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This paper aims to Solar photovoltaic (PV) power can significantly impact the power system because of its intermittent nature. Hence, an accurate solar PV power forecasting model is required for appropriate power system planning.

In this paper, a long short-term memory (LSTM)-based double deep Q-learning (DDQL) neural network (NN) is proposed for forecasting solar PV power indirectly over the long-term horizon. The past solar irradiance, temperature and wind speed are used for forecasting the solar PV power for a place using the proposed forecasting model.

The LSTM-based DDQL NN reduces over- and underestimation and avoids gradient vanishing. Thus, the proposed model improves the forecasting accuracy of solar PV power using deep learning techniques (DLTs). In addition, the proposed model requires less training time and forecasts solar PV power with improved stability.

The proposed model is trained and validated for several places with different climatic patterns and seasons. The proposed model is also tested for a place with a temperate climatic pattern by constructing an experimental solar PV system. The training, validation and testing results have confirmed the practicality of the proposed solar PV power forecasting model using LSTM-based DDQL NN.

Recycled fibres used as reinforcements are obtained from garment cut wastes of cotton, polyester and cotton/polyester fabrics to develop these composites. These composites are developed by using Epoxy resin, Kaolinite, Polypropylene sheet as matrices. Reinforcements and matrices have been used in different compositions and combinations to develop these composites. The main advantages of this type of composites are to combine the different properties of different materials to obtain unique and high-performance material.

Garment cut wastes from apparel industries are used for various applications in various industries. Normally, garment cut wastes and recycled fibres from garment cut wastes are used in textile, furniture, carpet, paper, automobile, construction and agricultural industries in low mechanical performance applications. In this research, composites are developed by using recycled fibres obtained from garment cut wastes as reinforcement and with different types of matrices.

Technical properties like thickness, mass per unit area, Tensile strength, Flexural strength, Impact strength, Water absorbency and Scanning Electron Microscope of developed composites were tested and analyzed. The outcome of the results demonstrates that many of the composite proportions with different blend, reinforcement and matrcies show superior mechanical performances when compared with each other, and it can be recommended for many potential applications.

The properties of composites are dependent on the different blend proportions of recycled fibres with reinforcement and matrices. Based on the result of tensile strength, polyester/cotton fibre reinforced composites show superior strength compared to other recycled fibre reinforced samples and it can be suitably tailored further by appropriate design of different lay-up angle and orientation with the number of different preformed layers of reinforcements to suit the intended applications.

In a pandemic framework (COVID-19), this chapter explores the impact of the global economy and socio-cultures concerning three axes: recreational, tourism, and hospitality. Although we slowly see an economic revival, it is well known that this sector of study is very susceptible to being affected by the context of nations. Following restrictions and measures taken by governments around the world to reduce the number of cases of coronavirus infections, many nations closed their borders, affecting international travel and by 2020 tourism had been reduced to the near cessation of operations due to the imminent fear of this poorly studied disease, and the service sector was negatively affected. It should be added that, according to the World Tourism Organization's projections, a decrease of between 20 and 30% is forecast for 2020 compared to the previous year.

This study investigated the impact of the coronavirus disease 2019 (COVID-19) pandemic on the Asian-Australasian cruise shipping network. The analysis was carried out using complex network analysis and data collected for two periods, before and after the pandemic outbreak. The analysis revealed that the network structure and properties have changed after the outbreak of the COVID-19 pandemic. Interestingly, the network’s density and the number of links have increased, but its scale-free property remains with the degree distribution follows the power law. The network has a higher connectivity efficiency with a smaller average path length and a higher clustering coefficient. Its hub ports still maintain an extensive connection. The network’s flow efficiency becomes higher and connectivity stronger after the pandemic. The role of cruise ports has changed as indicated by the degree, betweenness, closeness and eigenvector centralities. The study’s findings indicate that the cruise shipping sector could further enhance efficiency and identify strategies to assist the management in similar circumstances.

This paper aims to investigate whether the interest rate differentials Granger cause expected change in the exchange rate during the COVID-19 period. The study examines if the investors in the international assets and exchange rate markets take advantages of the relevant information obtained during the COVID-19 pandemic.

This paper used daily data ranging from January 31, 2020 to June 30, 2020 and considered BRIICS economies. The study implemented the Toda–Yamamoto’s Granger causality approach to identify the causality between interest rate differentials and exchange rates. For robustness checks, the study used ARLD short-run dynamics to infer causal relations.

Overall, the results indicate that the interest rate differentials improve the predictability of subsequent exchange rate changes in all six BRIICS economies during the COVID-19 period wherein investors are forward-looking. The empirical results pass the robustness checks.

There is a lack of studies exploring the relationship between interest rate differentials and exchange rates in the presence of an unanticipated event such as the current pandemic. To the best of the authors’ knowledge, this is the first study to explore the causal linkages between interest rate differentials and expected change in exchange rates, focusing on the COVID-19 outbreak period.

The purpose of this paper is to investigate the effects of exchange rate volatility, oil price return and COVID-19 cases on the stock market returns and volatility for selected emerging market economies. Additionally, this study compares the market performance in the emerging economies during the COVID-19 pandemic with the pre-COVID and global financial crisis (GFC) period.

The authors apply the arbitrage pricing theory to model the risk-return relationship between the risk-based factors (exchange rate volatility and COVID-19 cases) and stock market returns. By applying the exponential generalized autoregressive conditional heteroskedasticity model, the study captures the asymmetric volatility spillover from the stock markets to foreign exchange markets and vice versa.

Findings reveal that exchange rate volatility exerts a negative and significant effect on the market returns in Brazil (BOVESPA), Chile (S&P CLX IPSA), India (SENSEX), Mexico (S&P BMV IPC) and Russia (MOEX) during the coronavirus pandemic. Regarding the effect of oil price returns, the authors find a positive relationship between oil price and stock market returns across all the economies in the study. The market returns of Russia, India, Brazil and Peru appeared more volatile during the pandemic than the GFC period.

As the exchange rate volatility is causing higher risk and uncertainty in the stock market’s performance, the central bank’s effort to maintain a stabilizing effect on the exchange rate sale can be proven crucial for the economies under consideration. Emphasized should also be given to boost investors’ confidence in the stock market, and for this, the government policy actions in reducing the transmission of the disease are the need of the hour.

While a large volume of literature on stock market performance in times of COVID-19 has emerged from developed economies, this study adds to the literature by exploring the emerging economies’ stock market performance during the COVID-19 pandemic. Unlike previous literature, this study examines the volatility spillover between stock and exchange rate markets in the worst affected emerging economies during the crisis.

This study aims to examine the impact of Covid-19 on social and eco-environmental sustainability. It will also investigate the effect of advanced technologies in the post-pandemic era.

To get the robust findings, GMM (Generalized Method of Moments) modeling is employed on the panel data of 50 countries across the globe.

The outcomes indicate that gross fixed capital, logistical operations, knowledge spillover are positive, while Covid-19 is negatively associated with international trade. The results also revealed that Covid-19 spurs poverty and vulnerable employment, while the fertility rate increase creates pressure on economic growth. Also, fossil fuel and energy consumption contribute to carbon emission, while green and advanced technologies may mitigate the environment's adverse effects.

This study is the first of its kind to provide a solution to the challenges posed by the Covid-19 pandemic in the post-pandemic environment. Furthermore, researchers, managers and legislators can use this article's findings to formulate relevant policies for post-pandemic.

This paper aims to investigate the impact of the coronavirus (COVID-19) on major stock markets. Specifically, an event study analysis is executed to estimate the abnormal returns of selected stock indices from 15 countries to key events concerning the global pandemic.

Specifically, an event study analysis is executed to estimate the abnormal returns of selected stock indices from 15 countries to key events concerning the global pandemic. The study continues with a regression analysis that looks into cross-country variation of estimated abnormal returns by using country-specific characteristics as predictors.

The results indicate that stock markets of countries that have larger foreign direct investment exposure to China, higher democracy index, a higher number of confirmed COVID-19 cases and that accept a higher percentage of Chinese tourists are more prone to getting negatively affected by such a global health crisis. On the other hand, stock markets of countries with higher health expenditure, a higher level of preparedness for pandemics and higher gross domestic product per capita are likely to have less negative abnormal returns.

It is one of the first studies that focuses on determining the country-specific characteristics that influence the reaction of financial markets to a global health crisis that the world is experiencing today with the COVID-19 infectious disease. Investigating cross-country effects is very relevant and important today because countries and their relevant policymakers can take lessons and get better prepared for future pandemics only by recognizing the relevant points that are underlying and shape the response of the country’s economy to such a global health crisis.

This study aims to empirically explore and compare the dynamic dependency between health-care sector and Islamic industries before, during and after the COVID-19 pandemic.

Time-varying student-t copula is used for before, during and after COVID-19 periods. The data used are the daily frequency price series of the selected markets from February 2017 to October 2023.

Empirical results found strong evidence of significant impact of the COVID-19 pandemic on the dependence structure of the studied indexes: Co-movements between various sectors are certain. The authors assist also in the birth of new dependence structure with the health-care industry in response to the COVID-19 crisis. This reflects the contagion occurrence from the health-care sector to other sectors.

By specifically examining the Islamic industry, this study sheds light on the resilience, challenges and opportunities within this sector, contributing novel perspectives to the broader discourse on pandemic-related impacts on economies and industries. Also, this paper conducts a comprehensive temporal analysis, examining the dynamics before, during and after the COVID-19 lockdown. Such approach enables an understanding of how the relationship between the health-care sector and the Islamic industry evolves over time, accounting for both short-term disruptions and long-term effects. By considering the pre-pandemic context, the paper adopts a longitudinal perspective, enabling a deeper understanding of how historical trends, structural factors and institutional frameworks shape the interplay between the health-care sector and the Islamic industry.