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  1. 7741
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  3. 7743

    Benchmarking aplicado al catálogo en línea de los Servicios Bibliotecarios de la Universidad de Los Andes by Leonel Orangel Vivas Salas, María Alejandra Briceño Sosa, Janeyra del Carmen Colls Ojeda

    Published 2017-01-01
    “…Se analizaron los aspectos estructurales del OPAC de Serbiula con la finalidad de hacer mejoras en su diseño con base en indicadores en la estructura externa, el sistema de búsqueda, resultados y visualización de la búsqueda, y la gestión de los resultados; todo esto, basado en una metodología de benchmarking , utilizando un instrumento de evaluación denominado FORM50. …”
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  4. 7744
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  8. 7748

    Enhancing the mechanical properties’ performances coconut fiber and CDW composite in paver block: multiple AI techniques with a Performance analysis by G. Uday Kiran, G. Nakkeeran, Dipankar Roy, Sumant Nivarutti Shinde, George Uwadiegwu Alaneme

    Published 2024-12-01
    “…The outcomes from both the training and testing phases demonstrated the strong predictive power of RSM, SVM, GB, ANN, and RF with a criterion used Root Mean square error (RMSE), Mean square error (MSE), Mean Absolute Error (MAE) and correlation coefficient (R). …”
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  9. 7749
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  13. 7753

    Effects of Filled Nano-Al2O3 and Its Contents on Friction and Wear Properties of Hydrogenated Nitrile Butadiene Rubber by Xinyang Tan, Zenghui Liu

    Published 2024-01-01
    “…ATR–FTIR results show that mechanism of the nano-Al2O3 reinforcing HNBR for wear resistance is due to the graft reaction between the modified nano-Al2O3 and HNBR to form cross-linking networks around the Al2O3 nanoparticles, and self-polymerization of unsaturated groups on the surface of the nano-Al2O3 to form interpenetrating polymer networks with the HNBR molecular main chains.…”
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  14. 7754
  15. 7755

    Surface properties of friction stir welded dissimilar joints of AA7075 and Mg-WE43 alloys: Effect of positional arrangement by Tariq Ahmad, Nadeem Fayaz Lone, Noor Zaman Khan, Babar Ahmad, Arshad Noor Siddiquee, Daolun Chen

    Published 2025-01-01
    “…The EBSD analysis revealed that the average grain size reduced from 4.11 ± 0.7 µm to 2.04 ± 0.9 µm when AA7075 was shifted from advancing to retreating side. …”
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  16. 7756

    Effects of adding a kind of compound bio-enzyme to the diet on the production performance, serum immunity, and intestinal health of Pekin ducks by Yuxiao Li, Jie Zhou, Tong Guo, Huiya Zhang, Chang Cao, Yingjie Cai, Jiqiao Zhang, Tao Li, Jianqin Zhang

    Published 2025-01-01
    “…Results indicated a significant increase in ADG (P = 0.049) and a decrease in feed-to-gain ratio (F:G) (P = 0.020) in LG and HG compared to CG during rearing. …”
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  17. 7757
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    Rec. ad op.: Kuzembayuly A., Abil’ E., Alibek T. The Siberian ulus and Kazakhs: Problems of ethnic continuity and historical memory by Maslyuzhenko D.N.

    Published 2024-09-01
    “…It reveals some new sources, unknown or little known in earlier Russian historical science; there are interesting and original ideas on ethnic relations of the Siberian and Kazakh population and the origin of the Siberian princely dynasty of the Taibugids, which need further reflection. However, ignoring many achievements of the latest Russian historiography and its selective quoting, refusal to build an accurate chronology, skipping entire decades in the generalising conclusions, and the choice of the term “Siberian ulus”, which has not revealed its full potential, raise the question as to what extent the authors were really able to create a quality study on the role of the Shibanid statehood in the history of the formation of the Kazakh people.…”
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  20. 7760

    Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast-Furnace Slag and Fly Ash by Hai-Van Thi Mai, Thuy-Anh Nguyen, Hai-Bang Ly, Van Quan Tran

    Published 2021-01-01
    “…Next, the evaluation of the model is concluded over 100 simulations for the convergence analysis. The results show that ANN is a highly efficient predictor of the compressive strength using BFS and FA, with maximum values of the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) of 0.9437, 3.9474, and 2.9074, respectively, on the training part and 0.9285, 4.4266, and 3.2971, respectively, for the testing part. …”
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