Lake Batur is one of the national priorities, as it has economic value, and fish resources are used for food security and improving the local people’s welfare. The study examined the applicability of fisheries management status based on the ecosystem approach in lakes. The study was carried out from February to July 2023 using ecosystem approach methods in seven villages around Batur Lake, Bali, Indonesia, Data was collected through observations and interviews with 189 respondents. The success of fisheries management might be shown as a flag model after the composite domain and the total aggregate value of all dominants were rated. The results showed that the managed fish resources and stakeholders were unsatisfactory categories. Generally, social and fishing technology domains were classified as good categories. For that, ecosystem approach applications for sustainable fisheries in Batur Lake needed action under the five common scenario goals (a) reducing non-target fish (red devil) in the lakes by intensive capture and processing into other products of economic value; (b) regulations related to the reserve area as a place for fish to spawn and breed; (c) increasing the synergy of fisheries management policies; (d) increasing the stakeholder capacity; and (e) government support and related stakeholders regarding one regulation for fisheries management.

With the rapid increase in electric bicycle (e-bikes) use, the rate of associated traffic accidents has also escalated. Prior studies have extensively examined e-bike riders’ injury risks, yet there is a limited understanding of how their behavior contributes to these accidents. This study aims to explore the relationship between e-bike riders’ risk-taking behaviors and the incidence of traffic accidents, and to propose targeted safety measures based on these insights. Utilizing a mixed-methods approach, this research integrates quantitative data from traffic accident reports and qualitative observations from naturalistic studies. The study employs a binary logistic regression model to analyze risk factors and uses observational data to substantiate the model findings. The analysis reveals that assertive driving behaviors among e-bike riders, such as running red lights and speeding, significantly contribute to the high rate of accidents. Moreover, the lack of protective gear and inadequate safety training are identified as critical factors increasing the risk of severe injuries. The study concludes that comprehensive policy interventions, including stricter enforcement of traffic laws and mandatory safety training for e-bike riders, are essential to mitigate the risks associated with e-bike use. The findings advocate for an integrated approach to urban traffic management that enhances the safety of all road users, particularly vulnerable e-bike riders.

Resisting the adoption of medical artificial intelligence (AI), it is suggested that this opposition can be overcome by combining AI awareness, AI risks, and responsibility displacement. Through effective integration of public AI dangers and displacement of responsibility, some of these major concerns can be alleviated. The United Kingdom’s National Health Service has adopted the use of chatbots to provide medical advice, whereas heart disease diagnoses can be made by IBM’s Watson. This has the ability to improve healthcare by increasing accuracy, efficiency, and patient outcomes. The resistance may be due to concerns about losing jobs, anxieties about misdiagnosis or medical mistakes, and the consciousness of AI systems drifting more responsibility away from medical professionals. There is hesitancy among healthcare professionals and the general public about the deployment of AI, despite the fact that healthcare is being revolutionised by AI, its uses are pervasive. Participants’ awareness of AI in healthcare, AI risk, resistance to AI, responsibility displacement and ethical considerations were gathered through questionnaires. Descriptive statistics, chi-square tests and correlation analyses were used to establish the relationship between resistance and medical AI. The study’s objective seeks to collect data on primary and public AI awareness, perceptions of risk and feelings of displacement that the professionals have regarding medical AI. Some of these concerns can be resolved when AI awareness is effectively integrated and patients, healthcare providers, as well as the general public are well informed about AI’s potential advantages. Trust is built when, AI related issues such as bias, transparency, and data privacy are critically addressed. Another objective is to develop a seamless integration of risk management, communication and awareness of AI. Lastly to assess how this comprehensive approach has affected hospital settings’ ambitions to use medical AI. Fusing AI awareness, risk management, and effective communication can be used as a comprehensive strategy to address and promote the application of medical AI in hospital settings. An argument made by Chen et al. is that providing training in AI can improve adoption intentions while lowering complexity through the awareness of AI.

This research investigates the determinants of digital transformation among Vietnamese logistics service providers (LSPs). Employing the Technological-Organizational-Environmental framework and Resource Fit theory, the study identifies key factors influencing this process across different three stages: digitization, digitalization, digital transformation. Data from in-depth interviews with industry experts and a survey of 390 LSPs were analyzed using covariance-based structural equation modeling (CB-SEM). The findings reveal that the factors influencing the digital transformation of Vietnamese LSPs evolve across different stages. In the initial phase, information technology infrastructure, financial resources, employee capabilities, external pressures, and support services are key determinants. As digitalization progresses, leadership emerges as a crucial factor alongside the existing ones. In the final stage, the impact of these factors persists, with leadership and employee capabilities becoming increasingly important.

Countries employ various strategies to strengthen their soft power through education, public campaigns, mandatory service, and community involvement, essential for building a well-informed, prepared, and resilient citizenry. In Indonesia, the Civic Awareness for State Defence (CASD) program is designed to instil state defence awareness among citizens. This study introduces the Indonesia State Defence Index (SDI), a novel metric grounded in theoretical constructs such as national identity, nationalism, patriotism, and national pride. Differentiating from previous indices, our SDI employs advanced methodologies including Principal Component Analysis (PCA) and Structural Equation Modeling (SEM) to enhance measurement accuracy. Unlike earlier approaches that used traditional aggregation methods, our use of PCA ensures the reduction of dimensions for each state defence indicator, thereby guaranteeing that only the intended dimensions are measured. Utilising data from the State Defence Survey conducted by the Indonesian Ministry of Defence from 1 March to 26 June 2024, we aim to measure and benchmark SDI values across Indonesian regions, thereby elucidating the civic awareness profile in the context of state defence. The refined SDI provides critical insights for policymakers, highlighting regions that require focused interventions to bolster state defence preparedness.