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关键词： 五轴焊接机器人   不锈钢   空间相贯线焊缝   CAD/CAM   轨迹规划  

摘要： 针对不锈钢薄壁管材的空间相贯线焊缝焊接难的问题,设计了一款能够适应空间相贯线焊缝多变的五轴焊接机器人系统。该系统由机器人系统、控制系统和弧焊动力系统三部分组成,具有5个自由度的运动功能,可适用于复杂多变的焊接工况。同时,基于CAD/CAM技术完成空间相贯线焊缝轨迹的生成与仿真,利用MATLAB 软件模拟不锈钢相贯线的焊接轨迹和焊枪的姿态。运用样条曲线插值拟合补偿误差,通过焊接不锈钢管材实验验证该机器人系统的实用性。结果表明,该系统焊接路径贴合度高,焊接效果好,验证了五轴焊接机器人系统对不锈钢管件空间相贯线焊缝的适用性和可靠性。研究成果为相贯线焊缝焊接和焊缝轨迹规划提供了理论依据和技术支持,该五轴焊接机器人系统有一定的应用价值。

关键词： 18F-FDG PET myocardial viability study   CAD   Diabetic   OFT  

摘要： BackgroundStandard ASNC guidelines for 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) myocardial viability imaging often yield suboptimal image quality in patients with diabetes mellitus, primarily due to altered glucose-insulin kinetics. This study evaluated an optimized fast-track (OFT) patient preparation protocol designed to improve image quality and reduce preparation time in diabetic patients with coronary artery disease (CAD). MethodsWe conducted a prospective, randomized controlled trial involving 50 diabetic patients with known CAD. Patients were stratified based on diabetes duration, fasting blood sugar (FBS), and HbA1C levels, then assigned to either a standard preparation group or the OFT protocol group prior to undergoing 18F-FDG PET imaging. Image quality was assessed qualitatively using a visual grading scale and semi-quantitatively using the myocardium-to-blood pool (M/B) standardized uptake value ratio. Patient preparation time (PPT) was measured from oral glucose administration to 18F-FDG injection. ResultsThe OFT group demonstrated a higher proportion of interpretable scans (92% vs 64%;p = 0.017) and significantly greater M/B ratios (mean +/- SD: 12.44 +/- 5.37 vs 7.23 +/- 3.42;p < 0.001) compared to the standard group. Median PPT was also significantly shorter in the OFT group (82 min) than in the standard group (95 min;p = 0.003). Conclusions The optimized fast-track protocol significantly improves image quality and reduces patient preparation time in diabetic individuals undergoing 18F-FDG PET myocardial viability imaging. This protocol offers a practical and effective alternative to the standard ASNC approach, with potential for integration into routine clinical practice.

关键词： 纺织品文物   工艺优化   加固材料   丝织物   羊毛织物  

摘要： 为开发出适合纺织品文物加固的工艺条件,以脆弱丝、毛织物为例,采用浸渍法,系统探究戊二醛(Glutaraldehyde, GA)和丙烯酸树脂Paraloid B72(B72)处理对纺织品文物加固性能的影响,通过色差、硬挺度、厚度、断裂应力及微观形貌等指标综合评价加固效果,确定最优工艺条件。结果表明,对于丝织物,随GA浓度增加,色差和硬挺度呈增大趋势,断裂应力先升后降,浸渍时间延长有利于GA充分渗透与反应,GA加固丝织物的最佳工艺条件为质量分数1%、浸渍60 min,在该工艺条件下纤维表面可形成均匀致密的交联改性层;对于羊毛织物,随B72浓度增加,色差略有增大、硬挺度显著提升,断裂应力先升后降,随着浸渍时间延长,断裂应力先升后降,20~40 min为适宜时间范围,B72加固羊毛织物的最佳工艺条件为质量分数0.75%、浸渍20 min,在该工艺条件下纤维表面可形成连续完整的树脂薄膜,有效覆盖鳞片结构缺陷。实际文物保护中应根据纺织品文物材质与劣化状态选择适配的加固材料与工艺,以便最大限度延缓劣化、延长其保存寿命。

关键词： 3D parametric geometry   CAD program reconstruction   Model software application   Multi-modal learning  

摘要： Optical and photonic computing systems offer a high-performance, energy-efficient paradigm for next-generation AI hardware, but their scaling hinges on cross-layer hardware-algorithm co-design and advanced automation tools, where CAD modeling is indispensable. Reconstructing parametric CAD models from generic 3D data, such as point clouds, is a problem of practical significance, as it allows non-editable geometry to be modified and reused. However, the current mainstream approach for CAD reconstruction faces two principal challenges: (1) inherent structural defects in sequential command representations, and (2) neglecting the complementary information across multiple modalities leads to incomplete feature extraction in deep learning. To overcome these limitations, we employ a multi-modal reconstruction network. It integrates the point cloud with its rendered multi-view images as the input information, with a lightweight similarity gating module dynamically fusing features of these two modalities. To address the structural defects, we propose a novel grouped entity structure, with a decoder which separately decodes extrusion entities and corresponding sketches in two stages. Experiments demonstrate that our method achieves a reconstruction Chamfer Distance of 0.002 and reduces the inefficiency rate to 5.49% on about 8,000 test samples of the standard dataset, which are 1/4 and 2/5 of those achieved by the baseline method, respectively. More importantly, we develop an end-to-end practical pipeline that automatically translates the network’s output into fully editable parametric models within industrial CAD software (CATIA V5). This bridge from deep learning to application demonstrates the strong practical value of our work. The code is available at https://***/fzhe/GEDNet.

关键词： 3D printing   bond strength   CAD/CAM   color stability   interim restorations   PMMA   and surface treatments  

摘要： Background Mechanical properties of interim restorations, as well as fabrication methods, are important for the integrity of restorations, especially for a long interim period. Interim restorations are subjected to occlusal loads;therefore, an adequate bond strength should be maintained until the final restoration is delivered. Color stability is also crucial in the esthetic zone throughout the long-term treatment phase. Therefore, both color stability and bond strength are dependent on the fabrication methods. Aim of Study to evaluate the color stability and bond strength of 3-D printed and CAD/CAM milled interim restorations, and to compare between different groups regarding their color stability and bond strength. Materials and methods A total of 72 interim PMMA specimens were fabricated in two distinct shapes: disc-shaped (8 mm in diameter & times;2 mm in height) and square-shaped (10 mm & times;10 mm & times;2 mm thick). Half of the specimens (n = 36) were manufactured using CAD/CAM milling, while the remaining half (n = 36) were produced through 3D printing. Within each fabrication method, the specimens were categorized into two subgroups: Subgroup A, which consisted of 12 square-shaped specimens designated for color stability testing, and Subgroup B, comprising 24 disc-shaped specimens intended for shear bond strength testing. In Subgroup B, the discs were further divided based on surface treatment into three groups: surface roughening with a bur (n = 8), sandblasting (n = 8), and an untreated control group (n = 8). Color stability and bond strength measurements were recorded for all relevant specimens. Results In both CAD/CAM milled and 3D printed groups, there was a significant difference in the color stability of the specimens over time (p < 0.05). They had high discoloration values with CAD/CAM specimens, with a mean of 13.50 (0.10), while 3D printed specimens had a mean of 14.31(0.10). Shear bond strength was also significantly different between the expe

关键词： Image inpainting   generative adversarial network   Mamba, context aware module  

摘要： Achieving visually and semantically coherent image inpainting remains challenging, particularly for large missing regions with limited contextual cues. Although GAN-based models are widely used, they often lack contextual information with free-form holes, leading to significant structural inconsistencies, impaired global coherence and loss of fine-grained texture details. We propose CAM-Net, a context-aware, lightweight GAN architecture designed for improved and semantically consistent inpainting. In addition to the context-aware features, we explore long-range dependency features with linear time complexity given by Mamba to build a memory-efficient inpainting model. We evaluated CAM-Net on two widely used inpainting benchmarks: CelebA-HQ and Places2. The results demonstrate that the proposed model delivers slightly better inpainting quality with a great reduction in the parameters than the existing state-of-the-art approaches. The code implementation is available at https://***/saloni1126/CAM-Net.

关键词： 穿戴式   智能纺织品   传感响应技术  

摘要： 穿戴式智能纺织品在医疗健康、人机交互等领域需求激增,其传感机制研究成为实现纺织柔性智能化的关键。文章介绍了压力、温度、光学和磁场等主流传感机制的研究进展:压力传感中电阻式、电容式等机制在材料选用与结构设计上各有优劣;温度传感通过热敏电阻、热电转换及相变调温等机制实现精准监测与热管理;光学传感借助荧光、结构色和光纤技术展现抗干扰优势;磁场传感则通过磁电耦合实现非接触式交互。此外,多模态传感通过一体化集成突破单一功能局限,实现多维信号协同采集。最后,指出当前面临材料工艺兼容性、多信号解耦、能量自供等挑战,未来应聚焦高效复合材料、织物级封装技术及跨学科智能化应用,推动穿戴式智能纺织品的工程化与产业化。

关键词： Explainable Artificial Intelligence   Fake logo detection   Grad-CAM   Logo infringement detection  

摘要： The sudden upsurge in deployment of deep Convolutional Neural Networks (CNNs) for logo infringement detection has remarkably strengthened performance accuracy, yet their opaque decision-making process limits trustworthiness. This further hinders their interpretability and reduces their reliability in real-world situations. Therefore, this article builds upon the work of Gupta et al. on logo infringement detection employing a ResNet classifier and targets model explainability. This article utilises Gradient-weighted Class Activation Mapping (Grad-CAM) on a fine-tuned ResNet-101 model, generating heatmaps that facilitate model interpretation and explanation. This visualisation demarcates the most discriminative image regions that supervise the model's prediction. The Grad-CAM heatmaps illustrate that the model distinctly targets the salient logo features, ignoring the irrelevant ones. Furthermore, quantitative evaluation metrics such as Area Ratio, Energy Concentration and Intersection over Union (IoU) were calculated for the resultant heatmaps, which further demonstrate the efficacy of Grad-CAM.

关键词： 5-aminolevulinic acid   Cucurbituril   Protoporphyrin IX   Photodynamic therapy   Fluorescence detection  

摘要： The FDA-approved prodrug 5-aminolevulinic acid (ALA) is efficiently metabolized by cancer cells into protoporphyrin IX (PpIX), a potent photosensitizer widely used in photodynamic therapy (PDT) and fluorescence-guided tumor surgery. However, the poor membrane permeability of ALA led to the development of more lipophilic derivatives, such as the methyl (ALAm) and hexyl (ALAh) esters. Despite improved cellular uptake, ALA and its esters are highly unstable at physiological pH, resulting in premature decomposition, reduced PpIX accumulation, and limited therapeutic efficacy. In this study, the encapsulation of ALA, ALAm, and ALAh within cucurbit[7]uril (CB[7]) was evaluated as a strategy to enhance their stability and performance. Complex formation was confirmed by isothermal titration calorimetry, 1H NMR spectroscopy, and density functional theory (DFT) calculations. Decomposition kinetics at physiological pH showed that encapsulation significantly increased the stability of the ester derivatives. In vitro studies using 2D cell cultures demonstrated that the ALA@CB[7] complex enhanced intracellular conversion to PpIX, leading to increased phototoxicity, attributed to the improved stability of encapsulated ALA. In contrast, encapsulation of ALAh produced no significant change in phototoxicity, while encapsulated ALAm resulted in decreased phototoxicity in MCF-7 breast cancer cells. Experiments in 3D cell cultures revealed increased PpIX fluorescence and more homogeneous distribution for encapsulated ALA and ALAh, whereas ALAm showed no improvement. Finally, in vivo studies using the chorioallantoic membrane (CAM) model confirmed that encapsulated ALA and ALAh were more efficiently metabolized into PpIX, resulting in higher photosensitizer accumulation. Overall, these findings highlight CB[7] as a promising protective nanocapsule to improve the delivery, stability, and photodynamic efficacy of ALA-based prodrugs.