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The purpose of this chapter is to explore the effects of COVID-19 pandemic on tourism and hospitality. Motivation of the researchers in constructing the chapter was to logically alert the present-day and future world tourism and hospitality business leaders on how to respond to the disruptive forces attributed to deadly viral pandemics like that of COVID-19 pandemic. The purpose being to draw conclusions and identifying the research gaps. It is based on a structural analysis methodology to frame the categories of the major analysis in combination with scientific rigour to a broad and complex problem. Research results proved that the primary effects of COVID-19 pandemic on tourism and hospitality industry include technology disruption, globalisation problems and logistical inefficiencies. Implications to contemporary tourism and hospitality industry business leaders include bringing-in technologies, incubating survival plans of actions. The study results underscore the necessity of understanding effects of COVID-19 pandemic on tourism and hospitality so as to craft both proactive and reactive strategic stances. The research contributes to literature and theoretical novel introspections into the depth and breadth of how various tourism and hospitality industry metamorphoses can lead to navigation and survival in such COVID-19 pandemic environments.

Online feminist activism has opened a different path for ordinary Iranians who are not necessarily versed in post-revolutionary discourses on feminism and political activism, nor are familiar with the names and past achievements of Iranian women’s activist pioneers since the birth of the Islamic Republic in 1979. Social media has helped to tease apart government statecraft that continually touts and reemphasizes Islamic values, at the same time providing a platform for a feminist consciousness that more recently has passionately supported individual rights, especially the right to privacy. This chapter delves into this move toward a more individualized form of dissent, surveying the generational, ideological, and technological divides that have emerged among Iranian women’s activists following popular uprisings that have been happening domestically since 2009.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.