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Titlebook: Modern Survival Analysis in Clinical Research; Cox Regressions Vers Ton J. Cleophas,Aeilko H. Zwinderman Textbook 2023 The Editor(s) (if ap

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樓主: NO610
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發(fā)表于 2025-3-25 04:37:06 | 只看該作者
22#
發(fā)表于 2025-3-25 08:10:54 | 只看該作者
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發(fā)表于 2025-3-25 12:47:28 | 只看該作者
Robustness Issues in Control of Bilinear Discrete-Time Systems—Applied to the Control of Power Convelications to power converters have been studied. In the present work, robustness issues arising in these designs are studied. First, the issue of change of operating point is addressed, and relevant stability analysis is developed. For linear systems, one can simply “shift the origin” of the deviati
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發(fā)表于 2025-3-25 18:48:38 | 只看該作者
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發(fā)表于 2025-3-25 20:58:09 | 只看該作者
Mining in Educational Data: Review and Future Directionsof data obtained from the educational field. A vital part is played by data mining in the education field, particularly when behavior is being assessed in an online learning setting. This is because data mining is capable of analyzing and identifying the hidden information regarding the data itself,
26#
發(fā)表于 2025-3-26 03:51:36 | 只看該作者
Vitamin D Metabolism and the Implications for Atherosclerosisdiscuss both normal and disordered vitamin D metabolism as it pertains to atherosclerosis, and we review major clinical trials regarding vitamin D levels and effects of supplementation. Although there are no official recommendations for vitamin D as it applies to atherosclerosis, it is clear that th
27#
發(fā)表于 2025-3-26 06:56:26 | 只看該作者
Understanding Translated Authorship,Knoten, der bezüglich Prozes-sorzahl und Speicher ausbaubar ist. In diesem überblick werden die Bausteine und das Betriebssystem beschrieben und besondere Eigenschaften wie die automatische Systemgenerierung und die on-line-Konfigurierbarkeit vorgestellt.
28#
發(fā)表于 2025-3-26 11:47:00 | 只看該作者
29#
發(fā)表于 2025-3-26 15:22:22 | 只看該作者
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發(fā)表于 2025-3-26 17:49:09 | 只看該作者
https://doi.org/10.1007/978-3-540-73036-1ing, histogram equalization, grey scaling, and smoothening. We trained and validated two pre-trained deep learning models over the dataset and combined them using an ensemble approach to create a final model. Our proposed model achieved an F1 score of 0.927, which suggests that it could serve as a b
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