Search Results (1,164)

Huizhou covered bridges represent a unique and irreplaceable component of China′s architectural heritage, yet they are increasingly threatened by flash floods. In the Huizhou region, complex mountainous terrain, concentrated intense rainfall, and structural aging jointly exacerbate flood damage risks. Existing flood risk assessment approaches often prioritize external hydrodynamic hazards or assume linear additive effects, overlooking the complex interactions among inherent structural and physical attributes. To address this limitation, this study integrates Random Forest (RF) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to develop a flood risk assessment framework capable of capturing both nonlinear relationships and configurational (asymmetric) causal mechanisms. Based on field investigations of 89 covered bridges and 116 documented damage cases from 2020 to 2024, the RF model identifies six key risk factors (ACC = 0.79, AUC = 0.87), several of which exhibit pronounced nonlinear and threshold effects. Building on these results, fsQCA further reveals eight equivalent configurational pathways leading to covered bridge damage (solution coverage = 0.66, solution consistency = 0.94), highlighting multiple causal combinations rather than a single dominant driver. The results demonstrate that the disaster resilience of covered bridges emerges from interactions among structural characteristics, management conditions, and spatial scale attributes, rather than from any individual factor alone. Accordingly, this study advocates a shift in protection strategies from conventional “one-size-fits-all” structural reinforcement toward risk-pattern-oriented, precision-based non-structural interventions. By combining predictive modeling with configurational causal analysis, this research provides a system-level understanding of flood-induced damage mechanisms and offers actionable insights for flood risk mitigation and sustainable conservation of covered bridge heritage in Huizhou and comparable regions worldwide. Full article

Background: Exercise-induced fatigue (EIF) impairs performance and recovery and may contribute to overreaching/overtraining and adverse health outcomes. Beyond classical explanations (substrate depletion, metabolite accumulation, oxidative stress), accumulating evidence indicates that the gut microbiota modulates fatigue-related physiology through metabolic, immune, barrier, and neurobehavioral pathways. Methods: We conducted a structured narrative review of PubMed and Web of Science covering 1 January 2015 to 30 November 2025 using predefined keywords related to EIF, gut microbiota, recovery, and nutritional interventions. Human studies, animal experiments, and mechanistic preclinical work (in vivo/in vitro) were included when they linked exercise load, microbial features (taxa/functions/metabolites), and fatigue-relevant outcomes. Results: Across models, high-intensity or prolonged exercise is consistently associated with disrupted gut homeostasis, including altered community structure, reduced abundance of beneficial taxa, increased intestinal permeability, and shifts in microbial metabolites (e.g., short-chain fatty acids). Evidence converges on four interconnected microbiota-mediated pathways relevant to EIF: (1) energy availability and metabolic by-product clearance; (2) redox balance and inflammation; (3) intestinal barrier integrity and endotoxemia risk; and (4) central fatigue and exercise motivation via microbiota–gut–brain signaling. Nutritional strategies—particularly targeted probiotics, prebiotics/plant polysaccharides, and selected bioactive compounds—show potential to improve fatigue biomarkers and endurance-related outcomes, although effects appear context-dependent (exercise modality, baseline fitness, diet, and baseline microbiota). Conclusions: Current evidence supports a mechanistic role of the gut microbiota in EIF and highlights microbiota-targeted nutrition as a promising adjunct for recovery optimization. Future work should prioritize causal validation (e.g., fecal microbiota transplantation and metabolite supplementation), athlete-focused randomized trials with standardized fatigue endpoints, and precision approaches that stratify individuals by baseline microbiome features and training load. Full article

Emerging mid-sized cities in the Global South face growing challenges in aligning transport infrastructure with equity and safety objectives under conditions of rapid urban expansion. While accessibility metrics are widely used in transport planning, their ability to capture functional inequalities and safety-related dynamics remains limited, particularly in corridor-level assessments. This study examines structural and functional accessibility patterns along two planned BRT corridors in Querétaro, Mexico, an emerging mid-sized Latin American city. The analysis integrates spatial accessibility indicators, selected urban process proxies related to inequality and road safety, and AI-based modeling to explore non-linear spatial associations across fine-grained corridor segments. Structural accessibility is evaluated using network-based indicators, while functional accessibility reflects observed service dynamics and operational conditions. Spatial correlations and artificial neural networks are employed as exploratory tools to identify co-occurring patterns rather than causal relationships. Results reveal pronounced spatial mismatches between structural and functional accessibility, socio-spatial marginalization, and crash concentration, highlighting corridor segments where future BRT implementation may either reinforce or mitigate existing inequalities. By framing BRT corridors as test cases, the study contributes a transferable diagnostic framework for assessing accessibility–equity–safety tensions in emerging mid-sized cities. Full article

This study constructs an urban energy consumption structure transformation (UECST) index and utilizes a double machine learning model to investigate the impact and underlying mechanisms of green finance policies on this transformation. Based on panel data from 281 prefecture-level cities in China from 2010 to 2022, we find that green finance policies significantly promote the UECST. This finding holds after a series of robustness checks and endogeneity tests. Furthermore, our analysis reveals that these policies facilitate the transition not only through direct financial support but also indirectly by driving green technological innovation, enhancing green economic efficiency, and promoting industrial upgrading. The positive impact is more substantial in central cities, transportation hubs, non-resource-based cities, non-old industrial bases, and key environmental protection cities. By providing empirical evidence and policy insights, this study contributes to optimizing green finance policy design and addressing specific bottlenecks in energy transition, thereby supporting the achievement of the “Beautiful China” development goal. Full article

As social media influencers increasingly shape sustainable consumption, understanding the psychological mechanisms underlying consumer responses is essential. Drawing on social influence theory and reactance theory, this study examines how influencer characteristics affect sustainable behavioral intentions through perceived pressure and consumer reactance, while considering the moderating role of green self-identity. Using survey data from 382 respondents, the proposed model was tested using partial least squares structural equation modeling (PLS-SEM). Given the cross-sectional research design and the reliance on self-reported data, the findings should be interpreted as associational rather than strictly causal. The results show that influencer expertise, homophily, and social influence significantly increase perceived pressure. Perceived pressure, in turn, positively influences consumer reactance, which negatively affects sustainable purchase intention and positively affects avoidance intention. In addition, green self-identity significantly moderates the relationship between perceived pressure and reactance, such that consumers with a stronger green self-identity exhibit heightened sensitivity to perceived pressure and experience stronger reactance responses, indicating heightened sensitivity among environmentally self-identified consumers. These findings extend existing sustainability and influencer marketing research by revealing the dual and potentially counterproductive effects of persuasive communication. The study highlights the importance of autonomy-supportive and identity-consistent messaging for promoting sustainable consumption and provides practical guidance for designing effective influencer-based sustainability strategies. Full article

Social security systems constitute a structurally significant component of public finance in developing economies and often generate persistent fiscal pressures through budgetary transfers. Demographic transformation, widespread informality in labor markets, and weaknesses in contribution-based financing increase the dependence of social security systems on public resources. The objective of this study is to examine whether budget transfers to the social security system affect fiscal sustainability in Turkey by analyzing their relationship with the budget deficit and the public sector borrowing requirement. The analysis employs annual data for Turkey covering the period of 1984–2024. A comprehensive time-series econometric framework is adopted, incorporating conventional and structural-break unit root tests, the ARDL bounds testing approach with error correction modeling, and the Toda–Yamamoto causality method. The empirical findings provide evidence of a stable long-run relationship among the variables. The results indicate that social security budget transfers exert a statistically significant and persistent effect on the public sector borrowing requirement, while no direct long-run effect on the headline budget deficit is detected. Causality results further confirm that fiscal pressures associated with social security financing materialize primarily through borrowing dynamics rather than short-term budgetary imbalances. By explicitly modelling social security budget transfers as an independent fiscal channel over a long historical horizon, this study contributes to the literature by offering new empirical insights into the fiscal sustainability implications of social security financing in Turkey. The findings also provide policy-relevant evidence for developing economies facing similar institutional, demographic, and fiscal challenges. Full article

This article develops a structural–analogical framework to investigate conceptual resonances between Qur’an 24:35—the Verse of Light—and contemporary relational models in physics, while maintaining firm epistemic boundaries between theology, philosophy, and empirical science. The Qur’anic metaphors of niche, glass, tree, oil, and layered light depict a graded ontology of manifestation in which being unfolds through ordered relations grounded in a transcendent divine command (amr). By contrast, modern physics—as represented by quantum field theory, loop quantum gravity, and cosmological models—operates entirely within immanent causality, conceiving spacetime and matter as relational, dynamic, and structurally emergent. Despite their distinct registers, both discourses converge structurally around a shared grammar of potentiality, relation, and manifestation. Drawing on classical Islamic metaphysics—especially al-Ghazālī’s Mishkāt al-Anwār—alongside contemporary relational ontologies in physics (Smolin, Rovelli, Markopoulou), the article argues that “real time” functions as an ontological choice that conditions intelligibility, agency, and novelty. The Qur’anic notion of nūr is interpreted not as physical luminosity but as the metaphysical ground of determinability, while the quantum vacuum is treated as a field of latent potential—without suggesting empirical equivalence. Rather than concordism, the comparison highlights a structural resonance (used here as a heuristic notion indicating pattern-level affinity rather than equivalence, correspondence, or empirical verification): both traditions affirm that reality is neither static nor substance-based, but arises through dynamic relational processes grounded—whether transcendently or immanently—in principled order. Full article

Organic food consumption exemplifies the broader shift toward sustainable lifestyles and environmentally responsible choices in the current market generation. Despite the extensive use of the Theory of Planned Behavior (TPB) to explain consumer purchase intentions, its predictive power for decision making has come into question. This paper aims to enhance the relevance of using TPB in an emerging country to forecast consumer behavior in contemporary conditions. This study set out to investigate the determinants of organic food purchase intention and behavior among South African Generation Y consumers by applying the TPB model. By combining structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the research could capture both the symmetrical (linear) and asymmetrical (configurational) causal mechanisms underlying purchase intention. While symmetrical results highlight that attitude, subjective norms, and purchase intention predict purchase behavior, asymmetrical findings indicate that no single antecedent is necessary for high purchase intention; rather, two sufficient causal configurations emerge, in which either attitude or perceived behavioral control acts as a core condition, depending on its combination with other antecedents. Attitude and perceived behavioral control thus serve as core conditions leading to high purchase intention among South African Generation Y consumers. In sum, the findings suggest that strengthening pro-environmental attitudes through targeted communication strategies and improving the accessibility and perceived ease of purchasing organic food can serve as practical and implementable pathways to foster more sustainable consumption in emerging markets. Full article

The aim of this article is to identify and systematize the principal research directions in sustainable strategic management (SSM) at the intersection of eco-innovation and business performance. Despite the growing prominence of sustainability in management scholarship, systematic understanding of how SSM, eco-innovation, and business performance are connected in the academic literature remains limited. In particular, it is unclear whether this intersection constitutes a coherent research domain or instead reflects a set of loosely related and fragmented lines of inquiry. To address this gap, the study combines bibliometric analysis and science mapping of 181 Scopus-indexed publications (2006–2024) with a PRISMA-guided scoping review of five core papers that explicitly link SSM, eco-innovation, and business performance. VOSviewer was used to identify thematic clusters and structural gaps, including missing or weak linkages between eco-innovation and different dimensions of business performance. Building on these findings, the article proposes a dual-path conceptual model: (1) a mediated path in which eco-innovation functions as a transmission mechanism between SSM and multidimensional business performance, and (2) a direct path linking SSM to business performance without mediation. The model further distinguishes between internal organizational conditions, which predominantly support the direct path, and external business environment factors, which are critical in enabling the mediated path through eco-innovation. The main contributions are as follows: (a) a structured mapping of the SSM–eco-innovation research field and its emerging thematic architecture; and (b) a conceptual model specifying the dual role of eco-innovation in shaping business performance outcomes. The study also outlines implications for theory, managerial practice, and public policy, particularly in terms of how organizations and their environments influence the effectiveness of different strategic sustainability pathways. The proposed framework should be interpreted as an evidence-informed conceptual model derived from bibliometric patterns and focused qualitative synthesis, rather than as a statistically validated causal model. Full article

The first 1000 days of life, starting from conception to a child’s second birthday, constitute a pivotal period for fetal lung and pulmonary vascular development. Maternal nutrition during this period plays an important role in fetal growth, immune programming and organ development, including that of the pulmonary system. This narrative review consolidates evidence linking maternal nutrition and early-life nutrient intake during this period with the development of pulmonary hypertension in the newborn. We examine the influence of both nutrient deficiencies and excesses on fetal lung and vascular development. We performed a structured search of PubMed and Embase (conducted from February 2025 to March 2025) and screened reference lists. Twenty-eight peer-reviewed studies were included, comprising human clinical and observational evidence and studies on animal models. The findings suggest that imbalances in maternal diet can disrupt placental function, induce inflammation, and trigger epigenetic alterations, all contributing to pulmonary vascular dysfunction and increased pulmonary hypertension susceptibility in neonates. Notably, maternal undernutrition and thiamine deficiency during lactation have been directly linked to pulmonary hypertension in infants. Conversely, high-fat diets and excessive polyphenol intake have been associated with adverse fetal cardiovascular remodeling. While current evidence is primarily derived from animal models and observational studies, it highlights the urgent need for targeted nutritional strategies and clinical trials during pregnancy. Although human causality is unproven for most exposures, studying maternal nutrition in the first 1000 days could offer a cost-effective method for reducing the burden of pediatric pulmonary hypertension and its long-term consequences and for prospective trials aimed at preventing early-life pulmonary vascular disease. Full article

Digital transformation poses significant challenges to employee well-being, particularly in public administration, where hierarchical structures, increasing digitalization pressures, and high mental health-related absenteeism underscore the need to understand individual and job demands and resources. This study explores these aspects from the perspectives of employees and supervisors in public administration. Between September 2023 and February 2024, semi-structured interviews were conducted with eight employees and eleven supervisors from public administration organizations in Northern Germany and analyzed using deductive–inductive qualitative content analysis based on the Job Demands-Resources model. Identified individual resources included technical affinity, error tolerance, and willingness to learn, while key job resources involved early and transparent communication, attentive leadership, technical support, and counseling services, with most job resources linked to leadership behavior and work organization. Reported job demands comprised insufficient participation, inadequate planning, and lengthy procedures, whereas personal demands included fears and concerns about upcoming changes and negative attitudes toward transformation. The variation in perceived demands and resources highlights the individuality of the employees’ experiences. The findings provide initial insights into factors influencing psychological well-being at work during digital transformation, emphasizing the importance of participatory communication, employee involvement, leadership awareness of stressors, and competence development. Future research should employ longitudinal and interventional designs to improve causal understanding and generalizability. Full article

Traffic systems are quintessential complex systems, characterized by nonlinear interactions, multiscale dynamics, and emergent spatiotemporal patterns over complex networks. These properties make traffic prediction highly challenging, as it requires jointly modeling stable global topology and time-varying local dependencies. Existing graph neural networks often rely on predefined or static learnable graphs, overlooking hidden dynamic structures, while most RNN- or CNN-based approaches struggle with long-range temporal dependencies. This paper proposes a Spatiotemporal Causal–Trend Network (SCTN) tailored to complex transportation networks. First, we introduce a dual-path adaptive graph learning scheme: a static graph that captures global, topology-aligned dependencies of the complex network, and a dynamic graph that adapts to localized, time-varying interactions. Second, we design a Gated Temporal Attention Module (GTAM) with a causal–trend attention mechanism that integrates 1D and causal convolutions to reinforce temporal causality and local trend awareness while maintaining long-range attention. Extensive experiments on two real-world PeMS traffic flow datasets demonstrate that SCTN consistently achieves superior accuracy compared to strong baselines, reducing by 3.5–4.5% over the best-performing existing methods, highlighting its effectiveness for modeling the intrinsic complexity of urban traffic systems. Full article

We present a unified description of dense matter and neutron star structure based on simple but physically motivated models. Starting from the thermodynamics of degenerate Fermi gases, we construct an equation of state for cold, catalyzed matter by combining relativistic fermion statistics with the liquid drop model of nuclear binding. The internal stratification of matter in the outer crust is described by the $\beta$-equilibrium, neutron drip and a gradual transition to supranuclear matter. Short-range repulsive interactions inspired by Quantum Hadrodynamics are incorporated at high densities in order to ensure stability and causality. The resulting equation of state is used as input in the Tolman–Oppenheimer–Volkoff equations, yielding self-consistent neutron star models. We compute macroscopic stellar properties including the mass–radius relation, compactness and surface redshift that can be compared with recent observational data. Despite the simplicity of the underlying microphysics, the model produces neutron star masses and radii compatible with current observational constraints from X-ray timing and gravitational-wave measurements. This work demonstrates that physically transparent models can capture the essential features of neutron star structure and provide valuable insight into the connection between dense-matter physics and astrophysical observables; they can also be used as easy-to-handle models to test the impact of more complicated phenomena and variations in neutron stars. Full article

This article proposes a redefinition of scientific authorship under conditions of algorithmic mediation. We shift the discussion from the ontological dichotomy of “tool versus author” to an operationalizable epistemology of contribution. Building on the philosophical triad of instrumentality—intervention, representation, and hermeneutics—we argue that contemporary AI systems (notably large language models, LLMs) exceed the role of a merely “mute” accelerator of procedures. They now participate in the generation of explanatory structures, the reframing of research problems, and the semantic reconfiguration of the knowledge corpus. In response, we formulate the AI-AUTHorship framework, which remains compatible with an anthropocentric legal order while recognizing and measuring AI’s cognitive participation. We introduce TraceAuth, a protocol for tracing cognitive chains of reasoning, and AIEIS (AI epistemic impact score), a metric that stratifies contributions along the axes of procedural (P), semantic (S), and generative (G) participation. The threshold between “support” and “creation” is refined through a battery of operational tests (alteration of the problem space; causal/counterfactual load; independent reproducibility without AI; interpretability and traceability). We describe authorship as distributed epistemic authorship (DEA): a network of people, artifacts, algorithms, and institutions in which AI functions as a nonsubjective node whose contribution is nonetheless auditable. The framework closes the gap between the de facto involvement of AI and de jure norms by institutionalizing a regime of “recognized participation,” wherein transparency, interpretability, and reproducibility of cognitive trajectories become conditions for acknowledging contribution, whereas human responsibility remains nonnegotiable. Full article

This study formulates and verifies a network-based evaluation methodology for appraising the sustainable competitiveness of community enterprises. Based on Social Capital Theory, the Resource-Based View (RBV), and Network Theory, the model defines high-quality networks as structural relational circumstances that facilitate resource sharing and knowledge sharing, serving as mediating mechanisms that improve competitive outcomes. A quantitative study approach was utilized, gathering survey data from 451 representatives of community enterprises around Thailand, and Structural Equation Modeling (SEM) was applied to assess both measurement features and structural relationships. The model demonstrates satisfactory internal reliability, convergent validity, and discriminant validity, affirming measurement adequacy. Empirical evidence indicates that high-quality networks are positively correlated with sustainable competitiveness, both directly and indirectly, with 49.2% of the overall effect conveyed through resource and knowledge exchange, emphasizing the practical value of network-based processes. The suggested model offers practical utility for policymakers and development agencies in search of evidence-based instruments to enhance competitiveness, network capacity, and long-term resilience in community enterprises. The cross-sectional methodology and lack of contextual control variables restrict causal inference and external generalizability, highlighting the necessity for longitudinal or quasi-experimental expansions. By emphasizing model creation and empirical validation, this study develops a systematic and reproducible methodological framework for assessment. Full article