In recent years, the ongoing impact of the COVID-19 epidemic, irregular closed school life and frequent online teaching have negatively impacted the mental health and academic performance of many college students. Doing horticultural activities is an effective way to promote physical and mental health and enhance academic performance. This paper explores the relationship between horticultural activities participation, academic self-efficacy and positive mental characters under the perspective of disciplinary integration, with a view to promoting the mental health status and academic performance of college students and the application of horticultural therapy on college campuses. Questionnaires such as the Positive Mental Characters Scale for Chinese College Students (PMCS-CCS) and Academic Self-Efficacy Scale (ASES) are used to investigate 160 college students from four universities in China. The results show that horticultural activity participation is significantly positively correlated with academic self-efficacy (r = 0.345; p < 0.01) and positive mental characters (r = 0.298; p < 0.01), and horticultural activity participation can positively affect positive mental characters (B = 0.135, p < 0.01). At the same time, academic self-efficacy has a partial mediating effect between horticultural activity participation and positive mental characters. Universities can actively carry out campus horticultural activities to enhance students’ horticultural activity participation, which in turn promotes academic self-efficacy and further enhances the positive psychological level of college students.

The building sector, responsible for 40% of global energy consumption, faces increasing demands for sustainability and energy efficiency. Accurate energy consumption forecasting is essential to optimise performance and reduce environmental impact. This study introduces a hybrid machine learning framework grounded in Sparse, Interpretable, and Transparent (SIT) modelling to enhance building energy management. Leveraging the REFIT Smart Home Dataset, the framework integrates occupancy pattern analysis, appliance-level energy prediction, and probabilistic uncertainty quantification. The framework clusters occupancy-driven energy usage patterns using K-means and Gaussian Mixture Models, identifying three distinct household profiles: high-energy frequent occupancy, moderate-energy variable occupancy, and low-energy irregular occupancy. A Random Forest classifier is employed to pinpoint key appliances influencing occupancy, with a drop-in accuracy analysis verifying their predictive power. Uncertainty analysis quantifies classification confidence, revealing ambiguous periods linked to irregular appliance usage patterns. Additionally, time-series decomposition and appliance-level predictions are contextualised with seasonal and occupancy dynamics, enhancing interpretability. Comparative evaluations demonstrate the framework’s superior predictive accuracy and transparency over traditional single machine learning models, including Support Vector Machines (SVM) and XGBoost in Matlab 2024b and Python 3.10. By capturing occupancy-driven energy behaviours and accounting for inherent uncertainties, this research provides actionable insights for adaptive energy management. The proposed SIT hybrid model can contribute to sustainable and resilient smart energy systems, paving the way for efficient building energy management strategies.

The built environment of cities has increasingly affected the travel mode of residents and led to changes in energy consumption, which is crucial to maintaining urban sustainability. Ningbo is a typical representative of urbanization on the east coast of China, and its energy consumption is in a period of rapid growth. Therefore, using the survey data of 22,112 traffic trip samples from nine streets in Ningbo, this paper establishes a regression analysis model, systematically analyzes the relationship between the built environment and domestic energy consumption from multiple dimensions, and reveals the impact mechanism of the built environment on domestic energy consumption. We find that (1) social and economic conditions are the main factors affecting traffic energy consumption. (2) The population density has a significant negative correlation effect on the energy consumption of transportation trips. When the population density increases by 1%, the energy consumption of total transportation trips, commuting trips, high-energy-consumption trips, and low-energy-consumption trips decreases by 0.094%, 0.115%, 0.273%, and 0.124%, respectively. (3) When the degree of mixed use of land increases by one percentage point, the energy consumption of total transportation trips, commuting trips, high-energy-consumption trips, and low-energy-consumption trips decreases by 0.415%, 0.421%, 2.574%, and 1.197%, respectively. (4) The density of road intersections has a significant negative correlation effect on the energy consumption of traffic trips. (5) The impact of the built environment on the energy consumption of transportation trips is greater than that of residential buildings.

Salinization of the soil in Daqing in China is the main result of its geographical structure and climate condition. Oil exploration and overgrazing anthropogenic factors lead to the concentrated distribution in the form of plague, which threatens the inhabitation comfort and sustainable land use of the city environment as the oil operation and grazing zone expand. The treatment and subsequent development of saline/alkali land in Daqing are extremely crucial to curtail environmental desertification and promote the development of industries during the urbanization. This study compares the traditional physical, chemical, and biological technologies for the improvement of salinization land to establish the combined method of technology optimization. Considering the water conservancy as the guidance, the aim is to present the stable long- and short-term combined technological method of biological improvement from the view of eco-restoration and industrial renovation, supported by the physical and chemical improvement. The technology of network of the grading of trapezoidal terrace ditches, which combines the trapezoidal terrace with landscape using natural sedimentation, is proposed. The indoor terrace elution experiment indicates that the salinity reduced by 52%, alkalinity 50, and 50% for oily substances during 48 h; this technology is combined with comprehensive utilization of the waste, collaborative repair of the microorganisms and plants to offer new views and theoretical support for the protection and development of the saline/alkali land in Daqing area.

Aim: This study aimed to evaluate virtual reality restorative environments (VRREs)’ impact on university students’ mental well-being, investigate factors affecting VRRE perception and psychological recovery, understand virtual environments’ healing mechanisms, and provide recommendations for university virtual healing spaces. Methods: Semi-structured interviews were conducted with 32 participants to develop a VRRE perception evaluation system with five core and fourteen main categories. The system was then used to assess 13 virtual natural environments. Mental recovery effects were measured among 44 university students using the Schulte test (attention), Positive and Negative Affection Scale (mood), and physiological sensors (stress). Results: VRREs demonstrated significant positive effects on participants’ psychological recovery. Different virtual environments showed varying impacts on attention, negative affect, and stress levels, while effects on positive affect were consistent across environments. Virtual extraterrestrial space environments yielded the strongest improvements in attention and stress reduction, whereas mixed forest settings were most effective in decreasing negative affects. Structural equation modeling revealed that participants’ VRRE perceptions significantly influenced psychological recovery through seven of fifteen pathways. Conclusions: VRREs represent an effective intervention for supporting university students’ mental well-being. Different virtual environments offer distinct psychological benefits, with environment perception playing a crucial role in recovery outcomes. These findings provide valuable insights for designing targeted virtual healing spaces in university settings.