T15:56:48.225Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The increasing frequency and intensity of extreme events due to global warming and climate change is increasing flood risk. To act, rather than react, nature-based solutions (NBS) involving vegetation and wetlands are being explored on top of conventional solutions like dikes. WHY? There was a dire need for global study quantifying the potential of vegetation in reducing flood risk and eventually make a decision support tool which enables quick assessments about flood risk reduction in a vegetated hydrodynamic system. WHAT? The developed tool can predict flood risk anywhere in the world without rigorous modeling through user defined conditionalization of in-situ hydrodynamic or vegetation characteristics. HOW? Multivariate dependence among parameters of schematized system can exhibit characteristics of vegetated hydrodynamics. To ensure global representation of vegetated hydrodynamics a copula-based multivariate stochastic model has been developed which caters global ranges of each parameter, their probability distributions and the inter-parameter dependencies through ranked correlations. Numerical modeling has been carried out through XBeach non-hydrostatic model by resolving full spectrum of high and low frequency waves. A non-parametric Bayesian network-based flood risk prediction tool has been developed from the synthetic dataset developed from the simulations. SO? Bulk results conclude that saltmarshes attenuates waves by 87% and mangroves by 94% as a mean value. Wave attenuation, flood risk reduction and wave run-up manifests maximum dependence on offshore wave height, water depth, drag coefficient, vegetation height, frontal width, and forest length and least on offshore slope and vegetation density. NOW? The flood risk prediction tool would help decision makers in implementing NBS, in making better informed decisions about early warnings and policy making related to flood risk reduction and climate change adaptation by incorporating vegetation. NOVELTY? To the author's knowledge no such study exists which captures natural variability of hydrodynamics and vegetation together in a probabilistic model over global scales. Additionally, no such study exist which applies non-parametric Bayesian networks to predict flood risk. The dependence modeling of global vegetated hydrodynamic environments is also unique which skims out the most critical parameters.},\\n bibtype = {misc},\\n author = {Niazi, Muhammad Hassan Khan}\\n}\",\"author_short\":[\"Niazi,  M., H., K.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"niazi-floodriskpredictionunderglobalvegetatedhydrodynamicsabayesiannetwork-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks\",\"type\":\"article\",\"year\":\"2019\",\"keywords\":\"Arbitrage Pricing Model,Banking Stocks JEL: C11,Bayesian Networks,G11,G12,Machine Learning,Portfolio Selection Theory\",\"websites\":\"www.berjournal.com\",\"id\":\"87842199-6f11-388f-9ae4-465c6ad52ffb\",\"created\":\"2019-12-29T16:14:12.780Z\",\"accessed\":\"2019-12-29\",\"file_attached\":\"true\",\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2019-12-29T16:14:12.933Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"According to the modern portfolio theory, the direction of the relationship between the securities in the portfolio is stated to be effective in reducing the risk. Moreover, securities in high correlation are avoided by taking place in the same portfolio. The models structured by the Bayesian networks are capable of visually illustrate the probabilistic relationship. Also, portfolio returns could be refreshed simultaneously when new information has arrived. The study aims to provide dynamic information through Bayesian networks and to investigate the relationship between macroeconomic indicators and stock returns of Turkish major bank stocks based on the Arbitrage Pricing Model. The dataset includes stock returns of four banks listed in the Borsa Istanbul from June 2001 to January 2017. Besides, macroeconomic variables such as BIST-100 Index, oil prices, inflation, exchange, and interest rate & money supply are gathered for the same period. The results suggest that the Bayesian network models allow dynamics among stock returns could be investigated in more detail. Additionally, it determines that macroeconomic variables would have various impacts on stock returns on bank stocks by comparison of the conventional methods.\",\"bibtype\":\"article\",\"author\":\"Busem Hatipoglu, Fatma and Uyar, Umut\",\"doi\":\"10.20409/berj.2019.202\",\"bibtex\":\"@article{\\n title = {Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks},\\n type = {article},\\n year = {2019},\\n keywords = {Arbitrage Pricing Model,Banking Stocks JEL: C11,Bayesian Networks,G11,G12,Machine Learning,Portfolio Selection Theory},\\n websites = {www.berjournal.com},\\n id = {87842199-6f11-388f-9ae4-465c6ad52ffb},\\n created = {2019-12-29T16:14:12.780Z},\\n accessed = {2019-12-29},\\n file_attached = {true},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2019-12-29T16:14:12.933Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {According to the modern portfolio theory, the direction of the relationship between the securities in the portfolio is stated to be effective in reducing the risk. Moreover, securities in high correlation are avoided by taking place in the same portfolio. The models structured by the Bayesian networks are capable of visually illustrate the probabilistic relationship. Also, portfolio returns could be refreshed simultaneously when new information has arrived. The study aims to provide dynamic information through Bayesian networks and to investigate the relationship between macroeconomic indicators and stock returns of Turkish major bank stocks based on the Arbitrage Pricing Model. The dataset includes stock returns of four banks listed in the Borsa Istanbul from June 2001 to January 2017. Besides, macroeconomic variables such as BIST-100 Index, oil prices, inflation, exchange, and interest rate & money supply are gathered for the same period. The results suggest that the Bayesian network models allow dynamics among stock returns could be investigated in more detail. Additionally, it determines that macroeconomic variables would have various impacts on stock returns on bank stocks by comparison of the conventional methods.},\\n bibtype = {article},\\n author = {Busem Hatipoglu, Fatma and Uyar, Umut},\\n doi = {10.20409/berj.2019.202}\\n}\",\"author_short\":[\"Busem Hatipoglu,  F.\",\"Uyar,  U.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/1cefd92b-a220-0575-70be-5669e3c04614/full_text.pdf.pdf\",\"Website\":\"www.berjournal.com\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"busemhatipoglu-uyar-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019\",\"role\":\"author\",\"keyword\":[\"Arbitrage Pricing Model\",\"Banking Stocks JEL: C11\",\"Bayesian Networks\",\"G11\",\"G12\",\"Machine Learning\",\"Portfolio Selection Theory\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A Bayesian network approach to refining ecological risk assessments: Mercury and the Florida panther (Puma concolor coryi)\",\"type\":\"article\",\"year\":\"2020\",\"pages\":\"108911\",\"volume\":\"418\",\"websites\":\"https://linkinghub.elsevier.com/retrieve/pii/S0304380019304193\",\"month\":\"2\",\"id\":\"4adc18eb-ce64-3f83-8f9f-f08feb8f2625\",\"created\":\"2020-01-28T14:13:04.891Z\",\"accessed\":\"2020-01-28\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2020-01-28T14:13:04.965Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Carriger, John F. and Barron, Mace G.\",\"doi\":\"10.1016/j.ecolmodel.2019.108911\",\"journal\":\"Ecological Modelling\",\"bibtex\":\"@article{\\n title = {A Bayesian network approach to refining ecological risk assessments: Mercury and the Florida panther (Puma concolor coryi)},\\n type = {article},\\n year = {2020},\\n pages = {108911},\\n volume = {418},\\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0304380019304193},\\n month = {2},\\n id = {4adc18eb-ce64-3f83-8f9f-f08feb8f2625},\\n created = {2020-01-28T14:13:04.891Z},\\n accessed = {2020-01-28},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2020-01-28T14:13:04.965Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Carriger, John F. and Barron, Mace G.},\\n doi = {10.1016/j.ecolmodel.2019.108911},\\n journal = {Ecological Modelling}\\n}\",\"author_short\":[\"Carriger,  J., F.\",\"Barron,  M., G.\"],\"urls\":{\"Website\":\"https://linkinghub.elsevier.com/retrieve/pii/S0304380019304193\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"carriger-barron-abayesiannetworkapproachtorefiningecologicalriskassessmentsmercuryandthefloridapantherpumaconcolorcoryi-2020\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"(24) (PDF) An Application of Bayesian Networks to Antiterrorism Risk Management for Military Planners\",\"type\":\"misc\",\"websites\":\"https://www.researchgate.net/publication/2376453_An_Application_of_Bayesian_Networks_to_Antiterrorism_Risk_Management_for_Military_Planners\",\"id\":\"c5026614-7b5f-3a5f-b828-9640830e4569\",\"created\":\"2020-03-05T01:09:49.743Z\",\"accessed\":\"2020-03-04\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2020-03-05T01:09:49.814Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"misc\",\"author\":\"\",\"bibtex\":\"@misc{\\n title = {(24) (PDF) An Application of Bayesian Networks to Antiterrorism Risk Management for Military Planners},\\n type = {misc},\\n websites = {https://www.researchgate.net/publication/2376453_An_Application_of_Bayesian_Networks_to_Antiterrorism_Risk_Management_for_Military_Planners},\\n id = 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Application in Disaster Risk Assessment\",\"type\":\"article\",\"year\":\"2018\",\"keywords\":\"Bayesian network,Genetic algorithm,Grey relational analysis,Risk assessment\",\"pages\":\"237-248\",\"volume\":\"9\",\"month\":\"6\",\"publisher\":\"Beijing Normal University Press\",\"day\":\"1\",\"id\":\"6bf9daf9-fc8d-3688-a490-a6cbd5c416b2\",\"created\":\"2020-03-05T01:23:39.185Z\",\"accessed\":\"2020-03-04\",\"file_attached\":\"true\",\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2020-03-05T01:23:43.569Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"The application of Bayesian network (BN) theory in risk assessment is an emerging trend. But in cases where data are incomplete and variables are mutually related, its application is restricted. To overcome these problems, an improved BN assessment model with parameter retrieval and decorrelation ability is proposed. First, multivariate nonlinear planning is applied to the feedback error learning of parameters. A genetic algorithm is used to learn the probability distribution of nodes that lack quantitative data. Then, based on an improved grey relational analysis that considers the correlation of variation rate, the optimal weight that characterizes the correlation is calculated and the weighted BN is improved for decorrelation. An improved risk assessment model based on the weighted BN then is built. An assessment of sea ice disaster shows that the model can be applied for risk assessment with incomplete data and variable correlation.\",\"bibtype\":\"article\",\"author\":\"Li, Ming and Hong, Mei and Zhang, Ren\",\"doi\":\"10.1007/s13753-018-0171-z\",\"journal\":\"International Journal of Disaster Risk Science\",\"number\":\"2\",\"bibtex\":\"@article{\\n title = {Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment},\\n type = {article},\\n year = {2018},\\n keywords = {Bayesian network,Genetic algorithm,Grey relational analysis,Risk assessment},\\n pages = {237-248},\\n volume = {9},\\n month = {6},\\n publisher = {Beijing Normal University Press},\\n day = {1},\\n id = {6bf9daf9-fc8d-3688-a490-a6cbd5c416b2},\\n created = {2020-03-05T01:23:39.185Z},\\n accessed = {2020-03-04},\\n file_attached = {true},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2020-03-05T01:23:43.569Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The application of Bayesian network (BN) theory in risk assessment is an emerging trend. But in cases where data are incomplete and variables are mutually related, its application is restricted. To overcome these problems, an improved BN assessment model with parameter retrieval and decorrelation ability is proposed. First, multivariate nonlinear planning is applied to the feedback error learning of parameters. A genetic algorithm is used to learn the probability distribution of nodes that lack quantitative data. Then, based on an improved grey relational analysis that considers the correlation of variation rate, the optimal weight that characterizes the correlation is calculated and the weighted BN is improved for decorrelation. An improved risk assessment model based on the weighted BN then is built. An assessment of sea ice disaster shows that the model can be applied for risk assessment with incomplete data and variable correlation.},\\n bibtype = {article},\\n author = {Li, Ming and Hong, Mei and Zhang, Ren},\\n doi = {10.1007/s13753-018-0171-z},\\n journal = {International Journal of Disaster Risk Science},\\n number = {2}\\n}\",\"author_short\":[\"Li,  M.\",\"Hong,  M.\",\"Zhang,  R.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/59828c53-9f0d-04e1-3c35-486f9c780dc0/full_text.pdf.pdf\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"li-hong-zhang-improvedbayesiannetworkbasedriskmodelanditsapplicationindisasterriskassessment-2018\",\"role\":\"author\",\"keyword\":[\"Bayesian network\",\"Genetic algorithm\",\"Grey relational analysis\",\"Risk assessment\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"(99+) (PDF) Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children | paola berchialla - Academia.edu\",\"type\":\"misc\",\"websites\":\"https://www.academia.edu/20493958/Comparing_models_for_quantitative_risk_assessment_an_application_to_the_European_Registry_of_foreign_body_injuries_in_children?email_work_card=thumbnail\",\"id\":\"2cb55440-7c15-3f08-94e7-2da8fba5d230\",\"created\":\"2020-04-14T20:11:28.784Z\",\"accessed\":\"2020-04-14\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"0d75bba3-25d4-3416-a7fe-43a9d2675870\",\"last_modified\":\"2020-04-14T20:11:28.853Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"misc\",\"author\":\"\",\"bibtex\":\"@misc{\\n title = {(99+) (PDF) Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children | paola berchialla - Academia.edu},\\n type = {misc},\\n websites = {https://www.academia.edu/20493958/Comparing_models_for_quantitative_risk_assessment_an_application_to_the_European_Registry_of_foreign_body_injuries_in_children?email_work_card=thumbnail},\\n id = {2cb55440-7c15-3f08-94e7-2da8fba5d230},\\n created = {2020-04-14T20:11:28.784Z},\\n accessed = {2020-04-14},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\\n last_modified = {2020-04-14T20:11:28.853Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {misc},\\n author = {}\\n}\",\"author_short\":[],\"urls\":{\"Website\":\"https://www.academia.edu/20493958/Comparing_models_for_quantitative_risk_assessment_an_application_to_the_European_Registry_of_foreign_body_injuries_in_children?email_work_card=thumbnail\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"99pdfcomparingmodelsforquantitativeriskassessmentanapplicationtotheeuropeanregistryofforeignbodyinjuriesinchildrenpaolaberchiallaacademiaedu\",\"role\":\"\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"all.bib\" href=\"data:text/bibtex;base64,@article{
 title = {Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment.},
 type = {article},
 year = {2010},
 keywords = {Bayes Theorem,Consumer Product Safety,Food Microbiology,Models, Theoretical,Monte Carlo Method,Risk Assessment},
 pages = {S57-63},
 volume = {139 Suppl },
 websites = {http://www.sciencedirect.com/science/article/pii/S0168160509006680},
 month = {5},
 day = {30},
 id = {45a289ef-c69e-3346-9260-b60cad2cd92d},
 created = {2015-04-12T20:17:33.000Z},
 accessed = {2015-04-01},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2017-03-14T14:39:57.651Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {We discuss different aspects of farm-to-fork risk assessment from a modelling perspective. Stochastic simulation models as they are presented today represent a mathematical representation of nature. In food safety risk assessment, a common modelling approach consists of a logic chain beginning at the source of the hazard and ending with the unwanted consequences of interest. This 'farm-to-fork' approach usually begins with the hazard on the farm, sometimes with different compartments presenting different parts of the production chain, and ends with the 'dose' received by the consumer or in case a dose response model is available the number of cases of illness. These models are typically implemented as Monte Carlo simulations, which are unidirectional in nature, and the link between statistics and simulation model is not interactive. A possible solution could be the use of Bayesian belief networks (BBNs) and this paper tries to discuss in an intuitive way the possibilities of using BBNs as an alternative for Monte Carlo modelling. An inventory is made of the strengths and weaknesses of both approaches, and an example is given showing an additional use of BBNs in biotracing problems.},
 bibtype = {article},
 author = {Smid, J H and Verloo, D and Barker, G C and Havelaar, A H},
 doi = {10.1016/j.ijfoodmicro.2009.12.015},
 journal = {International journal of food microbiology}
}

@article{
 title = {Bayesian Networks for enterprise risk assessment},
 type = {article},
 year = {2007},
 keywords = {Bayesian Networks,Enterprise risk assessment,Mutual information},
 pages = {22-28},
 volume = {382},
 websites = {http://www.sciencedirect.com/science/article/pii/S037843710700132X},
 month = {8},
 id = {4a3b8b75-5751-3913-8fae-6e8c96544164},
 created = {2015-04-16T00:39:20.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2017-03-14T14:39:57.651Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. Risk, in general, is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover, qualitative data must be converted in numerical values or bounds to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Networks (BNs) are a useful tool to integrate different information and in particular to study the risk's joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a BN in the particular case in which only prior probabilities of node states and marginal correlations between nodes are available, and when the variables have only two states.},
 bibtype = {article},
 author = {Bonafede, C.E. and Giudici, P.},
 doi = {10.1016/j.physa.2007.02.065},
 journal = {Physica A: Statistical Mechanics and its Applications},
 number = {1}
}

@article{
 title = {A Bayesian network model to assess the public health risk associated with wet weather sewer overflows discharging into waterways},
 type = {article},
 year = {2012},
 keywords = {Bayesian networks,Risk assessment,Sanitary sewer overflows,Water pollution},
 pages = {4933-4940},
 volume = {46},
 websites = {http://www.sciencedirect.com/science/article/pii/S0043135412002187},
 month = {10},
 id = {b4ee6e90-27d4-3d22-8405-a99c04f106a3},
 created = {2015-04-16T00:39:20.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2017-03-14T14:39:57.651Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Overflows from sanitary sewers during wet weather, which occur when the hydraulic capacity of the sewer system is exceeded, are considered a potential threat to the ecological and public health of the waterways which receive these overflows. As a result, water retailers in Australia and internationally commit significant resources to manage and abate sewer overflows. However, whilst some studies have contributed to an increased understanding of the impacts and risks associated with these events, they are relatively few in number and there still is a general lack of knowledge in this area. A Bayesian network model to assess the public health risk associated with wet weather sewer overflows is presented in this paper. The Bayesian network approach is shown to provide significant benefits in the assessment of public health risks associated with wet weather sewer overflows. In particular, the ability for the model to account for the uncertainty inherent in sewer overflow events and subsequent impacts through the use of probabilities is a valuable function. In addition, the paper highlights the benefits of the probabilistic inference function of the Bayesian network in prioritising management options to minimise public health risks associated with sewer overflows.},
 bibtype = {article},
 author = {Goulding, R. and Jayasuriya, N. and Horan, E.},
 doi = {10.1016/j.watres.2012.03.044},
 journal = {Water Research},
 number = {16}
}

@article{
 title = {Statistical models for operational risk management},
 type = {article},
 year = {2004},
 keywords = {02.50.−r,89.65.Gh,Bayesian networks,Operational risk management,Predictive models,Value at risk},
 pages = {166-172},
 volume = {338},
 websites = {http://www.sciencedirect.com/science/article/pii/S0378437104002341},
 month = {7},
 id = {665e0f15-0663-37b7-8791-5303b0fdd603},
 created = {2015-04-16T00:39:20.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2017-03-14T14:39:57.651Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The Basel Committee on Banking Supervision has released, in the last few years, recommendations for the correct determination of the risks to which a banking organization is subject. This concerns, in particular, operational risks, which are all those management events that may determine unexpected losses. It is necessary to develop valid statistical models to measure and, consequently, predict, such operational risks. In the paper we present the possible approaches, including our own proposal, which is based on Bayesian networks.},
 bibtype = {article},
 author = {Cornalba, Chiara and Giudici, Paolo},
 doi = {10.1016/j.physa.2004.02.039},
 journal = {Physica A: Statistical Mechanics and its Applications},
 number = {1-2}
}

@article{
 title = {Safety and risk analysis of managed pressure drilling operation using Bayesian network},
 type = {article},
 year = {2015},
 keywords = {BHP,BN,BT,Bayesian Network,Bayesian network analysis,Blowout prevention,Bottom Hole Pressure,Bow-tie approach,CBHP,CCS,COBD,CPT,Conditional Probability Table,Constant Bottom Hole Pressure,Continuous Circulation System,Conventional Over-balanced Drilling,DAG,DAPC,DGD,Directed Acyclic Graph,Dual Gradient Drilling,Dynamic Annular Pressure Control,ECD,ET,Equivalent Circulating Density,FG,FIT,FT,Formation Integrity Test,Fracture Gradient,IADC,ICU,Intelligent Control Unit,International Association of Drilling Contractors,LHS,LOT,Leak off Test,Left Hand Side,MODU,MPD,Managed Pressure Drilling,Managed pressure drilling,Mobile Offshore Drilling Unit,NPT,PMCD,PP,PWD,Pore Pressure,Pressure measurement While Drilling,Pressurized Mud Cap Drilling,QRA,Quantitative Risk Analysis,RCD,RHS,Right Hand Side,Rotating control device,bow-tie,event tree,fault tree,non productive time,rotating control device},
 pages = {133-144},
 volume = {76},
 websites = {http://www.sciencedirect.com/science/article/pii/S0925753515000119},
 month = {7},
 id = {7370c67c-7dea-38d3-8b76-5fe12c2f5f70},
 created = {2015-04-16T00:39:20.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2017-03-14T14:39:57.651Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The exploration and development of oil and gas resources located in extreme and harsh offshore environments are characterized with high safety risk and drilling cost. Some of these resources would be either uneconomical if extracted using conventional overbalanced drilling due to increased drilling problems and prolonged non-productive time, or too risky to adopt underbalanced drilling technique. Seeking new ways to reduce drilling cost and minimize risks has led to the development of managed pressure drilling techniques. Managed pressure drilling methods address the drawbacks of conventional overbalanced and underbalanced drilling techniques. As managed pressure drilling techniques are evolving, there are many unanswered questions related to safety and operating pressure regime. This study investigates the safety and operational issues of constant bottom-hole pressure drilling technique which is used in managed pressure drilling compared to conventional overbalanced drilling. The study first uses bow-tie models to map safety challenges and operating pressure regimes in constant bottom-hole pressure drilling technique. Due to the difficulties in modeling dependencies and updating the belief on the operational data, the bow-ties are mapped into Bayesian networks. The Bayesian networks are thoroughly analyzed to assess the safety critical elements of constant bottom-hole pressure drilling techniques and their safe operating pressure regime.},
 bibtype = {article},
 author = {Abimbola, Majeed and Khan, Faisal and Khakzad, Nima and Butt, Stephen},
 doi = {10.1016/j.ssci.2015.01.010},
 journal = {Safety Science}
}

@article{
 title = {A Bayesian network to manage risks of maritime piracy against offshore oil fields},
 type = {article},
 year = {2014},
 keywords = {Bayesian networks,Offshore oil fields,Oil platforms,Pirate attacks,Quantitative and qualitative knowledge},
 pages = {222-230},
 volume = {68},
 id = {bd48a973-ffd3-36a1-b61e-c3c4e76dcd52},
 created = {2015-04-22T21:19:06.000Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2017-03-14T14:39:57.651Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In recent years, pirate attacks against shipping and oil field installations have become more frequent and more serious. This article proposes an innovative solution to the problem of offshore piracy from the perspective of the entire processing chain: from the detection of a potential threat to the implementation of a response. The response to an attack must take into account multiple variables: the characteristics of the threat and the potential target, existing protection tools, environmental constraints, etc. The potential of Bayesian networks is used to manage this large number of parameters and identify appropriate counter-measures. ?? 2014 Elsevier Ltd.},
 bibtype = {article},
 author = {Bouejla, Amal and Chaze, Xavier and Guarnieri, Franck and Napoli, Aldo},
 doi = {10.1016/j.ssci.2014.04.010},
 journal = {Safety Science}
}

@article{
 title = {Mathematical Decision Model for Reverse Supply Chains Inventory Literature review},
 type = {article},
 year = {2014},
 keywords = {bayesian networks,decision aid,inventory models,reverse supply chains},
 pages = {686-693},
 volume = {9},
 id = {ef05a9de-88df-321b-930e-079ea100bc9e},
 created = {2015-04-23T00:08:23.000Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2017-03-14T14:39:57.651Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Duta, L and Zamfirescu, C B and Filip, F G},
 journal = {INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL},
 number = {6}
}

@article{
 title = {Risk analysis of deepwater drilling operations using Bayesian network},
 type = {article},
 year = {2015},
 pages = {11-23},
 volume = {38},
 websites = {http://linkinghub.elsevier.com/retrieve/pii/S0950423015300188},
 month = {11},
 publisher = {Elsevier Ltd},
 id = {24fe5c2c-2a98-36f8-afbc-5f12277dc006},
 created = {2017-06-01T01:02:34.427Z},
 accessed = {2017-05-31},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2017-06-01T01:02:34.427Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Bhandari, Jyoti and Abbassi, Rouzbeh and Garaniya, Vikram and Khan, Faisal},
 doi = {10.1016/j.jlp.2015.08.004},
 journal = {Journal of Loss Prevention in the Process Industries}
}

@article{
 title = {Probabilistic risk assessment of major accidents: application to offshore blowouts in the Gulf of Mexico},
 type = {article},
 year = {2014},
 volume = {74},
 id = {50bddf4a-e099-3353-9f63-0da05bd9a677},
 created = {2017-08-23T21:32:33.818Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2017-08-23T21:34:10.828Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Major accidents are low-frequency, high-consequence accidents which are not well supported by conventional statistical methods due to the scarcity of directly relevant data. Modeling and decomposition techniques such as event tree have been proved as robust alternatives as they facilitate incorporation of partially relevant near accident data-accident precursor data-in probability estimation and risk analysis of major accidents. In this study, we developed a methodology based on event tree and hierarchical Bayesian analysis to establish informative distributions for offshore blowouts using data of near accidents, such as kicks, leaks, and failure of blowout preventers collected from a variety of offshore drilling rigs. These informative distributions can be used as predictive tools to estimate relevant failure probabilities in the future. Further, having a set of near accident data of a drilling rig of interest, the informative distributions can be updated to render case-specific posterior distributions which are of great importance in quantitative risk analysis. To cope with uncertainties, we implemented the methodology in a Markov Chain Monte Carlo framework and applied it to risk assessment of offshore blowouts in the Gulf of Mexico.},
 bibtype = {article},
 author = {Khakzad, Nima and Khakzad, Sina and Khan, Faisal},
 doi = {10.1007/s11069-014-1271-8},
 journal = {Natural Hazards},
 number = {3}
}

@article{
 title = {Network based approach for predictive accident modelling},
 type = {article},
 year = {2015},
 volume = {80},
 id = {431a0392-b88e-356a-8bd0-ca485c28fe29},
 created = {2017-08-23T21:37:06.090Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2017-08-23T21:37:06.090Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Accident modeling methodologies in literature such as the System Hazard Identification, Prediction and Prevention (SHIPP) consider accident precursors and the associated five engineering safety barriers to assess the likelihood of accident occurrence with the help of fault and event trees to model the cause-consequence relationship between the failure of safety barriers and potential adverse events and design preventive, controlling and mitigating measures for improving the industrial process safety. In the SHIPP method, a restrictive assumption is used that the severity of the adverse events progresses only through sequential failures of the five safety barriers considered.In the proposed methodology, shortcomings of the existing accident model are improved in the following ways. Firstly, the above mentioned restrictive sequential progression assumption is mitigated in the SHIPP methodology by allowing non-sequential failure of safety barriers to cause adverse events in any order. Secondly, in the prediction of posterior probabilities of adverse events for real time industrial data, an important mechanical safety barrier 'Damage Control Emergency Management Barrier' has been included. Further, posterior probabilities of the occurrence of the adverse events are calculated using a Bayesian network (BN) approach. The utility of this approach is tested and demonstrated with the data from a liquefied natural gas (LNG) process facility. The method allows for continual updating of occurrence probabilities for adverse events and failure probabilities of safety barriers for successive real time data from industry.},
 bibtype = {article},
 author = {Baksh, Al Amin and Khan, Faisal and Gadag, Veeresh and Ferdous, Refaul},
 doi = {10.1016/j.ssci.2015.08.003},
 journal = {Safety Science}
}

@article{
 title = {Dynamic Bayesian network modeling of reliability of subsea blowout preventer stack in presence of common cause failures},
 type = {article},
 year = {2015},
 volume = {38},
 id = {38063a61-ecb5-369c-a24b-d6016f0a17a3},
 created = {2017-08-23T21:39:19.143Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2017-08-23T21:39:19.143Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {A subsea blowout preventer (BOP) stack is used to seal, control and monitor oil and gas wells. It can be regarded as a series-parallel system consisting of several subsystems. This paper develops the dynamic Bayesian network (DBN) of a parallel system with n components, taking account of common cause failures and imperfect coverage. Multiple error shock model is used to model common cause failures. Based on the proposed generic model, DBNs of the two commonly used stack types, namely the conventional BOP and modern BOP are developed. In order to evaluate the effects of the failure rates and coverage factor on the reliability and availability of the stacks, sensitivity analysis is performed.},
 bibtype = {article},
 author = {Liu, Zengkai and Liu, Yonghong and Cai, Baoping and Zhang, Dawei and Zheng, Chao},
 doi = {10.1016/j.jlp.2015.09.001},
 journal = {Journal of Loss Prevention in the Process Industries}
}

@article{
 title = {A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study},
 type = {article},
 year = {2016},
 volume = {60},
 id = {3d0130c7-327c-38ae-8805-c5b66b3d8e29},
 created = {2017-08-23T21:40:59.017Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2017-08-23T21:40:59.017Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project.},
 bibtype = {article},
 author = {Yet, Barbaros and Constantinou, Anthony and Fenton, Norman and Neil, Martin and Luedeling, Eike and Shepherd, Keith},
 doi = {10.1016/j.eswa.2016.05.005},
 journal = {Expert Systems with Applications}
}

@article{
 title = {Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches},
 type = {article},
 year = {2017},
 volume = {45},
 id = {703fab28-be7f-3340-b2ca-f27e3b100b6c},
 created = {2018-03-31T13:52:13.308Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-03-31T13:52:13.308Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Industrial accidents are a major concern for companies and families alike. It is a high priority to all stakeholders that steps be taken to prevent accidents from occurring. In this paper, three approaches to safety are examined: fault trees (FT), Bayesian networks (BN), and the Functional Resonance Analysis Method (FRAM). A case study of a propane feed control system is used to apply these methods. In order to make safety improvements to industrial workplaces high understanding of the systems is required. It is shown that consideration of the chance of failure of the system components, as in the FT and BN approaches, may not provide enough understanding to fully inform safety assessments. The FT and BN methods are top-down approaches that are formed from the perspective of management in workplaces. The FRAM methodology uses a bottom-up approach from the operational perspective to improve the understanding of the industrial workplace. The FRAM approach can provide added insight to the human factor and context and increase the rate at which we learn by considering successes as well as failures. FRAM can be a valuable tool for industrial safety assessment and to consider industrial safety holistically, by providing a framework to examine the operations in detail. However, operations should be considered using both top-down and bottom-up perspectives and all operational experience to make the most informed safety decisions.},
 bibtype = {article},
 author = {Smith, Doug and Veitch, Brian and Khan, Faisal and Taylor, Rocky},
 doi = {10.1016/j.jlp.2016.11.016},
 journal = {Journal of Loss Prevention in the Process Industries}
}

@article{
 title = {A Bayesian network methodology for optimal security management of critical infrastructures},
 type = {article},
 year = {2018},
 websites = {http://linkinghub.elsevier.com/retrieve/pii/S0951832017311535},
 month = {3},
 id = {f32946ff-ce59-32d6-80db-4fd7062c5445},
 created = {2018-03-31T22:16:37.668Z},
 accessed = {2018-03-31},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-03-31T22:16:37.668Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Security management of critical infrastructures is a complex task as a great variety of technical and socio-political information is needed to realistically predict the risk of intentional malevolent acts. In the present study, a methodology based on Limited Memory Influence Diagram (LIMID) has been developed for the protection of critical infrastructures via cost-effective allocation of security measures. LIMID is an extension of Bayesian network (BN) intended for decision-making, allowing for efficient modeling of complex systems while accounting for interdependencies and interaction of system components. The probability updating feature of BN has been used to investigate the effect of vulnerabilities on adversaries’ preferences when planning attacks. Moreover, the proposed methodology has been shown to be able to identify an optimal defensive strategy given an attack through maximizing defenders’ expected utility. Despite being demonstrated via a chemical facility, the methodology can easily be tailored to a wide variety of critical infrastructures.},
 bibtype = {article},
 author = {Misuri, Alessio and Khakzad, Nima and Reniers, Genserik and Cozzani, Valerio},
 doi = {10.1016/j.ress.2018.03.028},
 journal = {Reliability Engineering & System Safety}
}

@article{
 title = {Risk identification of third-party damage on oil and gas pipelines through the Bayesian network},
 type = {article},
 year = {2018},
 pages = {163-178},
 volume = {54},
 websites = {http://linkinghub.elsevier.com/retrieve/pii/S0950423017310719},
 month = {7},
 id = {c01b2f9d-9a0b-3add-b06e-7f4d8d78fe8c},
 created = {2018-03-31T22:27:14.637Z},
 accessed = {2018-03-31},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-03-31T22:27:14.637Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper aims to identify the risks influencing oil and gas (O&G) pipeline safety caused by third-party damage (TPD). After comprehensive literature study, we found that the traditional risk identification of TPD suffers from defining binary states of risk only and ignores the risk factors after pipeline failure. To overcome this problem, we investigated incident reports to identify previously unrecognized additional factors. This work also developed a graphic model by using Bayesian theory to cope with the multistate risks arising from third parties and to present the incident evolution process explicitly. Furthermore, this paper included a leakage case study conducted to verify the logicality of this model. The results of case study inspire that the proposed methodology can be used in a dual assurance approach for risk mitigation, namely learning from previous incidents and continuously capturing new risk information for risk prevention.},
 bibtype = {article},
 author = {Guo, Xiaoyan and Zhang, Laibin and Liang, Wei and Haugen, Stein},
 doi = {10.1016/j.jlp.2018.03.012},
 journal = {Journal of Loss Prevention in the Process Industries}
}

@article{
 title = {Life-cycle Risk Modeling: Alternate Methods Using Bayesian Belief Networks},
 type = {article},
 year = {2014},
 keywords = {Bayesian Belief Network,Life-Cycle,Supply Chain Risk},
 pages = {320-325},
 volume = {17},
 websites = {http://www.sciencedirect.com/science/article/pii/S2212827114002881},
 id = {13cec4f1-25f9-313b-84fe-55538d5bbc6a},
 created = {2018-03-31T22:35:56.457Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-03-31T22:35:56.457Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In recent years natural and man-made disasters have highlighted the need for robust supply chain risk management (SCRM) in manufacturing firms from a life-cycle perspective (pre-manufacturing, manufacturing, use, post-use stages). Bayesian Belief Networks (BBN) provide a means to probabilistically represent risk interdependencies and to proactively identify and manage any existing vulnerabilities. In this work, the BBN method is implemented for a product in the aerospace industry. Risk network maps are developed to identify interdependencies and describe the potential risk propagation behavior during each life-cycle phase and from one phase to another. Due to limited number of respondents and lack of certainty with respect to the post-use phase, enhanced methods of risk likelihood assessment are necessary specifically for the post-use phase assessment. In this paper two alternate techniques are compared for risk modeling using BBN in such situations: Boolean nodes and numeric simulation nodes. Results show that numeric nodes provide a more thorough explanation of the interconnections of the risk items modeled. Further enhancement using an approach that combines both BBN and System Dynamics (SD) for SCRM is discussed and possible variations for linking variables between SD and BBN are also presented.},
 bibtype = {article},
 author = {Amundson, Joseph and Brown, Adam and Grabowski, Matthias and Badurdeen, Fazleena},
 doi = {10.1016/j.procir.2014.02.029},
 journal = {Procedia CIRP}
}

@article{
 title = {Evaluating risk of water mains failure using a Bayesian belief network model},
 type = {article},
 year = {2015},
 keywords = {Bayesian Belief Network (BBN),Consequence,Deterioration,Risk analysis,Water distribution network},
 pages = {220-234},
 volume = {240},
 websites = {http://www.sciencedirect.com/science/article/pii/S0377221714005360},
 month = {1},
 id = {27c1d147-4e5c-325d-959c-65a1f28e89a9},
 created = {2018-03-31T22:35:56.468Z},
 accessed = {2015-03-05},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-03-31T22:35:56.468Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {It has been reported that since year 2000, there have been an average 700 water main breaks per day only in Canada and the USA costing more than CAD 10 billions/year. Moreover, water main leaks affect other neighboring infrastructure that may lead to catastrophic failures. For this, municipality authorities or stakeholders are more concerned about preventive actions rather reacting to failure events. This paper presents a Bayesian Belief Network (BBN) model to evaluate the risk of failure of metallic water mains using structural integrity, hydraulic capacity, water quality, and consequence factors. BBN is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. The proposed model is capable of ranking water mains within distribution network that can identify vulnerable and sensitive pipes to justify proper decision action for maintenance/rehabilitation/replacement (M/R/R). To demonstrate the application of proposed model, water distribution network of City of Kelowna has been studied. Result indicates that almost 9% of the total 259 metallic pipes are at high risk in both summer and winter.},
 bibtype = {article},
 author = {Kabir, Golam and Tesfamariam, Solomon and Francisque, Alex and Sadiq, Rehan},
 doi = {10.1016/j.ejor.2014.06.033},
 journal = {European Journal of Operational Research},
 number = {1}
}

@article{
 title = {Low-capacity utilization of process plants: A cost-robust approach to tackle man-made domino effects},
 type = {article},
 year = {2018},
 websites = {http://linkinghub.elsevier.com/retrieve/pii/S0951832017309158},
 month = {3},
 id = {b6b43a9b-8093-3d54-aca0-93d57e805ebb},
 created = {2018-03-31T23:32:39.672Z},
 accessed = {2018-03-31},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-03-31T23:32:39.672Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Process plants can be potential targets to terrorist attacks with the aim of triggering domino effects. Compared to accidental domino effects where the possibility of having multiple primary events is very remote, man-made domino effects are likelier to be initiated from multiple units within the plant in order to increase the knock-on likelihood and thus causing maximum damage. In this regard, identification of critical units that - under attack - may lead to likelier and severer domino effects is crucial both to assess the vulnerability of process plants and subsequently to increase their robustness to such attacks. In the present work, we have applied graph theory and dynamic Bayesian network to identify critical units. Further, low-capacity utilization of process plants (e.g., by keeping some of the storage tanks empty) has been demonstrated as an effective strategy in the case of imminent terrorist attacks. As such, the robustness of the plant against intentional attacks can temporarily be increased while considering the cost incurred because of such a low-capacity utilization.},
 bibtype = {article},
 author = {Khakzad, Nima and Reniers, Genserik},
 doi = {10.1016/j.ress.2018.03.030},
 journal = {Reliability Engineering & System Safety}
}

@article{
 title = {Risk management in port and maritime logistics},
 type = {article},
 year = {2018},
 websites = {https://www.sciencedirect.com/science/article/abs/pii/S0001457518301416#!},
 month = {4},
 publisher = {Pergamon},
 day = {11},
 id = {d8b52b51-ce3a-327d-a2e3-41160dbf58a3},
 created = {2018-04-28T14:15:44.386Z},
 accessed = {2018-04-24},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-04-28T14:15:44.386Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Lam, Jasmine Siu Lee and Lun, Y.H. Venus and Bell, Michael G.H.},
 doi = {10.1016/J.AAP.2018.04.003},
 journal = {Accident Analysis & Prevention}
}

@article{
 title = {Assessment and Countermeasures for Offshore Wind Farm Risks Based on a Dynamic Bayesian Network},
 type = {article},
 year = {2018},
 pages = {368-384},
 volume = {09},
 websites = {http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jep.2018.94024},
 month = {4},
 publisher = {Scientific Research Publishing},
 day = {17},
 id = {3a4dfcfb-c5d9-3d33-95e0-8b555fc4a9e8},
 created = {2018-05-29T00:32:00.841Z},
 accessed = {2018-05-28},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-05-29T00:32:00.841Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Wind power is a kind of clean energy promising significant social and environmental benefits, and in The Peoples Republic of China, the government supports and encourages the development of wind power as one element in a shift to renewable energy. In recent years however, maritime safety issues have arisen during offshore wind power construction and attendant production processes associated with the rapid promotion and development of offshore wind farms. Therefore, it is necessary to carry out risk assessment for phases in the life cycle of offshore wind farms. This paper reports on a risk assessment model based on a Dynamic Bayesian network that performs offshore wind farms maritime risk assessment. The advantage of this approach is the way in which a Bayesian model expresses uncertainty. Furthermore, such models permit simulations and reenactment of accidents in a virtual environment. There were several goals in this research. Offshore wind power project risk identification and evaluation theories and methods were explored to identify the sources of risk during different phases of the offshore wind farm life cycle. Based on this foundation, a dynamic Bayesian network model with Genie was established, and evaluated, in terms of its effectiveness for analysis of risk during different phases of the offshore wind farm life cycle. Research results show that a dynamic Bayesian network method can perform risk assessments effectively and flexibly, responding to the actual context of offshore wind power construction. Historical data and almost real-time information are combined to analyze the risk of the construction of offshore wind power. Our results inform a discussion of security and risk mitigation measures that when implemented, could improve safety. This work has value as a reference and guide for the safe development of offshore wind power.},
 bibtype = {article},
 author = {Zhou, Chunhui and Liu, Xin and Gan, Langxiong},
 doi = {10.4236/jep.2018.94024},
 journal = {Journal of Environmental Protection},
 number = {04}
}

@article{
 title = {A Three-Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property},
 type = {article},
 year = {2018},
 keywords = {Bayesian network,benefit,cost,dwelling fires,human reaction,probability of fatality},
 websites = {http://doi.wiley.com/10.1111/risa.13113},
 month = {5},
 publisher = {Wiley/Blackwell (10.1111)},
 day = {17},
 id = {54568ea5-39f0-3290-9788-d5305e7ed240},
 created = {2018-05-29T00:38:58.160Z},
 accessed = {2018-05-28},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-05-29T00:38:58.160Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Matellini, D. B. and Wall, A. D. and Jenkinson, I. D. and Wang, J. and Pritchard, R.},
 doi = {10.1111/risa.13113},
 journal = {Risk Analysis}
}

@article{
 title = {Project Portfolio Risk Identification and Analysis, Considering Project Risk Interactions and Using Bayesian Networks},
 type = {article},
 year = {2018},
 keywords = {Bayesian networks,project portfolio risk,risk analysis,risk identification,risk interactions},
 pages = {1609},
 volume = {10},
 websites = {http://www.mdpi.com/2071-1050/10/5/1609},
 month = {5},
 publisher = {Multidisciplinary Digital Publishing Institute},
 day = {17},
 id = {f794f84b-0d80-3381-a929-3d81f93220c7},
 created = {2018-05-29T16:48:25.361Z},
 accessed = {2018-05-28},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-05-29T16:48:25.361Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {An organization&rsquo;s strategic objectives are accomplished through portfolios. However, the materialization of portfolio risks may affect a portfolio&rsquo;s sustainable success and the achievement of those objectives. Moreover, project interdependencies and cause&ndash;effect relationships between risks create complexity for portfolio risk analysis. This paper presents a model using Bayesian network (BN) methodology for modeling and analyzing portfolio risks. To develop this model, first, portfolio-level risks and risks caused by project interdependencies are identified. Then, based on their cause&ndash;effect relationships all portfolio risks are organized in a BN. Conditional probability distributions for this network are specified and the Bayesian networks method is used to estimate the probability of portfolio risk. This model was applied to a portfolio of a construction company located in Iran and proved effective in analyzing portfolio risk probability. Furthermore, the model provided valuable information for selecting a portfolio&rsquo;s projects and making strategic decisions.},
 bibtype = {article},
 author = {Ghasemi, Foroogh and Sari, Mohammad and Yousefi, Vahidreza and Falsafi, Reza and Tamošaitienė, Jolanta},
 doi = {10.3390/su10051609},
 journal = {Sustainability},
 number = {5}
}

@article{
 title = {Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network},
 type = {article},
 year = {2018},
 websites = {https://www.sciencedirect.com/science/article/pii/S1364815218301191},
 month = {10},
 publisher = {Elsevier},
 day = {26},
 id = {0cfa7415-7be3-3872-b2bb-6a249987c9bd},
 created = {2018-11-13T13:56:30.150Z},
 accessed = {2018-11-10},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-11-13T13:56:30.150Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper proposes a model for risk analysis of real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. The proposed model consists of three components: Monte Carlo simulations, dynamic Bayesian network establishing, and risk-informed inference for decision making. The Monte Carlo simulations provide basic data inputs for the dynamic Bayesian network establishing using the historical floods and operation models of the multi-reservoir system. The dynamic Bayesian network is built with expert knowledge and the relationships among the uncertainties. The component of risk-informed inference for decision making is to provide risk information about the operation schedules using the trained dynamic Bayesian network. We apply the proposed model to a multi-reservoir system in China. The results show that the proposed method has a capability for bi-directional inferences and can be served as a risk-informed decision-making tool under uncertainties in the real-time flood control operation of a multi-reservoir system.},
 bibtype = {article},
 author = {Chen, Juan and Zhong, Ping-An and An, Ru and Zhu, Feilin and Xu, Bin},
 doi = {10.1016/J.ENVSOFT.2018.10.007},
 journal = {Environmental Modelling & Software}
}

@inbook{
 type = {inbook},
 year = {2019},
 pages = {1193-1199},
 websites = {http://link.springer.com/10.1007/978-3-030-01057-7_88},
 month = {9},
 publisher = {Springer, Cham},
 day = {6},
 id = {4189eda4-c40a-31f0-a718-fd11c0d2e792},
 created = {2018-11-18T14:57:28.145Z},
 accessed = {2018-11-13},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-11-18T14:57:28.145Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {inbook},
 author = {Gerassis, Saki and Abad, Alberto and Saavedra, Ángeles and García, Julio F. and Taboada, Javier},
 doi = {10.1007/978-3-030-01057-7_88},
 chapter = {Women’s Occupational Health: Improving Medical Protocols with Artificial Intelligence Solutions}
}

@article{
 title = {Societal Risk and Resilience Analysis: Dynamic Bayesian Network Formulation of a Capability Approach},
 type = {article},
 year = {2019},
 pages = {04018046},
 volume = {5},
 websites = {http://ascelibrary.org/doi/10.1061/AJRUA6.0000996},
 month = {3},
 id = {9a652a37-3c70-3a62-9ddc-1be3e6e06406},
 created = {2018-11-23T14:18:02.616Z},
 accessed = {2018-11-18},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2018-11-23T14:18:02.616Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Tabandeh, Armin and Gardoni, Paolo and Murphy, Colleen and Myers, Natalie},
 doi = {10.1061/AJRUA6.0000996},
 journal = {ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering},
 number = {1}
}

@article{
 title = {Assessment of Safety Integrity Level by simulation of Dynamic Bayesian Networks considering test duration},
 type = {article},
 year = {2019},
 pages = {101-113},
 volume = {57},
 websites = {https://www.sciencedirect.com/science/article/pii/S0950423018306338},
 month = {1},
 publisher = {Elsevier},
 day = {1},
 id = {fce8ef87-dcf3-3df8-bcd9-d6354b5f165c},
 created = {2019-01-11T15:39:18.601Z},
 accessed = {2018-12-06},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2019-01-11T15:39:18.601Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper is devoted to model Safety Instrumented Systems (SISs) availability by Dynamic Bayesian Networks (DBNs). The models integrate several parameters but the main concerns of the study are the integration of test duration and test strategy. The proposed DBN are generic and can be reused for assessing performance and testing the effect of some parameters. More attention has been paid to the performance of the proof test, its harmlessness and particularly its duration. The duration increases the model complexity when considering the components availability given the test but it is more realistic. This parameter should be decided carefully to satisfy the Safety Integrity Level (SIL) objectives.},
 bibtype = {article},
 author = {Simon, Christophe and Mechri, Walid and Capizzi, Guillaume},
 doi = {10.1016/J.JLP.2018.11.002},
 journal = {Journal of Loss Prevention in the Process Industries}
}

@article{
 title = {Bayesian network model for quality control with categorical attribute data},
 type = {article},
 year = {2019},
 pages = {105746},
 volume = {84},
 websites = {https://www.sciencedirect.com/science/article/pii/S1568494619305277#!},
 month = {11},
 publisher = {Elsevier},
 day = {1},
 id = {e35caadc-5cd7-301b-8d2a-664262632d10},
 created = {2019-09-07T13:01:18.438Z},
 accessed = {2019-09-07},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2019-09-07T13:01:18.532Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {A Bayesian network is developed to monitor a production process where categorical attribute data are available. The number of sample items in each category is entered each time period, allowing the revised probability that the system is in-control or in one of multiple out-of-control states to be calculated. In contrast to other Bayesian methods, qualitative knowledge can be combined with sample data. The network permits the classification of the system into more than two states, so diagnostic analysis can be performed simultaneously with inference. The system state can be updated to reflect evidence on variables that complements the sample data.},
 bibtype = {article},
 author = {Cobb, Barry R. and Li, Linda},
 doi = {10.1016/J.ASOC.2019.105746},
 journal = {Applied Soft Computing}
}

@article{
 title = {A Bayesian Network Approach for Condition Monitoring of High-Speed Railway Catenaries},
 type = {article},
 year = {2019},
 pages = {1-15},
 websites = {https://ieeexplore.ieee.org/document/8805158/},
 id = {d8361035-c0be-3dab-a70f-66d746a49468},
 created = {2019-09-07T15:12:50.316Z},
 accessed = {2019-09-07},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2019-09-07T15:12:50.374Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Wang, Hongrui and Nunez, Alfredo and Liu, Zhigang and Zhang, Dongliang and Dollevoet, Rolf},
 doi = {10.1109/TITS.2019.2934346},
 journal = {IEEE Transactions on Intelligent Transportation Systems}
}

@article{
 title = {A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach},
 type = {article},
 year = {2019},
 publisher = {Springer US},
 id = {dc647355-529c-3374-ac4f-4b3445a2e862},
 created = {2019-09-30T14:09:25.592Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2019-09-30T14:09:25.592Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This is the first study that presents a supply chain (SC) resilience measure with the ripple effect considerations including both disruption and recovery stages. SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case- study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed.},
 bibtype = {article},
 author = {Hosseini, Seyedmohsen and Ivanov, Dmitry},
 doi = {10.1007/s10479-019-03350-8},
 journal = {Annals of Operations Research}
}

@article{
 title = {Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach},
 type = {article},
 year = {2019},
 keywords = {Bayesian methods,Markov modelling,ripple effect,supply chain dynamics,supply chain resilience,supply chain risk management},
 pages = {1-19},
 volume = {0},
 websites = {https://doi.org/00207543.2019.1661538},
 publisher = {Taylor & Francis},
 id = {4e7b8f37-b13a-3765-a051-bab4a68d08e7},
 created = {2019-09-30T14:09:25.751Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2019-09-30T14:09:25.751Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Hosseini, Seyedmohsen and Ivanov, Dmitry and Dolgui, Alexandre},
 doi = {10.1080/00207543.2019.1661538},
 journal = {International Journal of Production Research},
 number = {0}
}

@article{
 title = {A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach},
 type = {article},
 year = {2019},
 publisher = {Springer US},
 id = {40c54eef-72a2-37a2-b82e-380dd4e0bb65},
 created = {2019-11-23T23:40:00.765Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2019-11-23T23:40:00.765Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This is the first study that presents a supply chain (SC) resilience measure with the ripple effect considerations including both disruption and recovery stages. SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case- study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed.},
 bibtype = {article},
 author = {Hosseini, Seyedmohsen and Ivanov, Dmitry},
 doi = {10.1007/s10479-019-03350-8},
 journal = {Annals of Operations Research}
}

@article{
 title = {Use of Bayesian Network for Risk-Based Fatigue Integrity Assessment: Application for Topside Piping in an Arctic Environment},
 type = {article},
 year = {2019},
 pages = {421-428},
 volume = {29},
 websites = {http://legacy.isope.org/publications/journals/journalDecember19.htm},
 month = {12},
 day = {1},
 id = {cfa87aad-9ef5-3ba6-861e-bf24be1dd1bf},
 created = {2019-12-04T14:15:49.112Z},
 accessed = {2019-12-04},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2019-12-04T14:15:49.181Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Keprate, Arvind and Ratnayake, RM Chandima},
 doi = {10.17736/ijope.2019.bn19},
 journal = {International Journal of Offshore and Polar Engineering},
 number = {4}
}

@article{
 title = {Understanding the vulnerability of beef producers in Australia to an FMD outbreak using a Bayesian Network predictive model},
 type = {article},
 year = {2019},
 pages = {104872},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S0167587719306348},
 month = {12},
 id = {dde1afe5-4e27-3ca7-8ebc-7831619b86b3},
 created = {2019-12-21T18:28:26.312Z},
 accessed = {2019-12-21},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2019-12-21T18:28:26.312Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Manyweathers, Jennifer and Maru, Yiheyis and Hayes, Lynne and Loechel, Barton and Kruger, Heleen and Mankad, Aditi and Xie, Gang and Woodgate, Rob and Hernandez-Jover, Marta},
 doi = {10.1016/j.prevetmed.2019.104872},
 journal = {Preventive Veterinary Medicine}
}

@techreport{
 title = {Ship collision on temporary structures Combi-walls under collision loading Cover photo: Construction of the Maasdeltatunnel Photo by Rijkswaterstaat},
 type = {techreport},
 year = {2018},
 websites = {http://repository.tudelft.nl/},
 id = {467b614a-1429-3dd6-a33a-7c1b94463372},
 created = {2019-12-21T18:41:50.531Z},
 accessed = {2019-12-21},
 file_attached = {true},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2019-12-21T18:42:31.208Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {techreport},
 author = {Jansen, J}
}

@article{
 title = {Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines},
 type = {article},
 year = {2020},
 pages = {178-188},
 volume = {134},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S0957582019306664},
 month = {2},
 id = {96335c3c-c3eb-39e7-a873-ffffd85cbdb6},
 created = {2019-12-28T14:08:41.182Z},
 accessed = {2019-12-28},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2019-12-28T14:08:41.270Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Cui, Yan and Quddus, Noor and Mashuga, Chad V.},
 doi = {10.1016/j.psep.2019.11.038},
 journal = {Process Safety and Environmental Protection}
}

@misc{
 title = {Flood Risk Prediction under Global Vegetated Hydrodynamics: A Bayesian Network},
 type = {misc},
 year = {2019},
 id = {36c9fdda-4fc4-3b96-8f4d-f1f6da7244b0},
 created = {2019-12-29T15:56:48.150Z},
 accessed = {2019-12-29},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2019-12-29T15:56:48.225Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {The increasing frequency and intensity of extreme events due to global warming and climate change is increasing flood risk. To act, rather than react, nature-based solutions (NBS) involving vegetation and wetlands are being explored on top of conventional solutions like dikes. WHY? There was a dire need for global study quantifying the potential of vegetation in reducing flood risk and eventually make a decision support tool which enables quick assessments about flood risk reduction in a vegetated hydrodynamic system. WHAT? The developed tool can predict flood risk anywhere in the world without rigorous modeling through user defined conditionalization of in-situ hydrodynamic or vegetation characteristics. HOW? Multivariate dependence among parameters of schematized system can exhibit characteristics of vegetated hydrodynamics. To ensure global representation of vegetated hydrodynamics a copula-based multivariate stochastic model has been developed which caters global ranges of each parameter, their probability distributions and the inter-parameter dependencies through ranked correlations. Numerical modeling has been carried out through XBeach non-hydrostatic model by resolving full spectrum of high and low frequency waves. A non-parametric Bayesian network-based flood risk prediction tool has been developed from the synthetic dataset developed from the simulations. SO? Bulk results conclude that saltmarshes attenuates waves by 87% and mangroves by 94% as a mean value. Wave attenuation, flood risk reduction and wave run-up manifests maximum dependence on offshore wave height, water depth, drag coefficient, vegetation height, frontal width, and forest length and least on offshore slope and vegetation density. NOW? The flood risk prediction tool would help decision makers in implementing NBS, in making better informed decisions about early warnings and policy making related to flood risk reduction and climate change adaptation by incorporating vegetation. NOVELTY? To the author's knowledge no such study exists which captures natural variability of hydrodynamics and vegetation together in a probabilistic model over global scales. Additionally, no such study exist which applies non-parametric Bayesian networks to predict flood risk. The dependence modeling of global vegetated hydrodynamic environments is also unique which skims out the most critical parameters.},
 bibtype = {misc},
 author = {Niazi, Muhammad Hassan Khan}
}

@article{
 title = {Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks},
 type = {article},
 year = {2019},
 keywords = {Arbitrage Pricing Model,Banking Stocks JEL: C11,Bayesian Networks,G11,G12,Machine Learning,Portfolio Selection Theory},
 websites = {www.berjournal.com},
 id = {87842199-6f11-388f-9ae4-465c6ad52ffb},
 created = {2019-12-29T16:14:12.780Z},
 accessed = {2019-12-29},
 file_attached = {true},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2019-12-29T16:14:12.933Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {According to the modern portfolio theory, the direction of the relationship between the securities in the portfolio is stated to be effective in reducing the risk. Moreover, securities in high correlation are avoided by taking place in the same portfolio. The models structured by the Bayesian networks are capable of visually illustrate the probabilistic relationship. Also, portfolio returns could be refreshed simultaneously when new information has arrived. The study aims to provide dynamic information through Bayesian networks and to investigate the relationship between macroeconomic indicators and stock returns of Turkish major bank stocks based on the Arbitrage Pricing Model. The dataset includes stock returns of four banks listed in the Borsa Istanbul from June 2001 to January 2017. Besides, macroeconomic variables such as BIST-100 Index, oil prices, inflation, exchange, and interest rate & money supply are gathered for the same period. The results suggest that the Bayesian network models allow dynamics among stock returns could be investigated in more detail. Additionally, it determines that macroeconomic variables would have various impacts on stock returns on bank stocks by comparison of the conventional methods.},
 bibtype = {article},
 author = {Busem Hatipoglu, Fatma and Uyar, Umut},
 doi = {10.20409/berj.2019.202}
}

@article{
 title = {A Bayesian network approach to refining ecological risk assessments: Mercury and the Florida panther (Puma concolor coryi)},
 type = {article},
 year = {2020},
 pages = {108911},
 volume = {418},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S0304380019304193},
 month = {2},
 id = {4adc18eb-ce64-3f83-8f9f-f08feb8f2625},
 created = {2020-01-28T14:13:04.891Z},
 accessed = {2020-01-28},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2020-01-28T14:13:04.965Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Carriger, John F. and Barron, Mace G.},
 doi = {10.1016/j.ecolmodel.2019.108911},
 journal = {Ecological Modelling}
}

@misc{
 title = {(24) (PDF) An Application of Bayesian Networks to Antiterrorism Risk Management for Military Planners},
 type = {misc},
 websites = {https://www.researchgate.net/publication/2376453_An_Application_of_Bayesian_Networks_to_Antiterrorism_Risk_Management_for_Military_Planners},
 id = {c5026614-7b5f-3a5f-b828-9640830e4569},
 created = {2020-03-05T01:09:49.743Z},
 accessed = {2020-03-04},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2020-03-05T01:09:49.814Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {misc},
 author = {}
}

@article{
 title = {Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment},
 type = {article},
 year = {2018},
 keywords = {Bayesian network,Genetic algorithm,Grey relational analysis,Risk assessment},
 pages = {237-248},
 volume = {9},
 month = {6},
 publisher = {Beijing Normal University Press},
 day = {1},
 id = {6bf9daf9-fc8d-3688-a490-a6cbd5c416b2},
 created = {2020-03-05T01:23:39.185Z},
 accessed = {2020-03-04},
 file_attached = {true},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2020-03-05T01:23:43.569Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {The application of Bayesian network (BN) theory in risk assessment is an emerging trend. But in cases where data are incomplete and variables are mutually related, its application is restricted. To overcome these problems, an improved BN assessment model with parameter retrieval and decorrelation ability is proposed. First, multivariate nonlinear planning is applied to the feedback error learning of parameters. A genetic algorithm is used to learn the probability distribution of nodes that lack quantitative data. Then, based on an improved grey relational analysis that considers the correlation of variation rate, the optimal weight that characterizes the correlation is calculated and the weighted BN is improved for decorrelation. An improved risk assessment model based on the weighted BN then is built. An assessment of sea ice disaster shows that the model can be applied for risk assessment with incomplete data and variable correlation.},
 bibtype = {article},
 author = {Li, Ming and Hong, Mei and Zhang, Ren},
 doi = {10.1007/s13753-018-0171-z},
 journal = {International Journal of Disaster Risk Science},
 number = {2}
}

@misc{
 title = {(99+) (PDF) Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children | paola berchialla - Academia.edu},
 type = {misc},
 websites = {https://www.academia.edu/20493958/Comparing_models_for_quantitative_risk_assessment_an_application_to_the_European_Registry_of_foreign_body_injuries_in_children?email_work_card=thumbnail},
 id = {2cb55440-7c15-3f08-94e7-2da8fba5d230},
 created = {2020-04-14T20:11:28.784Z},
 accessed = {2020-04-14},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},
 last_modified = {2020-04-14T20:11:28.853Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {misc},
 author = {}
}\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! 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To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"cui-quddus-mashuga-bayesiannetworkandgametheoryriskassessmentmodelforthirdpartydamagetooilandgaspipelines-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0957582019306664\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'cui-quddus-mashuga-bayesiannetworkandgametheoryriskassessmentmodelforthirdpartydamagetooilandgaspipelines-2020', 'https://linkinghub.elsevier.com/retrieve/pii/S0957582019306664', event);\">\n    Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Cui,  Y.; Quddus,  N.;  and Mashuga,  C., V.\n</span>\n\n  \n\n</span>\n\n  <i>Process Safety and Environmental Protection</i>, 134: 178-188. 2 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0957582019306664\"\n     onclick=\"log_download('cui-quddus-mashuga-bayesiannetworkandgametheoryriskassessmentmodelforthirdpartydamagetooilandgaspipelines-2020', 'https://linkinghub.elsevier.com/retrieve/pii/S0957582019306664', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.psep.2019.11.038\"\n     onclick=\"log_download('cui-quddus-mashuga-bayesiannetworkandgametheoryriskassessmentmodelforthirdpartydamagetooilandgaspipelines-2020', 'http://doi.org/10.1016/j.psep.2019.11.038', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/cui-quddus-mashuga-bayesiannetworkandgametheoryriskassessmentmodelforthirdpartydamagetooilandgaspipelines-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('cui-quddus-mashuga-bayesiannetworkandgametheoryriskassessmentmodelforthirdpartydamagetooilandgaspipelines-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines},\n type = {article},\n year = {2020},\n pages = {178-188},\n volume = {134},\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0957582019306664},\n month = {2},\n id = {96335c3c-c3eb-39e7-a873-ffffd85cbdb6},\n created = {2019-12-28T14:08:41.182Z},\n accessed = {2019-12-28},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2019-12-28T14:08:41.270Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Cui, Yan and Quddus, Noor and Mashuga, Chad V.},\n doi = {10.1016/j.psep.2019.11.038},\n journal = {Process Safety and Environmental Protection}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"carriger-barron-abayesiannetworkapproachtorefiningecologicalriskassessmentsmercuryandthefloridapantherpumaconcolorcoryi-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0304380019304193\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'carriger-barron-abayesiannetworkapproachtorefiningecologicalriskassessmentsmercuryandthefloridapantherpumaconcolorcoryi-2020', 'https://linkinghub.elsevier.com/retrieve/pii/S0304380019304193', event);\">\n    A Bayesian network approach to refining ecological risk assessments: Mercury and the Florida panther (Puma concolor coryi).\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Carriger,  J., F.;  and Barron,  M., G.\n</span>\n\n  \n\n</span>\n\n  <i>Ecological Modelling</i>, 418: 108911. 2 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0304380019304193\"\n     onclick=\"log_download('carriger-barron-abayesiannetworkapproachtorefiningecologicalriskassessmentsmercuryandthefloridapantherpumaconcolorcoryi-2020', 'https://linkinghub.elsevier.com/retrieve/pii/S0304380019304193', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Bayesian network approach to refining ecological risk assessments: Mercury and the Florida panther (Puma concolor coryi) [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ecolmodel.2019.108911\"\n     onclick=\"log_download('carriger-barron-abayesiannetworkapproachtorefiningecologicalriskassessmentsmercuryandthefloridapantherpumaconcolorcoryi-2020', 'http://doi.org/10.1016/j.ecolmodel.2019.108911', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/carriger-barron-abayesiannetworkapproachtorefiningecologicalriskassessmentsmercuryandthefloridapantherpumaconcolorcoryi-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('carriger-barron-abayesiannetworkapproachtorefiningecologicalriskassessmentsmercuryandthefloridapantherpumaconcolorcoryi-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Bayesian network approach to refining ecological risk assessments: Mercury and the Florida panther (Puma concolor coryi)},\n type = {article},\n year = {2020},\n pages = {108911},\n volume = {418},\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0304380019304193},\n month = {2},\n id = {4adc18eb-ce64-3f83-8f9f-f08feb8f2625},\n created = {2020-01-28T14:13:04.891Z},\n accessed = {2020-01-28},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2020-01-28T14:13:04.965Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Carriger, John F. and Barron, Mace G.},\n doi = {10.1016/j.ecolmodel.2019.108911},\n journal = {Ecological Modelling}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (12)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"gerassis-abad-saavedra-garca-taboada--2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-01057-7_88\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'gerassis-abad-saavedra-garca-taboada--2019', 'http://link.springer.com/10.1007/978-3-030-01057-7_88', event);\">\n    Women’s Occupational Health: Improving Medical Protocols with Artificial Intelligence Solutions.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Gerassis,  S.; Abad,  A.; Saavedra,  Á.; García,  J., F.;  and Taboada,  J.\n</span>\n\n  \n\n</span>\n\n  pages 1193-1199. Springer, Cham, 9 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-01057-7_88\"\n     onclick=\"log_download('gerassis-abad-saavedra-garca-taboada--2019', 'http://link.springer.com/10.1007/978-3-030-01057-7_88', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\" [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-030-01057-7_88\"\n     onclick=\"log_download('gerassis-abad-saavedra-garca-taboada--2019', 'http://doi.org/10.1007/978-3-030-01057-7_88', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/gerassis-abad-saavedra-garca-taboada--2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('gerassis-abad-saavedra-garca-taboada--2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2019},\n pages = {1193-1199},\n websites = {http://link.springer.com/10.1007/978-3-030-01057-7_88},\n month = {9},\n publisher = {Springer, Cham},\n day = {6},\n id = {4189eda4-c40a-31f0-a718-fd11c0d2e792},\n created = {2018-11-18T14:57:28.145Z},\n accessed = {2018-11-13},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-11-18T14:57:28.145Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {inbook},\n author = {Gerassis, Saki and Abad, Alberto and Saavedra, Ángeles and García, Julio F. and Taboada, Javier},\n doi = {10.1007/978-3-030-01057-7_88},\n chapter = {Women’s Occupational Health: Improving Medical Protocols with Artificial Intelligence Solutions}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"tabandeh-gardoni-murphy-myers-societalriskandresilienceanalysisdynamicbayesiannetworkformulationofacapabilityapproach-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ascelibrary.org/doi/10.1061/AJRUA6.0000996\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'tabandeh-gardoni-murphy-myers-societalriskandresilienceanalysisdynamicbayesiannetworkformulationofacapabilityapproach-2019', 'http://ascelibrary.org/doi/10.1061/AJRUA6.0000996', event);\">\n    Societal Risk and Resilience Analysis: Dynamic Bayesian Network Formulation of a Capability Approach.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Tabandeh,  A.; Gardoni,  P.; Murphy,  C.;  and Myers,  N.\n</span>\n\n  \n\n</span>\n\n  <i>ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering</i>, 5(1): 04018046. 3 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ascelibrary.org/doi/10.1061/AJRUA6.0000996\"\n     onclick=\"log_download('tabandeh-gardoni-murphy-myers-societalriskandresilienceanalysisdynamicbayesiannetworkformulationofacapabilityapproach-2019', 'http://ascelibrary.org/doi/10.1061/AJRUA6.0000996', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Societal Risk and Resilience Analysis: Dynamic Bayesian Network Formulation of a Capability Approach [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1061/AJRUA6.0000996\"\n     onclick=\"log_download('tabandeh-gardoni-murphy-myers-societalriskandresilienceanalysisdynamicbayesiannetworkformulationofacapabilityapproach-2019', 'http://doi.org/10.1061/AJRUA6.0000996', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tabandeh-gardoni-murphy-myers-societalriskandresilienceanalysisdynamicbayesiannetworkformulationofacapabilityapproach-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tabandeh-gardoni-murphy-myers-societalriskandresilienceanalysisdynamicbayesiannetworkformulationofacapabilityapproach-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Societal Risk and Resilience Analysis: Dynamic Bayesian Network Formulation of a Capability Approach},\n type = {article},\n year = {2019},\n pages = {04018046},\n volume = {5},\n websites = {http://ascelibrary.org/doi/10.1061/AJRUA6.0000996},\n month = {3},\n id = {9a652a37-3c70-3a62-9ddc-1be3e6e06406},\n created = {2018-11-23T14:18:02.616Z},\n accessed = {2018-11-18},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-11-23T14:18:02.616Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Tabandeh, Armin and Gardoni, Paolo and Murphy, Colleen and Myers, Natalie},\n doi = {10.1061/AJRUA6.0000996},\n journal = {ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering},\n number = {1}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"simon-mechri-capizzi-assessmentofsafetyintegritylevelbysimulationofdynamicbayesiannetworksconsideringtestduration-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0950423018306338\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'simon-mechri-capizzi-assessmentofsafetyintegritylevelbysimulationofdynamicbayesiannetworksconsideringtestduration-2019', 'https://www.sciencedirect.com/science/article/pii/S0950423018306338', event);\">\n    Assessment of Safety Integrity Level by simulation of Dynamic Bayesian Networks considering test duration.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Simon,  C.; Mechri,  W.;  and Capizzi,  G.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Loss Prevention in the Process Industries</i>, 57: 101-113. 1 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0950423018306338\"\n     onclick=\"log_download('simon-mechri-capizzi-assessmentofsafetyintegritylevelbysimulationofdynamicbayesiannetworksconsideringtestduration-2019', 'https://www.sciencedirect.com/science/article/pii/S0950423018306338', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Assessment of Safety Integrity Level by simulation of Dynamic Bayesian Networks considering test duration [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/J.JLP.2018.11.002\"\n     onclick=\"log_download('simon-mechri-capizzi-assessmentofsafetyintegritylevelbysimulationofdynamicbayesiannetworksconsideringtestduration-2019', 'http://doi.org/10.1016/J.JLP.2018.11.002', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/simon-mechri-capizzi-assessmentofsafetyintegritylevelbysimulationofdynamicbayesiannetworksconsideringtestduration-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('simon-mechri-capizzi-assessmentofsafetyintegritylevelbysimulationofdynamicbayesiannetworksconsideringtestduration-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Assessment of Safety Integrity Level by simulation of Dynamic Bayesian Networks considering test duration},\n type = {article},\n year = {2019},\n pages = {101-113},\n volume = {57},\n websites = {https://www.sciencedirect.com/science/article/pii/S0950423018306338},\n month = {1},\n publisher = {Elsevier},\n day = {1},\n id = {fce8ef87-dcf3-3df8-bcd9-d6354b5f165c},\n created = {2019-01-11T15:39:18.601Z},\n accessed = {2018-12-06},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2019-01-11T15:39:18.601Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper is devoted to model Safety Instrumented Systems (SISs) availability by Dynamic Bayesian Networks (DBNs). The models integrate several parameters but the main concerns of the study are the integration of test duration and test strategy. The proposed DBN are generic and can be reused for assessing performance and testing the effect of some parameters. More attention has been paid to the performance of the proof test, its harmlessness and particularly its duration. The duration increases the model complexity when considering the components availability given the test but it is more realistic. This parameter should be decided carefully to satisfy the Safety Integrity Level (SIL) objectives.},\n bibtype = {article},\n author = {Simon, Christophe and Mechri, Walid and Capizzi, Guillaume},\n doi = {10.1016/J.JLP.2018.11.002},\n journal = {Journal of Loss Prevention in the Process Industries}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper is devoted to model Safety Instrumented Systems (SISs) availability by Dynamic Bayesian Networks (DBNs). The models integrate several parameters but the main concerns of the study are the integration of test duration and test strategy. The proposed DBN are generic and can be reused for assessing performance and testing the effect of some parameters. More attention has been paid to the performance of the proof test, its harmlessness and particularly its duration. The duration increases the model complexity when considering the components availability given the test but it is more realistic. This parameter should be decided carefully to satisfy the Safety Integrity Level (SIL) objectives.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"cobb-li-bayesiannetworkmodelforqualitycontrolwithcategoricalattributedata-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S1568494619305277#!\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'cobb-li-bayesiannetworkmodelforqualitycontrolwithcategoricalattributedata-2019', 'https://www.sciencedirect.com/science/article/pii/S1568494619305277#!', event);\">\n    Bayesian network model for quality control with categorical attribute data.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Cobb,  B., R.;  and Li,  L.\n</span>\n\n  \n\n</span>\n\n  <i>Applied Soft Computing</i>, 84: 105746. 11 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S1568494619305277#!\"\n     onclick=\"log_download('cobb-li-bayesiannetworkmodelforqualitycontrolwithcategoricalattributedata-2019', 'https://www.sciencedirect.com/science/article/pii/S1568494619305277#!', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Bayesian network model for quality control with categorical attribute data [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/J.ASOC.2019.105746\"\n     onclick=\"log_download('cobb-li-bayesiannetworkmodelforqualitycontrolwithcategoricalattributedata-2019', 'http://doi.org/10.1016/J.ASOC.2019.105746', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/cobb-li-bayesiannetworkmodelforqualitycontrolwithcategoricalattributedata-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('cobb-li-bayesiannetworkmodelforqualitycontrolwithcategoricalattributedata-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Bayesian network model for quality control with categorical attribute data},\n type = {article},\n year = {2019},\n pages = {105746},\n volume = {84},\n websites = {https://www.sciencedirect.com/science/article/pii/S1568494619305277#!},\n month = {11},\n publisher = {Elsevier},\n day = {1},\n id = {e35caadc-5cd7-301b-8d2a-664262632d10},\n created = {2019-09-07T13:01:18.438Z},\n accessed = {2019-09-07},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2019-09-07T13:01:18.532Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {A Bayesian network is developed to monitor a production process where categorical attribute data are available. The number of sample items in each category is entered each time period, allowing the revised probability that the system is in-control or in one of multiple out-of-control states to be calculated. In contrast to other Bayesian methods, qualitative knowledge can be combined with sample data. The network permits the classification of the system into more than two states, so diagnostic analysis can be performed simultaneously with inference. The system state can be updated to reflect evidence on variables that complements the sample data.},\n bibtype = {article},\n author = {Cobb, Barry R. and Li, Linda},\n doi = {10.1016/J.ASOC.2019.105746},\n journal = {Applied Soft Computing}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  A Bayesian network is developed to monitor a production process where categorical attribute data are available. The number of sample items in each category is entered each time period, allowing the revised probability that the system is in-control or in one of multiple out-of-control states to be calculated. In contrast to other Bayesian methods, qualitative knowledge can be combined with sample data. The network permits the classification of the system into more than two states, so diagnostic analysis can be performed simultaneously with inference. The system state can be updated to reflect evidence on variables that complements the sample data.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wang-nunez-liu-zhang-dollevoet-abayesiannetworkapproachforconditionmonitoringofhighspeedrailwaycatenaries-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/8805158/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'wang-nunez-liu-zhang-dollevoet-abayesiannetworkapproachforconditionmonitoringofhighspeedrailwaycatenaries-2019', 'https://ieeexplore.ieee.org/document/8805158/', event);\">\n    A Bayesian Network Approach for Condition Monitoring of High-Speed Railway Catenaries.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wang,  H.; Nunez,  A.; Liu,  Z.; Zhang,  D.;  and Dollevoet,  R.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Transactions on Intelligent Transportation Systems</i>,1-15.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/8805158/\"\n     onclick=\"log_download('wang-nunez-liu-zhang-dollevoet-abayesiannetworkapproachforconditionmonitoringofhighspeedrailwaycatenaries-2019', 'https://ieeexplore.ieee.org/document/8805158/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Bayesian Network Approach for Condition Monitoring of High-Speed Railway Catenaries [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/TITS.2019.2934346\"\n     onclick=\"log_download('wang-nunez-liu-zhang-dollevoet-abayesiannetworkapproachforconditionmonitoringofhighspeedrailwaycatenaries-2019', 'http://doi.org/10.1109/TITS.2019.2934346', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wang-nunez-liu-zhang-dollevoet-abayesiannetworkapproachforconditionmonitoringofhighspeedrailwaycatenaries-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wang-nunez-liu-zhang-dollevoet-abayesiannetworkapproachforconditionmonitoringofhighspeedrailwaycatenaries-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Bayesian Network Approach for Condition Monitoring of High-Speed Railway Catenaries},\n type = {article},\n year = {2019},\n pages = {1-15},\n websites = {https://ieeexplore.ieee.org/document/8805158/},\n id = {d8361035-c0be-3dab-a70f-66d746a49468},\n created = {2019-09-07T15:12:50.316Z},\n accessed = {2019-09-07},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2019-09-07T15:12:50.374Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Wang, Hongrui and Nunez, Alfredo and Liu, Zhigang and Zhang, Dongliang and Dollevoet, Rolf},\n doi = {10.1109/TITS.2019.2934346},\n journal = {IEEE Transactions on Intelligent Transportation Systems}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"hosseini-ivanov-anewresiliencemeasureforsupplynetworkswiththerippleeffectconsiderationsabayesiannetworkapproach-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Hosseini,  S.;  and Ivanov,  D.\n</span>\n\n  \n\n</span>\n\n  <i>Annals of Operations Research</i>.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s10479-019-03350-8\"\n     onclick=\"log_download('hosseini-ivanov-anewresiliencemeasureforsupplynetworkswiththerippleeffectconsiderationsabayesiannetworkapproach-2019', 'http://doi.org/10.1007/s10479-019-03350-8', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/hosseini-ivanov-anewresiliencemeasureforsupplynetworkswiththerippleeffectconsiderationsabayesiannetworkapproach-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('hosseini-ivanov-anewresiliencemeasureforsupplynetworkswiththerippleeffectconsiderationsabayesiannetworkapproach-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach},\n type = {article},\n year = {2019},\n publisher = {Springer US},\n id = {dc647355-529c-3374-ac4f-4b3445a2e862},\n created = {2019-09-30T14:09:25.592Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2019-09-30T14:09:25.592Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This is the first study that presents a supply chain (SC) resilience measure with the ripple effect considerations including both disruption and recovery stages. SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case- study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed.},\n bibtype = {article},\n author = {Hosseini, Seyedmohsen and Ivanov, Dmitry},\n doi = {10.1007/s10479-019-03350-8},\n journal = {Annals of Operations Research}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This is the first study that presents a supply chain (SC) resilience measure with the ripple effect considerations including both disruption and recovery stages. SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case- study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"hosseini-ivanov-dolgui-rippleeffectmodellingofsupplierdisruptionintegratedmarkovchainanddynamicbayesiannetworkapproach-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/00207543.2019.1661538\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'hosseini-ivanov-dolgui-rippleeffectmodellingofsupplierdisruptionintegratedmarkovchainanddynamicbayesiannetworkapproach-2019', 'https://doi.org/00207543.2019.1661538', event);\">\n    Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Hosseini,  S.; Ivanov,  D.;  and Dolgui,  A.\n</span>\n\n  \n\n</span>\n\n  <i>International Journal of Production Research</i>, 0(0): 1-19.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/00207543.2019.1661538\"\n     onclick=\"log_download('hosseini-ivanov-dolgui-rippleeffectmodellingofsupplierdisruptionintegratedmarkovchainanddynamicbayesiannetworkapproach-2019', 'https://doi.org/00207543.2019.1661538', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1080/00207543.2019.1661538\"\n     onclick=\"log_download('hosseini-ivanov-dolgui-rippleeffectmodellingofsupplierdisruptionintegratedmarkovchainanddynamicbayesiannetworkapproach-2019', 'http://doi.org/10.1080/00207543.2019.1661538', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/hosseini-ivanov-dolgui-rippleeffectmodellingofsupplierdisruptionintegratedmarkovchainanddynamicbayesiannetworkapproach-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('hosseini-ivanov-dolgui-rippleeffectmodellingofsupplierdisruptionintegratedmarkovchainanddynamicbayesiannetworkapproach-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach},\n type = {article},\n year = {2019},\n keywords = {Bayesian methods,Markov modelling,ripple effect,supply chain dynamics,supply chain resilience,supply chain risk management},\n pages = {1-19},\n volume = {0},\n websites = {https://doi.org/00207543.2019.1661538},\n publisher = {Taylor &amp; Francis},\n id = {4e7b8f37-b13a-3765-a051-bab4a68d08e7},\n created = {2019-09-30T14:09:25.751Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2019-09-30T14:09:25.751Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Hosseini, Seyedmohsen and Ivanov, Dmitry and Dolgui, Alexandre},\n doi = {10.1080/00207543.2019.1661538},\n journal = {International Journal of Production Research},\n number = {0}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"hosseini-ivanov-anewresiliencemeasureforsupplynetworkswiththerippleeffectconsiderationsabayesiannetworkapproach-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Hosseini,  S.;  and Ivanov,  D.\n</span>\n\n  \n\n</span>\n\n  <i>Annals of Operations Research</i>.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s10479-019-03350-8\"\n     onclick=\"log_download('hosseini-ivanov-anewresiliencemeasureforsupplynetworkswiththerippleeffectconsiderationsabayesiannetworkapproach-2019', 'http://doi.org/10.1007/s10479-019-03350-8', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/hosseini-ivanov-anewresiliencemeasureforsupplynetworkswiththerippleeffectconsiderationsabayesiannetworkapproach-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('hosseini-ivanov-anewresiliencemeasureforsupplynetworkswiththerippleeffectconsiderationsabayesiannetworkapproach-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach},\n type = {article},\n year = {2019},\n publisher = {Springer US},\n id = {40c54eef-72a2-37a2-b82e-380dd4e0bb65},\n created = {2019-11-23T23:40:00.765Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2019-11-23T23:40:00.765Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This is the first study that presents a supply chain (SC) resilience measure with the ripple effect considerations including both disruption and recovery stages. SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case- study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed.},\n bibtype = {article},\n author = {Hosseini, Seyedmohsen and Ivanov, Dmitry},\n doi = {10.1007/s10479-019-03350-8},\n journal = {Annals of Operations Research}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This is the first study that presents a supply chain (SC) resilience measure with the ripple effect considerations including both disruption and recovery stages. SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case- study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"keprate-ratnayake-useofbayesiannetworkforriskbasedfatigueintegrityassessmentapplicationfortopsidepipinginanarcticenvironment-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://legacy.isope.org/publications/journals/journalDecember19.htm\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'keprate-ratnayake-useofbayesiannetworkforriskbasedfatigueintegrityassessmentapplicationfortopsidepipinginanarcticenvironment-2019', 'http://legacy.isope.org/publications/journals/journalDecember19.htm', event);\">\n    Use of Bayesian Network for Risk-Based Fatigue Integrity Assessment: Application for Topside Piping in an Arctic Environment.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Keprate,  A.;  and Ratnayake,  R., C.\n</span>\n\n  \n\n</span>\n\n  <i>International Journal of Offshore and Polar Engineering</i>, 29(4): 421-428. 12 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://legacy.isope.org/publications/journals/journalDecember19.htm\"\n     onclick=\"log_download('keprate-ratnayake-useofbayesiannetworkforriskbasedfatigueintegrityassessmentapplicationfortopsidepipinginanarcticenvironment-2019', 'http://legacy.isope.org/publications/journals/journalDecember19.htm', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Use of Bayesian Network for Risk-Based Fatigue Integrity Assessment: Application for Topside Piping in an Arctic Environment [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.17736/ijope.2019.bn19\"\n     onclick=\"log_download('keprate-ratnayake-useofbayesiannetworkforriskbasedfatigueintegrityassessmentapplicationfortopsidepipinginanarcticenvironment-2019', 'http://doi.org/10.17736/ijope.2019.bn19', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/keprate-ratnayake-useofbayesiannetworkforriskbasedfatigueintegrityassessmentapplicationfortopsidepipinginanarcticenvironment-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('keprate-ratnayake-useofbayesiannetworkforriskbasedfatigueintegrityassessmentapplicationfortopsidepipinginanarcticenvironment-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Use of Bayesian Network for Risk-Based Fatigue Integrity Assessment: Application for Topside Piping in an Arctic Environment},\n type = {article},\n year = {2019},\n pages = {421-428},\n volume = {29},\n websites = {http://legacy.isope.org/publications/journals/journalDecember19.htm},\n month = {12},\n day = {1},\n id = {cfa87aad-9ef5-3ba6-861e-bf24be1dd1bf},\n created = {2019-12-04T14:15:49.112Z},\n accessed = {2019-12-04},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2019-12-04T14:15:49.181Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Keprate, Arvind and Ratnayake, RM Chandima},\n doi = {10.17736/ijope.2019.bn19},\n journal = {International Journal of Offshore and Polar Engineering},\n number = {4}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"manyweathers-maru-hayes-loechel-kruger-mankad-xie-woodgate-etal-understandingthevulnerabilityofbeefproducersinaustraliatoanfmdoutbreakusingabayesiannetworkpredictivemodel-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0167587719306348\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'manyweathers-maru-hayes-loechel-kruger-mankad-xie-woodgate-etal-understandingthevulnerabilityofbeefproducersinaustraliatoanfmdoutbreakusingabayesiannetworkpredictivemodel-2019', 'https://linkinghub.elsevier.com/retrieve/pii/S0167587719306348', event);\">\n    Understanding the vulnerability of beef producers in Australia to an FMD outbreak using a Bayesian Network predictive model.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Manyweathers,  J.; Maru,  Y.; Hayes,  L.; Loechel,  B.; Kruger,  H.; Mankad,  A.; Xie,  G.; Woodgate,  R.;  and Hernandez-Jover,  M.\n</span>\n\n  \n\n</span>\n\n  <i>Preventive Veterinary Medicine</i>,104872. 12 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0167587719306348\"\n     onclick=\"log_download('manyweathers-maru-hayes-loechel-kruger-mankad-xie-woodgate-etal-understandingthevulnerabilityofbeefproducersinaustraliatoanfmdoutbreakusingabayesiannetworkpredictivemodel-2019', 'https://linkinghub.elsevier.com/retrieve/pii/S0167587719306348', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Understanding the vulnerability of beef producers in Australia to an FMD outbreak using a Bayesian Network predictive model [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.prevetmed.2019.104872\"\n     onclick=\"log_download('manyweathers-maru-hayes-loechel-kruger-mankad-xie-woodgate-etal-understandingthevulnerabilityofbeefproducersinaustraliatoanfmdoutbreakusingabayesiannetworkpredictivemodel-2019', 'http://doi.org/10.1016/j.prevetmed.2019.104872', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/manyweathers-maru-hayes-loechel-kruger-mankad-xie-woodgate-etal-understandingthevulnerabilityofbeefproducersinaustraliatoanfmdoutbreakusingabayesiannetworkpredictivemodel-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('manyweathers-maru-hayes-loechel-kruger-mankad-xie-woodgate-etal-understandingthevulnerabilityofbeefproducersinaustraliatoanfmdoutbreakusingabayesiannetworkpredictivemodel-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Understanding the vulnerability of beef producers in Australia to an FMD outbreak using a Bayesian Network predictive model},\n type = {article},\n year = {2019},\n pages = {104872},\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0167587719306348},\n month = {12},\n id = {dde1afe5-4e27-3ca7-8ebc-7831619b86b3},\n created = {2019-12-21T18:28:26.312Z},\n accessed = {2019-12-21},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2019-12-21T18:28:26.312Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Manyweathers, Jennifer and Maru, Yiheyis and Hayes, Lynne and Loechel, Barton and Kruger, Heleen and Mankad, Aditi and Xie, Gang and Woodgate, Rob and Hernandez-Jover, Marta},\n doi = {10.1016/j.prevetmed.2019.104872},\n journal = {Preventive Veterinary Medicine}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"niazi-floodriskpredictionunderglobalvegetatedhydrodynamicsabayesiannetwork-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Flood Risk Prediction under Global Vegetated Hydrodynamics: A Bayesian Network.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Niazi,  M., H., K.\n</span>\n\n  \n\n</span>\n\n   2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/niazi-floodriskpredictionunderglobalvegetatedhydrodynamicsabayesiannetwork-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('niazi-floodriskpredictionunderglobalvegetatedhydrodynamicsabayesiannetwork-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{\n title = {Flood Risk Prediction under Global Vegetated Hydrodynamics: A Bayesian Network},\n type = {misc},\n year = {2019},\n id = {36c9fdda-4fc4-3b96-8f4d-f1f6da7244b0},\n created = {2019-12-29T15:56:48.150Z},\n accessed = {2019-12-29},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2019-12-29T15:56:48.225Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {The increasing frequency and intensity of extreme events due to global warming and climate change is increasing flood risk. To act, rather than react, nature-based solutions (NBS) involving vegetation and wetlands are being explored on top of conventional solutions like dikes. WHY? There was a dire need for global study quantifying the potential of vegetation in reducing flood risk and eventually make a decision support tool which enables quick assessments about flood risk reduction in a vegetated hydrodynamic system. WHAT? The developed tool can predict flood risk anywhere in the world without rigorous modeling through user defined conditionalization of in-situ hydrodynamic or vegetation characteristics. HOW? Multivariate dependence among parameters of schematized system can exhibit characteristics of vegetated hydrodynamics. To ensure global representation of vegetated hydrodynamics a copula-based multivariate stochastic model has been developed which caters global ranges of each parameter, their probability distributions and the inter-parameter dependencies through ranked correlations. Numerical modeling has been carried out through XBeach non-hydrostatic model by resolving full spectrum of high and low frequency waves. A non-parametric Bayesian network-based flood risk prediction tool has been developed from the synthetic dataset developed from the simulations. SO? Bulk results conclude that saltmarshes attenuates waves by 87% and mangroves by 94% as a mean value. Wave attenuation, flood risk reduction and wave run-up manifests maximum dependence on offshore wave height, water depth, drag coefficient, vegetation height, frontal width, and forest length and least on offshore slope and vegetation density. NOW? The flood risk prediction tool would help decision makers in implementing NBS, in making better informed decisions about early warnings and policy making related to flood risk reduction and climate change adaptation by incorporating vegetation. NOVELTY? To the author&#x27;s knowledge no such study exists which captures natural variability of hydrodynamics and vegetation together in a probabilistic model over global scales. Additionally, no such study exist which applies non-parametric Bayesian networks to predict flood risk. The dependence modeling of global vegetated hydrodynamic environments is also unique which skims out the most critical parameters.},\n bibtype = {misc},\n author = {Niazi, Muhammad Hassan Khan}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The increasing frequency and intensity of extreme events due to global warming and climate change is increasing flood risk. To act, rather than react, nature-based solutions (NBS) involving vegetation and wetlands are being explored on top of conventional solutions like dikes. WHY? There was a dire need for global study quantifying the potential of vegetation in reducing flood risk and eventually make a decision support tool which enables quick assessments about flood risk reduction in a vegetated hydrodynamic system. WHAT? The developed tool can predict flood risk anywhere in the world without rigorous modeling through user defined conditionalization of in-situ hydrodynamic or vegetation characteristics. HOW? Multivariate dependence among parameters of schematized system can exhibit characteristics of vegetated hydrodynamics. To ensure global representation of vegetated hydrodynamics a copula-based multivariate stochastic model has been developed which caters global ranges of each parameter, their probability distributions and the inter-parameter dependencies through ranked correlations. Numerical modeling has been carried out through XBeach non-hydrostatic model by resolving full spectrum of high and low frequency waves. A non-parametric Bayesian network-based flood risk prediction tool has been developed from the synthetic dataset developed from the simulations. SO? Bulk results conclude that saltmarshes attenuates waves by 87% and mangroves by 94% as a mean value. Wave attenuation, flood risk reduction and wave run-up manifests maximum dependence on offshore wave height, water depth, drag coefficient, vegetation height, frontal width, and forest length and least on offshore slope and vegetation density. NOW? The flood risk prediction tool would help decision makers in implementing NBS, in making better informed decisions about early warnings and policy making related to flood risk reduction and climate change adaptation by incorporating vegetation. NOVELTY? To the author's knowledge no such study exists which captures natural variability of hydrodynamics and vegetation together in a probabilistic model over global scales. Additionally, no such study exist which applies non-parametric Bayesian networks to predict flood risk. The dependence modeling of global vegetated hydrodynamic environments is also unique which skims out the most critical parameters.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"busemhatipoglu-uyar-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/1cefd92b-a220-0575-70be-5669e3c04614/full_text.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'busemhatipoglu-uyar-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019', 'https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/1cefd92b-a220-0575-70be-5669e3c04614/full_text.pdf.pdf', event);\">\n    Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Busem Hatipoglu,  F.;  and Uyar,  U.\n</span>\n\n  \n\n</span>\n\n  .  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/1cefd92b-a220-0575-70be-5669e3c04614/full_text.pdf.pdf\"\n     onclick=\"log_download('busemhatipoglu-uyar-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019', 'https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/1cefd92b-a220-0575-70be-5669e3c04614/full_text.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"www.berjournal.com\"\n     onclick=\"log_download('busemhatipoglu-uyar-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019', 'www.berjournal.com', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.20409/berj.2019.202\"\n     onclick=\"log_download('busemhatipoglu-uyar-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019', 'http://doi.org/10.20409/berj.2019.202', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/busemhatipoglu-uyar-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('busemhatipoglu-uyar-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks},\n type = {article},\n year = {2019},\n keywords = {Arbitrage Pricing Model,Banking Stocks JEL: C11,Bayesian Networks,G11,G12,Machine Learning,Portfolio Selection Theory},\n websites = {www.berjournal.com},\n id = {87842199-6f11-388f-9ae4-465c6ad52ffb},\n created = {2019-12-29T16:14:12.780Z},\n accessed = {2019-12-29},\n file_attached = {true},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2019-12-29T16:14:12.933Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {According to the modern portfolio theory, the direction of the relationship between the securities in the portfolio is stated to be effective in reducing the risk. Moreover, securities in high correlation are avoided by taking place in the same portfolio. The models structured by the Bayesian networks are capable of visually illustrate the probabilistic relationship. Also, portfolio returns could be refreshed simultaneously when new information has arrived. The study aims to provide dynamic information through Bayesian networks and to investigate the relationship between macroeconomic indicators and stock returns of Turkish major bank stocks based on the Arbitrage Pricing Model. The dataset includes stock returns of four banks listed in the Borsa Istanbul from June 2001 to January 2017. Besides, macroeconomic variables such as BIST-100 Index, oil prices, inflation, exchange, and interest rate &amp; money supply are gathered for the same period. The results suggest that the Bayesian network models allow dynamics among stock returns could be investigated in more detail. Additionally, it determines that macroeconomic variables would have various impacts on stock returns on bank stocks by comparison of the conventional methods.},\n bibtype = {article},\n author = {Busem Hatipoglu, Fatma and Uyar, Umut},\n doi = {10.20409/berj.2019.202}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  According to the modern portfolio theory, the direction of the relationship between the securities in the portfolio is stated to be effective in reducing the risk. Moreover, securities in high correlation are avoided by taking place in the same portfolio. The models structured by the Bayesian networks are capable of visually illustrate the probabilistic relationship. Also, portfolio returns could be refreshed simultaneously when new information has arrived. The study aims to provide dynamic information through Bayesian networks and to investigate the relationship between macroeconomic indicators and stock returns of Turkish major bank stocks based on the Arbitrage Pricing Model. The dataset includes stock returns of four banks listed in the Borsa Istanbul from June 2001 to January 2017. Besides, macroeconomic variables such as BIST-100 Index, oil prices, inflation, exchange, and interest rate & money supply are gathered for the same period. The results suggest that the Bayesian network models allow dynamics among stock returns could be investigated in more detail. Additionally, it determines that macroeconomic variables would have various impacts on stock returns on bank stocks by comparison of the conventional methods.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (10)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"misuri-khakzad-reniers-cozzani-abayesiannetworkmethodologyforoptimalsecuritymanagementofcriticalinfrastructures-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://linkinghub.elsevier.com/retrieve/pii/S0951832017311535\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'misuri-khakzad-reniers-cozzani-abayesiannetworkmethodologyforoptimalsecuritymanagementofcriticalinfrastructures-2018', 'http://linkinghub.elsevier.com/retrieve/pii/S0951832017311535', event);\">\n    A Bayesian network methodology for optimal security management of critical infrastructures.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Misuri,  A.; Khakzad,  N.; Reniers,  G.;  and Cozzani,  V.\n</span>\n\n  \n\n</span>\n\n  <i>Reliability Engineering & System Safety</i>. 3 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://linkinghub.elsevier.com/retrieve/pii/S0951832017311535\"\n     onclick=\"log_download('misuri-khakzad-reniers-cozzani-abayesiannetworkmethodologyforoptimalsecuritymanagementofcriticalinfrastructures-2018', 'http://linkinghub.elsevier.com/retrieve/pii/S0951832017311535', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Bayesian network methodology for optimal security management of critical infrastructures [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ress.2018.03.028\"\n     onclick=\"log_download('misuri-khakzad-reniers-cozzani-abayesiannetworkmethodologyforoptimalsecuritymanagementofcriticalinfrastructures-2018', 'http://doi.org/10.1016/j.ress.2018.03.028', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/misuri-khakzad-reniers-cozzani-abayesiannetworkmethodologyforoptimalsecuritymanagementofcriticalinfrastructures-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('misuri-khakzad-reniers-cozzani-abayesiannetworkmethodologyforoptimalsecuritymanagementofcriticalinfrastructures-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Bayesian network methodology for optimal security management of critical infrastructures},\n type = {article},\n year = {2018},\n websites = {http://linkinghub.elsevier.com/retrieve/pii/S0951832017311535},\n month = {3},\n id = {f32946ff-ce59-32d6-80db-4fd7062c5445},\n created = {2018-03-31T22:16:37.668Z},\n accessed = {2018-03-31},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-03-31T22:16:37.668Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Security management of critical infrastructures is a complex task as a great variety of technical and socio-political information is needed to realistically predict the risk of intentional malevolent acts. In the present study, a methodology based on Limited Memory Influence Diagram (LIMID) has been developed for the protection of critical infrastructures via cost-effective allocation of security measures. LIMID is an extension of Bayesian network (BN) intended for decision-making, allowing for efficient modeling of complex systems while accounting for interdependencies and interaction of system components. The probability updating feature of BN has been used to investigate the effect of vulnerabilities on adversaries’ preferences when planning attacks. Moreover, the proposed methodology has been shown to be able to identify an optimal defensive strategy given an attack through maximizing defenders’ expected utility. Despite being demonstrated via a chemical facility, the methodology can easily be tailored to a wide variety of critical infrastructures.},\n bibtype = {article},\n author = {Misuri, Alessio and Khakzad, Nima and Reniers, Genserik and Cozzani, Valerio},\n doi = {10.1016/j.ress.2018.03.028},\n journal = {Reliability Engineering &amp; System Safety}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Security management of critical infrastructures is a complex task as a great variety of technical and socio-political information is needed to realistically predict the risk of intentional malevolent acts. In the present study, a methodology based on Limited Memory Influence Diagram (LIMID) has been developed for the protection of critical infrastructures via cost-effective allocation of security measures. LIMID is an extension of Bayesian network (BN) intended for decision-making, allowing for efficient modeling of complex systems while accounting for interdependencies and interaction of system components. The probability updating feature of BN has been used to investigate the effect of vulnerabilities on adversaries’ preferences when planning attacks. Moreover, the proposed methodology has been shown to be able to identify an optimal defensive strategy given an attack through maximizing defenders’ expected utility. Despite being demonstrated via a chemical facility, the methodology can easily be tailored to a wide variety of critical infrastructures.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"guo-zhang-liang-haugen-riskidentificationofthirdpartydamageonoilandgaspipelinesthroughthebayesiannetwork-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://linkinghub.elsevier.com/retrieve/pii/S0950423017310719\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'guo-zhang-liang-haugen-riskidentificationofthirdpartydamageonoilandgaspipelinesthroughthebayesiannetwork-2018', 'http://linkinghub.elsevier.com/retrieve/pii/S0950423017310719', event);\">\n    Risk identification of third-party damage on oil and gas pipelines through the Bayesian network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Guo,  X.; Zhang,  L.; Liang,  W.;  and Haugen,  S.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Loss Prevention in the Process Industries</i>, 54: 163-178. 7 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://linkinghub.elsevier.com/retrieve/pii/S0950423017310719\"\n     onclick=\"log_download('guo-zhang-liang-haugen-riskidentificationofthirdpartydamageonoilandgaspipelinesthroughthebayesiannetwork-2018', 'http://linkinghub.elsevier.com/retrieve/pii/S0950423017310719', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Risk identification of third-party damage on oil and gas pipelines through the Bayesian network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jlp.2018.03.012\"\n     onclick=\"log_download('guo-zhang-liang-haugen-riskidentificationofthirdpartydamageonoilandgaspipelinesthroughthebayesiannetwork-2018', 'http://doi.org/10.1016/j.jlp.2018.03.012', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/guo-zhang-liang-haugen-riskidentificationofthirdpartydamageonoilandgaspipelinesthroughthebayesiannetwork-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('guo-zhang-liang-haugen-riskidentificationofthirdpartydamageonoilandgaspipelinesthroughthebayesiannetwork-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Risk identification of third-party damage on oil and gas pipelines through the Bayesian network},\n type = {article},\n year = {2018},\n pages = {163-178},\n volume = {54},\n websites = {http://linkinghub.elsevier.com/retrieve/pii/S0950423017310719},\n month = {7},\n id = {c01b2f9d-9a0b-3add-b06e-7f4d8d78fe8c},\n created = {2018-03-31T22:27:14.637Z},\n accessed = {2018-03-31},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-03-31T22:27:14.637Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper aims to identify the risks influencing oil and gas (O&amp;G) pipeline safety caused by third-party damage (TPD). After comprehensive literature study, we found that the traditional risk identification of TPD suffers from defining binary states of risk only and ignores the risk factors after pipeline failure. To overcome this problem, we investigated incident reports to identify previously unrecognized additional factors. This work also developed a graphic model by using Bayesian theory to cope with the multistate risks arising from third parties and to present the incident evolution process explicitly. Furthermore, this paper included a leakage case study conducted to verify the logicality of this model. The results of case study inspire that the proposed methodology can be used in a dual assurance approach for risk mitigation, namely learning from previous incidents and continuously capturing new risk information for risk prevention.},\n bibtype = {article},\n author = {Guo, Xiaoyan and Zhang, Laibin and Liang, Wei and Haugen, Stein},\n doi = {10.1016/j.jlp.2018.03.012},\n journal = {Journal of Loss Prevention in the Process Industries}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper aims to identify the risks influencing oil and gas (O&G) pipeline safety caused by third-party damage (TPD). After comprehensive literature study, we found that the traditional risk identification of TPD suffers from defining binary states of risk only and ignores the risk factors after pipeline failure. To overcome this problem, we investigated incident reports to identify previously unrecognized additional factors. This work also developed a graphic model by using Bayesian theory to cope with the multistate risks arising from third parties and to present the incident evolution process explicitly. Furthermore, this paper included a leakage case study conducted to verify the logicality of this model. The results of case study inspire that the proposed methodology can be used in a dual assurance approach for risk mitigation, namely learning from previous incidents and continuously capturing new risk information for risk prevention.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"khakzad-reniers-lowcapacityutilizationofprocessplantsacostrobustapproachtotacklemanmadedominoeffects-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://linkinghub.elsevier.com/retrieve/pii/S0951832017309158\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'khakzad-reniers-lowcapacityutilizationofprocessplantsacostrobustapproachtotacklemanmadedominoeffects-2018', 'http://linkinghub.elsevier.com/retrieve/pii/S0951832017309158', event);\">\n    Low-capacity utilization of process plants: A cost-robust approach to tackle man-made domino effects.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Khakzad,  N.;  and Reniers,  G.\n</span>\n\n  \n\n</span>\n\n  <i>Reliability Engineering & System Safety</i>. 3 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://linkinghub.elsevier.com/retrieve/pii/S0951832017309158\"\n     onclick=\"log_download('khakzad-reniers-lowcapacityutilizationofprocessplantsacostrobustapproachtotacklemanmadedominoeffects-2018', 'http://linkinghub.elsevier.com/retrieve/pii/S0951832017309158', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Low-capacity utilization of process plants: A cost-robust approach to tackle man-made domino effects [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ress.2018.03.030\"\n     onclick=\"log_download('khakzad-reniers-lowcapacityutilizationofprocessplantsacostrobustapproachtotacklemanmadedominoeffects-2018', 'http://doi.org/10.1016/j.ress.2018.03.030', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/khakzad-reniers-lowcapacityutilizationofprocessplantsacostrobustapproachtotacklemanmadedominoeffects-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('khakzad-reniers-lowcapacityutilizationofprocessplantsacostrobustapproachtotacklemanmadedominoeffects-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Low-capacity utilization of process plants: A cost-robust approach to tackle man-made domino effects},\n type = {article},\n year = {2018},\n websites = {http://linkinghub.elsevier.com/retrieve/pii/S0951832017309158},\n month = {3},\n id = {b6b43a9b-8093-3d54-aca0-93d57e805ebb},\n created = {2018-03-31T23:32:39.672Z},\n accessed = {2018-03-31},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-03-31T23:32:39.672Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Process plants can be potential targets to terrorist attacks with the aim of triggering domino effects. Compared to accidental domino effects where the possibility of having multiple primary events is very remote, man-made domino effects are likelier to be initiated from multiple units within the plant in order to increase the knock-on likelihood and thus causing maximum damage. In this regard, identification of critical units that - under attack - may lead to likelier and severer domino effects is crucial both to assess the vulnerability of process plants and subsequently to increase their robustness to such attacks. In the present work, we have applied graph theory and dynamic Bayesian network to identify critical units. Further, low-capacity utilization of process plants (e.g., by keeping some of the storage tanks empty) has been demonstrated as an effective strategy in the case of imminent terrorist attacks. As such, the robustness of the plant against intentional attacks can temporarily be increased while considering the cost incurred because of such a low-capacity utilization.},\n bibtype = {article},\n author = {Khakzad, Nima and Reniers, Genserik},\n doi = {10.1016/j.ress.2018.03.030},\n journal = {Reliability Engineering &amp; System Safety}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Process plants can be potential targets to terrorist attacks with the aim of triggering domino effects. Compared to accidental domino effects where the possibility of having multiple primary events is very remote, man-made domino effects are likelier to be initiated from multiple units within the plant in order to increase the knock-on likelihood and thus causing maximum damage. In this regard, identification of critical units that - under attack - may lead to likelier and severer domino effects is crucial both to assess the vulnerability of process plants and subsequently to increase their robustness to such attacks. In the present work, we have applied graph theory and dynamic Bayesian network to identify critical units. Further, low-capacity utilization of process plants (e.g., by keeping some of the storage tanks empty) has been demonstrated as an effective strategy in the case of imminent terrorist attacks. As such, the robustness of the plant against intentional attacks can temporarily be increased while considering the cost incurred because of such a low-capacity utilization.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lam-lun-bell-riskmanagementinportandmaritimelogistics-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/abs/pii/S0001457518301416#!\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lam-lun-bell-riskmanagementinportandmaritimelogistics-2018', 'https://www.sciencedirect.com/science/article/abs/pii/S0001457518301416#!', event);\">\n    Risk management in port and maritime logistics.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lam,  J., S., L.; Lun,  Y., V.;  and Bell,  M., G.\n</span>\n\n  \n\n</span>\n\n  <i>Accident Analysis & Prevention</i>. 4 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/abs/pii/S0001457518301416#!\"\n     onclick=\"log_download('lam-lun-bell-riskmanagementinportandmaritimelogistics-2018', 'https://www.sciencedirect.com/science/article/abs/pii/S0001457518301416#!', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Risk management in port and maritime logistics [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/J.AAP.2018.04.003\"\n     onclick=\"log_download('lam-lun-bell-riskmanagementinportandmaritimelogistics-2018', 'http://doi.org/10.1016/J.AAP.2018.04.003', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lam-lun-bell-riskmanagementinportandmaritimelogistics-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lam-lun-bell-riskmanagementinportandmaritimelogistics-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Risk management in port and maritime logistics},\n type = {article},\n year = {2018},\n websites = {https://www.sciencedirect.com/science/article/abs/pii/S0001457518301416#!},\n month = {4},\n publisher = {Pergamon},\n day = {11},\n id = {d8b52b51-ce3a-327d-a2e3-41160dbf58a3},\n created = {2018-04-28T14:15:44.386Z},\n accessed = {2018-04-24},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-04-28T14:15:44.386Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Lam, Jasmine Siu Lee and Lun, Y.H. Venus and Bell, Michael G.H.},\n doi = {10.1016/J.AAP.2018.04.003},\n journal = {Accident Analysis &amp; Prevention}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"zhou-liu-gan-assessmentandcountermeasuresforoffshorewindfarmrisksbasedonadynamicbayesiannetwork-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jep.2018.94024\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'zhou-liu-gan-assessmentandcountermeasuresforoffshorewindfarmrisksbasedonadynamicbayesiannetwork-2018', 'http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jep.2018.94024', event);\">\n    Assessment and Countermeasures for Offshore Wind Farm Risks Based on a Dynamic Bayesian Network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Zhou,  C.; Liu,  X.;  and Gan,  L.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Environmental Protection</i>, 09(04): 368-384. 4 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jep.2018.94024\"\n     onclick=\"log_download('zhou-liu-gan-assessmentandcountermeasuresforoffshorewindfarmrisksbasedonadynamicbayesiannetwork-2018', 'http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jep.2018.94024', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Assessment and Countermeasures for Offshore Wind Farm Risks Based on a Dynamic Bayesian Network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.4236/jep.2018.94024\"\n     onclick=\"log_download('zhou-liu-gan-assessmentandcountermeasuresforoffshorewindfarmrisksbasedonadynamicbayesiannetwork-2018', 'http://doi.org/10.4236/jep.2018.94024', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/zhou-liu-gan-assessmentandcountermeasuresforoffshorewindfarmrisksbasedonadynamicbayesiannetwork-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('zhou-liu-gan-assessmentandcountermeasuresforoffshorewindfarmrisksbasedonadynamicbayesiannetwork-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Assessment and Countermeasures for Offshore Wind Farm Risks Based on a Dynamic Bayesian Network},\n type = {article},\n year = {2018},\n pages = {368-384},\n volume = {09},\n websites = {http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jep.2018.94024},\n month = {4},\n publisher = {Scientific Research Publishing},\n day = {17},\n id = {3a4dfcfb-c5d9-3d33-95e0-8b555fc4a9e8},\n created = {2018-05-29T00:32:00.841Z},\n accessed = {2018-05-28},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-05-29T00:32:00.841Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Wind power is a kind of clean energy promising significant social and environmental benefits, and in The Peoples Republic of China, the government supports and encourages the development of wind power as one element in a shift to renewable energy. In recent years however, maritime safety issues have arisen during offshore wind power construction and attendant production processes associated with the rapid promotion and development of offshore wind farms. Therefore, it is necessary to carry out risk assessment for phases in the life cycle of offshore wind farms. This paper reports on a risk assessment model based on a Dynamic Bayesian network that performs offshore wind farms maritime risk assessment. The advantage of this approach is the way in which a Bayesian model expresses uncertainty. Furthermore, such models permit simulations and reenactment of accidents in a virtual environment. There were several goals in this research. Offshore wind power project risk identification and evaluation theories and methods were explored to identify the sources of risk during different phases of the offshore wind farm life cycle. Based on this foundation, a dynamic Bayesian network model with Genie was established, and evaluated, in terms of its effectiveness for analysis of risk during different phases of the offshore wind farm life cycle. Research results show that a dynamic Bayesian network method can perform risk assessments effectively and flexibly, responding to the actual context of offshore wind power construction. Historical data and almost real-time information are combined to analyze the risk of the construction of offshore wind power. Our results inform a discussion of security and risk mitigation measures that when implemented, could improve safety. This work has value as a reference and guide for the safe development of offshore wind power.},\n bibtype = {article},\n author = {Zhou, Chunhui and Liu, Xin and Gan, Langxiong},\n doi = {10.4236/jep.2018.94024},\n journal = {Journal of Environmental Protection},\n number = {04}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Wind power is a kind of clean energy promising significant social and environmental benefits, and in The Peoples Republic of China, the government supports and encourages the development of wind power as one element in a shift to renewable energy. In recent years however, maritime safety issues have arisen during offshore wind power construction and attendant production processes associated with the rapid promotion and development of offshore wind farms. Therefore, it is necessary to carry out risk assessment for phases in the life cycle of offshore wind farms. This paper reports on a risk assessment model based on a Dynamic Bayesian network that performs offshore wind farms maritime risk assessment. The advantage of this approach is the way in which a Bayesian model expresses uncertainty. Furthermore, such models permit simulations and reenactment of accidents in a virtual environment. There were several goals in this research. Offshore wind power project risk identification and evaluation theories and methods were explored to identify the sources of risk during different phases of the offshore wind farm life cycle. Based on this foundation, a dynamic Bayesian network model with Genie was established, and evaluated, in terms of its effectiveness for analysis of risk during different phases of the offshore wind farm life cycle. Research results show that a dynamic Bayesian network method can perform risk assessments effectively and flexibly, responding to the actual context of offshore wind power construction. Historical data and almost real-time information are combined to analyze the risk of the construction of offshore wind power. Our results inform a discussion of security and risk mitigation measures that when implemented, could improve safety. This work has value as a reference and guide for the safe development of offshore wind power.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"matellini-wall-jenkinson-wang-pritchard-athreepartbayesiannetworkformodelingdwellingfiresandtheirimpactuponpeopleandproperty-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.wiley.com/10.1111/risa.13113\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'matellini-wall-jenkinson-wang-pritchard-athreepartbayesiannetworkformodelingdwellingfiresandtheirimpactuponpeopleandproperty-2018', 'http://doi.wiley.com/10.1111/risa.13113', event);\">\n    A Three-Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Matellini,  D., B.; Wall,  A., D.; Jenkinson,  I., D.; Wang,  J.;  and Pritchard,  R.\n</span>\n\n  \n\n</span>\n\n  <i>Risk Analysis</i>. 5 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.wiley.com/10.1111/risa.13113\"\n     onclick=\"log_download('matellini-wall-jenkinson-wang-pritchard-athreepartbayesiannetworkformodelingdwellingfiresandtheirimpactuponpeopleandproperty-2018', 'http://doi.wiley.com/10.1111/risa.13113', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Three-Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1111/risa.13113\"\n     onclick=\"log_download('matellini-wall-jenkinson-wang-pritchard-athreepartbayesiannetworkformodelingdwellingfiresandtheirimpactuponpeopleandproperty-2018', 'http://doi.org/10.1111/risa.13113', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/matellini-wall-jenkinson-wang-pritchard-athreepartbayesiannetworkformodelingdwellingfiresandtheirimpactuponpeopleandproperty-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('matellini-wall-jenkinson-wang-pritchard-athreepartbayesiannetworkformodelingdwellingfiresandtheirimpactuponpeopleandproperty-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Three-Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property},\n type = {article},\n year = {2018},\n keywords = {Bayesian network,benefit,cost,dwelling fires,human reaction,probability of fatality},\n websites = {http://doi.wiley.com/10.1111/risa.13113},\n month = {5},\n publisher = {Wiley/Blackwell (10.1111)},\n day = {17},\n id = {54568ea5-39f0-3290-9788-d5305e7ed240},\n created = {2018-05-29T00:38:58.160Z},\n accessed = {2018-05-28},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-05-29T00:38:58.160Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Matellini, D. B. and Wall, A. D. and Jenkinson, I. D. and Wang, J. and Pritchard, R.},\n doi = {10.1111/risa.13113},\n journal = {Risk Analysis}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ghasemi-sari-yousefi-falsafi-tamoaitien-projectportfolioriskidentificationandanalysisconsideringprojectriskinteractionsandusingbayesiannetworks-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.mdpi.com/2071-1050/10/5/1609\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghasemi-sari-yousefi-falsafi-tamoaitien-projectportfolioriskidentificationandanalysisconsideringprojectriskinteractionsandusingbayesiannetworks-2018', 'http://www.mdpi.com/2071-1050/10/5/1609', event);\">\n    Project Portfolio Risk Identification and Analysis, Considering Project Risk Interactions and Using Bayesian Networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ghasemi,  F.; Sari,  M.; Yousefi,  V.; Falsafi,  R.;  and Tamošaitienė,  J.\n</span>\n\n  \n\n</span>\n\n  <i>Sustainability</i>, 10(5): 1609. 5 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.mdpi.com/2071-1050/10/5/1609\"\n     onclick=\"log_download('ghasemi-sari-yousefi-falsafi-tamoaitien-projectportfolioriskidentificationandanalysisconsideringprojectriskinteractionsandusingbayesiannetworks-2018', 'http://www.mdpi.com/2071-1050/10/5/1609', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Project Portfolio Risk Identification and Analysis, Considering Project Risk Interactions and Using Bayesian Networks [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.3390/su10051609\"\n     onclick=\"log_download('ghasemi-sari-yousefi-falsafi-tamoaitien-projectportfolioriskidentificationandanalysisconsideringprojectriskinteractionsandusingbayesiannetworks-2018', 'http://doi.org/10.3390/su10051609', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghasemi-sari-yousefi-falsafi-tamoaitien-projectportfolioriskidentificationandanalysisconsideringprojectriskinteractionsandusingbayesiannetworks-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghasemi-sari-yousefi-falsafi-tamoaitien-projectportfolioriskidentificationandanalysisconsideringprojectriskinteractionsandusingbayesiannetworks-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Project Portfolio Risk Identification and Analysis, Considering Project Risk Interactions and Using Bayesian Networks},\n type = {article},\n year = {2018},\n keywords = {Bayesian networks,project portfolio risk,risk analysis,risk identification,risk interactions},\n pages = {1609},\n volume = {10},\n websites = {http://www.mdpi.com/2071-1050/10/5/1609},\n month = {5},\n publisher = {Multidisciplinary Digital Publishing Institute},\n day = {17},\n id = {f794f84b-0d80-3381-a929-3d81f93220c7},\n created = {2018-05-29T16:48:25.361Z},\n accessed = {2018-05-28},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-05-29T16:48:25.361Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {An organization&amp;rsquo;s strategic objectives are accomplished through portfolios. However, the materialization of portfolio risks may affect a portfolio&amp;rsquo;s sustainable success and the achievement of those objectives. Moreover, project interdependencies and cause&amp;ndash;effect relationships between risks create complexity for portfolio risk analysis. This paper presents a model using Bayesian network (BN) methodology for modeling and analyzing portfolio risks. To develop this model, first, portfolio-level risks and risks caused by project interdependencies are identified. Then, based on their cause&amp;ndash;effect relationships all portfolio risks are organized in a BN. Conditional probability distributions for this network are specified and the Bayesian networks method is used to estimate the probability of portfolio risk. This model was applied to a portfolio of a construction company located in Iran and proved effective in analyzing portfolio risk probability. Furthermore, the model provided valuable information for selecting a portfolio&amp;rsquo;s projects and making strategic decisions.},\n bibtype = {article},\n author = {Ghasemi, Foroogh and Sari, Mohammad and Yousefi, Vahidreza and Falsafi, Reza and Tamošaitienė, Jolanta},\n doi = {10.3390/su10051609},\n journal = {Sustainability},\n number = {5}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  An organization&rsquo;s strategic objectives are accomplished through portfolios. However, the materialization of portfolio risks may affect a portfolio&rsquo;s sustainable success and the achievement of those objectives. Moreover, project interdependencies and cause&ndash;effect relationships between risks create complexity for portfolio risk analysis. This paper presents a model using Bayesian network (BN) methodology for modeling and analyzing portfolio risks. To develop this model, first, portfolio-level risks and risks caused by project interdependencies are identified. Then, based on their cause&ndash;effect relationships all portfolio risks are organized in a BN. Conditional probability distributions for this network are specified and the Bayesian networks method is used to estimate the probability of portfolio risk. This model was applied to a portfolio of a construction company located in Iran and proved effective in analyzing portfolio risk probability. Furthermore, the model provided valuable information for selecting a portfolio&rsquo;s projects and making strategic decisions.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chen-zhong-an-zhu-xu-riskanalysisforrealtimefloodcontroloperationofamultireservoirsystemusingadynamicbayesiannetwork-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S1364815218301191\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chen-zhong-an-zhu-xu-riskanalysisforrealtimefloodcontroloperationofamultireservoirsystemusingadynamicbayesiannetwork-2018', 'https://www.sciencedirect.com/science/article/pii/S1364815218301191', event);\">\n    Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chen,  J.; Zhong,  P.; An,  R.; Zhu,  F.;  and Xu,  B.\n</span>\n\n  \n\n</span>\n\n  <i>Environmental Modelling & Software</i>. 10 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S1364815218301191\"\n     onclick=\"log_download('chen-zhong-an-zhu-xu-riskanalysisforrealtimefloodcontroloperationofamultireservoirsystemusingadynamicbayesiannetwork-2018', 'https://www.sciencedirect.com/science/article/pii/S1364815218301191', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/J.ENVSOFT.2018.10.007\"\n     onclick=\"log_download('chen-zhong-an-zhu-xu-riskanalysisforrealtimefloodcontroloperationofamultireservoirsystemusingadynamicbayesiannetwork-2018', 'http://doi.org/10.1016/J.ENVSOFT.2018.10.007', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chen-zhong-an-zhu-xu-riskanalysisforrealtimefloodcontroloperationofamultireservoirsystemusingadynamicbayesiannetwork-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chen-zhong-an-zhu-xu-riskanalysisforrealtimefloodcontroloperationofamultireservoirsystemusingadynamicbayesiannetwork-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network},\n type = {article},\n year = {2018},\n websites = {https://www.sciencedirect.com/science/article/pii/S1364815218301191},\n month = {10},\n publisher = {Elsevier},\n day = {26},\n id = {0cfa7415-7be3-3872-b2bb-6a249987c9bd},\n created = {2018-11-13T13:56:30.150Z},\n accessed = {2018-11-10},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-11-13T13:56:30.150Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper proposes a model for risk analysis of real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. The proposed model consists of three components: Monte Carlo simulations, dynamic Bayesian network establishing, and risk-informed inference for decision making. The Monte Carlo simulations provide basic data inputs for the dynamic Bayesian network establishing using the historical floods and operation models of the multi-reservoir system. The dynamic Bayesian network is built with expert knowledge and the relationships among the uncertainties. The component of risk-informed inference for decision making is to provide risk information about the operation schedules using the trained dynamic Bayesian network. We apply the proposed model to a multi-reservoir system in China. The results show that the proposed method has a capability for bi-directional inferences and can be served as a risk-informed decision-making tool under uncertainties in the real-time flood control operation of a multi-reservoir system.},\n bibtype = {article},\n author = {Chen, Juan and Zhong, Ping-An and An, Ru and Zhu, Feilin and Xu, Bin},\n doi = {10.1016/J.ENVSOFT.2018.10.007},\n journal = {Environmental Modelling &amp; Software}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper proposes a model for risk analysis of real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. The proposed model consists of three components: Monte Carlo simulations, dynamic Bayesian network establishing, and risk-informed inference for decision making. The Monte Carlo simulations provide basic data inputs for the dynamic Bayesian network establishing using the historical floods and operation models of the multi-reservoir system. The dynamic Bayesian network is built with expert knowledge and the relationships among the uncertainties. The component of risk-informed inference for decision making is to provide risk information about the operation schedules using the trained dynamic Bayesian network. We apply the proposed model to a multi-reservoir system in China. The results show that the proposed method has a capability for bi-directional inferences and can be served as a risk-informed decision-making tool under uncertainties in the real-time flood control operation of a multi-reservoir system.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"jansen-shipcollisionontemporarystructurescombiwallsundercollisionloadingcoverphotoconstructionofthemaasdeltatunnelphotobyrijkswaterstaat-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/1128f09e-ec1a-512f-0965-652858c68b04/full_text.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'jansen-shipcollisionontemporarystructurescombiwallsundercollisionloadingcoverphotoconstructionofthemaasdeltatunnelphotobyrijkswaterstaat-2018', 'https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/1128f09e-ec1a-512f-0965-652858c68b04/full_text.pdf.pdf', event);\">\n    Ship collision on temporary structures Combi-walls under collision loading Cover photo: Construction of the Maasdeltatunnel Photo by Rijkswaterstaat.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Jansen,  J.\n</span>\n\n  \n\n</span>\n\n  Technical Report  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/1128f09e-ec1a-512f-0965-652858c68b04/full_text.pdf.pdf\"\n     onclick=\"log_download('jansen-shipcollisionontemporarystructurescombiwallsundercollisionloadingcoverphotoconstructionofthemaasdeltatunnelphotobyrijkswaterstaat-2018', 'https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/1128f09e-ec1a-512f-0965-652858c68b04/full_text.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Ship collision on temporary structures Combi-walls under collision loading Cover photo: Construction of the Maasdeltatunnel Photo by Rijkswaterstaat [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://repository.tudelft.nl/\"\n     onclick=\"log_download('jansen-shipcollisionontemporarystructurescombiwallsundercollisionloadingcoverphotoconstructionofthemaasdeltatunnelphotobyrijkswaterstaat-2018', 'http://repository.tudelft.nl/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Ship collision on temporary structures Combi-walls under collision loading Cover photo: Construction of the Maasdeltatunnel Photo by Rijkswaterstaat [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/jansen-shipcollisionontemporarystructurescombiwallsundercollisionloadingcoverphotoconstructionofthemaasdeltatunnelphotobyrijkswaterstaat-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('jansen-shipcollisionontemporarystructurescombiwallsundercollisionloadingcoverphotoconstructionofthemaasdeltatunnelphotobyrijkswaterstaat-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@techreport{\n title = {Ship collision on temporary structures Combi-walls under collision loading Cover photo: Construction of the Maasdeltatunnel Photo by Rijkswaterstaat},\n type = {techreport},\n year = {2018},\n websites = {http://repository.tudelft.nl/},\n id = {467b614a-1429-3dd6-a33a-7c1b94463372},\n created = {2019-12-21T18:41:50.531Z},\n accessed = {2019-12-21},\n file_attached = {true},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2019-12-21T18:42:31.208Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {techreport},\n author = {Jansen, J}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"li-hong-zhang-improvedbayesiannetworkbasedriskmodelanditsapplicationindisasterriskassessment-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/59828c53-9f0d-04e1-3c35-486f9c780dc0/full_text.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'li-hong-zhang-improvedbayesiannetworkbasedriskmodelanditsapplicationindisasterriskassessment-2018', 'https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/59828c53-9f0d-04e1-3c35-486f9c780dc0/full_text.pdf.pdf', event);\">\n    Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Li,  M.; Hong,  M.;  and Zhang,  R.\n</span>\n\n  \n\n</span>\n\n  <i>International Journal of Disaster Risk Science</i>, 9(2): 237-248. 6 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/59828c53-9f0d-04e1-3c35-486f9c780dc0/full_text.pdf.pdf\"\n     onclick=\"log_download('li-hong-zhang-improvedbayesiannetworkbasedriskmodelanditsapplicationindisasterriskassessment-2018', 'https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/59828c53-9f0d-04e1-3c35-486f9c780dc0/full_text.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s13753-018-0171-z\"\n     onclick=\"log_download('li-hong-zhang-improvedbayesiannetworkbasedriskmodelanditsapplicationindisasterriskassessment-2018', 'http://doi.org/10.1007/s13753-018-0171-z', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/li-hong-zhang-improvedbayesiannetworkbasedriskmodelanditsapplicationindisasterriskassessment-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('li-hong-zhang-improvedbayesiannetworkbasedriskmodelanditsapplicationindisasterriskassessment-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment},\n type = {article},\n year = {2018},\n keywords = {Bayesian network,Genetic algorithm,Grey relational analysis,Risk assessment},\n pages = {237-248},\n volume = {9},\n month = {6},\n publisher = {Beijing Normal University Press},\n day = {1},\n id = {6bf9daf9-fc8d-3688-a490-a6cbd5c416b2},\n created = {2020-03-05T01:23:39.185Z},\n accessed = {2020-03-04},\n file_attached = {true},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2020-03-05T01:23:43.569Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {The application of Bayesian network (BN) theory in risk assessment is an emerging trend. But in cases where data are incomplete and variables are mutually related, its application is restricted. To overcome these problems, an improved BN assessment model with parameter retrieval and decorrelation ability is proposed. First, multivariate nonlinear planning is applied to the feedback error learning of parameters. A genetic algorithm is used to learn the probability distribution of nodes that lack quantitative data. Then, based on an improved grey relational analysis that considers the correlation of variation rate, the optimal weight that characterizes the correlation is calculated and the weighted BN is improved for decorrelation. An improved risk assessment model based on the weighted BN then is built. An assessment of sea ice disaster shows that the model can be applied for risk assessment with incomplete data and variable correlation.},\n bibtype = {article},\n author = {Li, Ming and Hong, Mei and Zhang, Ren},\n doi = {10.1007/s13753-018-0171-z},\n journal = {International Journal of Disaster Risk Science},\n number = {2}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The application of Bayesian network (BN) theory in risk assessment is an emerging trend. But in cases where data are incomplete and variables are mutually related, its application is restricted. To overcome these problems, an improved BN assessment model with parameter retrieval and decorrelation ability is proposed. First, multivariate nonlinear planning is applied to the feedback error learning of parameters. A genetic algorithm is used to learn the probability distribution of nodes that lack quantitative data. Then, based on an improved grey relational analysis that considers the correlation of variation rate, the optimal weight that characterizes the correlation is calculated and the weighted BN is improved for decorrelation. An improved risk assessment model based on the weighted BN then is built. An assessment of sea ice disaster shows that the model can be applied for risk assessment with incomplete data and variable correlation.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2017\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2017')\"\n      onkeypress=\"toggleGroup('group_2017')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2017</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"smith-veitch-khan-taylor-understandingindustrialsafetycomparingfaulttreebayesiannetworkandframapproaches-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Smith,  D.; Veitch,  B.; Khan,  F.;  and Taylor,  R.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Loss Prevention in the Process Industries</i>, 45.  2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jlp.2016.11.016\"\n     onclick=\"log_download('smith-veitch-khan-taylor-understandingindustrialsafetycomparingfaulttreebayesiannetworkandframapproaches-2017', 'http://doi.org/10.1016/j.jlp.2016.11.016', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/smith-veitch-khan-taylor-understandingindustrialsafetycomparingfaulttreebayesiannetworkandframapproaches-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('smith-veitch-khan-taylor-understandingindustrialsafetycomparingfaulttreebayesiannetworkandframapproaches-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches},\n type = {article},\n year = {2017},\n volume = {45},\n id = {703fab28-be7f-3340-b2ca-f27e3b100b6c},\n created = {2018-03-31T13:52:13.308Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-03-31T13:52:13.308Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Industrial accidents are a major concern for companies and families alike. It is a high priority to all stakeholders that steps be taken to prevent accidents from occurring. In this paper, three approaches to safety are examined: fault trees (FT), Bayesian networks (BN), and the Functional Resonance Analysis Method (FRAM). A case study of a propane feed control system is used to apply these methods. In order to make safety improvements to industrial workplaces high understanding of the systems is required. It is shown that consideration of the chance of failure of the system components, as in the FT and BN approaches, may not provide enough understanding to fully inform safety assessments. The FT and BN methods are top-down approaches that are formed from the perspective of management in workplaces. The FRAM methodology uses a bottom-up approach from the operational perspective to improve the understanding of the industrial workplace. The FRAM approach can provide added insight to the human factor and context and increase the rate at which we learn by considering successes as well as failures. FRAM can be a valuable tool for industrial safety assessment and to consider industrial safety holistically, by providing a framework to examine the operations in detail. However, operations should be considered using both top-down and bottom-up perspectives and all operational experience to make the most informed safety decisions.},\n bibtype = {article},\n author = {Smith, Doug and Veitch, Brian and Khan, Faisal and Taylor, Rocky},\n doi = {10.1016/j.jlp.2016.11.016},\n journal = {Journal of Loss Prevention in the Process Industries}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Industrial accidents are a major concern for companies and families alike. It is a high priority to all stakeholders that steps be taken to prevent accidents from occurring. In this paper, three approaches to safety are examined: fault trees (FT), Bayesian networks (BN), and the Functional Resonance Analysis Method (FRAM). A case study of a propane feed control system is used to apply these methods. In order to make safety improvements to industrial workplaces high understanding of the systems is required. It is shown that consideration of the chance of failure of the system components, as in the FT and BN approaches, may not provide enough understanding to fully inform safety assessments. The FT and BN methods are top-down approaches that are formed from the perspective of management in workplaces. The FRAM methodology uses a bottom-up approach from the operational perspective to improve the understanding of the industrial workplace. The FRAM approach can provide added insight to the human factor and context and increase the rate at which we learn by considering successes as well as failures. FRAM can be a valuable tool for industrial safety assessment and to consider industrial safety holistically, by providing a framework to examine the operations in detail. However, operations should be considered using both top-down and bottom-up perspectives and all operational experience to make the most informed safety decisions.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2016\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2016')\"\n      onkeypress=\"toggleGroup('group_2016')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2016</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"yet-constantinou-fenton-neil-luedeling-shepherd-abayesiannetworkframeworkforprojectcostbenefitandriskanalysiswithanagriculturaldevelopmentcasestudy-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yet,  B.; Constantinou,  A.; Fenton,  N.; Neil,  M.; Luedeling,  E.;  and Shepherd,  K.\n</span>\n\n  \n\n</span>\n\n  <i>Expert Systems with Applications</i>, 60.  2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.eswa.2016.05.005\"\n     onclick=\"log_download('yet-constantinou-fenton-neil-luedeling-shepherd-abayesiannetworkframeworkforprojectcostbenefitandriskanalysiswithanagriculturaldevelopmentcasestudy-2016', 'http://doi.org/10.1016/j.eswa.2016.05.005', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yet-constantinou-fenton-neil-luedeling-shepherd-abayesiannetworkframeworkforprojectcostbenefitandriskanalysiswithanagriculturaldevelopmentcasestudy-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yet-constantinou-fenton-neil-luedeling-shepherd-abayesiannetworkframeworkforprojectcostbenefitandriskanalysiswithanagriculturaldevelopmentcasestudy-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study},\n type = {article},\n year = {2016},\n volume = {60},\n id = {3d0130c7-327c-38ae-8805-c5b66b3d8e29},\n created = {2017-08-23T21:40:59.017Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2017-08-23T21:40:59.017Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project.},\n bibtype = {article},\n author = {Yet, Barbaros and Constantinou, Anthony and Fenton, Norman and Neil, Martin and Luedeling, Eike and Shepherd, Keith},\n doi = {10.1016/j.eswa.2016.05.005},\n journal = {Expert Systems with Applications}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2015\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2015')\"\n      onkeypress=\"toggleGroup('group_2015')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2015</span>\n      <span class=\"bibbase_group_count\">\n        \n        (5)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"abimbola-khan-khakzad-butt-safetyandriskanalysisofmanagedpressuredrillingoperationusingbayesiannetwork-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0925753515000119\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'abimbola-khan-khakzad-butt-safetyandriskanalysisofmanagedpressuredrillingoperationusingbayesiannetwork-2015', 'http://www.sciencedirect.com/science/article/pii/S0925753515000119', event);\">\n    Safety and risk analysis of managed pressure drilling operation using Bayesian network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Abimbola,  M.; Khan,  F.; Khakzad,  N.;  and Butt,  S.\n</span>\n\n  \n\n</span>\n\n  <i>Safety Science</i>, 76: 133-144. 7 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0925753515000119\"\n     onclick=\"log_download('abimbola-khan-khakzad-butt-safetyandriskanalysisofmanagedpressuredrillingoperationusingbayesiannetwork-2015', 'http://www.sciencedirect.com/science/article/pii/S0925753515000119', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Safety and risk analysis of managed pressure drilling operation using Bayesian network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ssci.2015.01.010\"\n     onclick=\"log_download('abimbola-khan-khakzad-butt-safetyandriskanalysisofmanagedpressuredrillingoperationusingbayesiannetwork-2015', 'http://doi.org/10.1016/j.ssci.2015.01.010', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/abimbola-khan-khakzad-butt-safetyandriskanalysisofmanagedpressuredrillingoperationusingbayesiannetwork-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('abimbola-khan-khakzad-butt-safetyandriskanalysisofmanagedpressuredrillingoperationusingbayesiannetwork-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Safety and risk analysis of managed pressure drilling operation using Bayesian network},\n type = {article},\n year = {2015},\n keywords = {BHP,BN,BT,Bayesian Network,Bayesian network analysis,Blowout prevention,Bottom Hole Pressure,Bow-tie approach,CBHP,CCS,COBD,CPT,Conditional Probability Table,Constant Bottom Hole Pressure,Continuous Circulation System,Conventional Over-balanced Drilling,DAG,DAPC,DGD,Directed Acyclic Graph,Dual Gradient Drilling,Dynamic Annular Pressure Control,ECD,ET,Equivalent Circulating Density,FG,FIT,FT,Formation Integrity Test,Fracture Gradient,IADC,ICU,Intelligent Control Unit,International Association of Drilling Contractors,LHS,LOT,Leak off Test,Left Hand Side,MODU,MPD,Managed Pressure Drilling,Managed pressure drilling,Mobile Offshore Drilling Unit,NPT,PMCD,PP,PWD,Pore Pressure,Pressure measurement While Drilling,Pressurized Mud Cap Drilling,QRA,Quantitative Risk Analysis,RCD,RHS,Right Hand Side,Rotating control device,bow-tie,event tree,fault tree,non productive time,rotating control device},\n pages = {133-144},\n volume = {76},\n websites = {http://www.sciencedirect.com/science/article/pii/S0925753515000119},\n month = {7},\n id = {7370c67c-7dea-38d3-8b76-5fe12c2f5f70},\n created = {2015-04-16T00:39:20.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2017-03-14T14:39:57.651Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The exploration and development of oil and gas resources located in extreme and harsh offshore environments are characterized with high safety risk and drilling cost. Some of these resources would be either uneconomical if extracted using conventional overbalanced drilling due to increased drilling problems and prolonged non-productive time, or too risky to adopt underbalanced drilling technique. Seeking new ways to reduce drilling cost and minimize risks has led to the development of managed pressure drilling techniques. Managed pressure drilling methods address the drawbacks of conventional overbalanced and underbalanced drilling techniques. As managed pressure drilling techniques are evolving, there are many unanswered questions related to safety and operating pressure regime. This study investigates the safety and operational issues of constant bottom-hole pressure drilling technique which is used in managed pressure drilling compared to conventional overbalanced drilling. The study first uses bow-tie models to map safety challenges and operating pressure regimes in constant bottom-hole pressure drilling technique. Due to the difficulties in modeling dependencies and updating the belief on the operational data, the bow-ties are mapped into Bayesian networks. The Bayesian networks are thoroughly analyzed to assess the safety critical elements of constant bottom-hole pressure drilling techniques and their safe operating pressure regime.},\n bibtype = {article},\n author = {Abimbola, Majeed and Khan, Faisal and Khakzad, Nima and Butt, Stephen},\n doi = {10.1016/j.ssci.2015.01.010},\n journal = {Safety Science}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The exploration and development of oil and gas resources located in extreme and harsh offshore environments are characterized with high safety risk and drilling cost. Some of these resources would be either uneconomical if extracted using conventional overbalanced drilling due to increased drilling problems and prolonged non-productive time, or too risky to adopt underbalanced drilling technique. Seeking new ways to reduce drilling cost and minimize risks has led to the development of managed pressure drilling techniques. Managed pressure drilling methods address the drawbacks of conventional overbalanced and underbalanced drilling techniques. As managed pressure drilling techniques are evolving, there are many unanswered questions related to safety and operating pressure regime. This study investigates the safety and operational issues of constant bottom-hole pressure drilling technique which is used in managed pressure drilling compared to conventional overbalanced drilling. The study first uses bow-tie models to map safety challenges and operating pressure regimes in constant bottom-hole pressure drilling technique. Due to the difficulties in modeling dependencies and updating the belief on the operational data, the bow-ties are mapped into Bayesian networks. The Bayesian networks are thoroughly analyzed to assess the safety critical elements of constant bottom-hole pressure drilling techniques and their safe operating pressure regime.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"bhandari-abbassi-garaniya-khan-riskanalysisofdeepwaterdrillingoperationsusingbayesiannetwork-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://linkinghub.elsevier.com/retrieve/pii/S0950423015300188\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'bhandari-abbassi-garaniya-khan-riskanalysisofdeepwaterdrillingoperationsusingbayesiannetwork-2015', 'http://linkinghub.elsevier.com/retrieve/pii/S0950423015300188', event);\">\n    Risk analysis of deepwater drilling operations using Bayesian network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Bhandari,  J.; Abbassi,  R.; Garaniya,  V.;  and Khan,  F.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Loss Prevention in the Process Industries</i>, 38: 11-23. 11 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://linkinghub.elsevier.com/retrieve/pii/S0950423015300188\"\n     onclick=\"log_download('bhandari-abbassi-garaniya-khan-riskanalysisofdeepwaterdrillingoperationsusingbayesiannetwork-2015', 'http://linkinghub.elsevier.com/retrieve/pii/S0950423015300188', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Risk analysis of deepwater drilling operations using Bayesian network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jlp.2015.08.004\"\n     onclick=\"log_download('bhandari-abbassi-garaniya-khan-riskanalysisofdeepwaterdrillingoperationsusingbayesiannetwork-2015', 'http://doi.org/10.1016/j.jlp.2015.08.004', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/bhandari-abbassi-garaniya-khan-riskanalysisofdeepwaterdrillingoperationsusingbayesiannetwork-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('bhandari-abbassi-garaniya-khan-riskanalysisofdeepwaterdrillingoperationsusingbayesiannetwork-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Risk analysis of deepwater drilling operations using Bayesian network},\n type = {article},\n year = {2015},\n pages = {11-23},\n volume = {38},\n websites = {http://linkinghub.elsevier.com/retrieve/pii/S0950423015300188},\n month = {11},\n publisher = {Elsevier Ltd},\n id = {24fe5c2c-2a98-36f8-afbc-5f12277dc006},\n created = {2017-06-01T01:02:34.427Z},\n accessed = {2017-05-31},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2017-06-01T01:02:34.427Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Bhandari, Jyoti and Abbassi, Rouzbeh and Garaniya, Vikram and Khan, Faisal},\n doi = {10.1016/j.jlp.2015.08.004},\n journal = {Journal of Loss Prevention in the Process Industries}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"baksh-khan-gadag-ferdous-networkbasedapproachforpredictiveaccidentmodelling-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Network based approach for predictive accident modelling.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Baksh,  A., A.; Khan,  F.; Gadag,  V.;  and Ferdous,  R.\n</span>\n\n  \n\n</span>\n\n  <i>Safety Science</i>, 80.  2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ssci.2015.08.003\"\n     onclick=\"log_download('baksh-khan-gadag-ferdous-networkbasedapproachforpredictiveaccidentmodelling-2015', 'http://doi.org/10.1016/j.ssci.2015.08.003', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/baksh-khan-gadag-ferdous-networkbasedapproachforpredictiveaccidentmodelling-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('baksh-khan-gadag-ferdous-networkbasedapproachforpredictiveaccidentmodelling-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Network based approach for predictive accident modelling},\n type = {article},\n year = {2015},\n volume = {80},\n id = {431a0392-b88e-356a-8bd0-ca485c28fe29},\n created = {2017-08-23T21:37:06.090Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2017-08-23T21:37:06.090Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Accident modeling methodologies in literature such as the System Hazard Identification, Prediction and Prevention (SHIPP) consider accident precursors and the associated five engineering safety barriers to assess the likelihood of accident occurrence with the help of fault and event trees to model the cause-consequence relationship between the failure of safety barriers and potential adverse events and design preventive, controlling and mitigating measures for improving the industrial process safety. In the SHIPP method, a restrictive assumption is used that the severity of the adverse events progresses only through sequential failures of the five safety barriers considered.In the proposed methodology, shortcomings of the existing accident model are improved in the following ways. Firstly, the above mentioned restrictive sequential progression assumption is mitigated in the SHIPP methodology by allowing non-sequential failure of safety barriers to cause adverse events in any order. Secondly, in the prediction of posterior probabilities of adverse events for real time industrial data, an important mechanical safety barrier &#x27;Damage Control Emergency Management Barrier&#x27; has been included. Further, posterior probabilities of the occurrence of the adverse events are calculated using a Bayesian network (BN) approach. The utility of this approach is tested and demonstrated with the data from a liquefied natural gas (LNG) process facility. The method allows for continual updating of occurrence probabilities for adverse events and failure probabilities of safety barriers for successive real time data from industry.},\n bibtype = {article},\n author = {Baksh, Al Amin and Khan, Faisal and Gadag, Veeresh and Ferdous, Refaul},\n doi = {10.1016/j.ssci.2015.08.003},\n journal = {Safety Science}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Accident modeling methodologies in literature such as the System Hazard Identification, Prediction and Prevention (SHIPP) consider accident precursors and the associated five engineering safety barriers to assess the likelihood of accident occurrence with the help of fault and event trees to model the cause-consequence relationship between the failure of safety barriers and potential adverse events and design preventive, controlling and mitigating measures for improving the industrial process safety. In the SHIPP method, a restrictive assumption is used that the severity of the adverse events progresses only through sequential failures of the five safety barriers considered.In the proposed methodology, shortcomings of the existing accident model are improved in the following ways. Firstly, the above mentioned restrictive sequential progression assumption is mitigated in the SHIPP methodology by allowing non-sequential failure of safety barriers to cause adverse events in any order. Secondly, in the prediction of posterior probabilities of adverse events for real time industrial data, an important mechanical safety barrier 'Damage Control Emergency Management Barrier' has been included. Further, posterior probabilities of the occurrence of the adverse events are calculated using a Bayesian network (BN) approach. The utility of this approach is tested and demonstrated with the data from a liquefied natural gas (LNG) process facility. The method allows for continual updating of occurrence probabilities for adverse events and failure probabilities of safety barriers for successive real time data from industry.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"liu-liu-cai-zhang-zheng-dynamicbayesiannetworkmodelingofreliabilityofsubseablowoutpreventerstackinpresenceofcommoncausefailures-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Dynamic Bayesian network modeling of reliability of subsea blowout preventer stack in presence of common cause failures.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Liu,  Z.; Liu,  Y.; Cai,  B.; Zhang,  D.;  and Zheng,  C.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Loss Prevention in the Process Industries</i>, 38.  2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jlp.2015.09.001\"\n     onclick=\"log_download('liu-liu-cai-zhang-zheng-dynamicbayesiannetworkmodelingofreliabilityofsubseablowoutpreventerstackinpresenceofcommoncausefailures-2015', 'http://doi.org/10.1016/j.jlp.2015.09.001', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/liu-liu-cai-zhang-zheng-dynamicbayesiannetworkmodelingofreliabilityofsubseablowoutpreventerstackinpresenceofcommoncausefailures-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('liu-liu-cai-zhang-zheng-dynamicbayesiannetworkmodelingofreliabilityofsubseablowoutpreventerstackinpresenceofcommoncausefailures-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Dynamic Bayesian network modeling of reliability of subsea blowout preventer stack in presence of common cause failures},\n type = {article},\n year = {2015},\n volume = {38},\n id = {38063a61-ecb5-369c-a24b-d6016f0a17a3},\n created = {2017-08-23T21:39:19.143Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2017-08-23T21:39:19.143Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {A subsea blowout preventer (BOP) stack is used to seal, control and monitor oil and gas wells. It can be regarded as a series-parallel system consisting of several subsystems. This paper develops the dynamic Bayesian network (DBN) of a parallel system with n components, taking account of common cause failures and imperfect coverage. Multiple error shock model is used to model common cause failures. Based on the proposed generic model, DBNs of the two commonly used stack types, namely the conventional BOP and modern BOP are developed. In order to evaluate the effects of the failure rates and coverage factor on the reliability and availability of the stacks, sensitivity analysis is performed.},\n bibtype = {article},\n author = {Liu, Zengkai and Liu, Yonghong and Cai, Baoping and Zhang, Dawei and Zheng, Chao},\n doi = {10.1016/j.jlp.2015.09.001},\n journal = {Journal of Loss Prevention in the Process Industries}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  A subsea blowout preventer (BOP) stack is used to seal, control and monitor oil and gas wells. It can be regarded as a series-parallel system consisting of several subsystems. This paper develops the dynamic Bayesian network (DBN) of a parallel system with n components, taking account of common cause failures and imperfect coverage. Multiple error shock model is used to model common cause failures. Based on the proposed generic model, DBNs of the two commonly used stack types, namely the conventional BOP and modern BOP are developed. In order to evaluate the effects of the failure rates and coverage factor on the reliability and availability of the stacks, sensitivity analysis is performed.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kabir-tesfamariam-francisque-sadiq-evaluatingriskofwatermainsfailureusingabayesianbeliefnetworkmodel-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0377221714005360\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kabir-tesfamariam-francisque-sadiq-evaluatingriskofwatermainsfailureusingabayesianbeliefnetworkmodel-2015', 'http://www.sciencedirect.com/science/article/pii/S0377221714005360', event);\">\n    Evaluating risk of water mains failure using a Bayesian belief network model.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kabir,  G.; Tesfamariam,  S.; Francisque,  A.;  and Sadiq,  R.\n</span>\n\n  \n\n</span>\n\n  <i>European Journal of Operational Research</i>, 240(1): 220-234. 1 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0377221714005360\"\n     onclick=\"log_download('kabir-tesfamariam-francisque-sadiq-evaluatingriskofwatermainsfailureusingabayesianbeliefnetworkmodel-2015', 'http://www.sciencedirect.com/science/article/pii/S0377221714005360', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Evaluating risk of water mains failure using a Bayesian belief network model [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ejor.2014.06.033\"\n     onclick=\"log_download('kabir-tesfamariam-francisque-sadiq-evaluatingriskofwatermainsfailureusingabayesianbeliefnetworkmodel-2015', 'http://doi.org/10.1016/j.ejor.2014.06.033', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kabir-tesfamariam-francisque-sadiq-evaluatingriskofwatermainsfailureusingabayesianbeliefnetworkmodel-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kabir-tesfamariam-francisque-sadiq-evaluatingriskofwatermainsfailureusingabayesianbeliefnetworkmodel-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Evaluating risk of water mains failure using a Bayesian belief network model},\n type = {article},\n year = {2015},\n keywords = {Bayesian Belief Network (BBN),Consequence,Deterioration,Risk analysis,Water distribution network},\n pages = {220-234},\n volume = {240},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221714005360},\n month = {1},\n id = {27c1d147-4e5c-325d-959c-65a1f28e89a9},\n created = {2018-03-31T22:35:56.468Z},\n accessed = {2015-03-05},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-03-31T22:35:56.468Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {It has been reported that since year 2000, there have been an average 700 water main breaks per day only in Canada and the USA costing more than CAD 10 billions/year. Moreover, water main leaks affect other neighboring infrastructure that may lead to catastrophic failures. For this, municipality authorities or stakeholders are more concerned about preventive actions rather reacting to failure events. This paper presents a Bayesian Belief Network (BBN) model to evaluate the risk of failure of metallic water mains using structural integrity, hydraulic capacity, water quality, and consequence factors. BBN is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. The proposed model is capable of ranking water mains within distribution network that can identify vulnerable and sensitive pipes to justify proper decision action for maintenance/rehabilitation/replacement (M/R/R). To demonstrate the application of proposed model, water distribution network of City of Kelowna has been studied. Result indicates that almost 9% of the total 259 metallic pipes are at high risk in both summer and winter.},\n bibtype = {article},\n author = {Kabir, Golam and Tesfamariam, Solomon and Francisque, Alex and Sadiq, Rehan},\n doi = {10.1016/j.ejor.2014.06.033},\n journal = {European Journal of Operational Research},\n number = {1}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  It has been reported that since year 2000, there have been an average 700 water main breaks per day only in Canada and the USA costing more than CAD 10 billions/year. Moreover, water main leaks affect other neighboring infrastructure that may lead to catastrophic failures. For this, municipality authorities or stakeholders are more concerned about preventive actions rather reacting to failure events. This paper presents a Bayesian Belief Network (BBN) model to evaluate the risk of failure of metallic water mains using structural integrity, hydraulic capacity, water quality, and consequence factors. BBN is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. The proposed model is capable of ranking water mains within distribution network that can identify vulnerable and sensitive pipes to justify proper decision action for maintenance/rehabilitation/replacement (M/R/R). To demonstrate the application of proposed model, water distribution network of City of Kelowna has been studied. Result indicates that almost 9% of the total 259 metallic pipes are at high risk in both summer and winter.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2014\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2014')\"\n      onkeypress=\"toggleGroup('group_2014')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2014</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"bouejla-chaze-guarnieri-napoli-abayesiannetworktomanagerisksofmaritimepiracyagainstoffshoreoilfields-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  A Bayesian network to manage risks of maritime piracy against offshore oil fields.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Bouejla,  A.; Chaze,  X.; Guarnieri,  F.;  and Napoli,  A.\n</span>\n\n  \n\n</span>\n\n  <i>Safety Science</i>, 68: 222-230.  2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ssci.2014.04.010\"\n     onclick=\"log_download('bouejla-chaze-guarnieri-napoli-abayesiannetworktomanagerisksofmaritimepiracyagainstoffshoreoilfields-2014', 'http://doi.org/10.1016/j.ssci.2014.04.010', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/bouejla-chaze-guarnieri-napoli-abayesiannetworktomanagerisksofmaritimepiracyagainstoffshoreoilfields-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('bouejla-chaze-guarnieri-napoli-abayesiannetworktomanagerisksofmaritimepiracyagainstoffshoreoilfields-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Bayesian network to manage risks of maritime piracy against offshore oil fields},\n type = {article},\n year = {2014},\n keywords = {Bayesian networks,Offshore oil fields,Oil platforms,Pirate attacks,Quantitative and qualitative knowledge},\n pages = {222-230},\n volume = {68},\n id = {bd48a973-ffd3-36a1-b61e-c3c4e76dcd52},\n created = {2015-04-22T21:19:06.000Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2017-03-14T14:39:57.651Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In recent years, pirate attacks against shipping and oil field installations have become more frequent and more serious. This article proposes an innovative solution to the problem of offshore piracy from the perspective of the entire processing chain: from the detection of a potential threat to the implementation of a response. The response to an attack must take into account multiple variables: the characteristics of the threat and the potential target, existing protection tools, environmental constraints, etc. The potential of Bayesian networks is used to manage this large number of parameters and identify appropriate counter-measures. ?? 2014 Elsevier Ltd.},\n bibtype = {article},\n author = {Bouejla, Amal and Chaze, Xavier and Guarnieri, Franck and Napoli, Aldo},\n doi = {10.1016/j.ssci.2014.04.010},\n journal = {Safety Science}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In recent years, pirate attacks against shipping and oil field installations have become more frequent and more serious. This article proposes an innovative solution to the problem of offshore piracy from the perspective of the entire processing chain: from the detection of a potential threat to the implementation of a response. The response to an attack must take into account multiple variables: the characteristics of the threat and the potential target, existing protection tools, environmental constraints, etc. The potential of Bayesian networks is used to manage this large number of parameters and identify appropriate counter-measures. ?? 2014 Elsevier Ltd.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"duta-zamfirescu-filip-mathematicaldecisionmodelforreversesupplychainsinventoryliteraturereview-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Mathematical Decision Model for Reverse Supply Chains Inventory Literature review.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Duta,  L.; Zamfirescu,  C., B.;  and Filip,  F., G.\n</span>\n\n  \n\n</span>\n\n  <i>INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL</i>, 9(6): 686-693.  2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/duta-zamfirescu-filip-mathematicaldecisionmodelforreversesupplychainsinventoryliteraturereview-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('duta-zamfirescu-filip-mathematicaldecisionmodelforreversesupplychainsinventoryliteraturereview-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Mathematical Decision Model for Reverse Supply Chains Inventory Literature review},\n type = {article},\n year = {2014},\n keywords = {bayesian networks,decision aid,inventory models,reverse supply chains},\n pages = {686-693},\n volume = {9},\n id = {ef05a9de-88df-321b-930e-079ea100bc9e},\n created = {2015-04-23T00:08:23.000Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2017-03-14T14:39:57.651Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Duta, L and Zamfirescu, C B and Filip, F G},\n journal = {INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS &amp; CONTROL},\n number = {6}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"khakzad-khakzad-khan-probabilisticriskassessmentofmajoraccidentsapplicationtooffshoreblowoutsinthegulfofmexico-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Probabilistic risk assessment of major accidents: application to offshore blowouts in the Gulf of Mexico.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Khakzad,  N.; Khakzad,  S.;  and Khan,  F.\n</span>\n\n  \n\n</span>\n\n  <i>Natural Hazards</i>, 74(3).  2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s11069-014-1271-8\"\n     onclick=\"log_download('khakzad-khakzad-khan-probabilisticriskassessmentofmajoraccidentsapplicationtooffshoreblowoutsinthegulfofmexico-2014', 'http://doi.org/10.1007/s11069-014-1271-8', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/khakzad-khakzad-khan-probabilisticriskassessmentofmajoraccidentsapplicationtooffshoreblowoutsinthegulfofmexico-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('khakzad-khakzad-khan-probabilisticriskassessmentofmajoraccidentsapplicationtooffshoreblowoutsinthegulfofmexico-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Probabilistic risk assessment of major accidents: application to offshore blowouts in the Gulf of Mexico},\n type = {article},\n year = {2014},\n volume = {74},\n id = {50bddf4a-e099-3353-9f63-0da05bd9a677},\n created = {2017-08-23T21:32:33.818Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2017-08-23T21:34:10.828Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Major accidents are low-frequency, high-consequence accidents which are not well supported by conventional statistical methods due to the scarcity of directly relevant data. Modeling and decomposition techniques such as event tree have been proved as robust alternatives as they facilitate incorporation of partially relevant near accident data-accident precursor data-in probability estimation and risk analysis of major accidents. In this study, we developed a methodology based on event tree and hierarchical Bayesian analysis to establish informative distributions for offshore blowouts using data of near accidents, such as kicks, leaks, and failure of blowout preventers collected from a variety of offshore drilling rigs. These informative distributions can be used as predictive tools to estimate relevant failure probabilities in the future. Further, having a set of near accident data of a drilling rig of interest, the informative distributions can be updated to render case-specific posterior distributions which are of great importance in quantitative risk analysis. To cope with uncertainties, we implemented the methodology in a Markov Chain Monte Carlo framework and applied it to risk assessment of offshore blowouts in the Gulf of Mexico.},\n bibtype = {article},\n author = {Khakzad, Nima and Khakzad, Sina and Khan, Faisal},\n doi = {10.1007/s11069-014-1271-8},\n journal = {Natural Hazards},\n number = {3}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Major accidents are low-frequency, high-consequence accidents which are not well supported by conventional statistical methods due to the scarcity of directly relevant data. Modeling and decomposition techniques such as event tree have been proved as robust alternatives as they facilitate incorporation of partially relevant near accident data-accident precursor data-in probability estimation and risk analysis of major accidents. In this study, we developed a methodology based on event tree and hierarchical Bayesian analysis to establish informative distributions for offshore blowouts using data of near accidents, such as kicks, leaks, and failure of blowout preventers collected from a variety of offshore drilling rigs. These informative distributions can be used as predictive tools to estimate relevant failure probabilities in the future. Further, having a set of near accident data of a drilling rig of interest, the informative distributions can be updated to render case-specific posterior distributions which are of great importance in quantitative risk analysis. To cope with uncertainties, we implemented the methodology in a Markov Chain Monte Carlo framework and applied it to risk assessment of offshore blowouts in the Gulf of Mexico.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"amundson-brown-grabowski-badurdeen-lifecycleriskmodelingalternatemethodsusingbayesianbeliefnetworks-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S2212827114002881\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'amundson-brown-grabowski-badurdeen-lifecycleriskmodelingalternatemethodsusingbayesianbeliefnetworks-2014', 'http://www.sciencedirect.com/science/article/pii/S2212827114002881', event);\">\n    Life-cycle Risk Modeling: Alternate Methods Using Bayesian Belief Networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Amundson,  J.; Brown,  A.; Grabowski,  M.;  and Badurdeen,  F.\n</span>\n\n  \n\n</span>\n\n  <i>Procedia CIRP</i>, 17: 320-325.  2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S2212827114002881\"\n     onclick=\"log_download('amundson-brown-grabowski-badurdeen-lifecycleriskmodelingalternatemethodsusingbayesianbeliefnetworks-2014', 'http://www.sciencedirect.com/science/article/pii/S2212827114002881', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Life-cycle Risk Modeling: Alternate Methods Using Bayesian Belief Networks [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.procir.2014.02.029\"\n     onclick=\"log_download('amundson-brown-grabowski-badurdeen-lifecycleriskmodelingalternatemethodsusingbayesianbeliefnetworks-2014', 'http://doi.org/10.1016/j.procir.2014.02.029', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/amundson-brown-grabowski-badurdeen-lifecycleriskmodelingalternatemethodsusingbayesianbeliefnetworks-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('amundson-brown-grabowski-badurdeen-lifecycleriskmodelingalternatemethodsusingbayesianbeliefnetworks-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Life-cycle Risk Modeling: Alternate Methods Using Bayesian Belief Networks},\n type = {article},\n year = {2014},\n keywords = {Bayesian Belief Network,Life-Cycle,Supply Chain Risk},\n pages = {320-325},\n volume = {17},\n websites = {http://www.sciencedirect.com/science/article/pii/S2212827114002881},\n id = {13cec4f1-25f9-313b-84fe-55538d5bbc6a},\n created = {2018-03-31T22:35:56.457Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2018-03-31T22:35:56.457Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In recent years natural and man-made disasters have highlighted the need for robust supply chain risk management (SCRM) in manufacturing firms from a life-cycle perspective (pre-manufacturing, manufacturing, use, post-use stages). Bayesian Belief Networks (BBN) provide a means to probabilistically represent risk interdependencies and to proactively identify and manage any existing vulnerabilities. In this work, the BBN method is implemented for a product in the aerospace industry. Risk network maps are developed to identify interdependencies and describe the potential risk propagation behavior during each life-cycle phase and from one phase to another. Due to limited number of respondents and lack of certainty with respect to the post-use phase, enhanced methods of risk likelihood assessment are necessary specifically for the post-use phase assessment. In this paper two alternate techniques are compared for risk modeling using BBN in such situations: Boolean nodes and numeric simulation nodes. Results show that numeric nodes provide a more thorough explanation of the interconnections of the risk items modeled. Further enhancement using an approach that combines both BBN and System Dynamics (SD) for SCRM is discussed and possible variations for linking variables between SD and BBN are also presented.},\n bibtype = {article},\n author = {Amundson, Joseph and Brown, Adam and Grabowski, Matthias and Badurdeen, Fazleena},\n doi = {10.1016/j.procir.2014.02.029},\n journal = {Procedia CIRP}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In recent years natural and man-made disasters have highlighted the need for robust supply chain risk management (SCRM) in manufacturing firms from a life-cycle perspective (pre-manufacturing, manufacturing, use, post-use stages). Bayesian Belief Networks (BBN) provide a means to probabilistically represent risk interdependencies and to proactively identify and manage any existing vulnerabilities. In this work, the BBN method is implemented for a product in the aerospace industry. Risk network maps are developed to identify interdependencies and describe the potential risk propagation behavior during each life-cycle phase and from one phase to another. Due to limited number of respondents and lack of certainty with respect to the post-use phase, enhanced methods of risk likelihood assessment are necessary specifically for the post-use phase assessment. In this paper two alternate techniques are compared for risk modeling using BBN in such situations: Boolean nodes and numeric simulation nodes. Results show that numeric nodes provide a more thorough explanation of the interconnections of the risk items modeled. Further enhancement using an approach that combines both BBN and System Dynamics (SD) for SCRM is discussed and possible variations for linking variables between SD and BBN are also presented.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2012\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2012')\"\n      onkeypress=\"toggleGroup('group_2012')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2012</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"goulding-jayasuriya-horan-abayesiannetworkmodeltoassessthepublichealthriskassociatedwithwetweatherseweroverflowsdischargingintowaterways-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0043135412002187\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'goulding-jayasuriya-horan-abayesiannetworkmodeltoassessthepublichealthriskassociatedwithwetweatherseweroverflowsdischargingintowaterways-2012', 'http://www.sciencedirect.com/science/article/pii/S0043135412002187', event);\">\n    A Bayesian network model to assess the public health risk associated with wet weather sewer overflows discharging into waterways.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Goulding,  R.; Jayasuriya,  N.;  and Horan,  E.\n</span>\n\n  \n\n</span>\n\n  <i>Water Research</i>, 46(16): 4933-4940. 10 2012.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0043135412002187\"\n     onclick=\"log_download('goulding-jayasuriya-horan-abayesiannetworkmodeltoassessthepublichealthriskassociatedwithwetweatherseweroverflowsdischargingintowaterways-2012', 'http://www.sciencedirect.com/science/article/pii/S0043135412002187', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Bayesian network model to assess the public health risk associated with wet weather sewer overflows discharging into waterways [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.watres.2012.03.044\"\n     onclick=\"log_download('goulding-jayasuriya-horan-abayesiannetworkmodeltoassessthepublichealthriskassociatedwithwetweatherseweroverflowsdischargingintowaterways-2012', 'http://doi.org/10.1016/j.watres.2012.03.044', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/goulding-jayasuriya-horan-abayesiannetworkmodeltoassessthepublichealthriskassociatedwithwetweatherseweroverflowsdischargingintowaterways-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('goulding-jayasuriya-horan-abayesiannetworkmodeltoassessthepublichealthriskassociatedwithwetweatherseweroverflowsdischargingintowaterways-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Bayesian network model to assess the public health risk associated with wet weather sewer overflows discharging into waterways},\n type = {article},\n year = {2012},\n keywords = {Bayesian networks,Risk assessment,Sanitary sewer overflows,Water pollution},\n pages = {4933-4940},\n volume = {46},\n websites = {http://www.sciencedirect.com/science/article/pii/S0043135412002187},\n month = {10},\n id = {b4ee6e90-27d4-3d22-8405-a99c04f106a3},\n created = {2015-04-16T00:39:20.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2017-03-14T14:39:57.651Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Overflows from sanitary sewers during wet weather, which occur when the hydraulic capacity of the sewer system is exceeded, are considered a potential threat to the ecological and public health of the waterways which receive these overflows. As a result, water retailers in Australia and internationally commit significant resources to manage and abate sewer overflows. However, whilst some studies have contributed to an increased understanding of the impacts and risks associated with these events, they are relatively few in number and there still is a general lack of knowledge in this area. A Bayesian network model to assess the public health risk associated with wet weather sewer overflows is presented in this paper. The Bayesian network approach is shown to provide significant benefits in the assessment of public health risks associated with wet weather sewer overflows. In particular, the ability for the model to account for the uncertainty inherent in sewer overflow events and subsequent impacts through the use of probabilities is a valuable function. In addition, the paper highlights the benefits of the probabilistic inference function of the Bayesian network in prioritising management options to minimise public health risks associated with sewer overflows.},\n bibtype = {article},\n author = {Goulding, R. and Jayasuriya, N. and Horan, E.},\n doi = {10.1016/j.watres.2012.03.044},\n journal = {Water Research},\n number = {16}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Overflows from sanitary sewers during wet weather, which occur when the hydraulic capacity of the sewer system is exceeded, are considered a potential threat to the ecological and public health of the waterways which receive these overflows. As a result, water retailers in Australia and internationally commit significant resources to manage and abate sewer overflows. However, whilst some studies have contributed to an increased understanding of the impacts and risks associated with these events, they are relatively few in number and there still is a general lack of knowledge in this area. A Bayesian network model to assess the public health risk associated with wet weather sewer overflows is presented in this paper. The Bayesian network approach is shown to provide significant benefits in the assessment of public health risks associated with wet weather sewer overflows. In particular, the ability for the model to account for the uncertainty inherent in sewer overflow events and subsequent impacts through the use of probabilities is a valuable function. In addition, the paper highlights the benefits of the probabilistic inference function of the Bayesian network in prioritising management options to minimise public health risks associated with sewer overflows.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2010\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2010')\"\n      onkeypress=\"toggleGroup('group_2010')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2010</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"smid-verloo-barker-havelaar-strengthsandweaknessesofmontecarlosimulationmodelsandbayesianbeliefnetworksinmicrobialriskassessment-2010\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0168160509006680\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'smid-verloo-barker-havelaar-strengthsandweaknessesofmontecarlosimulationmodelsandbayesianbeliefnetworksinmicrobialriskassessment-2010', 'http://www.sciencedirect.com/science/article/pii/S0168160509006680', event);\">\n    Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Smid,  J., H.; Verloo,  D.; Barker,  G., C.;  and Havelaar,  A., H.\n</span>\n\n  \n\n</span>\n\n  <i>International journal of food microbiology</i>, 139 Suppl : S57-63. 5 2010.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0168160509006680\"\n     onclick=\"log_download('smid-verloo-barker-havelaar-strengthsandweaknessesofmontecarlosimulationmodelsandbayesianbeliefnetworksinmicrobialriskassessment-2010', 'http://www.sciencedirect.com/science/article/pii/S0168160509006680', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ijfoodmicro.2009.12.015\"\n     onclick=\"log_download('smid-verloo-barker-havelaar-strengthsandweaknessesofmontecarlosimulationmodelsandbayesianbeliefnetworksinmicrobialriskassessment-2010', 'http://doi.org/10.1016/j.ijfoodmicro.2009.12.015', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/smid-verloo-barker-havelaar-strengthsandweaknessesofmontecarlosimulationmodelsandbayesianbeliefnetworksinmicrobialriskassessment-2010\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('smid-verloo-barker-havelaar-strengthsandweaknessesofmontecarlosimulationmodelsandbayesianbeliefnetworksinmicrobialriskassessment-2010')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment.},\n type = {article},\n year = {2010},\n keywords = {Bayes Theorem,Consumer Product Safety,Food Microbiology,Models, Theoretical,Monte Carlo Method,Risk Assessment},\n pages = {S57-63},\n volume = {139 Suppl },\n websites = {http://www.sciencedirect.com/science/article/pii/S0168160509006680},\n month = {5},\n day = {30},\n id = {45a289ef-c69e-3346-9260-b60cad2cd92d},\n created = {2015-04-12T20:17:33.000Z},\n accessed = {2015-04-01},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2017-03-14T14:39:57.651Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We discuss different aspects of farm-to-fork risk assessment from a modelling perspective. Stochastic simulation models as they are presented today represent a mathematical representation of nature. In food safety risk assessment, a common modelling approach consists of a logic chain beginning at the source of the hazard and ending with the unwanted consequences of interest. This &#x27;farm-to-fork&#x27; approach usually begins with the hazard on the farm, sometimes with different compartments presenting different parts of the production chain, and ends with the &#x27;dose&#x27; received by the consumer or in case a dose response model is available the number of cases of illness. These models are typically implemented as Monte Carlo simulations, which are unidirectional in nature, and the link between statistics and simulation model is not interactive. A possible solution could be the use of Bayesian belief networks (BBNs) and this paper tries to discuss in an intuitive way the possibilities of using BBNs as an alternative for Monte Carlo modelling. An inventory is made of the strengths and weaknesses of both approaches, and an example is given showing an additional use of BBNs in biotracing problems.},\n bibtype = {article},\n author = {Smid, J H and Verloo, D and Barker, G C and Havelaar, A H},\n doi = {10.1016/j.ijfoodmicro.2009.12.015},\n journal = {International journal of food microbiology}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We discuss different aspects of farm-to-fork risk assessment from a modelling perspective. Stochastic simulation models as they are presented today represent a mathematical representation of nature. In food safety risk assessment, a common modelling approach consists of a logic chain beginning at the source of the hazard and ending with the unwanted consequences of interest. This 'farm-to-fork' approach usually begins with the hazard on the farm, sometimes with different compartments presenting different parts of the production chain, and ends with the 'dose' received by the consumer or in case a dose response model is available the number of cases of illness. These models are typically implemented as Monte Carlo simulations, which are unidirectional in nature, and the link between statistics and simulation model is not interactive. A possible solution could be the use of Bayesian belief networks (BBNs) and this paper tries to discuss in an intuitive way the possibilities of using BBNs as an alternative for Monte Carlo modelling. An inventory is made of the strengths and weaknesses of both approaches, and an example is given showing an additional use of BBNs in biotracing problems.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2007\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2007')\"\n      onkeypress=\"toggleGroup('group_2007')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2007</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"bonafede-giudici-bayesiannetworksforenterpriseriskassessment-2007\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S037843710700132X\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'bonafede-giudici-bayesiannetworksforenterpriseriskassessment-2007', 'http://www.sciencedirect.com/science/article/pii/S037843710700132X', event);\">\n    Bayesian Networks for enterprise risk assessment.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Bonafede,  C.;  and Giudici,  P.\n</span>\n\n  \n\n</span>\n\n  <i>Physica A: Statistical Mechanics and its Applications</i>, 382(1): 22-28. 8 2007.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S037843710700132X\"\n     onclick=\"log_download('bonafede-giudici-bayesiannetworksforenterpriseriskassessment-2007', 'http://www.sciencedirect.com/science/article/pii/S037843710700132X', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Bayesian Networks for enterprise risk assessment [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.physa.2007.02.065\"\n     onclick=\"log_download('bonafede-giudici-bayesiannetworksforenterpriseriskassessment-2007', 'http://doi.org/10.1016/j.physa.2007.02.065', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/bonafede-giudici-bayesiannetworksforenterpriseriskassessment-2007\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('bonafede-giudici-bayesiannetworksforenterpriseriskassessment-2007')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Bayesian Networks for enterprise risk assessment},\n type = {article},\n year = {2007},\n keywords = {Bayesian Networks,Enterprise risk assessment,Mutual information},\n pages = {22-28},\n volume = {382},\n websites = {http://www.sciencedirect.com/science/article/pii/S037843710700132X},\n month = {8},\n id = {4a3b8b75-5751-3913-8fae-6e8c96544164},\n created = {2015-04-16T00:39:20.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2017-03-14T14:39:57.651Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. Risk, in general, is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover, qualitative data must be converted in numerical values or bounds to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Networks (BNs) are a useful tool to integrate different information and in particular to study the risk&#x27;s joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a BN in the particular case in which only prior probabilities of node states and marginal correlations between nodes are available, and when the variables have only two states.},\n bibtype = {article},\n author = {Bonafede, C.E. and Giudici, P.},\n doi = {10.1016/j.physa.2007.02.065},\n journal = {Physica A: Statistical Mechanics and its Applications},\n number = {1}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. Risk, in general, is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover, qualitative data must be converted in numerical values or bounds to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Networks (BNs) are a useful tool to integrate different information and in particular to study the risk's joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a BN in the particular case in which only prior probabilities of node states and marginal correlations between nodes are available, and when the variables have only two states.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2004\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2004')\"\n      onkeypress=\"toggleGroup('group_2004')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2004</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"cornalba-giudici-statisticalmodelsforoperationalriskmanagement-2004\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0378437104002341\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'cornalba-giudici-statisticalmodelsforoperationalriskmanagement-2004', 'http://www.sciencedirect.com/science/article/pii/S0378437104002341', event);\">\n    Statistical models for operational risk management.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Cornalba,  C.;  and Giudici,  P.\n</span>\n\n  \n\n</span>\n\n  <i>Physica A: Statistical Mechanics and its Applications</i>, 338(1-2): 166-172. 7 2004.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0378437104002341\"\n     onclick=\"log_download('cornalba-giudici-statisticalmodelsforoperationalriskmanagement-2004', 'http://www.sciencedirect.com/science/article/pii/S0378437104002341', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Statistical models for operational risk management [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.physa.2004.02.039\"\n     onclick=\"log_download('cornalba-giudici-statisticalmodelsforoperationalriskmanagement-2004', 'http://doi.org/10.1016/j.physa.2004.02.039', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/cornalba-giudici-statisticalmodelsforoperationalriskmanagement-2004\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('cornalba-giudici-statisticalmodelsforoperationalriskmanagement-2004')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Statistical models for operational risk management},\n type = {article},\n year = {2004},\n keywords = {02.50.−r,89.65.Gh,Bayesian networks,Operational risk management,Predictive models,Value at risk},\n pages = {166-172},\n volume = {338},\n websites = {http://www.sciencedirect.com/science/article/pii/S0378437104002341},\n month = {7},\n id = {665e0f15-0663-37b7-8791-5303b0fdd603},\n created = {2015-04-16T00:39:20.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2017-03-14T14:39:57.651Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The Basel Committee on Banking Supervision has released, in the last few years, recommendations for the correct determination of the risks to which a banking organization is subject. This concerns, in particular, operational risks, which are all those management events that may determine unexpected losses. It is necessary to develop valid statistical models to measure and, consequently, predict, such operational risks. In the paper we present the possible approaches, including our own proposal, which is based on Bayesian networks.},\n bibtype = {article},\n author = {Cornalba, Chiara and Giudici, Paolo},\n doi = {10.1016/j.physa.2004.02.039},\n journal = {Physica A: Statistical Mechanics and its Applications},\n number = {1-2}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The Basel Committee on Banking Supervision has released, in the last few years, recommendations for the correct determination of the risks to which a banking organization is subject. This concerns, in particular, operational risks, which are all those management events that may determine unexpected losses. It is necessary to develop valid statistical models to measure and, consequently, predict, such operational risks. In the paper we present the possible approaches, including our own proposal, which is based on Bayesian networks.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_undefined\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_undefined')\"\n      onkeypress=\"toggleGroup('group_undefined')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>undefined</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"24pdfanapplicationofbayesiannetworkstoantiterrorismriskmanagementformilitaryplanners\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.researchgate.net/publication/2376453_An_Application_of_Bayesian_Networks_to_Antiterrorism_Risk_Management_for_Military_Planners\"\n     onclick=\"return trackOutboundLink('publications', 'click', '24pdfanapplicationofbayesiannetworkstoantiterrorismriskmanagementformilitaryplanners', 'https://www.researchgate.net/publication/2376453_An_Application_of_Bayesian_Networks_to_Antiterrorism_Risk_Management_for_Military_Planners', event);\">\n    (24) (PDF) An Application of Bayesian Networks to Antiterrorism Risk Management for Military Planners.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  \n</span>\n\n  \n\n</span>\n\n   \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.researchgate.net/publication/2376453_An_Application_of_Bayesian_Networks_to_Antiterrorism_Risk_Management_for_Military_Planners\"\n     onclick=\"log_download('24pdfanapplicationofbayesiannetworkstoantiterrorismriskmanagementformilitaryplanners', 'https://www.researchgate.net/publication/2376453_An_Application_of_Bayesian_Networks_to_Antiterrorism_Risk_Management_for_Military_Planners', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"(24) (PDF) An Application of Bayesian Networks to Antiterrorism Risk Management for Military Planners [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/24pdfanapplicationofbayesiannetworkstoantiterrorismriskmanagementformilitaryplanners\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('24pdfanapplicationofbayesiannetworkstoantiterrorismriskmanagementformilitaryplanners')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{\n title = {(24) (PDF) An Application of Bayesian Networks to Antiterrorism Risk Management for Military Planners},\n type = {misc},\n websites = {https://www.researchgate.net/publication/2376453_An_Application_of_Bayesian_Networks_to_Antiterrorism_Risk_Management_for_Military_Planners},\n id = {c5026614-7b5f-3a5f-b828-9640830e4569},\n created = {2020-03-05T01:09:49.743Z},\n accessed = {2020-03-04},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2020-03-05T01:09:49.814Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {misc},\n author = {}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"99pdfcomparingmodelsforquantitativeriskassessmentanapplicationtotheeuropeanregistryofforeignbodyinjuriesinchildrenpaolaberchiallaacademiaedu\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.academia.edu/20493958/Comparing_models_for_quantitative_risk_assessment_an_application_to_the_European_Registry_of_foreign_body_injuries_in_children?email_work_card=thumbnail\"\n     onclick=\"return trackOutboundLink('publications', 'click', '99pdfcomparingmodelsforquantitativeriskassessmentanapplicationtotheeuropeanregistryofforeignbodyinjuriesinchildrenpaolaberchiallaacademiaedu', 'https://www.academia.edu/20493958/Comparing_models_for_quantitative_risk_assessment_an_application_to_the_European_Registry_of_foreign_body_injuries_in_children?email_work_card=thumbnail', event);\">\n    (99+) (PDF) Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children | paola berchialla - Academia.edu.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  \n</span>\n\n  \n\n</span>\n\n   \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.academia.edu/20493958/Comparing_models_for_quantitative_risk_assessment_an_application_to_the_European_Registry_of_foreign_body_injuries_in_children?email_work_card=thumbnail\"\n     onclick=\"log_download('99pdfcomparingmodelsforquantitativeriskassessmentanapplicationtotheeuropeanregistryofforeignbodyinjuriesinchildrenpaolaberchiallaacademiaedu', 'https://www.academia.edu/20493958/Comparing_models_for_quantitative_risk_assessment_an_application_to_the_European_Registry_of_foreign_body_injuries_in_children?email_work_card=thumbnail', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"(99+) (PDF) Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children | paola berchialla - Academia.edu [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/99pdfcomparingmodelsforquantitativeriskassessmentanapplicationtotheeuropeanregistryofforeignbodyinjuriesinchildrenpaolaberchiallaacademiaedu\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('99pdfcomparingmodelsforquantitativeriskassessmentanapplicationtotheeuropeanregistryofforeignbodyinjuriesinchildrenpaolaberchiallaacademiaedu')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{\n title = {(99+) (PDF) Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children | paola berchialla - Academia.edu},\n type = {misc},\n websites = {https://www.academia.edu/20493958/Comparing_models_for_quantitative_risk_assessment_an_application_to_the_European_Registry_of_foreign_body_injuries_in_children?email_work_card=thumbnail},\n id = {2cb55440-7c15-3f08-94e7-2da8fba5d230},\n created = {2020-04-14T20:11:28.784Z},\n accessed = {2020-04-14},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {0d75bba3-25d4-3416-a7fe-43a9d2675870},\n last_modified = {2020-04-14T20:11:28.853Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {misc},\n author = {}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);