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关键词： HET-CAM test   Moisturizing creams   Ocular irritation   Sunscreen creams  

摘要： Cosmetic products applied around the eyes, such as moisturizing and sunscreen creams, can come into contact with the conjunctiva, potentially causing irritation due to the thinness of the ocular epithelium. These products, widely used by the general population, remain in contact with the skin for extended periods. In this study, 41 cosmetic creams marketed in Algeria (moisturizers and sunscreens) were evaluated using the Hen's Egg Test on the Chorioallantoic Membrane (HET-CAM), an alternative to the Draize test. The results showed that 36.6 % of the tested creams induced irritation, with varying degrees of severity. Moisturizing creams were more frequently associated with irritation (44.0 %) compared to sunscreens (25.0 %). Among products intended for children, 44.4 % elicited an irritation response, and 60 % of irritating products carried the precautionary statement “avoid contact with eyes.” These findings provide the first data on the ocular irritation potential of cosmetic creams available in the Algerian market and contribute to regional safety assessment by identifying specific product categories with irritant potential.

关键词： Knee cartilage degeneration   Artificial intelligence (AI)   Deep learning   Capsule Network (CapsNet)   Segmentation   Grad-CAM  

摘要： Knee cartilage degeneration is a major contributor to mobility disability, and affects millions of individuals worldwide. For effective intervention, early accurate detection of the condition is essential. Conventional approaches often lack sufficient sensitivity, whereas recent artificial intelligence (AI) techniques have demonstrated improved diagnostic accuracy.

关键词： 轮辐扩展   凸轮机构   参数计算   运动仿真   越障表现  

摘要： 针对轮式移动机器人越障能力与车轮尺寸之间的矛盾属性,设计了一种基于凸轮机构传动的可扩展车轮。首先,在对可扩展车轮运动步态解析的基础上,完成了主要参数和几何尺寸的计算;其次,运用Soildworks建立了可扩展车轮三维实体模型,并基于Adams构建了台阶攀爬运动模型;最后,为验证攀爬运动表现,进行了连续台阶攀爬对比仿真实验。结果表明:轮辐扩展后,机器人攀爬连续台阶的效率比未扩展前高出20%,同时质心加速度幅值变化明显更小,设计的可扩展车轮可以高效稳定地完成越障任务。

关键词： CAD model reconstruction   Lattice structures   Planar convex hulls   Point cloud   Primitive detection  

摘要： Lattice structures consist of spatially discrete bars interconnected by joints. The complex geometric configurations of these lightweight structures present a challenge for point cloud segmentation, which is essential for CAD model reconstruction. We propose an efficient and robust pipeline to reconstruct CAD models of lattice structures from point clouds. The point cloud is first fitted by a set of planar convex hulls, which are optimized by local geometric operators guided by an energy function. The edges of discrete convex hulls are stored in an AABB tree for efficient hull adjacency queries. The convex hulls are then segmented based on their consistently oriented normals. Structural primitives are classified using principal component analysis of these oriented normals, and their dimensions are determined with least squares fitting of the associated points. The final CAD model is assembled from the detected primitives. The method has been validated using point clouds of various scales and complexities including real-scanned data. It shows robustness in the presence of uniform noise, outliers and data loss. Furthermore, its advantages in terms of reconstruction accuracy and efficiency are further highlighted in comparison to conventional and deep learning approaches.

关键词： CAD   Fracture detection   Ghost convolution   Pediatric wrist trauma   YOLOv11  

摘要： Computer-aided diagnosis (CAD) systems have greatly improved the interpretation of medical images by radiologists and surgeons. However, current CAD systems for fracture detection in X-ray images primarily rely on large, resource-intensive detectors, which limits their practicality in clinical settings. To address this limitation, we propose a novel lightweight CAD system based on the YOLO detector for fracture detection.

关键词： Boundary representation   Command sequence   Reinforcement learning   Training gym  

摘要： A CAD command sequence is a typical parametric design paradigm in 3D CAD systems where a model is constructed by overlaying 2D sketches with operations such as extrusion, revolution, and Boolean operations. Although there is growing academic interest in the automatic generation of command sequences, existing methods and datasets only support operations such as 2D sketching, extrusion, and Boolean operations. This limitation makes it challenging to represent more complex geometries.

关键词： CAM   Insomnia classification   rs-fMRI   ST-GCN  

摘要： Chronic Insomnia Disorder (CID) is a common psychiatric condition that significantly affects quality of life. However, its neurobiological mechanisms remain largely unclear. Prior neuroimaging studies have mainly focused on isolated functional connections, with limited attention to within-network interactions. In this study, we propose a method based on Spatial–Temporal Graph Convolutional Networks (ST-GCN) to distinguish CID patients from healthy controls (HC) using resting-state functional MRI (rs-fMRI) data. A spatio-temporal graph is constructed to jointly model spatial and temporal features of brain connectivity. To interpret the model outputs, we further applied Class Activation Mapping (CAM) as a post-hoc analysis tool to identify brain regions most relevant to classification. CAM is not part of the core model but provides insights into discriminative features. The method was evaluated on two independent datasets, achieving classification accuracy above 90%. CAM results highlight the frontoparietal network as a key discriminator between CID and HC, supporting the hyperarousal theory of insomnia. These findings underscore the potential of graph-based deep learning for functional connectivity analysis. For details, see the code at https://***/AkishiroKura/ST-GCN-CID.

关键词： Computer-aided manufacturing (CAM)   Polygon   Tool path generation   Machinability   Cutting force prediction  

摘要： This study proposes a unified two-dimensional (2D) polygon-based computer-aided manufacturing (CAM) framework that enables tool path generation, machinability evaluation, material removal simulation, and cutting-force prediction within a single computational environment. The proposed method represents three-dimensional geometries as aggregates of orthogonal 2D polygon sets, obtained by slicing the model in the $xy$-, $yz$-, and $zx$-parallel planes and superposing the three polygonal datasets. A novel convolutional offsetting algorithm is developed to perform three-dimensional inflation and shrinkage by incorporating adjacent cross-sectional relationships, thereby achieving accurate 3D offsets independent of the slicing orientation. The inflated 2D polygons are directly utilized to generate contour and scanning tool paths, and sequential inflation–shrinkage analysis enables visualization of unmachinable regions for tool accessibility evaluation. Furthermore, the framework integrates an instantaneous cutting force model that accurately predicts the cutting force waveform by detecting intersections between the cutting edge points and 2D polygon aggregations. The system is experimentally validated via ball-end milling. The results demonstrate that tool paths can be generated in under one minute using only a CPU. Furthermore, the simulated cutting forces closely align with experimental measurements. These findings demonstrate that the proposed 2D polygon-based framework provides an efficient and extensible foundation for integrating mechanical simulation and tool-path generation.

关键词： CAM   Eddy covariance   COS   Stomatal conductance   Transpiration   Flux measurements   Ecosystem scale  

摘要： Crassulacean acid metabolism (CAM) helps plants in arid regions to reduce water loss by opening their stomata and taking up carbon dioxide (CO${}_2$) during nighttime. While gas exchange in CAM plants has been mainly studied under controlled laboratory conditions, only a few ecosystem scale studies exist. Moreover, carbonyl sulfide (COS) has been used as a tracer for stomatal conductance, transpiration and photosynthesis in C${}_3$ and C${}_4$ plants, but no studies on CAM ecosystems have yet been published. Here we present the first ecosystem scale measurements of COS fluxes over Agave sisalana (CAM plant), commercially cultivated for its fiber. The measurements were made during the wet season in Kenya. The ecosystem was a consistent sink of COS, with higher uptake observed during nighttime (−11.5 pmol m⁻² s⁻¹) than during daytime (-5.6 pmol m⁻² s⁻¹). The magnitude of COS fluxes was comparable to non-growing season daytime fluxes reported for C${}_3$ and C${}_4$ plant dominated ecosystems. The soil was a small COS source (0.3 pmol m⁻² s⁻¹), with highest emissions under high radiation and temperature conditions. Using random forest modeling, we found that vapor pressure deficit, air temperature and soil water content were the most important drivers of nighttime ecosystem COS exchange (variable

关键词： 2D-to-BIM conversion   BIM object generation   CAD material layer annotation   Semantic enrichment  

摘要： This study proposes a method for semantically enriching a building information model (BIM) by converting and supplementing material layer information in computer-aided-design (CAD) drawings into semantically rich BIM objects. Despite the widespread adoption of BIMs, practices relying on CAD drawings are still prevalent and fully automated conversion from CAD-to-BIM remains challenging. Previous studies on automatically converting 2D drawings into a BIM have primarily focused on geometric reconstruction, often overlooking textual data such as material layer annotations, which contain essential BIM object details. However, the recognition and conversion of material layer annotations to BIM objects pose several challenges, including inconsistent annotation formats, vague associations between annotations and objects, and missing material function and property information. To address these challenges, this paper introduces a four-step method: (1) semantic clustering of material layer annotations into groups corresponding to individual objects; (2) material layer function assignment using a newly proposed mistake-driven prompting technique, which incorporates feedback from common mistaken cases; (3) object type classification using a large language model based solely on material layer texts; and (4) automated generation of BIM objects semantically enriched with material property information in single-directional composite or light-framed structures. Validation on 90 real-world cases demonstrated a 99.9% weighted average adjusted Rand index for semantic clustering, a 99.5% weighted F1-score for layer function assignment, a 92.7% weighted F1-score for object type classification, and 100% accuracy in BIM object generation. This method is expected to enable a more complete 2D-to-BIM conversion by complementing geometric reconstruction with semantically rich material layer information.