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A unique extreme acute breathing syndrome emerged in China and spread rapidly globally due to a newly diagnosed human coronavirus and declared a pandemic. COVID-19 was formally named by WHO, and the Global Committee on Taxonomy referred to it as extreme Acute respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Currently there is no efficient method to control the extent of SARS-CoV-2 other than social distancing and hygiene activities. This study aims to present a simple medicinal strategy for combating fatal viral diseases like COVID-19 with minimum effort and intervention. Different Ayurveda medicines ( Curcuma longa , green tea, and Piper nigrum ) inhibit virus entrance and pathogen transmission while also enhancing immunity. Piperine (1-piperoylpiperidine), as well as curcumin, combine to create an intermolecular complex (π- π) that improves curcumin bioavailability by inhibiting glucuronidation of curcumin in the liver. The receptor- binding domains of the S-protein and also the angiotensin-converting enzyme 2 receptor of the recipient organism are directly occupied by curcumin and catechin, respectively, thereby preventing viruses from entering the cell. As a result, the infection will be tolerated by the animal host., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

National learning systems improve training and proficiency. In order to support education during the COVID-19 epidemic, the rate of online and remote learning accelerated. Since there were some available technologies, strong, flexible educational infrastructures were required to accommodate a range of student demands. Digital resources and inclusive education need government investment. This study highlights the vital role that adaptable educational frameworks play in lessening the effects of the crisis and fostering resilience in the face of uncertainty by examining the complex relationship between the COVID-19 pandemic and the creation of national education methodologies. This study offers a detailed analysis of the intricacy, challenges, and opportunities that have emerged in this significant field by investigating the ways in which the COVID-19 pandemic has affected educational institutions in developing countries. Twenty selected, peer-reviewed scientific journal articles from 2019 to 2023 were included in the research after a comprehensive search of relevant literature. Taking into consideration the viewpoints of parents, children, teachers, and administrators, this extensive and professionally handled research study provides a critical and nuanced examination of many consequences of the COVID-19 epidemic on the educational system. By using a comprehensive analysis of 25 academic articles, it achieves this. A broad number of useful tools and tactics are highlighted in this research, which offers an in-depth analysis of the intricate area of information and computational model deployment. Employing analysis of variance (ANOVA) as a robust statistical method, this analysis uncovers and scrutinizes the complex dynamics at play with the educational systems of developing nations amidst the unprecedented challenges brought by the global COVID-19 crisis. The COVID-19 epidemic has spurred rising countries to rethink and improve their education institutions, accelerating technology-driven education. The epidemic has underlined the need for inclusive and resilient learning infrastructures that respond to emergencies despite the digital device and access inequities.

The Corona Virus (COV-19) epidemic significantly affected the educational environment, requiring a quick transition to distance and blended learning methods. This extraordinary disruption had an incredible impact on pupil's levels of physical activity (PA), psycho-emotional health (PEH) and engagement with academic material. The research aims to examine the vital determinants that influenced various areas of learners' lives during CoV-19. The purpose of this 600-person study was to collect data on the subjects' overall health and PA levels for the CoV-19 pandemic. The SPSS application was used to process the questionnaire's collected data. The information given reveals the respondents' degree of PA throughout the quarantine. According to the breakdown, 15% indicated low levels of PA, 39% reported medium levels and 46% reported high levels. The data show that, despite the respondents' different levels of PA, little PA predominated for most of them. The limitations of distance learning throughout quarantine and the prevalent recommendation of leaving residence for necessary reasons were blamed for this tendency. There were fewer prospects for higher-intensity PA due to these circumstances.

Technology is playing an important role is healthcare particularly as it relates to disease prevention and detection. This is evident in the COVID-19 era as different technologies were deployed to test, detect and track patients and ensure COVID-19 protocol compliance. The White Spot Disease (WSD) is a very contagious disease caused by virus. It is widespread among shrimp farmers due to its mode of transmission and source. Considering the growing concern about the severity of the disease, this study provides a predictive model for diagnosis and detection of WSD among shrimp farmers using visualization and machine learning algorithms. The study made use of dataset from Mendeley repository. Machine learning algorithms; Random Forest classification and CHAID were applied for the study, while Python was used for implementation of algorithms and for visualization of results. The results achieved showed high prediction accuracy (98.28%) which is an indication of the suitability of the model for accurate prediction of the disease. The study would add to growing knowledge about use of technology to manage White Spot Disease among shrimp farmers and ensure real-time prediction during and post COVID-19., (© 2022. The Author(s).)

Background: COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization., Method: ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted., Results: Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions., Conclusions: Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings., (Copyright © 2022 Elsevier Ltd. All rights reserved.)