Search Results (22,398)

The impact of PM_2.5 pollution on the health inequality of vulnerable groups is a core issue in environmental justice research. However, existing studies in China mostly focus on severely polluted areas in northern China. They lack comparative cases in economically developed eastern regions. They also rarely consider changes in the impact of air pollution on residents’ health amid rapid urbanization. Based on multi-source data, this study employed spatial visualization, spatial autocorrelation analysis and spatial regression models. It investigated the impact of PM_2.5 pollution on the health inequality of vulnerable elderly groups in 92 districts and counties of Jiangsu Province from 2010 to 2020. The results show that: first, the regional pattern of health inequality between PM_2.5 pollution and vulnerable elderly groups in Jiangsu has continuously evolved, with a “lower in the south and higher in the north” pollution pattern and high overlap between high-pollution areas and high elderly health risk areas in northern Jiangsu. Second, the spatial coupling between PM_2.5 and elderly health risks has gradually strengthened, showing significant positive spatial agglomeration in 2020, confirming obvious spatial agglomeration characteristics of air pollution’s health impact. Third, the adverse health impact of PM_2.5 on vulnerable elderly groups became significant in 2020, exhibiting cumulative and lagged characteristics; urbanization and regional coordinated development have played a positive role in alleviating regional health inequality, while a lagging energy structure further exacerbates the health vulnerability of the elderly. This study fills the gap of insufficient research on economically developed eastern regions and provides targeted empirical references for urban refined governance and precise prevention and control of environmental health inequality. Full article

Bias temperature instability (BTI) degradation is commonly described using empirical power-law kinetics; however, extraction of the time exponent and projection of lifetime remain highly sensitive to baseline definition and data representation. In conventional approaches, the threshold voltage shift is referenced to an initial value that cannot be measured simultaneously with stress, introducing uncertainty that can produce apparent curvature and variability in the extracted exponent. In this work, a baseline-independent linearization method is applied to representative published datasets spanning advanced silicon, SiC MOSFETs, and GaN power devices. By analyzing the measured degradation trajectories directly in a transformed time coordinate, the method removes curvature associated with baseline ambiguity and enables consistent extraction of the effective power-law exponent. Across all material systems examined, the extracted exponent exhibits systematic dependence on applied stress once baseline effects are reduced. This behavior challenges the commonly assumed constant-exponent formulation used in conventional lifetime projections and shows that even modest variations in the exponent can produce large differences in projected time-to-failure. A transformed lifetime representation based on is introduced, in which the influence of exponent variation is separated from the intrinsic voltage and temperature acceleration of the degradation rate. In this representation, the extracted acceleration parameters become more stable and physically interpretable. This formulation is consistent with standard reliability frameworks, including JEDEC JEP122G, in which the time exponent enters directly into the lifetime expression. These results demonstrate that baseline-independent analysis provides a unified framework for interpreting BTI degradation across disparate semiconductor technologies and suggest that explicit treatment of stress-dependent exponents is required for physically consistent lifetime modeling. Full article

Efficient calibration is essential for the practical application of option pricing models. The Fractional Stochastic Volatility Jump Diffusion (FVSJ) model can reproduce several stylized features observed in option markets, including the volatility smile, volatility clustering, and long-memory effects. However, its multiple stochastic components make conventional calibration computationally expensive. This paper proposes a two-step calibration framework that combines a neural network with a differential evolution (DE) algorithm. In the first step, we construct a Physics-Informed Kolmogorov–Arnold Network (PCKAN) to approximate the FVSJ pricing map. Specifically, we replace the B-spline basis in KAN with second-kind Chebyshev polynomials and incorporate a Black–Scholes PDE residual as an additional penalty term in the training objective, aiming to improve global approximation and enhance numerical stability and interpretability. In the second step, the trained PCKAN is used as a fast surrogate pricer within the DE algorithm to accelerate parameter estimation. Empirical results show that the proposed method achieves calibration accuracy comparable to direct pricing while substantially reducing computational time. Full article

Although organisational climate is increasingly examined, explicit modelling of demographic interaction effects remains comparatively underrepresented. A search strategy was conducted (25 September 2025), and 358 records were identified and filtered in the Scopus and Covidence databases; subsequently, 60 peer-reviewed articles met the inclusion criteria following PRISMA-guided screening. R-project, reference to VOSviewer, and Biblioshiny were used to perform the bibliometric mapping to demonstrate three (3) large thematic clusters: (1) conceptual models with a focus on the Job Demands–Resources (JD–R) framework; (2) growing cross-sector and post-COVID literature; and (3) small but growing incorporation of interactive demographic variables (age, gender, tenure) other than control-variable treatment. The results show that organisational climate is always placed at the forefront as an important predictor of satisfaction, but intersectional demographic modelling is underdeveloped and geographically biased to Western and Asian factors. Yet improvements have been made in theoretical integration; however, a lack of constructs, methodological conservatism, and geographic skewness limit theoretical cumulation and practical translation. The proposed multi-factor model is conceptually derived from bibliometric patterns and requires empirical validation using CFA, SEM, and multilevel modelling. However, organisations should integrate satisfaction policies that reflect diverse demographic and contextual realities, rather than adopting a general approach. The study advances the model of employee satisfaction research by offering practical evidence and a theoretical framework to support the sustainability of industrial and organisational psychology. Full article

Japan’s import market for wood pellets has expanded rapidly since the introduction of the feed-in tariff (FIT) scheme in 2012, with imports exceeding six million tonnes in 2024, positioning Japan as the world’s second-largest wood pellet importer. Despite this expansion, empirical evidence on its demand structure remains limited. This study employs a Dynamic Linear Approximate Almost Ideal Demand System (Dynamic LA-AIDS) model incorporating demand inertia stemming from long-term fuel supply contracts to analyze Japan’s wood pellet import demand from 2012Q1 to 2025Q3. The results reveal a distinct two-tiered structure: North American pellets behave as a strategic necessity, exhibiting price-inelastic demand and a tendency toward a stable long-run procurement pattern following price and expenditure shocks, suggesting procurement practices that prioritize supply security under long-term contracts. In contrast, Vietnamese pellets behave as a price-sensitive commodity, displaying price-elastic demand and relatively sustained responsiveness following such shocks. These results indicate a dual procurement strategy under the FIT scheme that balances stability and cost flexibility. Importantly, the Japanese demand structure differs from the more uniformly price-inelastic patterns observed in the EU and South Korean markets, providing new insights into how institutional frameworks shape biomass allocation and market responsiveness in renewable energy systems. Full article

A novel method based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) combined with a modified multiple branch convolutional neural network (MMBCNN) is designed to detect fault events in distribution networks and to classify various faults in a distribution system. Given the presence of noise components in transient voltage signals, a moving time window technique integrated with the NA-MEMD method is employed to process high-frequency sampling and long-term series signals. This method is also utilized to reliably identify noise components in modal components through permutation entropy. On this basis, the Clarke transform is employed to convert transient voltage signals into the d–q axis, and three-phase voltage waveforms are transformed into a ring image. Moreover, an MMBCNN is developed to accurately detect and classify distribution network faults, and a modified pooling function is introduced to improve feature extraction ability and model convergence performance. Finally, the accuracy and effectiveness of the proposed algorithm are estimated and analyzed using measurement and fault simulation data from distribution networks. Full article

Artificial intelligence (AI) tools are increasingly integrated into mathematics education, yet most reviews emphasize achievement rather than how AI shapes mathematical thinking. This scoping review mapped literature published between 2020 and 2026 on AI-supported mathematics learning through three cognition frameworks: APOS (Action–Process–Object–Schema), Sfard’s process–object duality and reification, and Conceptual Image theory. Searches were conducted in Scopus, Web of Science, ERIC, PsycINFO, Education Source, and IEEE Xplore, followed by duplicate removal and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR)-aligned screening. Twenty-one peer-reviewed studies met inclusion criteria (18 empirical studies plus three theoretically oriented studies). Evidence growth accelerated after 2022, with most studies situated in secondary and higher education. Large language models (LLMs) and Intelligent Tutoring Systems (ITS) were the most frequently investigated modalities. Across studies, AI commonly supported theoretically inferred action-level execution and procedural management (APOS) via adaptive feedback, hinting, and stepwise scaffolding, and it often broadened learners’ conceptual images through multiple representations and generated explanations. However, these interpretations were necessarily cautious, because very few studies directly operationalized theory-linked conceptual mechanisms such as process internalization, object encapsulation, reification, or alignment between conceptual images and formal definitions. In LLM-supported contexts, gains in explanation quality coexisted with risks of procedural outsourcing when students relied on generated solutions without prior reasoning. By contrast, ITS-based environments more often supported tightly structured procedural engagement, suggesting that different AI modalities afford different forms of cognitive support and risk. Overall, AI’s conceptual impact appears to depend less on tool availability and more on instructional orchestration (task design, prompting, and teacher mediation). The findings also suggest that sustainability-related dimensions—particularly learner agency, transparency of AI support, and equitable participation—are closely connected to whether AI use promotes durable conceptual learning rather than superficial performance gains. Future research should operationalize cognitive transitions, assess structural understanding, and report AI-use conditions transparently to support cumulative, theory-driven synthesis. Full article

Flying Ad Hoc Networks (FANETs) composed of multiple unmanned aerial vehicles (UAVs) are a promising solution for emergency wireless communications when terrestrial infrastructure is unavailable. However, ensuring reliable Quality of Service (QoS) in these highly dynamic networks remains challenging due to topology changes, varying propagation conditions, and congestion. This work proposes a lightweight machine learning-based QoS optimization framework for multi-UAV emergency communications that combines realistic mobility modeling, empirical channel measurements, and adaptive traffic prioritization. UAV mobility patterns are generated with ArduSim, while LoS/NLoS propagation models are derived from real UAV flight experiments and integrated into ns-3. Multiple supervised machine learning algorithms—including Decision Trees, Random Forest, Support Vector Machines, k-NN, Gradient Boosting, and CatBoost—are trained using four input features derived from the network state: ${\mathrm{CBR}}_{\mathrm{src}}$, ${\mathrm{QP}}_{\mathrm{src}}$, ${\mathrm{CBR}}_{\mathrm{dst}}$, and ${\mathrm{QP}}_{\mathrm{dst}}$. Simulation results show that the proposed AI SMOTE EMERGENCY scheme, based on CatBoost, improves the Packet Delivery Ratio (PDR) by approximately 43% over the No-QoS baseline, achieving 89–93% delivery across all four application ports. Compared with EDCA, the proposed scheme maintains reliable delivery for all services, increases emergency throughput by 34–36%, and reduces end-to-end delay by about 70%. In addition, the higher delivery reliability translates into clear communication energy benefits, reducing energy waste across all evaluated topologies when compared with the No-QoS baseline. The inference time remains below 0.002 s, supporting real-time QoS adaptation in resource-constrained UAV networks. 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

Small and Medium-sized Enterprises (SMEs) are central to the industrial fabric of Sub-Saharan Africa (SSA). Yet, they confront increasing demands to implement sustainability practices originating from institutional contexts markedly different from their own. Existing research has tended to neglect the cultural and institutional negotiations inherent in this process, often framing sustainability adoption as a technical or compliance-oriented exercise rather than as a multifaceted cultural adaptation. This study proposes and empirically examines the concept of sustainability acculturation—the process by which firms align global sustainability norms with local business cultures. Drawing on Institutional Theory, the Resource-Based View, and Berry’s Acculturation Model, we present a context-specific framework, tested using a sequential explanatory mixed-methods approach: survey data from 284 manufacturing SMEs across six SSA countries, followed by 24 semi-structured interviews. Structural equation modeling reveals that international market pressure and owner–manager values are direct drivers, whereas local regulatory pressure exhibits only a weak association with deep cultural integration. Managerial commitment and organizational learning mediate these relationships, while Ubuntu values enhance social sustainability integration, and institutional voids diminish regulatory effectiveness. The model accounts for 57% of the variance in sustainability acculturation. Findings show that SSA SMEs employ distinct acculturation strategies—Integration, Assimilation, Resilient Adaptation, and Decoupling—shaped by the interplay of external pressures, internal capabilities, and contextual conditions. The study underscores the importance of culturally attuned, context-specific interventions for sustainable industrial development in SSA. Full article

Climate change poses a significant threat to water resources and agricultural sustainability, particularly in semi-arid and socio-economically vulnerable regions such as South Africa. This review synthesizes empirical, modelling, and policy-based evidence on the impacts of climate change on water availability, crop production, and adaptation strategies in the country, drawing on approximately 162 peer-reviewed studies and institutional reports published between 2010 and 2025. The findings indicate that rising temperatures, shifting rainfall patterns, and an increasing frequency of extreme events, such as droughts and floods, are intensifying water stress and disrupting agricultural systems. Hydrological models consistently project declines in runoff, soil moisture, and streamflow, while crop simulation models predict reductions in the yields of major staple crops, including maize, wheat, and sorghum, particularly under high-emission scenarios. Although localized improvements in water availability and crop productivity may occur, these tend to be limited and highly context-specific. In response, South Africa has implemented a range of adaptation strategies, including climate-smart agriculture, water-efficient irrigation, ecosystem-based approaches, and policy-driven interventions. However, their effectiveness remains constrained by institutional fragmentation, limited financial capacity, and persistent socio-economic inequalities, particularly among smallholder farmers. The review underscores the need for integrated, inclusive, and context-specific adaptation strategies that strengthen governance, enhance the science–policy interface, and improve access to climate finance. The insights provided offer valuable guidance for advancing climate resilience in South Africa and other vulnerable regions across the Global South. 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

This study examines the relationship between AI leadership, human-centered independence, and organizational innovation processes and outcomes, challenging the prevailing assumption that leadership-driven AI adoption is inherently associated with improved performance. The research draws on a dual-structured model of AI leadership—AI-driven innovation leadership (Sun) and reflective AI governance leadership (Moon)—to examine whether these approaches are associated with human capability development and innovation performance. Data were collected from 2754 respondents across diverse organizational contexts using a structured survey. The measurement model was validated through exploratory and confirmatory factor analysis, and the hypotheses were tested using structural equation modeling (SEM). The results indicate that none of the proposed positive relationships are empirically supported. Neither leadership dimension shows a statistically significant relationship with human-centered independence or innovation performance, while the only statistically significant relationship is negative, indicating that human-centered independence, when not integrated with AI, is associated with lower levels of innovation outcomes. The absence of mediation and negligible explained variance further indicate the lack of an integrated structural relationship among the examined constructs. These findings challenge linear models of AI leadership by showing that the coexistence of AI-oriented leadership and human-centered capabilities does not ensure their integration. The study proposes the AI–Human Misalignment Framework as an interpretative lens, suggesting that innovation outcomes may depend on alignment rather than the mere presence of capabilities. Full article

Higher technical and professional education is increasingly discussed in relation to workforce readiness, innovation, and societal inclusion. In Italy, the PNRR-funded Rome Technopole operates as a multi-institutional ecosystem in which universities, research organizations, industry, and public bodies interact through a Hub & Spoke model to support training and innovation activities. Among its components, Spoke 4 addresses professional higher technical education through the co-development of modular learning initiatives involving multiple stakeholders. This commentary examines the role and activities of the Italian National Institute of Health (ISS) within this context, with particular reference to the development of two pilot modules: one on Artificial Intelligence and Algorethics, and one on Accessibility and Assistive Technologies, including applications supported by AI. The paper combines a conceptual discussion of the approach with selected empirical insights derived from pilot implementation, including stakeholder engagement processes, structured evaluations, and thematic prioritization exercises. The findings suggest the perceived relevance of multi-stakeholder co-design, the use of flexible and modular learning formats, and the integration of technical and ethical dimensions in higher technical education. At the same time, they point to challenges related to coordination, scalability, and alignment across institutional actors. Rather than proposing a definitive model, the Spoke 4 experience is discussed as a context-specific case that may offer insights contributing to ongoing debates on the design and implementation of higher technical education in complex, multi-institutional settings. Full article

This study proposes an innovative instructional approach to promote scientific literacy by integrating the Nature of Science and the Nature of Scientific Inquiry with experimental practice and the history of physics. The aim is to foster a deep understanding of how scientific knowledge is constructed and to promote informed trust in science. Using an explicit and reflective methodology, the intervention combines experimental activities with historical reflection. The core of the learning sequence is the experimental reconstruction of Galileo’s studies on falling bodies, based on the historical manuscript folio 116v, an original document that provides the empirical evidence for the law of falling bodies, illustrating the transition from raw experimental data to mathematical formalization. Through this activity, students engage with key epistemic aspects of scientific practice, including the management of uncertainty—distinguished into statistical/aleatory and structural/epistemic forms—the probabilistic nature of scientific knowledge, the predictive power of models and theories, and the underdetermination of scientific theories. Additional themes addressed include the role of thought experiments, the importance of communicating results for scrutiny and validation, the function of models as mediators between theory and phenomena, and the process of de-idealization. The study also challenges the persistent myth of a single, linear “scientific method,” highlighting instead the theory-laden character of scientific inquiry and the central role of the scientific community. This dimension is explored through the historical comparison between Galileo and Mersenne, which illustrates elements of the scientific ethos and the role of peer review as a mechanism for the correction and refinement of knowledge. The results obtained with pre-service teachers, with whom this instructional sequence was implemented, indicate that this contextualized approach facilitates the overcoming of a view of science as a set of absolute truths. Instead, it promotes a more mature understanding of science as a dynamic, provisional, and self-correcting human enterprise, while equipping future citizens with the critical tools necessary to navigate the challenges of the twenty-first century. Full article