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Titlebook: Imaging of the Shoulder; Techniques and Appli A. Mark Davies,Jürg Hodler Book 2004 Springer-Verlag Berlin Heidelberg 2004 Arthritis.Arthrog

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41#
發(fā)表于 2025-3-28 17:35:38 | 只看該作者
42#
發(fā)表于 2025-3-28 21:52:59 | 只看該作者
s this search, revealing why this search is a fallacy. The purpose is to inform readers about results in neuroscience and biophysics that may guide us to some liberation needed in the current age of great complexity in life with a diverse burden of chores; a deliverance from some afflictions that pr
43#
發(fā)表于 2025-3-29 00:24:05 | 只看該作者
de a solid contribution to the spectacular growth in statistical methodologies for molecular evolution. .Evolutionary Genomics: Statistical and Computational Methods. is intended to bring together the more recent developments in the statistical methodology and the challenges that followed as a resul
44#
發(fā)表于 2025-3-29 04:39:35 | 只看該作者
45#
發(fā)表于 2025-3-29 07:32:50 | 只看該作者
46#
發(fā)表于 2025-3-29 13:11:13 | 只看該作者
LGWO: An Improved Grey Wolf Optimization for Function Optimizationrarchy and hunting mechanism of grey wolves in nature. It has exhibited promising performance in many fields. However, GWO algorithm has the drawback of slow convergence and low precision. In order to overcome this drawback, we propose an improved version of GWO enhanced by the Lévy-flight strategy,
47#
發(fā)表于 2025-3-29 16:22:09 | 只看該作者
48#
發(fā)表于 2025-3-29 20:50:37 | 只看該作者
Community Health Care Contexts,daries. However, in community health care settings care providers often have far less control over circumstances and environments. A well-stocked supply cupboard is far from the reach of a visiting nurse who drives, sometimes long distances, to see patients post-operatively for dressing changes and
49#
發(fā)表于 2025-3-30 02:10:38 | 只看該作者
A Triplet Contrast Learning of Global and Local Representations for Unannotated Medical Images,th the success of SSL, which showed performance improvement in natural images, SSL research is actively being conducted in medical image analysis. In this paper, we present a triplet network for the medical image representation learning to learn robust patterns of medical images against global and l
50#
發(fā)表于 2025-3-30 07:47:51 | 只看該作者
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