Introduction: Cafeteria environments within universities have a significant influence over sustainable and healthy dietary behaviours. Hence, this study aimed to understand the nutritional landscape of food options in university cafeterias and to identify the factors contributing to unhealthy food options from the perspective of food service vendors. Methods: Using a mixed-methods approach, 122 food options from all 12 residential campus cafeterias in Universiti Malaya (UM) were collected between October and December 2024. All food options were grouped into (i) bread, (ii) meat dishes, (iii) drinks, (iv) Western dishes, and (v) rice- or noodle-based fried food group; they were further analysed for calories, fat, saturated fat, dietary fibre, and salt using the Malaysian Food Composition Database (MyFCD) and the Singapore Food Composition Database (SFCD). Nutrient classification (high vs. low) was based on the criteria outlined in the Labelling Guidelines 2023. Additionally, food service vendors were interviewed to explore operational challenges, with data collection ceasing upon reaching thematic saturation. The interviews were then transcribed and analysed using thematic analysis. Results: Quantitative findings showed that there were 36 bread-based meals, 16 meat-based dishes, 18 beverages, 30 Westernstyle meals, and 22 rice- or noodle-based fried foods. The study also revealed an over-reliance on high-calorie, high-fat, and low-fibre foods, with limited availability of sustainable and nutritious options. Qualitative findings highlighted the influence of affordability, vendor practices, and student preferences on campus food choices. Conclusion: The study underscored the need for internal policy intervention, improved vendor education and sustainability practices, and increased accessibility to healthier meals on campus.

Dengue is an increasing threat in Malaysia, particularly in the more densely populated regions of the country. We present an Artificial Intelligence driven model in predicting Aedes outbreak, using predictors of weather variables and vector indices sourced from the Ministry of Health. Analysis and predictions to estimate Aedes populations were conducted, with its results being used to infer the possibility of dengue outbreaks at pre-determined localities around the Klang Valley, Malaysia. A Bayesian Network machine learning technique was employed, with the model being trained using predictor variables such as temperature, rainfall, date of onset and notification, and vector indices such as the Ae. albopictus count, Ae. aegypti count and larval count. The interfaces of the system were developed using the C# language for Server-side configuration and programming, and HTML, CSS and JavaScript for the Client Side programming. The model was then used to predict the population of Aedes at periods of 7, 14, and 30 days. Using the Bayesian Network technique utilising the above predictor variables we proposed a finalised model with predictive accuracy ranging from 79%-84%. This model was developed into a Graphical User Interface, which was purposed to assist and educate the general public of regions at risk of developing dengue outbreak. This remains a valuable case-study on the importance of public data in the context of combating a public health risk via the development of models for predicting outbreaks of dengue which will hopefully spur further sharing of data by all parties in combating public health threats.