Search Results (12,709)

Mountain floods, one of the most common and destructive natural disasters worldwide, pose significant challenges to disaster prevention due to their sudden onset, high destructive power, and severe localized impacts. This study proposes an innovative flash flood early warning system based on a distributed hydrological model ensemble. The main objective is to improve the prediction and early warning accuracy of flash flood disasters by integrating multi-source data and regional modeling. The system simulates flood flow and risk levels under different rainfall scenarios to provide timely warnings in mountainous areas. A case study of a heavy rainfall event in Ma Jia Natural Village, Jiangxi Province was used to validate the system’s performance. Through regionalized parameter calibration within the ensemble, the system achieved Nash–Sutcliffe Efficiency (NSE) values exceeding 0.88, while the simulated peak discharges deviated from observed values by only 1.5%, 9.5%, and 4.8% under 3 h, 6 h, and 24 h rainfall scenarios, respectively, demonstrating the improved quantitative accuracy of flood prediction enabled by the ensemble-based framework. The system showed high consistency with observed data, accurately predicting flood responses at 3, 6, and 24 h time scales and providing reliable risk warnings. This approach not only enhances warning accuracy across multiple temporal scales but also supports risk-level early warnings at both river-section and village scales, offering significant practical value for the prevention of mountainous flood disasters. Full article

Background/Objectives: Neurodegenerative diseases are driven by multiple interconnected pathological mechanisms involving both intrinsic and extrinsic molecular and cellular processes. Efficient bidirectional intracellular transport is essential for neuronal survival and function, enabling the movement of organelles, proteins, and vesicles between the neuronal soma and distal compartments. This process is primarily mediated by kinesin-dependent anterograde transport and dynein-dependent retrograde transport. Disruption of either motor protein compromises endosome–lysosome recycling, leading to cellular dysfunction and neurodegeneration. However, the mechanisms underlying motor protein impairment in Parkinson’s disease (PD) remain incompletely understood. Methods: We investigated the involvement of kinesin and dynein in intracellular transport dysfunction using both in vitro and in vivo models of PD. Cultured neuronal cells were exposed to MPP+ (1-methyl-4-phenylpyridinium) to model PD-associated neurotoxicity, and motor protein function, vesicular trafficking, and endosomal recycling were assessed. In parallel, an MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine)-induced mouse model of PD was used to evaluate dynein-positive fiber density in the spinal cord. The role of calpain-2 was examined by co-treatment with the selective calpain-2 inhibitor zLLYCH2F in both experimental systems. Results: MPP+ exposure disrupted kinesin- and dynein-mediated transport in neuronal cytoplasm, resulting in impaired vesicular trafficking and defective endosome–lysosome recycling. These alterations led to abnormal accumulation of vesicles in both perinuclear regions and at the cell periphery. Pharmacological inhibition of calpain-2 with zLLYCH2F restored motor protein function and normalized vesicle distribution in MPP+-treated cells. Consistent with in vitro findings, MPTP-treated mice exhibited a significant reduction in dynein-positive fiber density within the spinal cord, which was prevented by co-treatment with zLLYCH2F. Conclusions: Our findings demonstrate that calpain-2 activation contributes to kinesin and dynein dysfunction following MPP+/MPTP exposure, leading to impaired intracellular transport and vesicle recycling in PD models. Inhibition of calpain-2 preserves motor protein function, maintains cytoskeletal integrity, and supports normal intracellular trafficking. These results identify calpain-2 as a critical regulator of motor protein stability and suggest that targeting calpain-2 may represent a promising therapeutic strategy for mitigating intracellular transport defects in Parkinson’s disease. Full article

The increasing penetration of wind energy has led to reduced system inertia and heightened sensitivity to dynamic disturbances in modern power systems. This paper proposes a Newton–Raphson-Based Optimizer (NRBO) for tuning proportional, integral, and feedforward gains of a grid-forming converter applied to a wind energy conversion system operating in a low-inertia environment. The study considers an aggregated wind farm modeled as a single equivalent DFIG-based wind turbine connected to an infinite bus, with detailed dynamic representations of the converter control loops, synchronous generator dynamics, and network interactions formulated in the dq reference frame. The grid-forming converter operates in a grid-connected mode, regulating voltage and active–reactive power exchange. The NRBO algorithm is employed to optimize a composite objective function defined in terms of voltage deviation and active–reactive power mismatches. Performance is evaluated under two representative scenarios: small-signal disturbances induced by wind torque variations and short-duration symmetrical voltage disturbances of 20 ms. Comparative results demonstrate that NRBO achieves lower objective values, faster transient recovery, and reduced oscillatory behavior compared with Differential Evolution, Particle Swarm Optimization, Philosophical Proposition Optimizer, and Exponential Distribution Optimization. Statistical analyses over multiple independent runs confirm the robustness and consistency of NRBO through significantly reduced performance dispersion. The findings indicate that the proposed optimization framework provides an effective simulation-based approach for enhancing the transient performance of grid-forming wind energy converters in low-inertia systems, with potential relevance for supporting stable operation under increased renewable penetration. Improving the reliability and controllability of wind-dominated power grids enhances the delivery of cost-effective, cleaner, and more resilient energy systems, aiding in expanding sustainable electricity access in alignment with SDG7. Full article

Security requirements play a critical role in ensuring the trustworthiness and resilience of software systems; however, their automatic classification remains challenging due to limited labeled data, confidentiality constraints, and the heterogeneous nature of requirements across organizations. Existing approaches typically assume centralized access to training data and rely on costly manual annotation, making them unsuitable for distributed industrial settings. To address these challenges, we propose SeqFAL, a communication-efficient and privacy-preserving Federated Active Learning framework for natural language–based security requirements classification. SeqFAL integrates frozen pre-trained sentence embeddings, margin-based active learning, and lightweight federated aggregation of linear classifiers, enabling collaborative model training without sharing raw requirement text. We evaluate SeqFAL on a combined dataset of SeqReq dataset and the PROMISE-NFR dataset under varying federation sizes, query budgets, and communication rounds, and compare it against three baselines: centralized learning, active learning without federated aggregation, and federated learning without active querying. In addition to the proposed margin-based sampling strategy, we investigate alternative query strategies, including least-confidence and random sampling, as well as multiple linear classifiers such as LinearSVC and SGD-based classifiers with logistic and hinge losses. Results show that SeqFAL consistently outperforms FL-only and achieves performance comparable to AL-only centralized baselines, while approaching the optimal upper bound using significantly fewer labeled samples. These findings demonstrate that the joint integration of federated learning and active learning provides an effective and privacy-preserving strategy for security requirements classification in distributed software engineering environments. Full article

The extraction of the light stripe center is a pivotal step in line-structured light vision measurement. This paper addresses a key challenge in the online measurement of train wheel treads, where the diverse and complex profile characteristics of the tread surface lead to uneven gray-level distribution and varying width features in the stripe image, ultimately degrading the accuracy of center extraction. To solve this problem, a region-adaptive multiscale method for light stripe center extraction is proposed. First, potential light stripe regions are identified and enhanced based on the gray-gradient features of the image, enabling precise segmentation. Subsequently, by normalizing the feature responses under Gaussian kernels with different scales, the locally optimal scale parameter (σ) is determined adaptively for each stripe region. Sub-pixel center extraction is then performed using the Hessian matrix corresponding to this optimal σ. Experimental results demonstrate that under on-site conditions featuring uneven wheel surface reflectivity, the proposed method can reliably extract light stripe centers with high stability. It achieves a repeatability of 0.10 mm, with mean measurement errors of 0.12 mm for flange height and 0.10 mm for flange thickness, thereby enhancing both stability and accuracy in industrial measurement environments. The repeatability and reproducibility of the method were further validated through repeated testing of multiple wheels. Full article

Chronic obstructive pulmonary disease (COPD) is a major health burden worldwide and in Taiwan, ranking as the third leading cause of death globally, and its prevalence in Taiwan continues to rise. Readmission within 14 days is a key indicator of disease instability and care efficiency, driven jointly by patient-level physiological vulnerability (such as reduced lung function and multiple comorbidities) and healthcare system-level deficiencies in transitional care. To mitigate the growing burden and improve quality of care, it is urgently necessary to develop an AI-based prediction model for 14-day readmission. Such a model could enable early identification of high-risk patients and trigger multidisciplinary interventions, such as pulmonary rehabilitation and remote monitoring, to effectively reduce avoidable early readmissions. However, medical data are commonly characterized by severe class imbalance, which limits the ability of conventional machine learning methods to identify minority-class cases. In this study, we used real-world clinical data from multiple hospitals in Kaohsiung City to construct a prediction framework that integrates data generation and ensemble learning to forecast readmission risk among patients with chronic obstructive pulmonary disease (COPD). CTGAN and kernel density estimation (KDE) were employed to augment the minority class, and the impact of these two generation approaches on model performance was compared across different augmentation ratios. We adopted a stacking architecture composed of six base models as the core framework and conducted systematic comparisons against the baseline models XGBoost, AdaBoost, Random Forest, and LightGBM across multiple recall thresholds, different feature configurations, and alternative data generation strategies. Overall, the results show that, under high-recall targets, KDE combined with stacking achieves the most stable and superior overall performance relative to the baseline models. We further performed ablation experiments by sequentially removing each base model to evaluate and analyze its contribution. The results indicate that removing KNN yields the greatest negative impact on the stacking classifier, particularly under high-recall settings where the declines in precision and F1-score are most pronounced, suggesting that KNN is most sensitive to the distributional changes introduced by KDE-generated data. This configuration simultaneously improves precision, F1-score, and specificity, and is therefore adopted as the final recommended model setting in this study. Full article

When a fire occurs in a tunnel during construction, the smoke cannot be discharged in time and continues to settle near the ground, which threatens the safety of personnel. It is essential to understand smoke layer distribution for safe evacuation. To fill the knowledge gap for the spatio-temporal distribution of the smoke layer, a series of fire experiments are carried out in 1/20 reduced-scale tunnel models. Multiple variables are considered, including longitudinal fire location, heat release rate, aspect ratio of the main tunnel, and the inclined shaft length. Two fire scenarios are defined according to the longitudinal fire location in the main tunnel: near the upstream closed end (scenario 1) and near the downstream closed end (scenario 2). The results show that the structural evolution of the smoke layer inside the main tunnel experiences roughly three stages: single-layer smoke flow stage, transition stage, and two-layer smoke flow stage. In different fire scenarios, the reasonable N value is 10, determined by comparing the smoke layer interface height (h_s) predicted by the N-percentage method with the observed results. Moreover, we find that the FDS simulation method has significant deviation in predicting poor stratification situations. Furthermore, the spatio-temporal distributions of h_s in the main tunnel are predicted based on N = 10. The coupled effects of heat release rate and the longitudinal fire location on the h_s values are analyzed. The t_ar value (time of smoke arrival at the respiratory height) is determined, and its spatial variations are predicted. By comparing the t_ar values at position 2^# (near the inclined shaft) in different fire scenarios, we can provide a reference for the evacuation of personnel. Full article

The causes of agricultural drought are complex, and its actual occurrence process is often characterized by rapid onset in terms of time and small scale in terms of space. Monitoring agricultural drought using satellite remote sensing with low spatial resolution makes it difficult to accurately capture the details of small-scale drought events. High-resolution satellite remote sensing has relatively long revisit cycles, making it difficult to capture the rapid evolution of drought conditions. Furthermore, the occurrence of agricultural drought is linked to multiple factors including precipitation, evapotranspiration, soil properties, and crop physiological characteristics. Consequently, relying on a single variable or indicator is insufficient for multidimensional monitoring of agricultural drought. This study takes Hebi City, Henan Province as the research area. It uses Sentinel-1 satellite data (HV, VV), Sentinel-2 data (NDVI, B2, B11), elevation, slope, aspect, and GPM precipitation data from 2019 to 2024 as independent variables. Three machine learning algorithms—Random Forest (RF), Random Forest-Recursive Feature Elimination (RF-RFE), and eXtreme Gradient Boosting (XGBoost)—were employed to construct a multi-dimensional agricultural drought monitoring model at the field scale. Additionally, the study verified the sensitivity of different environmental variables to agricultural drought monitoring and analyzed the accuracy performance of different machine learning algorithms in agricultural drought monitoring. The research results indicate that under the condition of full-factor input, all three models exhibit the optimal predictive performance. Among them, the XGBoost model performs the best, with the smallest Relative Root Mean Square Error (RRMSE) of 0.45 and the highest Correlation Coefficient (R) of 0.79. The absence of Digital Elevation Model (DEM) data impairs the models’ ability to capture the patterns of key features, which in turn leads to a reduction in predictive accuracy. Meanwhile, there is a significant correlation between model performance and sample size. Ultimately, the constructed XGBoost model takes the lead with an accuracy of 89%, while the accuracies of Random Forest (RF) and Random Forest-Recursive Feature Elimination (RF-RFE) are 88% and 86%, respectively. Based on these three drought monitoring models, this study further monitored a drought event that occurred in Hebi City in 2023, presented the spatiotemporal distribution of agricultural drought in Hebi City, and applied the Mann–Kendall test for time series analysis, aiming to identify the abrupt change process of agricultural drought. Meanwhile, on the basis of the research results, the feasibility of verifying drought occurrence using irrigation signals was discussed, and the potential reasons for the significantly lower drought occurrence probability in the western mountainous areas of the study region were analyzed. Full article

To achieve accurate voltage sag prediction and early warning, thereby improving power quality, a hybrid voltage sag prediction framework is proposed by integrating Kernel Entropy Component Analysis (KECA) with an improved Northern Goshawk Optimization (NGO) algorithm for hyperparameter tuning of a CNN-LSTM model. First, to address the limitations of the original NGO, such as proneness to falling into local optima and high randomness of the initial population distribution, a refraction-opposition-based learning mechanism is introduced to enhance population diversity and expand the search space. Furthermore, a sine–cosine strategy (SCA) with nonlinear weight coefficients is integrated into the exploration phase to dynamically adjust the search step size, optimizing the balance between global exploration and local exploitation, thereby boosting convergence speed and accuracy. The improved algorithm (SCNGO) is then utilized to optimize the hyperparameters of the CNN-LSTM model. Second, KECA is applied to voltage-sag-related data to extract key features and eliminate redundant information, and the resulting dimensionally reduced data are fed as input to the SCNGO-CNN-LSTM model to further improve prediction performance. Experimental results demonstrate that the SCNGO-CNN-LSTM model outperforms other comparative models significantly across multiple evaluation metrics. Compared with NGO-CNN-LSTM, GWO-CNN-LSTM, and the original CNN-LSTM, the proposed method achieves a mean squared error (MSE) reduction of 53.45%, 44.68%, and 66.76%, respectively. The corresponding root mean squared error (RMSE) is decreased by 25.33%, 18.61%, and 36.92%, while the mean absolute error (MAE) is reduced by 81.23%, 77.04%, and 86.06%, respectively. These results confirm that the proposed framework exhibits superior feature representation capability and significantly improves voltage sag prediction accuracy. Full article

Recognizing the critical role of polar Sea Ice Concentration (SIC) in climate feedback mechanisms, this study presents the first comprehensive investigation of China’s Fengyun-3E(FY-3E) GNOS-II Global Navigation Satellite System Reflectometry (GNSS-R) for bipolar SIC retrieval. Specifically, reflected signals from multiple Global Navigation Satellite Systems (GNSS) are utilized to extract characteristic parameters from Delay Doppler Maps (DDMs). By integrating regional partitioning and dynamic thresholding for sea ice detection, a Random Forest Regression (RFR) model incorporating a rolling-window training strategy is developed to estimate SIC. The retrieved SIC products are generated at the native GNSS-R observation resolution of approximately 1 × 6 km, with each SIC estimate corresponding to an individual GNSS-R observation time. Owing to the limited daily spatial coverage of GNSS-R measurements, the retrieved SIC results are further aggregated into monthly composites for spatial distribution analysis. The model is trained and validated across both polar regions, including targeted ice–water boundary zones. Retrieved SIC estimates are compared with reference data from the OSI SAF Special Sensor Microwave Imager Sounder (SSMIS), demonstrating strong agreement. Based on an extensive dataset, the average correlation coefficient (R) reaches 0.9450 in the Arctic and 0.9602 in the Antarctic for the testing set, with corresponding Root Mean Squared Error (RMSE) of 0.1262 and 0.0818, respectively. Even in the more challenging ice–water transition zones, RMSE values remain within acceptable ranges, reaching 0.1486 in the Arctic and 0.1404 in the Antarctic. This study demonstrates the feasibility and accuracy of GNSS-R-based SIC retrieval, offering a robust and effective approach for cryospheric monitoring at high latitudes in both polar regions. Full article

China has numerous closed coal mines with abundant residual resources. The combined effect of multiple factors has driven research on gas resources in the goafs of these mines. Elucidating the characteristics of goaf gas reservoir space is crucial for analyzing gas distribution and extraction. Therefore, investigating gas reservoir space characteristics under coal seam group conditions is vital. This study uses the Xinzhuangzi closed coal mine as a case study and presents methods for calculating goaf space under both individual coal seam and coal seam group conditions. The volumes of the caving, fissure, and floor failure zones are determined. The results reveal that 14 coal seams have been mined at the Xinzhuangzi coal mine, with a total mined area of 4583.04 × 10⁴ m² across all seams. Under individual coal seam assumptions, the caving, fissure, and floor failure zones have volumes of 455.98 × 10⁶, 1648.40 × 10⁶, and 614.65 × 10⁶ m³, respectively, with a total goaf volume of 2719.03 × 10⁶ m³. Under coal seam group conditions, the caving, fissure, and floor failure zones have volumes of 438.22 × 10⁶, 871.24 × 10⁶, and 154.90 × 10⁶ m³, respectively, with a total goaf volume of 1464.36 × 10⁶ m³. The caving, fissure, floor failure zones and total volumes under coal seam group conditions are 96.11%, 52.85%, 25.20%, and 53.86% of the corresponding volumes calculated under individual coal seam assumptions. This indicates that the coal seam group goaf volumes are neither a simply the sum nor a proportional reduction in the individual coal seam goaf volumes. This study provides a concise approach for investigating goaf gas reservoir space under coal seam group conditions. Full article

Emotion recognition from physiological signals is pivotal for advancing human–computer interaction, yet unimodal pipelines frequently underperform due to limited information, constrained data diversity, and suboptimal cross-modal fusion. Addressing these limitations, the Self-Attention Wasserstein Generative Adversarial Network with Bidirectional Cross-Modal Attention (SAWGAN-BDCMA) framework is proposed. This framework reorganizes the learning process around three complementary components: (1) a Self-Attention Wasserstein GAN (SAWGAN) that synthesizes high-quality Electroencephalography (EEG) and Photoplethysmography (PPG) to expand diversity and alleviate distributional imbalance; (2) a dual-branch architecture that distills discriminative spatiotemporal representations within each modality; and (3) a Bidirectional Cross-Modal Attention (BDCMA) mechanism that enables deep two-way interaction and adaptive weighting for robust fusion. Evaluated on the DEAP and ECSMP datasets, SAWGAN-BDCMA significantly outperforms multiple contemporary methods, achieving 94.25% accuracy for binary and 87.93% for quaternary classification on DEAP. Furthermore, it attains 97.49% accuracy for six-class emotion recognition on the ECSMP dataset. Compared with state-of-the-art multimodal approaches, the proposed framework achieves an accuracy improvement ranging from 0.57% to 14.01% across various tasks. These findings offer a robust solution to the long-standing challenges of data scarcity and modal imbalance, providing a profound theoretical and technical foundation for fine-grained emotion recognition and intelligent human–computer collaboration. Full article

Background: The global increase in orthopedic implant use—both for trauma fixation and arthroplasty—has profoundly transformed musculoskeletal surgery. As a consequence, fractures occurring in the presence of implants have become more frequent and clinically relevant. Yet, these injuries are currently described using highly heterogeneous terminology, including periprosthetic (fracture occurring in the presence of a prosthetic joint replacement) peri-implant (fracture occurring around an osteosynthesis or fixation device), implant-related, and hardware-related fractures (umbrella terms encompassing both prosthetic and fixation devices, used descriptively rather than classificatorily). This coexistence of multiple, context-specific terminologies hinders clinical communication, complicates registry documentation, and limits research comparability across orthopedic subspecialties. Because fractures occurring in the presence of orthopedic implants significantly alter load transfer, muscle force distribution, and musculoskeletal biomechanics, a clear and unified terminology is also relevant for muscle-focused research addressing implant–tissue interaction and functional recovery. Objective: This systematic review aimed to critically analyze the terminology used to describe fractures influenced by orthopedic implants, quantify the heterogeneity of current usage across anatomical regions and publication periods, and explore the rationale for adopting a unified umbrella term—“artificial fracture.” Methods: A systematic search was performed in PubMed, Scopus, and Web of Science from January 2000 to December 2024, following PRISMA guidelines. Eligible studies included clinical investigations, reviews, registry analyses, and consensus statements explicitly employing or discussing terminology related to implant-associated fractures. Data were extracted on publication characteristics, anatomical site, terminology employed, and classification systems used. Quantitative bibliometric and qualitative thematic analyses were conducted to assess frequency patterns and conceptual trends. Results: Of 1142 records identified, 184 studies met the inclusion criteria. The most frequent descriptor in the literature was periprosthetic fracture (68%), reflecting its predominance in arthroplasty-focused studies, whereas broader and more practical terms such as implant-related and peri-implant fracture were more commonly used in musculoskeletal and fixation-related research. Terminological preferences varied according to anatomical site and implant type, and no universally accepted, cross-anatomical terminology was identified despite multiple consensus efforts. Discussion and Conclusions: The findings highlight persistent heterogeneity in terminology describing fractures influenced by orthopedic implants. A transversal, descriptive framework may facilitate communication across subspecialties and support registry-level harmonization. Beyond orthopedic traumatology, this approach may also benefit muscle and musculoskeletal research by enabling more consistent interpretation of data related to muscle–bone–implant interactions, rehabilitation strategies, and biomechanical adaptation. Full article

Mosquito-borne diseases, including dengue fever, chikungunya, and Zika, continue to pose a substantial global public health challenge. This is largely attributable to the absence of effective vaccines and the expanding distribution of vectors such as Aedes albopictus (Ae. albopictus). Repellents, therefore, remain a critical component of prevention strategies for disease prevention. However, existing formulations have notable limitations. Synthetic repellents such as DEET provide broad-spectrum efficacy but may raise safety concerns, especially at high concentrations. In contrast, botanical repellents, such as citronella and camphor oils, offer more favorable safety profiles but are restricted by short protection durations due to their high volatility. To overcome these drawbacks, this research developed a composite mosquito repellent through the strategic combination of DEET (5–15%), citronella oil (10–20%), and camphor oil (5–15%). This formulation leverages interactions across multiple olfactory pathways to simultaneously enhance efficacy and reduce the DEET concentration. Orthogonal experimental optimization identified an optimized formulation, Mix-3 (consisting of 15% DEET, 15% citronella oil, and 10% camphor oil in 75% ethanol), which achieved a mean complete protection time of 9.45 h. Mix-3 provided longer protection than 7% DEET (mean difference = 5.50 h, p < 0.001), 4.5% IR3535 (2.83 h, p < 0.001), 10% citronella oil (3.58 h, p < 0.001), and 15% DEET (6.50 h, p < 0.001). Catnip oil did not contribute significantly to repellency (p = 0.895). This study demonstrates that the rational combination of synthetic and botanical repellents effectively overcomes the limitations of single-agent formulations, providing a long-lasting and scalable approach for vector control. Full article

In the context of large-scale renewable energy integration driven by China’s dual-carbon goals, and under distribution network scenarios with continuously increasing shares of wind and photovoltaic generation, this paper proposes a source–grid–load–storage coordinated planning method embedded with a multi-agent game mechanism. First, the interest transmission pathways among distributed generation operators (DGOs), distribution network operators (DNOs), energy storage operators (ESOs), and electricity users are mapped, based on which a profit model is established for each stakeholder. Building on this, a coordinated planning framework for active distribution networks (DN) is developed under the assumption of bounded rationality. Through an evolutionary-game process among DGOs, DNOs, and ESOs, and in combination with user-side demand response, the model jointly determines the optimal network reinforcement scheme as well as the optimal allocation of distributed generation (DG) and energy storage system (ESS) resources. Case studies are then conducted to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the approach enables coordinated planning of DN, DG, and ESS, effectively guides users to participate in demand response, and improves both planning economy and renewable energy accommodation. Moreover, by explicitly capturing the trade-offs among multiple stakeholders through evolutionary-game interactions, the planning outcomes align better with real-world operational characteristics. Full article