This study aims to examine the evolution of the system of support sources in Hungary, focusing on the specific goals supporting higher education in the development programs Széchenyi 2020 (2014–2020) and Széchenyi Plan Plus (2021–2027). The study provides insights into development program evolution and changes, aiming to inform EU funding opportunities for Hungarian higher education institutions over a nearly 10-year period. By focusing on the operational programs that are the basis for the upcoming tenders, the study will display the target system of EU funds that can be utilized to bolster higher education institutions in Hungary. The study is based on document analysis, examining the Hungarian policy tools of the development programs and the operational program strategies of the ten-year time period from 2014 to 2024. By analyzing the support landscape for higher education institutions in Hungary, this study contributes to a better understanding of how the key objectives and criteria of strategic programs have evolved. It also examines the aspects and elements defined in two different development programs over the last ten years. The result of the study can contribute to anticipate the types of funding opportunities that may be available in the future and inform future decision-making processes.

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.

An important element in dealing with HIV/AIDS is to disclose of its status to others. One of the problems faced by HIV/AIDS sufferers in disclosing their status is finding people they can trust, who can keep information about their HIV/AIDS status and not divulge it to other parties without their permission. Not many people can accept them without prejudice and stigma. This article discusses the communication efforts carried out by female AIDS activists in the community as co-owners who receive information from people with HIV/AIDS and subsequently become confidants and assist them in medical and psychological and social aspects. This study used a qualitative method. Data was collected through in-depth interviews with 9 Community AIDS activist women from 7 regions. The results of the study reveal the process of housewives transforming into community AIDS activists, how they get personal information about the status of HIV/AIDS and eventually become co-owners of information who eventually become confidants, have responsibilities and help people with HIV/AIDS in health, psychological and social aspects.

This study investigates non-academic employees’ perceptions of their line managers’ leadership styles at a private university in Malaysia and how these perceptions influence their intention to remain employed. Employing a qualitative approach and the path-goal theory as a theoretical framework, data were collected through purposive sampling from 10 non-academic employees and analyzed thematically using NVivo 12 software. The findings reveal that a supportive and participative leadership style fosters an informal leadership dynamic between line managers and subordinates. Informal leadership behaviors encompass affective qualities and effective communication that enable the development of close relationships outside the workplace, facilitating increased employee engagement and motivation levels. Consequently, this approach notably improves employee retention. This study offers a comprehensive understanding of informal leadership styles contributing to enhanced human resource management at the private university while providing an inclusive perspective on employees’ perceptions and their intention to remain employed. Finally, we propose a model of employees’ perception of leadership styles as the main driver that better serves their intention to stay in organizations.

The 2019 Social Enterprise Promotion Act in Thailand represents a pivotal step towards promoting social enterprises by fostering self-reliance and a fair and sustainable future for the country. Despite their significance, there is a noticeable research gap focusing on the factors that motivate Thai entrepreneurs to venture into social entrepreneurship. This study seeks to fill that gap by analyzing data from 2000 respondents in Thailand, utilizing linear regression to explore whether the awareness of the United Nations Sustainable Development Goals (SDGs), the adoption of digital technologies, extrinsic motivations, such as the overall societal view of entrepreneurs, social awareness, and perceptions of entrepreneurial capabilities influence the decision to start a social enterprise. In a gender comparison, our findings reveal that the societal context plays a crucial role for both genders, although in distinct ways: Male entrepreneurs are more influenced by individualistic extrinsic values, with motivations linked to power, respect, and societal recognition. In contrast, female entrepreneurs display a collectivistic orientation, being more likely to be inspired by intrinsic motivations, such as the success and visibility of other successful startups within their society. These findings underline the need for a gender-sensitive approach by government bodies, educational institutions, and other relevant organizations aiming to boost start-up rates of enterprises who “make a difference in the world”. Tailored support and educational programs to address the unique motivations and perspectives of male and female entrepreneurs could play a crucial role in enhancing the effectiveness of strategies designed to promote social entrepreneurship in Thailand and beyond.

Infrastructure decision-making has traditionally been focused on the use of cost-benefit analysis (CBA) and multicriteria decision analysis (MCDA). Nevertheless, there remains no consensus in the infrastructure sector regarding a favored approach that comprehensively integrates resilience principles with those tools. This review focuses on how resilience has been evaluated in infrastructure projects. Initially, 400 papers were sourced from Web of Science and Scopus. After a preliminary review, 103 papers were selected, and ultimately, the focus was narrowed down to 56 papers. The primary aim was to uncover limitations in both CBA and MCDA, exploring various strategies for amalgamating them and enhancing their potential to foster resilience, sustainability, and other infrastructure performance aspects. Results were classified based on different rationalities: i) objectivist, ii) conformist, iii) adjustive, and iv) reflexive. The analysis revealed that while both CBA and MCDA contribute to decision-making, their perceived strengths and weaknesses differ depending on the chosen rationality. Nonetheless, embracing a broader perspective, fostering participatory methods, and potentially integrating both approaches seem to offer more promising avenues for assessing the resilience of infrastructures. The goal of this research proposal is to devise an integrated approach for evaluating the long-term sustainability and resilience of infrastructure projects and constructed assets.