The impact of volunteer service on moral education performance and mental health of college students.

BACKGROUND: Moral education in colleges and universities is an important part of the talent training system, including moral education curriculum, moral education practice, mental health education. Volunteer service is a public welfare act in which volunteers volunteer their time, knowledge, property, technology, with the ultimate goal of helping others and serving the society without personal compensation. As an innovative form of moral education practice in colleges and universities, college students' voluntary service is of great significance in promoting the reform and innovation of moral education, enhancing the affinity, appeal and influence of moral education, and building a positive psychology for college students. SUBJECTS AND METHODS: As an effective carrier of moral education practice in colleges and universities, voluntary service is helpful to enhance the effectiveness of moral education practice and construct the positive psychology of college students. This project is based on the actual situation of college students participating in volunteer services, and collected the volunteer services of 4545 college students in Zhejiang Province. Through model construction and data modeling, the correlation between college students' participation in volunteer service and their moral education performance and mental health was analyzed, and the basic path and guarantee measures to promote the role of volunteer service in moral education and positive psychological construction were deeply explored. RESULTS: From the correlation analysis of students' voluntary service participation, moral education performance and voluntary service motivation, students' attributes are determined according to their voluntary service participation, so as to predict their moral education performance and mental health level. CONCLUSION: College students' voluntary service is partially positively related to their moral education performance and mental health. In order to improve students' moral education performance and mental health, we can optimize the participation frequency, participation duration, participation ways and type structure of voluntary service, constantly increase the participation frequency of voluntary service, increase the duration of voluntary service, broaden the participation ways of voluntary activities, and enrich the types of voluntary service activities.

A multi-view contrastive learning for heterogeneous network embedding.

Graph contrastive learning has been developed to learn discriminative node representations on homogeneous graphs. However, it is not clear how to augment the heterogeneous graphs without substantially altering the underlying semantics or how to design appropriate pretext tasks to fully capture the rich semantics preserved in heterogeneous information networks (HINs). Moreover, early investigations demonstrate that contrastive learning suffer from sampling bias, whereas conventional debiasing techniques (e.g., hard negative mining) are empirically shown to be inadequate for graph contrastive learning. How to mitigate the sampling bias on heterogeneous graphs is another important yet neglected problem. To address the aforementioned challenges, we propose a novel multi-view heterogeneous graph contrastive learning framework in this paper. We use metapaths, each of which depicts a complementary element of HINs, as the augmentation to generate multiple subgraphs (i.e., multi-views), and propose a novel pretext task to maximize the coherence between each pair of metapath-induced views. Furthermore, we employ a positive sampling strategy to explicitly select hard positives by jointly considering semantics and structures preserved on each metapath view to alleviate the sampling bias. Extensive experiments demonstrate MCL consistently outperforms state-of-the-art baselines on five real-world benchmark datasets and even its supervised counterparts in some settings.

Multi-Attribute Decision Making with Einstein Aggregation Operators in Complex Q-Rung Orthopair Fuzzy Hypersoft Environments.

The purpose of our research is to extend the formal representation of the human mind to the concept of the complex q-rung orthopair fuzzy hypersoft set (Cq-ROFHSS), a more general hybrid theory. A great deal of imprecision and ambiguity can be captured by it, which is common in human interpretations. It provides a multiparameterized mathematical tool for the order-based fuzzy modeling of contradictory two-dimensional data, which provides a more effective way of expressing time-period problems as well as two-dimensional information within a dataset. Thus, the proposed theory combines the parametric structure of complex q-rung orthopair fuzzy sets and hypersoft sets. Through the use of the parameter q, the framework captures information beyond the limited space of complex intuitionistic fuzzy hypersoft sets and complex Pythagorean fuzzy hypersoft sets. By establishing basic set-theoretic operations, we demonstrate some of the fundamental properties of the model. To expand the mathematical toolbox in this field, Einstein and other basic operations will be introduced to complex q-rung orthopair fuzzy hypersoft values. The relationship between it and existing methods demonstrates its exceptional flexibility. The Einstein aggregation operator, score function, and accuracy function are used to develop two multi-attribute decision-making algorithms, which prioritize based on the score function and accuracy function to ideal schemes under Cq-ROFHSS, which captures subtle differences in periodically inconsistent data sets. The feasibility of the approach will be demonstrated through a case study of selected distributed control systems. The rationality of these strategies has been confirmed by comparison with mainstream technologies. Additionally, we demonstrate that these results are compatible with explicit histograms and Spearman correlation analyses. The strengths of each approach are analyzed in a comparative manner. The proposed model is then examined and compared with other theories, demonstrating its strength, validity, and flexibility.