Virtual learning is a medium that can enhance students’ understanding of a specific topic. The emergence of the COVID-19 pandemic provided an opportunity for dental education to shift from traditional learning to blended learning as it began to utilize technology to help students study effectively. In this study, we collaborated with experts in the field of dentistry to reach a consensus about which topics are appropriate to include in the virtual learning module about the biomechanics of tooth movement. We convened a panel of five experts who had a minimum of two years of experience in teaching orthodontics and introduced them to the Nominal Group Technique (NGT), which is a well-established, organized, multistep, assisted group meeting technique for generating consensus. The following ten key topics were identified for inclusion in the module: physiology of tooth movement; tooth movement–definition, type, theory, indications; force systems; anchorage; fixed appliances; biomaterials related to tooth movement; removable appliances; factors affecting tooth movement; iatrogenic effect of tooth movement; and current advances and evidence regarding tooth movement. The modified NGT approach led to the development of a ranked thematic list of the topics related to the biomechanics of tooth movement that can be delivered to students via virtual learning.

Data science is a dynamic field with ever-evolving job descriptions and salary structures. While data science offers high earning potential, the factors influencing data scientist salaries remain unclear. This lack of clarity makes it challenging for both employers to determine competitive compensation packages and for employees to understand how career choices like experience level and job title can impact their earning potential. Thus, this study aims to explore the interrelationship between related variables with salary. To achieve the objectives of this research, correlation analysis was employed to identify the strength and direction of linear relationships between these attributes in the dataset. Additionally, k-means clustering was utilized to group data scientists with similar characteristics, allowing for the exploration of potential salary segments within the data science field. It was found that there was a very strong correlation between employee residence and company location (r=0.90). There was a significant moderate positive correlation between salary with company location (r=0.46), residence (r=0.48) and experience level (r=0.41) respectively. Based on the clustering analysis, the group was divided into four different popular roles in data science salary group. Therefore, employers can leverage this knowledge to design the salary packages considering location and experience.

Life expectancy is a critical indicator of societal well-being and quality of life. Discovering and discussing the numerous factors that contribute to variations in life expectancy is crucial. Understanding these factors is important for shaping policies and interventions aimed at improving population health. With the advancement of the technology, prediction using machine learning is one of the alternatives in discovering the predictive factors that impact life expectancy. Therefore, the objective of this study is to identify and analyse the socio-health factors influencing life expectancy across countries using machine learning techniques. The study found that age group, immunisation status, and the presence of diseases such as HIV/AIDS were significant predictors of life expectancy. These insights are important for policymakers’ public health strategies and resource allocation.