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 November 2018, the sample plot survey method was used to analyze the population characteristics of Lithocarpus polystachyus in the natural secondary forest with different disturbance intensity in Jianning, Fujian Province, and compile its population static life table. The results showed that the number of individuals in the population was small, but it was clustered. With the increase of interference intensity, the first and second age seedlings and young trees decreased. The population types affected by human disturbance are all lacking level V trees, and the population type belongs to primary population (N₁); The undisturbed population lacks level I and II seedlings and young trees, but there are level V trees, and the population type belongs to medium decline population (S₂). In general, all populations of L. polystachyus are unstable and belong to the transitional type. In the static life table, the mortality of level I and II seedlings and young trees is high, the survival rate has a small peak in level III and IV, and then the survival rate decreases rapidly, and the average life expectation of level II is the highest. It shows that artificial conservation measures and appropriate space re-lease are needed to maintain the stability of the population.

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.

This quantitative survey was non-experimental and had two goals. An evaluation of predictor variables of empowerment, motivation, teamwork, interpersonal skills, and training and development in project environments was one goal to help explain the industry’s high project failure rate. Second, this research tested Bandura’s social learning theory and tested the hypothesis that empowerment and motivation boost performance. Using a survey-based questionnaire, the data was collected from 212 employees working in different IT companies in Pakistan. The results revealed that empowerment, motivation, teamwork, and training and development have a significant impact on project performance. Using the results, this study proposes theoretical implications for the researchers and managerial implications for the organizations.