Search Results (4,974)

With the increasing penetration of distributed energy resources (DERs) and the rapid development of active distribution networks, the traditional centrally controlled operation mode can no longer meet the flexibility and autonomy requirements under the multi-dimensional coupling of sources, networks, loads, and storage. To achieve regional energy self-balancing and autonomous operation, this paper proposes a partitioned configuration method for energy storage systems (ESSs) in energy-autonomous distribution networks based on autonomous unit division. First, the concept and hierarchical structure of the energy-autonomous distribution network and its autonomous units are clarified, identifying autonomous units as the fundamental carriers of the network’s autonomy. Then, following the principle of “tight coupling within units and loose coupling between units,” a comprehensive indicator system for autonomous unit division is constructed from three aspects: electrical modularity, active power balance, and reactive power balance. An improved genetic algorithm is applied to optimize the division results. Furthermore, based on the obtained division, an ESS partitioned configuration model is developed with the objective of minimizing the total cost, considering the investment and operation costs of ESSs, power purchase cost from the main grid, PV curtailment losses, and network loss cost. The model is solved using the CPLEX solver. Finally, a case study on a typical multi-substation, multi-feeder distribution network verifies the effectiveness of the proposed approach. The results demonstrate that the proposed model effectively improves voltage quality while reducing the total cost by 20.89%, ensuring optimal economic performance of storage configuration and enhancing the autonomy of EADNs. Full article

In the governance of intangible cultural heritage (ICH), traditional rituals often fall into a paradox of institutional exhibition. The Korean shamanic rites Ssitgim-gut and Byeolsin-gut, respectively, represent the two poles of ritual institutionalization, displaying semiotic logics of original iconicity and institutional textualization. This study, based on audiovisual materials, archival records, and performative documentation, constructs event-level coding of the signifier–subsystem–power relation and, through hierarchical regression and Mann–Whitney nonparametric tests, proposes the Dual-Axis Symbolic Regime Model (DSRR)—comprising the Symbolic Purification–Differentiation Axis (S) and the Textual–Institutional Axis (I). Results indicate that along the S-axis, the purification segments of Ssitgim-gut, dominated by iconic signifiers of soul pacification, manifest a shaman-centered unipolar power structure, whereas its performance segments, involving community participation, reveal a collaborative and co-performative power distribution. Moreover, institutionalization significantly affects the distribution of symbolic power. Along the I-axis, after Byeolsin-gut was incorporated into ICH stage performances, its ritual signifiers became scripted and codified, acquiring administrative value; consequently, the power gap between shamans and families narrowed, and interpretive authority shifted toward institutional agencies. These results remain robust after controlling for media-related variables.In conclusion, the DSRR model elucidates the correlation between symbols and power, offering empirical insights for ICH governance—specifically, how to preserve ritual integrity while avoiding the semantic attenuation of symbols caused by over-textualization. Full article

Atmospheric water harvesting (AWH) offers a decentralized and renewable solution to global freshwater scarcity. Bio-derived and hybrid aerogels, characterized by ultra-high porosity and hierarchical pore structures, show significant potential for high water uptake and energy-efficient, low-temperature regeneration. This PRISMA-guided systematic review synthesizes evidence on silica, carbon, MOF-integrated, and bio-polymer aerogels, emphasizing green synthesis and circular design. Our analysis shows that reported water uptake reaches up to 0.32 g·g⁻¹ at 25% relative humidity (RH) and 3.5 g·g⁻¹ at 90% RH under static laboratory conditions. Testing protocols vary significantly across studies, and dynamic testing typically reduces these values by 20–30%. Ambient-pressure drying and solar-photothermal integration enhance sustainability, but performance remains highly dependent on device architecture and thermal management. Techno-economic models estimate water costs from USD 0.05 to 0.40 per liter based on heterogeneous assumptions and system boundaries. However, long-term durability and real-world environmental stressor data are severely underreported. Bridging these gaps is essential to move from lab-scale promise to scalable, commercially viable deployment. We propose a strategic roadmap toward 2035, highlighting the need for improved material stability, standardized testing protocols, and comprehensive life cycle assessments to ensure the global viability of green aerogel technologies. Full article

Hierarchical Bayesian models based on Gaussian processes are considered useful for describing complex nonlinear statistical dependencies among variables in real-world data. However, effective Monte Carlo algorithms for inference with these models have not yet been established, except for several simple cases. In this study, we show that, compared with the slow inference achieved with existing program libraries, the performance of Riemannian manifold Hamiltonian Monte Carlo (RMHMC) can be drastically improved by applying the chain rule for the differentiation of the Hamiltonian in the optimal order determined by the model structure, and by dynamically programming the eigendecomposition of the Riemannian metric with the recursive update of the eigenvectors at the previous move. This improvement cannot be achieved when using a naive automatic differentiator included in commonly used libraries. We numerically demonstrate that RMHMC effectively samples from the posterior, allowing the calculation of model evidence, in a Bayesian logistic regression on simulated data and in the estimation of propensity functions for the American national medical expenditure data using several Bayesian multiple-kernel models. These results lay a foundation for implementing effective Monte Carlo algorithms for analysing real-world data with Gaussian processes, and highlight the need to develop a customisable library set that allows users to incorporate dynamically programmed objects and to finely optimise the mode of automatic differentiation depending on the model structure. Full article

This study investigates the coupled relationship between groundwater chemistry, lithology, and structural features in the dry zone of Netiyagama, Sri Lanka, within a fractured crystalline basement. Groundwater chemistry fundamentally reflects geological conditions determined by rock-water interactions, we hypothesized that the specific spatial patterns of groundwater chemistry in heterogeneous fractured systems are distinctly controlled by integrated effects of lithological variations, structurally driven flow pathways, aquifer stratification, and geochemical processes, including cation exchange and mineral-specific weathering. To test this, we integrated hydrogeochemical signatures with mapped hydrogeological data and applied multi-stage multivariate analyses, including Piper diagrams, Hierarchical Cluster Analysis (HCA), and Principal Component Analysis (PCA), and various bivariate plots. Piper diagrams identified five distinct hydrochemical facies, but these did not correlate directly with specific rock types, highlighting the limitations of traditional methods in heterogeneous settings. Employing a multi-stage multivariate analysis, we identified seven clusters (C1–C7) that exhibited unique spatial distributions across different rock types and provided a more refined classification of groundwater chemistries. These clusters align with a three-unit aquifer framework (shallow weathered zone, intermittent fracture zone at ~80–100 m MSL, and deeper persistent fractures) controlled by a regional syncline and lineaments. Further analysis through bivariate diagrams revealed insights into dominant weathering processes, cation-exchange mechanisms, and groundwater residence times across the identified clusters. Recharge-type clusters (C1, C2, C5) reflect plagioclase-dominated weathering and short flow paths; transitional clusters (C3, C7) show mixed sources and increasing exchange; evolved clusters (C4, C6) exhibit higher mineralization and longer residence. Overall, the integrated workflow (facies plots + PCA/HCA + bivariate/process diagrams) constrains aquifer dynamics, recharge pathways, and flow-path evolution without additional drilling, and provides practical guidance for well siting and treatment. Full article

This paper examines how county-level government in China formulates and implements solar photovoltaic (PV) policies through an adaptive-governance lens, using Lin’an District (Hangzhou) as a case study. Drawing on multi-level policy document analysis and 30 semi-structured interviews with government officials, developers, grid actors and experts, we identify three stages of local PV development (rooftop diffusion; rapid utility-scale expansion; and market-oriented regulatory adjustment). Key governance innovations include a district PV task force, an industry alliance, and a dual acceptance safety mechanism that together accelerated deployment while managing technical and political risks. We show how adaptive governance operates within an authoritarian, hierarchical system by combining top-down targets with bottom-up development and stakeholder coordination. The findings illuminate practical trade-offs between market liberalization and regulatory control, and provide transferable lessons for other developing countries pursuing decentralized renewable energy transitions. Full article

This paper establishes a rigorous analytical framework for a nonlinear multi-order fractional differential system governed by the generalized $\sigma$-Hilfer operator in weighted Banach spaces. In contrast to existing studies that often treat specific kernels or fixed fractional orders in isolation, our approach provides a unified treatment that simultaneously handles multiple fractional orders, a tunable kernel $\sigma \left(\varsigma \right)$, weighted integral conditions, and a nonlinearity depending on a fractional integral of the solution. By converting the hierarchical differential structure into an equivalent Volterra integral equation, we derive sufficient conditions for the existence and uniqueness of solutions using the Banach contraction principle and Mönch’s fixed-point theorem with measures of non-compactness. The analysis is extended to Ulam–Hyers stability, ensuring robustness under modeling perturbations. A principal contribution is the systematic classification of the system’s symmetric reductions—specifically the Riemann–Liouville, Caputo, Hadamard, and Katugampola forms—all governed by a single spectral condition dependent on $\sigma \left(\varsigma \right)$. The theoretical results are illustrated by numerical examples that highlight the sensitivity of solutions to the memory kernel and the fractional orders. This work provides a cohesive analytical tool for a broad class of fractional systems with memory, thereby unifying previously disparate fractional calculi under a single, consistent framework. Full article

Understanding the causal relationships between safety factors is essential for successful intervention in industries with intrinsically high-risk environments such as the construction industry. Therefore, the aim of this study is to employ the Interpretive Structural Modeling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques to analyze and map the interdependencies among various safety-related elements affecting construction safety. According to the results, resource allocation was shown to be the highest-level, most independent element in the analysis, highlighting its function as the primary facilitator of safety initiatives. This strategic commitment directly drives Management Commitment and Competence, which form the core organizational support structure. Mid-level elements that translate management intent into site-level practice include workers’ training, safety motivation, and communication structure. The frequency of safety observations, workers’ involvement in safety decisions, and subcontractor and procurement management—the immediate procedural controls—are then used to assess operational efficacy. Crucially, the most dependent factor was found to be Workers’ Compliance, indicating that frontline safety behavior is the result of efficient management at all higher levels. Therefore, in order to improve overall safety performance in construction, this research emphasizes the importance of improving resource provision and leadership commitment. The outputs of the current study provide an organized, evidence-based roadmap for selecting interventions. Full article

Object detection in adverse weather remains challenging due to the simultaneous degradation of visibility, structural boundaries, and semantic consistency. Existing restoration-driven or multi-branch detection approaches often fail to recover task-relevant features or introduce substantial computational overhead. To address this problem, DLC-SSD, a lightweight degradation-aware framework for detecting robust objects in adverse weather environments, is proposed. The framework integrates image enhancement and feature refinement into a single detection pipeline and adopts a hierarchical strategy in which global and local degradations are corrected at the image level, structural cues are reinforced in shallow high-resolution features, and semantic representations are refined in deep layers to suppress weather-induced noise. These components are jointly optimized end-to-end with the single-shot multibox detection (SSD) backbone. In rain, fog, and low-light conditions, DLC-SSD demonstrated more stable performance than conventional detectors and maintained a quasi-real-time inference speed, confirming its practicality in intelligent monitoring and autonomous driving environments. Full article

The development of shale gas reservoirs relies on complex fracture networks created via multistage hydraulic fracturing, yet most existing models still use oversimplified fracture geometries and therefore cannot fully capture the coupled effects of multiscale fracture topology on flow and production. To address this gap, in this study, we combine fractal geometry with the Embedded Discrete Fracture Model (EDFM) to analyze the production dynamics of hydraulically fractured horizontal wells in shale gas reservoirs. A tree-like fractal fracture network is first generated using a stochastic fractal growth algorithm, where the iteration number, branching number, scale factor, and deviation angle control the self-similar hierarchical structure and spatial distribution of fractures. The resulting fracture network is then embedded into an EDFM-based, fully implicit finite-volume simulator with Non-Neighboring Connections (NNCs) to represent multiscale fracture–matrix flow. A synthetic shale gas reservoir model, constructed using representative geological and engineering parameters and calibrated against field production data, is used for all numerical experiments. The results show that increasing the initial water saturation from 0.20 to 0.35 leads to a 26.4% reduction in cumulative gas production due to enhanced water trapping. Optimizing hydraulic fracture spacing to 200 m increases cumulative production by 3.71% compared with a 100 m spacing, while longer fracture half-lengths significantly improve both early-time and stabilized gas rates. Increasing the fractal iteration number from 1 to 3 yields a 36.4% increase in cumulative production and markedly enlarges the pressure disturbance region. The proposed fractal–EDFM framework provides a synthetic yet field-calibrated tool for quantifying the impact of fracture complexity and design parameters on shale gas well productivity and for guiding fracture network optimization. Full article

In this study, we present Hierarchical Local Interpretable Model-agnostic Explanations (H-LIME), an innovative extension of the LIME technique that provides interpretable machine learning insights across multiple levels of data hierarchy. While traditional local explanation methods focus on instance-level attributions, they often overlook systemic patterns embedded within educational structures. To address this limitation, H-LIME aggregates local explanations across hierarchical layers, Institution Type, Location, and Educational Level, thereby linking individual predictions to broader, policy-relevant trends. We evaluate H-LIME on a student adaptability dataset using a Random Forest model chosen for its superior explanation stability (approximately 4.5 times more stable than Decision Trees). The framework uncovers consistent global predictors of adaptability, such as education level and class duration, while revealing subgroup-specific factors, including network type and financial condition, whose influence varies across hierarchical contexts. This work demonstrates the effectiveness of H-LIME at uncovering multi-level patterns in educational data and its potential for supporting targeted interventions, strategic planning, and evidence-based decision-making. Beyond education, the hierarchical approach offers a scalable solution for enhancing interpretability in domains where structured data relationships are essential. Full article

In recent years, achieving ultra-wideband electromagnetic absorption has emerged as a critical challenge in confronting advanced broadband electromagnetic detection technologies. This capability is essential for effectively countering sophisticated radar systems. In this study, we present a novel multilayer metamaterial absorber that integrates an FR4 transmission layer, a periodic gradient dielectric structure designed for resonant impedance matching, and a magnetic skin layer for enhanced energy dissipation. By employing asymptotic gradients in both structure and composition, this design achieves dual-gradient electromagnetic parameter modulation, enabling efficient absorption across the X, Ku, and K bands (8.6–26.4 GHz) with a total thickness of 3.5 mm (effective thickness: 2 mm) and a density that is one-third that of conventional magnetic metamaterials. The proposed absorber demonstrates polarization insensitivity, stability across wide incident angles (up to 60°), and an absorption efficiency of 94%, as confirmed by full-wave simulations and experimental validation. Moreover, the fiber-reinforced hierarchical structure addresses the traditional trade-off between broadband absorption performance and mechanical load-bearing capacity. Full article

Moso bamboo, widely distributed in subtropical regions of China, plays an important role in forest management and carbon cycle research. However, accurate estimation of tree density and aboveground biomass (AGB) remains challenging due to the unique characteristics of Moso bamboo forests in their growth and stand structure. This research aims to develop a new procedure for bamboo tree density and AGB estimation based on UAV-LiDAR and sample plots from multiple sites through comparative analysis of the incorporation of two groups of variables—regular point cloud metrics (e.g., height, point density) and layered texture metrics—and three modeling methods—multiple linear regression (MLR), mixed-effects modeling (MEM), and hierarchical Bayesian modeling (HBM). The results showed that incorporating layered texture metrics with regular variables substantially improved the estimation accuracy of both tree density and AGB. Among these models, HBM achieved the highest predictive performance, yielding coefficient of determination (R²) values of 0.54 for tree density and 0.59 for AGB, with corresponding relative root mean square errors (rRMSE) of 21.46% and 17.97%. This study presents a novel and effective method for estimating Moso bamboo tree density and AGB using multi-site UAV-LiDAR and sample plots, offering a scientific basis for precise management and carbon stock assessment. Full article

This study examines the structural evolution of Knowledge Innovation Networks (KINs) and Technology Innovation Networks (TINs) across Chinese cities (2015–2024). Using SCI/SSCI co-authorship and prefecture-level patent data, we construct dual-layer networks and assess their resilience through metrics such as average clustering coefficient, path length, global efficiency, and largest-component ratio. Our framework clarifies how network structure, spatial proximity, and urban hierarchy jointly shape innovation dynamics and opportunity distribution. Three main findings emerge. First, KINs have moved toward polycentricity yet remain hierarchically rigid, with persistent core–periphery gaps despite improved connectivity in tier 2–4 cities. TINs show greater cross-tier adaptability, creating new innovation gateways while intensifying intra-tier polarization. Second, under simulated disruptions, KINs are vulnerable to targeted attacks and exhibit path-dependent degradation, whereas TINs maintain efficiency until a critical threshold, then collapse abruptly. Third, MRQAP analysis reveals that economic and geographic proximity facilitate collaboration in KIN but constrain linkages in TINs, with spatial proximity exerting a stronger influence on knowledge flows. These results demonstrate how innovation networks mediate urban–rural interactions, affect spatial inequality, and shape regional resilience. We argue for differentiated policies that strengthen core–periphery connectivity while mitigating proximity-induced lock-in, fostering more inclusive, resilient, and sustainable urban innovation systems. Full article

Conventional educational assessments enforce a rigid and symmetrical framework of identical question sequences upon a learner population inherently defined by asymmetry in cognitive capabilities and knowledge profiles. This mismatch results in inefficient measurement, where the uniform distribution of difficulty fails to mirror the heterogeneous nature of student learning. We address these topological and informational asymmetries through HARLA-ED, a hybrid framework combining deep knowledge modeling with intelligent question selection. The system integrates hierarchical cognitive graph networks to map the structural symmetries of concept dependencies while tracking evolving knowledge states across multiple time scales. By capturing both immediate working-memory constraints and long-term retention patterns, the model resolves the temporal asymmetry between learning and forgetting rates. A hierarchical reinforcement learning agent then orchestrates an assessment strategy through three decision levels: high-level planning determines diagnostic objectives, mid-level control sequences question types, and low-level actions select specific items. Crucially, the agent employs information-theoretic reward functions designed to restore distributional symmetry in assessment outcomes, ensuring demographic parity and minimizing algorithmic bias. Empirical results demonstrate a 47.5% average reduction in assessment duration compared to standard computer-adaptive tests while preserving measurement accuracy. The system successfully adapts to varying proficiency levels, effectively bridging the information asymmetry between the testing system and the learner’s true latent state. Full article