Search Results (39,344)

Energy systems are crucial to residential life and industrial production. During practical operation, these systems may experience various anomalies that disrupt the stability of system operation. Recent years have witnessed remarkable progress in power system anomaly detection. However, existing methods still suffer from two limitations. First, detection algorithms neglect privacy protection, although privacy security is also a critical issue in energy systems. Second, existing studies have difficulty characterizing latent dependencies and topology changes, which limits detection performance. To bridge these gaps, we present a power system anomaly detection method that integrates physics-informed sparse graph temporal modeling with homomorphic encryption, enabling anomalous-event identification and anomalous-bus localization under privacy-preserving conditions. Specifically, we construct a sparse graph using the power-grid topology and normal measurement residuals. We then obtain system-state predictions through polynomial graph temporal prediction and physics-guided affine correction and use anomaly scores to diagnose anomalous conditions. Furthermore, we employ homomorphic encryption to perform ciphertext computation for the affine prediction model without exposing historical measurement data, thereby enabling privacy-preserving remote anomaly detection. We conduct experiments on IEEE bus benchmarks to verify the effectiveness of the proposed method under multiple anomaly scenarios. Full article

This study developed a simple and scalable e-scooter riding evaluation platform that reduces on-site implementation effort by relying primarily on image analysis. The platform supports repeated riding trials under consistent conditions in an indoor environment. By combining the areas covered by two drones, the system recorded an entire long and narrow indoor riding lane. A pretrained YOLO model was fine-tuned to construct a rider detection model adapted to the experimental environment. ORB-based image registration and trajectory integration then transformed the riding trajectories obtained from the two cameras into a common coordinate system. Riding speed, riding duration, and the radius of curvature of the two curves were calculated. The results revealed differences among subjects in speed variation, stability across riding trials, and turning characteristics, including stable low-speed riding, sustained high-speed riding, and deceleration before turning. For most subjects, the inter-camera junction discrepancy was within 10 cm, indicating general internal consistency of trajectory integration under the experimental conditions. These results suggest that the proposed system can serve as a video-based platform for quantitatively evaluating observable riding behavior, including riding trajectory and speed characteristics, in an indoor riding lane. Full article

Matrix differential equations with time delay are crucial to the modeling of complex multivariate systems. However, the existing semi-analytic Laplace residual power series method (LRPSM) literature mainly focuses on scalar or no-delay problems, and lacks rigorous theoretical guarantees for matrix-valued time delay systems. This study systematically generalizes LRPSM to the linear time-delay matrix differential equation $X^{\prime }\left(t\right)=AX\left(t\right)+BX(t-\tau )+F\left(t\right)$ , where $X\left(t\right)\in {\mathbb{R}}^{n\times n}$, A and B are constant matrices, $\tau >0$ is a constant delay, and the forcing term $F\left(t\right)$ and the history function $\Phi \left(t\right)$ are analytic. The method of steps is employed to construct the solution piecewise: on each local interval, the solution is expanded asymptotically in the Laplace domain, and the coefficients are determined recursively via the Laplace residual function. We establish local error bounds on the initial interval and derive a global error propagation bound across successive delay interfaces using a variation-of-constants framework. Numerical experiments, including non-diagonal matrices, non-zero history functions, and multi-interval tests, illustrate the effectiveness of the approach. The proposed method reduces exactly to the standard LRPSM for scalar cases, demonstrating its validity as a natural and rigorous generalization of the existing semi-analytical framework. Full article

As critical biological components of freshwater ecosystems, aquatic insects are highly sensitive to cumulative anthropogenic disturbances and can effectively reflect long-term variations in river ecological quality, serving as ideal bioindicators for aquatic ecological monitoring and assessment. However, most existing benthic biotic integrity indices integrate diverse macroinvertebrate groups, which dilute the unique indicative value of aquatic insect communities, and few targeted evaluation systems have been independently developed for highly disturbed plain river basins in northern China. In this study, a field investigation of aquatic insect communities was conducted across 32 sampling sites in the Haihe River Basin from April to September 2023, aiming to construct a novel Aquatic Insect-based Index of Biotic Integrity (Ai-IBI) and systematically evaluate the basin’s river ecosystem health status. A total of 10,267 aquatic insect individuals belonging to eight orders and 43 families were identified, with Chironomidae dominating the community and predominantly distributing in midstream and downstream reaches. Based on ecological principles, discriminant ability analysis, and redundancy analysis, four core metrics were ultimately screened out to establish the Ai-IBI system, including total taxa richness, percentage of the top three dominant taxa, density of tolerant groups, and percentage of predators. Considering the widespread background ecological degradation of the Haihe River Basin, a localized reference-site selection framework integrating water quality standards, physical habitat assessment, and field ecological surveys was adopted to determine eight minimally disturbed reference sites, which effectively improved the regional applicability of the evaluation model. The health assessment results indicated that only a small proportion of river reaches maintained a healthy state, while most sites were classified as sub-healthy or fair, presenting a distinct spatial differentiation pattern wherein upstream mountainous reaches exhibited superior ecological integrity compared with downstream plain reaches. The constructed Ai-IBI showed high consistency with conventional physicochemical indicators and habitat evaluation results, verifying its excellent reliability and robustness. Different from traditional empirical IBI models suitable for near-natural watersheds, the Ai-IBI fully adapts to the unique ecological background of long-term water resource overexploitation, urbanization-induced habitat fragmentation, and nutrient enrichment in the Haihe River Basin. The Ai-IBI developed in this study provides a refined biological evaluation tool for quantitative diagnosis of river ecological degradation, offers empirical support for differentiated ecological protection and precise restoration strategies in the Haihe River Basin, and serves as a feasible methodological reference for ecological health assessment in other human-dominated plain river basins globally. Full article

Natural-language documents contain many causal claims, but those claims are unstable under paraphrase, granularity shifts, and contextual drift. A collection may express one mechanism in many surface forms, while neighboring studies may agree locally yet fail to glue globally because relation families, polarities, temporal scopes, or regimes differ. This paper studies that post-extraction problem through Democritus, an implemented system for extracting, normalizing, localizing, and diagnosing causal discourse. We do not claim to identify ground-truth causal structure from text alone. Instead, we formulate the post-extraction layer as a homotopical repair problem for a non-compositional causal-discourse sketch. A normalization functor induces a class of weak equivalences on textual mentions; we prove that these data form a relative category with two-out-of-three and that normalization factors through its localization. Strict repair requires a corpus diagram to satisfy the sketch exactly, whereas homotopy repair asks for a weakly equivalent diagram that factors coherently after localization. We also define the conditions under which grounded discourse localization maps functorially into the observational-equivalence spaces of causal models studied by higher algebraic K-theory. The implementation approximates these formal constructions through normalized claim classes, an observed compatibility complex, regime-sensitive gluing, and provenance-preserving database artifacts. On a frozen 404-passage AltLex/UniCausal comparison, a restricted verbatim-span Democritus extractor attains 57.1% causal-classification F1 and 39.1% macro span F1, compared with 59.2% and 43.6% for the public UniCausal baseline. This similar classification F1 reflects a clear precision–recall tradeoff: Democritus achieves higher recall (80.9% versus 68.7%) but lower precision (44.1% versus 52.0%). Case studies on Emperor-penguin climate discourse, Mediterranean-diet studies, red-wine cardiovascular studies, and rising-ocean-temperature corpora expose both stable repeated mechanisms and cases where local causal claims cannot be assembled into one coherent corpus-level account. Full article

This study applies a Craig–Bampton (CB) substructure reduction procedure to the prediction of natural frequency changes upon rotor replacement in a shaft generator shafting system and quantifies its accuracy and computational cost for that configuration. Because the shafting conditions are determined by the customer, performing full finite element analysis or experimental modal analysis (EMA) for every design change is impractical. The shafting system is divided into shaft and rotor substructures, and CB reduced-order models are constructed independently for each substructure. A three-stage verification framework—FE analysis versus EMA, the CB reduced-order model versus FE analysis, and the CB reduced-order model versus EMA—is employed to separate FE model error from CB reduction error. The CB pipeline reproduced the bending modes of the assembly with a maximum error of 0.26% against the parent finite element model, with a subspace MAC of 1.0000 for every mode group below 720 Hz, while reducing the model from 192,678 to 5379 degrees of freedom. With 20% of the interface degrees of freedom retained, the first three bending modes are predicted within 0.60%, and the reduced model comprises 1173 degrees of freedom, a reduction of 99.39%. The first bending mode error varies monotonically with the retention level, and the errors of all three bending modes remain bounded below 0.60% down to 20% retention. For the annular interface examined, the error-retention relation, therefore, provides a quantitative basis for selecting a retention level against a stated error tolerance, but its extension to other interface topologies, mesh densities, and mode ranges remains to be established. Full article

Unbalanced regional development is a long-standing spatial feature of China. Most extant studies are restricted to a single economic dimension and lack long-run comprehensive analysis that couples the economic, social and ecological dimensions. Drawing on China’s provincial panel data from 1990 to 2023, this paper constructs a comprehensive indicator system across three dimensions, measures comprehensive development levels with the entropy-weight TOPSIS method, and decomposes the overall imbalance into within-region disparity, between-region net disparity and the intensity of transvariation with the Dagum Gini coefficient, revealing the spatiotemporal evolution of imbalance across the four major zones of Southeast, Central, Western and Northeast China. The results show that the three types of imbalance are not synchronized: economic imbalance first rose and then declined, social imbalance fluctuated more strongly, and ecological imbalance changed mildly. The distribution of intra-provincial social and ecological disparities across zones is markedly crossed, whereas the cross-zone overlap of economic disparities has continuously weakened since 2000, and the gradient has become clear again. Accordingly, this paper proposes policy recommendations including three-dimensional assessment, classified governance, industrial collaboration and ecological compensation. This study enriches the multidimensional measurement paradigm of regional imbalance and provides quantitative support for coordinating innovation resources and promoting coordinated regional development. Full article

This study examines the perceived competitiveness of information technology companies in Kazakhstan from a governance and innovation-ecosystem perspective. In emerging digital economies, firm competitiveness depends not only on technological capabilities, but also on the state’s ability to coordinate fragmented actors, support human capital development, improve innovation infrastructure, and create functional mechanisms for cooperation between business, government, universities, and research institutions. Using a mixed-methods design, the study combines survey data from 200 respondents across all 20 regions of Kazakhstan with 26 semi-structured expert interviews. All constructs are perception-based: the study assesses how sectoral stakeholders evaluate the determinants of competitiveness and does not measure firm performance. The results show that stakeholders perceive IT-company competitiveness as a systemic outcome shaped by human capital, technological competencies, innovation infrastructure, government support, business–science interaction, and clusterization. Human capital, innovation infrastructure, technological competencies, and clusterization-related mechanisms received the strongest evaluations. Correlation analysis further indicates that competitiveness factors are positively interconnected, supporting an ecosystem-based interpretation rather than a firm-centered explanation. The findings also reveal persistent constraints, including talent shortages, weak investment depth, regulatory instability, technological dependence, and insufficient coordination among ecosystem actors. The study contributes to administrative and strategic management research by showing that stakeholders regard clusterization as a governance mechanism for strengthening state capacity, public–private–academic coordination, and digital-sector development in an institutionally uneven emerging economy. Full article

Under climate change, the demand for high-quality urban ecological security is rising. This study focuses on how rapid urbanization and climate change affect ecological risk, resilience, and land use functions (LUFs) spatial co-variation in the Pearl River Delta. Land use was simulated under Shared Socioeconomic Pathways (SSPs) using system dynamics (SD) and the interaction network–Patch-generating Land Use Simulation (intPLUS) model. Ecological risk was quantified via the landscape ecological risk index (LERI) and habitat degradation index (HDI), while ecological resilience was obtained using an adaptability–resistance–recovery framework, producing a comprehensive ecological risk–resilience index (CERRI). Ecosystem services were assessed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Spearman correlation, geographically weighted regression (GWR), constraint lines, and extreme gradient boosting with SHapley Additive exPlanations (XGBoost-SHAP) revealed LUFs patterns, nonlinear relationships and threshold effects, and driving factors. The results indicate that construction land expands mainly at the expense of cropland (~4049–4157 km²) from 2023 to 2035, while woodland and water remain largely stable. CERRI shows a concentric pattern (2023 domain mean = 0.638), with safer peripheral belts and a more risk-dominated central–southern core; under coupling-weight uncertainty with 2023-fixed common-reference normalization, SSP245 was preferred in all Monte Carlo iterations (best-scenario probability = 1.000). EF–LF, EF–PF and LF–PF retain stable nonlinear forms. Elevation, economic vitality and transport accessibility are the leading drivers, with model-derived breakpoints near low-elevation, high-vitality and moderately accessible transport nodes. This study provides the CERRI framework to support ecological security monitoring and adaptive land-use management, contributing to more sustainable regional development under climate change. Full article

With the continuous increase in renewable energy penetration, the traditional distribution network is gradually evolving into the active distribution network. Facing increasingly severe regulation pressure, relying solely on resource allocation from a single side of the distribution network can no longer provide adequate flexibility support. As an effective carrier integrating sources and loads, collaborative mutual assistance between the microgrid and the distribution network has become an inevitable trend to tap the flexibility potential of multiple entities. However, the current insufficient coordination of multi-type flexibility resources and the lack of deep interaction between the distribution network and the microgrid limit system flexibility, affecting the secure operation of the power grid. Therefore, this paper proposes an active distribution network–microgrid collaborative planning method for flexibility enhancement. Firstly, a collaborative optimal allocation model of nodal and grid-level flexibility resources for the active distribution network is established. The upper tier minimizes the annualized comprehensive cost, while the lower tier minimizes the annual operational cost and optimizes flexibility indices, comprehensively considering constraints like equipment investment, system security, and flexibility supply–demand balance. Secondly, the coupling relationship between the distribution network and the microgrid is established through tie-lines to construct the active distribution network–microgrid collaborative planning model. Finally, an accelerated and robust analytical target cascading solution strategy is proposed. By constructing a balancing coefficient, it eliminates the algorithm’s sensitivity to initial penalty weights, effectively improving the stability and efficiency of the model solution. Case study analysis verifies the effectiveness of the proposed method. Full article

With the increasing scale of centralized photovoltaic-plus-storage power stations, they are required not only to supply local load demands but also to provide power support to the upstream grid. The coupled interactions among renewable energy output, sharp fluctuations in local loads, and limited tie-line support capacity cause conventional dispatching methods to face issues in rare extreme scenarios, such as shortfalls in scheduled power delivery, increased imported power during peak support periods, load shedding, and voltage violations. To address these issues, this paper proposes a Wasserstein distributionally robust dispatch strategy for active distribution networks (ADNs) tailored for Upstream Power Support (UPS) tasks, incorporating tail risk assessment. An ADN operation model is established that integrates PV, energy storage, interruptible loads, and a bidirectional interface with the main grid, where the scheduled power delivery during UPS periods characterizes the support demand to the upper grid. A comprehensive risk loss function, encompassing load shedding, power shortfalls, peak-period power import, PV curtailment, and voltage violations, is constructed, and Conditional Value-at-Risk (CVaR) is employed to capture the tail risk caused by extreme scenarios. A Wasserstein ambiguity set is built around the empirical distribution of finite historical samples, and via dual reformulation, the worst-case distribution conditional risk model is transformed into a tractable mixed-integer second-order cone programming problem. Case studies on modified IEEE 33-bus and 69-bus ADN test systems demonstrate that the proposed method achieves a trade-off between routine operational costs and extreme-scenario security. By optimizing day-ahead charging/discharging schedules of energy storage, it reduces scheduled power shortfalls under low-PV and high-load conditions, as well as peak-period power import dependence, thereby enhancing the power support capability and supply resilience of the active distribution network. Full article

This study aims to propose a class of binary Darboux transformations for an integrable nonlinear Schrödinger system with two symmetric matrix potentials. The associated Lax pairs of AKNS type ensure the existence of these binary Darboux transformations, whose explicit single-step applications yield soliton solutions. Notably, the M-matrix appearing in the formulation of the binary Darboux transformations must be constructed within an extended framework, allowing for cases where eigenvalues coincide with adjoint eigenvalues, thereby encompassing generalized Darboux transformations as well. Full article

Vehicular ad hoc networks (VANETs) have become a vital component of intelligent transport systems, with their security concerns increasingly drawing attention. To safeguard user privacy and ensure data authenticity and integrity, researchers have devised numerous certificateless conditional privacy-preserving authentication (CLCPPA) schemes. However, existing schemes generally suffer from insufficient security or high computational and communication overhead. Moreover, most implicitly assume the existence of a secure channel between vehicles and trusted entities during pseudonym generation and transmission, making it difficult to meet the real-time demands and practical deployment requirements of VANETs. To address these issues, this paper constructs a certificateless aggregated conditional privacy-preserving authentication (CL-ACPPA) scheme under elliptic curve cryptography that does not require bilinear operations. Formal security analysis demonstrates that, under the Random Oracle Model and the elliptic curve discrete logarithm problem assumption, the proposed scheme resists adaptive chosen-message attacks from adversaries with varying capabilities. Performance analysis and experimental results demonstrate that, compared with existing schemes, the proposed scheme achieves higher security while maintaining low communication and computational overhead. Full article

Accurate carbon price prediction and fluctuation analysis are essential for carbon market risk management and achieving carbon neutrality goals. However, carbon prices exhibit complex multi-scale nonlinear dynamics intertwined with time-varying external factors, hindering reliable forecasting. This study constructs a hybrid prediction framework integrating CEEMDAN, Sample Entropy (SE) reconstruction, and the Coefficient of Variation (VC) ensemble algorithm. SHapley Additive exPlanations (SHAP) quantifies scale-specific nonlinear factor contributions, while TVP-SV-VAR captures dynamic carbon price-driver correlations. Empirical results indicate that the CEEMDAN-SE preprocessing strategy significantly improves the prediction accuracy of baseline models. The CEEMDAN-SE-GRU model achieves optimal performance, with an R² of 0.96 and over 60% reductions in both MSE and MAE relative to the baseline GRU model. Meanwhile, the VC ensemble outperforms single models and alternative fusion strategies. SHAP identifies scale-heterogeneous drivers: short-run prices follow sentiment and macro outlooks, medium-run trends tie to industrial costs and global markets, long-run paths align with energy transition and global climate governance. The TVP-SV-VAR model uncovers significant time-varying spillover effects on raw carbon prices. Based on these findings, we recommend establishing multi-layered dynamic monitoring and early warning systems, constructing a differentiated and adaptive policy toolkit, refining cross-market risk isolation and buffering mechanisms, and advancing institutional improvements through gradual implementation. Full article

Accurate mapping of rubber plantations is essential for sustainable land management and forest conservation in tropical regions. However, existing methods face two major challenges: persistent cloud cover limits the effectiveness of optical remote sensing in tropical areas, and regional phenological heterogeneity hinders the transferability of fixed-parameter approaches. This study proposes a Phenology-Adaptive Rubber Plantation Mapping (PARM) framework that integrates Sentinel-1 SAR time-series data with optimally selected spectral indices through a cascading constraint architecture. The framework operates as a structurally coherent system wherein SAR-derived phenological anchors explicitly govern downstream optical analysis across three internally dependent modules. First, three key phenological nodes—leaf-off start (LOS), fastest greening point (FGP), and full canopy point (FCP)—are extracted directly from SAR VH-polarization backscatter time series, enabling cloud-independent extraction of phenological temporal anchors. Second, the Jeffries–Matusita (JM) distance, evaluated within SAR-constrained phenological windows, is employed to identify the optimal vegetation and water indices for each region from six candidate spectral indices. Third, a time-weighted Rubber Plantation Discrimination Index (RPDI) is constructed using the selected indices and locally extracted phenological nodes, thereby amplifying the coupled signals of canopy greenness and moisture dynamics during critical phenological transitions. The framework was validated in Hainan Island and Vietnam, two regions with contrasting phenological regimes, using a spatial-block partitioning protocol (leave-one-subregion-out combined with DBSCAN-based clustering) designed to prevent samples from the same plantation from occurring in both training and test subsets. Within the Dynamic World forest mask, PARM achieved overall accuracies of 92.04% and 91.24%, respectively (93.57% and 91.00% on the fully held-out Qionghai City and Gia Lai province subregions), with Kappa coefficients exceeding 0.81 in both regions, consistently outperforming schemes based on raw spectral bands, individual spectral indices or direct multi-source time-series stacking. Error structure analysis revealed that residual classification failures are primarily associated with landscape fragmentation, stand immaturity, and residual cloud contamination, delineating the generalizability boundaries of the framework. These results demonstrate that tightly coupling SAR-based phenological characterization with adaptive optical index selection through a cascading constraint architecture provides a reliable foundation for rubber plantation mapping in cloud-prone tropical regions. Full article