Search Results (5,043)

The Gulong shale oil reservoir is characterized by high clay content and strong heterogeneity, with substantial variations in mineral composition among different intervals. However, existing fracability evaluation methods for such continental shales remain inconsistent and often rely on oversimplified two-dimensional fracture descriptors, lacking a multi-parameter quantitative framework derived from three-dimensional fracture characterization. In this study, the Q1 and Q9 members of the Gulong shale oil were selected, and laboratory-scale hydraulic fracturing simulation experiments were conducted using supercritical carbon dioxide (SC-CO₂), liquid CO₂, and water as the fracturing media. Within a fractal-theory framework based on CT-derived three-dimensional reconstructions, a multi-scale evaluation index system was established by integrating fractal dimension, fracture density, and spatial connectivity. The experimental results demonstrate that fluid properties exert a decisive influence on rock failure behavior. Owing to its ultra-low viscosity and strong diffusivity, SC-CO₂ can significantly reduce formation breakdown pressure while effectively activating natural weak planes to generate a more complex fracture network. For the Q9 shale, the breakdown pressure under SC-CO₂ is reduced by 11.91% and 8.33% relative to water and liquid CO₂, respectively. Moreover, the fracture fractal dimension reaches 2.41 under SC-CO₂, which is markedly higher than the values obtained under liquid CO₂ (2.18) and water (2.12). Mineral composition and densely developed bedding are the key factors inducing fracture branching and deflection, whereas injection rate and an asymmetric stress field regulate the internal energy-release rate and stress path, thereby influencing fracture crossing capability and aperture evolution. Based on the experimental dataset, a fracture complexity index (FCI) evaluation model was developed: under SC-CO₂ fracturing, the FCI values are 8.92 for the Q9 member and 4.43 for the Q1 member, and the model predictions are in good agreement with physical observations. This work elucidates the failure mechanism of the Gulong shale under multi-field coupling and provides a theoretical basis for optimizing hydraulic fracturing and evaluating fracability in unconventional reservoirs through the proposed FCI-based assessment framework. Full article

This study explores “incapacitated transcendence”—a divine presence that remains ontologically present but lacks sovereign power to intervene—within Paul Klee’s angel paintings. It reinterprets these figures as a critical counterpoint to Carl Schmitt’s theory of sovereignty. While Schmitt rationalized the “decision” of an omnipotent sovereign as a secularized miracle, Klee’s malformed or flightless angels present a starkly different theological device. Influenced by Nietzsche, Klee rejected traditional afterlife-oriented transcendence while preserving it as a domain inassimilable to human finitude. Following World War I, this evolved into “incapacity”—not an absence of the divine, but the structural impossibility of its manifestation through fragile human existence. By contrasting Klee’s Angelus Novus with Schmitt’s sovereign, this study argues that incapacitated transcendence deconstructs the myth of absolute political power. Drawing on Walter Benjamin’s “Angel of History,” this study concludes that this very incompetence constitutes a site of hope. This reinterpretation suggests that aesthetics can offer a profound critique of political theology, challenging absolutist models of power in contemporary crisis. Full article

Mountain railway engineering is constrained by fragile ecology, complex terrain, and intensive construction interfaces, making the project-level selection and implementation of green technologies a critical management issue. Existing studies mainly focus on single-technology assessment or static weighting and provide limited support for linking planning-stage prioritization with implementation-stage demonstration. This study proposes a multi-stage importance–performance evaluation framework for green technologies in mountain railway engineering. The framework integrates rough set theory for factor screening, the CRITIC–grey relational method for planning-stage importance assessment, Extremely Randomized Trees for implementation-stage importance identification, and the Logical Framework Approach for deviation diagnosis and management feedback. In the example application, four of seven candidate evaluation factors were retained after rough set reduction, and the corresponding weights for environmental impact, lifecycle, technical efficiency, and compatibility were 0.247, 0.263, 0.264, and 0.238, respectively. Under the simulated construction scenario, stage-to-stage deviations ranged from −0.24 to 0.11. These results show that the proposed framework can identify key factors, compare prescribed and implementation-stage importance patterns, and support adjustment-oriented management decisions. Because the implementation-stage demonstration is based on simulated rather than real project data, this study is positioned as a methodological and conceptual demonstration rather than an empirically validated case study. Full article

This study focuses on the transfer of the celebrity effect to live-stream e-commerce. It examines how the effectiveness of persuasion and the underlying mechanisms change when celebrities shift from live human appearances to AI avatars. Integrating Uncanny Valley Theory and Source Credibility Theory, and conducting a PLS-SEM analysis on 391 valid questionnaires collected from October to November 2025, reveals that, compared to live streaming by real celebrities, virtual streamers using celebrity avatars trigger significantly higher levels of perceived eeriness among consumers. This perceived eeriness systematically weakens audience evaluations of the streamer’s credibility, attractiveness, and expertise, ultimately leading to a decline in purchase intention. The findings suggest that, when the celebrity effect relies on an AI avatar, the persuasive pathway is negatively moderated by technological mediation. Among the dimensions of source credibility, trustworthiness is most directly eroded, while expertise remains the core factor driving purchase decisions. From a human-versus-avatar perspective, this study reveals the key psychological mechanisms underlying the digital migration of the celebrity effect. The results have important theoretical implications for understanding the boundaries of source credibility in digital communication and offer practical insights into the development and optimisation of AI avatar endorsement strategies in live-stream e-commerce. Full article

Background/Objectives: Mental health service utilization gaps remain a persistent global public health challenge. Among the 61.5 million adults with any mental illness in the United States, nearly half went without treatment in the past year, and dropout rates from outpatient services among those who do enter care range from 19.7% to 30.8%. Only 30 to 60% of individuals with lifetime mental illness are in active recovery at any given time. Existing theoretical frameworks, including Andersen’s Behavioral Model, the Health Belief Model, and the COM-B framework, each address isolated phases of the care continuum but offer no unified structure for understanding the complete, sequential journey from first contact through sustained recovery. This article introduces the Access, Initiation, Engagement, Retention, and Recovery (AIERR) model to address this theoretical gap. Methods: A conceptual review was conducted following Hulland’s framework for theory development through narrative synthesis. Literature was identified through targeted searches in PubMed, PsycINFO, and Google Scholar, prioritizing peer-reviewed empirical studies, systematic reviews, and foundational theoretical frameworks. Sources were assigned to AIERR stages using predefined decision rules corresponding to each phase’s defining characteristics. Results: AIERR maps five sequential, interconnected stages: Access (structural, cultural, and systemic conditions enabling service reach), Initiation (the transition from provider identification to first appointment attendance), Engagement (active and meaningful treatment participation), Retention (sustained continuity of care), and Recovery (long-term reclamation of life quality and community belonging). For each stage, the framework identifies individual-level and structural-level barriers, facilitating conditions, and targeted intervention points. Conclusions: AIERR advances mental health services theory by unifying previously siloed frameworks, establishing stage-specificity as a core theoretical principle, and reorienting research and intervention strategy toward the upstream structural conditions that produce downstream utilization failures. These theoretical contributions require empirical testing to confirm. Implications for health equity research, clinical practice, and health systems design are discussed. Full article

The rapid proliferation of generative artificial intelligence (GenAI) tools has created an urgent need for instruments to evaluate their educational value as teachers, faculty, administrators, and instructional designers consider adopting them. While rubrics exist to assess mobile applications and virtual reality tools, no comparable instrument has been developed specifically for large language models (LLMs) and AI media generators. The authors reviewed existing evaluation rubrics for edtech and GenAI tools, with edtech meaning digital tools that support ethical teaching to improve student learning and GenAI referring to neural networks that simulate human interactions by contextualizing relevant content based on learning needs. Grounded in Waks’ framework, the resulting Edu-GenAI Rubric comprises multiple dimensions organized into five domains: the Instrumental, Technical, Hedonic, Use, and Beneficial values. Dimensions include accuracy, productivity, personalization, citation, user interface, user experience, sharing, storage, and ethical dimensions encompassing data privacy, data transparency, guardrails, fair use, and algorithmic discrimination. The Edu-GenAI Rubric offers decision-makers with a preliminary, theory-informed instrument for evaluating GenAI tools in educational contexts that can be applied to institutional adoption decisions, developer benchmarking, and future research. Full article

This paper presents a theoretically grounded integration of deterministic Q-learning with relational game theory (QLRG) for efficiently identifying minimal winning coalitions in Online Social Networks (OSNs). We address the fundamental challenge that coalition formation is NP-hard under traditional approaches by leveraging structural properties of relational dependencies and Armstrong’s axioms to transform the problem into one solvable in polynomial time. Our framework reduces the state space from exponential O(2ⁿ) to O(n²) through a sufficient statistic representation based on coalition size, follower reach, and terminal status, while achieving O(n⁴) time complexity under deterministic, static, and sufficiently symmetric influence structures. The QLRG framework introduces three critical innovations: (1) a principled agent selection mechanism derived directly from the Q-function that eliminates heuristic weight tuning; (2) a formal Boost action defined through temporal closure operators that captures influence spread dynamics; and (3) a constrained MDP formulation that enforces relational consistency through action elimination rather than penalty terms. We prove that the Bellman optimality operator forms a contraction mapping, guaranteeing deterministic convergence to optimal policies with established rates of O(1/√k) for decreasing learning rates or linear convergence up to bias for constant rates. To bridge the gap between this idealized model and the asymmetry inherent in real OSNs, we further develop a cluster-based sufficient statistics approach. By partitioning the network into communities with bounded internal variation, we relax the global symmetry requirement while preserving polynomial state space complexity, and obtaining a single within-community swap changes the optimal Q-value by at most ε_i/(1−γ), which is a local Lipschitz continuity result. The implications of this are both theoretical and practical, and they form the bedrock for relaxing the global symmetry assumption in the QLRG framework. Empirical validation on synthetic networks satisfying the symmetry assumption demonstrates that QLRG consistently identifies minimal winning coalitions matching the optimal solutions found by exhaustive search, while operating with polynomial-time complexity. Unlike conventional approaches, our framework simultaneously satisfies four critical properties: deterministic convergence, policy optimality, minimal coalition identification, and computational tractability. The work bridges computational social science and operations research, providing a mathematically rigorous foundation for strategic decision-making in influencer marketing and coalition formation. While the framework requires symmetry assumptions that may only hold approximately in real-world OSNs, it establishes an idealized baseline for future extensions addressing stochasticity, dynamics, and partial observability. This research represents a paradigm shift from empirical improvements to theoretically grounded convergence guarantees for coalition formation problems, demonstrating how structural mathematical insights can transform intractable problems into efficiently solvable ones without sacrificing solution quality. Full article

Refined decision-making of the design intent is a key factor affecting the iterative design of complex equipment products. While current research on design intent decision-making generally emphasizes methodological innovation, it often neglects the individualized and fuzzy expressive characteristics of cognitive agents, as well as the actual status of the research object. This oversight leads to uncertainty in both design intent and design outcomes. To address these issues, in this paper, a refined decision-making method for the multimodal fuzzy design intent of complex products based on noncooperative–cooperative game serialization is proposed. First, through scenario analysis, the fuzzy design intent evaluation process of different cognitive agents is transformed into a cooperative game model based on a fuzzy network, achieving a preliminary assessment of design intent. On this basis, a noncooperative game-based refined matching and decision-making model for design intent across different dimensions is constructed, thereby completing the final design intent decision-making for a specific product model. Finally, the proposed method is applied to the design intent decision-making process of a CKA6180 CNC machine tool, yielding the conclusion that the two design intents of “good protective performance” and “grand appearance” should be prioritized, thereby verifying the practicality and effectiveness of the method. The analysis of the results reveals the following: ① The application of scenario analysis theory enables a more comprehensive and precise characterization of the design intents of different cognitive agents; ② The construction of a model combining a fuzzy network with a cooperative game facilitates a more complete representation and evaluation of multimodal fuzzy design intent data; ③ The integration of a refined design concept with a noncooperative game model leads to more definitive design intent decision outcomes, thereby reducing the “disturbance” of experience dependence in the early design phase and consequently enhancing subsequent design satisfaction. Full article

In fault diagnosis, multi-source information fusion (MSIF) is usually more reliable than single-source information, and Dempster–Shafer (D-S) evidence theory provides a universal and popular decision-level fusion framework for MSIF. However, existing evidence fusion methods still have two limitations: (1) the overweighting effect of high information volume on unreliable evidence is ignored, and (2) the fusion accuracy cannot be further improved, as only one-time evidence correction is considered. To overcome these limitations, an evidence fusion method based on optimal coordination and iterative correction is proposed for fault diagnosis. Firstly, the credibility and information volume of each piece of evidence are quantified by the Jousselme distance and Deng entropy, respectively. Then, using game theory combination weighting (GTCW), credibility and information volume are optimally coordinated to correct all pieces of evidence, which are then initially fused with Dempster’s rule. Ultimately, taking the initial fusion result as the reference, the credibility is iteratively recalculated to correct and fuse all pieces of evidence until the fusion result converges. The optimal coordination suppresses the overweighting effect caused by high information volume, and the iterative correction breaks the limitation of one-time fusion. Experimental results demonstrate that the proposed method outperforms existing methods and can significantly improve the fusion results in fault diagnosis. Full article

While most existing studies addressing greenwashing emphasize governmental regulation, they often overlook the role of public participation. This study examines how managers’ risk preferences affect enterprise decision-making under public scrutiny. In this study, an agent-based simulation model incorporating enterprises, media, and consumers is established on the basis of signaling theory and the wolf-sheep model. The conclusions are as follows: (1) Both positive and negative public opinion can curb greenwashing behavior in building material enterprises, but negative public opinion intensity is more effective in this context. (2) Regardless of whether building material enterprises operate in positive or negative public opinion scenarios, constraining managers’ risk preferences consistently serves as an effective internal mechanism for curbing greenwashing behavior. (3) The effectiveness of constraint managers’ risk preferences in suppressing greenwashing behavior is influenced by the external public opinion scenario, with its inhibitory effect being more pronounced under negative public opinion scenarios. This study incorporates public opinion as a key external factor and integrates dynamic managers’ risk preferences into a behavioral simulation framework. It expands the theoretical understanding of greenwashing decision-making mechanisms and offers practical implications for regulatory enforcement and enterprise governance. Full article

This article presents methods and hardware for the multi-criteria non-destructive determination of chicken egg quality parameters, implemented using a multifunctional rotary system. Unlike traditional single-criteria sorting, which relies primarily on weight, the proposed approach utilizes a combination of physical and geometric parameters, including weight, linear dimensions, cross-sectional area and perimeter, volume, density, and shape. The experimental framework for the study was formed by measuring the parameters of 750 chicken eggs, covering the entire range of product categories and morphological variations. Geometric parameters were determined using machine vision methods, weight was determined using a strain gauge, and derived parameters were calculated using formalized models. A multi-criteria evaluation algorithm based on fuzzy set theory was used to make the classification decision, accounting for overlapping feature ranges and regulatory differences between EU and EAEU standards. The results of statistical and correlation analysis showed that egg density is identified as a relatively independent diagnostic parameter, weakly correlated with weight and geometric characteristics, justifying its inclusion in the quality model. A comparison of manual and automatic classification revealed differences in boundary categories during single-criteria sorting and indicated the potential of a multi-criteria approach. The obtained results support the feasibility of the developed methods and hardware under the conditions of the present study. Full article

In future active distribution networks with high penetrations of renewable energy, flexible loads are expected to play an increasingly important role as reserve resources to support the sustainable and reliable operation of power grids. Accurate evaluation of flexible load reserve capacity is therefore essential for reliable reserve scheduling. Existing research mainly focuses on the operational characteristics and physical constraints of flexible loads, while insufficiently accounting for user response willingness and the uncertainty of user decision-making behavior, which may lead to biased reserve capacity assessments and impair the sustainability of reserve supply in actual grid operation. To address this issue, this paper proposes a results-oriented reserve capacity evaluation method for flexible loads that explicitly incorporates user response willingness. Specifically, a fuzzy logic system is developed to quantitatively characterize the response willingness of electric vehicle (EV) and air-conditioning (AC) users under multiple influencing factors. Then, a probabilistic modeling approach for user decision-making behavior is established using the theory of planned behavior, enabling explicit representation of behavioral uncertainty. Furthermore, a comprehensive reserve capacity evaluation framework for flexible loads is constructed by integrating user willingness states, sustainable response duration, and operational power constraints. Finally, the case studies demonstrate that the proposed method can effectively improve the objectivity of flexible load reserve capacity assessments while maintaining high user participation willingness, thus supporting the long-term sustainable application of flexible loads as grid reserve resources. Full article

This study examined the critical sustainability challenge of regional demographic decline in South Korea by analyzing how young people’s mobility decisions are intricately influenced by structurally and socially constructed meaning systems. Countering strictly economic deterministic views, this research posited that youth out-migration is a complex socio-cognitive process mediated by social representations of place—collectively constructed and circulated meanings attached to regions. Applying a secondary analysis of Q-sort data from 24 undergraduate students at a regional national university, the study integrated Q methodology with Social Representation Theory to systematically identify youth typologies regarding regional identity, territorial stigma, and local extinction. Participants sorted 44 statements encompassing place attachment, local consumption, cultural experiences, and policy effectiveness. Rigorous factor analysis revealed four distinct perception typologies: identity-based strategic mobility, conditional leaving based on internalized success norms, re-anchoring toward alternative lifestyles, and skeptical leaving rooted in profound institutional distrust. The findings empirically demonstrated that identical structural constraints can produce highly divergent mobility trajectories—ranging from active retention to complete resignation—depending entirely on the region’s socio-cognitive representation. This study demonstrates that local extinction is not merely a demographic condition, but a socially constructed framework of meaning and an object of social representation that shapes youth perception typologies and mobility judgments. Accordingly, moving beyond conventional technical interventions, meaning governance, and strategic communication are needed to help reimagine regional futures. Full article

Urbanization increases pluvial flood risk by expanding impermeable surfaces, which is a trend likely to intensify with climate change. Permeable pavement (PePav) made from industrial byproducts, in accordance with circular economy principles, may improve soil permeability. Public acceptance remains a critical barrier to its implementation. Existing measures of willingness to accept (WtA) new technologies are inconsistent, limiting interdisciplinary collaboration. Therefore, a concise WtA scale was adapted from the Bogardus Social Distance Scale to assess acceptance of PePav at varying levels of proximity in residential contexts, from public flood-prone roads to private yards. The scale was evaluated across three studies: Study 1 (N = 195) and Study 2 (N = 187) utilized mixed student samples, while Study 3 (N = 625) involved a non-student sample. The 5-item solution, identified through factor analysis in Study 1, consistently demonstrated a unidimensional and cumulative structure and satisfactory reliability, even after the proposed PePav ingredient modification in subsequent studies. The scale correlated with recycling experience and professional background, indicating convergent validity, but not with flooding or informal construction experience, across all samples. Study 3 provided evidence of external validity by incorporating empirically well-established Theory of Planned Behavior (TPB) constructs and showing that WtA predicted PePav use beyond TPB variables and demographics. The scale also showed measurement invariance across sample type (student vs. general population) and different levels of construction experience. The constructed WtA scale is suitable for efficiently assessing professional and public acceptance of circular building materials and may have broad cross-disciplinary relevance. This enables timely, targeted interventions and informed policy decisions to advance sustainable technologies in the built environment, with substantial implications for education, professional policy, and sustainable engineering. Nevertheless, further validation is required. Full article

To investigate the inevitable aging of composite insulators under the coupled effects of electrical, thermal, ice, and fog stresses, as well as to explore their aging mechanisms and residual strength prediction methods, this study collected operational insulator samples from four environmental regions: Tibet, Yunnan, Hunan Xuefeng Mountain, and Anhui/Chongqing. Mechanical properties, including tensile strength, elongation at break, and shear resistance, were tested. The results indicate that the degradation of mechanical performance in composite insulation components can be attributed to the synergistic interaction of operational environments and material characteristics, with the aging behavior of high-temperature vulcanized (HTV) silicone rubber exhibiting significant non-linearity. Based on existing research, molecular dynamics simulations were employed to construct microstructural models at different aging stages, and it was verified that main chain scission, reduced system density, and changes in the elemental chemical environment during aging are closely related to the degradation of material mechanical properties. Based on hyper-elastic constitutive theory and fracture mechanics, a quantitative method for assessing the comprehensive aging degree was proposed, with “service years” and “operational altitude” as the core dimensions. A negative exponential model was established to describe the strength degradation of silicone rubber materials. This model enables the non-destructive estimation of the residual mechanical strength of in-service insulators in complex regions without power interruption, providing a decision-making framework for grid operation and maintenance. Full article