Search

This paper presents a numerical strategy for solving boundary value problems (BVPs) that is based on the Haar wavelets method (HWM). BVPs having high Prandtl numbers are discussed, Because they are very important in many practical problems of science and engineering. By using group-theoretic method, the considered model of partial differential equations (PDEs) are converted to system of nonlinear ordinary differential equations. By using HWM, the numerical results are established. Further, solutions obtained on a coarse resolution with low accuracy is refined towards higher accuracy by increasing the level of resolution. Superiority of the HWM has been established over the commercial software NDSolve and available numerical and approximated methods.

This article presents clean core criticality calculations and control rod worth calculations for TRIGA (Training, Research, Isotope production-General Atomics) Mark II research reactor benchmark cores using Winfrith Improved Multi-group Scheme-D/4 (WIMS-D/4) and Program for Reactor In-core Analysis using Diffusion Equation (PRIDE) codes. Cores 133 and 134 were analyzed in 2-D (r, θ) and 3-D (r, θ, z), using WIMS-D/4 and PRIDE codes. Moreover, the influence of cross-section data was also studied using various libraries based on Evaluated Nuclear Data File (ENDF/B-VI.8 and VII.0), Joint Evaluated Fission and Fusion File (JEFF-3.1), Japanese Evaluated Nuclear Data Library (JENDL-3.2), and Joint Evaluated File (JEF-2.2) nuclear data. The simulation results showed that the multiplication factor calculated for all these data libraries is within 1% of the experimental results. The reactivity worth of the control rods of core 134 was also calculated with different homogenization approaches. A comparison was made with experimental and reported Monte Carlo results, and it was found that, using proper homogenization of absorber regions and surrounding fuel regions, the results obtained with PRIDE code are significantly improved.

Renewable energy is considered to be an alternate option for limiting the consumption of fossil fuel along with reducing environmental pollution. Among the possible renewable energy resources, solar energy is considered to be the key candidate as it is the most economical and energy efficient. Keeping in view its importance, there has been an exponential increase in energy harvesting using photovoltaic (PV) systems across the globe in recent years. However, to ensure optimal performance, system health monitoring is essential for such a system. Current traditional monitoring methods are laborious, expensive, time-consuming, and prone to error. To address the limitations of these approaches, the proposed work follows two main approaches. First, an effective deep-learning method is proposed for the identification of the types of cracks in the PV cell such as microcracks and deep cracks. In microcracks, the crack’s orientation is crucial and therefore classified accordingly. Next, the power analysis is performed based on the severity of the cracks. In case of deep cracks, it is observed that the output power efficiency is proportional to crack size. For crack identification, four deep learning models, namely U-net, LinkNet, FPN, and attention U-net, are trained, evaluated, and compared using an online public dataset of electroluminescence images. The efficiency of the models is evaluated using different metrics, including intersection over union (IoU) and F1-Score. These models are then subjected to an ensemble learning technique, which results in an accurate and robust segmentation, IoU, and F1-Score.

COVID-19 is an established challenge to the health care professionals (HCPs) having physical, emotional, and psychological repercussions. We determine the perceptions of Pakistani HCPs regarding the risks and deaths related to COVID-19. A cross-sectional study was conducted among HCPs throughout Pakistan from 16th February 2021 to 6th March 2021 by disseminating an online Google questionnaire via all possible social media platforms including WhatsApp, Facebook, Twitter, and emails. Out of total of 462 HCPs, 33.55% tested COVID-19 positive and 49.43% had received their first dose of COVID-19 vaccine. The proportion of HCPs worried about contracting COVID-19 was 46.97%. A total of 69.48% mentioned that doctors are at a higher risk of dying from COVID-19 infection compared to the general population . In a dichotomous analysis done for risk perception, 72.06% of doctors reported a high-risk perception of contracting COVID-19 in hospital settings. The main reasons identified to be associated with higher mortality amongst the HCPs, specifically doctors, included comorbidities (47.26%), working in high-risk wards (24.02%) and lack of adequate personal protective equipment (PPE) (28.72%). Using univariate and multivariate regression the HCPs tested positive rated two-fold high risk while performing professional duty during COVID-19 pandemic [unadjusted odds ratio (OR) (95% confidence interval (CI)): 2.23 (1.24–4.02), P = .007 ; adjusted OR (95% CI) 2.27 (1.22–4.19), P = .009 ]. In conclusion, Pakistani HCPs have significant COVID–19-related concerns and anxieties. The doctors perceived to be at a higher risk of dying with COVID-19, due to a variety of personal and professional reasons.