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The social distancing imposed by the Covid-19 pandemic accelerated the digitalisation of societies, which also influenced habits related to the consumption and dissemination of news. In this context, older individuals are often blamed for contributing to disinformation, which is associated with the echo chambers fostered by social media. Mass media, social media and personal communication tools act as mass, social or personal intermediaries when it comes to keeping up to date with the news. This paper analyses the preferred intermediaries of older online adults (aged 60 and over) for following the news and how they change over time. We analysed two waves of an online survey-based longitudinal study conducted in Canada and Spain, before Covid-19 pandemic (2016/17), and during Covid-19 (in 2020). We found that most participants exclusively use mass intermediaries or combine mass with social and personal intermediaries to keep abreast of the news. However, only 28% of respondents inform themselves exclusively through the alleged echo chambers of social and personal intermediaries. Results also show that media ecologies evolve in different directions, and, despite the forced digitalisation driven by the pandemic, digital media usage did not always increase or evolve towards newer technologies. This paper contributes to understanding the diverse intermediaries used by older adults to obtain news and how such media ecologies can contribute to contrasting different sources of information beyond the alleged echo chambers of social media. [ABSTRACT FROM AUTHOR]

Future state prediction for nonlinear dynamical systems is a challenging task. Classical prediction theory is based on a, typically long, sequence of prior observations and is rooted in assumptions on statistical stationarity of the underlying stochastic process. These algorithms have trouble predicting chaotic dynamics, "Black Swans" (events which have never previously been seen in the observed data), or systems where the underlying driving process fundamentally changes. In this paper we develop (1) a global and local prediction algorithm that can handle these types of systems, (2) a method of switching between local and global prediction, and (3) a retouching method that tracks what predictions would have been if the underlying dynamics had not changed and uses these predictions when the underlying process reverts back to the original dynamics. The methodology is rooted in Koopman operator theory from dynamical systems. An advantage is that it is model-free, purely data-driven and adapts organically to changes in the system. While we showcase the algorithms on predicting the number of infected cases for COVID-19 and influenza cases, we emphasize that this is a general prediction methodology that has applications far outside of epidemiology. [ABSTRACT FROM AUTHOR]

Mining is an important enterprise that will supply essential minerals for the country's infrastructural development. Mining is a key industry and a development engine in the Indian economy. The epidemic has had both immediate and long-term effects on the labour and industries in India. Short-term effects include the cessation of mineral production, salary loss due to absenteeism and pandemic limitations, unemployment, and the selling of minerals on the market. The reputation and economic health of industries, fresh mineral deposit exploration, and new mining complex development have all been impacted in long-term. Aside from the mining industry-specific impact of COVID-19, the social fabric of the mining workers has been impacted since they are living in an environment of fear and uncertainty. This paper attempts to analyse the effect of COVID-19 on extractive industries in India. It also focuses the steps which taken by the Indian government. The paper concluded with the negative effect of COVID-19 which disrupts the mining sector and the contribution declined during COVID-19. However, for instance from mineral rich-Odisha, the mining sector takes up by its growth in post COVID-19 period with remarkable increment from negative growth to high positive rate. [ABSTRACT FROM AUTHOR]

IRTree models have been receiving increasing attention. However, to date, there are limited sources that provide a systematic introduction to Bayesian modeling techniques using modern probabilistic programming frameworks for the implementation of IRTree models. To facilitate the research and application of IRTree models, this paper introduces how to perform two families of Bayesian IRTree models (i.e., response tree models and latent tree models) in Stan and how to extend them in an explanatory way. Some suggestions on executing Stan codes and checking convergence are also provided. An empirical study based on the Oxford Achieving Resilience during COVID-19 data was conducted as an example to further illustrate how to apply Bayesian IRTree models to address research questions. Finally, strengths and future directions are discussed. [ABSTRACT FROM AUTHOR]

While the link between financial market movement and economic policy uncertainty indices is well-established in literature, uncertainty in the form of 'foreboding' emanating from catastrophic events has not been explored in literature. This paper explores "foreboding", which reflects uncertainty at its extreme, following the Covid-19 pandemic. Using Natural Language Processing on minute-by-minute news data, I construct two Foreboding Indices, representing 'foreboding' or 'fearful apprehension', for 28,622 Covid-related news for the period July 2020–August 2021. The impact of foreboding on financial market volatility is explored using a logistic regression model. Both the indices show a marked increase in June–July, 2020, in January 2021, April, 2021, and July–August, 2021 and have a positive impact on volatility for hourly S&P 500 Index. Understanding of foreboding sentiment is crucial for central banks looking to monitor financial market volatility. Appropriate signaling in accordance to sentiment can help central banks handle detrimental impacts of market volatility. Moreover, FI can be used for market practitioners to gauge the sentiment and take effective trading decisions. [ABSTRACT FROM AUTHOR]

This paper reviews the literature on (potential) green recovery measures in the context of the global financial crisis and the COVID-19 pandemic, focusing on their macroeconomic effects. We find that spending for renewables and energy efficiency is particularly promising with regard to macroeconomic impacts. Moreover, the empirical evidence suggests that green recovery measures are associated with larger macroeconomic effects compared to conventional non-green recovery spending. We also derive lessons learned with regard to open questions and issues as well as accompanying framework conditions which could enhance a macroeconomically successful implementation of green recovery measures. [ABSTRACT FROM AUTHOR]

The 43rd issue of Financial Innovation (FIN), Volume 10, No. 1 (2024) features 22 papers from authors and co-authors representing twenty-seven countries and areas. The papers cover four main themes: corporate finance, behavior finance, fintech, and cryptocurrency and COVID-19. The corporate finance papers explore topics such as firm performance, performance measurement in the banking industry, and the impact of legal certification signals on IPO investors. The behavior finance papers examine investment advisory decisions, herding behavior in the stock market, and the relationship between sentiment and Bitcoin returns. The fintech papers focus on factors influencing the intention to use biometric technology in FinTech, the current state of Fintech research, and predictive models for financial time series. The cryptocurrency and COVID-19 papers investigate the effects of the pandemic on asset returns, the efficiency of cryptocurrencies during the pandemic, and the relationship between cryptocurrencies and stock markets. The issue concludes with an integrated framework for evaluating cryptocurrency exchanges. [Extracted from the article]

This paper examines the extent to which refugee girls and young women were able to access learning during COVID-19 education closures in Pakistan, and the role that EdTech played in their learning access. It is based on findings from a survey with 403 Afghan refugee students, along with in-depth interviews with six young female refugees. The research shows that, while the majority of female refugee students were able to continue accessing education in some form during school closures, learning access was nevertheless limited, and a sizable minority were not engaged in any learning during this time. Teacher and institutional support was either absent or inadequate for many students, and infrastructure and devices that serve to support remote learning were not always reliable or accessible. Although male respondents were less likely than females to engage in independent study during the closures, refugee girls and young women were significantly less likely than their male counterparts to own the devices they needed for learning. The findings demonstrate how targeted investment in specific types of EdTech and teacher professional development, as well as supporting educational institutions in the establishment of remote learning opportunities, could help sustain learning during future periods of educational restriction. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic negatively affected many sectors including aviation and travel. Travel bans and forced lockdowns prevented transportation activity, especially air travel. Accordingly, huge amounts of emission reductions occurred. On the other hand, travel restrictions are not the only cause of emissions reductions. Changing travel intention in the era of Covid-19 is another important factor that affects aviation emissions. This paper aims to investigate the Landing/Take-Off (LTO) emission changes at Turkish airports. An emission inventory has been implemented for the years 2019 and 2020 to reveal the impacts of Covid-19 on aviation emissions. Domestic, international, and cargo flights have been included in the inventory. According to the results, total emissions of SO2, CO2, CO, NOx, NMVOC, CH4, N2O, and PM2.5 have decreased in 2020 compared to 2019 by 49.8%, 49.7%, 41.0%, 52.6%, 40.0%, 33.8%, 49.8%, and 50.3%, respectively. Total CO2 reductions in the Q2, Q3, and Q4 periods of 2020 compared to that of 2019 are 87%, 50% and 43%, respectively. Another aim of this paper is to find the underlying reasons for emission reductions. For Turkish airports, emission reductions have resulted from travel bans in Q2. After the relaxation of restrictions with the declaration of the "New Normal" in Turkey, flight traffic rebounded to a certain level but was lower than 2019 levels. Therefore, changing travel intention is the main cause of emission reductions in Q3 and Q4 of 2020. The results of this study contribute to both the areas of air pollution and tourism management. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic negatively affected healthcare in countries all over the world. When making support decisions related to reducing the pandemic's effects, government agencies should monitor various issues such as cumulative total cases, deaths, newly recorded case numbers, and their interactive effects on public health. The recovery policies used in the countries depend on the sufficient analysis of COVID-19 case-related daily data. The helpful big data sets are provided by the World Health Organization (WHO) day by day. In this paper, instead of using unique performance criteria, an overall performance score is calculated for a selected country in the world using the fuzzy TOPSIS method. We use various attributes to determine which countries are most negatively impacted by the COVID-19 pandemic. We illustrate the applicability of the developed decision support framework in this paper. The fuzzy TOPSIS approach developed in this paper has many primary benefits. For example, we can determine the level of influence of the COVID-19 pandemic for an individual country. Another advantage of the fuzzy TOPSIS approach is that it allows for the comparison of the local, rather than ignoring the population effect, country-wide impact of the COVID-19 pandemic with that of other countries. In the modeling stage, we incorporate the Value at Risk (VaR) integrated IF–Then rules to convert the cognitive evaluation of the criteria weights by the decision maker to the corresponding fuzzy numbers. [ABSTRACT FROM AUTHOR]