In the parallel stage of China's economic growth shift and industrial structure adjustment, the high-quality development of regional economy is no longer just a macro goal, but a dynamic process that needs continuous observation and feedback. When combing the data, the author noticed that the innovation indicators in some years were obviously out of line with the green development indicators, which prompted the author to rethink the limitations of the traditional static evaluation system. Based on this, this paper constructs a comprehensive evaluation framework including innovation, industrial structural optimization, green transformation, coordinated development, and shared prosperity. In this study, the mixed weighting method of analytic hierarchy process and entropy weight method is adopted, and the fluctuation of weight is monitored by time series changes, and the self-correction of the model is realized through dynamic feedback mechanism. This study provides data monitoring and trend identification for green development.
Against the backdrop of rural revitalization and culture-tourism integration, rural study travel suffers from fragmented resources, poor stakeholder coordination, and product homogenization. This study constructs a four-stage value co-creation model, clarifies multi-agent roles, designs a transparent benefit distribution mechanism (villagers: 25-30% of revenue), and builds a digital platform for intelligent resource allocation and data-driven decision-making. Empirical analysis of Zhejiang's County M (2018–2023) reveals a 127.0% net treatment effect of the model on rural tourism income relative to the synthetic control counterfactual group, with benefit-sharing and subject collaboration as core drivers. Cross-regional tests in Henan's County N and Guizhou's County G verified the model's universal applicability via adaptive adjustments, realizing win-win for all stakeholders. Rural study travel cultivates stronger student practical competence and cultural identity than conventional classroom education, and the digital-enabled framework realizes coordinated multi-value development.
Digital economy is promoting the reconstruction of rural ecological governance collaborative mechanism. This paper aims to explore the dynamic evolution characteristics and distribution heterogeneity of multi-regional collaborative efficiency. Based on 2386 questionnaires and multi-source data, this paper takes five typical counties in Gansu Province as empirical objects to analyze the collaborative mechanism of digital economy empowering rural ecological governance. The results show that the platform permeability has a significant impact on the collaborative activity (β=0.43), but there are regional differences, with the highest value of 0.48 in region C and only 0.29 in region E. Information flow quality can improve policy incentive efficiency (β=0.32), and dynamic feedback can enhance system robustness (β=0.19). The research reveals the spatial heterogeneity of “strong center-weak edge” and points out that it is necessary to differentiate and strengthen digital infrastructure and optimize information closed loop to improve governance efficiency.
To address trust and conversion challenges in green agricultural e-commerce, this study introduces an integrated model of scene embedding and strategy vector adaptation. Scenes are conceptualized as a synergy of technological fidelity and social presence, while the elaboration likelihood model explains how embedding shifts cognitive processing from central to peripheral routes. Using latent Dirichlet allocation topic modeling and structural equation modeling on survey data, results show that high-embedding scenes activate emotion-driven, interpersonal trust; marketing effectiveness hinges on the projection efficiency of strategies onto scene demands; and strategic fit nonlinearly enhances how scene embedding drives purchase intention. This work provides a theoretical framework for trust construction in digital agricultural systems and practical insight for artificial intelligence-enabled, scene-responsive marketing.
The assessment of rural ecotourism management quality, given contemporary urbanization development, comprises a multiple-attribute decision-making problem necessitating innovative integrated assessment methodologies grounded in DVNNLD. Accordingly, this study presents several methodological contributions: first, by creatively linking the CRITIC and CoCoSo methods in a decision-making approach, thus generating a DVNN-CoCoSo model; second, through an innovative introduction of logarithm distance measures into the assessment to accommodate non-linear relationships between the assessment's attributes and significantly enhance the CoCoSo model's capability by tackling complex educational data; and third, to constitute an applicability of quality assessment regarding rural ecotourism management in the backdrop of urbanization development through real-case applications.
This study aims to establish a new framework for evaluating the quality of teaching regarding the integration of Chinese excellent traditional culture into college Guzheng classes. This framework seeks to go beyond the common practice of evaluating general classroom instruction, and evaluates the level of integration, as well as how effective in enhancing learning. Traditionally, evaluation may involve assessing the order of teaching, students' engagement, or performance, but seldom involves the degree to which traditional culture is integrated in the college Guzheng classes teaching process. In order to overcome this limitation, a new approach combining artificial intelligent technology with neutrosophy has been established for a more comprehensive evaluation of the quality of college Guzheng classes teaching involving cultural integration. This framework includes the dimensions of classroom evidence, instrumental teaching guidance, quality of integration, and student inheritance feedback.
The informational leadership of college instructors for English teaching is becoming increasingly important for modern schooling, but its evaluation is somewhat non-rigorous due to the unclear indicator system and the unrestrained expert appraisal. To this end, a new neutrosophic modeling method combining fuzzy expression and MCDM is proposed to establish a rigorous indicator system and determine the key factors. It consists of three parts: establishing and verifying the indicator system; coding experts' comments into the neutrosophic-fuzzy format to express their support, rejection, and indeterminacy of the indicators (and multiple hesitant values); and applying a correlation-based MCDM approach for ranking against the ideal. One complete case study, including stepwise computations, a comparative study with the conventional fuzzy MCDM, and a sensitivity analysis, is presented. The results produce a stable ranking with acceptable perturbations and provide transparent core indicators and driving factors for education decision-makers.
This study constructs a multimodal intelligent monitoring system empowered by biosensing technology, which synchronously collects tourists’ physiological signals such as heart rate and scenic environmental data including temperature and humidity. The Spatial-Temporal Graph Attention Network (STGAT) is adopted to complete dual-task prediction of tourist interest and environmental comfort, and Deep Deterministic Policy Gradient (DDPG) reinforcement learning is leveraged to generate dynamic personalized tour paths. Experiments based on Huangshan Yungusi scenic area verify that the STGAT model achieves 85.2% tourist interest prediction accuracy with a comfort prediction RMSE of 0.45 under the optimal 5-minute time window. Compared with classic Dijkstra, A*, and mainstream reinforcement learning recommendation baselines, the proposed method raises the average tourist stay time at core scenic spots to 14.2 min, effectively balances passenger flow distribution across attractions, mitigates overcrowding at hot spots, and significantly optimizes tourists’ immersive sightseeing experience.
The assessment of rural ecotourism management quality, given contemporary urbanization development, comprises a multiple-attribute decision-making problem necessitating innovative integrated assessment methodologies grounded in DVNNLD. Accordingly, this study presents several methodological contributions: first, by creatively linking the CRITIC and CoCoSo methods in a decision-making approach, thus generating a DVNN-CoCoSo model; second, through an innovative introduction of logarithm distance measures into the assessment to accommodate non-linear relationships between the assessment's attributes and significantly enhance the CoCoSo model's capability by tackling complex educational data; and third, to constitute an applicability of quality assessment regarding rural ecotourism management in the backdrop of urbanization development through real-case applications.
To address trust and conversion challenges in green agricultural e-commerce, this study introduces an integrated model of scene embedding and strategy vector adaptation. Scenes are conceptualized as a synergy of technological fidelity and social presence, while the elaboration likelihood model explains how embedding shifts cognitive processing from central to peripheral routes. Using latent Dirichlet allocation topic modeling and structural equation modeling on survey data, results show that high-embedding scenes activate emotion-driven, interpersonal trust; marketing effectiveness hinges on the projection efficiency of strategies onto scene demands; and strategic fit nonlinearly enhances how scene embedding drives purchase intention. This work provides a theoretical framework for trust construction in digital agricultural systems and practical insight for artificial intelligence-enabled, scene-responsive marketing.
At this stage, people’s living standards have been greatly improved. Nevertheless, it must be recognized that while industrial development brings convenience to daily life, it also causes substantial damage to the ecological environment. The quality evaluation of environmental art design from the perspective of green building is multiple attribute group decision-making (MAGDM) problem. Intuitionistic fuzzy sets (IFSs) are an appropriate framework to model uncertainty in information when evaluating the quality of environmental art design from a green building perspective. Then, under IFSs, the intuitionistic fuzzy Combined Compromise Solution (IF-CoCoSo) method was developed. IF-CoCoSo is then used to solve the Multiple Attribute Group Decision-Making (MAGDM). Finally, an example of quality evaluation of environmental art design from the perspective of a green building is given to prove the practicability of the IF-CoCoSo method. Some comparative analyses are also carried out to verify the effectiveness of the IF-CoCoSo method.
A significant part of teaching evaluation in college English classes within agricultural universities involves teaching evaluation. It can give feedback in many ways, manage teaching, and guarantee quality. In addition, these ways could serve students effectively in improving methods and enhancing learning efficiency. Class teaching quality in College English classes at agricultural colleges constitutes an MADM problem. This research presents a novel approach to the interval-valued intuitionistic fuzzy (IVIF) Hamacher interactive power averaging (IVIFHIPA) operator to overcome such problems. The main properties of the IVIFHIPA operator are reviewed here, and a numerical study on the application of this operator regarding the assessment of college English classroom teaching quality in agricultural colleges and universities in the New Liberal Arts context is carried out to demonstrate its practical efficiency under IVIFSs environment.
University students’ mental health affects learning engagement, academic performance, retention, and long-term outcomes, but choosing which campus interventions to prioritize is still hard to justify quantitatively. This paper presents an auditable evaluation pipeline that prioritizes agricultural university mental health interventions throughout the analysis. The proposed framework integrates three tightly connected components. First, expert inputs are modeled using single-valued neutrosophic evaluations. Second, repeated or multisource judgments are reconciled using a Gaussian-structured evidential fusion layer that treats assessments as probabilistic evidence and controls dispersion, reducing sensitivity to noise and outliers without imposing artificial certainty. Third, the fused outputs are processed by a neutrosophic–fuzzy multicriteria decision-making that produces criterion weights and alternative scores through structured sensitivity and robustness analysis. A case study is provided using six criteria relevant to higher education mental health planning.
This study builds an intelligent recommendation system based on Improved Collaborative Filtering (CF). The system combines agronomy, economics, media communication, and computer science. This work addresses data sparsity and rating bias in rural livestream e-commerce. It optimizes traditional CF with adjusted cosine similarity and agronomic weighting. Economic metrics and livestream media communication features are embedded in user preference modeling. Tests use datasets from a central China rural e-commerce platform. The improved algorithm outperforms conventional CF. Its Mean Absolute Error equals 0.84, 11.5% below user-based CF. Precision@10 is 0.78 and Recall@10 is 0.65. Diversity stands at 0.72. Domain adaptability hits 0.83, up 50.9% from old models. Ablation tests verify agronomic and economic traits jointly boost recommendation outcomes. This work extends theories on agricultural information dissemination via live media. It delivers technical support for agricultural informatization and rural revitalization.
This study develops a quantitative framework to evaluate the coordination efficiency of red education and cultural tourism integration in five typical red villages within the Jiujiang region of northern Jiangxi. Multi-source data—points of interest, user-generated content, and mobile signaling data—are integrated. A spatial vitality index (Vᵢ) is constructed using the entropy weight method. A coupling coordination degree model quantifies coordination efficiency, revealing strong subsystem correlation but significant quality variation and a tentative spatial decay pattern from core sites. Villages are classified into four exploratory types: dual-high synergy (Village D), education dominant (Village A), tourism dominant (Village B), and dual-low stagnation (Villages C and E). Given the small sample (N = 5), these typologies inform targeted optimization strategies, including immersive digital scenarios, differentiated resource allocation, and cross-village connectivity. This framework offers a replicable reference for data-driven renewal of similar red rural spaces.
Rural residents account for a substantial proportion of China’s total population, and improving their physical fitness is pivotal to elevating the overall national fitness level. A thorough insight into public sports services for the rural elderly is essential to analyze the current service system, clarify its underlying logic, and formulate targeted optimization strategies. The demand evaluation for these services falls into the multi-attribute group decision-making (MAGDM) category. Spherical fuzzy sets (SFSs) provide a more effective and comprehensive tool to tackle the uncertainty and vagueness in subjective demand assessment. This study develops spherical fuzzy bidirectional projection (SFBP) and weighted SFWBP techniques by introducing the bidirectional projection method into the SFS environment. The authors build a complete MAGDM framework with detailed computational steps, validate the SFWBP method via a numerical case of rural elderly sports service demand evaluation, and conduct comparative analyses to verify its superiority in the SFS context.
From the perspective of sports geography, in this study, the author aims to deeply explore the influence of geographical environment on national traditional sports and provide technical support for related research. Basing research on digital elevation model data, the author proposes an improved A* algorithm-based 3D path-planning method: the algorithm optimizes the cost function by introducing slope factors and dynamic weighting strategies to ensure path gentleness and walkability. Combined with geographic information system technology (the Cesium framework), the method realizes real-time 3D visualization of planned mountain paths. This research provides effective technical support for the analysis of geographical environments in ethnic minority areas related to traditional sports, and it lays a solid foundation for in-depth exploration of the development laws of national traditional sports and the inheritance of national sports culture.
Digital monitoring and ESG disclosure are important for agricultural carbon sink measurement and market-oriented transactions. However, data fragmentation, information opacity, and low conversion efficiency still limit carbon sink assetization. This paper analyzes the coupling mechanism of digital monitoring, ESG disclosure, and agricultural carbon sink assetization efficiency. Based on data from typical agricultural regions, this study builds an indicator system and uses modeling and visualization methods to verify a multi-loop closed-loop mechanism with anomaly handling. Results show that high-quality monitoring and ESG disclosure significantly improve conversion efficiency, while inconsistent policy and lagged market response remain constraints. This study provides a practical framework for agricultural environmental information system integration and supports low-carbon agricultural transformation and carbon finance development.
In the face of frequent extreme weather and limited architectural space in modern cities, traditional landscape design methods cannot effectively balance ecological performance and public experience. In this study, a digital twin system is established, and quantitative evaluation models of Ecological Adaptation Degree (EAD) and Interactive Experience Utility (IEU) are constructed respectively. Taking a typical public square as an example, the multi-objective optimization is carried out by integrating GIS (Geographic Information System), sensors, and simulation data. Finally, three typical schemes are obtained, revealing the relationship between resource competition and cost constraint among design variables. The results show that the dynamic balance between ecology and experience can be achieved by calculation optimization, which also provides a set of feasible methods for data-driven urban landscape design.
Urbanization and industrial transfer aggravate soil and water pollution and public health risks. This study analyzes ecological risks in urban construction and spatial transfer rules of polluting industries. It uses ecological footprint, ecological carrying capacity, and econometric models to quantify regional ecological pressure. It also applies machine learning and spatial analysis to evaluate urban ecological vulnerability. Results show that unregulated industrial transfer leads to the pollution shelter effect and regional ecological inequality. Current legal systems cannot fully coordinate economic growth and environmental protection. This study proposes spatial zoning of polluting industries and data-driven governance strategies. It integrates ecological risk assessment into urban planning and policy making. The findings provide a feasible framework to balance urban development, ecological protection, and public health for equitable and sustainable urban growth.
