Effective end-of-life vehicle (ELV) management is crucial for minimizing the environmental and health impacts of Indonesia's growing automotive industry. However, proper ELV management has received limited attention. To bridge this gap, we conducted a qualitative study to identify barriers to effective ELV management in Indonesia's automotive sector. Through in-depth interviews with key stakeholders and a strengths, weaknesses, opportunities, and threats analysis, we identified internal and external factors influencing ELV management. Our findings reveal major barriers, including inadequate government regulation and enforcement, insufficient infrastructure and technology, low education and awareness, and a lack of financial incentives. We also identified internal factors such as limited infrastructure, inadequate strategic planning, and challenges in waste management and cost collection methods. Based on these findings, we recommend a comprehensive and integrated approach to ELV management involving enhanced coordination among government, industry, and stakeholders. The government should enforce regulations and provide financial incentives to encourage proper ELV management practices. Industry players should invest in technology and infrastructure to support effective ELV treatment. By addressing these barriers and implementing our recommendations, policymakers can develop sustainable ELV management policies and decisions in Indonesia's fast-paced automotive sector. Our study contributes valuable insights to guide the development of effective strategies for ELV management and sustainability in Indonesia.
The management of end-of-life vehicles (ELVs) conserves natural resources, provides economic benefits, and reduces water, air, and soil pollution. In an effort to adequately manage flow of ELVs, modern infrastructure is considered a prerequisite. Thus, development of an effective performance evaluation tool for monitoring and continuous improvement of ELV management systems is strongly desired. In this paper, a performance evaluation tool is proposed for ELV management system implementation, based on the analytic hierarchy process. A real-life case study in Malaysia was conducted in order to demonstrate the potential and applicability of the presented methodology. The scores of eight key success factors in establishing an ELV management system (i.e., management responsibility, performance management, capacity management, resource management, stakeholders' responsibility, education and awareness, improvement and enforcement, and cost management) are presented. The overall score of the ELV management system implementation in Malaysia is equal to 2.13. Therefore, its performance level is average. The presented multi-criteria decision analysis tool can be of assistance not only to stakeholders in the Malaysian ELV management system, but also to vehicle recycling managers from other countries in order to monitor and continuously improve their ELV management systems.
Effective management of end-of-life vehicles (ELVs) represents a sound strategy to mitigate global climate change. ELVs are contaminants that pollute water, air, soil, and landscape. This waste flow must be adequately treated, but no proper rule oversees the disposal of ELV waste in Malaysia. This study aims to determine the extent of implementing the ELV policy and the social readiness in implementing environmentally friendly ELV disposal in Malaysia. The questionnaire seeks public input on critical ELV concerns such as public perception of the phenomena, environmental and safety standards, and recycling and treatment facilities. This research uses a cross-sectional design with 448 respondents in the survey. Fit models in structural equation modeling are evaluated using a variety of goodness-of-fit indicators to ensure an actual hypothesis. This study's advantages include the availability of representative samples and allowing for comparable and generalizable conclusions to larger communities throughout Malaysia. It is found that personal experience is significantly correlated with social readiness. The cause of ELV vehicles knowledge was the vital mediator, along with recycling costs knowledge. Thus, knowledge regarding ELV management costs is the most decisive mediation variable to predict public acceptance. The recommended strategy to reduce resentment and rejection of ELV policy is to disseminate information about the negative ELV impact on environmental and social sustainability.
Parkinson's disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on clinical evaluation, lacking reliable diagnostic tests and being inherently imprecise and subjective. Early detection of PD is crucial for initiating treatments that, while unable to cure the chronic condition, can enhance the life quality of patients and alleviate symptoms. This study explores the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson's disease based on dual-task walking test data. Given that the performance of networks is significantly inductance by architecture and training parameter choices, a modified version of the recently introduced crayfish optimization algorithm (COA) is proposed, specifically tailored to the requirements of this investigation. The proposed optimizer is assessed on a publicly accessible real-world clinical gait in Parkinson's disease dataset, and the results demonstrate its promise, achieving an accuracy of 87.4187 % for the best-constructed models.