This study addresses the rising concerns of technostress experienced by teachers due to the increased reliance on educational technology in both classroom and online settings. Technostress, defined as the adverse psychological effects arising from the use of information communication technologies, has been documented to impact teacher performance and overall well-being. Despite the importance of educational technology in enhancing teaching and learning experiences, many educators report elevated levels of anxiety, stress, and pressures associated with their use of these tools. This study presents practical strategies to help teachers alleviate or prevent technostress while using educational technology. This study used a quantitative approach with a survey conducted among 113 university and schoolteachers. The data analysis included frequency and percentage distribution of categorical variables, Cronbach's alpha for reliability, chi-square test, and exploratory factor analysis to identify strategies for symptom prevention. The results indicated that while many teachers experienced symptoms of technostress due to several factors, some did not. The study concluded with specific strategies, and many teachers agreed highly. The implications of this study are profound for educational institutions, policymakers, and teacher training programs as they underscore the necessity of providing comprehensive training, support, and resources to help educators manage technostress effectively. By integrating these strategies into professional developmental programs and fostering a supportive teaching environment, schools and universities can promote better mental health for teachers, improving students' educational outcomes.

The increasing demand for electricity and the need to reduce carbon emissions have made optimizing energy usage and promoting sustainability critical in the modern economy. This research paper explores the design and implementation of an Intelligent-Electricity Consumption and Billing Information System (IEBCIS), focusing on its role in addressing electricity sustainability challenges. Using the Design Science Research (DSR) methodology, the system's architecture collects, analyses, and visualizes electricity usage data, providing users with valuable insights into their consumption patterns. The research involved developing and validating the IEBCIS prototype, with results demonstrating enhanced real-time monitoring, load shedding schedules, and billing information. These results were validated through user testing and feedback, contributing to the scientific knowledge of intelligent energy management systems. The contributions of this research include the development of a framework for intelligent energy management and the integration of data-driven insights to optimize electricity consumption, reduce costs, and promote sustainable energy use. This research was conducted over a time scope of two years (24 months) and entails design, development, pilot test implementation and validation phases.

The growth of buildings in big cities necessitates Design Review (DR) to ensure good urban planning. Design Review involves the city community in various forms; however, community participation remains very limited or even non-existent. There are indications that the community has not been involved in the Design Review process. Currently, DR tends to involve only experts and local government, without including the community. Therefore, this research aimed to analyze the extent of opportunities for community participation by exploring DR analysis in developed countries and related policies. In-depth interviews were also carried out with experts and Jakarta was selected as a case study since the city possessed the most intensive development. The results showed that the implementation of DR did not consider community participation. A constructivist paradigm was also applied with qualitative interpretive method by interpreting DR data and community participation. The strategy selected was a case study and library research adopted by examining theories from related literature. Additionally, the data was collected by reconstructing different sources such as books, journals, existing research, and secondary data from related agencies. Content and descriptive analysis methods were also used, where literature obtained from various references was analyzed to support research propositions and ideas.