Search Results (309)

Forecasting fine particulate matter with a diameter of 2.5 μm (PM_2.5) is critically important due to its adverse effects on human health and environmental sustainability. Although ground-based monitoring stations provide accurate measurements, their limited spatial coverage restricts large-scale PM_2.5 assessment, especially in complex urban regions. Consequently, aerosol optical depth (AOD) derived from satellite imagery, combined with advanced deep learning (DL) techniques, has emerged as an effective alternative by offering wide spatial coverage and rich spatio-temporal information. This paper proposed an optimization-driven fuzzy transformer–deep belief network (ODFT-DBN) for accurate PM_2.5 air pollution prediction. The proposed framework integrates a fuzzy inference module to model uncertainty and nonlinear environmental relationships, a transformer encoder to capture long-range spatio-temporal dependencies, and a DBN to extract hierarchical features and improve prediction robustness. In addition, a novel multi-objective gray wolf optimizer (NMOGWO) is employed to jointly optimize the model hyper-parameters and fuzzy membership functions. The proposed approach is implemented for the city of Tehran, Iran, using meteorological variables, topographical features, ground-based PM_2.5 measurements, and satellite-derived AOD data. The ODFT-DBN model is compared with several benchmark methods, including bidirectional encoder representations from transformers (BERT), transformer, long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), DBN, and extreme gradient boosting (XGBoost). Experimental results demonstrate that the proposed framework achieves superior predictive performance, attaining an R² value of 0.94 and root mean square error (RMSE) of 0.8 μg/m³. Scatter plot analyses indicate a strong agreement between predicted and observed PM_2.5 values, while the proposed model exhibits low variance, stable convergence behavior, and acceptable computational time. Overall, the results confirm the effectiveness, robustness, and practical applicability of the proposed ODFT-DBN framework for spatio-temporal PM_2.5 forecasting. Full article

U.S. renewable energy consumption has expanded substantially over the past five decades, but this transition cannot be adequately characterized by aggregate growth alone. This study developed an integrated empirical, forecasting, uncertainty, reconciliation, scenario, and planning framework for U.S. renewable energy consumption using a complete monthly multisector panel from January 1973 through December 2025. The analytic dataset contained 3180 sector–month observations across 636 monthly periods and five reporting sectors: Commercial, Electric Power, Industrial, Residential, and Transportation. The framework combined data harmonization, mutually exclusive source-family construction, long-run trend analysis, source-mix diversification metrics, structural-regime diagnostics, sector–source panel analysis, rolling-origin forecast benchmarking, probabilistic interval assessment, hierarchical reconciliation, future scenario analysis, and decision-focused planning evaluation. Annual reported total renewable energy consumption increased from 2475.547 trillion Btu in 1973 to 7050.214 trillion Btu in 2025, equivalent to approximately 2.476 quadrillion Btu and 7.050 quadrillion Btu, respectively. The results show that U.S. renewable energy growth was also a source-mix transformation: the portfolio became less concentrated as wind, solar, transportation biofuels, renewable diesel, waste, and other emerging sources gained importance alongside legacy wood and hydroelectric power. Sector–source heterogeneity was substantial, with Electric Power, Industrial, and Transportation showing distinct renewable-source profiles. Forecasting performance depended strongly on model family, horizon, validation window, target group, and evaluation lens. Strong statistical baselines and feature-based tree models remained competitive or superior to several deep learning architectures, while time-series foundation models provided useful modern comparators but required calibration and horizon-specific interpretation. All five selected foundation model comparators completed successfully. ChronosBolt was the fastest and strongest completed foundation model comparator, followed in runtime by TimesFM, Moirai/Uni2TS, TimeGPT, and LagLlama; however, foundation model forecasts remained too smooth for peak-sensitive planning and did not displace the strongest feature-based tree models in point-forecast benchmarking. Probabilistic diagnostics showed that nominal coverage alone was insufficient because interval width, Winkler score, CRPS, and visual inspection revealed target-specific miscalibration, underforecast bias, and weak peak coverage. Hierarchical and decision-focused evaluation changed the model-selection narrative: bottom-up and reconciled hierarchical forecasts produced stronger planning-loss and planning-value profiles than many nominally advanced alternatives, while selected tree-based models were particularly useful for preserving source-share allocation. Scenario analysis showed that solar acceleration increased projected totals but also increased concentration and coherence divergence, whereas diversification reduced concentration but required wider uncertainty buffers. Overall, U.S. renewable energy consumption should be analyzed as a dynamic, diversified, hierarchical, and planning-sensitive system. The proposed framework provides a reproducible basis for evaluating renewable energy growth, source-mix evolution, forecast reliability, uncertainty, source allocation, scenario trade-offs, and planning value beyond single-model forecasting claims. Full article

This paper investigates the interaction and price discovery mechanisms among the day-ahead, intraday, and balancing segments of the Greek wholesale electricity market under the European Target Model, emphasizing their contribution to a sustainable and flexible energy transition. Using a Vector Error Correction Model with exogenous variables (VECMX), hourly data from 2023 to September 2025 are analyzed, incorporating key system fundamentals and regime-dependent dynamics. The results reveal a hierarchical market structure in which the day-ahead market dominates long-run price discovery, the intraday market acts as a short-run adjustment mechanism, and the balancing market reflects real-time system conditions associated with renewable energy variability and system reliability. Forecast Error Variance Decomposition shows that day-ahead shocks explain most long-run price variation, while balancing market effects are mainly transitory. Cointegration analysis confirms stable long-run relationships among market segments, with imbalance prices anchored to forward market outcomes and moderated by intraday adjustments. Robustness tests based on alternative recursive orderings and Generalized Impulse Response Functions (GIRFs) confirm the stability of the results and the dominant role of the day-ahead market in price discovery. The findings have important policy implications for market design and sustainability, highlighting the role of integrated day-ahead, intraday, and balancing markets in supporting renewable energy integration, system flexibility, and the transition toward a resilient low-carbon electricity system. The Greek electricity market is gradually evolving toward a mature and resilient Target Model structure capable of supporting higher renewable energy penetration, improved operational flexibility, and enhanced market efficiency within the European decarbonization framework. Full article

Vertically linked commodity markets—global futures, regional spot, and farm-gate prices—transmit information through directed cross-market channels whose strength varies with latent volatility regimes. Standard deep learning forecasters absorb both the directed cross-market dependence and the regime dependence of intrinsic-mode-aligned latent components into shared model weights, with no explicit architectural mechanism that exposes either as an inspectable structure. This paper proposes HVB-RA, a modular framework that combines two such mechanisms with a per-tier Variational Mode Decomposition and bidirectional LSTM backbone: (i) a directed cross-market attention layer in which the upstream-to-downstream topology is supplied from domain knowledge and the time-varying upstream-source attention intensities at the farm-gate tier (the regional-spot tier, with a single upstream key, reduces algebraically to a fixed residual upstream fusion) are extracted from data, and (ii) a regime-informed modal-weighting layer that mixes two trainable softmax weight profiles over IMF-aligned latent components through a filtered Markov-switching state probability fitted in a separate stage. An auxiliary post hoc projection enforces an exact linear constraint defined by long-run sample-mean ratios across tiers; the paper does not claim that these descriptive ratios are cointegrating relations or equilibrium coefficients. The framework is evaluated on three tiers of daily natural-rubber prices spanning 2038 trading days, against three external benchmarks (random walk, ARIMA(2,0,2), and an exogenous-only LSTM) and a contemporary neural hierarchical-interpolation forecaster (NHITS). Root mean squared error is reported per tier-horizon cell; a decision-aware income-smoothing metric quantifies the operational value of $h=5$ farm-gate forecasts under a 5-day selling rule; and a within-method comparison evaluates the marginal contribution of the auxiliary constraint projection. On the present single-regime test window, HVB-RA attains a lower point error than the contemporary NHITS baseline at every tier-horizon cell, while no method—including HVB-RA—improves on the random-walk floor at most cells; the regime-conditional components of the architecture are not identifiable because every calibration and test origin is classified as a high-volatility regime by the trained Markov-switching model. The paper contributes to machine learning and knowledge extraction by demonstrating how time-varying upstream-source attention intensities at the farm-gate tier and regime-dependent latent-component-weight profiles—two forms of latent structure typically absorbed into model weights—can be exposed as explicit, inspectable, and individually testable components of a multi-tier forecasting architecture, and by providing a reproducibility package documenting the conditions under which each component is expected to be identifiable. Full article

Power data terminals deployed in smart-grid environments generate massive amounts of operational data, yet the sensitive nature of these data and the existence of cross-region silos make centralized model training difficult in practice. Federated learning offers a natural alternative by enabling collaborative model optimization without transferring raw data, but its direct use in power terminal scenarios is still limited by four coupled challenges: update leakage, malicious or abnormal client behavior, constrained terminal-side resources, and severe Non-IID data heterogeneity. To address these issues, we develop SFL-PDT, a hierarchical federated learning framework tailored to power data terminals. The proposed method is built on a server–edge–terminal architecture. Within this architecture, edge nodes aggregate terminal updates from relatively homogeneous regional groups and perform local robustness screening, while the central server aggregates edge-level updates across heterogeneous regions and coordinates the privacy budget schedule for protected updates. It combines adaptive privacy-aware update perturbation, robust suppression of suspicious regional updates, terminal-oriented update compression, and similarity-guided aggregation for heterogeneous data distributions. Experiments on two representative power-system tasks, load forecasting and fault diagnosis, demonstrate that SFL-PDT achieves a superior overall balance among privacy protection, robustness, efficiency, and predictive performance. Compared with the evaluated baselines, the proposed method more effectively reduces reconstruction-related leakage under different privacy budgets, lowers leakage similarity under gradient inversion attacks, and maintains robust performance when malicious clients participate. It also converges faster and more stably under heterogeneous data partitions. In addition, SFL-PDT achieves the best overall predictive results, reaching an MAE of 0.021 for load forecasting and an accuracy of 88.2% for fault diagnosis, while reducing average terminal-side local training time from 4.3 s to 2.9 s and per-round upload volume from 4.2 MB to 1.5 MB relative to FedAvg. These results suggest that SFL-PDT is a practical solution for secure, efficient, and heterogeneity-aware collaborative learning in power data terminal environments. Full article

This study proposes VulnPattern-TKG, an end-to-end temporal knowledge graph framework that forecasts the emergence of CVE-derived vulnerability-pattern relations from Common Vulnerabilities and Exposures (CVE) descriptions. The framework does not aim to predict the real-world exploitation of individual CVEs; instead, it models how standardized relations among Weakness Factor (WF), Exploitation Outcome (EO), and Exploitation Prerequisite (EP) categories evolve over time in vulnerability disclosure text. It processes 205,600 National Vulnerability Database (NVD) CVE descriptions from 2014 to 2024 using a hybrid pipeline combining SecureBERT+CRF-based entity extraction, dependency-parsing-based relation rules, and four-stage hierarchical standardization. The resulting compact Knowledge Layer contains 26 standardized category nodes and 48,371 confidence-filtered triples. VulnTEC is a lightweight confidence- and time-weighted Node2Vec graph embedding framework that ranks relation-compatible candidate tails using cosine similarity over shared node embeddings. An internal four-component priority-score framework, integrating prediction confidence, temporal rise, exploitation-prerequisite prevalence-risk proxy, and extraction confidence, supports an analyst-side review of the forecasted relations. Under the novel-only triggers evaluation, VulnTEC achieves a mean MRR of 0.410 ± 0.020; however, the theoretical random baseline already reaches 0.408 because the candidate tail space contains only six EO categories. The results are interpreted as directional ranking evidence, and query-level Top-K results are reported only as descriptive analyst-side review evidence. Full article

This study evaluates the 20-year operational performance (2006–2025) of a preseason prediction system for Atlantic hurricane activity developed at North Carolina State University (NCSU) and compares it with forecasts from Colorado State University (CSU), Tropical Storm Risk (TSR), and NOAA. Unlike previous studies based primarily on hindcast experiments, this analysis uses real-time forecasts generated under evolving model configurations, providing a realistic assessment of operational forecast skill. Results show that NCSU April forecasts exhibit lower mean absolute error than other April-issued forecasts and achieve performance comparable to later-issued forecasts from NOAA and CSU, indicating that improved model formulation can partially offset the advantage of later initialization. To identify the sources of forecast improvement, regression and ensemble analyses are conducted. Forecast adjustments between early- and late-season forecasts are primarily explained by changes in tropical North Atlantic sea surface temperature (SST), while ENSO contributes secondarily as forecast uncertainty decreases beyond the spring predictability barrier. These results establish a clear hierarchy of predictors, with Atlantic SST providing the dominant source of preseason predictability. Multi-model ensemble experiments further show that simple averaging does not outperform the best individual models; instead, selective combinations yield the highest skill, with optimal configurations differing between named storm and hurricane predictions, demonstrating that forecast improvement depends on combining complementary information rather than increasing ensemble size. Forecast performance is also shown to be predictand-dependent, with named storm counts more sensitive to late-spring environmental evolution and hurricane counts more strongly constrained by basin-scale thermodynamic conditions. Despite these advances, all models exhibit reduced skill during extreme seasons, reflecting the intrinsic limits of seasonal predictability. Overall, these results demonstrate that preseason hurricane forecast skill is governed by the interaction of forecast timing, model capability, and a hierarchical structure of environmental predictors, providing a unified framework for interpreting differences among forecasting systems and guiding future model development. Full article

Accelerated electric vehicle (EV) adoption imposes complex requirements on grid integration and energy dispatch. Current Vehicle-to-Vehicle (V2V) trading research frequently utilizes monolithic forecasting architectures that fail to account for the stochastic nature of mobility data. Furthermore, traditional optimization strategies often prioritize financial yields at the expense of user-centric utilities, hindering global system optimality. To resolve these limitations, this paper proposes a hierarchical optimization framework, designed to reconcile the interests of stakeholders. The approach first employs a hybrid deep learning architecture, integrating long short-term memory (LSTM), gated recurrent unit (GRU), and Transformer architectures, dynamically weight predictions and refine available dwell time estimations. Then, a multi-objective optimization model is formulated to identify Pareto-optimal solutions that balance economic efficiency with user convenience. Finally, a dynamic greedy matching algorithm is introduced to facilitate rapid transaction pairing for large-scale, real-time V2V requests under multiple constraints. Simulation results demonstrate that this hierarchical framework improves trading success rates, optimizes resource distribution, and enhances overall user satisfaction. Full article

To address the demand for high-precision, high-efficiency, and standardized hydrographic information in intelligent shipping, this study systematically investigates key technologies for high-resolution hydrographic data parsing and intelligent information services. Focusing on the East China Sea, a space–air–ground integrated monitoring data access system is established. A hybrid data assimilation method combining four-dimensional variational (4D-Var) and ensemble Kalman filter is adopted to realize quality control, deep fusion, and optimal state estimation of multi-source heterogeneous hydrographic observations. A hybrid tidal harmonic response model is further developed to improve the refined forecasting accuracy of tide levels and ocean currents. A hierarchically decoupled system architecture is designed, and modules for data production, sharing, exchange, and visualization are developed in compliance with the international S-100 standard. By integrating hybrid spatiotemporal indexing, multi-level caching, and intelligent query optimization, the system achieves low-latency and high-concurrency service capabilities. Experimental results show that, compared with conventional models, the proposed framework reduces tidal forecast RMSE by approximately 15.8% under extreme weather, raises the continuity index of current vectors to 0.93, and cuts the S-100 product generation latency to less than 30 s. This research establishes a full-chain technical system from data parsing and product generation to intelligent services, providing a reliable technical support platform for ship intelligent navigation, dynamic route planning, and maritime safety assurance. Full article

Systematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain. In recent years, several brain-inspired deep learning architectures, such as long short-term memory networks, graph neural networks, and convolutional neural networks, have been introduced for intelligent decision-making tasks. From a biomimetic perspective, these models are inspired by biological information-processing mechanisms. Convolutional neural networks reflect hierarchical procedures similar to those in the visual cortex, graph neural networks mimic communication among biological neurons, and LSTM networks are motivated by short-term and long-term memory mechanisms in the brain. Inspired by these biomimetic computational principles, this study proposes a novel hybrid deep learning strategy composed of LSTM, convolutional layers and GraphSAGE geometric layers for smart supply chain logistics management. This strategy enables leveraging information pertaining to LSTM-based long-term dependencies, convolutional local patterns and graph-related hidden connections of the supply chain dataset for intelligent decision-making. The GraphSAGE framework helps with scalable graph learning, which enhances predictive accuracy in the case of unseen data. The optimizer in the proposed methodology performs sequential optimization using the biomimetic particle swarm optimizer and the Adam approach (PSO-Adam), considering the hybrid cost function. The prediction of logistics parameters is investigated using five datasets, including DataCo, Shipping, Smart Logistics, Hospital Supply Chain, and Pharmaceutical Supply Chain. The average accuracies of 97.8%, 100%, 96.6%, 98.7% and 99.4% are obtained for practical multi-category logistics parameter forecasts. The evaluation metrics for ten logistics predictions confirm the effectiveness of the proposed intelligent logistics model and highlight the potential of biomimetic geometric networks for complex supply chain decision-making. The model is a cost-efficient approach with consideration of the prediction capabilities, helping to reduce the occurrence of logistics risks, increase the productivity of the supply chain and affect the supply chain visibility, customer satisfaction, and industry reputation. Full article

Electric Vehicle Charging Stations (EVCSs) have become increasingly important due to the growing penetration of electric vehicles (EVs) and renewable-based power generation. However, challenges such as fluctuating renewable energy availability, increasing charging demand, power losses, operational cost, and charging delays continue to affect overall grid performance and stability. To address these issues, this study proposes a hybrid Goat Optimization Algorithm–Hierarchical Multi-Granularity Interaction Graph Convolutional Network (GOA–HMGIGCN) framework for intelligent smart grid energy management and EV charging coordination. The proposed framework combines the Goat Optimization Algorithm (GOA) for optimal EVCS placement and charging scheduling with the Hierarchical Multi-Granularity Interaction Graph Convolutional Network (HMGIGCN) for forecasting renewable generation, charging demand, and load variations. The framework was implemented and evaluated in MATLAB/Simulink R2024a using the IEEE 14-bus smart grid test system under varying operating conditions. Simulation results demonstrated that the proposed framework achieved superior performance compared with the Coot Optimization Algorithm–Fractional Backpropagation Physics-Informed Neural Network (COA-FBPINN), Dingo Optimization Algorithm–Convolutional Hypergraph Graph Neural Network (DOA-CHGNN), Self-Feedback Feedforward Artificial Neural Network (SFFANN), Deep Neural Network (DNN), and Golden Jackal Optimization–Attention-Based Probabilistic Convolutional Neural Network (GJO-APCNN) techniques by attaining the lowest operational cost of USD 1561, the highest efficiency of 99.2%, the minimum power loss of 10.6 kW, and the shortest charging time of 32 min. In addition, the proposed framework and overall grid reliability, confirming its effectiveness for intelligent renewable-integrated smart grid applications. Full article

Clarifying the mutual feedback relationship and coordinated evolution characteristics between low-carbon transportation (LCT) and high-quality economic development (HQED) is of great significance for the green transformation of resource-based and ecologically fragile urban agglomerations. Taking 18 cities in the Yellow River Jiziwan Metropolitan Area as the research objects, this paper constructs an evaluation indicator system for LCT and HQED based on panel data from 2013 to 2022, and comprehensively applies the ISM-MICMAC model, a modified coupling coordination degree model, a gravity model, an obstacle degree model, and a combined GM-ARIMA forecasting model to analyze the interaction relationships, spatiotemporal evolution, spatial correlations, and scenario differences between the two systems. The results indicate that: (1) A hierarchical mutual feedback relationship exists between LCT and HQED, in which the relevant factors exhibit a hierarchical association within the system structure, extending from basic input, transportation supply, and economic operation to green and low-carbon outcomes. (2) During the study period, the comprehensive development levels of the two systems generally improved, with the mean coupling coordination degree rising from 0.4374 in 2013 to 0.4702 in 2022, remaining overall at a borderline coordination stage, while inter-city divergence was relatively pronounced. (3) The spatial connection network gradually exhibited multi-node linkage characteristics, yet strong connections remained concentrated in a few core cities. (4) Scenario predictions reveal that the synergistic development scenario is most conducive to enhancing the coupling coordination level, and the differences among scenarios gradually widen after 2026. Simultaneously advancing LCT and HQED is an important pathway to enhance the regional synergy level of the Yellow River Jiziwan Metropolitan Area. Full article

The efficient management of power systems requires balancing electricity generation costs with associated environmental emissions under dynamically varying demand. This paper proposes a two-stage approach that combines machine learning (ML) with a metaheuristic optimization algorithm to address the dynamic economic–environmental load dispatch (DEELD) challenge. In the first stage, electricity consumption data are enriched with temporal features to capture demand patterns and enable accurate forecasting. In the second stage, the daily scheduling horizon is divided into multiple periods, and dispatch solutions are generated sequentially while enforcing ramp-rate constraints. To enhance operational realism, a priority-based generator scheduling mechanism is explicitly introduced, enforcing hierarchical unit commitment and reflecting practical dispatch policies. Rather than focusing on a single optimal solution, the proposed framework generates multiple feasible dispatch solutions and evaluates them using economic, environmental, and operational performance indicators. These solutions are then ranked according to predefined decision profiles, enabling system operators to select dispatch strategies that align with specific priorities. This transforms the dispatch process into a flexible decision-support tool capable of addressing diverse real-world requirements. Full article

Accurate traffic flow prediction is fundamental to Intelligent Transportation Systems (ITSs), critical for transportation management and logistics. Despite advances in spatio-temporal prediction methods, existing approaches suffer from two key limitations: (i) multi-scale fusion methods inadequately capture hierarchical constraints between cross-scale features, and (ii) models rely on single spatio-temporal views, neglecting multi-source relationship complementarity. To address these issues, we propose ST-MAFNet, a spatio-temporal multi-scale adaptive fusion network comprising three key components, specifically, a Cross-Scale Hierarchical Anchoring strategy (CSHA) that anchors short-term predictions with multi-scale temporal patterns to mitigate noise; a Dual Spatial Perception Module (DSPM) that learns node heterogeneity and dynamic correlations through node embeddings and adaptive graph attention; and a Spatio-Temporal Adaptive Fusion Module (STAFM) that captures time-varying connectivity by integrating multi-scale temporal features with multi-source spatial relationships. Experiments on four real-world datasets demonstrate that ST-MAFNet is particularly effective for short-term traffic forecasting. Compared with the best previously reported MAE results, ST-MAFNet reduces MAE by 2.95%, 1.43%, 1.25%, and 0.37% on PEMS03, PEMS04, PEMS07, and PEMS08, respectively, and achieves the best or second-best performance on most evaluation metrics. Full article

Intensive mining over recent decades has caused severe ground subsidence in mining regions, threatening safety and long-term sustainability. High-precision, continuous monitoring and prediction of subsidence are therefore urgently needed. Traditional methods—terrestrial surveying and GPS—offer limited coverage, sparse measurement points, high costs, and poor scalability, making them unsuitable for large-scale, long-term surface deformation monitoring. InSAR is widely used for ground deformation monitoring due to its wide-area coverage, long-term sampling, high spatial resolution, and millimeter-scale precision. However, conventional InSAR often fails in vegetated areas and under steep deformation gradients—common in mining zones. To overcome these limitations, this study applied SBAS-InSAR, a method better suited for large-magnitude, continuous subsidence monitoring in mining areas. This study proposed an enhanced hierarchical spatiotemporal dependency graph neural network (HSDGNN) integrated with a Long Short-Term Memory (LSTM) module to improve temporal feature representation. Using this model, this study predicted surface subsidence at the Dexing Copper Mine under environmental drivers. Key findings are as follows: (1) Surface subsidence exhibited pronounced spatial heterogeneity and strong temporal nonlinearity; major subsidence zones were localized in open-pit excavation areas and waste rock dumps, with peak subsidence rates reaching −126.121 mm/yr. (2) Precipitation and soil moisture emerged as the dominant environmental controls on subsidence, displaying distinct seasonal modulation and quantifiable lagged responses—up to several months—relative to subsidence onset. (3) The HSDGNN model achieved high predictive accuracy for both Mine 1 and Mine 2, attaining R² values of up to 0.9950. This work establishes a robust, scalable, and operationally viable framework for high-precision subsidence monitoring and forecasting in geologically and anthropogenically complex mining environments. Full article