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.
An ever increasing number of electronic devices integrated into the Internet of Things (IoT) generates vast amounts of data, which gets transported via network and stored for further analysis. However, besides the undisputed advantages of this technology, it also brings risks of unauthorized access and data compromise, situations where machine learning (ML) and artificial intelligence (AI) can help with detection of potential threats, intrusions and automation of the diagnostic process. The effectiveness of the applied algorithms largely depends on the previously performed optimization, i.e., predetermined values of hyperparameters and training conducted to achieve the desired result. Therefore, to address very important issue of IoT security, this article proposes an AI framework based on the simple convolutional neural network (CNN) and extreme machine learning machine (ELM) tuned by modified sine cosine algorithm (SCA). Not withstanding that many methods for addressing security issues have been developed, there is always a possibility for further improvements and proposed research tried to fill in this gap. The introduced framework was evaluated on two ToN IoT intrusion detection datasets, that consist of the network traffic data generated in Windows 7 and Windows 10 environments. The analysis of the results suggests that the proposed model achieved superior level of classification performance for the observed datasets. Additionally, besides conducting rigid statistical tests, best derived model is interpreted by SHapley Additive exPlanations (SHAP) analysis and results findings can be used by security experts to further enhance security of IoT systems.
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.