With the rapid development of society and the advent of the information age, counselors in higher vocational colleges and universities are facing the double test of burnout and network security. Burnout affects counselors’ work efficacy and psychological health, while cybersecurity poses certain hazards to counselors’ occupational safety. Based on the social ecology perspective, this paper explores the measurement of burnout and puts forward corresponding countermeasure suggestions, with a view to improving the work efficiency and occupational safety of counselors in higher vocational colleges and universities, and providing useful references for the construction and management of counselor teams in higher vocational colleges and universities. This paper takes the job burnout status and network security structure of vocational college counselors as the research object, and explores its causes. Corresponding countermeasures have been proposed. This article selects 100 counselors from a vocational college in X city as the research objects. The latest version of China’s job burnout scale, Maslach Burnout Inventory-General Survey (MBI-GS), was used to study it. The experimental results showed that in the dimension of emotional exhaustion, 55% of the subjects were mild. 40% were moderate and 5% were severe. In terms of cynicism, 65% were mild. 30% were moderate and 5% were moderate. On the “low achievement” dimension, the participants were “slightly” rated at 10%. “Moderate” was 75% and “Severe” was 15%. Across the three dimensions, the results showed that job burnout was widespread among vocational college counselors.

This article is devoted to studying the principles of the relationship between democracy and demoethics as tools for transforming the sustainable development of society. The study is based on the assumption that the effective functioning of democracy is associated with such social phenomena as elections and electoral behavior. The study examined electoral behavior and surveyed members of society about the qualities of candidates to which they pay special attention. An analysis of qualitative and quantitative data demonstrating the democratic foundations of elections of members of society was conducted, and an analysis of the choice of voters in the extraordinary elections of the President of the Republic of Kazakhstan by region was conducted. In this study, Bayesian network modeling is experimentally applied to formalize the problem of identifying and analyzing the behavior of virtuous personality traits. A sociological survey of public opinion was conducted using the questionnaire method with the participation of 826 people from all regions of Kazakhstan from May to June 2023. A questionnaire was used to collect data, the main purpose of which was to compare attitudes and find out what values are considered important for people, what norms of behavior are considered acceptable, and to understand what values and norms prevail in society. It is concluded that the concept of demoethics promotes a positive transformation of humanity and helps to form a new leader of virtue, a ruler of the city, capable of making ethical rational decisions that can ensure a balance between the economic, social, and environmental needs of humanity.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

The technological development and growth of the telecommunications industry have had a great positive impact on the education, health, and economic sectors, among others. However, they have also increased rivalry between companies in the market to keep and acquire new customers. A lower level of market concentration is related to a higher level of competitiveness among companies in the sector that drives a country’s socioeconomic development. To guarantee and improve the level of competition, it is necessary to monitor the concentration level in the telecommunications market to plan and develop appropriate strategies by governments. With this in mind, the present work aims to analyze the concentration prediction in the telecommunications market through recurrent neural networks and the Herfindahl-Hirschman index. The results show a slight gradual increase in competition in terms of traffic and access, while a more stable concentration level is observed in revenues.

Food security presents a complex challenge that spans multiple sectors and levels, involving diverse stakeholders. Such a challenge necessitates collaborative efforts and the creation of shared value among participants. Through the lens of service-dominant logic (S-D logic), food security can be redefined to achieve a more comprehensive understanding and sheds light on the dynamic interplay among stakeholders, enabling the realization of potential value co-creation. As a theoretical contribution, this research addresses the gap in explaining stakeholder interactions. This aspect is crucial for fostering collaboration, and the study accomplishes this by leveraging Social Network Analysis to identify clusters and assign them roles as sub-orchestrators to support the National Food Agency as the main orchestrator who responsible to implement co-creation management strategy (involvement, curation, and empowerment). The study also proposes stakeholder roles in the context of food security: regulator, operator, dominator, niche player, and supporter. Moreover, the practical significance of this research is highly relevant to the early stages of the National Food Agency (NFA) since its establishment in 2021. As the NFA seeks optimal structure, networks, and resources to enhance Indonesia’s existing food system, the study offers valuable insights. This comprehensive study highlights key issues in developing food security in Indonesia and provides recommendations for overcoming future challenges.