Search Results (39,948)

In recent years, extreme weather events have occurred more frequently. The resulting equipment failure, renewable energy extreme output, and other extreme operation scenarios affect the smooth operation of power grids. The occurrence probability of extreme operation scenarios is small, and the occurrence frequency in historical operation data is low, which affects the modeling accuracy for scenario generation. Meanwhile, extreme operation scenarios in the form of discrete temporal data lack corresponding modeling methods. Therefore, this paper proposes a definition and generation framework for extreme power grid operation scenarios triggered by extreme weather events. Extreme operation scenario expansion is realized based on the sequential Monte Carlo sampling method and the distribution shifting algorithm. To generate equipment failure scenarios in discrete temporal data form and extreme output scenarios in continuous temporal data form for renewable energy, a Gumbel-Softmax variational autoencoder and an extreme conditional generative adversarial network are respectively proposed. Numerical examples show that the proposed models can effectively overcome limitations related to insufficient historical extreme data and discrete extreme scenario training. Additionally, they can generate improved-quality equipment failure scenarios and renewable energy extreme output scenarios and provide scenario support for power grid planning and operation. Full article

The spread of COVID-19 is influenced by multiple factors, including control policies, virus characteristics, individual behaviors, and environmental conditions, exhibiting highly complex nonlinear dynamic features. The time series of new confirmed cases shows significant nonlinearity and non-stationarity. Traditional prediction methods that rely solely on one-dimensional case data struggle to capture the multi-dimensional features of the data and are limited in handling nonlinear and non-stationary characteristics. Their prediction accuracy and generalization capabilities remain insufficient, and most existing studies focus on single-step forecasting, with limited attention to multi-step prediction. To address these challenges, this paper proposes a multi-module fusion prediction model—TSMixer-BiKSA network—that integrates multi-feature inputs, Variational Mode Decomposition (VMD), and a dual-branch parallel architecture for 1- to 3-day-ahead multi-step forecasting of new COVID-19 cases. First, variables highly correlated with the target sequence are selected through correlation analysis to construct a feature matrix, which serves as one input branch. Simultaneously, the case sequence is decomposed using VMD to extract low-complexity, highly regular multi-scale modal components as the other input branch, enhancing the model’s ability to perceive and represent multi-source information. The two input branches are then processed in parallel by the TSMixer-BiKSA network model. Specifically, the TSMixer module employs a multilayer perceptron (MLP) structure to alternately model along the temporal and feature dimensions, capturing cross-time and cross-variable dependencies. The BiGRU module extracts bidirectional dynamic features of the sequence, improving long-term dependency modeling. The KAN module introduces hierarchical nonlinear transformations to enhance high-order feature interactions. Finally, the SA attention mechanism enables the adaptive weighted fusion of multi-source information, reinforcing inter-module synergy and enhancing the overall feature extraction and representation capability. Experimental results based on COVID-19 case data from Italy and the United States demonstrate that the proposed model significantly outperforms existing mainstream methods across various error metrics, achieving higher prediction accuracy and robustness. Full article

Telemedicine in emergency contexts presents unique challenges, particularly in multilingual and low-resource settings where accurate, clinical understanding and triage decision support are critical. This paper introduces TriagE-NLU, a novel multilingual natural language understanding system designed to perform both semantic parsing and clinical intervention classification from emergency dialogues. The system is built on a federated learning architecture to ensure data privacy and adaptability across regions and is trained using TriageX, a synthetic, clinically grounded dataset covering five languages (English, Spanish, Romanian, Arabic, and Mandarin). TriagE-NLU integrates fine-tuned multilingual transformers with a hybrid rules-and-policy decision engine, enabling it to parse structured medical information (symptoms, risk factors, temporal markers) and recommend appropriate interventions based on recognized patterns. Evaluation against strong multilingual baselines, including mT5, mBART, and XLM-RoBERTa, demonstrates superior performance by TriagE-NLU, achieving F1 scores of 0.91 for semantic parsing and 0.89 for intervention classification, along with 0.92 accuracy and a BLEU score of 0.87. These results validate the system’s robustness in multilingual emergency telehealth and its ability to generalize across diverse input scenarios. This paper establishes a new direction for privacy-preserving, AI-assisted triage systems. Full article

The synergistic development of ecosystems and socioeconomic systems constitutes a critical foundation for achieving Sustainable Development Goals (SDGs). Large river basins characterized by ecological and socioeconomic spatial heterogeneity frequently present contradictions and conflicts in regional sustainable development, thereby impeding the realization of SDGs. This study employed the Yellow River Basin (YRB), a typical large sediment-laden river system, as a case study. Based on the secondary water resource zones, the spatial variability and temporal evolution of ecosystem service value (ESV), population (POP), GDP, nighttime light (NTL), and Human Development Index (HDI) were analyzed at the water resource partition scale. A consistent mode was applied to quantify the spatiotemporal consistency between ESV and socioeconomic indicators across water resource partitions. The results indicated that from 1980 to 2020, the ESV of the YRB increased from 1079.83 × 10⁹ to 1139.20 × 10⁹ yuan, with no notable spatial pattern variation. From upstream to downstream, the population density, GDP per unit area, and NTL per unit area displayed increasing trends along the river course, whereas the total population, GDP, and NTL initially increased and then declined. Temporally, the population fluctuated with an overall upward tendency, while GDP and NTL experienced significant growth. The spatial distribution and temporal evolution of HDI remained comparatively stable. The coefficients of variation for population, GDP, and NTL were significantly higher than those for ecosystem services and HDI. The study highlighted an overall lack of coordination between ESV and socioeconomic development in the YRB, with relatively stable spatial patterns. These findings could offer a theoretical reference for the formulation of policies to enhance the synergistic development of ecosystems and socioeconomic systems in the YRB. Full article

As the largest freshwater lake in China, Poyang Lake plays a crucial role in hydrological processes. Conventional models often fail to capture the time-lagged relationships between meteorological drivers and runoff responses, while lacking regional generalization capability. To address these limitations, this study proposes a novel XAJ-LSTM-TFM hybrid model that accounts for time-lagged hydrological responses and enhances the regional applicability of the Xinanjiang model. The model innovatively integrates the physical mechanisms of the Xinanjiang model with the temporal learning capacity of LSTM networks. By incorporating intermediate hydrological variables (including interflow and groundwater flow) along with 1–3 day lagged meteorological features, the model achieves an average 15.3% improvement in Nash–Sutcliffe Efficiency (NSE) across five sub-basins, with the Ganjiang Basin attaining an NSE of 0.812 and a 25.7% reduction in flood peak errors. The results demonstrate superior runoff simulation performance and reliable generalization capability under intensive anthropogenic activities. Full article

This study explores the application of spiking neural networks (SNNs) for click-through rate (CTR) prediction in personalized online advertising systems, introducing a novel hybrid model, the Temporal Rate Spike with Attention Neural Network (TRA–SNN). By leveraging the biological plausibility and energy efficiency of SNNs, combined with attention-based mechanisms, the TRA–SNN model captures temporal dynamics and rate-based patterns to achieve performance comparable to state-of-the-art Artificial Neural Network (ANN)-based models, such as Deep & Cross Network v2 (DCN-V2) and FinalMLP. The models were trained and evaluated on the Avazu and Digix datasets, using standard metrics like AUC-ROC and accuracy. Through rigorous hyperparameter tuning and standardized preprocessing, this study ensures fair comparisons across models, highlighting SNNs’ potential for scalable, sustainable deployment in resource-constrained environments like mobile devices and large-scale ad platforms. This work is the first to apply SNNs to CTR prediction, setting a new benchmark for energy-efficient predictive modeling and opening avenues for future research in hybrid SNN–ANN architectures across domains like finance, healthcare, and autonomous systems. Full article

With the increasing application of sodium-ion batteries in energy storage systems, accurate state of charge (SOC) estimation plays a vital role in ensuring both available battery capacity and operational safety. Traditional Kalman-filter-based methods often suffer from limited model expressiveness or oversimplified physical assumptions, making it difficult to balance accuracy and robustness under complex operating conditions, which may lead to unreliable estimation results. To address these challenges, this paper proposes a hybrid framework that combines an unscented Kalman filter (UKF) with a long short-term memory (LSTM) neural network for SOC estimation. Under various driving conditions, the UKF—based on a second-order equivalent circuit model with online parameter identification—provides physically interpretable estimates, while LSTM effectively captures complex temporal dependencies. Experimental results under CLTC, NEDC, and WLTC cycles demonstrate that the proposed LSTM-UKF approach reduces the mean absolute error (MAE) by an average of 2% and the root mean square error (RMSE) by an average of 3% compared to standalone methods. The proposed framework exhibits excellent adaptability across different scenarios, offering a precise, stable, and robust solution for SOC estimation in sodium-ion batteries. Full article

This research investigated the temporal dynamics of the anthropogenic impact of fishing pressure on the resilience of the fish species Sarotherodon melanotheron (Rüppel, 1858) in the African Lake Toho, located in southwest Benin. The sampling and analysis of monthly length frequency data were conducted from April 2002 to March 2003 and from April 2022 to March 2023 using the FAO-ICLARM Stock Assessment Tool (FiSAT II software program (version 1.2.2.). The analysis of the S. melanotheron population in Lake Toho revealed a significantly diminishing resilience potential, reflected mainly in general reductions in both the average size and weight of individuals. There was a notable reduction in the size of Sarotherodon melanotheron individuals caught between 2002–2003 and 2022–2023, reflecting the increased pressure on juvenile size classes. Catches are now concentrated mainly on immature fish, revealing increasing exploitation before sexual maturity is reached. An analysis of maturity stages showed a decrease in the percentage of mature individuals in the catches (69.27% in 2002–2003 compared to 55.07% in 2022–2023) and a reduction in the number of mega-spawners (4.53% in 2002–2003 compared to 1.56% in 2022–2023). Growth parameters revealed a decrease in asymptotic length (from 32.2 cm to 23.8 cm) and longevity (from 9.37 years to 7.89 years), while the growth coefficient slightly increased. The mean size at first capture and optimal size significantly declined, indicating increased juvenile exploitation. The total and natural mortalities increased, whereas the fishing mortality remained stable. The exploitation rate remained high, despite a slight decrease from 0.69 to 0.65. Finally, the declines in the yield per recruit, maximum sustainable yield, and biomass confirm the increasing fishing pressure, leading to growth overfishing, recruitment overfishing, reproductive overfishing, and, last but not least, a decreasing resilience potential. These findings highlight the growing overexploitation of S. melanotheron in Lake Toho, compromising stock renewal, fish population resilience, sustainability, and production while jeopardizing local food safety. Full article

Reservoir group flood control scheduling decision-making faces multiple uncertainties, such as dynamic fluctuations of evaluation indicators and conflicts in weight assignment. This study proposes a risk analysis model for the decision-making process: capturing the temporal uncertainties of flood control indicators (such as reservoir maximum water level and downstream control section flow) through the Long Short-Term Memory (LSTM) network, constructing a feasible weight space including four scenarios (unique fixed value, uniform distribution, etc.), resolving conflicts among the weight results from four methods (Analytic Hierarchy Process (AHP), Entropy Weight, Criteria Importance Through Intercriteria Correlation (CRITIC), Principal Component Analysis (PCA)) using game theory, defining decision-making risk as the probability that the actual safety level fails to reach the evaluation threshold, and quantifying risks based on the First-Order Second-Moment (FOSM) method. Case verification in the cascade reservoirs of the Qiantang River Basin of China shows that the model provides a risk assessment framework integrating multi-source uncertainties for flood control scheduling decisions through probabilistic description of indicator uncertainties (e.g., Zmax1 with μ = 65.3 and σ = 8.5) and definition of weight feasible regions (99% weight distribution covered by the 3σ criterion), filling the methodological gap in risk quantification during the decision-making process in existing research. Full article

In the era of precision medicine, effective data visualisation plays a pivotal role in supporting clinical decision-making by translating complex, multidimensional datasets into intuitive and actionable insights. This paper explores the foundational principles of visual perception, with a specific focus on pre-attentive attributes such as colour, shape, size, orientation, and spatial position, which are processed automatically by the human visual system. Drawing from cognitive psychology and perceptual science, we demonstrate how these attributes can enhance the clarity and usability of medical visualisations, reducing cognitive load and improving interpretive speed in high-stakes clinical environments. Through detailed case studies and visual examples, particularly within the field of oncology, we highlight best practices and common pitfalls in the design of dashboards, nomograms, and interactive platforms. We further examine the integration of advanced tools—such as genomic heatmaps and temporal timelines—into multidisciplinary workflows to support personalised care. Our findings underscore that visually intelligent design is not merely an aesthetic concern but a critical factor in clinical safety, efficiency, and communication, advocating for user-centred and evidence-based approaches in the development of health data interfaces. Full article

With the exponential growth of engineering monitoring data, data-driven neural networks have gained widespread application in predicting retaining structure deformation in foundation pit engineering. However, existing models often overlook the spatial deflection correlations among monitoring points. Therefore, this study proposes a novel deep learning framework, CGCA (Convolutional Gated Recurrent Unit with Cross-Attention), which integrates ConvGRU and cross-attention mechanisms. The model achieves spatio-temporal feature extraction and deformation prediction via an encoder–decoder architecture. Specifically, the convolutional structure captures spatial dependencies between monitoring points, while the recurrent unit extracts time-series characteristics of deformation. A cross-attention mechanism is introduced to dynamically weight the interactions between spatial and temporal data. Additionally, the model incorporates multi-dimensional inputs, including full-depth inclinometer measurements, construction parameters, and tube burial depths. The optimization strategy combines AdamW and Lookahead to enhance training stability and generalization capability in geotechnical engineering scenarios. Case studies of foundation pit engineering demonstrate that the CGCA model exhibits superior performance and robust generalization capabilities. When validated against standard section (CX1) and complex working condition (CX2) datasets involving adjacent bridge structures, the model achieves determination coefficients (R²) of 0.996 and 0.994, respectively. The root mean square error (RMSE) remains below 0.44 mm, while the mean absolute error (MAE) is less than 0.36 mm. Comparative experiments confirm the effectiveness of the proposed model architecture and the optimization strategy. This framework offers an efficient and reliable technical solution for deformation early warning and dynamic decision-making in foundation pit engineering. Full article

A novel multi-scale copula-based model predictive control (MPC) method is proposed to address the core regulation challenges of runoff hydropower and distributed photovoltaic systems within high-penetration renewable energy grids. Complex spatio-temporal complementarity under ecological constraints and the limitations of conventional methods were critically analyzed. The core innovation lies in integrating copula theory with MPC, enabling adaptive spatio-temporal optimization and weight adjustment to significantly enhance the efficiency of complementary regulation and overcome traditional performance bottlenecks. Key nonlinear dependencies of water–solar resources were investigated, and mainstream techniques (copula analysis, MPC, rolling optimization, adaptive weighting) were evaluated for their applicability. Future directions for improving modeling precision and intelligent adaptive control are outlined. Full article

Successfully detecting ground deformation, especially landslides, using InSAR has not always been possible. Improvements to existing InSAR tools are needed to address this issue. This study develops and evaluates two novel approaches that use multidimensional InSAR products to detect surface displacements in the landslide-prone region of Büyükalan, Antalya. Multi-temporal InSAR analysis of Sentinel-1 data (2015–2020) is performed using LiCSAR–LiCSBAS, followed by two novel approaches: multi-dimensional InSAR research and analysis (MIRA) and Crosta’s InSAR application (InCROSS). Cumulative LOS velocity maps reveal deformation rates of −1.1 cm/year to 1.0 cm/year for descending tracks and −3.8 cm/year to 3.8 cm/year for ascending tracks. Vertical displacements range from −1.9 cm/year to 2.3 cm/year and east–west components from −2.8 cm/year to 2.9 cm/year. MIRA uses an n-Dimensional Visualizer and SVM classifier to identify deformation clusters, and InCROSS applies PCA to enhance deformation features. MIRA increases the deformation detection capacity compared to conventional InSAR products, and InCROSS integrates these products. A comparison of the results reveals 80.48% consistency between them. Overall, the integration of InSAR with statistical and multidimensional analysis significantly enhances the detection and interpretation of ground deformation patterns in landslide-prone areas. Full article

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article

This study evaluates the human health risks associated with exposure to arsenic in groundwater from a semi-arid region of Mexico, focusing on concentration levels and their temporal variation. Arsenic concentrations were analyzed using ordinary kriging for spatial interpolation, along with descriptive and inferential statistical methods. Human health risk was assessed through the following two key indicators: the Hazard Quotient (HQ), which estimates non-carcinogenic risk by comparing exposure levels to reference doses and carcinogenic risk (CR), which represents the estimated lifetime probability of developing cancer due to arsenic exposure. The mean arsenic concentration across both study years was 0.0200 mg/L, with median values of 0.0151 mg/L in 2015 and 0.0200 mg/L in 2020. The average HQ was 2.13 in 2015 and 2.17 in 2020, both exceeding the safety threshold of one. Mean CR values were 0.00096 and 0.00097 for 2015 and 2020, respectively, with a consistent median of 0.00072 across both years. A t-test was applied to compare the distributions between years. Both HQ and CR values significantly exceeded the recommended safety limits (p < 0.05), indicating that groundwater in the study area poses a potential carcinogenic and non-carcinogenic health risk. These findings underscore the urgent need for water quality monitoring and the implementation of mitigation measures to safeguard public health in the region. Full article