This work contributes to the comprehension of Bayes’ theorem inclusive Bayesian probabilities and Bayesian inferencing within the framework of STEM (Science, Technology, Engineering, Arts, and Mathematics) and cognitive learning w.r.t Bloom’s taxonomy (BT). Bayes’ theorem is taken as a crucial statistical instrument employed in the development of intelligent systems and the management of risks, commonly utilized by engineers for tasks in machine learning and managerial decision-making. The fundamental concept behind Bayes’ theorem revolves around comprehending the degree of truth within the confines of an explicit perspective. This involves partitioning the entire sample space of possible evidence and utilizing the subset containing the relevant perspective to estimate the uncertainty of an event or the reliability of a model. However, it is often found difficult for students to understand Bayes’ theorem to the level of applying it to real-world problems. Considering this, the proposed learning method in this paper elucidated the acquisition of Bayes’ mathematical formulation by leveraging computational thinking, leading to the development of a computational model. The proposed model is named the Bayesian Computational Learning Model (BCLM). Subsequently we have probed the utility of BCLM in the design and plan of learning activities, coherent to STEM paradigm and BT cognitive learning hierarchy.

This paper disseminates our research findings that we conducted on university students in the years 2021, 2022, and 2023, with the year 2021 taken as the base year. Our research mined and excavated a concealed prevalence of social anxiety as an important and crucial facet of study anxiety in the university students of Pakistan. Using the Liebowitz Social Anxiety Scale (LSAS), we found a significant increase in the social anxiety level among university students in the past three years after the COVID-19 lockdown. Our data showed that the ‘very severe anxiety’ level soared up to 52.94% in the year 2023 from just 5.98% in the year 2021, showing a net increase of 47.06%. Statistical analyses demonstrate noteworthy differences in the overall social anxiety levels among the students, reaching significance at the 5% level and a discernable upward trend in the social anxiety levels as study anxiety. We also employed predictive analytics, including binary classifiers and generalized linear models with a 95% confidence interval, to identify individuals at risk. This study highlights a dynamic shift with escalating social anxiety levels among the university students and thus emphasizing its awareness, which is significantly important for the timely intervention, potentially preventing symptom escalation and improving outcomes.

Breast cancer is considered a leading cause of deaths among women. Researches state that women around the world still face this problem, and because of its unawareness, it is many times left unattended in the budding stages. If correctly screened and detected early, then with proper treatment, this could stop the metastasis and reduce the pains and difficulties of the later stages. Screening methods such as x-ray-based mammography, ultrasound, PET scan, and magnetic resonance imaging (MRI) clinically exist for breast tumor investigation. It is very important that screening procedures should have high specificity and sensitivity for the detection of tumors. Additionally, these methods also have to placate concerns such as ease of the patient during imaging, high-resolution images for added precise elucidation, cost effectiveness, and the capacity to detect the malignant-leading tumors in the early stage. Existing imaging techniques do not meet all of these conditions concurrently. In this scenario, ultra-wide band (UWB) technology has come into play the role of a useful alternative for screening and detection of breast tumors. This chapter discusses firstly probabilistic qualitative metrics which are used in measuring the quality of testing procedures, and then later UWB testing methods are discussed in brief.

In electromagnetics and antenna engineering, reflection and transmission coefficients are important parameters to be evaluated statistically and numerically to obtain effective computer simulations. This paper proposes a pedagogy to understand the development methodology of numerical reflection and transmission coefficient models. The proposed method employs relatively easy mathematics instead of the conventional ways that use difficult mathematics. These are then simulated as computer models. The study in this paper included a medium comprising of air and glass placed in it. Firstly, the region was divided into a gridded structure where the grids were mathematically formulated. Points on the grid were distinguished between different media using electrical permittivity and conductivity. The grids included boundary conditions and electric field wave propagation through them. Both boundary conditions and field equations were numerically modelled and discretized using the finite difference method with a proportional h2 error. This method provided a system of linear equations, which were then solved linearly to obtain the required reflection and transmission coefficients. An important aspect of the presented work is to provide an example approach for computational thinking (CT), which is now considered an important part of any engineering curriculum