We propose a dynamic monitoring and precision fertilization decision system for agricultural soil nutrients, integrating UAV remote sensing and GIS technologies to address the limitations of traditional soil nutrient assessment methods. The proposed method combines multi-source data fusion, including hyperspectral and multispectral UAV imagery with ground sensor data, to achieve high-resolution spatial and spectral analysis of soil nutrients. Real-time data processing algorithms enable rapid updates of soil nutrient status, while a time-series dynamic model captures seasonal variations and crop growth stage influences, improving prediction accuracy (RMSE reductions of 43–70% for nitrogen, phosphorus, and potassium compared to conventional laboratory-based methods and satellite NDVI approaches). The experimental validation compared the proposed system against two conventional approaches: (1) laboratory soil testing with standardized fertilization recommendations and (2) satellite NDVI-based fertilization. Field trials across three distinct agroecological zones demonstrated that the proposed system reduced fertilizer inputs by 18–27% while increasing crop yields by 4–11%, outperforming both conventional methods. Furthermore, an intelligent fertilization decision model generates tailored fertilization plans by analyzing real-time soil conditions, crop demands, and climate factors, with continuous learning enhancing its precision over time. The system also incorporates GIS-based visualization tools, providing intuitive spatial representations of nutrient distributions and interactive functionalities for detailed insights. Our approach significantly advances precision agriculture by automating the entire workflow from data collection to decision-making, reducing resource waste and optimizing crop yields. The integration of UAV remote sensing, dynamic modeling, and machine learning distinguishes this work from conventional static systems, offering a scalable and adaptive framework for sustainable farming practices.

The dynamic scheduling optimization of sports facilities faces challenges posed by real-time demand fluctuations and complex interdependencies between facilities. To address the adaptability limitations of traditional centralized approaches, this study proposes a decentralized multi-agent reinforcement learning framework based on graph neural networks (GNNs). Experimental results demonstrate that in a simulated environment comprising 12 heterogeneous sports facilities, the proposed method achieves an operational efficiency of 0.89 ± 0.02, representing a 13% improvement over Centralized PPO, while user satisfaction reaches 0.85 ± 0.03, a 9% enhancement. When confronted with a sudden 30% surge in demand, the system recovers in just 90 steps, 33% faster than centralized methods. The GNN attention mechanism successfully captures critical dependencies between facilities, such as the connection weight of 0.32 ± 0.04 between swimming pools and locker rooms. Computational efficiency tests show that the system maintains real-time decision-making capability within 800 ms even when scaled to 50 facilities. These results verify that the method effectively balances decentralized decision-making with global coordination while maintaining low communication overhead (0.09 ± 0.01), offering a scalable and practical solution for resource management in complex built environments.

The present study aims to investigate how the safety cognition bias of high-altitude workers in different age groups in the construction industry affects their unsafe behaviors. A theoretical framework involving safety cognition bias, risk-taking propensity, work experience, and unsafe behaviors was constructed. The empirical analysis was conducted using structural equation modeling. The results indicate that safety cognition bias has a significant positive effect on the unsafe behaviors of high-altitude workers. Risk-taking propensity plays a mediating role in this relationship, with partial mediation for the new generation of high-altitude workers and full mediation for the older generation. Moreover, work experience plays a crucial role in moderating the relationship between safety cognition bias and unsafe behaviors, specifically showing a significant moderating effect on the new generation of high-altitude workers, while its impact on the older generation is not significant. These findings provide theoretical support and practical guidance for reducing unsafe behaviors in high-altitude construction work, enhancing the safety management level of construction workers.

In the context of high turnover rates in the construction industry, team leisure sports activities have been increasingly promoted, with growing attention on their potential impact on the sustainability of construction workers’ occupational commitment. However, there is a lack of systematic research on the mechanism through which team sports promote occupational commitment. This study explores how team leisure sports activities enhance the sustainability of construction workers’ occupational commitment through the mediating effect of team cohesion, integrating perspectives from social psychology and organizational behavior. Data were collected from 509 Chinese construction workers using a structured questionnaire. The results revealed the following: (1) Four dimensions of team sports experience—social interaction quality, emotional engagement, team culture perception, and work pressure relief—positively affected workers’ commitment to sustainability. (2) Team cohesion mediated the relationship between the team sports experience and sustainability. This study provides insights into career sustainability in the construction industry and highlights the importance of team cohesion in enhancing workers’ professional commitment. The findings offer practical implications for optimizing team-building and human resource management strategies, with a focus on retaining employees in the construction industry.

As urban architecture continues to emphasize integration with natural environments, the concept of waterfront buildings and blue–green spaces has been widely applied in the site selection of large urban structures. While existing research has extensively explored architectural types such as waterfront landscapes and sports venues, systematic studies on waterfront concert halls, as an important category of cultural architecture, remain limited. Specifically, the interaction mechanisms between such halls and their aquatic environments, as well as their impact on users’ psychological satisfaction, have not been thoroughly investigated. This study takes waterfront concert halls as representative cultural buildings and examines 1267 users from 12 typical waterfront concert halls across eight cities in China. A theoretical model was constructed with water visibility, water accessibility, water interactivity, and water integration as independent variables, biophilia and a sense of nature’s presence as parallel mediators, and user satisfaction as the dependent variable. Data were analyzed using covariance based structural equation modeling CB-SEM. The findings reveal that (1) water visibility, water accessibility, and water integration positively influence user satisfaction; (2) biophilia mediates the relationship between water visibility, water accessibility, water interactivity, water integration, and user satisfaction; (3) a sense of nature’s presence also mediates the relationship between these water-related variables and user satisfaction. This study empirically demonstrates the dual pathway psychological mechanism through which water elements influence user satisfaction, providing a new perspective for the design of waterfront cultural architecture. The research suggests that architects can enhance users’ biophilic instincts and sense of nature’s presence through specific design strategies, such as strengthening water visibility, optimizing waterfront circulation, and enriching water interaction experiences. These findings offer theoretical support for shifting contemporary architectural practice from physical space creation to environmental well-being promotion, while also establishing a practical foundation for developing human-centered evaluation systems for built environments.

VR technology is increasingly being integrated into daily life, with virtual book communities emerging as novel platforms for immersive reading and interaction. This study investigates how internal and external factors jointly influence users’ usage intention from psychological and behavioral science perspectives. A multivariate structural equation model based on three-dimensional perception theory was developed and tested through a survey of individuals with prior VR reading experience. The model examines the roles of task–technology fit, privacy and security risks, emotional resonance, self-expression, and the sense of belonging. The results reveal that task–technology fit positively influences usage intention, while privacy and security risk has a negative effect. Internally, emotional resonance and a sense of belonging significantly enhance usage intention. Furthermore, emotional resonance mediates the relationship between self-expression and both sense of belonging and usage intention, while sense of belonging also mediates between emotional resonance and usage intention. These findings underscore the critical interplay between technical attributes and affective factors in shaping engagement with VR-based reading platforms. This study offers new insights into user acceptance mechanisms in virtual book communities, and provides a theoretical foundation and practical implications for enhancing user experience and adoption in digital library systems.