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关键词： design workflow   digital fabrication   open systems   point-cloud modeling   script-based rendering   CAD  

摘要： Script-based computer-aided design tools offer accessible and customizable environments, but their broader adoption is limited by the cognitive and computational difficulty of describing curved, irregular, or free-form geometries through code. This study addresses this challenge by contributing a unified, open-source framework that enables concept-to-model transformation through 2D point-based representations. Unlike previous ad hoc methods, this framework systematically integrates an interactive point-cloud modeling layer with modular systems for curve construction, point generation, transformation, sequencing, and formatting, together with script-based rendering functions. This framework allows users to generate geometrically valid models without navigating the heavy geometric calculations, strict syntax requirements, and debugging demands typical of script-based workflows. Structured case studies demonstrate the underlying workflow across mechanical, artistic, and handcrafted forms, contributing empirical evidence of its applicability to diverse tasks ranging from mechanical component modeling to cultural heritage digitization and reverse engineering. Comparative analysis demonstrates that the framework reduces user-facing code volume by over 97% compared to traditional scripting and provides a lightweight, noise-free alternative to traditional hardware-based reverse engineering by allowing users to define clean geometry from the outset. The findings confirm that the framework generates fabrication-ready outputs-including volumetric models and vector representations-suitable for various manufacturing contexts. All systems and rendering functions are made publicly available, enabling the entire pipeline to be performed using free tools. By establishing a practical and reproducible basis for point-based modeling, this study contributes to the advancement of computational design practice and supports the wider adoption of script-based design workflows.

关键词： resin luting agents   bioactive glass   adhesive   multifunctional materials   hydrophilicity   nano-ceramics   hybrid materials   color stability   spectrophotometry   restorative dentistry   functional gel  

摘要： Using multifunctional dual-cure smart bioactive resin luting agents (DRLs) offers benefits in adhesive dentistry, but their optical stability remains a concern. Their pre-cured form is a shear-thinning structure with thixotropic gel-like behavior. The effect of their hydrophilicity and different thicknesses of nanoceramic hybrid on the final shade of milled esthetic restorations needs further investigation. This study examined how the optical function deterioration of dual-cure smart bioactive resin luting agents used to bond a CAD/CAM nano-ceramic hybrid composite would influence the restoration's final shade at three different thicknesses. A nanoceramic hybrid composite (GD) was cut into blocks and grouped by thickness (0.8, 1.0, 1.5 mm). Ten blocks from each group were assigned to subgroups based on the DRL type: Panavia SA Universal (PN), Predicta Bioactive (PR), and ACTIVA BioACTIVE (AC). Color and whiteness changes after a 24 h/day (24 days) coffee immersion were analyzed using statistical methods (ANOVA and Tukey's HSD for Delta E-00;Welch's ANOVA and Games-Howell for Delta WID and Delta L*). DRL type significantly affected Delta E-00, Delta WID, and Delta L* (p < 0.001). All materials showed the least color change and optical function deterioration at a restoration thickness of 1.5 mm, which was below the acceptability threshold (AT). Despite PR's bioactive functionality, it maintained its primary optical function with the least color change at GD thicknesses of 1.0 and 1.5 mm (p < 0.001). AC exhibited the greatest Delta E-00 above AT, especially at a thickness of 0.8 mm (p < 0.001). Delta L*, Delta E-00, and Delta WID varied significantly based on DRL type, GD thickness, and the interaction between DRL and thickness (p < 0.05). This suggests that although dual-cure smart DRLs containing bioactive glasses are advantageous, their optical function shifts may become more noticeable in thin, translucent restorations. Increasing the restoration thickness can help

关键词： composite   interfacial bond strength   self-adhesive resin cements  

摘要： This in vitro study evaluated the shear bond strength (SBS) of four CAD/CAM (Computer aided design/Computer aided manufacturing) polymer-based indirect composites bonded to dentin and microhybrid composite substrates using two resin cements. Gradia Plus (GP), Ceramage (Ce), Tescera ATL (TA), and Lava Ultimate (LA) were fabricated into cylindrical specimens (3 x 3 mm). Dentin substrates were obtained from extracted molars, while composite substrates were prepared from Filtek Z250 (4 mm x 2 mm). Bonding was performed using either a self-adhesive resin cement (RelyX U200;RU200) or a dual-cure adhesive resin cement (RelyX Ultimate;RU), resulting in 16 experimental groups (n = 12 per group). SBS was measured using a universal testing machine (1 mm/min), and failure modes were assessed under stereomicroscopy. Bond strength was significantly higher on composite substrates than on dentin (p < 0.001), primarily due to favorable polymer-polymer compatibility and matrix interdiffusion, which improved stress accommodation at the adhesive interface. TA and Ce showed superior adhesion when combined with RU, while LA exhibited the lowest values, particularly on dentin bonded with RU200. Overall, the dual-cure adhesive system provided stronger bonding than the self-adhesive system (p < 0.05). These findings highlight the influence of substrate type, composite architecture, and cement chemistry on interfacial performance in indirect polymer-based restorations.

关键词： Centroid radial distribution   Convolution neural network   Grad-CAM   Regional maritime collision risk assessment   Relative traffic representation   Risk influence distribution map  

摘要： Maritime transport handles over 80 % of global trade but rising traffic density and congestion have increased collision risk. Traditional assessment methods, including the ship domain and closest point-of-approach approaches, are limited to pairwise interactions and cannot capture regional traffic dynamics. To overcome these shortcomings, we propose a regional collision risk assessment framework integrating AIS-derived relative traffic representations, convolutional neural networks, and explainable artificial intelligence. A gradient-weighted class activation mapping framework generated risk influence distribution maps and a centroid radial distribution, while a novel indicator, the risk influence radius (RIR), quantified radial influence patterns. Experiments with 3,660 low-risk and 366 high-risk cases from South Korean coastal waters revealed that the Visual Geometry Group (VGG) network backbone achieved the highest performance, with an average F1-score of 0.9324, outperforming ResNeXt, EfficientNet, ResNet, and MobileNetV3 models. Statistical tests revealed a significant difference in RIR (p < 0.05) between high- and low-risk collision areas at 10 and 20 km, but not at 30 km. These results confirm RIR as an interpretable indicator of collision risk at regional scales, while highlighting its limitations at broader ranges. The framework combines accuracy with interpretability and supports practical applications such as maritime monitoring, route planning, and early warning systems.

关键词： Batch normalization   Bayesian optimized SVM   CNN   KNN   Layer fusion   RF   Spectral features  

摘要： Coronary Artery Disease (CAD) and Congestive Heart Failure (CHF) are significant contributors to global mortality. Early diagnosis and intervention in cardiac subjects are crucial for reducing mortality rates and minimizing the associated treatment costs linked to these conditions. This highlights the critical need for efficient healthcare strategies aimed at the early detection and classification of cardiac diseases to improve patient outcomes. This study proposed a hybrid model combination of layer fusion Convolutional Neural Network (LF-CNN) and Machine Learning (ML) techniques, including Bayesian Optimized Support Vector Machine (BO-SVM), k-nearest neighbor (KNN), and Random Forest (RF) for detecting Normal (healthy), CAD, and CHF arrhythmia conditions. For training and validation, time-series and spectral features are extracted from HRV database of cardiac diseases to feed input of proposed model. Additionaly, to speedup the training, validation and to overcome overfitting, batch normalization was employed to adjust the distribution of features. This study also employed t-distributed Stochastic Neighbor Embedding (t-SNE) within deep CNN to visualize data and ensuring that fusion of different features. The simulation results demonstrated that the proposed method achieved an accuracy (Ac) of 99.88 %, a precision (Pr) of 99.89 %, a recall (sensitivity) of 99.68 %, and an F1-score of 99.79 % by integrating BO-SVM, KNN, and RF ML techniques with LF-CNN. The findings indicate that classifier performance can be significantly enhanced by fusion of multiple features instead of relying on a single feature and emphasizing the benefits of integrating ML with deep learning (DL) techniques.

关键词： coronary artery bypass grafting   coronary artery disease   familial hypercholesterolemia   hybrid coronary revascularization   minimally invasive cardiac surgery   BIMA   bilateral internal mammary artery   CABG   coronary artery bypass grafting   CAD   coronary artery disease   LAD   left anterior descending   LIMA   left internal mammary artery   RIMA   right internal mammary artery  

摘要： Robotic-assisted minimally invasive direct coronary artery bypass (MIDCAB) with bilateral internal mammary artery (BIMA) harvesting is an emerging approach for durable revascularization of coronary artery disease (CAD). Intraoperative cardiac luxation is a rare but potentially life-threatening complication during this procedure, yet guidance on recognition and management is limited.

关键词： Erosion   Shear bond   Grit-blasting   Resin cement   Universal adhesive  

摘要： This laboratory study evaluated the effect of surface pretreatments and erosion on the shear bond strength (SBS) of additive (3D-printed;Crowntec (TM)) and subtractive (milled;Cerasmart (TM) 270) CAD/CAM resin-based materials bonded with a self-adhesive resin cement. Seventy-two specimens of each material were assigned to three surface pretreatment groups: control, grit-blasting, and grit-blasting + universal adhesive (UA). After treatment, self-adhesive resin cement (Panavia (TM) SA Cement Plus) was applied using a mold (2.38 mm diameter, 3 mm height). Specimens were then divided into control, gastric acid, and citric acid groups and subjected to 96 h erosive aging (n = 8). SBS was tested with a universal testing machine, failure modes were examined under a stereomicroscope, and data analyzed using SPSS 21.0 (alpha = 0.05). Material type and erosion significantly affected SBS (p < 0.001), whereas surface pretreatments did not (p = 0.698). Crowntec showed decreased SBS in the grit-blasting + UA group after gastric acid (p = 0.02), while Cerasmart had reduced SBS in all pretreated groups after both acids (p < 0.05). Overall, Crowntec exhibited higher SBS with predominant cohesive failures, while Cerasmart showed mainly adhesive failures, except in the grit-blasting + UA group. The findings indicate that SBS of self-adhesive resin cement to resin-based CAD/CAM materials depends on material type and erosive aging, while surface pretreatments mainly affect failure mode.

关键词： contrast media   encephalopathy   BBB   blood-brain barrier   BP   blood pressure   CAD   coronary artery disease   CIE   contrast-induced encephalopathy   eGFR   estimated glomerular filtration rate   LAD   left anterior descending artery   MRA   magnetic resonance angiography   PCI   percutaneous coronary intervention   RCA   right coronary artery  

摘要： Iatrogenic contrast-induced encephalopathy related to the use of iodinated contrast media is a rare neurologic complication. It tends to be self-limiting and has most commonly been reported with high-osmolar agents. However, it may also occur with low-osmolar, nonionic contrast media during coronary angiography, agents that are more commonly used in current clinical practice for medical imaging. We present 3 cases of contrast-induced encephalopathy that occurred temporally very close to each other in a single-center high-volume cardiac catheterization laboratory among a series of 21 consecutive angiograms performed at our institution over a 1-week period.

关键词： feature fusion   convolutional neural network   Transformer   cross-attention mechanism   fault diagnosis  

摘要： As a core component of the gearbox, bearings are crucial to the stability and reliability of the transmission system. However, dynamic variations in operating conditions and complex noise interference present limitations for existing fault diagnosis methods in processing non-stationary signals and capturing complex features. To address the aforementioned challenges, this paper proposes a bearing fault diagnosis method based on a multi-feature fusion dual-channel CNN-Transformer-CAM framework. The model cross-fuses the two-dimensional feature images from Gramian Angular Difference Field (GADF) and Generalized S Transform (GST), preserving complete time-frequency domain information. First, a dual-channel parallel convolutional structure is employed to separately sample the generalized S-transform (GST) maps and the Gramian Angular Difference Field (GADF) maps, enriching fault information from different dimensions and effectively enhancing the model's feature extraction capability. Subsequently, a Transformer structure is introduced at the backend of the convolutional neural network to strengthen the representation and analysis of complex time-frequency features. Finally, a cross-attention mechanism is applied to dynamically adjust features from the two channels, achieving adaptive weighted fusion. Test results demonstrate that under conditions of noise interference, limited samples, and multiple operating states, the proposed method can effectively achieve the accurate assessment of bearing fault conditions.

关键词： chicken chorioallantoic membrane (CAM)   CAR-T   CD19   Raji lymphoma   new approach methodologies (NAMs)   3Rs  

摘要： Chimeric antigen receptor (CAR)-T-cell therapy is a revolutionary approach in immunotherapy that has shown remarkable success in the treatment of blood cancers. Many preclinical studies are currently underway worldwide to extend the CAR-T-cell therapy benefits to a broad spectrum of cancers, using rodent models. Alternative in vivo platforms are essential for overcoming the drawbacks associated with rodent models, including immunodeficiency in humanized models, ethical concerns, extended time requirements, and cost. In this work, we used the chicken chorioallantoic membrane (CAM) assay to evaluate the in vivo efficacy of cluster-of-differentiation 19 (CD19)-targeting CAR-T cells expressing a second-generation CAR construct against human lymphoma derived from the Raji cell line. Our results confirm the efficacy of selected CAR-T cells on tumor growth, metastasis, and angiogenesis. Further, the chicken embryo has an intrinsic active immune system. Therefore, the dialog between CAR-T cells and endogenous immune cells, as well as their participation in the tumor challenge, has also been studied. In conclusion, our study demonstrates that the chicken CAM assay provides a relevant in vivo, 3Rs (Replacement, Reduction and Refinement)-compliant new approach methodology (NAM), which is well-suited for the current needs of preclinical research on CAR-T-cell therapy.