Search Results (2,028)

Gender-based violence (GBV) and traffic offenses pose significant public health challenges and contribute to widespread social issues globally. This study examines the sociodemographic and psychological profiles of individuals who commit traffic offenses and GBV, focusing on three alternative penal programs: TASEVAL (for traffic offenses), PRIA-MA, and reGENER@r (both for GBV). The study involved 54 participants distributed across these programs, using various psychometric tests to assess their profiles. Participants across the three programs (TASEVAL, PRIA-MA, and reGENER@R) were comparable in age (mean range 39.13–40.69 years) and nationality, with roughly half having prior contact with the justice system. Educational levels varied, with TASEVAL participants mainly completing secondary education (43.8%), PRIA-MA participants primary education (43.8%), and reGENER@R participants post-secondary education (59.1%). Employment status differed slightly, with TASEVAL and reGENER@R participants mainly employed (62.5% and 63.6%, respectively), while most PRIA-MA participants were unemployed (56.3%). Family characteristics varied across groups. In TASEVAL, having a partner and no children predominated (62.5% and 31.3%); in PRIA-MA, not having a partner and having two children predominated (62.5% and 37.5%); and, in reGENER@R, not having a partner and having one child predominated (59.1% and 31.8%). No significant differences were observed in sociodemographic variables. Regarding psychological characteristics, results across all groups indicate a marked presence of psychopathological symptoms and difficulties in emotional intelligence domains, with a significant correlation between psychological traits and coping strategies. These findings highlight the importance of tailoring alternative penal measures to the specific characteristics of each group to enhance effectiveness and reduce recidivism. Full article

There are various mechanisms through which water droplets can be present in compressor flows, e.g., rain ingestion in aeroengines or overspray fogging used in heavy-duty gas turbines to boost power output. For the latter, droplet evaporation within the compressor leads to a cooling of the flow as well as to a shift in the fluid properties, which is beneficial to the overall process. However, due to their inertia, the majority of droplets are deposited in the first stages of a multistage compressor. While this phenomenon is generally considered in CFD computations of droplet-laden flows, the subsequent re-entrainment of collected water, the formation of new droplets, and the impact on the overall evaporation are mostly neglected because of the additional computational effort required, especially with regard to the modeling of films formed by the deposited water. The work presented here shows an approach that allows for the integration of the process of droplet deposition and re-entrainment based on relatively simple correlations and experimental observations from the literature. Thus, the two-phase flow in multistage compressors can be modelled and analyzed very efficiently. In this paper, the models and assumptions used are described first, then the results of a study performed based on a generic multistage compressor are presented, whereby the various models are integrated step by step to allow an assessment of their impact on the droplet evaporation throughout the compressor and overall performance. It can be shown that evaporation becomes largely independent of droplet size when deposition on both rotor and stator and subsequent re-entrainment of collected water is considered. In addition, open issues with regard to the future improvement of models and correlations of two-phase flow phenomena are highlighted based on the results of the current investigation. Full article

Color grading of boiled shrimp is a critical factor influencing market price, yet the process is usually conducted visually by buyers such as middlemen and processing plants. This subjective practice raises concerns about accuracy, impartiality, and fairness, often resulting in disputes with farmers. To address this issue, this study proposes a standardized and automated grading approach based on image processing and artificial intelligence. The method requires only a photograph of boiled shrimp placed alongside a color grading ruler. The grading process involves two stages: segmentation of shrimp and ruler regions in the image, followed by color comparison. For segmentation, deep learning models based on Mask R-CNN with a Feature Pyramid Network backbone were employed. Four model configurations were tested, using ResNet and ResNeXt backbones with and without a Boundary Loss function. Results show that the ResNet + Boundary Loss model achieved the highest segmentation performance, with IoU scores of 91.2% for shrimp and 87.8% for the color ruler. In the grading step, color similarity was evaluated in the CIELAB color space by computing Euclidean distances in the L (lightness) and a (red–green) channels, which align closely with human perception of shrimp coloration. The system achieved grading accuracy comparable to human experts, with a mean absolute error of 1.2, demonstrating its potential to provide consistent, objective, and transparent shrimp quality assessment. Full article

The widespread application of WE43 (Mg-4Y-2Nd-1Gd-0.5Zr) alloy castings in aerospace components is hindered by the frequent formation of defects such as cracks, pores, and especially yttria inclusions. These defects necessitate subsequent welding. However, using homologous WE43 filler wires often exacerbates these issues, leading to high crack susceptibility and reintroduction of inclusions. Herein, we propose a novel strategy of tailoring Y content in filler wires to achieve high-quality welded joint of WE43 sand castings. Systematic investigations reveal that reducing Y content to 2 wt.% (WE23) effectively suppresses oxide inclusion formation and significantly enhances the integrity of the joint. The fusion zone microstructure evolves distinctly with varying Y levels: grain size initially increases, peaking at 24 μm with WE43 wire, then decreases with further Y addition. Moreover, eutectic compounds transition from a semi-continuous to a continuous network structure with increasing Y content, deteriorating mechanical performance. Notably, joints welded with WE23 filler exhibit minimal performance loss, with ultimate tensile strength, yield strength, and elongation reaching 93.0%, 98.0%, and 97.4% of the sand-cast base metal, respectively. The underlying strengthening mechanisms and solute-second phase relationships are elucidated, highlighting the efficacy of optimizing Y content in welding wire design. This study provides valuable insights toward defect-free welding of high-performance Mg-RE alloy castings. Full article

In remote sensing imagery, objects smaller than $32\times 32$ pixels suffer from three persistent challenges that existing detectors inadequately resolve: (1) their weak signal is easily submerged in background clutter, causing high miss rates; (2) the scarcity of valid pixels yields few geometric or textural cues, hindering discriminative feature extraction; and (3) successive down-sampling irreversibly discards high-frequency details, while multi-scale pyramids still fail to compensate. To counteract these issues, we propose MS-YOLOv11, an enhanced YOLOv11 variant that integrates “frequency-domain detail preservation, lightweight receptive-field expansion, and adaptive cross-scale fusion.” Specifically, a 2D Haar wavelet first decomposes the image into multiple frequency sub-bands to explicitly isolate and retain high-frequency edges and textures while suppressing noise. Each sub-band is then processed independently by small-kernel depthwise convolutions that enlarge the receptive field without over-smoothing. Finally, the Mix Structure Block (MSB) employs the MSPLCK module to perform densely sampled multi-scale atrous convolutions for rich context of diminutive objects, followed by the EPA module that adaptively fuses and re-weights features via residual connections to suppress background interference. Extensive experiments on DOTA and DIOR demonstrate that MS-YOLOv11 surpasses the baseline in mAP@50, mAP@95, parameter efficiency, and inference speed, validating its targeted efficacy for small-object detection. Full article

High-resolution remote sensing imagery is crucial for ship extraction in ocean-related applications. Existing object detection and semantic segmentation methods for ship extraction have limitations: the former cannot precisely obtain ship shapes, while the latter struggles with small targets and complex backgrounds. This study addresses these issues by constructing two datasets, DIOR_SHIP and LEVIR_SHIP, using the SAM model and morphological operations. A novel MambaSegNet is then designed based on the advanced Mamba architecture. It is an encoder–decoder network with MambaLayer and ResMambaBlock for effective multi-scale feature processing. The experiments conducted with seven mainstream models show that the IOU of MambaSegNet is 0.8208, the Accuracy is 0.9176, the Precision is 0.9276, the Recall is 0.9076, and the F1-score is 0.9176. Compared with other models, it acquired the best performance. This research offers a valuable dataset and a novel model for ship extraction, with potential cross-domain application prospects. Full article

Blockchain is an emerging technology that is being used to create innovative solutions in many areas, including healthcare. Nowadays healthcare systems face challenges, especially with security, trust, and remote data access. As patient records are digitized and medical systems become more interconnected, the risk of sensitive data being exposed to cyber threats has grown. In this evolving time for healthcare, it is important to find a balance between the advantages of new technology and the protection of patient information. The combination of blockchain–InterPlanetary File System technology and conventional electronic health record (EHR) management has the potential to transform the healthcare industry by enhancing data security, interoperability, and transparency. However, a major issue that still exists in traditional healthcare systems is the continuous problem of remote data unavailability. This research examines practical methods for safely accessing patient data from any location at any time, with a special focus on IPFS servers and blockchain technology in addition to group signature encryption. Essential processes like maintaining the confidentiality of medical records and safe data transmission could be made easier by these technologies. Our proposed framework enables secure, remote access to patient data while preserving accessibility, integrity, and confidentiality using Ethereum blockchain, IPFS, and group signature encryption, demonstrating hospital-scale scalability and efficiency. Experiments show predictable throughput reduction with file size (200 → 90 tps), controlled latency growth (90 → 200 ms), and moderate gas increase (85k → 98k), confirming scalability and efficiency under varying healthcare workloads. Unlike prior blockchain–IPFS–encryption frameworks, our system demonstrates hospital-scale feasibility through the practical integration of group signatures, hierarchical key management, and off-chain erasure compliance. This design enables scalable anonymous authentication, immediate blocking of compromised credentials, and efficient key rotation without costly re-encryption. Full article

Climate change poses significant challenges to agriculture, leading to increased crop damage owing to extreme weather conditions. Detecting and analyzing such damage is crucial for mitigating its effects on crop yield. This study proposes a novel autoencoder (AE)-based model, termed “Memory Ganomaly,” designed to detect and analyze weather-induced crop damage under conditions of significant class imbalance. The model integrates memory modules into the Ganomaly architecture, thereby enhancing its ability to identify anomalies by focusing on normal (undamaged) states. The proposed model was evaluated using apple and peach datasets, which included both damaged and undamaged images, and was compared with existing robust Convolutional neural network (CNN) models (ResNet-50, EfficientNet-B3, and ResNeXt-50) and AE models (Ganomaly and MemAE). Although these CNN models are not the latest technologies, they are still highly effective for image classification tasks and are deemed suitable for comparative analyses. The results showed that CNN and Transformer baselines achieved very high overall accuracy (94–98%) but completely failed to identify damaged samples, with precision and recall equal to zero under severe class imbalance. Few-shot learning partially alleviated this issue (up to 75.1% recall in the 20-shot setting for the apple dataset) but still lagged behind AE-based approaches in terms of accuracy and precision. In contrast, the proposed Memory Ganomaly delivered a more balanced performance across accuracy, precision, and recall (Apple: 80.32% accuracy, 79.4% precision, 79.1% recall; Peach: 81.06% accuracy, 83.23% precision, 80.3% recall), outperforming AE baselines in precision and recall while maintaining comparable accuracy. This study concludes that the Memory Ganomaly model offers a robust solution for detecting anomalies in agricultural datasets, where data imbalance is prevalent, and suggests its potential for broader applications in agricultural monitoring and beyond. While both Ganomaly and MemAE have shown promise in anomaly detection, they suffer from limitations—Ganomaly often lacks long-term pattern recall, and MemAE may miss contextual cues. Our proposed Memory Ganomaly integrates the strengths of both, leveraging contextual reconstruction with pattern recall to enhance detection of subtle weather-related anomalies under class imbalance. Full article

Heating and cooling (H&C) account for nearly half of the EU’s energy consumption, with significant potential for decarbonization through renewable energy sources (RES) integrated in district heating and cooling (DHC) systems. This study evaluates the environmental and social impacts of RES-powered DHC solutions implemented in three European small-scale demo sites (Bucharest, Luleå, Córdoba) under the Horizon 2020 WEDISTRICT project. Using the Life Cycle Assessment (LCA) and Social Life Cycle Assessment (S-LCA) methodologies, the research compares baseline fossil-based energy scenarios with post-implementation renewable scenarios. Results reveal substantial greenhouse gas emission reductions (up to 67%) and positive environmental trade-offs, though increased mineral and metal resource use and site-specific impacts on water and land use highlight important sustainability challenges. Social assessments demonstrate improvements in gender parity, local employment, and occupational safety, yet reveal persistent issues in wage equity, union representation, and inclusion of vulnerable populations. The findings emphasize that while renewable DHC systems offer significant climate benefits, social sustainability requires tailored local strategies and robust governance to avoid exacerbating inequalities. This integrated environmental-social perspective underscores the need for holistic policies that balance technical innovation with equitable social outcomes to ensure truly sustainable energy transitions. Full article

Due to the growing complexity of video encoders, the optimization of the parameters of the encoding process is becoming an important issue. In recent years, this has become an important field of application of neural networks. Artificial neural networks in video encoders are used to accelerate the video encoder operation. This paper demonstrates the use of different ResNet- and DenseNet-type architectures to accelerate the CTU partitioning algorithm in HEVC in All Intra mode. The paper demonstrates the results of an exhaustive evaluation of different proposed architectures, considering compression efficiency, network size, and encoding time reduction. Multiple pros and cons of the proposed architectures are presented in the Conclusions, considering various limitations that may be important for a given application, like hardware-constrained sensor networks or standalone small devices operating with images and videos. Full article

Accurate and efficient pest monitoring in complex rice field environments is vital for food security. Existing detection methods often struggle with small targets and high computational redundancy, limiting deployment on resource-constrained edge devices. To address these issues, we propose YOLO-RP, a lightweight and efficient rice pest detection method based on YOLO11n. YOLO-RP reduces model complexity while maintaining detection accuracy. The model removes the redundant P5 detection head and introduces a high-resolution P2 head to enhance small-object detection. A lightweight partial convolution detection head (LPCHead) decouples task branches and shares feature extraction, reducing redundancy and boosting performance. The re-parameterizable DBELCSP module strengthens feature representation and robustness while cutting parameters and computation. Wavelet pooling preserves essential edge and texture information during downsampling, improving accuracy under complex backgrounds. Experiments show that YOLO-RP achieves a precision of 90.62%, recall of 87.38%, mAP@0.5 of 90.99%, and mAP@0.5:0.95 of 63.84%, while reducing parameters, GFLOPs, and model size by 61.3%, 50.8%, and 49.1% to 1.00 M, 3.1, and 2.8 MB. Cross-dataset tests on Common Rice Pests (Philippines), IP102, and Pest24 confirm strong robustness and generalization. On NVIDIA Jetson Nano, YOLO-RP attains 20.8 FPS—66.4% faster than the baseline—validating its potential for edge deployment. These results indicate that YOLO-RP provides an effective solution for real-time rice pest detection in complex, resource-limited environments. Full article

Climatic heat stress in Hong Kong’s construction industry has been exacerbated by global climate change in recent times and the city has been taking proactive measures in protecting its workforce. Heat stress management systems refer to integrated frameworks, including policies, technologies, and practices, designed to monitor, mitigate, and prevent heat-related risks to workers’ health and productivity in hot environments. This scoping review investigates the existing heat stress management systems within Hong Kong’s construction industry, analyzing policies and academic research, and highlighting challenges and proposing solutions. A systematic scoping method was used to review and synthesize findings from 50 peer-reviewed articles (updated to 2025) and nine policy documents. This study highlights the interplay between research innovations like AI-driven models and wearable cooling technologies and policy frameworks. The results indicate substantial progress in Hong Kong’s drive to manage heat strain and accidents among construction workers over the years, with advancements in real-time advisory systems and protective equipment, improving worker safety and productivity. However, limited scalability, costs, socio-cultural compliance issues, gaps in addressing equity concerns among vulnerable workers, policy implementation, and other challenges persist. This review underscores the importance of building resilient systems against the escalating heat stress risks by proposing the integration of research-based technological innovation with policies and socio-organizational considerations. It contributes to providing the first updated scoping review post-2020, identifying implementation gaps (e.g., 40% non-compliance rate) and proposing a concrete action framework for future interventions. Recommendations for future research include cross-regional adaptations, cost-effective solutions for medium-sized construction enterprises, and the continuous re-evaluation and improvement of current interventions. Full article

Facial beauty prediction (FBP) is a cutting-edge topic in deep learning, aiming to endow computers with human-like esthetic judgment capabilities. Current facial beauty datasets are characterized by multi-class classification and imbalanced sample distributions. Most FBP methods focus on improving accuracy (ACC) as their primary goal, aiming to indirectly optimize other metrics. In contrast to ACC, which is well known to be a poor metric in cases of highly imbalanced datasets, the recall measures the proportion of correctly identified samples for each class, effectively evaluating classification performance across all classes without being affected by sample imbalances, thereby providing a fairer assessment of minority class performance. Therefore, targeting recall improvement facilitates balanced classification across all classes. The Macro Recall (MR), which averages the recall of all the classes, serves as a comprehensive metric for evaluating a model’s performance. Among numerous classic models, ConvNeXt, which integrates the designs of the Swin Transformer and ResNet, performs exceptionally well regarding its MR but still suffers from inter-class confusion in certain categories. To address this issue, this paper introduces contrastive learning (CL) to enhance the class separability by optimizing feature representations and reducing confusion. However, directly applying CL to all the classes may degrade the performance for high-recall categories. To this end, we propose using an ensemble strategy, ECL-ConvNeXt: First, ConvNeXt is used for multi-class prediction on the whole of dataset A to identify the most confused class pairs. Second, samples predicted to belong to these class pairs are extracted from the multi-class results to form dataset B. Third, true samples of these class pairs are extracted from dataset A to form dataset C, and CL is applied to improve their separability, training a dedicated auxiliary binary classifier (ConvNeXtCL-ABC) based on ConvNeXt. Subsequently, ConvNeXtCL-ABC is used to reclassify dataset B. Finally, the predictions of ConvNeXtCL-ABC replace the corresponding class predictions of ConvNeXt, while preserving the high recall performance for the other classes. The experimental results demonstrate that ECL-ConvNeXt significantly improves the classification performance for confused class pairs while maintaining strong performance for high-recall classes. On the LSAFBD dataset, it achieves 72.09% ACC and 75.43% MR; on the MEBeauty dataset, 73.23% ACC and 67.50% MR; on the HotOrNot dataset, 62.62% ACC and 49.29% MR. The approach is also generalizable to other multi-class imbalanced data scenarios. Full article

Ancient Chinese scripts—including oracle bone carvings, bronze inscriptions, stone steles, Dunhuang scrolls, and bamboo slips—are rich in historical value but often degraded due to centuries of erosion, damage, and stylistic variability. These issues severely hinder manual transcription and render conventional OCR techniques inadequate, as they are typically trained on modern printed or handwritten text and lack interpretability. To tackle these challenges, we propose FAIR-Net, a hybrid architecture that combines the unsupervised feature learning capacity of a deep autoencoder with the semantic transparency of a fuzzy rule-based classifier. In FAIR-Net, the deep autoencoder first compresses high-resolution character images into low-dimensional, noise-robust embeddings. These embeddings are then passed into a Fuzzy Neural Network (FNN), whose hidden layer leverages Fuzzy C-Means (FCM) clustering to model soft membership degrees and generate human-readable fuzzy rules. The output layer uses Iteratively Reweighted Least Squares Estimation (IRLSE) combined with a Softmax function to produce probabilistic predictions, with all weights constrained as linear mappings to maintain model transparency. We evaluate FAIR-Net on CASIA-HWDB1.0, HWDB1.1, and ICDAR 2013 CompetitionDB, where it achieves a recognition accuracy of 97.91%, significantly outperforming baseline CNNs (p < 0.01, Cohen’s d > 0.8) while maintaining the tightest confidence interval (96.88–98.94%) and lowest standard deviation (±1.03%). Additionally, FAIR-Net reduces inference time to 25 s, improving processing efficiency by 41.9% over AlexNet and up to 98.9% over CNN-Fujitsu, while preserving >97.5% accuracy across evaluations. To further assess generalization to historical scripts, FAIR-Net was tested on the Ancient Chinese Character Dataset (9233 classes; 979,907 images), achieving 83.25% accuracy—slightly higher than ResNet101 but 2.49% lower than SwinT-v2-small—while reducing training time by over 5.5× compared to transformer-based baselines. Fuzzy rule visualization confirms enhanced robustness to glyph ambiguities and erosion. Overall, FAIR-Net provides a practical, interpretable, and highly efficient solution for the digitization and preservation of ancient Chinese character corpora. Full article

Background/Objectives: Return to work is an important goal of cardiac rehabilitation, yet individuals recovering from cardiovascular disease often face significant challenges in achieving it. As a result, a significant proportion of individuals with coronary artery disease experience work disability, negatively impacting both their economic well-being and quality of life while imposing a substantial financial burden on society. This less-studied issue is often treated as a secondary outcome in research, resulting in return to work findings that are frequently underreported. As such, this study aimed to identify the factors associated with adequate levels of functional capacity enabling the engagement in professional work and to develop a model for predicting the potential return to work of patients with coronary artery disease. Methods: During 2024, we enrolled 250 consecutive patients with chronic coronary syndrome less than 65 years of age who were referred to the National Institute for Medical Assessment and Work Capacity Rehabilitation (INEMRCM) for medical evaluation to establish eligibility-to-work disability benefits. Patients underwent a revascularization procedure either using PTCA or CABG, with a few having had no revascularization until the moment of assessment. Detailed demographic, socioeconomic, and clinical data were collected via interviews. Logistic regression was used to develop a multivariable model for predicting return to work. Results: Six months after discharge from the INEMRCM, around 20% of participants had returned to work. A better functional status was determinant for individuals’ re-employment (p = 0.026) along with an absence of cardiovascular comorbidities (p = 0.045) and holding a higher-skilled occupation (p = 0.037). The multivariate analysis identified professional specialization and the absence of cardiovascular comorbidities as the strongest predictors of return to work. Conclusions: Cardiac patients with coexisting cardiovascular conditions engaged in less-specialized types of work tend to experience poorer return to work outcomes. As such, individuals in this category should be carefully assessed and prioritized in the development of targeted rehabilitation programs. Full article