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.