Hilya Mudrika Arini

Aim: This study aims to explore the role of digital technologies across the entire life cycle of Urban Green Infrastructure, which to date remains insufficiently mapped and understood. Methodology and Results: This review examined 75 peer-reviewed journal articles published between 2019 and 2024 to understand where digital tools are used in planning, construction, and operation and maintenance. The literature shows a clear concentration of digital use in planning and in operation, with construction receiving far less attention. Planning and design studies rely mainly on Geographic Information Systems for spatial analysis and scenario comparison, while studies on operation and maintenance focus on monitoring and performance assessment through Internet of Things systems and remote sensing. Reported impacts relate mainly to data quality, system oversight, and management efficiency. Conclusion, significance and impact study: Across studies, this pattern does not appear to depend on the type of technology applied. Instead, digital use follows institutional responsibility and reporting routines. Phases with stable mandates show regular digital application, while construction remains weakly documented, which interrupts continuity between design intent and operational outcomes and limits interpretation of UGI performance across the life cycle.

Manual lifting tasks are a leading cause of workplace injuries, necessitating reliable methods to evaluate and mitigate associated risks. Accurately identifying lifting stages is a critical step in ergonomic risk assessment tools such as the Revised NIOSH Lifting Equation (RNLE), which requires precise determination of the lifting action's origin and destination points. This study addresses the challenge of lifting stage identification by developing a hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Using a dataset of 117 videos, representing single lifting actions, 75,920 labeled frames were generated and classified into four stages:

In emergencies, a comprehensive solution must be determined using large‐group emergency decision making (LGEDM), which relies on the perspectives of experts from various fields. The inherent complexity and uncertainty of these situations necessitate the adoption of multicriteria decision‐making approaches. LGEDM is often clustered into smaller subgroups using algorithms, such as k ‐means and hierarchical clustering, to manage this complexity and facilitate analysis. Although achieving consensus among participants is ideal, LGEDM can be time‐consuming and resource‐intensive, particularly in emergencies where time is essential. The proposed LGEDM framework addresses these challenges by incorporating an objective feedback mechanism. This ensures that the final solution reflects the original preferences of the participants, even after adjustments are made to improve group consensus. An emergency case study demonstrates the efficiency of the proposed framework. Simultaneously, a thorough discussion outlines its advantages through comparative analysis, highlighting its improved decision‐making speed, higher consensus achievement with fewer iterations, and enhanced robustness in overcoming the limitations of previous studies, enhancing the consensus and final solution. Some limitations of the current study for improvements in more comprehensive future studies are also discussed.

The use of social media as a platform to access news and information has the potential to lead to the spread of fake news in Indonesia. This study aims to (1) understand the trust characteristics in information of Indonesians during COVID-19; (2) identify Indonesians' ability to detect COVID-19 fake news; and (3) analyze the relationship between people's trust characteristics in information with regard to COVID-19 information and their ability to detect fake news. An online survey was conducted with 751 Indonesians who use social media to access information about COVID-19. Cultural theory is used to categorize people's trust characteristics in information, while signal detection theory is employed to identify people's ability to discriminate between fake and real news. The results showed that 61% of respondents were categorized as having hierarchy trust characteristics. Concerning the detectability of fake news, most respondents could discriminate between fake and real news. Lastly, there was a relationship between trust characteristics in information and bias tendencies in detecting fake news. The respondents have hierarchy trust characteristics, indicating they trusted government information related to COVID-19 issues. The respondents also have high ability to discriminate between fake and real news, even though they tended to miss more errors than identify false alarms when detecting fake news. The findings showed that respondents who had hierarchy and egalitarian characteristics tended to perceive real news as fake news and had a better ability to distinguish fake news compared to other trust characteristics in information.

This study aims to: (1) find out the variables that influence people’s trust in the virtual team in a project, (2) find out the relationship of these variables with people trust in the virtual team in a project, and (3) the relationship between the presence of experienced members on a virtual team in leading an organization to determine the success of a project. The experiment was conducted using online Werewolf game and collected questionnaires from 30 respondents that divided into 3 groups, i.e., (1) having experience of being leaders, (2) a combination of having and having no experience of being leaders, and (3) having no experience of being leaders. A correlation test and a comparative test were used to analyze the result. The results found four variables that could influence people’s trust, i.e., (1) ability, (2) benevolence, (3) integrity, and (4) task faithfulness. All variables had a positive effect, except for task faithfulness. There is a relationship between the presence of leaders in a virtual team to the success of the project.