Showing 281 - 300 results of 645 for search 'The X Factor', query time: 0.08s Refine Results
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    Comparison of fit of poly (etheretherketone) and cobalt-chromium removable partial denture frames: an in-vitro study. by Ary Yaqoub Putros, Jawad Mohammed Mikaeel

    Published 2023-06-01
    “…Eighty VPS samples were made to present the gap between rest and rest seats; these samples were measured using a digital micrometre under a microscope with a magnification of 40x. The data were analysed using SPSS programs with an independent T-test. …”
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    An Intelligent Approach for the Challenges in the Evaluation of Public Accountant Firms Under Uncertainties by Zijie Yu

    Published 2025-01-01
    “…Flexibility evaluation is additionally built into the framework to guarantee the findings’ reliability and pinpoint ideal factor setups. …”
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    Healthcare Resource Utilization Associated with Leukopenia and Neutropenia in Kidney Transplant Recipients Receiving Valganciclovir in the United States by Qinghua Li, Vladimir Turzhitsky, Pamela Moise, Harry Jin, Kaylen Brzozowski, Irina Kolobova

    Published 2025-01-01
    “…**Methods:** Using TriNetX Dataworks-USA, a federated, de-identified electronic medical record database, we identified adult KTRs who underwent their first kidney transplant from January 2012 to September 2020. …”
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    Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data by Jiannan He, Yongbin Zhang, Mingyue Liu, Lin Chen, Weidong Man, Hua Fang, Xiang Li, Xuan Yin, Jianping Liang, Wenke Bai, Fuping Li

    Published 2025-01-01
    “…The results show that the following. 1) The prediction accuracy is improved by classifying the data into three types: unlodging <italic>S. alterniflora</italic> (ULSA), lodging <italic>S. alterniflora</italic> (LSA), and mudflats. 2) XGBoost outperformed RF and SVM in accurately predicting SOC content, with <italic>R</italic><sup>2</sup>; values of 0.743 for ULSA, 0.731 for LSA, and 0.705 for mudflats; 3) In the XGBoost models constructed for ULSA, LSA, and mudflats, spectral features contributed 75.7&#x0025;, 73.1&#x0025;, and 63.1&#x0025;, respectively, with the normalized difference vegetation index emerging as the most critical spectral feature. …”
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