It has been demonstrated that music can be an effective tool for shaping the future of college students. This study aimed to investigate the effects of college students’ participation in group music activities on their pro-social behavior, and for the first time introduced the psychological variables of “positive social connections” and “perceived peer support”. The results of this study show the following: (1) Group music activity participation is positively associated with pro-social behavior. (2) Positive social connections serve as a mediator between college student group music activity participation and college students’ pro-social behavior. (3) Perceived peer support serves as a mediator between college student group music activity participation and college students’ pro-social behavior. (4) Positive social connections and perceived peer support interlock to shape the relationship between group music activity participation and pro-social behavior. This study explores the intrinsic factors of college students’ participation in group music activities to promote their pro-social behavior from the dual perspectives of music psychology and behavioral psychology, provides empirical support for educational practice, emphasizes the importance of group music activities in promoting the development of college students’ pro-social behavior, and provides insights into new perspectives for college students’ overall healthy development.

This study aims to provide a precise assessment of the distribution of educational facilities within the Guangdong–Hong Kong–Macao Greater Bay Area, serving as a crucial foundation for managing educational resource allocation and enhancing the quality of educational services. Utilizing a kernel density analysis, global autocorrelation analysis, and geographic detectors, this research systematically analyzes the spatial distribution characteristics and influencing factors of educational facilities in the area. The findings reveal significant geographical disparities in facility distribution with dense clusters in urban centers such as Guangzhou and Shenzhen, and less dense distributions in peripheral areas like Zhongshan and Macau. These facilities exhibit a multi-center cluster pattern with strong spatial autocorrelation, mainly influenced by the population size and economic and urban development levels. The results provide actionable insights for refining educational planning and resource allocation, contributing to the enhancement of educational quality across diverse urban landscapes.

The objective of this inquiry is to illuminate the attributes of heavy metal contamination and evaluate the potential ecological hazards inherent in the surface soil of Chenzhou City. A comprehensive analysis was conducted on 600 systematically collected soil samples within the study area, utilizing enrichment factors, geo-accumulation indices, comprehensive pollution indices, potential ecological hazard indices, and health risk assessment models to evaluate the degree of heavy metal contamination in the soil, potential ecological risks, and associated health hazards. The findings reveal that the average enrichment factor (EF) for each heavy metal is below 2, with the hierarchy from highest to lowest being Hg > Cd > Cu > Pb > Ni > Zn > Cr > As. Approximately 78.67% of soil samples exhibit no pollution to weak pollution levels based on heavy metal enrichment factors. Moreover, the comprehensive pollution index (IPI_N) indicates that 95.17% of soil samples are within safe and pollution-free levels, indicating an overall environmentally secure setting. However, 2.67% of samples display heightened potential ecological risk levels, primarily concentrated in the southwestern region of the study area, influenced by nearby industrial activities. Additionally, it is noteworthy that both the non-carcinogenic and carcinogenic health hazards emanating from soil heavy metals to adult individuals lie within tolerable thresholds. Among these, arsenic (As), chromium (Cr), and lead (Pb) have been discerned as the principal non-carcinogenic agents. It is of particular significance that only a solitary soil specimen, located in the southwestern quadrant of the investigative region, manifests detectable health perils for children.

As urbanization accelerates, the contamination of urban soil and the consequent health implications stemming from urban expansion are increasingly salient. In recent years, a plethora of cities and regions nationwide have embarked on rigorous soil geological surveys with a focus on environmental quality, yielding invaluable foundational data. This research aims to develop scientifically robust and rational land-use planning strategies while assessing the levels of heavy metal pollution and associated risks. The urban agglomeration encompassing Zhengzhou, Luoyang, and Kaifeng (referred to as Zheng–Bian–Luo Urban Agglomeration) in Henan Province was designated as the study area. Leveraging the Nemerow comprehensive index method alongside the Hakanson potential ecological risk assessment method, this study delved into the pollution levels and potential ecological ramifications of nine heavy metals, namely Cr, Mn, Ni, Cu, Zn, As, Cd, Pb, and Co. Research indicates that the hierarchy of individual potential ecological risks ranges from most to least significant as follows: Cd > Pb > Cr > Ni > Cu > Zn > As > Mn > Co. The concentrations of Cd in both Zhengzhou and Kaifeng surpassed the established background levels. Furthermore, the mean single-factor pollution index values for the heavy metals Cd and Zn exceeded 1, signifying a state of minor pollution. The Nemerow comprehensive index P of Cd and Zn is between 1 < P_comp ≤ 2, which is considered mild pollution. The comprehensive P values of the other seven metal elements are all less than 0.7, reaching a clean (alert) level. Predominantly, the primary potential risk factor in the superficial soil of the Zheng–Bian–Luo urban agglomeration is Cd, while the ecological risk implications associated with other heavy metal elements are comparatively minimal. The soil environmental quality within the designated study area remains secure, although certain localized areas pose potential risks of heavy metal pollution. A comprehensive assessment of the current state of soil heavy metal pollution is essential to establish a theoretical foundation and provide technical support for soil environmental protection, pollution mitigation, and sustainable utilization.

The primary object of this study is to survey the spatial heterogeneity of sustainable land use in the Guangdong–Hong Kong–Macao Greater Bay Area, The introduction of GIS technology into the evaluation index system under the traditional concept of circular economy, combined with the “double carbon target” and the methods of entropy weight analysis and superposition analysis led to the establishment of the evaluation index system for sustainable land use in the GIS model. The evaluation’s findings indicate that: (1) Spatially, the horizontal gravity center of sustainable land use in the Guangdong–Hong Kong–Macao Greater Bay Area changed dimensionally from 2010 to 2021, and the spatial gravity center shifted from north to south. (2) In terms of time characteristics, sustainable land use showed a steady upward trend in the 11 years from 2010 to 2021. (3) There were regional differences and uneven development levels in the comprehensive evaluation of sustainable land use in the Guangdong–Hong Kong–Macao Greater Bay Area. It shows that there are great differences in the degree of social and economic development among federation-level cities in the Guangdong–Hong Kong–Macao Greater Bay Area. From the current research on the sustainable use of land resources, the evaluation of sustainable use of land based on the concept of a circular economy is less favorable. Thus far, there has been no case study on land sustainability in the Guangdong–Hong Kong–Macao Greater Bay Area based on carbon cycles. In this study, the results are systematically sorted out, and the influencing factors are analyzed in depth to provide theoretical guidance on the sustainable and circular development of society, culture, and economy in the Guangdong–Hong Kong–Macao Greater Bay Area.

The increasing complexity of interconnected systems in the Internet of Things (IoT) demands advanced methodologies for real-time security and management. This study presents MAS-LSTM, an anomaly-detection framework that combines multi-agent systems (MASs) with long short-term memory (LSTM) networks. By training agents on IoT traffic datasets (NF-ToN-IoT, NF-BoT-IoT, and their V2 versions), MAS-LSTM offers scalable, decentralized anomaly detection. The LSTM networks capture temporal dependencies, enhancing anomaly detection in time-series data. This framework overcomes key limitations of existing methods, such as scalability in heterogeneous traffic and computational efficiency in resource-constrained IIoT environments. Additionally, it leverages graph signal processing for adaptive and modular detection across diverse IoT scenarios. Experimental results demonstrate its effectiveness, achieving F1 scores of 0.9861 and 0.8413 on NF-BoT-IoT and NF-ToN-IoT, respectively. For V2 versions, MAS-LSTM achieves F1 scores of 0.9965 and 0.9678. These results highlight its robustness in handling large-scale IIoT traffic. Despite challenges in real-world deployment, such as adversarial attacks and communication overhead, future research could focus on self-supervised learning and lightweight architectures for resource-constrained environments.