This study aims to investigate the phenomenon of non-disclosure of personal information among male individuals, employing the Communication Privacy Management Theory as a guiding framework. The objectives of the study encompass identifying the specific types of personal information male students refrain from disclosing, examining the underlying reasons for their non-disclosure practices, and assessing the impact of non-disclosure on their interpersonal relationships. Qualitative research methods, primarily in-depth interviews, were employed to gather insights, with six male students from Sultan Idris Education University (UPSI) participating in the interviews. The findings reveal that male students at UPSI do engage in non-disclosure of personal information, albeit to a certain extent. Specifically, the findings discovered four types of personal information—secrets, traumas, dark history, and family matters—that these students commonly choose not to disclose. Notably, there are four categories of personal information they tend to withhold, namely secrets, traumas, dark history, and family matters. The reluctance to disclose stems from factors such as insecure attachment, a reluctance to worry about their parents, and strained relationships with their family members. Furthermore, the study highlights that non-disclosure of personal information has both negative and positive repercussions on the participants’ relationships with others. Moreover, the study underscores that non-disclosure of personal information can have both negative and positive effects on the participants’ relationships, shedding light on the complexities of navigating personal privacy choices in the university and job-seeking context. The study contributes valuable insights into the challenges of employability dilemmas faced by male university students concerning the management of personal information.

The research is focused on the evolution of the enterprises, in the field of specialized professional services, medium-period, enterprises that implemented projects financed within Regional Operational Program (ROP) during the 2007–2013 financial programming period. The analysis of the economic performance of the micro-enterprises corresponds to general objectives, but there can be outlined connections between these performances and other economic indicators that were not considered or followed through the financing program. The study case is focused on the development of micro-enterprises in the services area, in the Central Region, Romania (one of the eight development regions in Romania). The scientific approach for this article was based on a regressive statistical analysis. The analysis included the economic parameters for the enterprises selected, comparing the economic efficiency of these enterprises, during implementation with the economic efficiency after the implementation of the projects, during medium periods, including the sustainability period. The purpose of the research was to analyse the economic efficiency of the selected micro-enterprises, after finalizing the projects’ implementation. The authors intend to point out the need for a managerial instrument based on the economic efficiency of companies that are benefiting from non-reimbursable funds. This instrument should be taken into consideration in planning regional development at the national level, regarding the conditions and results expected. Although the authors used regressive statistical analysis the purpose was to prove that there is a need for additional managerial instruments when the financial allocations are being designed at the regional level. This study follows the interest of the authors in proving that the efficiency of non-reimbursable funds should be analysed distinctively on the activity sectors.

Human resource management practices are crucial, especially in the private healthcare sector. This could be because managing personnel in the healthcare sector is particularly challenging; therefore, meeting every employee's needs is crucial. Recently, the healthcare sector has experienced a scarcity and unbalanced distribution of employees due to job turnover. In addition, employee performance in the private healthcare sector has shown a slight drop due to the dissatisfaction of employees toward human resource practices such as unattractive compensation and rewards packages, bias in performance appraisal, lack of training and development, and many more. Therefore, this study is conducted to examine the impact of human resource practices on employees' job performance. Specifically, there are three main human resource practices observed as factors that contribute to an employee's job performance. The three human resource practices are compensation and benefits, performance appraisal, and training and development. There were four private hospitals operating in Selangor, Malaysia, chosen as a sample for this study. The private hospitals are KPJ Selangor Specialist Hospital, Columbia Asia Hospital Puchong, Assunta Hospital PJ, and Sunway Medical Centre. Out of these four private hospitals, there were about 291 employees working at the front desk: nurses, clinical workers, and administration staff were chosen as respondents in this study. The questionnaires were distributed to the respondents by hand. The data collected was analyzed using SPSS version 29. The findings indicate that employee job performance in Malaysian private hospitals is positively correlated with compensation and benefits. Employees feel motivated by compensation, which encourages them to increase their production and work more efficiently. Additionally, the findings also suggest that performance appraisal and training and development significantly contribute to employee job performance.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.