Unlike the experimental group, the students in the control group received all the instructions in the classroom and were assigned homework. The findings obtained through the ANOVA and t-test indicated that the students in the experimental group significantly outperformed their counterparts in the control group in terms of their writing. A probable conclusion could be that by requiring students to study in advance and take responsibility for their learning, flipped classroom can provide the opportunity for learners to actively construct knowledge rather than receive the information passively in the classroom. Flipped classroom can also cultivate interactive class time for teachers and enable them to invest in more fruitful academic practices, instead of asking students to spend a substantial amount of time each week doing homework independently.At the end of 2019, a novel coronavirus COVID-19 broke out. Due to its high contagiousness, more than 74 million people have been infected worldwide. Automatic segmentation of the COVID-19 lesion area in CT images is an effective auxiliary medical technology which can quantitatively diagnose and judge the severity of the disease. In this paper, a multi-class COVID-19 CT image segmentation network is proposed, which includes a pyramid attention module to extract multi-scale contextual attention information, and a residual convolution module to improve the discriminative ability of the network. A wavelet edge loss function is also proposed to extract edge features of the lesion area to improve the segmentation accuracy. For the experiment, a dataset of 4369 CT slices is constructed, including three symptoms ground glass opacities, interstitial infiltrates, and lung consolidation. The dice similarity coefficients of three symptoms of the model achieve 0.7704, 0.7900, 0.8241 respectively. The performance of the proposed network on public dataset COVID-SemiSeg is also evaluated. The results demonstrate that this model outperforms other state-of-the-art methods and can be a powerful tool to assist in the diagnosis of positive infection cases, and promote the development of intelligent technology in the medical field. The COVID-19 pandemic has spread across 87 million people with more than 1·8 million deaths in the world. As there is no definite treatment modality, the use of convalescent plasma has become increasingly popular worldwide. This study aimed to identify an appropriate strategy of donor recruitment and to evaluate the appropriateness of pre-set plasma donation guidelines. In this prospective study conducted from May to September 2020, the donors were recruited under the following two circumstances Group I, patients in the post-COVID-19 follow-up in the clinic, and Group II, patients recovered from COVID-19 recruited through mass and electronic media. A pre-set donor selection criteria and laboratory investigation was designed according to national and international guidelines. Approximately 500ml of COVID-19 convalescent plasma (CCP) was collected from recovered individuals in each group by two different cell separators. The overall donor's attendance rate, deferral rate, adverse events and donor compliance was analysed and compared between the two groups. There was a significant difference in attendance in relation to registration between the groups ( <0·0001). Donor deferral was significantly higher in group II compared with group I. The single most frequent cause of donor deferral was low antibody index ( =0·0001). The total donor adverse event rate in CCP donation was significantly lower compared with routine plateletpheresis procedures. The donor's compliance to blood centre's protocol was satisfactory in both the groups. Recruitment of patients in the post-COVID-19 follow-up in the clinic was more effective than the general recruitment through mass and electronic media for convalescence plasma donation in a resource-constrained blood centre. Recruitment of patients in the post-COVID-19 follow-up in the clinic was more effective than the general recruitment through mass and electronic media for convalescence plasma donation in a resource-constrained blood centre.Vaccines against SARS-CoV-2 continue to be developed at an astonishingly quick speed and the early ones, like Pfizer and Moderna, have been shown to be more effective than many public health scientists had dared to hope. As COVID-19 vaccine research continues to progress, the world's eyes are turning toward medicine regulators. COVID-19 vaccines need to be authorized for use in each country in which the pharmaceutical industry intends to commercialize its product. https://www.selleckchem.com/products/ecc5004-azd5004.html This results in a patchwork of regulations that can influence the speed at which products are launched and the standards that govern them. In this research forum article, we discuss several key questions about COVID-19 vaccine regulations that should shape research on the next stage of the pandemic response. We call for a research agenda that looks into the political economy of pharmaceutical regulation, particularly from a comparative perspective, including Global South countries.The unprecedented scale and impact of the COVID-19 pandemic have required organizations to adapt all facets of their operations. The impact on the UK water sector extends beyond engineering and treatment processes, with social, economic and environmental consequences. Semi-structured interviews were conducted with executives from 10 UK water companies to investigate the organizational response to the pandemic, and how their response impacted operational delivery. The Safe and SuRe framework was used to structure interview questions and analysis. Emergent themes of changes to customer behaviour, changes to operational practices and industry collaboration were mapped onto the framework and a ripple effect map developed. Lessons learnt highlight a failure to adequately prepare for the scale of the threat, the success of sector-level collaboration and a need to embrace new ways of working.Covid-19 (Coronavirus Disease-2019) is the most recent coronavirus-related disease that has been announced as a pandemic by the World Health Organization (WHO). Furthermore, it has brought the whole planet to a halt as a result of the worldwide introduction of lockdown and killed millions of people. While this virus has a low fatality rate, the problem is that it is highly infectious, and as a result, it has infected a large number of people, putting a strain on the healthcare system, hence, Covid-19 identification in patients has become critical. The goal of this research is to use X-rays images and computed tomography (CT) images to introduce a deep learning strategy based on the Convolutional Neural Network (CNN) to automatically detect and identify the Covid-19 disease. We have implemented two different classifications using CNN, i.e., binary and multiclass classification. A total of 3,877 images dataset of CT and X-ray images has been utilised to train the model in binary classification, out of which the 1,917 images are of Covid-19 infected individuals .