The growth of Indonesia’s automotive sector has increased the number of end-of-life vehicles (ELVs), making ELV waste management a major issue. Most countries, such as Japan, China, and Europe, manage ELV waste well, but developing countries still do not. In developing countries, little is known about ELV social admissions. This study analyzes ELV management social acceptance in developing countries. Three hundred nine respondents from Jakarta, Bogor, Depok, Tangerang, and Bekasi (the district in Indonesia) were surveyed in a cross-sectional. A set of questions was designed to determine social acceptance (attitude, knowledge, social influence, institutional trust, health issues, and acceptance). After passing validity and reliability tests, the hypothesized research model was estimated using structural equitation. According to this study, social influence, attitude, knowledge, institutional trust, and health issues influenced public acceptance. The health issues variable was also a good moderator (Adj. R2 = 0.173, p < 0.001, average path coefficient = 0.299). The analysis of social acceptance models related to ELV management found that social influence, attitude, knowledge, and institutional trust play a role in one’s desire to accept a new rule, and health issues can strengthen a person in the admission process.

The huge automotive industry in Indonesia has had a major impact on the environment and health caused by ELV. An ELV is a vehicle that has reached the end of life due to age or cannot be reused because of accidents and high repair costs. The absence of procedures and laws in Indonesia related to the driving of this vehicle resulted in the dismantling of ELV, and its management was carried out on an original and unstructured basis. As a result, emissions of gases and toxic substances are released into the environment. To reduce this problem, implementing ELV management must be done. Implementing this ELV policy requires the cooperation of all stakeholders (government, automotive industry, and the community). Therefore, this study aims to understand the laws related to ELV and its implementation in neighboring countries and explore public perceptions of ELV management in Indonesia. The study was divided into two phases. The first phase reviewed literature related to ELV laws, and the second was surveyed with questionnaires. The results obtained from this research show that public awareness and acceptance of the application of ELV are still very low. In addition, the regulations that have been applied to check the feasibility of vehicles are proven unable to cut down the number of old vehicles.

Hydrogen peroxide (H2O2) enrichment is known to enhance combustion efficiency in diesel engines; however, its application as an emulsion is restricted by phase instability in the absence of chemical surfactants. This study investigates the stability and temperature characteristics of non-surfactant emulsions produced using an inline mixer known as the Real-Time Emulsion Supply System (RTES). Four emulsion types were formulated using water and aqueous H2O2 (5-15 vol%) as the dispersed phase, with neat diesel (B0) and biodiesel (B35) serving as the continuous phase: water-in-B0 (WD), H2O2-in-B0 (HD), water-in-B35 (WB), and H2O2-in-B35 (HB). The emulsions were prepared using RTES with a residence time of 180 s. During the preparation, the temperature was continuously recorded using a Graphtec GL840 data logger. The phase separation was monitored over 600 s using a Canon-EM3 camera. Results demonstrate that B35-based emulsions exhibit higher stability compared to B0-based emulsions. The stability tests showed distinct phase separation ratios for each formulation: 12.67% for WD, 10.67% for HD, 4.67% for WB, and 4.27% for HB. Meanwhile, the temperature rise (ΔT) varied slightly by formulation, ranging from 1.67-1.97 °C for WD and HD, and from 2.33-2.73 °C for WB and HB.

The use of hydrogen peroxide or H2O2 as a fuel additive is known to improve the combustion quality of conventional fuels, including Indonesia’s biodiesel B35. Because H2O2 and B35 do not mix naturally, the H2O2 must be emulsified into B35 to ensure stable fuel delivery and consistent combustion. Conventional emulsification methods typically rely on chemical surfactants, which require a separate preparation step. This study therefore aimed to evaluate the effects of an H2O2–B35 emulsion (HPBE) produced without surfactants on diesel engine performance and emissions. The emulsion was prepared using B35 as the base fuel and 10% (v/v) of 30% aqueous H2O2 as the dispersed phase. Emulsification was carried out using an inline mixer known as the Real-Time Non-Surfactant Emulsion Fuel Supply System (RTES). Engine tests were performed on a Kubota Z482 operating at a constant speed of 2800 rpm under loads of 2.7, 8, 13, 19, and 26 Nm, applied using a Cussons eddy current dynamometer. The parameters measured include Brake Specific Fuel Consumption (BSFC), exhaust gas temperature (EGT) as well as Carbon Monoxide (CO), Carbon Dioxide (CO2), Nitrogen Oxide (NOx) emissions, and smoke opacity. The results showed that, compared to B35, HPBE improved all measured parameters across all loads. BSFC decreased, with the maximum reduction of 22.9% occurring at 8 Nm. At full load, HPBE produced the maximum reductions in smoke opacity (31.3%), CO (29.5%), and CO2 (11.2%). Meanwhile, the maximum NOx reduction of 14.3% was observed at 75% load.

Developing an engineering design is resource-intensive and time-consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL)-based IBCD method for modeling floats' lift-to-drag coefficient ratio (CL/CD), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted CL/CD of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost-effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.