Search Results (874)

Against the backdrop of the “dual-carbon” goals, the penetration of distributed photovoltaics (PV) in distribution networks continues to increase. Under the combined effects of stochastic PV generation and load variability, assessing PV hosting capacity in distribution networks under fault conditions has become increasingly challenging. To address the limited consideration of uncertainty and fault conditions in existing studies, this paper proposes a stochastic optimization framework for evaluating PV hosting capacity under fault conditions that considers load uncertainty and mobile shared energy storage (MSES). First, historical data are used to generate representative uncertainty scenarios through Latin hypercube sampling. An optimization model is then formulated subject to secure distribution network operation constraints to determine the optimal capacities and locations of distributed PV installations. Meanwhile, MSES is incorporated to further enhance PV hosting capacity through flexible spatial and temporal energy transfer. Finally, simulations on the modified IEEE 33-bus system demonstrate that MSES can effectively increase PV hosting capacity and renewable energy accommodation. However, under fault conditions, changes in voltage profiles and the emergence of reverse power flows further reduce PV hosting capacity. Although incorporating load uncertainty increases the system cost, the resulting PV hosting capacity is more representative of practical operating conditions, thereby providing quantitative decision-making support for the planning and operation of distribution networks with high PV penetration. Full article

Rice is a staple grain crop central to China’s food security. As the core production region of single-cropping rice, the middle and lower reaches of the Yangtze River face escalating high daytime and nighttime temperatures and compound drought–heat stress amid global warming. The accurate assessment of heat hazards is therefore pivotal for regional yield stability and disaster mitigation. Based on meteorological, remote-sensing, and soil data, together with county-level rice yield statistics from 150 major producing counties spanning 1991 to 2024 (5009 county-year calibration units), we first constructed a composite heat damage index (CHI) by integrating daytime harmful accumulated temperature (Ha), nighttime harmful accumulated temperature (HNa), and the Vegetation Health Index (VHI). We then implemented a gradient boosting decision tree (GBDT) machine learning framework in which yield loss was imposed as a physical constraint. This framework was benchmarked against convolutional neural network (CNN), random forest (RF), and support vector machine (SVM) models, with the Shapley additive explanations (SHAP) method used for attribution analysis and an independent temporal partitioning strategy applied for model validation. The results indicate the following: (1) compared to the single daytime heat damage index, the CHI elevated the yield correlation coefficient from 0.52 to 0.63; (2) with yield constraint calibration, the model attained a balanced accuracy of 92.6% and 94.0% consistency with historical disaster records; (3) regional heat hazard presents a spatial pattern of “high in inland areas and low in coastal areas,” with the heading–flowering stage as the critical sensitive period; and (4) high nighttime temperature accounts for approximately 20% of the model’s relative importance, with higher discriminative sensitivity for high-grade hazards, while the amplifying effect of water deficit on heat stress maintains a stable relative importance of around 16%. In this study, the coupled optimization of traditional assessment paradigms and data-driven approaches is achieved, providing a methodological reference for refined growth stage–specific heat hazard assessment. Its cross-regional portability and independent predictive validity require further validation. Full article

This study proposes a surrogate model, Graph-Structured Physics-Informed Deep Operator Network (GS-PI-DeepONet), to simulate two-dimensional hydrodynamics in a tidal river network. The model was coupled with Graph Convolutional Networks (GCNs) for spatial feature extraction and Long Short-Term Memory (LSTM) network for temporal prediction, with 2D shallow-water equations (2D SWEs) embedded as physical constraints. To handle complex river network topologies, a mapping mechanism was proposed to transform discrete irregular boundaries into differentiable neural network constraints. A dynamic weighting strategy was developed to improve model training efficiency. GS-PI-DeepONet was applied to a river network within the Pearl River Basin in Zhuhai. Trained on high-fidelity Delft3D data, it achieved precise flow field reconstruction and millisecond-level extrapolation predictions, outperforming traditional data-driven models. The model can be a valuable tool for real-time hydrodynamic simulations and flood management strategies in tidal river networks. Full article

Coastal humid-heat and photochemical-oxidant episodes are commonly studied through concentration estimation, leaving it unclear whether satellite observations improve warning decisions under explicit false-alarm constraints. This study introduces CoAST-EWS Japan, a six-station, validation-locked hindcast benchmark across Osaka Bay and Tokyo Bay. Models were developed using June–July 2023 data, calibrated and thresholded on August 2023 predictions, and retrospectively evaluated on June–August 2025 station-hour observations. The strong non-satellite route combines recent ground history, ERA5 meteorology, CAMS composition, and static station geometry. Adding previous-day MODIS thermal context to an otherwise identical XGBoost route increased average precision from 0.3153 to 0.3429, reduced the Brier score from 0.05032 to 0.04874, and improved recall/F1 under a validation-locked budget of 0.5 false alarms per station-day (FPDs) from 0.1864/0.2511 to 0.2402/0.3042. Japan-local calendar-day intervals supported the improvements in Brier score, recall, and F1. In a dimension-matched comparison using the same 18 MODIS variables, previous-day context increased average precision over the same-day route by 0.0378 (95% CI: 0.0144–0.0603), demonstrating that the timing advantage was not attributable to a larger satellite feature set. The MODIS increment was strongest during high-heat issue times and in Osaka Bay, and its ranking value was reproduced by a 36 h Temporal FLOW model. Matched spatial controls identified distance-based coastal context as the most stable 24 h graph component, while wind-aligned information operated as a complementary route. These results establish latency-aware MODIS thermal context as a measurable decision input for neighborhood-scale coastal compound warning. Strict station-level localization, cross-bay transfer, and forecast-consistent deployment define the next validation frontier. Full article

A Transmission Control Protocol (TCP) throughput map represents communication quality over road networks and supports communication-aware applications in intelligent transportation systems. Maintaining such a map online is challenging because vehicular measurements are sparse and unevenly distributed, network conditions vary rapidly, and sensing-budget constraints limit the number of vehicles from which onboard communication measurements can be uploaded at each time step. This work addresses online TCP throughput map maintenance under sparse vehicular observations and sensing-budget constraints. To support budget-constrained sensing, we combine discoverability-guided vehicle selection and probabilistic map updating within a digital twin (DT)-assisted vehicular sensing architecture. The resulting sensing-and-mapping method, referred to as Discoverability-aware and Statistical Mapping (DISMAP), maintains a spatio-temporal discoverability map to characterize historical sensing coverage and select vehicles that improve the coverage of under-represented regions. It then uses Gaussian Process Regression (GPR) as a probabilistic mapping engine to estimate the mean TCP throughput and predictive standard deviation, where the standard deviation is adjusted using local vehicle density. Simulation results show that DISMAP reduces the mean absolute error (MAE) and mean standard deviation (MSTD) by up to 23.7% and 37.5%, respectively, and achieves a prediction-interval miss rate (PIMR) of 0.048, which is close to the nominal value of 0.05. These results indicate a favorable balance among prediction accuracy, interval sharpness, calibration, and spatial representativeness across different traffic-density conditions. Full article

Multi-site water quality sequences are jointly affected by seasonal periodicity, meteorological disturbances, and inter-site differences in the Jianghuai Watershed region. Conventional quality prediction models struggle to simultaneously achieve multi-scale feature extraction, spatial heterogeneity characterization, and prediction uncertainty expression. This study used daily-scale monitoring data on dissolved oxygen (DO), pH, and ammonia nitrogen (NH3N) from 32 monitoring stations within the region in 2025 and proposed the FT-TransONet (Fourier-enhanced Temporal Transformer Operator Network) multi-site probabilistic water quality prediction model. Within a Transformer framework, the model employed a FourierTime learnable frequency-domain filtering module, a GeoBias (Geographic Bias) attention bias mechanism, and a multi-residual ensemble strategy composed of a multilayer perceptron (MLP), a gated recurrent unit (GRU), and a temporal convolutional network (TCN) combined with a mass conservation constraint, thereby achieving both point and interval prediction of key water quality indicators. The results showed that FT-TransONet achieved the lowest Macro_RMSE among all compared methods on the multi-site water quality prediction task. At a prediction horizon of three days, its Macro_RMSE reached 0.2255, which was 21.89% lower than that of the long short-term memory network and 5.57% lower than that of the strongest baseline MC-Dropout. For the three individual indicators, the model attained coefficients of determination of 0.9135, 0.9338, and 0.8660 for dissolved oxygen, pH, and ammonia nitrogen, with corresponding root-mean-square errors of 0.5145, 0.1098, and 0.0523, confirming its potential to characterize the temporal variation in the main water quality indicators. Under multi-step prediction, the error grew gently, with the Macro_RMSE rising only from 0.2255 to 0.2384 as the horizon extended from three to seven days, and the ablation experiments, together with the probabilistic prediction results, further supported the effectiveness of the proposed structural design. Validated on 32 water quality monitoring stations in the Jianghuai Watershed, the method improved multi-site prediction accuracy while accounting for stability and uncertainty quantification, providing a preliminary reference for regional water quality early warning and management. Full article

Historic buildings occupy a sensitive position in contemporary energy and heritage management because they combine building-performance requirements, material-conservation constraints, and dynamic patterns of use. This study maps the broader historic-building literature at the intersection of energy-efficient restoration, building decarbonization, heritage conservation, and sustainable tourism, and interprets the resulting bibliometric structure with particular implications for historic stone places of worship. Records were retrieved from the Web of Science Core Collection and screened through a PRISMA 2020-aligned protocol, yielding 804 publications. Accordingly, the 804-record corpus represents a broad historic-building and heritage-management evidence base rather than an exhaustive dataset restricted exclusively to stone religious buildings. Performance analysis and science mapping were conducted using Bibliometrix, while bibliographic coupling and longitudinal thematic-evolution analyses were added to examine contemporary research fronts and their temporal continuity. Co-citation analysis identified two partially separated intellectual schools—European conservation microclimate and adaptive reuse management—whereas bibliographic coupling revealed five current research-front communities, including adaptive reuse and sustainability, microclimate-conservation-energy, and an emerging HBIM/digital-documentation front. Thematic evolution showed increasing post-2019 convergence of tourism and management with conservation and cultural-heritage research, but continued separation from technical energy and microclimate streams. The study therefore moves beyond descriptive rankings to identify structural fragmentation and three priority directions: integrated monitoring of visitor loads, energy, and conservation performance; HBIM-supported spatial integration; and stronger cross-national research collaboration. The findings are interpreted as bibliometric evidence and research-agenda signals rather than as causal proof of building or policy performance. Full article

Unstable rock blocks and cliffs pose a widespread geohazard, and their mechanical state can be tracked through their ambient-vibration resonance frequencies, whose decrease anticipates progressive failure. Such monitoring is usually performed with expensive broadband instrumentation, limiting spatial and temporal coverage. Here we assess whether a low-cost, IoT-enabled node—built around a Raspberry Pi single-board computer, a 24-bit sigma-delta digitiser and a force- balance accelerometer (Geobit FBA-200)—can identify the resonance of an unstable coastal rock block at the celebrated “moving rock” of Kounopetra (Paliki peninsula, Kefalonia, Greece), a site historically renowned for visually perceptible rocking boulders. We stress that the low-amplitude 7.7 Hz structural eigenvibration characterised here is a distinct phenomenon from the historically documented ∼0.3 Hz macroscopic, quasi-rigid rocking of the boulder: the former is the ambient–vibration resonance of the fractured rock mass, the latter a large-amplitude rigid-body oscillation. Two identical nodes recorded ground acceleration simultaneously for nine hours: one on the fractured Kounopetra rock mass and one on stable ground 25 m away, used as a reference. The rock station exhibits a clear, temporally stable fundamental resonance at f₀ = 7.7 Hz (Q ≈ 50, damping ζ ≈ 1%), amplified by up to an order of magnitude relative to the reference and entirely absent from it, whereas a narrow 20.5 Hz line present on both nodes is identified as instrument-related and discarded. A simultaneous two-station analysis further shows that the ambient sources are extremely local (only 0.4% of transient activity is common to the two nodes 25 m apart), quantifying a design constraint for differential schemes. The results demonstrate that a low-cost force-balance node is sufficient to establish a resonance baseline for an unstable rock block, opening the way to dense, affordable early-warning networks; the main limitations are the single vertical component and the short record, which preclude polarisation analysis and long-term tracking of f₀. Full article

In continuous sign language recognition (CSLR), existing methods predominantly adopt frame-wise feature extraction, neglecting temporal continuity and motion trajectory modeling, thereby struggling to capture complete spatio-temporal dynamics. Meanwhile, global cross-modal attention approaches typically directly model interactions between text and the entire video sequence but lack structural constraints, making them susceptible to interference from redundant frames, which leads to attention distribution dilution and undermines fine-grained motion alignment capability. To address these issues, this paper proposes CMST-Net, a Cross-modal Interaction and Spatio-temporal Feature Enhancement Method for Continuous Sign Language Recognition. CMST-Net comprises two principal modules: the Local–Global Cross-modal Fusion Module (LGCFM) and the Spatio-Temporal Feature Enhancement Module (STFEM). LGCFM introduces a synergistic modeling mechanism that combines local sliding-window attention with global attention, achieving a unified fusion of structured local alignment and global semantic modeling. STFEM incorporates multi-scale spatial dilated convolution and coordinate attention to extract fine-grained spatial features while leveraging channel partitioning and a hierarchical residual structure to enhance long-range temporal modeling capability; the two modules collaboratively yield high-quality spatio-temporal feature representations. Experiments on three public benchmark datasets (PHOENIX2014, PHOENIX2014-T, and CSL-Daily) demonstrate that CMST-Net can effectively improve continuous sign language recognition performance, achieving state-of-the-art performance on the PHOENIX2014 and CSL-Daily datasets and competitive results on the PHOENIX2014-T dataset. Full article

Precise spatiotemporal assessment of habitat suitability is essential for crop pest and disease risk warning and food security. However, most existing approaches focus on disease occurrence, overlook spatial heterogeneity and time series information, and therefore, struggle to capture the habitat dynamics from occurrence to epidemic. We propose a dynamic framework for rice sheath blight (RSB) habitat suitability assessment that integrates rice phenology and disease time series to reveal fine-grained intra-annual spatiotemporal variability via a spatiotemporally adaptive strategy beyond the reach of traditional static models. Remote sensing and meteorological time series data, together with RSB survey data and crowdsourced records from southern China, are integrated in this study. First, the study area is partitioned into sub-regions and sensitive time windows (STWs) based on climate and rice-cropping systems. MaxEnt, combined with natural breaks, is then used to assess RSB occurrence suitability and delineate multi-level suitable areas. Geographical and temporal weighted regression (GTWR) and the coefficient of variation (CV) are finally applied within moderate-to-high occurrence-suitability areas to reconstruct time series of intra-STW epidemic potential dynamics and quantify their temporal variation. Results indicate the optimal phenology-based scheme yields one STW for single-cropping sub-regions and three STWs for double- and mixed-cropping sub-regions. MaxEnt AUC ranges from 0.610 to 0.768, with natural-break thresholds at 0.312 and 0.473. GTWR produces generally robust intra-STW fits (most local R² > 0.4), and the CV highlights localized, time-varying high-fluctuation zones within occurrence-suitable areas that static maps do not reveal. Overall, our method extends crop disease habitat suitability assessment from a static paradigm to a spatiotemporally adaptive, dynamic one, providing useful habitat background constraints for monitoring, early warning, and forecasting of crop pests and diseases under complex cropping systems. Full article

Event cameras offer microsecond-level temporal resolution and high dynamic range, but their spatial resolution remains much lower than that of modern RGB cameras. This paper studies high-resolution novel-view synthesis from low-resolution (i.e., low-spatial-resolution) event streams alone. Given multi-view low-resolution events of a static scene, without RGB images or any high-resolution signal, our goal is to reconstruct a 3D Gaussian radiance field that can be rendered beyond the native event-sensor resolution. To this end, we propose an event-only framework that integrates event super-resolution into event-driven 3D Gaussian optimization. The framework exploits two types of cues. Temporal cues convert the high temporal resolution of event streams into dense local multi-view constraints by constructing event observations between nearby viewpoints. Spatial cues provide target-resolution event priors by lifting low-resolution event increments with a 2D event super-resolution module. To make these priors compatible with the physical measurements, we apply pool correction so that each high-resolution prior reproduces the original low-resolution event increment after downsampling. The corrected high-resolution priors and the native low-resolution measurements are jointly used to optimize a shared 3D Gaussian radiance field, enforcing multi-view consistency during reconstruction. Experiments on synthetic multi-view scenes with paired low- and high-resolution event ground truth show that our method outperforms Pre-SR and Post-SR baselines in both quantitative metrics and visual quality, demonstrating the effectiveness of reconstructing high-resolution radiance fields from low-resolution events alone. Full article

Despite substantial progress in biomarker discovery and multi-omics profiling, several features of Alzheimer’s disease (AD), including prolonged compensated states, heterogeneous clinical trajectories, and marked stage-dependent therapeutic responses, remain difficult to integrate into a single mechanistic framework. In this review, we propose an integrative and testable conceptual framework that reframes AD as a single, progressive multi-layer network disorder whose dynamics arise from hierarchical constraint propagation and progressive loss of cross-scale coordination. Integrating evidence from human genetics, epigenomics, transcriptomics, proteomics, metabolomics, spatial biology, connectomics, and longitudinal biomarker studies, we examine how molecular, cellular, and circuit-level processes interact over time to shape disease progression. Within this framework, different omics measurements are interpreted as complementary representations of disease-related changes, rather than as independent molecular signatures. Disease progression reflects the gradual convergence of immune, metabolic, proteostatic, cytoskeletal, and synaptic stress, with overt cognitive impairment emerging when compensatory capacity is exceeded, producing threshold-like network destabilization. By explicitly linking biological scale, temporal hierarchy, and network structure, this synthesis extends prior network-medicine, connectomic, and multi-omics approaches into a testable framework for state-aware stratification, integrative analysis, and stage-appropriate therapeutic investigation in AD. Full article

Architecture has often represented itself through permanence, stability, and formal autonomy while materially existing through irreversible exchanges, dissipation, aging, maintenance, and transformation. Situated within a cumulative research program on architectural conception, thermodynamics, constructal law, environmental transfer, digital design, and spatial morphogenesis, this article isolates a more specific question: how can irreversibility and constructal law become generative principles within architectural design rather than corrective considerations applied after form has been established? The study develops a situated theoretical-methodological framework in which the building is approached as a finite open configuration traversed by thermal, material, environmental, and occupational flows. Drawing on the second law of thermodynamics, exergy degradation, finite-time processes, boundary-layer reasoning, and constructal access, the framework organizes design through the definition of the finite domain, the identification of gradients, the localization of resistance and irreversible loss, the spatial and temporal staging of differences, the organization of access, and the evaluation of geometry through dynamic persistence. The framework is applied to an unbuilt amphibious dwelling as a research-by-design case study, examining its implications for site interpretation, flood accommodation, sectional organization, envelope depth, material differentiation, circulation, and maintenance. The application is not presented as a complete numerical performance validation, but as an architectural translation of the method that establishes the basis for subsequent computational, structural, material, and life-cycle testing. The article contributes a focused account of architecture as the organization of irreversible processes under finite constraints and proposes a design methodology through which gradients, resistances, transfers, and temporal transformations may participate directly in the generation of form. Full article

The economic scheduling of grid-connected microgrids requires the coordinated dispatch of renewable energy sources, controllable distributed generators, battery energy storage systems, and power exchange with the utility grid while satisfying various operational constraints. Owing to the time-varying nature of renewable generation and load demand, this problem often exhibits strong nonlinearity, temporal coupling, and complex constraint characteristics. To enhance the optimization capability of the original Coati Optimization Algorithm (COA) for such constrained scheduling tasks, this paper proposes an Improved Coati Optimization Algorithm, termed ICOA. Different from the original COA, which mainly depends on random initialization, single-best individual guidance, and simple local perturbation, the proposed ICOA redesigns the search process through several urban-traffic-inspired mechanisms. First, a road-network stratified initialization strategy is employed to improve the spatial coverage and diversity of the initial population. Second, a traffic-signal-guided exploration strategy adaptively adjusts the search direction by considering population congestion and elite information. Third, a lane-changing local exploitation operator is introduced to refine promising solutions with the aid of neighborhood information. Finally, a traffic-rule-based repair mechanism is incorporated to enhance the feasibility of candidate scheduling solutions under operational constraints. The performance of ICOA is first assessed on the CEC2017 benchmark suite with 10-, 30-, 50-, and 100-dimensional test settings. The results obtained from convergence curves, boxplots, Wilcoxon signed-rank tests, and Friedman mean rank tests demonstrate that ICOA achieves competitive performance in terms of convergence accuracy, robustness, and scalability when compared with 11 advanced algorithms. In addition, ICOA is applied to a 24 h grid-connected microgrid economic scheduling case. The simulation results show that ICOA obtains the lowest mean operating cost of 1393.58, which is 13.10% lower than that of the best competing algorithm in terms of mean cost. These results suggest that ICOA is an effective and reliable optimization method for both benchmark function optimization and constrained microgrid scheduling problems. Full article

To address the challenges of copyright protection for marine environmental datasets in open sharing environments, this paper proposes ST-Mark, a robust watermarking framework that synergistically integrates established geometric invariants and Quantization Index Modulation (QIM) techniques to meet the strict physical constraints of marine environmental datasets. The proposed method first extracts feature points by analyzing the spatiotemporal distribution of the data. It then constructs a local reference frame from the convex hull vertices and computes the geometrically invariant angles and distance ratios of the feature points relative to this reference pair to achieve robust partitioning. Finally, the watermark is embedded into the attribute domain of the grouped data through the Quantization Index Modulation (QIM) strategy while constraining perturbations within observational uncertainty bounds. Extensive experiments demonstrate that ST-Mark exhibits strong robustness: under extreme conditions such as temporal deletion attacks with an intensity of 0.9, the average normalized correlation (NC) remains above the robustness threshold of 0.75, with peak values reaching 0.99 on high-resolution datasets, although performance may fluctuate or fall near this threshold under severe spatial restrictions and numerical quantization, while still supporting reliable copyright verification under typical operational conditions. Full article