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Abstract Exploring ancient Chinese artifacts is crucial for analyzing East Asian technological development, with bronze vessel being the critical element. Bronze vessels, typically featuring intricate carvings, hold historical significance and provide valuable insights into past civilizations. However, identifying bronze patterns can be challenging for human vision, and most RGB-domain methods fail to capture periodic designs. Addressing these issues, we propose the Siamese Fourier Networks (SFN), a parallel network model designed for few-shot regular pattern classification. The Siamese network can differentiate between intricate shapes, while Fourier features enable the extraction of regular textures. To optimize parallel networks, we combine the BCE loss and focal contrastive loss, balancing positive and negative samples. Moreover, we introduce the Bronze Vessel Dataset, featuring 527 samples with diverse shapes and unbalanced distributions. Extensive experiments with advanced few-shot methods demonstrate the superiority of SFN and focal mechanism, significantly improving accuracy.

Abstract Background Total endovascular technique with fenestrated endovascular graft might be hampered for the late dilatation of proximal landing zone, which may cause endografts migration. We describe a successful urgent hybrid procedure for extent III thoracoabdominal aortic aneurysm with aortic intramural hematoma. Case Presentation A 55-year-old female with thoracoabdominal aortic aneurysm was considered at high surgical risk and unfit for open repair due to multiple comorbidities. Therefore, a hybrid procedure of surgeon-modified fenestrated endovascular graft combined with thoracoscope-assisted Transaortic epicardial fixation of endograft was finally chosen and performed in the endovascular operating room. A 3-port technique was performed through a left video-assisted thoracoscopic approach. After the first tampering stent-graft was deployed, a double-needle suture was penetrated both the aortic wall and stent-graft to fixate it in the proximal descending aorta. Then the second endograft, which had been fenestrated on table, was introduced and oriented extracorporeally by rotating superior mesenteric artery and left renal artery fenestration radiopaque markers and deployed with perfect apposition between the fenestrations and target visceral artery. Each vessel was sequentially stented using Viabahn self-expandable stent to finish target vessel stenting. An Ankura cuff stent was deployed in the distal abdominal aortic artery. Conclusion Surgeon-modified fenestrated endovascular graft combined with thoracoscope-assisted fixation may be an innovative and viable alternative for selected high-risk patients with extent III thoracoabdominal aortic aneurysm. A longer follow-up is needed to ascertain the success of this approach.

This article proposes an advanced classification algorithm for bronze drinking utensils, taking into account the complexity of their cultural characteristics and the challenges of dynasty classification. The SSA-CBAM-GNNs algorithm integrates the Sparrow Search Algorithm (SSA), Spatial and Spectral Attention (CBAM) modules, and Graph Neural Networks (GNNs). The CBAM module is essential for optimizing feature extraction weights in graph neural networks, while SSA enhances the weighted network and expedites the convergence process. Experimental results, validated through various performance evaluation indicators, illustrate the outstanding performance of the improved SSA-CBAM-GNNs algorithm in accurately identifying and classifying cultural features of bronze drinking utensils. Comparative experiments confirm the algorithm's superiority over other methods. Overall, this study proposes a highly efficient identification and classification algorithm, and its effectiveness and excellence in extracting and identifying cultural features of bronze drinking utensils are experimentally demonstrated.