Root turnover is a key process of terrestrial ecosystem carbon cycle, which is of great significance to the study of soil carbon pool changes and global climate change. However, because there are many measurement and calculation methods of root turnover, the results obtained by different methods are quite different, and the current research on root turnover of forest ecosystem on the global regional scale is not sufficient, so the change law of root turnover of global forest ecosystem is still unclear. By collecting literature data and unifying the calculation method of turnover rate, this study integrates the spatial pattern of fine root turnover of five forest types in the world, and obtains the factors affecting fine root turnover of forest ecosystem in combination with soil physical and chemical properties and climate data. The results showed that there were significant differences in fine root turnover rate among different forest types, and it gradually decreased with the increase of latitude; the turnover rate of fine roots in forest ecosystem is positively correlated with annual average temperature and annual average precipitation; fine root turnover rate of forest ecosystem is positively correlated with soil organic carbon content, but negatively correlated with soil pH value. This study provides a scientific basis for revealing the law and mechanism of fine root turnover in forest ecosystem.

Research into electro-conductive textiles based on conductive polymers like polypyrrole has increased in recent years due to their high potential applications in various fields. Conductive polymers behave like insulators in their neutral states, with typical electrical conductivity in the range 10^–10 to 10^–25 Scm^–1. These neutral polymers can be converted into semi-conductive or conductive states with conductivities ranging from 1 Scm^–1 to 10^–4 Scm^–1 through chemical or electro-chemical redox reactions. By applying these polymers to a textile surface, we can obtain novel composites that are strong, flexible, lightweight, and highly electroconductive. These textile composites are suitable for applications such as heating pads, sensors, corrosion-protecting materials, actuators, electrochromic devices, EMI shielding, etc. The methods of application of conductive polymers onto the textile surface, such as in-situ chemical, in-situ electrochemical, in-situ vapor phase, in-situ polymerization in a supercritical fluid, and solution coating processes, are described here briefly. The merits and demerits of these methods are mentioned here. The reaction mechanisms of chemical and electrochemical polymerization proposed by the different researchers are described. Different factors affecting the kinetics of chemical and electrochemical polymerization are accounted for. The influence of textile materials on the kinetics of chemical polymerization is reviewed and reported.

Universities play a key role in university-industry-government interactions and are important in innovation ecosystem studies. Universities are also expected to engage with industries and governments and contribute to economic development. In the age of artificial intelligence (AI), governments have introduced relevant policies regarding the AI-enabled innovation ecosystem in universities. Previous studies have not focused on the provision of a dynamic capabilities perspective on such an ecosystem based on policy analysis. This research work takes China as a case and provides a framework of AI-enabled dynamic capabilities to guide how universities should manage this based on China’s AI policy analysis. Drawing on two main concepts, which are the innovation ecosystem and dynamic capabilities, we analyzed the importance of the AI-enabled innovation ecosystem in universities with governance regulations, shedding light on the theoretical framework that is simultaneously analytical and normative, practical, and policy-relevant. We conducted a text analysis of policy instruments to illustrate the specificities of the AI innovation ecosystem in China’s universities. This allowed us to address the complexity of emerging environments of innovation and draw meaningful conclusions. The results show the broad adoption of AI in a favorable context, where talents and governance are boosting the advance of such an ecosystem in China’s universities.

The main objective of the study was to examine factors that influence employee performance in general and, more specifically, in public enterprises. The research approach was qualitative, with an in-depth literature review and content analysis. The findings of the study reflect that some factors have a positive and some have a negative influence on employee performance. The study also shows a significant relationship between factors and employee performance, which in turn has a multiplier effect on employee development. Recommendations include the need to provide resources for employee training and development, and the strategic aims and objectives of public enterprises should be aligned with the training and development programs.

Several studies have discussed the benefits of blockchain in human resources management (HRM) policies to support the efficiency of HRM routine practices in organizations. The discussion ranges from selection and recruitment to employee separation. With the growing interest in digital application usage, research focused on utilization and effective measurement is needed. However, the existing literature review on blockchain-based HRM practices linked to cost efficiency still needs to be improved. Hence, this study aims to review current studies on blockchain human resources management systematically. This study investigates the trends in blockchain application usage in terms of practices, methodologies, and settings. This study used a literature survey and Publish or Perish software with Google Scholar and Scopus as the databases. 123 articles published in 19 journals from 2010 to 2022 were selected. This study used systematic data to reveal trends in HRM practices and qualitative inductive analysis to define relevant themes within the topic. The results show that blockchain applications for efficiency are used mainly in the recruitment and selection process, ranging from personal data verification to the quality of decision-making in skill development and maintenance. Five HRM practices have been discussed, indicating potential explorative and exploitative future research to improve the effectiveness of using blockchain in HRM practices.

This study aimed to analyze government policies in education during the Covid-19 pandemic and how teachers exercised discretion in dealing with limitations in policy implementation. This research work used the desk review method to obtain data on government policies in the field of education during the Covid-19 pandemic. In addition, interviews were conducted to determine the discretion taken in implementing the learning-from-home policy. There were three learning models during the pandemic: face-to-face learning in turns (shifts), online learning, and home visits. Online learning policies did not work well at the pandemic’s beginning due to limited infrastructure and human resources. To overcome various limitations, the government provided internet quota assistance and curriculum adjustments and improved online learning infrastructure. The discretion taken by the teachers in implementing the learning-from-home policy was very dependent on the student’s condition and the availability of the internet network. The practical implication of this research is that street-level bureaucrats need to pay attention to discretionary standards when deciding to provide satisfaction to the people they serve.