Search Results (212)

The 2020 southern Puerto Rico earthquake sequence highlighted the severe seismic vulnerability of informally constructed two-story reinforced concrete (RC) houses. This study examines the failure mechanisms of these structures and assesses the effectiveness of first-floor RC shear-wall retrofitting. Nonlinear pushover and dynamic time–history analyses were performed using fiber-based distributed plasticity models for RC frames and nonlinear macro-elements for second-floor masonry infills, which introduced a significant inter-story stiffness imbalance. A bi-directional seismic input was applied using spectrally matched, near-fault pulse-like ground motions. The findings for the as-built structures showed that stiffness mismatches between stories, along with substantial strength and stiffness differences between orthogonal axes, resulted in concentrated plastic deformations and displacement-driven failures in the first story—consistent with damage observed during the 2020 earthquakes. Retrofitting the first floor with RC shear walls notably improved the performance, doubling the lateral load capacity and enhancing the overall stiffness. However, the retrofitted structures still exhibited a concentration of inelastic action—albeit with lower demands—shifted to the second floor, indicating potential for further optimization. Full article

Background: The waist–calf circumference ratio (WCR) is an index that combines waist and calf circumference measurements, offering a potentially effective method for evaluating the imbalance between abdominal fat and leg muscle mass in older adults. Objective: To assess the association between WCR and indicators of body composition, muscle strength, and physical performance in community-dwelling older women. Methods: This was a cross-sectional study involving 133 older women (≥65 years) from an urban-marginal community in Guayaquil, Ecuador. The WCR was categorized into quartiles (Q1: 2.07–2.57; Q2: 2.58–2.75; Q3: 2.76–3.05; Q4: 3.06–4.76). Body indicators included fat-free mass (FFM), skeletal muscle mass (SMM), appendicular muscle mass (ASM), appendicular muscle mass index (ASMI), visceral fat (VF), fat mass (FM), and fat mass index (FMI). Handgrip strength (HGS) and the Short Physical Performance Battery test (SPPB) score were used to assess muscle strength and function, respectively. Results: The median age of the participants was 75 [IQR: 65–82] years. The mean WCR was 2.92 ± 0.93. Statistically significant associations were found between WCR and VF (p < 0.001), WCR and SMM (p = 0.039), and WCR and ASM (p = 0.016). Regarding muscle function, WCR was associated with HGS (p = 0.025) and SPPB score (p = 0.029). Conclusions: A significant association was observed between WCR and body composition, and muscle strength and function in older women. Full article

Internal crack defects in high-strength low-alloy (HSLA) steels during continuous casting pose significant challenges to downstream processing and product reliability. However, due to the inherent class imbalance in industrial defect datasets, conventional machine learning models often suffer from poor sensitivity to minority class instances. This study proposes a predictive framework that integrates conditional tabular generative adversarial network (CTGAN) for synthetic minority sample generation and CatBoost for classification. A dataset of 733 process records was collected from a continuous caster, and 25 informative features were selected using mutual information. CTGAN was employed to augment the minority class (crack) samples, achieving a balanced training set. Feature distribution analysis and principal component visualization indicated that the synthetic data effectively preserved the statistical structure of the original minority class. Compared with the other machine learning methods, including KNN, SVM, and MLP, CatBoost achieved the highest metrics, with an accuracy of 0.9239, precision of 0.9041, recall of 0.9018, and F1-score of 0.9022. Results show that CTGAN-based augmentation improves classification performance across all models. These findings highlight the effectiveness of GAN-based augmentation for imbalanced industrial data and validate the CTGAN–CatBoost model as a robust solution for online defect prediction in steel manufacturing. Full article

This study aimed to compare isometric strength, range of motion (RoM), and strength ratios of shoulder internal and external rotators between female volleyball and hand ball players Twenty-five volleyball players (age = 21.8 ± 4.8 years, height = 178.5 ± 7.1 cm, mass = 69.3 ± 7.7 kg) and twenty-four handball players (age = 19.5 ± 2.9 years, height = 169.7 ± 6.4 cm, mass = 67.6 ± 8.4 kg), all competing in the Slovenian 1st national league, participated. Maximal isometric strength and passive RoM of internal and external rotation were measured bilaterally using a handheld dynamometer and goniometer, respectively. A significant group × side interaction was observed for internal rotation RoM (F = 5.41; p = 0.024; η² = 0.10), with volleyball players showing lower RoM on the dominant side (p = 0.001; d = 0.89), but this was not the case for handball players (p = 0.304). External rotation strength also showed a significant interaction (F = 9.34; p = 0.004; η² = 0.17); volleyball players were stronger in the non-dominant arm (p = 0.033), while handball players were stronger in the dominant arm (p = 0.041). The external-to-internal rotation strength ratio was significantly lower on the dominant side in volleyball players compared to handball players (p = 0.047; d = 0.59). Findings suggest sport-specific adaptations and asymmetries in shoulder function, emphasizing the need for sport-specific and individually tailored injury prevention strategies. Volleyball players, in particular, may benefit from targeted strengthening of external rotators and flexibility training to address imbalances. Full article

In recent years, the classification of underwater images has become one of the most remarkable areas of research in computer vision due to its useful applications in marine sciences, aquatic robotics, and sea exploration. Underwater imaging is pivotal for the evaluation of marine eco-systems, analysis of biological habitats, and monitoring underwater infrastructure. Extracting useful information from underwater images is highly challenging due to factors such as light distortion, scattering, poor contrast, and complex foreground patterns. These difficulties make traditional image processing and machine learning techniques struggle to analyze images accurately. As a result, these challenges and complexities make the classification difficult or poor to perform. Recently, deep learning techniques, especially convolutional neural network (CNN), have emerged as influential tools for underwater image classification, contributing noteworthy improvements in accuracy and performance in the presence of all these challenges. In this paper, we have proposed a modified ResNet-18 model for the binary classification of underwater images into raw and enhanced images. In the proposed modified ResNet-18 model, we have added new layers such as Linear, rectified linear unit (ReLU) and dropout layers, arranged in a block that was repeated three times to enhance feature extraction and improve learning. This enables our model to learn the complex patterns present in the image in more detail, which helps the model to perform the classification very well. Due to these newly added layers, our proposed model addresses various complexities such as noise, distortion, varying illumination conditions, and complex patterns by learning vigorous features from underwater image datasets. To handle the issue of class imbalance present in the dataset, we applied a data augmentation technique. The proposed model achieved outstanding performance, with 96% accuracy, 99% precision, 92% sensitivity, 99% specificity, 95% F1-score, and a 96% Area under the Receiver Operating Characteristic Curve (AUC-ROC) score. These results demonstrate the strength and reliability of our proposed model in handling the challenges posed by the underwater imagery and making it a favorable solution for advancing underwater image classification tasks. Full article

Amid the global shift toward clean energy, wind power has emerged as a critical pillar of the modern energy matrix. To improve the reliability and maintainability of wind farms, this work proposes a novel hybrid fault detection approach that combines expert-driven diagnostic knowledge with data-driven modeling. The framework integrates autoencoder-based neural networks with Failure Mode and Symptoms Analysis, leveraging the strengths of both methodologies to enhance anomaly detection, feature selection, and fault localization. The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. The approach adopts a fault-specific modeling strategy, in which each turbine and failure mode is associated with a customized autoencoder. The methodology was first validated using OpenFAST 3.5 simulated data with induced faults comprising normal conditions and a 1% mass imbalance fault on a blade, enabling the verification of its effectiveness under controlled conditions. Subsequently, the methodology was applied to a real-world SCADA data case study from wind turbines operated by EDP, employing historical operational data from turbines, including thermal measurements and operational variables such as wind speed and generated power. The proposed system achieved 99% classification accuracy on simulated data detect anomalies up to 60 days before reported failures in real operational conditions, successfully identifying degradations in components such as the transformer, gearbox, generator, and hydraulic group. The integration of FMSA improves feature selection and fault localization, enhancing both the interpretability and precision of the detection system. This hybrid approach demonstrates the potential to support predictive maintenance in complex industrial environments. Full article

Background and Objectives: This study aims to investigate the relationship between isokinetic knee strength and competition performance in elite male ski mountaineering sprint athletes and to identify strength parameters that predict performance and contribute to injury prevention. Materials and Methods: Thirteen male athletes participating in the Ski Mountaineering Turkey Cup final stage were included. Isokinetic knee flexion (FLX) and extension (EXT) strength of dominant (DM) and non-dominant (NDM) legs were measured at angular velocities of 60°/s and 180°/s using the DIERS-Myolin Isometric Muscle Strength Analysis System. Competition performance was evaluated using the ISMF scoring system. Data were analyzed using SPSS 26.0 with Pearson correlation and multiple regression analyses after normality, linearity, and homoscedasticity checks. Results: Strong positive correlations were found between hamstring strength at high angular velocities (180°/s) and performance (DM FLX: r = 0.809; NDM FLX: r = 0.880). Extension strength showed moderate correlations at low velocities (60°/s) (DM EXT: r = 0.677; NDM EXT: r = 0.699). Regression analysis revealed that DM FLX at 180°/s and DM EXT at 60°/s explained 49% of performance variance (Adj. R² = 0.498). For NDM legs, only 180°/s FLX was a significant predictor (β = 1.468). Conclusions: High-velocity hamstring strength plays a critical role in ski mountaineering sprint performance, particularly during sudden directional changes and dynamic balance. Quadriceps strength at low velocities contributes to prolonged climbing phases. Moreover, identifying and addressing bilateral strength asymmetries may support injury prevention strategies in elite ski mountaineering athletes. These findings provide scientific support for designing training programs targeting explosive hamstring strength, bilateral symmetry, and injury risk reduction, essential for optimizing performance in the 2026 Winter Olympics sprint discipline. Full article

Rural areas are experiencing significant changes in socio-economic and spatial patterns, and research on the characteristics of rural restructuring is conducive to the planning of rural revitalization. However, few studies have focused on the changes in regional development imbalances in the process of rural restructuring. This study aims to explore whether rural restructuring mitigates or exacerbates existing regional disparities, and to assess the degree of coordination among economic, social, and spatial restructuring dimensions. In this study, the evolution of spatio-temporal patterns and divergence characteristics of unevenly developed regions in the process of rural restructuring from 2010 to 2020 were investigated by using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model and the coupled coordination model. We found the following: (1) The level of rural development has increased significantly and the overall pattern has not changed. Meanwhile, the degree of regional imbalance has deepened, evolving from a low level of disequilibrium to a pattern of high levels but more pronounced spatial polarization. (2) The impacts of different dimensions of rural restructuring on regional imbalance are not consistent, and the social and spatial dimensions are significantly more unbalanced than the economic dimension. (3) The analysis of the driving mechanism shows that there are significant spatial and temporal differences between a variety of driving factors, the strength of their role, positive and negative have evolved in stages, and the transition from a government-led to a market-driven trend is gradually obvious. In the future, rural planning should pay more attention to resource inputs in the social and spatial dimensions, and improve the equilibrium of the social and spatial dimensions, which is more conducive to mitigating the trend of regional polarization. Full article

With the rapid development of remote sensing technology, satellites can now capture multispectral (MS) and panchromatic (PAN) images simultaneously. MS images offer rich spectral details, while PAN images provide high spatial resolutions. Effectively leveraging their complementary strengths and addressing modality gaps are key challenges in improving the classification performance. From the perspective of deep learning, this paper proposes a novel dual-source remote sensing classification framework named the Diversity Extraction and Fusion Classifier (DEFC-Net). A central innovation of our method lies in introducing a modality-specific intra-class diversity modeling mechanism for the first time in dual-source classification. Specifically, the intra-class diversity identification and splitting (IDIS) module independently analyzes the intra-class variance within each modality to identify semantically broad classes, and it applies an optimized K-means method to split such classes into fine-grained sub-classes. In particular, due to the inherent representation differences between the MS and PAN modalities, the same class may be split differently in each modality, allowing modality-aware class refinement that better captures fine-grained discriminative features in dual perspectives. To handle the class imbalance introduced by both natural long-tailed distributions and class splitting, we design a long-tailed ensemble learning module (LELM) based on a multi-expert structure to reduce bias toward head classes. Furthermore, a dual-modal knowledge distillation (DKD) module is developed to align cross-modal feature spaces and reconcile the label inconsistency arising from modality-specific class splitting, thereby facilitating effective information fusion across modalities. Extensive experiments on datasets show that our method significantly improves the classification performance. The code was accessed on 11 April 2025. Full article

Powdery mildew is one of the most common diseases affecting strawberry yield and quality. Accurate and timely detection is essential to reduce pesticide usage and labor costs. However, recognizing strawberry powdery mildew in complex field environments remains a significant challenge. In this study, an HSV-based image segmentation method was employed to enhance the extraction of disease regions from complex backgrounds. A total of 14 widely used deep learning models—including SqueezeNet, GoogLeNet, ResNet-50, AlexNet, and others—were systematically evaluated for their classification performance. To address sample imbalance, data augmentation was applied to 2372 healthy and 553 diseased leaf images, resulting in 11,860 training samples. Experimental results showed that InceptionV4, DenseNet-121, and ResNet-50 achieved superior performance across metrics such as accuracy, F1-score, recall, and loss. Models such as MobileNetV2, AlexNet, VGG-16, and InceptionV3 demonstrated certain strengths, and models like SqueezeNet, VGG-19, EfficientNet, and even ResNet-50 showed room for further improvement in performance. These findings demonstrate that CNN models originally developed for other crop diseases can be effectively adapted to detect strawberry powdery mildew under complex conditions. Future work will focus on enhancing model robustness and deploying the system for real-time field monitoring. Full article

Accurate fish species identification is crucial for marine biodiversity conservation, environmental monitoring, and sustainable fishery management, particularly as marine ecosystems face increasing pressures from human activities and climate change. Traditional morphological identification methods are inherently labor-intensive and resource-demanding, while contemporary automated approaches, particularly deep learning models, often suffer from significant computational overhead and struggle with the pervasive issue of class imbalance inherent in ecological datasets. Addressing these limitations, this research introduces a novel computationally parsimonious fish classification framework leveraging the hybrid Mobile–Former neural network architecture. This architecture strategically combines the local feature extraction strengths of convolutional layers with the global context modeling capabilities of transformers, optimized for efficiency. To specifically mitigate the detrimental effects of the skewed data distributions frequently observed in real-world fish surveys, the framework incorporates a sophisticated robust asymmetric loss function designed to enhance model focus on under-represented categories and improve resilience against noisy labels. The proposed system was rigorously evaluated using the comprehensive FishNet dataset, comprising 74,935 images distributed across a detailed taxonomic hierarchy including eight classes, seventy-two orders, and three-hundred-forty-eight families, reflecting realistic ecological diversity. Our model demonstrates superior classification accuracy, achieving 93.97 percent at the class level, 88.28 percent at the order level, and 84.02 percent at the family level. Crucially, these high accuracies are attained with remarkable computational efficiency, requiring merely 508 million floating-point operations, significantly outperforming comparable state-of-the-art models in balancing performance and resource utilization. This advancement provides a streamlined, effective, and resource-conscious methodology for automated fish species identification, thereby strengthening ecological monitoring capabilities and contributing significantly to the informed conservation and management of vital marine ecosystems. Full article

To address the challenges of low displacement efficiency and gas channeling in the Lukqin thick oil reservoir, characterized by high viscosity (286 mPa·s) and strong heterogeneity (permeability contrast 5–10), this study systematically investigated water flooding and foam flooding mechanisms using a large-scale 2D sandpack model (5 m × 1 m × 0.04 m). Experimental results indicate that water flooding achieves only 30% oil recovery due to a mobility ratio imbalance (M = 128) and preferential channeling. In contrast, foam flooding enhances recovery by 15–20% (final recovery: 45%) through synergistic mechanisms of dynamic high-permeability channel plugging and mobility ratio optimization. By innovatively integrating electrical resistivity tomography with HSV color mapping, this work achieves the first visualization of foam migration pathways in meter-scale heterogeneous reservoirs at a spatial resolution of ≤0.5 cm, reducing monitoring costs by approximately 30% compared to conventional CT techniques. Key controlling factors for gas channeling (injection rate, foam quality, permeability contrast) are identified, and a nonlinear predictive model for plugging strength ((S = 0.70C^0.6 k_r^−0.28) (R² = 0.91)) is established. A composite optimization strategy—combining high-concentration slugs (0.7% AOS), salt-resistant polymer-enhanced foaming, and multi-round profile control—achieves a 67% reduction in gas channeling. This study elucidates the dynamic plugging mechanisms of foam flooding in heterogeneous thick oil reservoirs through large-scale physical simulations and data fusion, offering direct technical guidance for optimizing foam flooding operations in the Lukqin Oilfield and analogous reservoirs. Full article

In the context of global population aging, identifying reliable, objective tools to assess physical function and postural stability in older adults is increasingly important to mitigate fall risk. This study presents a non-contact platform that uses a Microsoft Azure Kinect depth camera to evaluate functional performance related to lower-limb muscular capacity and static balance through self-selected depth squats and four progressively challenging stances (feet apart, feet together, semitandem, and tandem). By applying markerless motion capture algorithms, the system provides key biomechanical parameters such as center of mass displacement, knee angles, and sway trajectories. A comparison of older and younger individuals showed that the older group tended to perform shallower squats and exhibit greater mediolateral and anteroposterior sway, aligning with age-related declines in strength and postural control. Longitudinal tracking also illustrated how performance varied following a fall, indicating potential for ongoing risk assessment. Notably, in 30 s balance trials, the first 10 s often captured meaningful differences in stability, suggesting that short-duration stance tests can reliably detect early signs of imbalance. These findings highlight the feasibility of low-cost, user-friendly depth-camera technologies to complement traditional clinical measures and guide targeted fall-prevention strategies in older populations. Full article

The bioeconomy and biotechnology sectors present transformative opportunities for sustainable development by harnessing biological resources and promoting innovation. This study investigates the potential for bilateral collaboration between Colombia and China, highlighting their complementary strengths: Colombia’s remarkable biodiversity and China’s advanced technological capabilities and policy frameworks. This article aimed to analyze the current landscape of bioeconomy and biotechnology in both countries, identify key areas for cooperation, evaluate regulatory frameworks, and propose strategies to strengthen bilateral efforts. This paper combines a qualitative approach with an extensive literature review, secondary data analysis, and case studies. The findings indicate that Colombia’s rich biodiversity offers significant opportunities in bioprospecting, biofuels, and agricultural biotechnology. Meanwhile, China’s expertise in bioeconomic innovation can facilitate technological advancements and capacity building. However, these opportunities remain despite challenges such as trade imbalances, regulatory gaps, and cultural differences. Collaborative initiatives focused on bioplastics, bioenergy, and circular economy principles have the potential to diversify Colombia’s exports and enhance its global competitiveness. This study emphasizes that integrating Colombia’s natural resources with China’s technological advancements has the potential to drive innovation, improve participation in global value chains, and foster sustainability. Effective governance, inclusive policies, and strategic investments are crucial to fully realizing this partnership’s transformative potential in tackling global challenges like climate change and food security. Full article

Homologous recombination deficiency (HRD) is a key biomarker associated with increased sensitivity to PARP inhibitors (PARPi) in advanced epithelial ovarian cancer. Accurate identification of HRD status is essential for selecting patients most likely to benefit from these therapies. Current diagnostic approaches combine sequencing to detect mutations in homologous recombination repair genes—particularly BRCA1 and BRCA2—with genome-wide analysis of structural genomic alterations indicative of HRD. This review briefly outlines the biological basis of HRD and its clinical significance and then focuses on currently available assays for HRD assessment. We compare their molecular strategies, including the use of targeted gene panels and genomic instability metrics such as loss of heterozygosity, telomeric allelic imbalance, and large-scale state transitions. The review also highlights the strengths and limitations of each platform and discusses their role in guiding clinical decision-making. Challenges related to dynamic tumor evolution and the interpretation of HRD status in recurrent disease settings are also addressed. Full article