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\n  \n 2026\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Decoupled Geometric Measurement and Machine Learning Classification for Automated Post-Harvest Quality Assessment of Ruscus hypophyllum Foliage.\n \n \n \n \n\n\n \n Ortega-Loza, F.; Toapanta-Ramos, F.; Peña, D.; Safhi, M., H.; and Peluffo-Ordóñez, D., H.\n\n\n \n\n\n\n Horticulturae, 12(8). 2026.\n \n\n\n\n
\n\n\n\n \n \n \"DecoupledWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Decoupled Geometric Measurement and Machine Learning Classification for Automated Post-Harvest Quality Assessment of Ruscus hypophyllum Foliage},\n type = {article},\n year = {2026},\n volume = {12},\n websites = {https://www.mdpi.com/2311-7524/12/8/935},\n id = {77a0b9bf-3c69-305c-9a1d-f5f978d7b6a1},\n created = {2026-08-06T16:45:55.997Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {daab818b-67d8-34c7-bb2b-5037177341c3},\n last_modified = {2026-08-06T16:45:55.997Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {Article},\n private_publication = {false},\n abstract = {This paper presents a hybrid computer vision framework that explicitly decouples geometric stem measurement from visual foliage condition classification for automated post-harvest quality assessment of Ruscus hypophyllum ornamental foliage. The proposed approach addresses a gap in the literature where heterogeneous quality attributes are typically treated within a single unified learning framework. In the first stage, stem size is estimated using a pixel-based geometric method that incorporates trigonometric orientation correction and spatial calibration via a reference marker of known length, enabling accurate conversion of image measurements to real-world physical dimensions. In the second stage, foliage condition is classified as good or poor using supervised machine learning models trained on Bag of Features representations extracted with the SIFT descriptor. A dataset of 1233 Ruscus hypophyllum images was acquired under controlled conditions using a consumer-grade smartphone camera and processed using open-source Python 3.11 libraries. Twenty-four classifier configurations across six model families were evaluated using stratified 10-fold cross-validation. The geometric estimation stage achieved a Mean Absolute Error (MAE) of 1.2 mm, a Root Mean Square Error (RMSE) of approximately 1.3 mm, and a size categorization accuracy of 99.84%. For foliage condition classification, the Linear Support Vector Machine achieved the best performance, with an accuracy of 92.4±1.0% and an F1-score of 91.1±1.2%, outperforming all other evaluated configurations. The proposed framework provides an interpretable, computationally efficient, and accessible solution for automated foliage quality grading, with potential applications in export-oriented ornamental foliage processing facilities.},\n bibtype = {article},\n author = {Ortega-Loza, Fernando and Toapanta-Ramos, Fernando and Peña, Diego and Safhi, Moad Hicham and Peluffo-Ordóñez, Diego H},\n doi = {10.3390/horticulturae12080935},\n journal = {Horticulturae},\n number = {8}\n}
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\n This paper presents a hybrid computer vision framework that explicitly decouples geometric stem measurement from visual foliage condition classification for automated post-harvest quality assessment of Ruscus hypophyllum ornamental foliage. The proposed approach addresses a gap in the literature where heterogeneous quality attributes are typically treated within a single unified learning framework. In the first stage, stem size is estimated using a pixel-based geometric method that incorporates trigonometric orientation correction and spatial calibration via a reference marker of known length, enabling accurate conversion of image measurements to real-world physical dimensions. In the second stage, foliage condition is classified as good or poor using supervised machine learning models trained on Bag of Features representations extracted with the SIFT descriptor. A dataset of 1233 Ruscus hypophyllum images was acquired under controlled conditions using a consumer-grade smartphone camera and processed using open-source Python 3.11 libraries. Twenty-four classifier configurations across six model families were evaluated using stratified 10-fold cross-validation. The geometric estimation stage achieved a Mean Absolute Error (MAE) of 1.2 mm, a Root Mean Square Error (RMSE) of approximately 1.3 mm, and a size categorization accuracy of 99.84%. For foliage condition classification, the Linear Support Vector Machine achieved the best performance, with an accuracy of 92.4±1.0% and an F1-score of 91.1±1.2%, outperforming all other evaluated configurations. The proposed framework provides an interpretable, computationally efficient, and accessible solution for automated foliage quality grading, with potential applications in export-oriented ornamental foliage processing facilities.\n
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\n  \n 2025\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Numerical Study of 3D Heat Transfer in Heat Sinks with Circular Profile Fins Using CFD.\n \n \n \n \n\n\n \n Toapanta-Ramos, F.; Guashco Rubio, M.; Ortega-Loza, F.; and Diaz, W.\n\n\n \n\n\n\n Processes, 13(10). 2025.\n \n\n\n\n
\n\n\n\n \n \n \"NumericalWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Numerical Study of 3D Heat Transfer in Heat Sinks with Circular Profile Fins Using CFD},\n type = {article},\n year = {2025},\n volume = {13},\n websites = {https://www.mdpi.com/2227-9717/13/10/3199},\n id = {91ae5c7b-9a99-36ce-a92f-5be22fe966b6},\n created = {2026-05-08T16:38:05.491Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {daab818b-67d8-34c7-bb2b-5037177341c3},\n last_modified = {2026-05-08T16:38:05.491Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {Article},\n private_publication = {false},\n abstract = {A 3D numerical study using computational fluid dynamics simulations is carried out on a heat sink with circular fins. These devices are used to reject heat on motherboards and graphics cards. The software used in this investigation was ANSYS Fluent-CFD, with energy- and momentum-conservation models, as well as two-equation κ−ϵ turbulence models. Three temperatures are set at the base of the heat sink: 80 °C, 90 °C, and 100 °C; as well as three air velocities for cooling: 10 m/s, 15 m/s, and 20 m/s. The analysis determined that the temperature at the fins depends on the length of time the heat sink is exposed to high temperatures. Furthermore, the temperature in the center of the heat sink is lower than at the edges. On the other hand, the analysis times with periods of 2 s, 5 s, and 10 s, this variable being the most fluctuating since significant changes in the temperature of the fins and the surrounding air are observed; increases are determined ranging from 7.96% for the shortest time of exposure to forced convective air, up to 54.55%, for the longest heat-transfer time. However, in the simulations it was observed that from the eighth second the heat transfer stabilizes.},\n bibtype = {article},\n author = {Toapanta-Ramos, Fernando and Guashco Rubio, Mayra and Ortega-Loza, Fernando and Diaz, William},\n doi = {10.3390/pr13103199},\n journal = {Processes},\n number = {10}\n}
\n
\n\n\n
\n A 3D numerical study using computational fluid dynamics simulations is carried out on a heat sink with circular fins. These devices are used to reject heat on motherboards and graphics cards. The software used in this investigation was ANSYS Fluent-CFD, with energy- and momentum-conservation models, as well as two-equation κ−ϵ turbulence models. Three temperatures are set at the base of the heat sink: 80 °C, 90 °C, and 100 °C; as well as three air velocities for cooling: 10 m/s, 15 m/s, and 20 m/s. The analysis determined that the temperature at the fins depends on the length of time the heat sink is exposed to high temperatures. Furthermore, the temperature in the center of the heat sink is lower than at the edges. On the other hand, the analysis times with periods of 2 s, 5 s, and 10 s, this variable being the most fluctuating since significant changes in the temperature of the fins and the surrounding air are observed; increases are determined ranging from 7.96% for the shortest time of exposure to forced convective air, up to 54.55%, for the longest heat-transfer time. However, in the simulations it was observed that from the eighth second the heat transfer stabilizes.\n
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