Petersenlin8372

The COVID-19 pandemic led to drastically altered working practices. During the UK lockdown, a questionnaire was distributed to water professionals to understand their experiences and perceptions of organisational response. Findings were evaluated on the measures of mitigation, adaptation, coping and learning. Employees' perceived there were adequate procedures to mitigate a threat, partly due to preparations for Brexit. Participants quickly adapted, with eighty-four percent working from home. Coping was experienced at an individual and sector level. IT issues and care responsibilities made it harder for individuals to cope, but good communication and signposting of support helped. Eighty percent felt able to continue their usual role, implying coping mechanisms were effective. At the sector level, coping involved the ability to meet an increased water demand with a remote workforce. Lessons learned highlight the importance of communication and collaboration. Future crisis plans should prepare for prolonged crises of international magnitude and multiple threats.The emergence of Coronavirus Disease 2019 (COVID-19) in early December 2019 has caused immense damage to health and global well-being. Currently, there are approximately five million confirmed cases and the novel virus is still spreading rapidly all over the world. Many hospitals across the globe are not yet equipped with an adequate amount of testing kits and the manual Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test is time-consuming and troublesome. It is hence very important to design an automated and early diagnosis system which can provide fast decision and greatly reduce the diagnosis error. The chest X-ray images along with emerging Artificial Intelligence (AI) methodologies, in particular Deep Learning (DL) algorithms have recently become a worthy choice for early COVID-19 screening. This paper proposes a DL assisted automated method using X-ray images for early diagnosis of COVID-19 infection. We evaluate the effectiveness of eight pre-trained Convolutional Neural Network (CNN) models such as AlexNet, VGG-16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, ResNet-50 and Inception-V3 for classification of COVID-19 from normal cases. Also, comparative analyses have been made among these models by considering several important factors such as batch size, learning rate, number of epochs, and type of optimizers with an aim to find the best suited model. BAY1000394 The models have been validated on publicly available chest X-ray images and the best performance is obtained by ResNet-34 with an accuracy of 98.33%. This study will be useful for researchers to think for the design of more effective CNN based models for early COVID-19 detection.This study explored whether the global temperature had any role in the spread and vulnerability to COVID-19 and how that knowledge can be used to arrest the fast-spreading disease. It highlighted that for transmitting the virus, global temperature played an important role and a moderately cool environment was the most favourable state. The risk from the virus was reduced significantly for warm places and countries. Based on the temperature of March and April, various degrees of vulnerability were identified and countries were specified. The maximum reported case, as well as death, was noted when the temperature was in the range of around 275 °K (2 °C) to 290 °K (17 °C). Countries like the USA, UK, Italy and Spain belonged to this category. The vulnerability was moderate when the temperature was less than around 275 °K (2 °C), e.g. Russia, parts of Canada and a few Scandinavian countries. For temperature 300 °K (27 °C) and above, a significantly lesser degree of vulnerability was noted. Countries from South Asian Association for Regional Cooperation, South-East Asia, the African continent and Australia fell in that category. This work discussed that based on the variability of temperature, countries can switch from one vulnerability state to another. That influence of temperature on the virus and results of previous clinical trials with similar viruses provided a useful insight that regulating the level of temperature can offer remarkable results to arrest and stop the outbreak. Based on that knowledge, some urgent and simple solutions are proposed, which are practically without side effects and very cost-effective too.Coronavirus disease 2019 (COVID-2019), which emerged in Wuhan, China in 2019 and has spread rapidly all over the world since the beginning of 2020, has infected millions of people and caused many deaths. For this pandemic, which is still in effect, mobilization has started all over the world, and various restrictions and precautions have been taken to prevent the spread of this disease. In addition, infected people must be identified in order to control the infection. However, due to the inadequate number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, Chest computed tomography (CT) becomes a popular tool to assist the diagnosis of COVID-19. In this study, two deep learning architectures have been proposed that automatically detect positive COVID-19 cases using Chest CT X-ray images. Lung segmentation (preprocessing) in CT images, which are given as input to these proposed architectures, is performed automatically with Artificial Neural Networks (ANN). Since both architectures contain AlexNet architecture, the recommended method is a transfer learning application. However, the second proposed architecture is a hybrid structure as it contains a Bidirectional Long Short-Term Memories (BiLSTM) layer, which also takes into account the temporal properties. While the COVID-19 classification accuracy of the first architecture is 98.14%, this value is 98.70% in the second hybrid architecture. The results prove that the proposed architecture shows outstanding success in infection detection and, therefore this study contributes to previous studies in terms of both deep architectural design and high classification success.Hopelessness is an important vulnerability factor for depressive symptomology and suicidal ideations. It may also play an important role in the fear of coronavirus 2019 (COVID-19). Therefore, the present study tested the mediating role of mindful awareness and humor (both identified as coping strategies for dealing with stressful situations) in the relationship between fear of COVID-19 and hopelessness. Participants comprised 786 Turkish individuals (562 females and 224 males; aged between 18 and 67 years) from 71 of 81 cities in Turkey. An online convenience sampling method was used to recruit participants. Participants completed surveys including the Fear of COVID-19 Scale, Beck Hopelessness Scale, Mindful Attention Awareness Scale, and Coping Humor Scale. The model was tested using structural equation modeling (SEM) and utilizing bootstrapping. The results of SEM showed that the effect of fear of COVID-19 on hopelessness was partly mediated by mindfulness and humor, and which was supported by bootstrapping.