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| http://dx.doi.org/10.1007/s00068-024-02656-y | DOI Listing |
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Long-lived entanglement of molecules in magic-wavelength optical tweezers.
Nature
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Department of Physics, Durham University, Durham, United Kingdom.
Realizing quantum control and entanglement of particles is crucial for advancing both quantum technologies and fundamental science. Substantial developments in this domain have been achieved in a variety of systems. In this context, ultracold polar molecules offer new and unique opportunities because of their more complex internal structure associated with vibration and rotation, coupled with the existence of long-range interactions.
View Article and Find Full Text PDFA review of novel methods to assist digital planning and execution of osteotomy for upper limb deformities.
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Department of Orthopaedic Surgery, NHO Mito Medical Center Hospital, Ibaraki, Ibaraki, 311-3193, Japan.
Corrective osteotomy for upper limb deformities caused by fractures, trauma, or degeneration necessitates detailed preoperative planning to ensure accurate anatomical alignment, restore limb length, and correct angular deformities. This review evaluates the effectiveness of a three-dimensional (3D) preoperative planning program and an image fusion system designed for intraoperative guidance during corrective osteotomy procedures. The application processes and clinical outcomes observed with these technologies in various surgical scenarios involving the upper extremities were summarized.
View Article and Find Full Text PDFDiffusion-weighted MRI of advanced gastric cancer: correlations of the apparent diffusion coefficient with Borrmann classification, proliferation and aggressiveness.
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January 2025
Department of Medical Imaging, Tongji Hospital, Tongji University, No. 389, Xincun Road, Putuo District, Shanghai, 200065, China.
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Performance of Four AI Chatbots in Answering Endodontic Questions.
J Endod
January 2025
Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Jazan University, Jazan, Saudi Arabia.
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Dual-view global and local category-attentive domain alignment for unsupervised conditional adversarial domain adaptation.
Neural Netw
January 2025
School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China. Electronic address:
Conditional adversarial domain adaptation (CADA) is one of the most commonly used unsupervised domain adaptation (UDA) methods. CADA introduces multimodal information to the adversarial learning process to align the distributions of the labeled source domain and unlabeled target domain with mode match. However, CADA provides wrong multimodal information for challenging target features due to utilizing classifier predictions as the multimodal information, leading to distribution mismatch and less robust domain-invariant features.
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