In the highly competitive employment environment, most college students have left their jobs for a short time after employment, and attention should be paid to students’ career adaptation. However, the further influence of skilled goal orientation, social support and career-determined self-efficacy on college students’ career adaptation needs to be confirmed. This study analyzes the effects of these factors on college students’ career adaptation. This study aims to analyze the impact of mastery goal orientation, social support, and vocational decision self-efficacy on career adaptation among 224 university students in East China. The results indicated that university students generally exhibit positive levels of mastery goal orientation, social support, vocational decision self-efficacy, and overall career adaptation. Female students demonstrate higher levels of mastery goal orientation, social support, vocational decision self-efficacy, and career adaptation compared to male students. As students progress in their academic years, their levels of mastery goal orientation, social support, vocational decision self-efficacy, and career adaptation tend to increase. Students majoring in humanities and social sciences have higher level than students majoring in science and engineering in all factors. Students majoring in humanities and social sciences exhibit more optimism in all factors compared to students in science and technology fields. The relationships among these factors show positive correlations. Mastery goal orientation, social support, and vocational decision self-efficacy all have positive effects on career adaptation. Among these, family support stands out as the most influential subordinate factor of social support on career adaptation. The most influential subordinate factor of vocational decision self-efficacy on career adaptation is conscious decision-making. Therefore, male, lower grade, science and engineering college students are the groups that need to be paid attention to in improving career adaptation. Skilled goal orientation, family support and conscious decision making have a better effect on the improvement of career adaptation. These results can provide important reference information for universities, counselors and college students in the training of career planning, and theoretically enrich the relevant research on college students’ career adaptation, and provide certain enlightenment for future researchers.

Poverty, and especially the widening disparity between the rich and the poor, leads to social unrest that can interrupt the harmonious development of human society. Understanding the reasons for income inequality, and supporting the development of an effective strategy to reduce this inequality, have been major goals in socioeconomic research around the world. To identify the determinants of the income gap, we calculated the Gini coefficients for Chinese provinces and performed regression analysis and contribution analysis for heterogeneity, using data from 30 Chinese provinces from 2002 to 2018. We found that urbanization, higher education, and foreign direct investment in eastern China and energy in central and western China were important factors that increased the Gini coefficient (i.e., decreased equality). Therefore, paying more attention to the fair distribution of the factors that can increase the Gini coefficient and investing more in the factors that can reduce the Gini coefficient will be the keys to narrowing the income gap. Our approach revealed factors that should be targeted for solutions both in China and in other developing countries that are facing similar difficulties, although the details will vary among countries and contexts.

The rising trend of tourists selecting agrotourism as a tourist destination has become an intriguing study issue. Seremban is a well-known tourist attraction that is popular among visitors. As a result, Seremban has been selected as the study site. However, river pollution may have an influence on Seremban’s natural environment and agrotourism potential. Furthermore, inadequate infrastructure, such as unauthorized parking, exacerbated the inhabitants’ problems. A growing number of young people leave Seremban to pursue employment or further education in other cities, with no desire to work as farmers. The labor scarcity has also made it difficult for farmers to grow their farms. Consequently, the study aims to examine how factors such as the natural environment, tourist infrastructure, perceived social advantages, and perceived barriers influence the attitudes of Seremban residents towards agrotourism, with a focus on its potential for driving economic growth. This study adopts quantitative research methods, employing descriptive and causal research designs. Primary data collection is conducted through questionnaires, supplemented by secondary data. Non-probability quota sampling is utilized due to the absence of a specific sampling frame, with a sample size of 385 respondents determined using G*Power software. Constructs are developed based on previous research, and the questionnaire comprises Likert-scale items to gauge attitudes and perceptions. A pilot study assesses the instrument’s reliability. Data analysis is performed using SPSS software, encompassing multiple linear regression and Pearson correlation analyses in addition to descriptive statistics. The findings provide valuable insights into the factors driving residents’ perceptions of agrotourism in Seremban, emphasizing the importance of the natural environment, tourism infrastructure, perceived social benefits, and perceived barriers in shaping attitudes. Additionally, the study highlights the resilience of residents’ positive attitudes toward agrotourism, despite potential challenges and barriers identified. Overall, these results offer implications for policymakers and stakeholders involved in tourism development in the region.

As China’s urbanization process accelerates, it has become common for rural men to go out to work and women to stay at home. The implementation of China’s rural revitalization strategy is in dire need of a large amount of high-quality human capital, and education and training are an important way to improve human capital and empower left-behind women. Starting from the background of China’s rural revitalization, this study focuses on the education and training of rural left-behind women, a topic that has received less attention. Through in-depth interviews and participatory observation, we analyzed the factors affecting rural left-behind women’s participation in education and training, as well as the problems that exist in China’s rural education and training process, and proposed strategies to solve them. The study found that education level, traditional attitudes, economic income, knowledge of education and training, and mental health are important factors affecting the participation of left-behind women in education and training in rural China. At the same time, there are some problems in the process of education and training, such as a single main body of supply and training methods, a lack of teachers, and a lack of management, etc., which affect the development of education and training, and thus also the promotion of rural revitalization.

Countering cyber extremism is a crucial challenge in the digital age. Social media algorithms, if designed and used properly, have the potential to be a powerful tool in this fight, development of technological solutions that can make social networks a safer and healthier space for all users. this study mainly aims to provide a comprehensive view of the role played by the algorithms of social networking sites in countering electronic extremism, and clarifying the expected ease of use by programmers in limiting the dissemination of extremist data. Additionally, to analyzing the intended benefit in controlling and organizing digital content for users from all societal groups. Through the systematic review tool, a variety of previous literature related to the applications of algorithms in the field of online radicalization reduction was evaluated. Algorithms use machine learning and analysis of text and images to detect content that may be harmful, hateful, or call for violence. Posts, comments, photos and videos are analyzed to detect any signs of extremism. Algorithms also contribute to enhancing content that promotes positive values, tolerance and understanding between individuals, which reduces the impact of extremist content. Algorithms are also constantly updated to be able to discover new methods used by extremists to spread their ideas and avoid detection. The results indicate that it is possible to make the most of these algorithms and use them to enhance electronic security and reduce digital threats.