Search Results (61)

The rapid proliferation of fintech has created unprecedented opportunities for enhancing bank credit-risk management and promoting financial sustainability. Using an unbalanced panel dataset of Chinese commercial banks spanning 2013–2023, we construct a bank-specific fintech index through text mining of annual reports combined with an entropy-weighted methodology, and systematically examine the relationship between fintech adoption and credit risk. Our empirical findings reveal that fintech adoption significantly mitigates credit risk, reducing the non-performing loan ratio by an average of 0.9 percentage points. This effect is more pronounced among non-state-owned banks and in regions with less developed service sectors. Mechanism analysis further demonstrates that financial sustainability is a critical transmission mechanism: fintech mitigates credit risk by improving both cost efficiency and asset efficiency, thereby enhancing banks’ economic resilience. Additionally, we find that regional green development is a powerful moderator that significantly amplifies the risk-reducing impact of fintech. These findings offer robust empirical evidence for guiding commercial banks’ digital transformation strategies and informing regulators’ green finance policy formulation. Our results underscore the strategic importance of fintech investment in building more resilient and sustainable banking systems. Full article

Against the backdrop of inclusive development and modernization of employment governance, the limitations of traditional approaches to promoting employment for persons with disabilities—such as information asymmetries and inefficient resource allocation—have become increasingly salient. Building a systematic promotion framework for disability employment has therefore emerged as a critical agenda for advancing modern social governance. Drawing on bounded rationality and information asymmetry theories, this study develops a tripartite evolutionary game model encompassing government, employers, and persons with disabilities. By incorporating key elements such as initial intentions, skill matching, and policy signal transmission, the model analyzes the strategic choices and dynamic interactions among stakeholders. We conduct numerical simulations using delay differential equations (DDEs), perform stability and sensitivity analyses in MATLAB R2024b, and triangulate findings with a practice-based case from Shanghai. The results indicate that persons with disabilities exhibit the highest policy responsiveness within the employment ecosystem and act as the core driver of convergence toward desirable equilibria through four mechanisms: skill-matching effects, policy signal diffusion, perceived institutional fairness, and system-level synergy gains. Although employer subsidies and penalties directly target firms, they exert the strongest psychological incentive effects on persons with disabilities, revealing a “misaligned incentives” feature in policy signaling. Systemic synergy gains activate market network effects, facilitating a pivotal shift from “policy transfusion” to “market self-sustenance.” Based on these findings, we propose a diversified policy toolkit, enhanced policy signaling mechanisms, and innovations in concentrated employment models to support the modernization of disability employment governance. Full article

Under the background of uncertainty brought by the rapid development of AI, participation in AI standardisation is becoming the key for strategic emerging enterprises (SEEs) to break through and achieve sustainable development. This paper selects listed SEEs from the China Strategic Emerging Industries Composite Index jointly issued by China Securities Index Co., Ltd. and the Shanghai Stock Exchange in 2017 as the initial sample. We collect 3430 observations from 380 companies spanning 2010 to 2023. This paper employs a two-way fixed effects model incorporating enterprise clustering. It thoroughly investigates and empirically tests how participation in AI standardisation affects the sustainable development of SEEs under uncertainty. It is found that participation in AI standardisation in the context of uncertainty has a significant positive effect on the sustainable development of SEEs, and this conclusion still holds after employing instrumental variables, difference-in-difference, and a series of robustness tests. Mechanism tests indicate that two transmission paths exist between participation in AI standardisation and the sustainable development of SEEs under uncertainty: digital technology innovation and the dynamic capabilities in the dimensions of learning and absorption as well as change and reconfiguration. However, the dynamic capabilities in the coordination and integration dimensions do not play a significant mediating role. Heterogeneity analyses indicate that participation in AI standardisation contributes more significantly to the sustainable development of SEEs that are not state-owned, face lower environmental and information uncertainty, and are under higher economic policy uncertainty. The findings enrich the research related to AI standardisation and firm sustainability and provide policy recommendations for the sustainable development of SEEs in the context of uncertainty. Full article

Human–environment interactions in antiquity were fundamentally shaped by environmental constraints, with spatial patterns of human construction works reflecting strategic resource optimization. This study employed Geographic Information System (GIS) and binary logistic regression (BLR) to analyze the siting principles of ancient postal buildings in Fujian, China, integrating related environmental factors of elevation, slope, relief amplitude, and distance to rivers. The results revealed significant spatial differentiation, with elevation exhibiting the strongest influence on siting preference, followed by slope, relief amplitude, and distance to rivers. Clustering patterns along coasts and rivers indicated a strategic balance between transmission efficiency and military defense needs. The applicability of the integrated GIS–BLR approach in studying the ancient postal system demonstrates its extensibility to other ancient settlement systems while offering insights for contemporary conservation practice and sustainable development. Full article

Amid intensifying global ESG regulations and the expanding influence of green finance, China’s digital economy policies have emerged as key institutional instruments for promoting corporate sustainability. Leveraging the implementation of the National Big Data Comprehensive Pilot Zone as a quasi-natural experiment, this study utilizes panel data of Chinese listed firms from 2009 to 2023 and applies multi-period Difference-in-Differences (DID) and Spatial DID models to rigorously identify the policy’s effects on corporate ESG performance. Empirical results indicate that the impact of digital economy policy is not exerted through a direct linear pathway but operates via three institutional mechanisms, enhanced information transparency, eased financing constraints, and expanded fiscal support, collectively constructing a logic of “institutional embedding–governance restructuring.” Moreover, disruptive technological innovation significantly amplifies the effects of the transparency and fiscal mechanisms, but exhibits no statistically significant moderating effect on the financing constraint pathway, suggesting a misalignment between innovation heterogeneity and financial responsiveness. Further heterogeneity analysis confirms that the policy effect is concentrated among firms characterized by robust governance structures, high levels of property rights marketization, and greater digital maturity. This study contributes to the literature by developing an integrated moderated mediation framework rooted in institutional theory, agency theory, and dynamic capabilities theory. The findings advance the theoretical understanding of ESG policy transmission by unpacking the micro-foundations of institutional response under digital policy regimes, while offering actionable insights into the strategic alignment of digital transformation and sustainability-oriented governance. Full article

In recent years, the concept of the myofascial network has transformed biomechanical understanding by emphasizing the body as an integrated, multidirectional system. This study advances that paradigm by applying graph theory to model the osteo-myofascial system as an anatomical network, enabling the identification of topologically central nodes involved in force transmission, stability, and coordination. Using the aNETomy model and the BIOMECH 3.4 database, we constructed an undirected network of 2208 anatomical nodes and 7377 biomechanical relationships. Centrality analysis (degree, betweenness, and closeness) revealed that structures such as the sacrum and thoracolumbar fascia exhibit high connectivity and strategic importance within the network. These findings, while derived from a theoretical modeling approach, suggest that such key nodes may inform targeted treatment strategies, particularly in complex or compensatory musculoskeletal conditions. The proposed concept of a polyconnective skeleton (PCS) synthesizes the most influential anatomical hubs into a functional core of the system. This framework may support future clinical and technological applications, including integration with imaging modalities, real-time monitoring, and predictive modeling for personalized and preventive medicine. Full article

Influenza A virus (IAV) poses a significant threat to animal and public health due to its wide host range and potential for interspecies transmission. This study aimed to conduct a comprehensive survey of IAV in a wide range of wildlife in the Orinoco flooded savannas of Colombia, a region of high biodiversity and a strategic location for monitoring viral transmission. Sampling was conducted during both dry and rainy seasons in two localities. ELISA and qPCR targeted 2028 individuals from 173 bird, mammal, and reptile species, 124 of them without previously published reports. There were positive results for 54 species, with 34 representing first-time world reports. Of the qPCR, 13.4% were positive from birds, and 2.9% were from mammals. Seropositivity was identified in 5.7% of birds, 2.7% of mammals, and 1.3% of reptiles. These findings underscore the potential role of these diverse species as reservoirs or incidental hosts in the transmission cycle of IAV, emphasizing the need for expanded research on less-studied taxa and their ecological interactions. The results also contribute to our understanding of the epidemiology of IAV in the Neotropics and can inform future surveillance and mitigation strategies. Full article

This paper investigates integrating a sensory data model for managing an existing 50-year-old building. A primary challenge in retrofitting older structures is the optimal deployment of high-quality sensors, systematic data acquisition, and subsequent data management. To address this, the study implemented a network of over 50 sensors connected via 270 m of wired infrastructure, deliberately avoiding wireless transmission to ensure data reliability. This configuration generates 5568 data points daily, which are archived on a dedicated server. The data is planned for integration into the Campus Geographical Information System (GIS), enabling private and public access. A methodology was employed, involving the strategic placement of sensors based on building use patterns, continuous data monitoring, and iterative sensor performance evaluation. The findings from the study indicate that integrating sensory data through this structured approach significantly enhances building management capabilities. Specifically, the results demonstrate improved energy efficiency and environmental performance, which is particularly relevant for public and educational facilities. The research highlights that a data-driven, monitoring-based management system can optimize operational functions and inform future retrofitting strategies for aging buildings. Full article

As key nodes in the energy transmission network, oil and gas pipeline stations are crucial in ensuring national energy security and stable economic development. The traditional construction mode of “on-site prefabrication and installation” has problems, such as low efficiency, high cost, and large quality fluctuations, which make it difficult to meet current construction needs. Factory prefabrication technology for pipelines has become a key path to solving industry pain points. This article focuses on the factory prefabrication technology of oil and gas station pipelines. By integrating key technologies, such as 3D modeling, automated welding, modular transportation, and intelligent detection, the visualization and digitization of station pipeline design are achieved, providing a basis for prefabrication and processing. They also improve welding quality and efficiency through automated welding technology and non-destructive testing technology. Through research on the planning and construction of prefabrication factories, construction organization and quality management, supply chain management, and information technology applications, real-time monitoring and information management of the construction process have been achieved. Case analysis shows that factory prefabrication can achieve a prefabrication rate of 70% for DN50–DN600 pipelines in the station, 80% for automated welding seams, a total construction period reduction of about 30%, a one-time welding qualification rate of over 96%, and a significant cost reduction, reflecting the significant advantages of factory prefabrication in terms of construction period, quality, and cost. Further research has clarified that factory prefabrication technology can effectively improve the efficiency, quality, and economic benefits of pipeline construction in oil and gas stations, promote the transformation of construction towards a high-efficiency, low-carbon, and sustainable direction, and provide support for the strategic goal of “One National Network”. Full article

Bovine pestiviruses, namely bovine viral diarrhea virus (BVDV) and HoBi-like pestiviruses (HoBiPevs), are endemic viruses in Latin America, and the disease causes significant losses in the agricultural sector. The present review aims to perform a systematic assessment and meta-analysis of the prevalence of bovine pestiviruses in Latin America and their risk factors. Notable heterogeneity was observed in the analyzed groups, with significant prevalence variations based on age and country. However, no differences were found between temporal trends, production systems, or models. Identified risk factors included age, breed, location, reproductive practices, animal purchase, farm management, and biosecurity measures. This systematic review and meta-analysis of BVDV in Latin America provides critical insights to inform decision-making and strategic actions for disease control in the region. The high serological prevalence of bovine pestivirus across Latin America underscores the urgent need for standardized surveillance programs, biosecurity reinforcement, and targeted vaccination strategies. The presence of HoBiPev further complicates current diagnostic and control measures. Future research should focus on disease transmission dynamics, economic impact assessments, and the effectiveness of intervention programs tailored to the region’s diverse livestock production systems. Full article

China has a large number of transmission lines laid in the mountains and forests and other regions, and these transmission lines enable national strategic projects such as the west-east power transmission project. However, the occurrence of mountain fires in the corresponding areas will seriously affect these transmission projects. At the same time, these mountain fires yield fewer image samples and complex backgrounds. Based on this, this paper proposes a transmission line hill fire detection model with YOLOv11 as the basic framework, named meta-learning attention YOLO (MA-YOLO). Firstly, the feature extraction module in it is replaced with a meta-feature extraction module, and the scale of the detection head is adjusted to detect smaller-sized hill fire targets. After this, the re-weighting module learns class-specific re-weighting vectors from the support set samples and uses them to recalibrate the mapping of meta-features. To enhance the model’s ability to learn target hill fire features from complex backgrounds, adaptive feature fusion (AFF) is integrated into the feature extraction process of YOLOv11 to improve the model’s feature fusion capabilities, filter out useless information in the features, and reduce the interference of complex backgrounds in detection. The experimental results show that the accuracy of MA-YOLO is improved by 10.8% in few-shot scenarios. MA-YOLO misses fewer hill fire targets in different scenarios and is less likely to be affected by complex backgrounds. Full article

The rapid evolution of Integrated Energy Systems (IESs) demands robust management of information transmission, which is critical for real-time monitoring, coordination, and operational efficiency. However, the increasing complexity and costs associated with information exchange necessitate effective pricing mechanisms to ensure economic sustainability and optimal resource allocation. This paper presents an evolutionary game-theoretic framework to analyze regulatory strategies governing information transmission within IES. In the context of market dynamics, both market regulators and communication network operators are considered as actors with bounded rationality, emphasizing their strategic interplay within the system. The main contributions include formulating a model that treats communication network operators as independent entities, enhancing the understanding of IES member diversity and interactivity. This study introduces evolutionary game dynamics, providing new insights into optimizing regulatory policies. This paper also innovates by considering asset utilization in defining effective assets, potentially curbing excessive investment by communication network operators and preventing information transmission prices from soaring. A case study is provided to reveal the dynamics and equilibrium states of the regulatory game, offering theoretical support for refining regulatory strategies in IESs. Full article

Rabies virus (RABV; species Lyssavirus rabies) causes rabies, a disease of the central nervous system that invariably results in the death of the host. In South Africa, studies have indicated that RABV is maintained by animal species that include four wildlife carnivore species—the black-backed jackal (Canis mesomelas), bat-eared fox (Otocyon megalotis), yellow mongoose (Cynictis penicillata), and aardwolf (Proteles cristatus)—and domestic dogs (Canis lupus familiaris). The complex natural ecology holds significant implications for the control and elimination of rabies. In this study, confirmed animal rabies case data, including geospatial features, were analyzed for 12,879 laboratory-confirmed animal cases reported on a database managed by the Department of Agriculture, Land Reform and Rural Development (DALRRD). Sequence data generated from animal rabies cases in South Africa were also analyzed, which included 1374 cytoplasmic domain of the glycoprotein and the G-L intergenic sequences using maximum likelihood (ML) and Bayesian inference. The analysis provides insights into the transmission dynamics involving several wildlife species and domestic dogs in South Africa. This information is crucial for the strategic planning for rabies control and elimination programs, and particularly in understanding the interlinked nature of some lineages and the importance of the cross-border spread of rabies. This analysis provided an improved understanding of the distribution of the RABV lineages in South Africa and identified areas that can be targeted for rabies control strategies to limit future spread of RABV, which is important due to the limited available resources that must be carefully managed to allow optimal control. Full article

An information system (IS) is an organic system that integrates personnel, technology, data, and other resources for information collection, processing, storage, transmission, and utilization, supporting organizational decision making, management, and business development. Against the backdrop of sustainable development being integrated into the strategic goals of enterprises, the impact of accounting information systems (AISs) on corporate sustainability performance has garnered significant attention. This study employs the IS success model as its theoretical underpinning and incorporates both the quality of AISs and sustainability performance into the research framework. Likert scales were adopted to collect data, and structural equation modeling was conducted to test our hypotheses. The findings reveal that information quality and service quality exert a notably positive influence on intention to use and satisfaction. Meanwhile, system quality only positively impacts intention to use. There exists an interactive relationship between intention to use and satisfaction. Satisfaction positively contributes to corporate sustainability performance, whereas intention to use only positively affects environmental performance. This research offers a theoretical foundation and practical guidelines for enterprises aiming to optimize their AISs and enhance sustainability performance. Full article

The decarbonization of the electricity system coupled with the electrification of transport, heat, and industry represents a practical and cost-effective approach to deep decarbonization. A key question is as follows: where to build new solar and wind farms? This study presents a cost-based approach to evaluate land parcels for solar and wind farm suitability using colour-coded heatmaps that visually depict favourable locations. An indicative cost of electricity is calculated and classified for each pixel by focusing on key factors including the resource availability, proximity to transmission infrastructure and load centres, and exclusion of sensitive areas. The proposed approach mitigates the subjectivity associated with traditional multi-criteria decision-making methods, in which both the selection of siting factors and the assignment of their associated weightings rely highly on the subjective judgements of experts. The methodology is applied to Australia, South Korea, and Indonesia, and the results show that proximity to high-voltage transmission and load centres is a key factor affecting site selection in Australia and Indonesia, while connection costs are less critical in South Korea due to its smaller land area and extensive infrastructure. The outcomes of this study, including heatmaps and detailed statistics, are made publicly available to provide both qualitative and quantitative information that allows comparisons between regions and within a region. This study aims to empower policymakers, developers, communities, and individual landholders to make informed decisions and, ultimately, to facilitate strategic renewable energy deployment and contribute to global decarbonization. Full article