Search Results (15,893)

Promoting farmers’ adoption of sustainable agricultural practices is essential for advancing agricultural green transformation and ecological conservation in the Poyang Lake Basin. Current research frequently relies on a single theoretical perspective and insufficiently reveals the synergistic mechanism linking knowledge conversion, psychological cognition, and institutional support. This study integrates the Theory of Planned Behavior (TPB) and Organizational Support Theory (OST) to construct a holistic “knowledge–psychology–behavior–institution” analytical framework. Based on a questionnaire survey of 485 farmers from 12 districts and counties surrounding Poyang Lake, we use structural equation modeling and the Process macro to examine direct effects, mediating effects, and the moderating role of government support. The results show that sustainable knowledge sharing and application significantly improve farmers’ behavioral intention through attitude, subjective norms, and perceived behavioral control, thereby positively promoting actual sustainable practices. Government support plays a significant positive moderating role in the translation of knowledge and psychological factors into behavioral intention. This study enriches the theoretical interpretation of farmers’ pro-environmental behavior from the synergistic perspective of individual cognition and external institutional constraints. The findings provide empirical support for local governments to optimize agricultural extension services, improve policy support systems, and promote coordinated development between ecological protection and high-quality agriculture. Full article

In the context of global climate change and industrial low-carbon transition, whether environmental taxes can simultaneously promote environmental and economic benefits by stimulating corporate green investment remains a central issue in academic research. Existing studies have reached mixed conclusions regarding the effects of environmental taxes, emphasizing either the “innovation compensation” effect or the “crowding-out” effect. However, this binary perspective overlooks the internal boundary conditions under which environmental taxes operate, particularly the roles of market competition and firm-level resource endowments. In particular, limited attention has been paid to how competitive market environments shape firms’ responses to environmental regulation. To address this gap, this study develops an integrated analytical framework that combines external market competition with internal firm endowments. Using China’s 2018 Environmental Protection Tax Law as a quasi-natural experiment and a panel dataset of Chinese listed firms from 2009 to 2024, this study employs a Difference-in-Differences (DID) approach to examine the impact of environmental taxation on corporate green investment. The results show that: (1) the environmental protection tax significantly promotes corporate green investment, with substantial heterogeneity across firm size, ownership structure, and regional institutional environments; (2) market competition serves as an important external moderating mechanism, as intensified competition strengthens firms’ incentives to pursue technological differentiation through green investment, thereby generating an “escape-competition effect”; and (3) from an internal perspective, the effectiveness of environmental taxation is also shaped by firm endowments. High investment activity provides the necessary resource buffer to support strategic pivots, whereas rapid revenue growth and high financial slack (excessive cash ratio) generate strategic inertia, thereby attenuating firms’ responsiveness to the tax shock. This study not only provides empirical evidence from China on the mechanisms through which environmental taxes influence corporate green transformation, but also offers important policy implications for improving environmental tax systems in other countries. Full article

Inspired by the limited research regarding the interrelationships among Environmental, Social, and Governance (ESG) performance, common institutional investors and open innovation, this study adopts stakeholder theory and social network theory as the analytical framework to examine this issue. This study uses data from China’s A-share listed firms between 2018 and 2024, comprising 25,440 firm-year observations. Fixed-effects OLS models are employed to estimate the main relationships. Empirical findings demonstrate that ESG performance significantly promotes open innovation among Chinese companies. Furthermore, the moderating results indicate common institutional investors strengthen the positive association between ESG performance and open innovation. Further analysis confirms that each of the three dimensions of ESG can independently drive open innovation, yet the moderating effect of common institutional investors positively regulates only the relationships between social performance and open innovation as well as between corporate governance performance and open innovation, while exerting no significant impact on the relationship between environmental performance and open innovation. Overall, this study underscores the positive effects of ESG practices by focusing on the perspective of open innovation and integrating common institutional investors, which provides insights for enterprises to optimize their ESG practices and enhance their open innovation capabilities by virtue of external governance. Full article

This study examines the transformative role of digital and artificial intelligence (AI)-enabled public procurement systems in enhancing governance efficiency within smart city environments, with a specific focus on Yerevan, Armenia. As urban administrations increasingly adopt data-driven governance models and digital infrastructures, public procurement remains a critical yet underexplored domain for innovation in transition economies. Despite ongoing e-government reforms in Armenia, procurement systems continue to face challenges related to procedural inefficiencies, limited transparency, and institutional constraints. To address these challenges, the paper develops a Governance Efficiency Framework that integrates digitalization, AI capabilities, and multi-criteria decision-making principles to assess and optimize public procurement processes in urban settings. The framework incorporates key dimensions such as transparency, operational efficiency, accountability, and data integration, enabling a comprehensive evaluation of procurement performance. The empirical application of the framework to the case of Yerevan provides insights into the structural and technological determinants of procurement efficiency in a transition economy context. The findings indicate that while digitalization has contributed to improvements in transparency, significant limitations remain in efficiency and system integration. A scenario-based analysis further suggests that AI-enabled analytics, process automation, and digital procurement platforms have the potential to reduce administrative delays, enhance transparency, and support more strategic and evidence-based decision-making under assumed implementation conditions. By bridging the fields of public procurement, digital governance, and smart city research, this study contributes both theoretically and practically. It offers a structured and adaptable framework for policymakers and urban administrators seeking to modernize procurement systems and strengthen governance efficiency in evolving digital environments. Full article

Heap bioleaching is widely used to extract copper from low-grade sulfide ores thanks to its operational simplicity, low cost, and environmental sustainability. However, current control strategies rely primarily on single-factor optimization and often overlook the synergistic interactions of multiple key parameters, such as ore particle size, pore structure, pH, temperature, microbial activity, and oxygen transfer efficiency. As a result, issues such as low recovery rates, extended leaching periods, and high operational costs persist. Moreover, the “gray-box” nature of heap systems impedes real-time monitoring of internal physical, chemical, and biological processes. In addition, empirical multi-parameter optimization is time-consuming and inadequate for capturing complex interdependencies. This review was conducted to systematically examine the key factors influencing heap bioleaching efficiency and critically evaluate recent advances in numerical simulation and intelligent control strategies. As a result, we identified a major research gap: the existing models—including microscale shrinking core models (SCMs), mesoscale pore-network models based on CT reconstruction, and macroscale continuum models—have inherent limitations. SCMs assume idealized spherical particles with uniform mineral distribution while neglecting pore structure evolution and biofilm dynamics. Mesoscale models offer detailed pore characterization but lack robust multi-physics coupling (thermal–hydro–mechanical–chemical–biological, or THMCB). Macroscale models rely on homogenization assumptions that oversimplify spatial heterogeneity and temporal variations in permeability. This analysis covers the relevant literature from 1985 to 2025, with a focus on three methodological scales (micro, meso, and macro) and their integration with machine learning approaches. A notable finding is that hybrid neural network models (e.g., BP and RBF architectures) outperform purely physics-based models in predicting leaching kinetics under varying operational conditions. However, their accuracy depends heavily on high-quality field data—a limitation rarely addressed in prior reviews. By clearly delineating these model-specific limitations and scale-dependent trade-offs, this review makes two unique contributions: a structured framework for selecting and coupling numerical methods according to process requirements and a roadmap for integrating artificial neural networks with multi-physics simulations to achieve real-time intelligent control of heap bioleaching. The findings offer both theoretical guidance and practical references for optimizing the processing of low-grade copper sulfide ores. Full article

The rapid integration of large language models (LLMs) into education has shifted research focus from questions of capability, such as what LLMs can do and how accurately—to questions of deployability, including how they can be operated effectively for many learners at once. In school environments, system reliability, scalability, and real-time responsiveness are critical, as delays or interruptions can directly reduce learner engagement, particularly during synchronous activities. This study evaluates the performance of open-source LLaMA models ranging from 1 billion to 70 billion parameters across one-, dual-, triple-, and quad-GPU configurations suitable for educational settings. Performance is assessed using four key indicators: success rate (percentage of completed requests), generation speed (tokens per second), throughput (completed responses per second), and latency (time until full response generation). These metrics were measured under progressively increasing numbers of simultaneous users to identify system capacity limits and trade-offs between model size, responsiveness, and scalability. The results indicate that smaller models (1B–3B) deliver faster, more stable performance under concurrent use, while larger models (8B–70B) experience substantial slowdowns and reduced reliability, even on high-end GPU systems. These findings suggest that effective educational deployment should prioritize empirical performance and infrastructure compatibility over model size alone. The paper concludes by proposing a practical framework to guide educators, administrators, and developers in selecting and configuring locally hosted GPU systems that balance model capability, response speed, and resource efficiency for real-time applications such as AI tutors, classroom chatbots, and automated feedback tools. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly used in dental diagnostics, treatment planning, documentation, and research. However, there is limited synthesis of how informed consent should be understood and operationalized in AI-augmented dentistry. This scoping review aimed to map the existing literature on informed consent in AI-assisted dental care and dental research, identify conceptual and practical gaps, and synthesize key domains relevant to ethically robust implementation. Methods: This review was conducted in accordance with PRISMA-ScR and the review question was developed using the Population–Concept–Context framework. Searches were performed in PubMed, Web of Science, and ClinicalKey, supplemented by Google Scholar and reference list screening. English-language sources published between January 2015 and January 2026 were considered if they addressed informed consent, patient information, autonomy, transparency, accountability, or governance in relation to AI use in dentistry or dental research. Results: Of 2624 records identified, 30 sources were included. The reviewed literature consistently emphasized the importance of disclosing AI involvement, clarifying clinician accountability, communicating uncertainty and bias, distinguishing clinical care from research-related consent, and addressing secondary data use. Most included sources were conceptual, ethical, regulatory, or narrative in nature, with limited empirical evidence on implementation or patient outcomes. Conclusions: The available literature suggests that informed consent in AI-augmented dentistry should extend beyond traditional clinician–patient models to explicitly address AI involvement, human oversight, and data governance. Based on recurring themes across the included sources, we propose the ACCOUNT-AI framework as a conceptual synthesis to support future research, policy development, and implementation efforts. Full article

Turbot (Scophthalmus maximus) is a globally important species in both capture fisheries and aquaculture. With the development of the turbot farming industry in China and several European countries, enhancing its aquaculture eco-economic performance has become a key concern among stakeholders. Turbot is a major species in marine fish aquaculture in China. As the world’s leading producer of farmed turbot, the bioeconomic dynamics of this species under recirculating aquaculture systems (RASs) remain poorly understood, which hinders optimal resource allocation, green development, and industrial upgrading of the turbot farming sector. In this study, a bioeconomic model for turbot cultured in industrial RASs was developed based on empirical production data and published literature. The optimal harvesting strategies under the industrial RAS production mode were analyzed. The results indicated the following: (1) for a two-year grow-out cycle commencing with stocking at the beginning of the year, at a farm-gate price of 7.56 USD/kg, the maximum cumulative profit of 41,846.08 USD occurred at t = 22.69 months, while the maximum monthly average profit of 1937.65 USD/month occurred at t = 20.49 months. The optimal harvesting time for single-batch culture was t = 22.69 months, whereas for continuous culture, it was t = 20.49 months. (2) Extended analysis incorporating fish price variation revealed that higher market prices corresponded to later optimal harvesting times. (3) February to April was identified as the optimal stocking window. Based on the bioeconomic dynamics elucidated herein, this study provides a theoretical foundation for related research and proposes producer-oriented strategy recommendations for reference by relevant stakeholders. Full article

This Perspective is dedicated to the memory of Professor Joseph Mark (Joe) Gani (1924–2016) and Professor Christopher Charles (Chris) Heyde (1939–2008), two scholars whose intellectual leadership profoundly shaped applied probability, mathematical statistics, and their interface with finance, insurance, and risk management. Their contributions extend beyond specific technical results to the development of research cultures grounded in probabilistic rigor, empirical relevance, and methodological transparency. We emphasize three enduring themes central to modern quantitative risk analysis. First, the systematic incorporation of heavy-tailed and non-Gaussian features in stochastic modeling, reflecting persistent empirical deviations from classical Gaussian assumptions in financial data. Second, the development of stochastic and time-series methodologies capable of handling dependence structures, including conditional heteroskedasticity and long-range dependence. Third, the principled integration of probabilistic modeling with data-driven and machine learning approaches, ensuring predictive performance is accompanied by interpretability and robustness. We situate these contributions within contemporary challenges in financial risk management, including systemic risk, environmental, social and governance (ESG) considerations, and climate finance. In particular, climate-related financial risks arise from both physical impacts (such as extreme weather events and long-term environmental change) and transition dynamics associated with the shift toward a low-carbon economy (including policy, technological, and market adjustments). These sources of risk introduce additional forms of dependence, nonlinearity, and model uncertainty, particularly in high-dimensional, data-rich settings. This Perspective highlights a forward-looking research agenda that preserves the foundational principles of applied probability while adapting them to modern financial systems characterized by real-time information flows and evolving risk structures. This legacy continues to shape how financial risk is modeled, measured, and understood in increasingly complex and interconnected environments. Full article

Healing agent encapsulated in polymeric microcapsules has proven its ability to seal surface and internal cracks. Focused on mitigating the negative impact of capsules on the properties of fresh cement paste and hardened cementitious matrix, uncertainties in self-healing triggering, and poor control of the released quantity, researchers report technological improvements in predominantly experimental studies. However, practical applications will necessitate lightweight models that capture all the characteristics of practical importance. Analysis of the scientific literature reveals the lack of such models adapted for cementitious composites. In this paper, a model rooted in continuum damage mechanics, tuned based on empirical data, is used in the finite element analysis of concrete specimens containing polymer self-healing microcapsules to quantify self-healing efficiency and local damage-healing behavior. The predicted increase in the self-healing rate is more pronounced for specimens subjected to compression compared to that for elements subjected to four-point bending. Thus, for a 20% increase in healing efficiency, strength recovery in compression increases from 18.5% to 32% for C25 and C30, respectively, whereas the corresponding values for tension in the tension-be-flexure setup are 3.5% and 5.3%. Full article

In the context of revitalizing rural development, farmer entrepreneurship has emerged as a significant driver of rural economic growth. However, existing research has not sufficiently examined the specific mechanisms or heterogeneous effects through which digital skills influence farm household entrepreneurial behavior. This gap is the focus of the present study. Utilizing micro-level survey data collected from 936 farm households across Shandong, Shaanxi, and Henan provinces in 2021, we construct a digital skills index using factor analysis. We then employ a Probit model and an Interaction term model to examine the impact of digital skills on entrepreneurial behavior among Chinese rural households and its underlying mechanisms. Additionally, we explore heterogeneity across different household types. The results show that digital skills are positively associated with entrepreneurial decision-making. Further analysis provides suggestive evidence that this relationship may operate through three channels: shaping risk preferences, expanding relational networks, and improving access to credit. Heterogeneity tests reveal that the promoting effect of digital skills is stronger among disadvantaged households, households with a head younger than 45, and those engaged in opportunity-driven or online entrepreneurship. Theoretically, this study contributes by empirically validating a multi-pathway mechanism framework and identifying relevant boundary conditions. Practically, it offers targeted insights for policymakers to design skill-based interventions and foster inclusive entrepreneurial ecosystems in rural areas. Full article

Rural sustainable development is a core component of the global Sustainable Development Goals, and building digital villages and enhancing the resilience of rural economies are key pathways for underdeveloped regions to achieve rural sustainable development. The coordination and synergy between these two areas are central to rural revitalization. Taking 122 counties in Hunan Province as research units and using 2013–2023 spatial panel data, this study employs an improved coupling coordination model, spatial autocorrelation analysis and geographically weighted regression to explore their spatiotemporal evolution, clustering patterns and driving factors. The results show that both systems improved steadily: digital villages expanded from core areas, while economic resilience developed more balancedly. The coupling coordination evolved from near-disorder to a pattern characterized by regional equilibrium. The coupling coordination degree displayed significant positive spatial autocorrelation, forming an “High-High (H-H)” cluster in the Changsha-Zhuzhou-Xiangtan-Dongting Lake Plain and an “Low-Low (L-L)” cluster in western Hunan. Driving factors showed marked spatial heterogeneity. These findings provide empirical support for differentiated digital village policies in Hunan. Full article

Material selection in architectural design necessitates a multifaceted evaluation of economic, technical, esthetic, and cultural variables. Beyond fundamental requirements such as cost, structural integrity, and transparency, architects must synthesize subjective attributes, including warmth and formality, with objective constraints like multifunctionality and cultural heritage. Despite the strategic impact of material choice on project performance, empirical research systematically categorizing these governing criteria remains sparse. Furthermore, existing methodologies often overlook the psychophysical principles of human perception essential for construction material evaluation. Thus, this study identifies the fundamental factors influencing material selection and establishes a hierarchical framework to prioritize their relative significance within the design process. The research employs a weighted Analytic Hierarchy Process integrated with the Weber–Fechner law (W-AHP) to structure and quantify selection criteria. By incorporating perceptual scaling principles into the AHP framework, the methodology accounts for variations in judgment sensitivity across different evaluation scales. A hierarchical decision model was developed to categorize criteria and sub-criteria, followed by pairwise comparisons to derive priority weights. Results reveal a distinct priority hierarchy among the identified criteria and confirm that judgment sensitivity varies significantly across evaluation scales. The W-AHP method produced differentiated weightings that accurately reflect the psychological intensity of professional decision-making, offering a structured mechanism to balance functional performance with complex design intentions. This study contributes to the field of construction management by introducing the W-AHP method as a novel decision-support tool. The integration of Weber–Fechner perceptual principles enhances weight differentiation and addresses the inherent subjectivity of architectural evaluation, providing a transparent methodology to justify material procurement within a rigorous engineering management context. Full article

Against the backdrop of environmental governance systems transitioning from command-and-control to multi-stakeholder collaboration, elucidating the mechanisms and pathways through which voluntary environmental regulations influence green technological innovation in heavily polluting enterprises holds significant implications for advancing green innovation and high-quality development. This paper systematically examines the synergistic mechanisms of command-and-control versus voluntary environmental regulations on green technological innovation in heavily polluting enterprises, utilising data from listed companies in China’s high-pollution industries between 2008 and 2024. Unlike previous studies predominantly focused on the impact of a single regulatory type, this study reveals an interactive effect between the two: moderate command-and-control regulation provides essential institutional support for voluntary environmental regulation, such as ISO 14001 certification, thereby generating a complementary enhancement effect. However, overly stringent command-and-control regulation diverts innovation resources from enterprises, thereby suppressing the incentive effect of voluntary regulation. This conclusion transcends the traditional analytical paradigm within environmental regulation theory that treats command-and-control and voluntary regulations as mutually exclusive opposites, revealing instead a dynamic relationship where both synergistic and constraining effects coexist. This discovery provides crucial theoretical underpinnings and empirical evidence for constructing an environmental governance system that combines command-and-control constraints with flexible incentives, ensuring compatibility between policy objectives and corporate behaviour. Full article

To address the power optimization problem of LCC-HVDC systems in multi-infeed receiving-end grids under system strength constraints, this paper systematically analyzes the influence mechanism of AC system strength on conventional DC transmission power, clarifying the quantitative relationship between the critical short circuit ratio and the system’s power transmission limit. A novel day-ahead power optimization method for multiple DC links is proposed, incorporating operational constraints such as frequency stability and voltage stiffness. Empirical simulation analysis of the Chinese Zhejiang Power Grid under a low-voltage typical operation mode in the summer of 2025 demonstrates that the optimized DC power transmission scheme significantly improves the system’s frequency response and voltage recovery characteristics under fault conditions, enhancing the overall security and stability level of the multi-infeed HVDC receiving-end grid. This research holds significant reference value for practical engineering applications. Full article