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关键词： 自动换刀机械手   弧面分度凸轮机构   运动学仿真   模态分析  

摘要： 以自动换刀机械手的弧面分度凸轮机构为研究对象,针对机构运动及模态问题进行研究。首先基于包络曲面的共轭原理进行数学推导,建立齐次坐标变换法的弧面分度凸轮轮廓曲面方程;然后通过软件编程采集工作轮廓曲面数据点云,利用“点—线—面—体”的建模方法建立其实体模型,基于ADAMS对其进行机构运动学仿真分析,基于ANSYS对其输入输出轴进行模态分析,得出其前6阶固有频率和振型图。结果表明,弧面分度凸轮的临界转速远大于实际规定转速,不会发生共振,设计的弧面分度凸轮机构满足预期要求。

关键词： Attention mechanisms   Dual-phase CT   Dual-task learning   Lymph node metastasis   Rectal cancer  

摘要： Accurate preoperative prediction of lymph node metastasis and precise tumor segmentation are critical for treatment planning in rectal cancer. However, existing approaches often rely on single-task models or handcrafted features, limiting generalization and diagnostic robustness. In this work, we propose CAD-Net, a novel cross-phase attention-driven dual-task learning framework that jointly performs tumor segmentation and LNM classification from dual-phase contrast-enhanced CT images. CAD-Net introduces a Cross-phase Attention Feature Interaction Unit to dynamically fuse arterial and portal venous phase features, and a Dual-path Hierarchical Fusion Codec to integrate multi-scale contextual and morphological cues. Additionally, a dual-task consistency loss enforces spatial alignment between segmentation and classification outputs. We evaluate CAD-Net on the public LNMCR dataset, achieving state-of-the-art performance with a Dice score of 87.24% for segmentation and 87.4% accuracy for LNM diagnosis. Ablation and hyperparameter studies demonstrate the contribution of each module, while cosine similarity visualizations confirm improved feature coherence. The findings indicate that CAD-Net is a promising approach for improving preoperative decision-making in rectal cancer management.

关键词： Speaker verification   Deep learning   Speaker embeddings   Multi-scale   Selective kernel  

摘要： Speaker verification technology plays a crucial role in biometric authentication and security applications. However, the development of systems for low-resource languages such as Tibetan continues to encounter unique challenges. The complex phonology and distinctive acoustics of Tibetan, combined with the limited availability of speech data, make it difficult for conventional models to achieve strong performance. To address these challenges, this paper proposes an improved CAM-SKANF (CAM-Selective Kernel and Kolmogorov-Arnold Networks Fusion) network, built on the CAM++ framework and tailored to Tibetan speaker verification. First, we integrate Selective Kernel 2D Convolution (SK-2D) into the front-end ResBlock, forming the SKResBlend module. This module employs a multi-scale parallel convolution structure with a dynamic kernel selection mechanism, enhancing acoustic feature extraction and enabling more precise modeling of fine-grained variations and long-term dependencies. Second, we replace the original transition layer after the Dense Block with a KANs layer. Using the Kolmogorov-Arnold function decomposition, this layer optimizes high-dimensional feature representation, reduces dependence on large-scale data, and helps prevent overfitting. Finally, we introduce the Multi-scale Attention Normalization Fusion (MANF) module between adjacent KANs layers to fuse shallow local features with deep global semantics, strengthening the model's robustness and representational capacity. Experimental results show that CAM-SKANF achieves an EER (Equal Error Rate) of 5.5476%, an improvement of about 34% over CAM++. The MinDCF (Minimum Detection Cost Function) is reduced to 0.7291, an 11.5% improvement relative to the baseline. These results demonstrate that CAM-SKANF significantly enhances discrimination ability in Tibetan speaker verification, reducing errors and improving system reliability.

关键词： aggregate index of systemic inflammation   coronary artery disease   chronic kidney disease   all-cause mortality   cardiovascular mortality  

摘要： Chronic kidney disease (CKD) is a complex disease that may exacerbate the inflammatory state in patients with coronary artery disease (CAD). The predictive value of the Aggregate Index of Systemic Inflammation (AISI) for individuals with CAD and chronic kidney disease (CKD) remains unclear. The data for this study were obtained from the Cardiorenal Improvement II (CIN-II) cohort. Patients (n = 15,133) with CAD combined with CKD were included. The AISI was calculated using the formula: neutrophil*platelet*monocyte/lymphocyte counts. During a median follow-up of 4.26 years, 3979 deaths occurred, including 2239 cardiovascular (CV) deaths. With adjustment for potential confounding factors, the level of AISI was associated with a higher risk of all-cause mortality (Q [quartile]2, Q3, Q4: hazard ratio [HR 95 CI%] = 1.20 [1.09-1.32 1.34 [1.22-1.48 and 1.59 [1.44-1.75 respectively;p(trend) < 0.001) and CV mortality (Q2, Q3, Q4: HR [95 CI%] = 1.27 [1.11-1.45 1.50 [1.32-1.71 1.78 [1.55-2.03 respectively;p(trend) < 0.001). Elevated AISI level was independently associated with increased risks of all-cause and CV-specific mortality in patients with CAD and CKD.

关键词： rapid palatal expansion   customized orthodontic appliances   chairside time   patient comfort   bond failure  

摘要： Background: Rapid palatal expansion (RPE) is the gold standard for maxillary deficiency in growing patients. Conventional soldered expanders often present challenges in adaptation, chairside procedures and bond stability. This study compares traditional RPEs with customized CAD (Computer Aided Design)/CAM (Computer Aided Manufacturing) expanders regarding clinical efficiency and patient experience. Methods: Thirty growing patients (mean age: 9.2 +/- 1.1 years) were allocated to two groups: traditional RPEs (n = 15) and customized CAD/CAM RPEs (n = 15). Outcomes included bond failures, chairside time (from initial try into cementation) and short-term patient-reported discomfort via a 10-point Visual Analogue Scale (VAS) 24 h after appliance placement. Significance was set at p < 0.05. Results: The CAD/CAM group showed significantly fewer bond failures (0.2 +/- 0.4 vs. 1.1 +/- 0.8;p < 0.05) and shorter chairside time (12.4 +/- 2.1 min vs. 24.6 +/- 3.8 min;p < 0.001). Patient discomfort was also significantly lower in the CAD/CAM group (VAS: 4.1 +/- 1.0 vs. 6.3 +/- 1.2;p < 0.05). Conclusions: Within the limitations of this pilot exploratory non-randomized study, customized CAD/CAM RPEs were associated with fewer bond failures, shorter chairside application time, and lower short-term discomfort at 24 h compared with traditional appliances. These preliminary findings should be interpreted with caution and confirmed by larger randomized controlled studies.