Gasoline is the primary fuel used in small cars, and the exhaust emissions from gasoline combustion have a significant impact on the atmosphere. Efforts to clean up gasoline have therefore focused primarily on reducing the olefin and sulfur content of gasoline, while maintaining as much of the octane content as possible. With the aim of minimizing the loss of octane, this study investigated various machine learning algorithms to identify the best self-fitness function. An improved octane loss optimization model was developed, and the best octane loss calculation algorithm was identified.

The Mekong Delta region has been seriously affected by climate change, with increasing temperatures, sea-level rise, and salinization strongly impacting agricultural activities of the region. Recent studies have shown that groundwater exploitation also contributes significantly to land subsidence throughout the delta. Thus, combating climate change now makes it necessary to design strategies and policies for adapting to and mitigating climate change and subsidence, not only at the individual level (mainly farmers), but also at the institutional level (province and region).

Plastic bag pollution in the marine environment is an urgent issue that has negatively impacted the sustainability of marine biodiversity. Studying effective ways to design advocacy messages that can promote individuals’ intentions to reduce, reuse, and recycle plastic bags in order to mitigate plastic bag pollution in the effort to help restore marine biodiversity is necessary. Utilizing emotional appeal messages, such as messages that are designed to elicit audiences’ feelings of empathy, can promote a variety of pro-environmental behaviors.