The Jianghan Plain (JHP) is a key agricultural area in China where efficient agricultural water use (AWUE) is vital for sustainable water management, food security, environmental sustainability, and economic growth. This study introduces a novel AWUE prediction model for the JHP, combining a BP neural network with the Sparrow Search Algorithm (SSA) and an improved Tent Mixing Algorithm (Tent-SSA-BPNN). This hybrid model addresses the limitations of traditional methods by enhancing AWUE forecast accuracy and stability. By integrating historical AWUE data and environmental factors, the model provides a detailed understanding of AWUE’s spatial and temporal variations. Compared to traditional BP neural networks and other methods, the Tent-SSA-BPNN model significantly improves prediction accuracy and stability, achieving an accuracy (ACC) of 96.218%, a root mean square error (RMSE) of 0.952, and a coefficient of determination (R²) of 0.9939, surpassing previous models. The results show that (1) from 2010 to 2022, the average AWUE in the JHP fluctuated within a specific range, exhibiting a decrease of 0.69%, with significant differences in the spatial and temporal distributions across various cities; (2) the accuracy (ACC) of the Tent-SSA-BPNN prediction model was 96.218%, the root mean square error (RMSE) was 0.952, and the coefficient of determination (R²) value was 0.9939. (3) Compared with those of the preoptimization model, the ACC, RMSE, and R² values of the Tent-SSA-BPNN model significantly improved in terms of accuracy and stability, clearly indicating the efficacy of the optimization. (4) The prediction results reveal that the proportion of agricultural water consumption has a significant impact on AWUE. These results provide actionable insights for optimizing water resource allocation, particularly in water-scarce regions, and guide policymakers in enhancing agricultural water management strategies, supporting sustainable agricultural development.

This study aims to address the challenges of insufficient regional adaptability and complex non-linear relationships in land carrying capacity (LCC) assessment by constructing a comprehensive evaluation framework tailored to geographically diverse regions such as Hubei Province (HP). We developed an improved projection pursuit model optimized by a differential evolution algorithm (DEA-PTM) to evaluate LCC through a four-dimensional system encompassing water and soil resource carrying capacity (WSCC), social carrying capacity (SCC), economic carrying capacity (ECC), and ecological and environmental carrying capacity (EECC). Empirical analysis was conducted using data from HP from 2010 to 2023. An obstacle degree model (ODM) and an autoregressive integrated moving average (ARIMA) model were further integrated to identify limiting factors and predict future trends. The results indicate that the LCC of HP showed a sustained growth trend from 2010 to 2023, with a cumulative increase of 24.7%, and exhibited a distinct “east-high, west-low” spatial pattern. Significant heterogeneity existed among subsystems, with WSCC higher in the east and EECC stronger in the west. Ecological pressure and water-soil resource constraints were identified as the core obstacles to LCC improvement. The ARIMA model predicts a continued increase in overall LCC to 1.671 by 2035, but a decline in EECC, highlighting the risk of ecological degradation. This study provides a scientifically robust framework for regional land resource optimization and sustainable development policy-making.

School-enterprise cooperation is a crucial strategy in China's vocational education and a vital aspect of vocational education reform. Conducting a comprehensive empirical study and evaluation of the effectiveness of school-enterprise cooperation in China's vocational education can enhance the quality and efficiency of such cooperation. Nevertheless, current evaluations of school-enterprise cooperation are predominantly theoretical, and there is a scarcity of systematic indicators and empirical research methods. To address this gap, the author adopts scientific, systematic, goal-oriented, and operational principles and utilizes stakeholder theory and system contract theory as the theoretical basis and framework. The author constructs three primary indicators, namely,participation, return, and potential, and several secondary indicators from the viewpoints of cooperation objectives, process, and results of vocational education school-enterprise cooperation for both schools and enterprises using factor analysis and the fuzzy comprehensive evaluation method. This evaluation offers precise empirical verification and a theoretical and practical reference for the high-quality practice of school-enterprise cooperation in China's vocational education.

Rapid urbanization has led to habitat fragmentation in peri-urban areas, continuously eroding the ecological foundation of sustainable food systems in urban–rural transition zones and posing a real threat to regional food security. Against the backdrop of urbanization disturbances, traditional nature-based solutions have limitations in addressing socioecological nonlinear responses, whereas digital tools offer new governance pathways for enhancing food system resilience. To elucidate the intrinsic mechanisms through which digital technology empowers the resilience of peri-urban food systems, this study, which is grounded in ecological wisdom theory, constructs a system dynamics model that integrates “digital technology-ecological perception-ecological wisdom capital” in a three-dimensional linkage. This model simulates the dynamic process through which sustainable food systems in urban–rural transition zones resist the risks of habitat fragmentation and achieve synergistic steady-state evolution. According to the simulation results, a synthesized steady-state transition in sustainable food systems can be regarded as a self-organizing phase transition process. During resource metabolism, system elements show strong nonlinear symbiotic and mutually beneficial features. Further, there is a significant time-lag effect on improving food system resilience through digital technology empowerment and policy coordination. Also, the effects of governance are not immediately visible. Further, as an important instrumental empowerment carrier, urban–rural spatial and information barriers can be broken through means like digital ecological monitoring. Moderate investment in this regard can promote the acceleration of the system’s self-organizing phase transition. Also, this can enhance resilience against disturbance from habitat fragmentation while ensuring food production and supply. Finally, the ecological carrying capacity of core food production spaces does not increase monotonically. This means that the system possesses an adaptive cyclical fluctuation mechanism, with a periodic oscillatory evolution of carrying capacity. This study breaks through static analytical paradigms, fills the quantitative research gap on the resilience evolution of peri-urban food systems driven by the integration of digital technology and ecological wisdom, and can provide scientific evidence and decision-making support for food–ecological collaborative governance in China’s urban–rural transition zones.