Artificial intelligence (AI) is rapidly transforming the way human resources (HR) functions are carried out in the health sector of China. This study aims to scrutinize the impact of artificial intelligence on the human resource functions operating in the healthcare sector through technological awareness, social media influence, and personal innovativeness. Additionally, this study examines the moderating role of perceived risk between technological awareness and human resources functions. An online questionnaire was administered to human resources professionals in the health sector of China to gather data from 363 respondents. Partial least squares structural equation modeling (PLS-SEM), a statistical procedure, is implemented to investigate the hypothesis of the projected model of artificial intelligence and human resource functions. The research findings reveal that artificial intelligence significantly influences human resource functions through technological awareness, social media influence, and personal innovativeness. Furthermore, perceived risk significantly moderates the relationship between technological awareness and human resource functions. The findings of this study have important implications for HR practitioners and policymakers in the health sectors of China, who can leverage artificial intelligence technologies to optimize and improve organizational performance. However, its adoption needs to be carefully planned and managed to reap the full benefits of this transformative technology.

In the modern digital world, electronic devices are being widely employed for various applications where thermal performance represents a significant technical challenge due to continued miniaturization, high heat generated in the system, and non-uniform high-temperature causing failure. Phase change materials (PCMs) owing to the immense heat of fusion are primarily considered for thermal management, but their insulating properties hedge their applications in electronics cooling. Nano-enhanced phase change materials (NePCMs) have the ability to improve the thermal conductivity of PCM, decrease system temperature and escalate the operating time of devices. Accordingly, the current study focused on the experimental investigations for the thermal performance of three heat sinks (HS) with different configurations such as a simple heat sink (SHS), a square pin-fins heat sink (S_pfHS), and Cu foam integrated heat sink (Cu_fmHS) with various alumina nanoparticles mass concentrations (0.15, 0.20 and 0.25 wt%) incorporated in PCM (RT-54HC) and at heat flux (0.98–2.94 kW/m²). All HSs reduced the base temperature with the insertion of NePCM compared to the empty SHS. The experimental results identified that the thermal performance of Cu_fmHS was found to be superior in reducing base temperature and enhancing working time at two different setpoint temperatures (SPTs). The maximum drop in base temperature was 36.95%, and a 288% maximum working time enhancement was observed for Cu_fmHS. Therefore, NePCMs are highly recommended for the thermal management of the electronic cooling system.