Search Results (284)

Road Traffic Accidents (RTAs) represent a significant public safety issue in rapidly urbanising nations, resulting in considerable fatalities, injuries, and economic losses. This research investigates the spatio-temporal distribution and hotspot dynamics of RTAs in Siliguri City, India, a principal transnational transport corridor connecting northeastern India with adjacent countries. A geocoded dataset comprising RTA incidents from 2021 to 2023 was analysed using integrated GIS-based machine learning and statistical methods. Temporal clusters were identified through Kulldorff’s purely temporal scan statistics, while Kernel Density Estimation (KDE) quantified accident density during morning peak, midday/off-peak, evening peak, and lean/night-time intervals. Spatial clustering was further assessed using LISA-Moran’s I, purely spatial scan statistics, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Emerging Hotspot Analysis (EHA) was employed to detect evolving hotspot patterns over time. The findings indicate that major accident hotspots are concentrated at key intersections and transport corridors, such as Hill Cart Road, Darjeeling More, Sevoke Road, Eastern Bypass, and Burdwan Road. Moran’s I (0.157; p = 0.007) demonstrates significant but moderate spatial autocorrelation, and spatial scan statistics identified three principal high-risk zones. HDBSCAN classified 81.90% of incidents within clustered areas. Lean/night-time periods exhibited the highest accident densities, reaching 14.21 accidents/km² at critical intersections. These results underscore the utility of integrating GIS and machine learning techniques for urban traffic safety planning and hotspot-focused intervention strategies. Full article

As the power density of office computing clusters rises to 200–250 W per chip, the substantial heat generated during operation not only impairs chip performance and shortens lifespan but also compels heating, ventilation, and air conditioning (HVAC) systems to operate at high loads. This increases energy consumption by 30–40% and causes indoor temperature fluctuations that reduce office workers’ comfort. Targeting centralized thermal management for such clusters, this study proposes a hybrid cooling strategy integrating outdoor natural cold air (as a continuous heat sink) with phase change materials (PCMs, for transient heat peak absorption). Six adjustable heating plates (power range: 50–250 W per unit, simulating 7 nm office chips) mimicked heat dissipation in a six-chip cluster. Latent heat storage (LHS) units served as passive cooling, with fan coils as auxiliary for natural/forced convection. By using PCMs (melting point: 48 °C) to absorb transient peaks and coils to utilize outdoor cold air, the system maintained circulating water at approximately 60 °C (steady-state equilibrium temperature under full-load conditions) and kept chip temperatures below 80 °C (industrial safety threshold). The hybrid system reduced combined pump and fan power to 125 W, achieving 75% energy savings compared to the HVAC system (500 W) and 40% savings compared to using only natural cold air (210 W pump and fan power). Positive pressure in the outdoor unit (increasing coil air velocity by 1.2 m/s relative to natural convection) further improved heat dissipation efficiency by 15%. Finally, this study quantifies the influence of PCM thermal conductivity and filling mass on the system’s temperature control performance through numerical simulations, providing direct evidence for parameter design of LHS units. Full article

Under the background of global climate change and China’s “carbon peak and carbon neutrality” strategy, it is of great significance to assess the carbon sink benefits of urban park plant communities. This study took 20 plant community plots of 20 m × 20 m (400 m²) in Tientsin Water Park as the research object. Carbon sequestration capacity was characterized by carbon stock (CS) and annual carbon sequestration (ACS), and six community structure indicators were quantified: Vegetation Coverage (VC), Canopy Density (CD), Three-Dimensional Green Volume (3DGV), Tree-to-Shrub Ratio (TSR), Vertical Complexity (CV), and Number of Individuals (N). Spearman correlation analysis, principal component analysis, and regression analysis were adopted, and K-means clustering was introduced to identify vegetation structure–function groups, thereby exploring the statistical correlations between these structural characteristic indicators and carbon sink capacity indicators (CS and ACS). The results showed that (1) VC, CD, and 3DGV were significantly positively correlated with CS, suggesting that these factors may be more conducive to long-term carbon pool accumulation; (2) N was significantly positively correlated with ACS, and a nonlinear decreasing trend was observed in the current observation data; (3) the influence of TSR and CV on carbon storage and sequestration also showed a nonlinear correlation. Based on the above correlation findings, the community combinations with higher carbon sink performance in this case were screened out. And suggestions for low-carbon configuration of plant communities, centered on optimizing canopy structure, configuring high-carbon-sequestration tree species, and regulating reasonable density, were proposed, which can be used as a reference for forming hypotheses in subsequent confirmatory studies. Full article

With the advancement of carbon peaking and carbon neutrality goals, the increasing penetration of renewable energy sources such as wind and photovoltaic power poses severe challenges to the power supply reliability of AC/DC hybrid distribution networks due to their fluctuating, intermittent, and stochastic outputs. This paper proposes a reliability assessment method for AC/DC hybrid distribution networks under large-scale renewable energy integration based on clustering of typical operating scenarios. The net load duration curve is adopted as the feature variable to characterize typical operating scenarios. An improved t-distributed Stochastic Neighbor Embedding (t-SNE) nonlinear dimensionality reduction method with Kullback–Leibler (KL) divergence elbow correction is proposed for effective reduction of high-dimensional time-series data. An adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) parameter optimization method based on the k-nearest-neighbor curve and a secondary K-means clustering method based on entropy-weighted multi-objective optimization are further developed, forming a hybrid t-SNE-DBSCAN–K-means clustering algorithm. The power supply reliability is then assessed based on the clustered typical operating scenarios. A typical AC/DC hybrid distribution network is used as the test system. Results show that the DB index of the proposed clustering method improves by at least 22% compared with conventional methods, the maximum relative error between the typical-day-based and full time-series simulation results is less than 6%, and the computational efficiency improves by about 8.8 times, achieving a good balance between accuracy and efficiency. Full article

Under China’s carbon peaking and carbon neutrality goals, building-integrated photovoltaics (BIPV) are a key option for low-carbon urban transition. However, how urban morphology shapes effective PV potential in severe cold cities remains poorly understood. Previous work focuses on single buildings or citywide resource mapping and rarely yields actionable planning controls. Using Harbin as a case, this study integrates GIS with explainable machine learning to relate building-cluster morphology to effective PV generation potential. An XGBoost model is interpreted with SHAP and partial dependence analysis to quantify factor importance and response ranges. Building density (BD) and floor area ratio (FAR) are the dominant predictors, ranking above the other morphological indicators. PV density peaks at moderate BD (≈0.20–0.35) under medium-to-high development intensity, and it increases when building distribution is moderately even (NNI ≈ 1.3–1.5) with moderate height differentiation. These coupled responses define a Morphological Sweet Spot, indicating that higher PV performance depends on coordinated morphological configurations rather than on any single parameter. The framework provides an interpretable, data-driven basis for building-cluster BIPV assessment and for translating model outputs into morphology-based planning guidance for low-carbon renewal in severe cold regions. Full article

This study implements a hybrid unsupervised machine learning approach for detecting anomalies in electricity consumption patterns from Advanced Metering Infrastructure (AMI) systems. The proposed methodology integrates dimensionality reduction techniques using Principal Component Analysis (PCA) with the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm to identify anomalous behavior. The dataset, which encompasses 26,230 m and spans a one-year period, is segmented based on user type for the purpose of analysis. The findings indicate a high degree of temporal stability in residential consumption patterns, with detection performance varying depending on the nature of the simulated anomaly. The “Sudden Drop” anomaly pattern shows an average detection rate of 61%, with monthly peaks reaching up to 96%, while more subtle anomalies such as flattening remain considerably harder to identify, with detection rates ranging between 4% and 35%. These findings contribute to the development of automated surveillance systems for reducing non-technical losses in electrical distribution networks. Full article

The assembly interface topography of precision machinery exhibits complex multi-scale geometric features, including roughness, waviness, and form error, which critically influence assembly accuracy and tolerance analysis. To address the lack of adaptivity in existing separation criteria, this paper proposes a multi-scale digital modeling approach oriented toward tolerance analysis of precision assembly interfaces, based on a fractal-wavelet framework. Firstly, multiple Weierstrass–Mandelbrot functions with independent fractal dimensions are superposed to construct a multi-fractal topography model with controllable multi-scale characteristics, grounded in the power spectral density energy additivity property. Subsequently, wavelet functions are employed to hierarchically decompose the topography height field information. The effects of the compact support length and vanishing moments of the wavelet functions on the decomposition performance are analyzed to establish a clear basis for their selection. Finally, an adaptive multi-scale separation criterion based on wavelet energy K-means clustering is then proposed, with the optimal number of scale classes determined by maximizing the silhouette coefficient, eliminating reliance on empirical thresholds. Case study results show that the fused waviness-and-form-error model retains 94.8% of the original energy while reducing convex peak count by over 90%, significantly simplifying the interface microstructure for downstream tolerance computation. The proposed method provides a high-fidelity, adaptive digital foundation for assembly accuracy prediction of precision interfaces. Full article

With the increasing depth of mining operations, accurate identification and assessment of rock mass instability risks are crucial for ensuring mine safety. This study proposes an integrated framework combining the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), fuzzy comprehensive evaluation (FCE) and kernel density estimation (KDE) for the identification and dynamic assessment of high-risk zones in deep mining. Using microseismic monitoring data from a lead–zinc mine in Northwest China (January–June 2023), the HDBSCAN algorithm adaptively identified 86 high-density clusters from 11,638 events. The weights of five evaluation indicators (moment magnitude, apparent stress, stress drop, peak ground acceleration, and ringing count) were determined objectively using the Euclidean distance method. FCE was then applied to classify cluster risk levels, revealing that 70.9% of the clusters were rated as high-risk (Level IV). KDE further illustrated the spatiotemporal migration of high-risk zones, showing a systematic shift from northeast to southwest along stopes and roadways, driven by mining unloading and geological structures. The integrated HDBSCAN-FCE-KDE framework demonstrates strong applicability and reliability in identifying and predicting rock mass instability risks, providing a scientific basis for proactive risk management in deep mining environments. Full article

The growing flexibility of load dispatching in modern smart grids has exposed critical limitations in conventional capacity pricing mechanisms, which calculate charges based solely on monthly maximum demand without distinguishing when peak demand occurs. This approach fails to reflect the temporal value of capacity and provides insufficient incentives for demand-side optimization. To address these challenges, this paper proposes a time-of-use (TOU) capacity pricing method that integrates user load characteristics to enable more equitable cost allocation and optimized electricity consumption patterns. The methodology employs K-means clustering analysis of user load profiles to partition pricing periods, accurately capturing differential capacity value across temporal intervals. We validate the clustering approach through the elbow method and silhouette analysis, confirming k = 3 as optimal and demonstrating K-means superiority over hierarchical and density-based alternatives. This data-driven approach ensures that period delineation reflects actual consumption patterns of commercial and industrial users. A capacity cost allocation model is established using the Shapley value method, incorporating maximum demand in each designated period while maintaining revenue neutrality for the grid operator. The 80% load simultaneity factor is empirically validated using 12 months of Shanghai industrial data (May 2023–April 2024). A Stackelberg game-based pricing model for TOU capacity tariffs is developed, incentivizing users to deploy energy storage systems and optimize charging strategies. We prove game convergence theoretically and demonstrate equilibrium achievement within 3–5 iterations across diverse initialization scenarios. Energy storage capacity is optimized by sector (3.5–6.5% of peak demand) rather than uniformly, and realistic battery self-discharge rates (0.006%/hour) are incorporated. Case study analysis using real operational data from 11 commercial and industrial sub-sectors in Shanghai demonstrates effectiveness. Extended to 12 months with seasonal analysis, results show the proposed strategy reduces the peak-to-valley difference ratio by 2.4% [95% CI: 1.9%, 2.9%], p < 0.001; increases the system load factor by 1.3% [95% CI: 0.9%, 1.7%], p < 0.001; and achieves reductions in users’ total capacity costs of 3.6% [95% CI: −4.2%, −3.0%], p < 0.001. Comparative analysis shows the proposed method significantly outperforms simple TOU (improvement +1.2 pp) and peak-responsibility pricing (improvement +0.6 pp). Monte Carlo robustness analysis (1000 scenarios) confirms performance stability under demand uncertainty. This research provides theoretical foundations and practical methodologies for capacity cost allocation, offering valuable insights for policymakers and utilities seeking to enhance demand-side response mechanisms and improve power resource allocation efficiency. Full article

Understanding the spatiotemporal dynamics and underlying drivers of invasive species is crucial for moving beyond descriptive monitoring to predictive management. The red imported fire ant (Solenopsis invicta Buren, RIFA) continues to spread globally, yet studies often lack the seasonal and cross-habitat resolution needed to explain the puzzling heterogeneity of infestations within urban landscapes—such as the stark contrast between high-density agricultural zones and low-density urban green spaces. To address this gap, we conducted a four-season, city-wide survey of 129 sites across four dominant habitat types (farmlands, fishponds, orchards, and urban green spaces) in Guangzhou, a core city of the GBA. Using inverse distance weighting interpolation, kernel density estimation, and spatial autocorrelation, we sought to examine not only the spatial patterns of RIFA distribution but also its potential contributing factors. Our analysis points to three key observations. First, the occurrence level of RIFA appears to follow a significant gradient (farmlands > fishponds > orchards > urban green spaces), suggesting that idle agricultural lands may serve as core reservoirs. Second, we observed a pronounced seasonal bimodal pattern, with peak infestation indices in spring and autumn—a dynamic that seems closely associated with agricultural disturbance cycles. Third, spatial analysis (Global Moran’s I = 0.346, p < 0.001) revealed significant clustering, with “high-high” clusters concentrated in peripheral suburban districts. Notably, abandoned or idle farmlands emerged as a potentially important factor, possibly acting as dispersal hubs that help bridge these spatial and temporal peaks and offering one explanation for how local outbreaks may spread across the landscape. Collectively, these findings suggest that RIFA distribution may not be driven solely by static habitat suitability or climate; instead, they point to the importance of considering the dynamic interplay between land-use legacies (such as abandonment), seasonal agricultural practices, and spatial connectivity. By elucidating these drivers, this study refines the theoretical framework of urban invasion biology and provides a replicable, evidence-based control paradigm. We suggest implementing a “zoned, seasonal, and pathway-specific” management strategy that prioritizes suburban farmland complexes during critical seasons and targets abandoned lands for intervention, offering a path towards more sustainable and precise regional RIFA control in the GBA and beyond. Full article

This research examines the structural evolution and functional performance of urban spatial expansion in Changchun, Northeast China. Utilizing an integrated framework of the Adjusted Sprawl Index, Gaussian two-step floating catchment area (Gaussian 2SFCA) accessibility modeling, and XGBoost-SHAP machine learning, the study identifies a decoupled growth pattern where land development and infrastructure construction proceed without a corresponding increase in population density, reflecting a structural-demographic divergence. Empirical results demonstrate that land expansion reached a significant peak between 2015 and 2020, followed by a transition toward morphological equalization and stabilization after 2020. This process manifests as asynchronous urbanism, where the strategic deployment of physical infrastructure frameworks systematically precedes the functional integration of essential social services. The analysis reveals the emergence of localized service-value misalignment clusters in peripheral zones. The phenomenon represents a deviation from the traditional monocentric paradigm toward McCann’s framework of modern urban economics, as high residential valuations are sustained by social capital and institutional expectations despite physical service gaps. Within these clusters, the club realm and private enclosure function as critical forward-looking mechanisms, where the presence of influential groups signals future social and infrastructural investment. A negative interaction effect between property management levels and regional accessibility confirms that these private governance structures effectively substitute for maturing public resources. These findings suggest that future development should prioritize the functional integration of social systems over mere material expansion. Full article

Dengue is a mosquito-borne viral disease which is predominantly endemic in tropical and subtropical countries. In Bangladesh, 321,179 dengue cases were reported in 2023, followed by 101,214 cases in 2024, which highlights a severe and ongoing public health challenge. Dengue transmission risks are shaped by climatic variability, rapid urbanization, socio-economic vulnerability, and healthcare strain. But existing dengue surveillance models remain limited in their ability to capture district-level disparities in Bangladesh. This study aimed to develop a district-level dengue early warning system that integrates climatic, socio-demographic, economic, healthcare, and environmental determinants to generate accurate and interpretable predictions. We examined dengue cases across all 64 districts in Bangladesh from 2017 to 2024, integrating Directorate General of Health Services (DGHS) case records with climate, socio-demographic, economic, and healthcare indicators. Machine learning and deep learning approaches, including Multi-Layer Perceptron (MLP) and Convolutional Long Short-Term Memory (ConvLSTM), were combined with SHAP (Shapley Additive Explanations)-based explainable artificial intelligence. We also used Bayesian spatio-temporal models to capture spatial clustering, temporal dependence, and the lagged transmission effects of dengue. Dengue outbreaks peaked in September 2023, with Dhaka recording 113,233 cases. DENV-4 (Dengue Virus type 4) emerged in 2022, accounting for 27% of infections in 2023. Climate was the strongest predictor of dengue transmission (humidity SHAP = 0.314; minimum temperature SHAP = 0.146; rainfall RR = 1.303). Poverty (SHAP = 0.193) and healthcare capacity (nursing/midwifery density SHAP = 0.073) mostly contributed to dengue prediction. The MLP model achieved the best yearly performance (accuracy = 0.93; ROC-AUC = 0.99), ConvLSTM was the best model in monthly prediction (recall = 0.88; ROC-AUC = 0.81), and Bayesian BYM2_RW2 with lagged effects improved predictive fit (DIC = 3671.055). Our integrated framework delivers transparent, interpretable predictions and district-level early warnings, supporting adaptive dengue outbreak preparedness and resource allocation in Bangladesh. Full article

Event cameras provide microsecond-level temporal resolution, low latency, and high dynamic range, enabling robust perception under fast motion and challenging lighting conditions. Nevertheless, event streams are susceptible to background activity, thermal noise, and hot pixels. Their sparse and irregular patterns can corrupt event structures and degrade downstream tasks. We propose MSF, a multi-level spatiotemporal filtering framework that couples motion-compensated aggregation with neighborhood-level verification. In each temporal window, MSF estimates a constant 2D optical flow by maximizing a robust, density-normalized contrast objective on the image of warped events (IWE). We further incorporate polarity–gradient decorrelation to suppress mixed-polarity noise and an explicit peak-suppression regularizer to avoid hot-pixel-induced degeneracy. The motion parameters are optimized via coarse grid initialization followed by gradient-ascent refinement. Based on the estimated motion, MSF performs hierarchical event selection: central events are extracted from high-confidence aggregated regions, local events are recovered through joint spatial–temporal–directional–polarity consistency, and weak border events are identified using a density-normalized probabilistic support model that rewards support from reliable structures while penalizing self-clustering. Experiments on four public benchmarks (DVSNOISE20, DVSMOTION20, DVSCLEAN, and E-MLB) show that MSF consistently improves the Event Structural Ratio (ESR) and outperforms representative baselines across diverse motion regimes and severe low-light noise. Full article

Population ageing is intensifying pressure on elderly-care provision in megacity suburbs, but spatially explicit evidence on who benefits and where gaps persist remains limited. Using Daxing District, Beijing, as a case study, under the 15-min community living circle framework, we integrate cleaned elderly-care facility POIs from the municipal government portal (209 points), census-calibrated age-stratified WorldPop 100 m grids, and an OpenStreetMap road network to evaluate walking-based supply–demand matching. Kernel density estimation (KDE) characterizes facility agglomeration; the Gaussian Two-Step Floating Catchment Area (Ga2SFCA) method (1 km threshold) measures accessibility for two cohorts (60–80 and 80+); and global Moran’s I with bivariate LISA identifies spatial coupling between accessibility and elderly population density. The results indicate the following: (1) pronounced spatial imbalance—facilities are concentrated in the northwest and east but remain sparse in central and southern areas, while elderly population density follows a center–periphery gradient, peaking at 12,000 persons/km² in core areas (e.g., Jiugong and Huangcun); (2) clear accessibility stratification—overall accessibility is low and spatially clustered, yet the 80+ cohort (13.6% of the elderly population) exhibits markedly higher accessibility than the 60–80 cohort; and (3) differentiated coupling types—global bivariate Moran’s I = 0.773143 (p < 0.01), with LISA dominated by low-demand–low-accessibility (LL) areas and additional high-demand–low-accessibility (HL) shortage zones and low-demand–high-accessibility (LH) potential redundancy zones, while HH areas are scarce. These diagnostics support zone-specific gap filling to mitigate spatial inequities and age–structural mismatches. Full article

The global energy transition faces the critical challenge of intermittency in renewable sources, which causes grid imbalances and estimated annual losses of USD 42 billion. Within the framework of circular economy and sustainability, mechanical energy storage (MES) systems—such as compressed air energy storage (CAES) and flywheels—emerge as scalable, long-lived solutions (over 30 years), reducing dependence on fossil fuels by up to 94%. To provide a comprehensive assessment, this study applies a Technology–Economy–Policy (TEP) framework to differentiate the maturity and iteration rates of MES sub-technologies (CAES, flywheels, pumped hydro). Furthermore, it integrates core circular economy indicators—lifespan extension, material efficiency, and multi-vector synergy—to evaluate the sustainability impact of these systems. To assess their impact and evolution, a quantitative bibliometric methodology was applied, analyzing 706 documents from the Scopus database (2010–2025). The study employed tools such as R Studio (Bibliometrix), VOSviewer, and Plotly for co-occurrence mapping, cluster density analysis, and keyword burst detection. Results reveal exponential growth in research, fitted to a logistic model (R² = 0.969), with a projected productivity peak in 2032. A technological shift toward high-efficiency solutions, such as adiabatic CAES (75%) and flywheels (95%), is evident, with grid stability prioritized. Furthermore, artificial intelligence is already applied in 40% of new management models to optimize these hybrid systems. The analysis, which quantitatively identifies underexplored areas such as socio-technical integration and standardized testing protocols, concludes that integrating MES is essential for the sustainability and circularity of the power system, enabling synergy with other vectors such as green hydrogen and fostering scalable business models that strengthen the circular economy in the energy sector. Full article