In obesity modelling studies, researchers have been seeking to identify the effective indicators of obesity by using appropriate statistical or mathematical techniques. The main objective of the present study is addressed in three stages. First, a new framework for modelling obesity in university students is introduced. The second stage involves data analysis based on Bayesian Structural Equation Modelling (BSEM) for estimating the Body Mass Index (BMI) (representative of the obesity level) of students at three university levels: Bachelor, Master and PhD. In the third stage, the highest significant correlation is determined between the BMI and other variables in the research model that were found significant through the second phase. The data for this study were collected from students at selected Malaysian universities. The results indicate that unhealthy food intake (fast food and soft drinks), social media use and stress exhibit the highest weightage contributing to overweight and obesity issues for Malaysian university students.

The feasibility of artificial intelligence (AI) as a predictive model for thorough efficacy analysis on environmental pollution applied on mangrove forests are discussed. Mangrove forests are among the most productive and biological diverse ecosystems on the planet. However, due to environmental pollution and climate change, mangrove forests are in serious decline. Despite crucial issues pertaining mangrove forests, the law enforcement on the ecosystem is still dubious due to the lack of evidence and data that could provide accurate analysis and prediction. The main highlight of this review elaborates on pollutant markers in soil, water, and air, by correlating these three aspects to the sustainability of mangrove ecosystem. The research gap identified from this review suggests the application of an integrated environmental prediction system for practical environmental insights. A predictive model for environmental decision-making could be developed by integrating meteorological, climatological, hydrological, atmospheric, and heavy metal concentration to understand the interaction between each factor for an efficient solution of pollutant reduction scheme involving mangrove ecosystems.

Strays and companion animal management is a sensitive issue in Malaysia that incites solid and conflicting views. Through structured questionnaires administered to a random sample of 704 respondents, this study explored public opinion on a) causes of the stray animal population, b) the management of the stray animal population, and c) the national strategy on strays and companion Animal Management. The results show that 70.3% of respondents agreed that a lack of public awareness regarding animal care was the major contributor to the stray animal population. In addition, 58.1% of respondents felt that treating and vaccinating animals exposed to zoonotic diseases is a viable approach that could be instituted as a reasonable measure in stray animal population management. Finally, developing animal protection areas through a multi-stakeholder partnership strategy initiative recorded the highest support (48.4%) for intervention planning for stray animal management at a national level. Notably, a significant percentage of public responses were implicitly influenced by demographic variables. These findings provide valuable insights into public opinion regarding stray and companion animal management in Malaysia. These findings could inform the development of future legislation aimed at reducing the unfavorable effects of stray animal populations on humans and the ecology of Malaysia.

Dengue remains the most prevalent vector-borne viral infection in the current century, contributing significantly to global morbidity and mortality. Despite being a preventable disease, the effectiveness of dengue control is strongly influenced by the level of knowledge, attitudes, and practices demonstrated by communities. This study systematically reviews and synthesises two decades of empirical research on household-level knowledge, attitudes, and practices concerning dengue in Asian countries. A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, which utilised major academic databases to identify publications from 2010 to 2022. The analysis revealed that although most respondents exhibited satisfactory knowledge and positive attitudes, their preventive practices remained inconsistent and suboptimal. These findings underscore the importance of sustained health education efforts and community engagement initiatives to enhance household-level awareness and behaviour towards dengue prevention.

Using natural deep eutectic solvents (NADESs) as a green reagent is a step toward producing environmentally friendly and sustainable technology. This study screened three natural DESs developed using quaternary ammonium salt and organic acid to analyse their capability to extract nickel ions from contaminated mangrove soil, which are ChCl: Acetic Acid (ChCl-AceA), ChCl: Levulinic Acid (ChCl-LevA), and ChCl: Ethylene Glycol(ChCl-Eg) at molar ratio 1:2. The impact of various operating parameters such as washing agent concentration, pH solution, and contact time on the NADES performance in the dissolution of Ni ions batch experiments were performed. The optimal soil washing conditions for metal removal were 30% and 15% concentration, a 1:5 soil-liquid ratio, and pH 2 of ChCl-LevA and ChCl-AceA, respectively. A single removal washing may remove 70.8% and 70.0% Ni ions from the contaminated soil. The dissolution kinetic of Ni ions extraction onto NADES was explained using the linear kinetic pseudo and intraparticle mass transfer diffusion models. The kinetic validation demonstrates a good fit between the experimental and pseudo-second-order Lagergren data. The model's maximum Ni dissolution capacity, Qe are 51.56 mg g(-1) and 52.00 mg g(-1) of ChCl-LevA and ChCl-AceA, respectively. The synthesised natural-based DES has the potential to be a cost-effective, efficient, green alternative extractant to conventional solvent extraction of heavy metals. [GRAPHICS]