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  1. 10761
  2. 10762

    Análisis bibliométrico de la inteligencia artificial en el deporte (Bibliometric analysis of artificial intelligence in sport) by José Ramón Sanabria Navarro, William Alejandro Niebles Núñez, Yahilina Silveira Pérez

    Published 2024-05-01
    “…The objective of this research was to bibliometrically analyze the elements of artificial intelligence in sports. …”
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    Article
  3. 10763

    Effect of bin width on variogram model accuracy: a case study of teak tree volume specific to Solomon clone in Tawau, Sabah, Malaysia by Johannah Jamalul Kiram, Rossita Mohamad Yunus, Yani Japarudin, Mahadir Lapammu, Olivier Monteuuis, Doreen Kim Soh Goh

    Published 2025-04-01
    “…The volume of teak trees (Tectona grandis Linn. f.) was analyzed using data from a teak plantation managed by the research and development team at Sabah Softwood Berhad in Brumas Camp, Tawau, Sabah, Malaysia. …”
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  4. 10764

    Anthropometric Landmark Detection Network via Geodesic Heatmap on 3D Human Scan by Min Hee Cha, Jae Hyeon Park, Ji Sun Byun, Sangyeon Ahn, Gyoomin Lee, Seung Hyun Yoon, Sung In Cho

    Published 2024-01-01
    “…As a result, our method provides the lowest average detection error on the SHREC’14 dataset over the six anthropometric landmarks, demonstrating a maximum error reduction of 76.14%. …”
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  15. 10775

    Adaptive rolling runoff forecasting model: Combining multi-source correlated sequences and extreme value encoding by Tao Wang, Jingzhe Liu, Yongming Cheng, Jingjing Duan, Yifei Zhao, Jing Zhao, Peiling Wang, Jiaqi Zhai

    Published 2025-04-01
    “…Study region: The research subject of this study is the control watershed at the inlet cross-section of the Linjiacun Reservoir in the Baoji Gorge Irrigation Area, China. …”
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  16. 10776
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    FREE VIBRATION ANALYSIS IN INNOVATIVE 3D PRINTING SANDWICH PANAELS FOR AIRCRAFT STRUCTURE by Sadiq Emad Sadiq, Hayder Zuhair Zainy, Roaa Mohammed Muneer, Luay S. Al-Ansari

    Published 2025-02-01
    “…Experimental tests validated the finite element models with an error margin below 12%. The key parameters explored were spherical diameter (3-12mm), offset distance (10-33mm), and face thickness (1-5mm). …”
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    Addressing data scarcity in deep learning: Leveraging real and artificial datasets to predict compaction of composites by Anatoly Koptelov, Hanna Beketova, Jonathan P.-H. Belnoue, Stephen R. Hallett, Iryna Tretiak

    Published 2025-09-01
    “…The bespoke predictive system accurately captured key stages of material behaviour with an average final thickness prediction error of 5.5%. A major outcome of this research was the creation of the large real compaction dataset for toughened prepreg materials, offering a valuable resource for the development of new material models. …”
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