In recent years, the pathological diagnosis of glomerular diseases typically involves the study of glomerular his-to pathology by specialized pathologists, who analyze tissue sections stained with Periodic Acid-Schiff (PAS) to assess tissue and cellular abnormalities. In recent years, the rapid development of generative adversarial networks composed of generators and discriminators has led to further developments in image colorization tasks. In this paper, we present a generative adversarial network by Spectral Normalization colorization designed for color restoration of grayscale images depicting glomerular cell tissue elements. The network consists of two structures: the generator and the discriminator. The generator incorporates a U-shaped decoder and encoder network to extract feature information from input images, extract features from Lab color space images, and predict color distribution. The discriminator network is responsible for optimizing the generated colorized images by comparing them with real stained images. On the Human Biomolecular Atlas Program (HubMAP)—Hacking the Kidney FTU segmentation challenge dataset, we achieved a peak signal-to-noise ratio of 29.802 dB, along with high structural similarity results as other colorization methods. This colorization method offers an approach to add color to grayscale images of glomerular cell tissue units. It facilitates the observation of physiological information in pathological images by doctors and patients, enabling better pathological-assisted diagnosis of certain kidney diseases.

In engineering, a design is best described based on its alternative performance operation. In this paper, an electric power plant is analysed based on its effective operational performance even during critical situation or crisis. Data is generated and analysed using both quantitative and qualitative research approach. During maintenance operation of an electric power plant, some components are susceptible to wide range of issues or crises. These includes natural disasters, supply chain disruptions, cyberattacks, and economic downturns. These crises significantly impact power plant operations and its maintenance strategies. Also, the reliable operation of power plants is often challenged by various technical, operational, and environmental issues. In this research, an investigation is conducted on the problems associated with electric power plants by proposing a comprehensive and novel framework to maintenance the power plant during crises. Based on the achieved results discussed, the framework impact and contribution are the integration of proactive maintenance planning, resilient maintenance strategies, advanced technologies, and adaptive measures to ensure the reliability and resilience of electric power plant during power generation operations in the face of unforeseen challenges/crisis. Hypothetical inferences are used ranging from mechanical failures to environmental constraints. The research also presents a structured approach to ensure continuous operation and effective maintenance in the electric power plant, particularly during crisis (such as environmental issues and COVID-19 pandemic issues).

Nanoparticle V₂O₅ is prepared by the measurement of X-ray diffraction (XRD) and atomic force microscopy (AFM) analyses. The crystallite size = 19.59 nm, optical energy gap = 2.6 eV, an average particle size of 29.58 nm and, RMS roughness of ~6.8 nm. Also, Fourier transformer infrared spectrophotometer (FTIR) showed a porous free morphology with homogeneity and uniformity on the sample surface. The film surface exhibited no apparent cracking and, the grains exhibited large nicely separated conical columnar growth combined grains throughout the surface with coalescence of some columnar grains at a few places. The fabrication of a thin film of V₂O₅ NPs/PSi heterojunction photodetector was characterized and investigated.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

This paper highlights the opportunities as well as challenges posed for Bangladesh by the Belt and Road Initiative (BRI) of China. BRI is being considered as the most expensive project ever initiated connecting more than half of the world population from Asia, Europe and Africa. For writing this paper, the authors utilized published sources such as journal articles, newspaper articles and web-based information published from 2013 to 2024. The article proposes that although the involvement of Bangladesh in the BRI is not absolutely free of challenges, it can serve the ultimate national interest through greater connectivity with other countries, increased volume of trade and economic activities and socio-cultural exchange. Although, as the originator and major contributor of the BRI, China will be the principal benefiter, other partner countries can also attain considerable benefits out of this historical mega scheme through the application of appropriate vision and strategic implementation. This paper has highlighted those benefits/opportunities and challenges for Bangladesh that can be beneficial for upcoming research projects particularity aimed at development studies, political economy and international relations. On the other hand, based on the arguments made on this paper, policymakers and businessmen can formulate their best policies as well as trading strategies with mutual benefits for all the stakeholders involved.