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Objective: To estimate the economic costs of selected tobacco-related illnesses (TRI) in Kenya in 2022. Research Design and Methods: This study was conducted in 2 phases. Phase 1, conducted between 2021 and 2022, entailed conducting a cross-sectional study conducted in 4 national public referral hospitals in Kenya. Patients with cardiovascular disease, cancer, chronic obstructive pulmonary disease, or tuberculosis were interviewed to compute the indirect and direct medical costs related to the illness. Activity-Based Costing approach was used to capture costs for services along the continuum of care pathway. In the second phase, the Tobacco Attributable Factor was used to estimate the direct, indirect, and ultimately economic cost due to tobacco smoking. Results: The estimated health care cost attributed to tobacco use in Kenya is US$396,107,364. Among TRIs included in the study, myocardial infarction had the highest health care cost at US$158,687,627, followed by peripheral arterial disease and stroke with health care cost of US$64,723,181 and US$44,746,700 respectively. The main cost driver across all the illnesses is the cost for medication accounting for over 90% of the total health care cost. The productivity losses from the diseases ranged between US$148 to US$360 and accounted for 27% to 48% of the economic costs. The total cost attributable to tobacco use to Kenya's economy for the selected TRIs was between US$544.74 million and US$756.22 million. Conclusions/interpretation: Tobacco related illnesses impose a significant economic burden as reported for direct and indirect costs. These findings underscore the need for strengthened implementation of the provision of the Framework Convention on Tobacco Control and the Tobacco Control Act (2007) to facilitate a reduction in tobacco consumption in the population. [ABSTRACT FROM AUTHOR]

3D printing is an additive manufacturing technique that produces three dimensional objects through a layering process. Due to its ability to print complex structures, 3D printing has been applied in many disciplines including textiles. Different textiles structures have been printed using 3D printing techniques. 3D printed polymers have also been combined with textile structures to create composites and also enhance mechanical properties. The challenge with those composites has been the adherence of the 3D printed polymer to the textile substrate. Research has been done to improve the adhesion properties by varying 3D printed polymers, modifying fabric properties and also varying the fabric to polymer combinations. In this study a Polylactic Acid (PLA) model was printed on 8 fabric samples that differed in fabric weight, warp density, weft density, warp linear density and weft linear density. The samples were printed using the Athena 3D printer that uses the Fused Deposition Modelling (FDM) technique. The adhesion strength of the polymer to the fabric was then tested according to DIN53530 standard. The results were analysed based on the different elements of the fabric structure. Results showed that while some elements had a great effect on adhesion, others showed no direct effect on adhesion force.

Phishing emails have experienced a rapid surge in cyber threats globally, especially following the emergence of the COVID-19 pandemic. This form of attack has led to substantial financial losses for numerous organizations. Although various models have been constructed to differentiate legitimate emails from phishing attempts, attackers continuously employ novel strategies to manipulate their targets into falling victim to their schemes. This form of attack has led to substantial financial losses for numerous organizations. While efforts are ongoing to create phishing detection models, their current level of accuracy and speed in identifying phishing emails is less than satisfactory. Additionally, there has been a concerning rise in the frequency of phished emails recently. Consequently, there is a pressing need for more efficient and high-performing phishing detection models to mitigate the adverse impact of such fraudulent messages. In the context of this research, a comprehensive analysis is conducted on both components of an email message—namely, the email header and body. Sentence-level characteristics are extracted and leveraged in the construction of a new phishing detection model. This model utilizes K Nearest Neighbor (KNN) introducing the novel dimension of sentence-level analysis. Established datasets from Kaggle were employed to train and validate the model. The evaluation of this model's effectiveness relies on key performance metrics including accuracy of 0.97, precision, recall, and F1-measure. [ABSTRACT FROM AUTHOR]