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关键词： CAD/CAM   FDI criteria   Inlay   Lithium disilicate   Randomised controlled trial   Split-mouth  

摘要： To compare the clinical performance of Amber Mill (AM) and IPS e.max CAD (EM) lithium disilicate computer-aided design/computer-aided manufacturing (CAD/CAM) materials in posterior Class II inlay restorations and characterise their baseline properties.

关键词： complementary and alternative medicine   herd health management   dairy cows   qualitative research   document analysis  

摘要： The use of complementary and alternative medicines (CAM) in UK dairy farming has sparked considerable debate. The Royal College of Veterinary Surgeons (RCVS) released a position statement in 2017, emphasizing that treatments should be underpinned by evidence. Since then, there are ongoing debates around CAM use in farming media. We conducted a document analysis to examine how these debates may influence how CAM is used on UK dairy farms.

关键词： Attention mechanism   Brain tumor   EfficientNetV2   Grad-CAM   UNet  

摘要： Automated brain tumor segmentation provides precise evaluations of tumor dimensions and progression across time, which is beneficial for longitudinal studies and treatment oversight. This study provides a novel brain tumor segmentation method that combines a two-path UNet architecture with an upgraded EfficientNetV2 model due to an attention mechanism. By utilizing the advantages of both designs, our technique handles 3D MRI data efficiently and increases segmentation accuracy. By using two distinct encoding pathways, the UNet model is improved and can extract a greater variety of information from MRI images. The pretrained EfficientNetV2 model is also included to further improve the model’s capabilities. By including an attention mechanism, the model is able to concentrate on pertinent features, which further enhances segmentation performance. Using the BraTS 2020 dataset and the clinical dataset, we assessed the performance of the two-path UNet with EfficientNetV2B0 and EfficientNetV2S backbones. The real-clinical dataset comprises MRI scans from 99 patients acquired at Ankara Bilkent City Hospital. The findings showed that particular input combinations produced superior segmentation results, and some models performed exceptionally well at correctly categorizing various tumor kinds. This emphasizes how these models may be useful in clinical settings and how crucial it is to choose the right input configurations and model backbones to get the best segmentation results. Furthermore, the Grad-CAM technique improved the models’ reliability and interpretability, providing crucial insights into their decision-making processes.

关键词： Cancer metabolism   Metabolic reprogramming   Warburg effect   Tumor microenvironment   Immunometabolism   Oncometabolites   Ferroptosis   Epigenetics   Glycolysis   Oxidative phosphorylation   2-DG   2-Deoxyglucose   2-HG   2-Hydroxyglutarate   6-AN   6-Aminonicotinamide   6PGD   6-Phosphogluconate dehydrogenase   A2A/A2B   Adenosine receptors A2A and A2B   ACC1/2   Acetyl-CoA carboxylase 1/2   ACLY   ATP-citrate lyase   ACOD1   Aconitate decarboxylase 1 (see IRG1)   ACSL3/4   Acyl-CoA synthetase long-chain family member 3/4   ACSS2   Acyl-CoA short-chain synthetase 2   ADSS/ADSL   Adenylosuccinate synthetase/lyase   AHR   Aryl hydrocarbon receptor   AML   Acute myeloid leukemia   AMD1   S-Adenosylmethionine decarboxylase 1   AMPK   AMP-activated protein kinase   APRT   Adenine phosphoribosyltransferase   ARG1/2   Arginase 1/2   ASCT2   Alanine, serine, cysteine transporter 2 (see SLC1A5)   ASNS   Asparagine synthetase   ATF4   Activating transcription factor 4   ATR   Ataxia telangiectasia and Rad3-related protein   BCAT1/2   Branched-chain amino acid transaminase 1/2   BCAA   Branched-chain amino acid   CAD   Carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, dihydroorotase   CAF   Cancer-associated fibroblast   CAR T   Chimeric antigen receptor T cell   CASTOR1   Cytosolic arginine sensor for mTORC1 subunit 1   cDC1   Type 1 conventional dendritic cell   cGAS   Cyclic GMP-AMP synthase   CHK1   Checkpoint kinase 1   CIMP   CpG island methylator phenotype   COX2   Cyclooxygenase 2   CPT1A   Carnitine palmitoyltransferase 1 A   CPS1   Carbamoyl phosphate synthetase 1   DC   Dendritic cell   DFMO   Difluoromethylornithine (eflornithine)   DHFR   Dihydrofolate reductase   DHPS   Deoxyhypusine synthase   DHODH   Dihydroorotate dehydrogenase   dNTP   Deoxynucleoside triphosphate   DOHH   Deoxyhypusine hydroxylase   DRP1   Dynamin-related protein 1   DTP   Drug-tolerant persister   eIF5A   Eukaryotic initiation factor 5 A   EMT   Epithelial-mesenchymal transition   ETC   Electron transport chain   FAO   Fatty acid oxidation   FASN   Fatty acid synthase   FATP   Fatty acid transport protein   FBP1   Fructose-1,6-bisphosphatase 1   FH   Fumarate hydratase   FIGLU   Formiminoglutamate   FPP   Farnesyl pyrophosphate   FSP1   Ferroptosis su  

摘要： Cancer cells undergo profound metabolic reprogramming to sustain uncontrolled proliferation within a nutrient-limited and often hypoxic tumor microenvironment (TME). Metabolic rewiring is an active driver of oncogenesis, immune evasion, epigenetic remodeling, and therapy resistance. Over the past century, our understanding of tumor metabolism has grown from Warburg's seminal description of aerobic glycolysis to a comprehensive adaptive network. Cancer cells coordinate glucose catabolism, mitochondrial oxidative metabolism, fatty acid synthesis and oxidation, amino acid catabolism, nucleotide biosynthesis, and one‑carbon metabolism into an integrated metabolic framework. These pathways form a deeply interconnected web in which metabolic intermediates serve as biosynthetic building blocks, bioenergetic substrates, redox buffers, signaling molecules, and epigenetic cofactors. Within the TME, metabolic competition between tumor cells and immune cells, together with the accumulation of immunosuppressive metabolites such as lactate, kynurenine, and adenosine, creates a profoundly immune-hostile landscape. Recent work has further revealed that key post-translational modifications, directly driven by metabolic flux, reshape the chromatin and proteome of both cancer cells and tumor-infiltrating immune cells, linking metabolism to gene regulation in previously unanticipated ways. Therapeutically, the FDA approval of IDH1/IDH2 inhibitors for acute myeloid leukemia demonstrated that metabolic enzymes are tractable oncology drug targets. Yet the broader effort to translate metabolic insights into robust clinical benefit has encountered formidable obstacles, including metabolic plasticity, intratumoral heterogeneity, overlap with normal tissue function, and inadequate biomarkers. This review traces the evolution of our understanding of cancer metabolism from its origins to therapeutic targeting. It further examines how anabolic and catabolic pathways, energy production, redox bal

关键词： Additive manufacturing   Accuracy   Lithium disilicate   CAD CAM  

摘要： This study compared subtractive and additive fabrication methods regarding their impact on the marginal/internal fit, trueness, and precision of lithium disilicate ceramic veneers.

关键词： Cylindrical cam–tapered roller mechanism   Coupled shaft bending and contact deformation   Driving torque   Mechanical efficiency   Structural optimization   Hybrid optimization algorithm  

摘要： In high-load cylindrical cam–tapered roller transmissions, roller-shaft bending and local contact deformation jointly govern the load distribution. However, existing studies generally treat system-level force and efficiency modeling separately from local contact analysis. We designed a force-balanced cylindrical cam–tapered roller transmission mechanism and established its cam profile using the enveloping theory of a one-parameter family of space curves. We then developed driving-torque and mechanical-efficiency models that couple roller-shaft bending with Hertzian contact deformation at the cam–roller interfaces. Structural optimization maximized mechanical efficiency subject to constraints on peak driving torque and contact geometry, using an adaptive-grouping particle-swarm–genetic hybrid algorithm. Prototype tests showed a maximum relative error of 2.9% between theoretical peak driving torque and the experimental mean. The maximum absolute deviation between theoretical and experimental mechanical efficiency was 2.19 percentage points. Optimization reduced the theoretical peak driving torque from 55.07 to 26.94 N·m and the total cam–roller volume by 14.6%. The theoretical mechanical efficiency decreased from 87.37% to 82.71%. At a comparable peak driving torque of approximately 27.1 N·m, the maximum experimentally validated load increased from 1497 to 3101 N, a gain of 107.2%. At a common load of approximately 1500 N, the experimental mechanical efficiency decreased by about 4.4 percentage points. The proposed method therefore reduces driving torque, increases load capacity and decreases mechanism size at a quantifiable efficiency cost, supporting the design of high-load cylindrical cam transmissions.

关键词： Reverse engineering  

摘要： Reverse engineering of degraded service components becomes difficult if there is uncertainty in the nominal geometry due to corrosion, fracture or missing material, and original drawings are not available. This study proposes a two-stage dimensional validation procedure for a worn cast-iron foot valve, which can be broken down into reconstruction-stage deviation and fabrication-stage deviation. The valve was scanned with structured light and converted to a watertight mesh of around 3.1 million facets. The damaged geometry was recreated in the CAD environment using a combination of NURBS surface fitting, symmetry guided repair and controlled fitting of missing or corroded geometry. A PLA prototype was then fabricated by fused deposition modelling using 0.15 mm layer height, 100% infill, and 45 mm/s print speed. Dimensional accuracy was evaluated using signed deviation mapping, rigid alignment, and a ± 2 mm tolerance band. In Stage 1, the CAD-scan comparison produced an RMS deviation of 2.67 mm, with 36.5% of the surface lying within the tolerance band. In Stage 2, the CAD-printed-part comparison showed an RMS deviation of 2.9 mm and 35% in-tolerance surface. The small RMS increase of + 0.23 mm indicates that, under the selected printing conditions, FDM reproduction introduced only a limited additional global deviation relative to the CAD-scan deviation. The main contribution of this work is the proposed two-stage error-separation strategy, which provides traceable evidence of dimensional error propagation across scanning, CAD repair, and physical prototyping. The workflow is suitable for preliminary geometric prototyping of degraded cast components, while functional reuse requires further datum-based and feature-level validation.

关键词： Endoscopy  

摘要： Artificial Intelligence (AI), particularly in the domains of computer vision and deep learning (DL), is increasingly being integrated into medical diagnostics. Although it has not yet entirely transformed clinical practice, its role in medical image analysis is rapidly expanding, offering the potential for more accurate, efficient, and accessible diagnostic capabilities that contribute to improved patient outcomes. Currently, DL techniques are employed in a variety of medical imaging tasks, such as anomaly detection, pathology classification, and anatomical structure segmentation across modalities, including endoscopy, MRI, and CT scans. Despite these advancements, the widespread clinical adoption of DL models remains constrained by several limitations, including the opacity of model decision-making, data imbalance, and lack of interpretability. To address these challenges, this study proposes an explainable knowledge distillation (KD) framework that integrates Gradient-weighted Class Activation Mapping (Grad-CAM) with a ResNet-based teacher-student architecture. The proposed methodology was evaluated on a class-imbalanced dataset comprising ten categories of gastrointestinal (GI) abnormalities derived from the Capsule Vision 2024 Challenge. The resulting student model achieved a classification accuracy of 96.52% and an F1-score of 96.37%, thereby surpassing previously reported methods.

关键词： Inspection  

摘要： Deep learning-based autonomous inspection of power grid insulators is challenged by data imbalance and model opacity. This paper presents an end-to-end solution integrating advanced data synthesis, detection, classification, and explainability. First, a conditional diffusion model generates realistic synthetic fault images to balance the dataset. A two-stage architecture based on You Only Look Once version 26 (YOLO26) extra-large and Shifted windows (Swin)-V2-B, called YOLO26-Swin, fine-tuned with Bayesian optimization, performs robust insulator detection and then fault classification. Finally, a novel SHapley Additive exPlanations with Class Activation Mapping (SHAP-CAM) method provides intuitive visual explanations for model predictions. Extensive experiments validate our framework’s superiority: it achieves an F1-score of 0.98149 and a mean Average Precision (mAP)@[0.5] of 0.98951, exceeding leading detection and classification models. This work highlights the efficacy of diffusion models for data augmentation in critical infrastructure and advances the interpretability of vision-based inspection systems.

关键词： Artificial intelligence  

摘要： Gender recognition is a significant subfield in artificial intelligence and can be used in many applications, for example: security systems, suggestions, and biometric identification. This work evaluates the performance of MobileNet and ViT architectures for gender classification using real world and synthetic datasets. From the experimental results, we find that the overall performance of MobileNet is outstanding in real-life applications with the highest accuracy, recall, and F1 score due to feature extraction with its lightweight architecture. On the other hand, ViT is powerful, but it needs a lot of training data to fine-tune its performance. Various Explainable AI (XAI) techniques, including Grad CAM and LIME, were utilized to improve model interpretability by shedding light on the rationale behind the decision-making process and highlighting the most salient features that contribute to the classification process. These results indicate that MobileNet is suitable for real-time applications, while ViT shows potential for optimization through fine tuning and additional data. Future studies should also investigate hybrid models, strategies for mitigating bias, and improved explainability for fairness and reliability in gender classification systems.