Search Results (1,025)

High penetrations of distributed energy resources make active distribution networks strongly time-varying, nonlinear, and spatially coupled, which limits the online applicability of centralized voltage/reactive-power optimization. This paper proposes a data–physics fusion dynamic partitioning method for fast power coordination. A physics-based rolling partition baseline is first developed by integrating node operating behavior, voltage/reactive sensitivity, electrical distance, and feeder topology, providing an interpretable and efficient partitioning scheme for normal operating conditions. For high-volatility and strongly coupled scenarios, a heterogeneous dynamic graph and a heterogeneous spatio-temporal graph attention network are introduced to learn control-oriented latent node embeddings. Physical regularization, boundary-coupling penalties, and temporal smoothing constraints are further embedded into soft clustering to reduce cross-partition coupling and partition fluctuation. Tests on the IEEE 33-bus, IEEE 123-bus, and practical Feeder Z systems show that the dynamic partition closely approximates global OPF results, achieving normalized costs of 1.00017 and 1.00099 on the two IEEE systems with 74.3% and 83.2% time reductions. It further reduces the Feeder Z fixed-partition cost gap by 88.0%, while HST-GAT lowers boundary P/Q exchanges by 1.55%/6.57% under volatile conditions. Full article

Background: Heat exposure is a leading climate-related health threat, yet whether the heat–disease burden relationship is moderated by national adaptive capacity remains poorly quantified at the global level. We examined associations between heat exposure and cause-specific disability-adjusted life year (DALY) burden across climate vulnerability strata and projected future burden to 2050 under IPCC AR6 warming scenarios. Methods: We constructed a country–year panel spanning 187 countries and 34 years (1990–2023) by merging ERA5 reanalysis temperature data; GBD 2023 DALY rates for cardiovascular diseases (CVD), chronic kidney disease (CKD), and chronic respiratory diseases (CRD); ND-GAIN adaptive-capacity scores; and WHO GHO health system indicators. Countries were stratified into adaptive-capacity tertiles (Low: n = 63; Medium: n = 62; High: n = 62). We used two-way fixed-effects panel regression with country-clustered standard errors, a formal Chow test of slope equality, lagged exposure models, and a benefit-of-adaptation counterfactual. Future DALY burden was projected to 2030, 2045, and 2050 using country-specific ERA5 warming trends scaled to IPCC AR6 SSP scenario multipliers. Findings: The heat–CVD dose–response was 26 times larger in Low versus High adaptive-capacity countries (β = −346.2 vs. −13.1 DALY years per 100,000 per °C). The Chow test confirmed statistically significant slope heterogeneity across tertiles for all three outcomes (CVD: F = 22.0, p < 0.0001; CKD: F = 14.9, p < 0.0001; CRD: F = 9.4, p < 0.0001). CKD burden rose 47·8% globally between 1990 and 2023, with the strongest within-country heat–CKD association in Medium adaptive-capacity countries (β = −61.5, p < 0.0001). These findings were robust to lagged exposure specifications. Under SSP5-8.5 by 2050, Low adaptive-capacity countries face a projected CVD DALY rate change 23 times larger than High adaptive-capacity countries (−16.2% vs. −0.7%). Upgrading Low adaptive-capacity countries to High tertile standards would avert 15.6% of projected CVD DALY burden under SSP5-8.5 by 2050. Conclusions: Adaptive capacity substantially moderates the health consequences of heat exposure. The quantified benefit of adaptation investment—expressed as averted DALY burden—provides a direct metric for health-system strengthening and climate adaptation financing, particularly in low-income settings facing the steepest projected burden increases. These results position adaptive capacity as a critical social determinant of planetary health, linking Earth-system boundary transgression to inequitably distributed human disease burden across the global community. Full article

This study investigates digital leadership as a networked social process by analyzing how influential actors operating across professional and institutional domains construct leadership discourse and draw on transformational leadership (TFL) principles within Twitter (X) networks, with particular attention to the skill-transfer gaps that persist between formal academic preparation and workforce demands. Social Network Analysis (SNA) using the NodeXL program was used to examine the relational structure of that discourse across a dataset of 1186 Twitter accounts and 1362 relational ties. The analysis identified 27 prominent actors operating within a distinct community cluster whose discourse spanned politics, health, technology, media, and education, with thematically diverse but uneven engagement with leadership topics. Combining semantic cluster analyses, inductive thematic mapping, and a supplementary exploratory factor analysis (EFA), the study finds that the four TFL principles (individualized consideration, intellectual stimulation, inspirational motivation, and idealized influence) are unevenly represented in this discourse. The EFA condensed the co-occurrence structure into three platform-shaped factors, with the strongest support for individualized consideration and no coherent factor for idealized influence, indicating partial rather than comprehensive alignment with the four-dimensional TFL model. The findings position digital leadership as a relational and iterative social process, sustained through repeated interactions, endorsements, and positional recognition within platform-based publics that extend across academic, industry, and socio-political boundaries. The study highlights social media as a networked yet uneven environment for leadership development and the broader social negotiation of skill-transfer challenges across digital professional contexts. Full article

Graph-based narrative extraction relies on a coherence function to score transitions between events, but the coherence metrics in current use are defined operationally and lack an information-theoretic foundation. We study the composite metric $C=\sqrt{A·T}$, where A is the angular similarity of document embeddings and $T=1-d_{\mathrm{JS}}$ is the topic proximity through the Jensen–Shannon distance of soft cluster memberships, and we provide an information-geometric reading of this metric together with an axiomatic characterization of the geometric-mean combinator. On the product manifold ${\mathbb{S}}^{d-1}\times {\Delta }_{+}^{K-1}$, the negative log-coherence decomposes additively into an angular and a topic cost. Because the Riemannian metric tensor induced by the Jensen–Shannon distance on the simplex is proportional to the Fisher information matrix, the topic component is locally consistent with the Fisher–Rao metric singled out by Chentsov’s theorem. Within a parametric family of combinators (the compensability spectrum), the geometric mean is the unique combinator consistent with four natural axioms (a boundary/veto condition, symmetry, log-additivity, normalization), and the construction also motivates a proper product metric $d_{\times }$ that we use as a reference distance. Experiments on four corpora spanning news and academic domains (40 to 6000 documents), three general-purpose embedding families (GPT-4/ada-002, MPNet, MiniLM-L6) plus citation-aware SPECTER2, and three alternative topic models (LDA, soft k-means, GMM) are consistent with the framework: the Fisher identity holds with $R\ge 0.99$, the geometric mean tracks $d_{\times }$ closely ($\rho =0.999$), and a downstream LLM-as-judge consistency check shows that the geometric mean is not empirically dominated by any alternative combinator or single-channel baseline. Sweeping the compensability spectrum, the bottleneck-coherence gap between extracted storylines and random sequences splits into a symmetric component—maximized at the geometric mean on the four corpora above and a fifth, human-navigation corpus—and a displacement term; a cross-modal case study on a human-curated image narrative reproduces the same effect in a second modality. Together, these results provide an information-geometric justification for the composite coherence metric and articulate the conditions under which the geometric mean is the natural choice. Full article

Against the global imperative to address climate change and accelerate energy transitions, the rapid growth of the electric vehicle (EV) industry has turned public charging infrastructure into a key foundation of urban operations, driven by carbon peaking and carbon neutrality goals. However, effective supply depends not only on scale but on deep association with urban functional spaces. This study compares Gusu District (a historic preservation district) and Industrial Park District (a new development district) in Suzhou. The goal is to reveal how public electric vehicle charging stations (EVCSes) associate with functional spaces under different urban development models. The study employs Standard Deviational Ellipse (SDE), Kernel Density Estimation (KDE), Bivariate Spatial Autocorrelation, and Coupling Coordination Degree (CCD) models to compare layout patterns, clustering features and functional association. The research findings are as follows: (1) The EVCS layout in Gusu District shows strong dependence on roads and administrative boundaries, while EVCSes in Industrial Park District show clear planning intervention, less constrained by its administrative boundary. (2) KDE analysis confirms that Gusu District has continuous clustering centered on the ancient city, but Industrial Park District shows a multi-center layout. (3) Bivariate Spatial Autocorrelation reveals different priorities in facility allocation. In Gusu District, spatial association is mainly driven by high-mobility nodes, while in Industrial Park District, EVCSes are more deeply embedded in social services and daily life scenarios. (4) CCD analysis reveals that the coordination in Gusu District forms a monocentric, spatially continuous gradient centered on the ancient city, whereas in Industrial Park District it displays a polycentric but fragmented pattern, with high coordination areas confined to planned cores. This comparative study reveals the EVCS spatial layout, which is shaped by both administrative boundaries and policy constraints, and the heterogeneity in spatial association between two districts. It provides scientific evidence and decision support for different spatial governance and facility optimization in various types of urban areas. Full article

Standard scalar indicators of systemic dependence, such as the mean pairwise correlation, the absorption ratio, and the dispersion of the eigenvalue spectrum, summarise the magnitude of co-movement but are by construction blind to its spatial arrangement. We propose treating the time-varying cross-asset correlation matrix as a greyscale image and quantifying its spatial organisation with the multiscale gliding-box lacunarity. Using a controlled block-factor generative model in which the average correlation is held fixed while the sectoral block strength is varied, we show that lacunarity recovers the planted block structure almost perfectly (partial Spearman $\rho =0.92$ at fixed mean correlation), a recovery that persists under fat-tailed innovations, time-varying loadings, and overlapping communities, whereas the mean correlation and the absorption ratio remain flat. Applied to twenty years of daily data for sixty-two sector-spanning United States equities, lacunarity tracks a model-free index of block heterogeneity after controlling for correlation magnitude (partial Spearman $\rho =0.46$, ninety-five percent bootstrap interval $[0.33,0.58]$) and improves the out-of-sample prediction of block structure beyond the magnitude baselines. We are explicit about two boundaries. A simple permutation-invariant dispersion statistic, the standard deviation of the off-diagonal correlations, tracks block heterogeneity even more strongly than lacunarity, so lacunarity is not the most efficient estimator of that quantity; its distinct role, confirmed by a scrambling test, is that it responds to the spatial arrangement of dependence, which dispersion measures are invariant to, and it remains informative under a canonical clustering or spectral ordering. The measure is descriptive rather than predictive of future drawdowns. The results position correlation-image lacunarity as an interpretable, computationally light, and arrangement-sensitive complement to the existing magnitude and dispersion descriptors of systemic dependence. Full article

Canopy base height (CBH) is a key descriptor of forest vertical structure and an essential input for fire behavior modeling and ecosystem assessments, yet it remains difficult to retrieve reliably from satellite observations. Spaceborne waveform LiDAR from the Global Ecosystem Dynamics Investigation (GEDI) mission provides detailed information on vertical vegetation structure through relative height (RH) metrics, but existing CBH studies have largely relied on empirically selected percentiles or indirect calibration approaches. Here, we present a physically informed framework for CBH estimation that interprets the full GEDI RH profile as a continuous representation of vertical energy distribution and identifies CBH as a structural transition within this profile. Three RH-based approaches—the first-derivative, clustering-threshold, and crown-length methods—were evaluated against independent UAV LiDAR observations. Among them, the clustering-threshold approach achieved the best agreement with UAV-derived CBH (R² = 0.71, RMSE = 1.27 m) and was selected for regional-scale mapping. Sparse GEDI-derived CBH samples were further integrated with Sentinel-2 optical data using a gradient boosting regression model to generate wall-to-wall CBH maps for the Jiagedaqi District, northeastern China, achieving an RMSE of 1.01 m against independent validation data. The results demonstrate that CBH can be retrieved directly from GEDI RH metrics without requiring region-specific airborne LiDAR calibration of the GEDI-based CBH retrieval itself, while UAV LiDAR is used only for independent validation. By advancing the interpretation of spaceborne waveform LiDAR for structural boundary detection, this study expands the utility of GEDI data for large-scale mapping of fire-relevant forest structural attributes. Full article

Traditional dynamic community detection methods often struggle to simultaneously preserve local structural consistency, capture global topological relationships, and efficiently adapt to continuous graph updates in large-scale environments. To solve these limitations, this paper proposes a novel dynamic community detection framework called Embedded Clustering Boundary Refinement (ECBR). The proposed method integrates unsupervised GraphSAGE and Node2Vec embeddings to jointly capture local neighborhood aggregation patterns and global structural equivalence among nodes. The generated embeddings are fused through feature concatenation and z-score normalization to construct a unified latent representation space. Subsequently, Mini-Batch KMeans clustering is employed to efficiently generate the initial community structure while maintaining scalability for large-scale graphs. To further improve partition quality, ECBR introduces a boundary-aware refinement mechanism that identifies structurally ambiguous nodes using neighborhood consistency analysis and reassigns them according to embedding-space similarity. In addition, the framework incorporates an adaptive dynamic update strategy capable of distinguishing between major topological shifts and localized structural changes. Significant graph perturbations trigger complete model retraining, whereas minor modifications are handled through computationally efficient incremental updates on local subgraphs. Experimental evaluations were conducted on synthetic LFR benchmark networks and several real-world dynamic interaction datasets, including high school, workplace, and hospital contact networks. The results demonstrate that ECBR consistently outperforms several state-of-the-art methods, including QCA, DyPerm, DCDID, IncNSA, and DCDBFE, achieving better NMI and ARI scores across diverse network conditions. The experimental findings confirm that ECBR provides a scalable, robust, and highly effective solution for dynamic community detection in evolving large-scale social networks. Full article

To address the issues of neglecting population heterogeneity and the difficulties in determining constraint parameters in residential load adjustable capacity forecasting, this paper proposes a data-driven forecasting method that considers profile heterogeneity. First, K-means++ is utilized to extract diverse user electricity consumption profiles. Second, to solve the problem of real response data scarcity, the difference-in-differences (DID) method is employed to empirically calibrate the true physical constraint boundaries of different clusters, and high-quality response samples are generated in batches based on an electricity cost minimization model. Finally, a Long Short-Term Memory (LSTM) time-series forecasting model is constructed to achieve the precise quantitative evaluation of adjustable capacity. Case studies demonstrate that after introducing user profile labels, the three accuracy metrics of the predictive model are improved by 16.29%, 24.52%, and 20.21%, respectively. Although the practical application of synthetic labels faces minor limitations caused by uncertain user behaviors, this scalable framework supports seamless incremental retraining using future empirical response data to realize continuous model evolution and persistent accuracy improvement, thereby providing technical support for load aggregators’ market bidding and the precise dispatch of power grid demand response. Full article

Understanding the spatial hierarchy, distribution patterns, and driving mechanisms of A-level tourist attractions is essential for optimizing tourism resource allocation and promoting sustainable regional development. This study integrates core–periphery theory with a sustainability perspective to examine hierarchical differentiation of China’s A-level tourist attractions, using 15,699 POI data points collected in 2024 and applying the nearest neighbor index (NNI), kernel density estimation, spatial autocorrelation analysis, and the geographical detector model. The results indicate that these attractions exhibit an unbalanced spatial distribution characterized by a “dense east and sparse west” pattern, with the Hu Huanyong Line (Hu Line) as an important spatial boundary, showing east–west hierarchical disparities. The attractions demonstrate a clustered distribution pattern, although the degree of agglomeration decreases as attraction grades increase. Spatial associations exhibit a pattern of coordination in eastern regions and polarization in western regions, forming a three-tier spatial hierarchy of core–sub-core–periphery. Population density exhibits the strongest explanatory power. Interaction detector results reveal grade-dependent differences. 2A attractions show weak factor associations, whereas 5A attractions are more strongly linked to resource endowment, population density, and economic development. These findings advance the theoretical understanding of the hierarchical spatial structure and differentiated development mechanisms of tourist attractions. Full article

Urban environments are saturated with imaging sensors deployed for purposes unrelated to vehicle identification, from ATM and dashboard cameras to pole-mounted CCTV and smartphones. We term the use of such non-purpose-built sensors for secondary inference “opportunistic sensing”; its central question is where, under uncontrolled capture conditions, AI-enabled restoration remains reliable. This paper introduces recoverability maps, a task-agnostic methodology for quantifying that boundary, and applies it to oblique-view license plate recognition (LPR). It pairs a full-grid synthetic sweep of the degradation space with two summary measures: a boundary area-under-curve for coverage and a reliability score $F$ for the frequency and depth of interior unrecovered pockets. For LPR, the space is the oblique-angle grid $[0°,89°{]}^2$ sampled by Scrambled Sobol sequences, and the utility is plate-level optical character recognition (OCR) accuracy. Within this synthetic benchmark, approximately $90$–$92\%$ of the angle grid is recoverable (best single model to union of restoration arms), recovery degrades sharply beyond roughly $80°$ in both axes, and lateral rotations are harder to reconstruct than elevational ones. Five restoration architectures cluster within a narrow AUC band of $0.89$–$0.93$, and share the same $\alpha /\beta$ asymmetry, so the recoverable region is set primarily by sensing geometry, with architecture affecting efficiency and interior consistency; discriminative architectures outperform generative models. The methodology is validated on real plates: on CCPD and the Brazilian legacy and Mercosur layouts of RodoSol-ALPR, restoration raises held-out extreme-angle recognition by $+15$ to $+38$ exact-match points under plate-specialized recognizers, and the discriminative-over-generative ordering reproduces on real data. Full article

The dynamic strain aging (DSA) behavior of an extruded Mg–3Gd–1Zn (wt.%) alloy was investigated under compressive deformation at intermediate temperatures (150–250 °C) and strain rates ranging from 5 × 10⁻⁵ to 10⁻³ s⁻¹. The as-extruded alloy exhibited equiaxed grains (~20 µm), with all alloying elements retained in solid solution and a weak basal texture. Serrated flow was observed under different temperature and strain-rate conditions. The critical strain, which denotes the onset of serrations, decreased with increasing temperature and increased with strain rate. Notably, the temperature dependence of the critical strain exhibited anomalous non-linear behavior, with a sharp increase at 250 °C, attributed to the formation of low-mobility Gd–Zn clusters/precipitates that depleted mobile solutes from the matrix. In situ synchrotron radiation diffraction revealed the activation of {10.2}⟨10.1⟩ tensile twinning as the dominant deformation mechanism, with periodic plateaus in twin intensity coinciding with macroscopic stress serrations. HAADF-STEM analysis confirmed Gd and/or Zn segregation at twin boundaries and dislocations, leading to the formation of nanoscale clusters during deformation. The overall mechanical response is rationalized by a temperature-dependent competition between solute diffusion and solute clustering at elevated temperatures. Full article

Leftover spaces, often associated with neglected urban corners, bear physical and conceptual similarities to ignored parts of designed wholes. This study proposes an analytical approach to develop resilient intervention strategies by analyzing the production of leftover spaces through users’ emotional experiences. An experimental pilot study was conducted along Söğütlüçeşme Street in Kadıköy, Istanbul, where all corner points were typologically classified based on morphological characteristics. To measure the impact of these configurations on spatial emotional characters, a survey was implemented using Plutchik’s wheel of emotions. Following a quantitative analysis of emotion frequencies and intensities, findings were visualized via radar charts and spatialized using QGIS 3.40 to generate an emotional map. The resulting emotional maps were further used to identify spatial vulnerabilities and resilience priorities across the study area. By making the gaps between point-based emotional clusters continuous through the IDW interpolation method, the emotional topography of the study area was modeled, thereby presenting an analytical framework that identifies emotional thresholds, spatial vulnerabilities, and resilience priorities. Results indicate that as the physical boundaries of corner voids expand, influenced by angling and massing decisions, public diversity increases, creating a positive emotional atmosphere. Conversely, compressed voids demonstrate a higher potential for producing leftover spaces. This study reveals that mapping user emotions as a data layer is critical for constructing more inclusive and resilient urban environments. Full article

Local impedance variations in structural waveguides partially reflect and absorb incident
flexural waves, motivating wave-based strategies for passive vibration control. This study
develops and experimentally validates a wave-energy framework to quantify and optimize
flexural wave absorption by Kelvin–Voigt attachments on a finite Timoshenko beam.
A finite element model is validated against Scanning Laser Doppler Vibrometry measurements
from a clamped–clamped aluminum beam with a passive damper mounted near
one end, with dashpot parameters identified through two independent approaches and
the discrepancies attributed to parameter uncertainty. Wave decomposition of the simulated
and measured velocity fields yields the power reflection coefficient ρ(ω) and power
absorption coefficient α(ω) over the 0–15.3 kHz band. The spring stiffness and damping
coefficient exhibit frequency-dependent optima and act as complementary, jointly tuned design
variables. Expressing dashpot location in wavelength-normalized coordinates reveals
a recurring spatial pattern in which absorption minima cluster around half-wavelength
multiples, while multiple spanwise positions yield near-peak absorption at any given
frequency. This pattern is governed primarily by the flexural wavelength, decoupling
placement from parameter tuning, and persists across clamped–clamped, clamped–free,
and free–free boundary conditions. Two independently tuned dampers further broaden the
effective absorption band by suppressing local minima in α(ω). These results demonstrate
that measurement-driven wave decomposition provides compact, physically grounded
guidelines for passive damper placement in beam structures. Full article

Optimal coordination of directional overcurrent relays (DOCRs) in medium-to-large power systems constitutes a computationally demanding, mixed-integer, nonlinear optimisation problem whose complexity escalates rapidly with system size, making the simultaneous minimisation of relay operating time and computational cost a critical open challenge. This study presents a two-level hierarchical framework in which Type-2 Fuzzy C-Means (T2FCM) clustering partitions 226 fault scenarios into subproblems at the upper level, while the Hybrid Fractional Entropy Evolution (HFEE) algorithm independently optimises relay settings for each cluster at the lower level. HFEE integrates fractional-order velocity updates—derived from the Grünwald–Letnikov formulation—with a Shannon entropy diversity-control mechanism to prevent premature convergence. T2FCM captures inherent fault-current uncertainty through interval-valued type-2 fuzzy memberships, yielding more robust cluster assignments near protection-zone boundaries than crisp partitioning methods. The framework is validated on the extended IEEE 30-bus system. An ablation study demonstrates that standalone HFEE achieves a 29.19% improvement in $T_{\mathrm{op}}$ over the prior best-reported result; however, a comprehensive parameter sweep over cluster counts $K\in \{2,\dots ,8\}$ and fractional orders $\alpha \in \{0.1,\dots ,0.9\}$ across 50 independent runs per configuration shows that the proposed clustering-decomposed method achieves 3.68–66.67% lower wall-clock computation time while maintaining zero CTI violations across all active relay pairs. The communicationless, entirely offline framework demonstrates scalability for simultaneous sub-transmission and distribution protection coordination and offers a practically deployable strategy for modern power networks. Full article