Search Results (208)

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

Children with autism spectrum disorder (ASD) often exhibit atypical patterns of visual attention allocation and social-cue processing. Eye-tracking scanpath (ETSP) retains information about fixation points, saccade paths and their temporal changes in the form of images, providing an intuitive and computable data representation for analyzing ASD-related visual attention patterns. However, in ASD auxiliary identification studies, the same participant often generates multiple eye-tracking recordings or multiple visual representation samples. If participant independence is not properly considered during model evaluation, the training and test sets may share individualized eye-movement patterns from the same child. In such cases, the model may learn subject-specific characteristics rather than stable and transferable ASD-related visual attention features, leading to an overestimation of its recognition ability on unseen participants. To address this issue, we propose a Global–Local Collaborative Fusion Network (GLCF-Net) under a strict participant-independent splitting protocol. Specifically, the proposed method first maps ETSP images into patch token sequences through a shared Patch Embedding layer. A CNN-based local branch is then used to extract local trajectory morphology, path density, and spatial neighborhood structure, while a ViT-based global branch models cross-region gaze transitions and the overall attention distribution. Finally, a gated adaptive fusion module dynamically integrates local and global information to enhance the representation of stable visual attention features. In the primary repeated stratified five-fold participant-level evaluation, averaging the two out-of-fold probabilities for each participant yielded an Accuracy of 87.0% and a ROC-AUC of 93.7%; the original participant split, retained as a secondary analysis, yielded an Accuracy of 83.52% and a ROC-AUC of 90.27%. Under the reported frozen-backbone configurations, the model also showed a balanced pattern across Accuracy, Recall, and F1-score. These results characterize performance for unseen participants within the same dataset and acquisition conditions. Full article

High-resolution precipitation fields are essential for flash-flood forecasting and hydrological risk management, especially in small and medium-sized basins, yet single-source precipitation products often show limited accuracy over complex terrain. This study develops a two-stage machine-learning framework for 1 km/1 h multi-source precipitation fusion over Shaoxing, China, during the 2025 flood season. In the first stage, a machine-learning classifier identifies precipitation occurrence and reduces zero-inflated noise; in the second stage, an optimized tree-based residual-regression model corrects precipitation estimates for rainy samples. A 61-dimensional feature set was constructed by integrating satellite precipitation estimates, weather-radar precipitation estimates from the Zhejiang radar network, temporal-lag and accumulation statistics, neighborhood descriptors, cyclic time variables, and terrain-derived interaction features, with gauge observations used as the training target. After quality control, the dataset comprised 41,458 hourly station samples from 72 rain gauges. The stations were divided at the station level into a 57-station development set and a fixed 15-station held-out spatial test set containing 8637 hourly samples. Station-blocked fivefold cross-validation within the development set was used for model selection, hyperparameter tuning, and probability-threshold selection, whereas the held-out stations were used only for final performance evaluation. On the fixed held-out test set, the occurrence classifier achieved an overall accuracy of 0.947, with a probability of detection of 0.806, a false alarm ratio of 0.158, a critical success index of 0.700, and an F1 score of 0.823. For quantitative estimation, the two-stage fusion product reduced root mean square error from 2.342 mm for satellite precipitation estimates to 1.189 mm, corresponding to a 49.22% reduction, and decreased mean absolute error from 0.712 mm to 0.262 mm, while increasing the coefficient of determination to 0.685. The fused precipitation product also improved the detection of intense rainfall events, with probability of detection and critical success index reaching 0.511 and 0.442, respectively, for events exceeding 10 mm/h, while reducing false weak precipitation and showing closer agreement with observed station-level spatial variability. By separating precipitation-occurrence identification from rainfall-intensity correction, the framework reduces zero-inflated bias, improves heavy-rainfall representation, and demonstrates predictive skill at gauges excluded from model development during the 2025 flood season. Full article

Event cameras provide high temporal resolution and sparse asynchronous output for small-target monitoring. However, distant weak targets often generate sparse and fragmented events that are easily obscured by responses from background structures and sensor background activity (BA) noise. This paper proposes a Spatio-Temporal Polarity Interaction Filter (SPIF), which assigns a reliability score to each incoming event. SPIF constructs weighted neighborhood support from temporal proximity, spatial distance, and polarity relationships, and uses the recent firing history of the center pixel to discount unreliable evidence caused by persistent activation. On the complete test split of the event-based unmanned aerial vehicle (EV-UAV) benchmark, SPIF achieves a macro-averaged F1 score (macro-F1) of 0.7732 and a target retention rate of 0.8896, exceeding the corresponding values of 0.7583 and 0.6740 obtained by the supervised EV-SpSegNet. Driving and the ED24 real-world denoising dataset further evaluate separation between valid events and BA noise. SPIF obtains the highest area under the receiver operating characteristic curve (AUC) on Driving at 3–10 Hz/pixel and under all evaluated ED24 settings. The C++ implementation processes 6.72 million events per second on the tested CPU. Full article

Urban areas contribute a dominant share of global carbon dioxide (CO₂) emissions, yet city-scale emission estimates remain highly uncertain due to the limited spatial and temporal resolution of existing observation methods. This review paper examined the potential of horizontally scanning Differential Absorption LiDAR (DIAL) systems to address these gaps in urban CO₂ monitoring. We reviewed current top-down and bottom-up approaches, identified their limitations in resolving intra-urban variability, and outlined a conceptual framework for deploying compact, eye-safe CO₂ DIAL systems on elevated urban structures. We reviewed methods that combined horizontal CO₂ scanning with wind measurements to enable high-resolution mapping of CO₂ distributions, assessment of boundary-layer effects, and estimation of urban carbon fluxes using established mass-balance and inversion techniques. Conceptual deployment scenarios, illustrated using Boston as an example, demonstrated how a single elevated LiDAR unit could provide near-city-wide coverage and capture neighborhood-scale emission patterns. We further discussed calibration strategies, safety considerations, and integration with existing sensor networks and column observations. This review concluded horizontal CO₂ LiDAR represents a promising pathway toward high-resolution, observation-based urban carbon monitoring, offering a complementary tool to existing methods and supporting future efforts to verify emission inventories, evaluate mitigation policies, and advance city-scale carbon management. Full article

In Spain, inbound European tourism exhibits marked territorial imbalances whose evolution is difficult to characterize using aggregate indicators. This study analyzes its spatiotemporal patterns at the municipal level between July 2019 and December 2025 based on experimental statistics from the National Institute of Statistics compiled using mobile phone data. The objective is to identify processes of growth, persistence, and spatial intensification using a geospatial methodology based on the Space–Time Cube (STC) and Emerging Hot Spot Analysis (EHSA). The analysis covers the 1000 municipalities with the highest cumulative volume of European tourists, which account for most of the flows recorded during the period. The results show positive and statistically significant temporal trends in 911 municipalities, although the formation of persistent spatial clusters is considerably less widespread. EHSA identified 48 municipalities classified as hot spots when applying a one-month temporal neighborhood and 77 when using a three-month configuration. The two classifications showed an observed agreement of 96.0% and, for the four shared categories, a Cohen’s kappa coefficient of 0.660. The post-pandemic recovery in tourism did not, therefore, result in a homogeneous territorial consolidation of stable spatial patterns. We identify persistent hubs, areas undergoing intensification, and destinations with episodic behavior, located primarily in metropolitan, coastal, and island areas. The main contribution of the study lies in the development of a reproducible workflow based on the STC–EHSA integration, capable of distinguishing between temporal growth, persistence, intensification, and spatial intermittency, and of evaluating the stability of the results under different temporal neighborhood configurations. Full article

The scarcity of resources is a major challenge for the livability of cities and sustainable urbanization. This study explores the socioeconomic dimensions of water scarcity in four low-income neighborhoods of Amman, the capital of Jordan, using a qualitative approach. The study investigates how water scarcity intersects with socioeconomic vulnerability, spatial injustice, and behavioral adaptation. The main research findings are that income, the quality of the neighborhood, and infrastructure access commonly shape residents’ coping strategies and perceptions of fairness. While households adopt a range of adaptive behaviors, limited affordability and institutional neglect intensify existing inequalities. Temporal shifts in service provision, spatial disparities within and between neighborhoods, and gaps in awareness and governance deepen the sense of water insecurity. The study concludes that water scarcity in Amman is not only a technical or economic challenge but a socially embedded problem that requires equity-oriented policies, participatory planning, and spatially targeted investments. Full article

Urban noise pollution is considered to be a significant challenge to neighborhood livability, particularly in cities with limited noise governance. This has resulted in raising the environmental noise as a critical health and livability concern. Existing approaches rely on decibel thresholds that fail to capture the spatial, social and perceptual complexity of noise conditions at the neighborhood scale. This study offers a multi-criteria soundscape framework integrating acoustic measurements and urban parameters (urban design, landscape, regulations and perception). The aim is to support the optimal location for noise planning. The framework was applied as a pilot study across five residential neighborhoods in Tripoli, Lebanon. The OpeNoise mobile application was used across four temporal sessions. Recordings were combined with qualitative resident interviews. Average Equivalent Continuous Sound level LAeq(t) values ranged from 60.3 to 76.8 dBA. While all neighborhoods exceeded the World Health Organization WHO guidelines, acoustic severity alone did not determine the best case for intervention. The neighborhood with the highest measured noise did not achieve the highest priority score. This demonstrates that decibel planning is insufficient. The alignment of acoustic severity, feasibility, and community demand is more effective for intervention. The framework’s criteria are based on observable urban indicators, suggesting applicability to similar urban contexts, further pending validation. Full article

A better understanding of the associations between urban morphology and performance can support more evidence-based urban governance and design evaluation. However, conventional hand-crafted metrics may omit important configurational information, making it difficult to model morphology–performance relationships consistently across multiple performance domains and under changing urban conditions. To address this issue, this study tests a modeling pathway based on variational autoencoder (VAE) latent representations of urban morphology, combined with neighborhood de-averaging to reduce the influence of locational context, and evaluates it through a four-phase design covering explanatory gain, cross-dimensional response, contextual-scale and spatial robustness, and temporal generalizability. Using Shanghai as the empirical case, the results show that the latent-representation modeling pathway consistently outperforms multiple hand-crafted metric-based baselines. This relative advantage remained evident across multiple contextual scales, under spatially grouped validation, and in temporal validation. The pathway enables complex urban morphology to enter multidimensional performance models through a unified representation and has practical application potential for data-driven urban evaluation and performance-oriented planning support. Full article

Pattern similarity analysis is widely used to assess response reliability in repeated-stimulus fMRI designs, but pairwise similarity estimates may be confounded by temporal structure, including slow drift, autocorrelation, hemodynamic smoothing, and fixed repetition order. This study presents a simulation-supported workflow for detecting and reducing such confounding. A fixed design with 40 events, corresponding to ten stimuli repeated four times, was simulated with and without stable stimulus-specific structure. Naive within-versus-between similarity was compared with a time-neighborhood-matched contrast and a distance-adjusted regression estimator. In 500 null simulations, temporal structure alone produced a biased naive contrast (mean ± SD = −0.1309 ± 0.0021), whereas the time-neighborhood-matched contrast was near zero (−0.0013 ± 0.0018). The time-neighborhood-matched contrast increased monotonically with stimulus-effect strength and remained robust after introducing hemodynamic convolution, spatial dependence, structured nuisance components, and variation in autocorrelation, voxel count, and drift specification. In an OpenNeuro music-fMRI case study, the naive contrast was strongly negative, whereas temporal matching yielded a small positive residual contrast. Cross-validated selection of functionally responsive voxels increased this residual effect, indicating partial dilution in the whole-EPI analysis, while the naive contrast remained negative under all mask specifications. The workflow provides a practical diagnostic framework for repeated-stimulus fMRI pattern similarity analyses. Full article

Accurate characterization of flood dynamics in large river–lake systems remains challenging due to the difficulty of simultaneously capturing inundation extent and water surface elevation (WSE) variations under rapidly changing hydrological conditions. This study develops an integrated Synthetic Aperture Radar (SAR) and wide-swath interferometric altimetry framework to reconstruct the spatiotemporal evolution and storage dynamics of the 2024 flood event in the East Dongting Lake system, China. Sentinel-1 SAR imagery is utilized to derive high-resolution inundation extent, while the Surface Water and Ocean Topography (SWOT) mission, equipped with the Ka-band Radar Interferometer (KaRIn), provides two-dimensional WSE observations. To improve SAR-based flood extraction in heterogeneous floodplain environments, an Adaptive Spatially-Constrained Fuzzy C-Means (AS-FCM) algorithm is proposed by incorporating adaptive spatial regularization and structure-aware neighborhood weighting. Quantitative evaluation demonstrates that the proposed method achieves the highest performance among the evaluated conventional approaches, with an Overall Accuracy of 93.6%, an Intersection over Union of 0.89, and a Kappa coefficient of 0.87. The multi-temporal inundation sequence reveals a distinct flood evolution pattern characterized by rapid expansion during the rising stage and gradual recession during the post-peak period. SWOT-derived WSE observations exhibit strong agreement with synchronous in situ measurements after bias adjustment, with a correlation coefficient of 0.988. By integrating SAR-derived inundation extent with temporally matched water-level observations constrained by bias-adjusted SWOT and in situ gauge data, an empirical WSE–area relationship ($R^2=0.937$) is established to reconstruct daily flood dynamics and estimate cumulative water storage variation. The results indicate that the East Dongting Lake floodplain played an important buffering role during the 2024 flood event, with cumulative storage variation reaching approximately $10.7{\mathrm{km}}^3$ during the peak stage. Overall, the proposed framework demonstrates strong potential for flood monitoring and hydrological storage assessment in complex river–lake systems. Full article

Past Citi Bike rebalancing research has relied on optimization and geospatial models but has treated spatial and temporal structures separately, leaving a gap in understanding stations as long-term behavioral entities. This study exploits the frequent spatiotemporal structure in Citi Bike daily trip data and treats the station’s bike net flow rate (NFR) time-series as the study object. Stations are grouped into regimes using time-series clustering, cluster stability, and the spatial context surrounding each station. Stations were assigned operational roles based on their hourly NFRs and potential contribution to the rebalancing truck. A priority-queue-based heuristic routing (PQHR) algorithm is introduced to design a single-vehicle route that accounts for stations’ regimes, roles, rebalancing urgency, and priority during rush hours. Therefore, this study formally introduces the Station Behavior Regime Model (SBRM) that defines station regimes, rebalancing roles, and routing. The result achieved a >90% reduction in the number of stations with extreme bike accumulation or unavailability and reduced the NFR of affected stations by >30% in busy areas. The spatial context derived from station behavior modes suggests new ways to define neighborhood boundaries. The methodologies provide new avenues for rebalancing operations and routing plans across a broad range of station-centric transportation network studies. Full article

Industrial defect image generation is an effective way to alleviate data scarcity and class imbalance in visual inspection. In industrial images, defects usually appear as local asymmetric perturbations on globally regular background structures, which makes defect synthesis dependent on both background consistency and local anomaly fidelity. Existing generative methods still face difficulties when only limited anomalous samples are available, especially in representing fine-grained discrepancies among defect categories, coordinating global and local branches across diffusion stages, and constraining small defect regions and their boundary transitions. To address these issues, this paper develops a symmetry-aware multi-constraint diffusion framework based on the dual-branch architecture of DualAnoDiff. The framework treats multi-class industrial defect generation as a joint optimization problem involving class-conditioned discrepancy representation, diffusion-stage-aware branch coordination, and saliency-guided regional supervision. First, Class-Conditioned Shared-Basis LoRA (CSB-LoRA) models category-specific defect characteristics by combining cross-class shared low-rank bases with class-dependent coefficients, allowing common structural priors and class-specific asymmetric patterns to be represented simultaneously. Second, Temporal Dual-branch Attention Modulation (TDAM) adjusts branch interaction, background information injection, and residual feature fusion according to the denoising stage, so that the generation process can gradually shift from global structure restoration to local defect refinement. Third, Saliency-Guided Reconstruction Loss (SGRL) applies stronger spatial constraints to defect regions and boundary neighborhoods, improving local detail preservation and defect-background continuity. Experiments on the MVTec AD dataset show that the proposed method improves both generation quality and perceptual diversity compared with DualAnoDiff. The average IS increases from 1.93 to 2.07, and IC-LPIPS increases from 0.38 to 0.41. When the generated samples are used for downstream defect segmentation, AP-P improves from 84.5% to 85.7%, and F1-P improves from 78.8% to 79.3%. These results indicate that the generated samples can serve as useful synthetic training data for few-shot and class-imbalanced industrial inspection. Full article

Gas pipeline leakages in urban underground utility tunnels pose a severe threat to public safety. Leakages of varying aperture sizes trigger differentiated risks of diffusion and explosion; thus, achieving precise identification of leakage hole size has become a critical issue in safety management. To address the difficulty of traditional methods in effectively separating the acoustic features of different leakage levels within complex utility tunnel environments, this paper proposes a gas pipeline leakage risk level identification method based on a lightweight Spatial–Temporal Graph Neural Network (ST-GNN). First, relying on a real utility tunnel simulation platform, acoustic signals under different pressures and leakage hole size are collected, and time-frequency magnitude features are constructed through Short-Time Fourier Transform (STFT). Furthermore, each acoustic sample is independently converted into a graph with STFT time frames as nodes, where temporal neighborhood edges and K-nearest neighbor edges jointly encode local dynamics and non-local spectral similarities. This transforms unstructured acoustic signals into graph-structured data that embodies spatial–temporal coupling relationships. Building upon this, a lightweight Chebyshev graph convolutional network is designed to progressively extract discriminative features strongly correlated with leakage levels using multi-layer convolution. Experimental results on the actual utility tunnel simulation platform dataset demonstrate that the proposed method achieves excellent performance in a three-level leakage classification task. The t-SNE visualization reveals the effective separation of features, progressing from complete mixing in the input layer to distinct separation in the output layer. Through multiple training statistics and ablation experiments, the impact of dataset size and the number of network layers on the identification performance is analyzed, validating the robustness of the proposed model under limited samples and the effectiveness of its lightweight structure. This provides a feasible solution for the automated and refined identification of gas pipeline leakage levels in underground utility tunnels. Full article

Data centers have become essential infrastructure for digital services, while their rapidly growing electricity demand makes coordinated workload and power management an important optimization problem. This paper studies the multi-data-center operation problem under time-of-use electricity pricing and formulates it as a multi-data-center mixed-integer nonlinear programming model (MDC-MINLP). The model jointly represents binary task scheduling decisions, including temporal workload shifting and spatial task migration, and continuous power-side variables, including device-level utilization, IT and auxiliary power consumption, energy storage dynamics, grid power procurement, and quality-of-service constraints. The objective is to minimize the total operating cost by integrating electricity purchasing cost, IT operation loss, storage degradation cost, and migration cost. To solve the resulting large-scale discrete–continuous coupled problem, an Adaptive Large Neighborhood Search algorithm with Hierarchical Collaboration (HC-ALNS) is proposed. HC-ALNS reconstructs feasible task action sets, employs a surrogate objective for fast candidate screening, performs accurate power-layer evaluation for selected solutions, and adaptively adjusts search intensity according to convergence behavior. Numerical results show that HC-ALNS reduces the total operating cost by 3.67% and achieves better convergence and solution quality than NSGA-II and PSO. These findings demonstrate that the proposed MDC-MINLP and HC-ALNS provide an effective mathematical optimization framework for coordinated computation–power scheduling. Full article