Search Results (1,904)

Dairy cows are economically significant ruminants in China, and the dairy industry is closely linked to food safety and the agricultural economy. However, various factors such as pathogenic microorganisms often lead to frequent diseases in dairy cows. Furthermore, as potential hosts for diverse viruses, dairy cows can harbor zoonotic pathogens, which pose a threat to public health. The Shaanxi–Gansu–Ningxia region boasts abundant natural resources and extensive pastures. It is a major animal husbandry base in Northwest China, and dairy farming plays a significant role in the local economy. However, research on dairy cow virus diversity in this region remains limited; epidemic prevention and control capabilities are constrained, and the risk of disease outbreaks is elevated. In this study, 790 dairy cow samples were collected from 13 large-scale farms and free-range households in the Shaanxi–Gansu–Ningxia region from 2021 to 2023. Sample types consisted of nasal and anal swabs. Six viral metagenomic libraries were constructed and analyzed using high-throughput sequencing and bioinformatics methods, leading to the identification of 51 viral families. These comprised 16 positive-sense single-stranded RNA virus families, one Retroviridae family, four double-stranded RNA virus families, 21 double-stranded DNA virus families, and nine single-stranded DNA virus families. Among these, RNA viruses were represented by families such as Astroviridae, Coronaviridae, Caliciviridae, Picornaviridae, and Picobirnaviridae; DNA viruses were primarily detected in Circoviridae, Papillomaviridae, Genomoviridae, and Smacoviridae. Alpha diversity analysis revealed no significant differences in viral diversity and abundance among the three regions (p > 0.05); however, significant differences were observed in the read counts and proportions of RNA and DNA viruses across the provinces. Phylogenetic analysis further indicated that viruses carried by dairy cows exhibit considerable genetic diversity and pose potential cross-species transmission risks. This study established a reference database for the dairy cow virome in the Shaanxi–Gansu–Ningxia region, elucidated the phylogenetic relationships of key viruses, and provided a scientific basis for future monitoring and prevention of dairy cow viruses. Full article

Mountain photovoltaic (PV) stations are increasingly deployed in complex terrain, where generation is jointly controlled by solar-resource variability, near-surface meteorology, and local topography. However, the quantitative contribution of topographic factors to regional-scale PV generation remains insufficiently evaluated, and many prediction studies rely on single-station or short-term records. In this study, monthly measured generation from 118 standardized village-level mountain PV stations in Badong County, western Hubei Province, China (2019–2021), was integrated with Solargis Global Horizontal Irradiance (GHI)-related solar-resource data, high-resolution gridded meteorological data, a 25 m digital elevation model, seasonal-cycle variables, and historical-generation features. After seasonally grouped median-absolute-deviation (MAD) outlier screening, GIS-based spatial matching, terrain extraction, and viewshed-derived shading analysis, regression models and climatology baselines were compared under both chronological validation and station-exclusion spatial cross-validation. Under the strict chronological validation, CatBoost achieved the best temporal performance among the tested models (R² = 0.3119, MAE = 2719.7 kWh, RMSE = 3245.6 kWh), slightly outperforming the monthly climatology baseline. In the station-exclusion spatial cross-validation, XGBoost achieved the highest mean R² (0.8659), indicating good spatial transferability to unseen stations. Correlation and partial-correlation analyses showed that the temperature-related variable group and monthly radiation were the dominant meteorological controls, whereas elevation, slope, and terrain shading showed weak direct correlations with monthly generation for already-sited stations. Annual 90% prediction intervals were further estimated using residual bootstrapping, with an empirical coverage of 94.9%. The proposed framework provides a practical basis for monthly generation forecasting and operational assessment of already-built distributed PV stations in mountainous regions, while its application to greenfield site selection requires additional site engineering and near-field obstruction information. Full article

The pinewood nematode (PWN, Bursaphelenchus xylophilus (Steiner & Buhrer) Nickle)), first introduced into China in 1982, has since spread rapidly, posing a serious threat to forest resource security and ecological balance. This study aimed to analyze the genetic diversity and genetic structure of PWN in eight geographic populations (60 individuals) of Guizhou Province using single nucleotide polymorphisms (SNPs). Results revealed low genetic diversity (Ho values varied from 0.123 to 0.229; He values ranged between 0.117 and 0.212) across the eight sampled populations, along with low levels of genetic differentiation (pairwise Fst values varied from 0.005 to 0.183) among them. Gene flow was generally high between populations, and no clear geographical clustering was observed based on ADMIXTURE, PCA and phylogenetic analysis. These findings provided a scientific basis for tracking the dispersal and identifying the origins of PWN infestations in China. Full article

Against the backdrop of China’s strategic goals of achieving carbon peaking and carbon neutrality, a key question is whether renewable electricity generation (REG) is associated with lower carbon emission intensity (CEI). To address this issue, this study employs panel data from 30 Chinese provinces from 2005 to 2024 and combines the Tapio decoupling model, Moran’s I test, and the spatial Durbin model (SDM), with the ordinary least squares (OLS) used as a benchmark to analyze the decoupling evolution, spatial spillover associations, and potential transmission channels between REG and CEI. The findings show that: (1) the relationship between REG and CEI evolves from weak decoupling to strong decoupling, suggesting a potentially nonlinear relationship; (2) CEI exhibits significant spatial autocorrelation and regional clustering; (3) REG is significantly associated with lower CEI, with both local and spatial spillover associations; (4) the local mitigation association is stronger in eastern and higher-CEI provinces, while spillover effects are more pronounced in western, northeastern, and resource-based provinces; and (5) the REG-CEI association may operate through energy structure (ES) optimization and energy intensity (EI) reduction, while environmental regulation (ER) may strengthen this association. The endogeneity tests provide supplementary evidence consistent with these findings, although they should not be interpreted as definitive causal proof. Overall, this study contributes to the sustainability literature by showing that the REG-CEI relationship is not merely a static local association, but a dynamic and spatially differentiated pattern shaped by regional coordination and energy-system adjustment. These findings provide evidence relevant to sustainability-oriented energy policy by suggesting that renewable electricity development should be assessed not only by generation scale, but also by its association with carbon-intensity reduction, spatial coordination, and energy-system efficiency Full article

Digital transformation has become an essential component of sustainable rural development, yet substantial inequalities persist in how communities access, adopt, and benefit from digital technologies. Understanding these disparities requires not only information about technological resources but also knowledge of the relational structures through which information, support, and opportunities circulate. This article presents a reproducible synthetic socio-digital network dataset designed to support the analysis of digital gaps in community-based tourism (CBT) environments. Rather than containing original respondent-level observations, the repository was computationally reconstructed from aggregate statistics derived from field studies conducted in three rural communities in the province of Guayas, Ecuador: Bucay (5 de Septiembre), Manglares Churute, and Ruta de los Chirijos. All node-level records, survey variables, and support relationships included in the repository were synthetically generated to preserve aggregate community characteristics while protecting participant confidentiality and preventing individual re-identification. The repository contains synthetic actor metadata, reconstructed socio-digital variables, directed support networks, graph representations in interoperable formats, and precomputed Social Network Analysis (SNA) indicators. The dataset includes 90 synthetic actors, more than one thousand generated support interactions distributed across multiple socio-digital dimensions, machine-readable metadata, and reusable scripts for preprocessing, validation, graph construction, and metric computation. The represented dimensions include financial assistance, training support, information exchange, technological support, social media promotion, institutional collaboration, trust, and emotional closeness. To facilitate reuse, all resources are distributed in standardized formats compatible with NetworkX, Gephi, Neo4j, and graph-learning frameworks. The repository follows FAIR principles and includes documentation intended to support transparency, reproducibility, and methodological benchmarking. Potential applications include social network analysis, graph mining, graph neural networks, digital inequality research, computational social science, community resilience studies, and educational activities. By providing an openly documented synthetic dataset and reproducible computational workflow, the repository contributes to the study of socio-digital systems, privacy-preserving data sharing, and community-level digital transformation processes. Full article

Introduction: College student-athletes must often balance academic responsibilities with intensive training and competition, placing them under considerable pressure and potentially increasing their risk of mental health difficulties. Against this background, the present study focused on the link between mindfulness and psychological distress and examined whether cognitive reappraisal and subjective vitality were statistically involved in this association as indirect associations. Methods: Participants were 430 college student-athletes recruited from five universities in Hunan Province, China. Using a cross-sectional survey design, the hypothesized model was tested using structural equation modeling in AMOS 23.0, and indirect associations were examined with bootstrap analysis based on 5000 resamples. Results: Mindfulness was positively associated with both cognitive reappraisal and subjective vitality. Cognitive reappraisal was positively associated with subjective vitality but negatively associated with psychological distress. Subjective vitality also showed a negative association with distress. Moreover, mindfulness showed an indirect association with lower distress through cognitive reappraisal and subjective vitality. Discussion: The findings may contribute to a better understanding of the psychological correlates associated with mental health in college student-athletes. They also suggest that mindfulness-related psychological resources may be associated with lower distress and may help guide future longitudinal and intervention research in this group. Full article

Soil is the principal physical space for water conservation (WC), so analyzing the driving forces of soil on WC is significant for studying WC services and integrated environmental management. Guangdong Province, a major economic province in China, was taken as a research case to deeply analyze the spatiotemporal pattern of WC function from 2000 to 2020 with InVEST, and to reveal its soil driving forces using a classical mathematical statistics method. We found that, from 2000 to 2020, the WC functions in Guangdong Province exhibited significant spatiotemporal differences. High-value regions were mainly concentrated in the northern and western mountainous regions, while low-value areas were primarily in the Pearl River Delta. The total WC in Guangdong showed a fluctuating upward trend, with 10.71% of its area experiencing extremely significant improvement in the Pearl River Delta, followed by Northern Guangdong. Moreover, WC is influenced by the types and distribution areas of different soils. Red soil has the highest WC depth and volume, followed by paddy soil, while lateritic red soil has the lowest WC depth. Furthermore, soil components exhibited complex stratified relationships with precipitation-normalized WC (PNWC). Components characterized by cation exchange capacity (CEC), pH, and total exchangeable bases (TEB) were positively associated with PNWC, whereas aluminum saturation (ALSA) showed a negative association within the corresponding soil components. The findings provide an important scientific basis for the ecological governance of ecosystem WC functions and water resource management. Full article

Green prepared dishes are an emerging food-consumption format that links convenience, food safety, and sustainable consumption. In this study, “green” denotes a sustainability-oriented product profile involving food-safety assurance, resource-conscious packaging or sourcing, and waste-reduction potential, rather than formal organic certification. However, existing studies have mainly relied on linear behavioral models and have paid limited attention to nonlinear and asymmetric consumer decision mechanisms. This study integrates the stimulus–organism–response framework with explainable machine learning to predict consumers’ sustainable purchase intention toward green prepared dishes. Based on 805 valid questionnaires collected in Jilin Province, China, predictors were organized into three dimensions: environmental and health cognition, socioeconomic and infrastructural conditions, and sustainable behavioral propensity. The sample represents a regional online consumer profile in Jilin Province rather than a national probability sample. Six classifiers were trained using SMOTE–Tomek resampling and Optuna-based hyperparameter optimization. XGBoost achieved the best predictive performance, with an F1-score of 0.894, an AUC of 0.934, and an MCC of 0.702. Unlike conventional black-box machine learning, the SHAP-based interpretation translated ensemble predictions into transparent feature-level and case-level explanations. Accordingly, the model interpretations are framed as predictive associations rather than causal mechanisms. The study reveals an asymmetric decision pattern in which core behavioral willingness functions as a non-compensatory barrier, while channel convenience, delivery efficiency, and after-sales support facilitate purchase intention among consumers who already show high behavioral readiness. The findings suggest that green prepared-dish strategies should prioritize trust-based advocacy and word-of-mouth, reliable channel design, low-risk trial experiences, and collaborative food-safety governance rather than relying only on short-term traffic acquisition. Full article

To address the issues of low intelligence and weak knowledge support in traditional water resource security risk analysis and response strategy generation, this paper proposes a novel framework based on hybrid retrieval augmentation and multi-agent collaboration. First, the proposed method integrates the DPSIR-CRITIC-TOPSIS framework with an obstacle degree model to construct an evaluation agent. This agent enables the intelligent assessment of regional water resource security and the precise extraction of key obstacle factors. Second, a water resource security knowledge graph and a vector knowledge base are constructed utilizing textual data, including policies and regulations, technical standards, the academic literature, and typical case studies. A hybrid retrieval augmentation mechanism—integrating graph reasoning, dual-path recall, and relation expansion—is designed to enhance the precision and relevance of the generated risk response strategies. Finally, a collaborative workflow comprising a master control agent alongside evaluation, retrieval, generation, and review agents is established to iteratively optimize the strategies through cross-validation and compliance reviews. In an empirical case study utilizing multi-year data from Henan Province and its 18 prefecture-level cities, experimental results demonstrate that the proposed method significantly outperforms baseline models across multi-dimensional semantic evaluation metrics, as well as BLEU-4 and ROUGE-L scores. The multi-agent collaborative architecture developed in this study, which integrates data-driven evaluation with knowledge-based hybrid retrieval augmentation, significantly elevates the intelligence level of water resource security assessment. It provides robust technical support for the analysis of regional water resource security situations and the intelligent generation of actionable response strategies. Full article

The shift to circular agrosystems necessitates using new ideas like sustainable biochar, which provides many eco-beneficial attributes like enhancing soil fertility, storing atmospheric carbon dioxide, and retaining soil moisture. However, there is still a small number of farmers worldwide (particularly those located in low-income countries) adopting biochar. Accordingly, this research is focused primarily on determining how factors affecting behavior will influence the decision of wheat producers in Marvdasht County, in Iran’s Fars Province, to use biochar as a circular technology for farming. The study will focus on addressing issues related to environmental challenges (e.g., degradation of soil and drought) through the implementation of resource-efficient, sustainable agricultural technologies. The intent of this paper was to research the behavioral characteristics associated with wheat farmers who choose to use biochar in the city of Marvdasht, Fars Region, Iran, using a new Theory of Planned Behavior (TPB). The model is theoretically enriched through the inclusion of personal norms and connectedness to the land, allowing for a more comprehensive understanding of pro-environmental decision-making. Data was collected from a total of 386 wheat farmers through the use of a structured survey. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the software Smart-PLS 3.0. The results reveal that attitude (β = 0.342, p < 0.001) and personal norms (β = 0.278, p < 0.001) are the strongest predictors of behavioral intention, while perceived behavioral control showed a weaker but significant effect (β = 0.178, p = 0.049). Subjective norms do not have a significant direct effect (β = 0.115, p = 0.199) but significantly influence intention indirectly through personal norms (β = 0.100, p < 0.001). Furthermore, connectedness to the land strongly affects personal norms (β = 0.420, p < 0.001) and exerts a significant indirect effect on intention (β = 0.117, p < 0.001), highlighting the importance of emotional attachment to land. The findings are significant because they demonstrated that farmers’ biochar adoption decisions are shaped not only by rational evaluations but also by moral obligations and emotional relationships with land. This study makes significant theoretical contributions by extending TPB with moral and relational constructs and empirically demonstrating their mediating roles in agricultural innovation adoption. The novelty of this study lies in integrating personal norms and connectedness to the land into the TPB framework to explain biochar adoption behavior within the context of circular agriculture in a developing country. Practically, the findings provide evidence-based insights for designing policies that integrate cognitive, ethical, and emotional drivers to promote biochar adoption and advance circular agriculture. Specifically, policymakers and extension agencies should prioritize behavioral-level strategies such as awareness campaigns, farmer training programs, and community-based initiatives that strengthen positive attitudes, environmental responsibility, and farmers’ emotional connection to land in order to enhance biochar adoption. Full article

This study aimed to systematically categorize and critically analyze the feasibility of developing a cultural resource-based tourism route in Betong District, Yala Province, the southernmost area of Thailand, which is called “the city in the mist.” Research and development techniques were employed using a simulated map from an information system and community forums to create and revise a cultural resource-based tourism map in these areas: the Aiyoeweng, Tano Maero, Betong, and Than Nam Thip Subdistricts. The participants from five communities, 10 people per community, totaling 50 participants, were selected through purposive sampling to join in drafting a cultural resource map by pinpointing important areas in each subdistrict. The fieldwork data collected in each subdistrict were categorized and the content was analyzed to examine the feasibility of the approach to creating a map based on cultural resources. The results found that the tourism patterns resulting from a strong resource base could be divided into tangible and intangible cultural resources. The selected resources include local food, learning centers, tourist attractions, interesting entertainment activities, and community service centers. These were then used to create a simulated map, which was analyzed to determine the feasibility of a tourism route based on resource capital, abundant forests, cultural capital in historical sites, and social capital that were covered in community tourism policies, plans, and guidelines for tourism management to achieve maximum benefits, resulting from the community process that had to jointly design the process. The results of this study are part of the restoration of tourism based on resources for income management and for local organizations to expand and upgrade tourism to the regional economic zones in the southern border provinces. Full article

In the context of stock-based development, assessing the development potential of idle rural resources and formulating differentiated utilization pathways are important for improving resource-use efficiency and stimulating endogenous rural development in mountainous counties. However, existing studies mainly focus on single resource types and lack both an integrated framework for multiple idle rural resources and effective links between potential assessment and classified utilization. Taking Suichang County, Zhejiang Province, as a case study, this study constructs an evaluation index system for idle rural resource development potential. GIS-based spatial analysis and geographically weighted regression (GWR) reveal the spatial differentiation of development potential and its driving factors. On this basis, a three-dimensional framework of “potential–driving force–resistance” is used to classify resource utilization types and formulate differentiated pathways. The results show significant spatial heterogeneity in the development potential of idle rural resources in Suichang County, characterized by “central agglomeration, two-wing diffusion, and peripheral weakening” and a “three cores and two zones” pattern. The driving factors display significant spatial non-stationarity. Idle rural resources are classified into six utilization types with corresponding utilization strategies. This study provides a scientific basis and practical reference for classified revitalization, zoned policy implementation, and sustainable rural transformation in similar mountainous counties. Full article

Sustainable higher education governance requires durable institutional mechanisms that support student engagement, faculty participation in education, and organizational coordination. Against the backdrop of China’s “Three-All Education” framework, academic culture has become a broader governance issue involving student development, faculty engagement, and organizational operation. This study aims to examine the current conditions and associated factors of academic culture governance in resource-constrained undergraduate universities. Based on survey data from three undergraduate institutions in Gansu Province, China, including 6120 valid student questionnaires and 735 valid faculty questionnaires, this study combines group difference tests with multiple regression analysis. The findings show that the sampled institutions face three main challenges: insufficient continuity in students’ academic goal commitment, limited faculty educational engagement under research-oriented incentives, and weak organizational coordination. Regression results further indicate that clarity of learning goals is significantly and positively associated with students’ learning status; perceived research orientation is significantly and negatively associated with faculty engagement in education; and information sharing and clarity of responsibility are significantly and positively associated with organizational coordination. These findings suggest that sustainable academic culture governance depends on the alignment of student goal support, faculty incentive structures, and collaborative organizational operation. Full article

The conventional “resources–energy–environment–economy” growth paradigm has imposed severe environmental pressures on China, including land desertification and smog pollution. In the context of carbon peaking and carbon neutrality, the synergistic advancement of pollution reduction, carbon mitigation, ecological expansion, and economic growth (PCEG) has become a critical development pathway. Drawing on Pareto improvement theory, this study applies a super-efficient slack-based measure (SBM) model to evaluate PCEG synergistic efficiency across 30 Chinese provinces from 2003 to 2020. We further investigate its temporal evolution, regional heterogeneity, and convergence characteristics. The empirical results reveal that (1) PCEG synergistic efficiency follows a U-shaped trajectory; (2) both technological change and efficiency change contribute positively to post-2018 recovery; (3) substantial regional heterogeneity and cross-regional overlap are observed, with intra-regional disparities playing an equally important role in shaping overall inequality as inter-regional differences; and (4) no σ-convergence is observed at the national or regional level; β-convergence is significant in the non-spatial setting but drops sharply once spatial dependence is incorporated, indicating that administrative barriers, market segmentation, and frictions in factor mobility hinder the convergence process. These results inform a policy mix that addresses within-region heterogeneity, sustains the post-2018 momentum of technological progress, and dismantles spatial barriers to factor mobility. Full article

Enhancing mariculture’s green total factor productivity (GTFP) is essential to balance industrial growth with ecology, safeguard global food security, and meet UN Sustainable Development Goal 14 amid mounting marine stress. As a global leading mariculture producer, China provides a typical research sample. This study constructs a mariculture GTFP measurement index system, estimates GTFP in China’s coastal provinces via the global Super-SBM model, identifies root causes of efficiency loss, and explores influencing factors and spatial spillover effects using a spatial econometric model. The results show that the overall mariculture GTFP of China’s coastal provinces exhibits a fluctuating upward trend with significant regional heterogeneity, specifically presenting a distribution pattern of “the highest in the South China Sea Region, followed by the East China Sea Region, and the lowest in the Yellow Sea and Bohai Sea Region”. Meanwhile, mariculture GTFP shows significant positive spatial autocorrelation, with distinct High-High and Low-Low agglomeration characteristics. Excessive resource consumption and undesirable output discharge are the core drivers of efficiency loss. For direct effects, industrial scale, industrial structure, fishermen’s income, transportation accessibility, internet development, technology adoption, and environmental regulation significantly boost local GTFP, while fishery disasters exert a significant negative impact. For spatial spillovers, industrial scale, industrial structure, and internet development show significant positive effects, while fishermen’s income and urbanization present negative effects. Based on these findings, this study proposes targeted multi-stakeholder optimization paths, providing decision support for China’s mariculture green development and replicable experience for global coastal countries. Full article