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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. 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. Disufenton 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. Therefore, higher fear of COVID-19 was associated with lower mindfulness and humor. In turn, lower mindfulness and humor were related with higher hopelessness. Findings are discussed in the context of COVID-19 and the hopelessness literature, and practical implications for counselors are also provided.The current research examined personality and individual difference factors associated with the perceived ability to adapt to the significant challenges accompanying the ongoing public health crisis concerning the COVID-19 pandemic. This cross-sectional study investigated the associations among self-reported adaptability to the pandemic and personality predispositions (dependency, self-criticism, mattering, and self-esteem), cognitive factors (positive, negative, and loneliness automatic thoughts), loneliness, distress, and mood states. A sample of 462 college students from Israel completed an online questionnaire after 10 weeks of social distancing during the COVID-19 pandemic. The results confirmed that personality vulnerability factors underscored by a negative sense of self (i.e., self-criticism and dependency) and individual difference factors reflecting self-esteem, feelings of mattering, and fear of not mattering are associated in meaningful ways with adaptability to the pandemic, loneliness, distress, negative mood states, and positive mood states. Most notably, higher self-reported adaptability to the pandemic is associated with lower dependency, self-criticism, and fear of not mattering, and higher levels of self-esteem and mattering. The findings attest to the central role of adaptability and related individual difference factors in acclimatizing to the numerous changes and challenges associated with the COVID-19 crisis. The theoretical and practical implications of our findings are discussed.The COVID-19 pandemic has thrust the world into a crisis - and the child welfare system is particularly susceptible to its effects. This pandemic has exacerbated some of the most problematic aspects of the system, and its impacts will reverberate long after the immediate crisis ends. As COVID-19 spread, families were instantly impacted - in-person family time was cancelled, youth and families were unable to access basic resources, services, and technology, and access to the courts was curtailed. Those short-term effects may give way to long-term harms such as disrupted attachments and delays in achieving permanency. The pandemic also reinforced the importance of key tenets of a well-functioning child welfare system high-quality legal representation, creativity, and youth and family engagement. Attorneys must learn from the fallout of the pandemic, retain the best responsive practices, and use the lessons learned from this crisis to transform dependency cases, and the system writ large, into what families need and deserve.We describe in this paper an approach for predicting the COVID-19 time series in the world using a hybrid ensemble modular neural network, which combines nonlinear autoregressive neural networks. At the level of the modular neural network, which is formed with several modules (ensembles in this case), the modules are designed to be efficient predictors for each country. In this case, an integrator is used to combine the outputs of the modules, in this way achieving the goal of predicting a set of countries. At the level of the ensembles, forming a part of the modular network, these are constituted by a set of modules, which are nonlinear autoregressive neural networks that are designed to be efficient predictors under particular conditions for each country. In each ensemble, the results of the modules are combined with an aggregator to achieve a better and improved result for the ensemble. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained that could be helpful in deciding the best strategies in dealing with this virus for countries in their fight against the coronavirus pandemic. In addition, the proposed approach could be helpful in proposing strategies for similar countries.In the first part, this work reports that during the global "anthropopause" period, that was imposed in March and April 2020 for limiting the spread of COVID-19, the concentrations of basic air pollutants over Europe were reduced by up to 70%. During May and June, the gradual lift of the stringent measures resulted in the recovery of these reductions with pollution concentrations approaching the levels before the lockdown by the end of June 2020. In the second part, this work examines the alleged correlations between the reported cases of COVID-19 and temperature, humidity and particulate matter for March and April 2020 in Europe. It was found that decreasing temperatures and relative humidity with increasing concentrations of particulate matter are correlated with an increase in the number of reported cases during these 2 months. However, when these calculations were repeated for May and June, we found a remarkable drop in the significance of the correlations which leads us to question the generally acceptedly recovered to pre-pandemic levels and therefore any possible climatic feedbacks were negligible; (2) no robust relationship between atmospheric parameters and the spread of COVID-19 cases can be justified in the warmer part of the year and (3) more research needs to be done regarding the possible links between climate change and the release of new pathogens from thawing of permafrost areas.