Land use changes have been demonstrated to exert a significant influence on urban planning and sustainable development, particularly in regions undergoing rapid urbanization. Tehran Province, as the political and economic capital of Iran, has undergone substantial growth in recent decades. The present study employs sophisticated Geographic Information System (GIS) instruments and the Google Earth Engine (GEE) platform to comprehensively track and analyze land use change over the past two decades. A comprehensive analysis of Landsat images of the Tehran metropolitan area from 2003 to 2023 has yielded significant insights into the patterns of land use change. The methodology encompasses the utilization of GIS, GEE, and TerrSet techniques for image classification, accuracy assessment, and change detection. The Kappa coefficients for the maps obtained for 2016 and 2023 were 0.82 and 0.87 for four classes: built-up, vegetation cover, barren land, and water bodies. The findings suggest that, over the past two decades, Tehran Province has undergone a substantial decline in ecological and vegetative areas, amounting to 2.4% (458.3 km2). Concurrently, the urban area and the barren lands have expanded by 287.5 and 125.5 km2, respectively. The increase in water bodies during this period is likely attributable to the reduction of vegetation cover and dam construction in the region. The present study demonstrates that remote sensing and GIS are excellent tools for monitoring environmental and sustainable urban development in areas experiencing rapid urbanization and land use changes.

This study investigates the impact of human resource management (HRM) practices on employee retention and job satisfaction within Malaysia’s IT industry. The research centered on middle-management executives from the top 10 IT companies in the Greater Klang Valley and Penang. Using a self-administered questionnaire, the study gathered data on demographic characteristics, HRM practices, and employee retention, with the questionnaire design drawing from established literature and validated measuring scales. The study employed the PLS 4.0 method for analyzing structural relationships and tested various hypotheses regarding HRM practices and employee retention. Key findings revealed that work-life balance did not significantly impact employee retention. Conversely, job security positively influenced employee retention. Notably, rewards, recognition, and training and development were found to be insignificant in predicting employee retention. Additionally, the study explored the mediating role of job satisfaction but found it did not mediate the relationship between work-life balance and employee retention nor between job security and employee retention. The research highlighted that HRM practices have diverse effects on employee retention in Malaysia’s IT sector. Acknowledging limitations like sample size and research design, the study suggests the need for further research to deepen understanding in this area.

The idea of a smart city has evolved in recent years from limiting the city’s physical growth to a comprehensive idea that includes physical, social, information, and knowledge infrastructure. As of right now, many studies indicate the potential advantages of smart cities in the fields of education, transportation, and entertainment to achieve more sustainability, efficiency, optimization, collaboration, and creativity. So, it is necessary to survey some technical knowledge and technology to establish the smart city and digitize its services. Traffic and transportation management, together with other subsystems, is one of the key components of creating a smart city. We specify this research by exploring digital twin (DT) technologies and 3D model information in the context of traffic management as well as the need to acquire them in the modern world. Despite the abundance of research in this field, the majority of them concentrate on the technical aspects of its design in diverse sectors. More details are required on the application of DTs in the creation of intelligent transportation systems. Results from the literature indicate that implementing the Internet of Things (IoT) to the scope of traffic addresses the traffic management issues in densely populated cities and somewhat affects the air pollution reduction caused by transportation systems. Leading countries are moving towards integrated systems and platforms using Building Information Modelling (BIM), IoT, and Spatial Data Infrastructure (SDI) to make cities smarter. There has been limited research on the application of digital twin technology in traffic control. One reason for this could be the complexity of the traffic system, which involves multiple variables and interactions between different components. Developing an accurate digital twin model for traffic control would require a significant amount of data collection and analysis, as well as advanced modeling techniques to account for the dynamic nature of traffic flow. We explore the requirements for the implementation of the digital twin in the traffic control industry and a proper architecture based on 6 main layers is investigated for the deployment of this system. In addition, an emphasis on the particular function of DT in simulating high traffic flow, keeping track of accidents, and choosing the optimal path for vehicles has been reviewed. Furthermore, incorporating user-generated content and volunteered geographic information (VGI), considering the idea of the human as a sensor, together with IoT can be a future direction to provide a more accurate and up-to-date representation of the physical environment, especially for traffic control, according to the literature review. The results show there are some limitations in digital twins for traffic control. The current digital twins are only a 3D representation of the real world. The difficulty of synchronizing real and virtual world information is another challenge. Eventually, in order to employ this technology as effectively as feasible in urban management, the researchers must address these drawbacks.

Business organizations use job advertisements to find and attract the high-quality workforce they need. Skillfully crafted job advertisements not only provide job-related information to job seekers but also help develop a strong employer brand in the employee market. Based on signaling theory and person-environment fit theory, we propose that the content and specificity of information provided in job advertisements influence job advertisement effectiveness through various mechanisms. In a scenario-based experiment on 310 young job seekers, we probed the direct and indirect effects of job advertisement informativeness on job pursuit intentions. Using structural equations modelling and multi-group path analysis, the mediating roles of perceived job appropriateness and ad truthfulness, along with the moderating role of previous employment experience, were examined. By manipulating the information content of a hypothetical job advertisement, we demonstrated that: a) both advertisement informativeness and perceived job appropriateness had positive direct effects on application intentions, while the latter had a greater effect; b) perceived job appropriateness mediated the relationship between advertisement informativeness and job pursuit intentions; c) the indirect (mediated) effect of advertisement informativeness on application intentions was moderated by previous employment experience; d) perceived ad truthfulness did not exert any significant effect on application intentions. These findings imply that HR practitioners should provide specific information in job postings to help candidates, especially those with less work experience, evaluate how well the job suits them and increase their motivation to apply.

This study assesses Vietnam’s state-level implementation of artificial intelligence (AI) technology and analyses the government’s efforts to encourage AI implementation by focusing on the National Strategy on AI Development Program. This study emphasizes the possibility of implementing AI at the state level in Vietnam and the importance of conducting continuous reviews and enhancements to achieve sustainable and inclusive AI growth. Impact evaluations were conducted in public organizations alone, and implication evaluations were considered optional. AI impact assessments were constrained by societal norms that necessitated establishing relationships among findings. There is a lack of official information regarding the positive impact of Vietnam’s AI policy on the development of AI infrastructure, research, and talent pools. The study’s findings highlight the necessity of facilitating extensive AI legislation, and strengthening international cooperation. The study concludes with the following recommendations for improving Vietnam’s AI policy: implementing a strong AI governance structure and supporting AI education and awareness.