Search Results (1,853)

Structural deviation detection in calligraphic layout is an important problem in intelligent calligraphy tutoring systems. Existing approaches typically rely on isolated geometric or pixel-level statistics and lack a unified representation across spatial levels and scales. To address this issue, this study formulated a layout analysis for hard-pen regular script written in Tianzigē grids as a structural deviation detection task. A continuous writing density field was first constructed from the binary stroke foreground, and a three-level spatial partition consisting of page level, row-column level, and single cell level regions was established. Multi-scale structural responses (MSRs) were then computed within these regions to characterize layout deviations in a unified manner. Under controlled parametric perturbations, an original dataset of 1200 pages was evaluated to assess detection performance. In repeated experiments, the joint MSR features achieved an AUC of 0.94 and an F1-score of 0.90, outperforming geometric, pixel-statistical, page-level structural, and traditional machine-learning baselines. The results indicate that multi-level MSRs provide complementary structural information for reliable layout deviation detection and offer a useful basis for hierarchical diagnostic feedback in intelligent calligraphy tutoring systems. Full article

This study presents an analytical formulation for predicting the additional elastic deflection of tapered steel beams with variable-diameter circular web openings, a configuration that is not addressed by existing analytical models or current design codes. The proposed formulation accounts for the coupled effects of cross-section tapering, progressive variation in opening diameter along the span, and shear–bending interaction within perforated regions. To the best of the authors’ knowledge, this is the first analytical model addressing such complex non-prismatic cellular beam configurations. The formulation is implemented in MATLAB R2019a, enabling fast and automated deflection calculations over a wide parametric range, including various loading cases, tapering ratios, beam spans, web-post widths, and opening dimensions. For prismatic configurations, the analytical predictions are benchmarked against Eurocode 3 and the SCI P355 design guide, both originally developed for beams with constant cross-sections and regular openings. The results demonstrate the improved accuracy and broader applicability of the proposed approach. For tapered configurations with variable-diameter openings, the formulation is assessed against finite element simulations performed in Abaqus/CAE 2017, with the numerical model previously validated against experimental results available in the literature. The proposed method provides a reliable and practical analytical tool for the serviceability assessment of tapered perforated steel beams in structural engineering applications. Full article

Infrared (IR) spectroscopy is a powerful tool for characterizing molecular structures and chemical groups, offering advantages such as low cost, rapid analysis, and non-destructive testing. When analyzing heterogeneous objects, spectra are typically measured from different regions to capture the local variations, presenting a multi-instance learning (MIL) problem. However, existing methods primarily rely on multi-instance assumptions or explicit bag representations, often failing to fully capture the intrinsic information from implicit representations. We introduce a bag dissimilarity regularized MIL framework for analyzing IR spectra of heterogeneous objects, which integrates both explicit and implicit representations to effectively learn the MIL bags. Specifically, a bag dissimilarity regularization term is utilized to extract implicit representations, which subsequently guide the classifier based on explicit representations to enhance generalization performance. The proposed method was validated on two heterogeneous detection tasks: polydimethylsiloxane (PDMS) block assessment and polyethylene terephthalate (PET) fiber inspection. Experimental results demonstrate that our approach significantly outperforms existing methods on both datasets with a considerable margin. Full article

Laser Powder Bed Fusion for metals (PBF-LB/M) is a complex additive manufacturing process in which metal powder is selectively melted layer-by-layer to fabricate 3D parts. Process parameters critically influence the resulting microstructure in nickel alloys, with features such as melt pool marks, grain size and orientation, porosity, and cracks serving as key process signatures. These features are typically analyzed post-process to identify suboptimal conditions. This research aims to develop automated post-process measurement and analysis techniques using image processing, pattern recognition, and statistical learning to correlate process parameters with part quality. Optical microscopy images of build surfaces are analyzed using machine learning algorithms to evaluate porosity, grain size, and relative density in fabricated test coupons. Effect plots are generated to identify trends related to increasing energy density. A novel deep learning approach based on Mask R-CNN is used to detect and segment melt pool regions in optical microscopy images. From the segmented regions, melt pool dimensions—such as width, depth, and area—are extracted using bounding geometry coordinates. Manually labeled images (Type I and Type II) are used to train the model. A comparison between ResNet-50 and ResNet-101 backbones shows that the ResNet-50-based model (Model 2) achieves superior performance, with lower training loss (0.1781 vs. 0.1907) and validation loss (8.6140 vs. 9.4228). Quantitative evaluation using the Jaccard index, precision, and recall metrics shows that the ResNet-101 backbone outperforms ResNet-50, achieving about 4% higher mean Intersection-over-Union, with values of 0.85 for Type I and 0.82 for Type II melt pools, where Type I is detected more accurately due to its more regular morphology and clearer boundaries. By extending Faster R-CNNs with a mask prediction branch, the method allows for precise melt pool measurements, providing valuable insights into process quality and dimensional accuracy, and aiding in the detection of defects in PBF-LB-fabricated parts. Full article

Background: The Magnetic Resonance Imaging (MRI)-based tumor segmentation remains a challenging problem in medical imaging due to tumor heterogeneity, unpredictable morphological features, and the high complexity of calculations needed to implement it in clinical practice, putting it out of the scope of real-time applications. Although neural networks have significantly improved segmentation performance, they still struggle to capture morphological tumor features while maintaining computational efficiency. This work introduces Hybrid Adaptive Attention U-Net (HAAU-Net) framework, combining context-aware morphologically stable features and spatial channel attention to achieve high-quality tumor segmentation with less computational cost. Methods: The proposed HAAU-Net framework integrates multi-scale Adaptive Attention Blocks (AAB), Context-Aware Morphological Feature Module (CAMFM) and Spatial-Channel Hybrid Attention Mechanism (SCHAM). CAMFM is used to maintain the stability of morphological features by hierarchical aggregation and dynamic normalization of features. SCHAM enhances feature representation by modelling channels and spatial regions where the strongest feature are determined to use in segmentation. On the BRaTS 2022/2023 data, the proposed HAAU-Net is evaluated using four modalities including T1, T1GD, T2 and T2-FLAIR sequences. Results: The proposed model able to obtain 96.8% segmentation accuracy with a Dice coefficient of 0.89 on the entire tumor region, outperforming the alternative U-Net (0.83) and conventional CNN methods of segmentation (0.81). The proposed HAAU-Net architecture cuts the computational complexity of the standard deep learning models by 43% and still achieve real-time inference (28 FPS on a regular GPU). The hybrid model used to predict survival has a C-Index of 0.91 which is higher than the traditional SVM-based methods (0.72). Conclusions: Spatial-channel attention, combined with morphologically stable features, can be combined to allow clinically significant interpretability in attention maps. The proposed framework significantly improves segmentation performance while maintaining computational effeciency. This broad system has a serious potential of AI-enabled clinical decision support system and early prognostic diagnosis in neuro-oncology with practical deployment capability. Full article

Total variation (TV)-based image denoising effectively suppresses noise while preserving edges, but it often introduces staircase artifacts in flat regions. To address this limitation, we propose a novel denoising model that combines adaptive fractional-order total variation with a minimax-concave (MC) penalty in the regularization term. The adaptive fractional-order TV alleviates staircase effects in homogeneous areas while preserving fine details in textured regions. The MC penalty provides a more accurate estimation of image sparsity, improving restoration fidelity compared to traditional L1-based regularization. The resulting model, termed AFTVMC, is efficiently solved using an alternating direction method of multipliers (ADMM). Extensive numerical experiments on synthetic and natural images demonstrate that AFTVMC outperforms classical TV, higher-order LLT, adaptive ATV, and state-of-the-art MCFOTV models in both objective metrics—peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)—and subjective visual quality, particularly in suppressing staircase artifacts and preserving complex texture details. Full article

Background: Medical emergencies occur at varying rates across the globe. Given the significant effort invested in identifying them and assessing dentists’ preparedness to deliver treatment in these life-threatening conditions, a global overview was needed. Materials and Methods: In this scoping review, data from PubMed, Cochrane Library, and Google Scholar databases were examined to identify all relevant studies reporting on the impact of medical emergencies on dentists and determine their operational preparedness at a national or regional level. Operational preparedness was determined in accordance with existing emergency operational preparedness frameworks across six domains: Anticipate, Assess, Prevent, Prepare, Respond, and Recover. Significant Findings: Global data show that dentists will invariably encounter medical emergencies across their careers. However, our investigation found that in countries where there is strong foundational training and regular refresher training, fewer frequent emergencies and stronger operational preparedness are reported. Governmental regulation emerged as a key facilitator of operational preparedness. Still, barriers exist, primarily limited access to medical emergency courses, shortages of office supplies for emergency drugs and materials, and the absence of medical emergency registries. Conclusions: A reassessment of the medical emergency training courses’ content appropriateness is paramount. Training interventions should also focus on raising awareness about the importance of preventive measures and office optimization through planning. Further research is needed to identify any overlooked facilitators and barriers to operational preparedness in medical emergencies. This will help identify opportunities for improvement and minimize the impact of emergencies on dental practices. Full article

Prescription drug misuse disproportionately impacts American Indian communities, yet limited research explores how trust in healthcare settings affects behaviors related to prescription drug use. Using data from a 2017 cross-sectional survey of 781 American Indian adults in the Plains region, this study aims to examine the association between trust in health information provided by physicians and the misuse of prescribed medication, while identifying demographic and structural factors that influence trust levels. To assess trust, the study utilized a tool consisting of questions adapted from the Health Information National Trends (HINTS) survey, which asked respondents to rate how much they trust health and medical information from their doctors. Results showed that 29.3% of participants reported high trust in provider information. Trust was significantly higher among women, individuals with private insurance, and those with a personal healthcare provider. Notably, participants who misused prescription drugs reported significantly lower trust (30.0%) than those who did not (40.0%). The study concludes that while historical trauma influences mistrust, structural factors like continuity of care and regular provider access are vital. Improving patient–provider relationships may reduce medication misuse and associated risks like antibiotic resistance. Full article

Background: Overweight and obesity are emerging public health concerns among young adults. However, evidence on associated sociodemographic, dietary, and lifestyle factors among young industrial workers in low- and middle-income countries remains limited. This study aimed to identify factors associated with overweight and obesity among Vietnamese young industrial workers aged 18–30 years. Methods: A cross-sectional study was conducted among 2295 young industrial workers (55.6% men and 44.4% women) recruited from factories and industrial zones in three geographic regions of Vietnam. Sociodemographic characteristics, dietary habits, lifestyle behaviors, and physical activity were assessed using a structured questionnaire. Body mass index (BMI) was calculated from self-reported height and weight and classified using WHO Western Pacific Region (WPRO) cut-offs; overweight/obesity was defined as BMI ≥ 23.0 kg/m². Physical activity was assessed using the International Physical Activity Questionnaire—Long Form (IPAQ-LF) and categorized by total MET-min/week according to IPAQ scoring guidelines. Logistic regression analyses were performed to estimate crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Results: Overall, 10.4% of participants were overweight (BMI 23.0–24.9 kg/m²) and 7.0% were obese (BMI ≥ 25.0 kg/m²), yielding a combined prevalence of 17.4%. After multivariable adjustment, increasing age (aOR = 1.15; 95% CI: 1.10–1.20), male sex (aOR = 2.10; 95% CI: 1.59–2.76), and regular alcohol consumption (aOR = 1.37; 95% CI: 1.04–1.81) were independently associated with higher odds of overweight/obesity, while residence in the Southern region was inversely associated (aOR = 0.57; 95% CI: 0.42–0.76). High total physical activity (vs. low activity) was positively associated with overweight/obesity, whereas moderate physical activity was not independently associated. Other dietary behaviors were not significantly associated after adjustment. Conclusions: Among Vietnamese young industrial workers, overweight and obesity were associated with age, sex, alcohol consumption, and geographic region. The observed association with high total physical activity likely reflects the occupational context of physical activity in this population, highlighting the importance of distinguishing between occupational and leisure time physical activity when interpreting physical activity obesity relationships. These findings underscore the relevance of early, workplace relevant prevention strategies targeting modifiable behaviors, particularly alcohol use. Full article

In this paper, a moving horizon estimation (MHE)-based state estimation problem is studied for asynchronous multi-rate nonlinear systems. First, the asynchronous multi-rate system is transformed into a synchronous system at measurement sampling points through pseudo-measurement synchronization modeling. Secondly, a MHE strategy with a time-discounted quadratic objective is proposed. Under the detectability assumption, the exponential stability of the proposed MHE is established via the Lyapunov method, and the corresponding linear matrix inequality (LMI) constraints are derived. Moreover, to address the model mismatch after synchronization, a deep learning-based framework is proposed to approximate and learn the weighting parameters of the MHE. Then, barrier-function regularization is introduced to enforce the aforementioned LMI feasibility conditions, keeping the learned weights within the feasible region throughout training. Finally, the result is illustrated by a target tracking example. Full article

Background: Measles and rubella remain major public health concerns in the Eastern Mediterranean Region (EMR), despite regional elimination goals. In 2023, the region experienced an increase in measles outbreaks. This study assessed outbreak detection and response challenges in either case definition or data analysis, in addition to gaps in laboratory and genotyping data integration to improve preparedness and response. Method: A retrospective epidemiological study was conducted using official World Health Organization (WHO) data on measles and rubella (MR) in EMR countries, from 1 January to 31 December 2023. Routine MR surveillance line list, genotyping data and supplemental immunization activity (SIA) reported by countries were used. Results: In 2023, 1206 suspected measles outbreaks were reported in 13 countries; 942 (78%) were confirmed. Rubella accounted for 158 confirmed outbreaks. Children under 5 years old comprised 76% of cases, with 62% zero dose. Timely detection was achieved in only 46% of outbreaks, with wide national variation. Genotype B3 predominated, but missing genotyping data limited verification. Six immunization campaigns occurred; however, outbreaks persisted due to high zero dose, limited targeting, and delayed responses. Conclusions: Persistent immunity gaps, under detection, inconsistent genotyping, and delayed response hindered MR control in EMR. Strengthening surveillance, integrating epidemiological and molecular data, expanding targeted supplementary immunization activities, and ensuring timely response are essential tasks. Standardized outbreak definitions, capacity building, and regular subnational analyses remain critical to regional elimination goals. Full article

Insulators are essential components in high-voltage transmission lines and require regular inspection to ensure reliable power delivery. Traditional manual inspection methods are inefficient and labor intensive, highlighting the need for intelligent and automated solutions. In this study, we propose a missing insulator detection method that integrates Unmanned Aerial Vehicle (UAV) imaging with deep learning techniques. Firstly, an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) is employed to detect and localize insulators in aerial images. Secondly, the localized insulators are segmented using an improved U-Net to reduce background interference. A bounding box regression approach is adopted to obtain the minimum enclosing rectangles, and the insulators are aligned vertically. Adaptive thresholding is then applied to extract binary images of the insulators. These binary images are further transformed into defect curves, from which missing insulators are identified based on curve distribution. To address the limited availability of labeled samples, a transfer learning-based strategy is adopted to improve model generalization. A dataset of glass insulators was collected using a DJI M300 UAV equipped with an H20T camera along a 330 kV overhead transmission line. On the collected UAV insulator dataset, the proposed method achieved an AP@0.5 of 99.85% and an average IoU of 88.56% for insulator string detection, while the improved U-Net achieved an mIoU of 89.73% for insulator string segmentation. Outdoor flight experiments further verified performance under varying backgrounds and illumination conditions in our UAV inspection scenarios. Full article

Hemorrhagic fever with renal syndrome (HFRS) is the most common zoonotic disease in Russia, with about a 98% abundance of Puumala virus in all HFRS cases. We report clinical manifestations and genomic characteristics of the Puumala virus strain that caused an unconventional course of HFRS with sudden death. The patient was admitted to the hospital on the third day from fever onset with hyperthermia, cough, shortness of breath, and severe weakness, and died 28 h after hospitalization despite intensive care. Further analyses of autopsy samples led to Puumala virus detection. The viral genome was sequenced, followed by phylogenetic and similarity plot analyses. The genomic sequences of three viral segments were identified as endemic for the Moscow region strain. Phylogenetic and similarity plot analysis revealed the reassortant origin of the strain via M segment exchange. This finding increases the explored molecular diversity of Puumala virus in the Central Federal District and underscores the need for heightened awareness of HFRS manifestations that deviate from regular clinical presentation. Full article

Background/Objectives: Police work has been associated with increased cardiometabolic risk due to occupational stress, shift work, and lifestyle factors. This study aimed to determine the prevalence of MetS and 10-year cardiovascular risk, and to analyse differences by sex and occupational function among police officers (POs) in Madeira. Methods: A cross-sectional study was conducted among 109 POs from the Autonomous Region of Madeira. MetS was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria, and 10-year cardiovascular risk was estimated using the Framingham Risk Score. Health-related quality of life (HRQoL) was evaluated using the SF-36 questionnaire (SF-36v2). Comparisons were performed by sex and professional role (indoor versus outdoor). Results: (i) The prevalence of MetS was 28.4%; (ii) Male POs had significantly higher Framingham Risk Scores than female POs, although no sex differences in MetS prevalence were observed; (iii) Approximately 20% of POs were classified as high cardiovascular risk; and (iv) Among male POs, those performing indoor duties showed higher cardiovascular risk scores. Conclusions: POs in Madeira present a considerable burden of cardiometabolic risk factors. These findings highlight the need for targeted occupational health strategies and regular cardiovascular screening within police organisations. Full article

Environments with sparse or repetitive geometric structures, such as long corridors and narrow stairwells, remain challenging for LiDAR–inertial simultaneous localization and mapping (LiDAR–IMU SLAM) due to insufficient geometric observability and unreliable data associations. To address these issues, we propose VINA-SLAM, a novel LiDAR–IMU SLAM framework that constructs a unified global voxel map to explicitly exploit structural consistency. VINA-SLAM continuously tracks surface normals stored in the global voxel map using a normal-guided correspondence strategy, enabling stable scan-to-map alignment in degenerate scenes. Furthermore, a tangent-space metric is introduced to supplement missing rotational constraints around planar regions, providing reliable initial pose estimates for local optimization. A tightly coupled sliding-window bundle adjustment is then formulated by jointly incorporating IMU factors, voxel normal consistency factors, and planar regularization terms. In particular, the minimum eigenvalue of each voxel’s covariance is used as a statistically principled planar constraint, improving the Hessian conditioning and cross-view geometric consistency. The proposed system directly aligns raw LiDAR scans to the voxelized map without explicit feature extraction or loop closure. Experiments on 25 sequences from the HILTI and MARS-LVIG datasets show that VINA-SLAM reduces ATE by 25–40% on average while maintaining real-time performance at 10 Hz in the evaluated geometrically degenerate environments. Full article