The aim of the present study was to test an explanatory model for individual and social wellbeing which incorporates the advantages of using digital technologies during the COVID-19 pandemic. The study was carried out in Italy, one of the countries that has been most severely affected by the pandemic worldwide. The study was designed to include variables that might be specifically pertinent to the uniqueness of the restrictions imposed by the pandemic. Adults living in Italy (n = 1412) completed an online survey during the lockdown period in March 2020. https://www.selleckchem.com/screening/chemical-library.html Results showed two distinct digital interaction processes highlighted by the facilitating use of online emotions ("e-motions") and online social support ("e-support"). In short, e-motions were positively related to posttraumatic growth, which in turn was positively associated with positive mental health and higher engagement in prosocial behaviors. Moreover, individuals who perceived themselves as having greater e-support were characterized by higher levels of positive mental health, which it turn was positively associated with prosocial behaviors. Collectively, these two digital interaction processes suggest that digital technologies appear to be critical resources in helping individuals cope with difficulties raised by the COVID-19 pandemic.This paper deals with the recognition of selected burning liquids by convolutional neural networks (CNNs). Three CNNs (AlexNet, GoogLeNet and ResNet-50) were trained, validated and tested (in the MATLAB 2020b software) for the recognition of selected liquids (ethanol, propanol and pentane) using photographs of the flames they produce. For training, validation and test photographs of the liquids under investigation burning in a 106-mm-diameter vessel were used. The accuracy of all the CNNs under investigation during the tests was above 99%. In addition the trained CNNs were tested using photographs of the flames generated by the liquids under investigation burning in a vessel with a diameter of 75 mm. The accuracy of the trained CNNs in this additional test ranged from 37 to 42% (GoogLeNet) through 62-73% (ResNet-50) up to 51-80% (AlexNet) - the results varied dependent upon the relative size of the flame in the photograph under analysis (in most cases an increase in the relative size caused an increase in accuracy). The accuracy of the AlexNet can be improved from 80% to almost 96% using an algorithm. The principle of the algorithm is the analysis of 10 photographs of the same liquid in the same vessel (taken over a few seconds) followed by the recognition based on an identical classification for at least 6 out of 10 photographs. An accuracy of 96% is sufficient for the rapid recognition of burning liquids in practical applications. The online version contains supplementary material available at 10.1007/s10973-021-10903-2. The online version contains supplementary material available at 10.1007/s10973-021-10903-2.The paper aims to identify the factors that cause prospective tourists' hesitation to travel. The study also examines whether this relationship is mediated by the tourist perception in Bangladesh. The study is of quantitative design, and the relationships between tourist knowledge, tourist health risk, and destination personality with tourist hesitation were explored using a sample of 322 Bangladeshi prospective tourists. The three relationships were also examined through tourist perception. By using cross-sectional data, the researchers hypothesized that tourist knowledge, tourist health risk, and destination personality have a positive and significant effect on tourist hesitation. Besides, the researchers also hypothesized that tourist perception mediates the relationships between tourist knowledge, tourist health risk, and destination personality with tourist hesitation. In this respect, the Smart PLS 3.0 was employed to analyze the data. The results of the study confirm findings of previous related studie. Besides, the results may assist stakeholders of tourist destinations in understanding tourist perception and the causes of tourist's hesitation.The growing availability of data and the emergence of business analytics ecosystems offer possibilities for companies developing innovative business models. However, the disruptive impact of these business models on society is not always judged favourably. This paper explores the growing tensions in the relationship between disruptive Big Data companies and society through the lens of legitimacy - a judgement about the fit and propriety of an entity, such as a company, to society. The study is based on four instrumental cases where Big Data organisations were faced with challenges to their legitimacy. The findings elaborate how digital transformations require companies to understand and manage how much to disrupt and how much to conform to social norms and values. Big Data businesses face a dynamic and paradoxical tension between the potential costs and benefits of their disruptive business models. The topic of legitimacy management is also addressed, drawing out implications for practice. The online version contains supplementary material available at 10.1007/s10796-021-10155-3. The online version contains supplementary material available at 10.1007/s10796-021-10155-3.Sustaining patient portal use is a major problem for many healthcare organizations and providers. If this problem can be successfully addressed, it could have a positive impact on various stakeholders. Through the lens of cognitive dissonance theory, this study investigates the role of health professional encouragement as well as patients' security concerns in influencing continuous use intention and deep structure usage among users of a patient portal. The analysis of data collected from 177 patients at a major medical center in the Midwestern region of the United States shows that health professional encouragement helps increase the continuous use intention and deep structure usage of the patient portal, while security concerns impede them. Interestingly, health professional encouragement not only has a direct positive influence on continuous use intention and deep structure usage but also lowers the negative impact of security concerns on them. The research model explains a substantial variance in continuous use intention (i.