The pandemic has severely disrupted the activity and economic growth of co-working spaces, ridesharing, couch surfing, and other services known collectively as the "sharing economy." Academicians and managers are exploring and experimenting with various strategies to recover losses, build meaningful connections with consumers , and optimize consumer engagement. As the sharing economy strives to recover from pandemic-induced losses, it is crucial to consider factors that might help sharing-economy firms navigate through difficult times. Moreover, value-co creation has been addressed previously in sharing economy research as a unidimensional construct with constrained applicability. Guided by the stimulus-organism-response (S-OR) framework, the present study focuses on ethical marketing as a strategy for identifying consumer responses-including value co-creation, self-brand connection, and consumers' willingness to pay-that may benefit sharing-economy firms. We have collected the empirical data from n = 403 consumers in the sharing economy. Analyses through structural equation modelling tests reveal that ethical marketing influence value co-creation, self-brand connection, and consumers' willingness to pay. Contrary to our expectations, value co-creation has no influence on consumers' willingness to pay; however, the relationship between value co-creation and consumers' willingness to pay is fully mediated by self-brand connection. Based on empirical results, this study contributes to existing theoretical knowledge regarding ethical marketing and value co-creation literature in the sharing economy and proposes practical implications, including how consumers would be willing to co-create value, establish a self-brand connection, and are more likely to pay in response to ethical marketing.
We investigate the relationship between global risk aversion and safe-haven assets using the causality-in-quantiles test and the quantile-on-quantile regression method. Our empirical results show the predictability of global risk aversion on the returns of safe-haven assets. Furthermore, we find that several assets have consistent safe haven attributes regardless of the level of global risk aversion, while gold and Bitcoin cannot be considered consistent safe havens. Based on these findings, non-cash flow-induced shocks are not only an important predictor of asset returns but also their relevance cuts across general financial markets.
Nowadays, researchers are investing their time and devoting their efforts in developing and motivating the 6G vision and resources that are not available in 5G. Edge computing and autonomous vehicular driving applications are more enhanced under the 6G services that are provided to successfully operate tasks. The huge volume of data resulting from such applications can be a plus in the AI and Machine Learning (ML) world. Traditional ML models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated Learning (FL) plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients in real-time wherever and whenever needed. In fact, some mobile and vehicular devices are not available to serve as clients in the FL due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in FL offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using any type of client devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the deployment of clients whenever and wherever needed.
We investigate the asymmetric nonlinear link between foreign direct investment, oil prices, and CO2 emissions for the Gulf Cooperation Council nations, using foreign direct investment and oil price data. As foreign direct investment is positively associated with carbon emissions in the long run and oil prices have positive, significant effects on CO2 emissions, our findings support the pollution haven hypothesis. Furthermore, these variables have an asymmetric nonlinear relationship, which corresponds to the theoretical expectations of the pollution haven hypothesis. We also find that negative changes in foreign direct investment have positive, significant impacts on carbon emissions in the short run, implying that foreign enterprises utilize green technologies in their manufacturing processes in the short run. In the long run, however, negative changes in oil prices are positively associated with carbon emissions. These findings should help Gulf Cooperation Council economies focus on policies that encourage foreign direct investment in green rather than dirty industries in order to ensure environmental sustainability.
This study explores the relationship between extremist propaganda and the process of radicalisation. Two theories of the radicalisation process are explored which include a linear approach and a non-linear approach. The Dabiq magazines published by ISIS were analysed qualitatively to understand the possible link between propaganda and radicalising future ISIS fighters. The findings found that the Dabiq magazines were in line with Sageman’s radicalisation process which is non-linear. All of the magazine issues contained various aspects that fit into the four stages of Sageman’s model which suggests that ISIS is attempting to radicalise future enlistments by using multiple methods within the Dabiq issues.
Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one’s opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either ‘human’ or ‘bot.’ We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework ‘DeeProBot,’ which stands for Deep Profile-based Bot detection framework. The raw text from the description field of the Twitter account is also considered a feature for training the model by embedding the raw text using pre-trained Global Vectors (GLoVe) for word representation. Using only the user profile-based features considerably reduces the feature engineering overhead compared with that of user timeline-based features like user tweets and retweets. DeeProBot handles mixed types of features including numerical, binary, and text data, making the model hybrid. The network is designed with long short-term memory (LSTM) units and dense layers to accept and process the mixed input types. The proposed model is evaluated on a collection of publicly available labeled datasets. We have designed the model to make it generalizable across different datasets. The model is evaluated using two ways: testing on a hold-out set of the same dataset; and training with one dataset and testing with a different dataset. With these experiments, the proposed model achieved AUC as high as 0.97 with a selected set of features.
January 2022 witnessed the violent eruption of Hunga Tonga–Hunga Haʻapai submarine volcano in the South Pacific. With a volcanic explosivity index possibly equivalent to VEI 5, this represents the largest seaborne eruption for nearly one and a half centuries since Indonesia’s cataclysmic explosion of Krakatau in AD 1883. The Tongan eruption remarkably produced ocean-wide tsunamis, never documented before in the Pacific instrumental record. Volcanically generated tsunamis have been referred to as a ‘blind spot’ in our understanding of tsunami hazards, particularly in the Pacific Ocean. This event therefore presents a unique opportunity for investigating the multiple processes contributing to volcanic tsunamigenesis. It is argued that, although challenges exist, integrating theoretical, observational, field and modelling techniques offers the best approach to improving volcanic tsunami hazard assessment across Oceania.
This paper explores carbon capture and storage (CCS) through carbide lime waste (CLW), a by-product of acetylene production, under different conditions. This process is specifically designed to provide an onsite waste management solution for several industries that can easily be integrated into existing systems. In addition, the effect of the carbonation process on collected solids morphology and average particle size was studied. The structural and chemical characteristics of the carbonated carbide lime samples were investigated using X-ray diffraction, scanning electron microscopy, TGA analysis, and Raman spectroscopy. The effect of carbonation conditions on the total dissolved solids and change in pH was studied. All carbonated products exhibited a calcite crystal structure with a specific morphology at each carbonation condition. High CLW concentration helped to form singular long rods and agglomerated spheroidal particles. In contrast, low CLW concentration promoted truncated prismatic morphology. The maximum pH reduction was honored at the highest CLW to water ratio. In addition, a maximum conductivity reduction of 96.87% was obtained at pH 12.7, and a CLW to water ratio of 1:10. Raman analyzer, X-ray diffraction, and scanning electron microscopy confirmed the minimum CO2 uptake value for the higher carbide lime to distilled water ratio. This is due to the increase in the concentration of calcium species in the CLW–water mixture, which will form a thin carbonation layer that is distributed among calcium species.
Objective Illness perceptions (IPs) are important in understanding human reactions to illnesses, including mental health disorders. They influence risk perceptions and several variables relevant to the adjustment to a disorder, treatment seeking, and health outcomes. This study sought to compare IP, risk perception, and help-seeking intention for depression and schizophrenia in a community sample and to assess the mediating role of risk perception in the relationship between IP and help-seeking intention. Materials and methods A total of 380 adults participated in this study and filled out self-report measures of IPs, risk perceptions, and help-seeking intention. The previous diagnosis of depression was used to control the comparisons between the two disorders. A structural equation model (SEM) was used to test the mediation relationship. Results Perceived consequences, expected timeline, lack of personal control, and symptom identity were higher for schizophrenia, while lack of treatment control and concern were higher for depression. An interaction occurred with a previous diagnosis of depression for several dimensions of IP. Concerning the SEM, a valid model was obtained for depression, explaining 15.5% of help-seeking intentions, but not for schizophrenia. Conclusion The results show that the general population represents depression and schizophrenia differently. These representations are influenced by having experienced depression, and that illness and risk perceptions contribute to explaining the intention to seek help. Considering these illness representations makes it possible to understand the general population’s emotional and cognitive reactions to mental health disorders.
Can a short-squeeze incident trigger financial contagion over heavily shorted companies? The recent GameStop frenzy provides a unique natural experiment to explore this question. This study examines the static and dynamic return and volatility connectedness among the GameStop stock, the novel market-wide and sectoral short-interest indices, and the U.S. stock market. Contrary to anecdotal evidence, we find that the GameStop stock is not a net transmitter but a net recipient of return and volatility spillovers from other companies shorted in the market. This result agrees with the view that short-interest indices provide price discovery for shorted stocks. Therefore, although David might have won a battle against Goliath, he does not seem to win the war.
This article studies a collection of legal terms and their interpretation by Jordanian courts in matters related to the Šarīʿa. It outlines the method through which the meaning of terms is determined by returning to Islamic foundations of jurisprudence ( uṣūl al-fiqh ), a source specified by Jordanian law which can be used to define legal terms as well as the context, scope, and application of legal texts. The article examines a set of judicial interpretations ( iğtihād ) of terms which have carried different points of view in both courts of first instance and appeals. The methodology of the study combines between theoretical discussions derived from Islamic foundations of jurisprudence ( uṣūl al-fiqh ) and the application of interpretive principles through a focus on determining the purpose of the legislator. The article highlights the role of the contemporary Muslim judiciary in developing personal status law through the interpretation of terms that carry multiple meanings and explores the essential principles relied upon in this process, establishing a path for future legal reform.
An activity theory method is used to analyse the knowledge-sharing practices. The activity theory emphasises the necessity of analysing the SME organisation as a whole. In the context of knowledge-sharing practices, activity theory is used to collect interconnected parts of SME practices. A cross-sectional design was used to study the relationship among relationship commitment, knowledge-sharing practices, employee development, team performance, and a moderating role of social identification. The majority of the SMEs were established 3–5 years ago (46.3%), and 84.4% were private, with an employee range of less than 50 (73.1%). Furthermore, 82.1% of the SMEs in this study were in the growth stage. Knowledge-sharing practices have a significant positive effect on team performance (0.278, [Formula: see text]), with a moderating impact of role and behaviour on knowledge-sharing practices and team performance (0.178, [Formula: see text]). The findings have confirmed the significant and positive effects of knowledge-sharing practices on the mediation of employee development (0.045, [Formula: see text]). The activity theory models for knowledge-sharing practices emphasise the contextual nature of knowledge sharing and ensure systematic evaluation.
This study analyzes the impact of Covid-19 on stock market liquidity of China and four worst hit countries by the pandemic. Using daily data for the stock market illiquidity spanning over July 1, 2019 to July 10, 2020 and the data for new cases and deaths over the period from December 31, 2019 to July 10, 2020, the results of our GARCH analysis show that liquidity in stock markets of all the sampled countries hit hard by the news of the Covid-19 outbreak. We find that for all sampled countries increase in illiquidity due to temporary shocks reverts to long term trend shortly, suggesting that the liquidity shocks due to the incidence of Covid-19 were short lived. The findings of our VAR analysis show an absence of any short-term relationship between Covid-19 new cases or deaths and illiquidity. Since the series are not integrated at same level, long-term relationship between Covid-19 and stock market illiquidity do not exist as well suggesting no evidence of the effect of Covid-19 on stock market liquidity.
Recently, social network applications were developed intensively due to the increasing compaction and user demands. These applications provide different services to their users like learning, awareness, chatting with friends, sharing global news, etc. Simply, this work introduces the advantages of these software applications, specifically in the field of education during the COVID 19 spread. Google Classroom and Zoom meetings had gained the attention of many educational institutes for using them as a learning platform for students and educators. This research used two methodologies SWOT analysis and the information system success model of DeLone and McLean's updated to evaluate the effectiveness of these applications. SWOT analysis was performed for the Zoom meetings and google classroom, then evaluated their effectiveness. Likewise, DeLone and McLean's model was deployed for evaluation, an empirical survey was used and distributed in our college. The results were collected, analyzed, and studied using various statistical parameters. Practically, each application has its pros and cons. However, google classroom showed more functionality for the learning process than the Zoom application.
Introduction: Tobacco smokers are at high risk of developing severe COVID-19. Lockdown was a chosen strategy to deal with the spread of infectious diseases; nonetheless, it influenced people's eating and smoking behaviors. The main objective of this study is to determine the impact of the COVID-19 lockdown on smoking (waterpipe and cigarette) behavior and its associations with sociodemographic characteristics and body mass index. Methods: The data were derived from a large-scale retrospective cross-sectional study using a validated online international survey from 38 countries (n=37207) conducted between 17 April and 25 June 2020. The Eastern Mediterranean Region (WHO-EMR countries) data related to 10 Arabic countries that participated in this survey have been selected for analysis in this study. A total of 12433 participants were included in the analysis of this study, reporting their smoking behavior and their BMI before and during the COVID-19 lockdown. Descriptive and regression analyses were conducted to examine the association between smoking practices and the participant's country of origin, sociodemographic characteristics, and BMI (kg/m2). Results: Overall, the prevalence rate of smoking decreased significantly during the lockdown from 29.8% to 23.5% (p<0.05). The percentage of females who smoke was higher than males among the studied population. The highest smoking prevalence was found in Lebanon (33.2%), and the lowest was in Oman (7.9%). In Egypt, Kuwait, Lebanon, and Saudi Arabia, the data showed a significant difference in the education level of smokers before and during the lockdown (p<0.05). Smokers in Lebanon had lower education levels than those in other countries, where the majority of smokers had a Bachelor's degree. The findings show that the BMI rates in Jordan, Lebanon, Oman, and Saudi Arabia significantly increased during the lockdown (p<0.05). The highest percentages of obesity among smokers before the lockdown were in Oman (33.3%), followed by Bahrain (28.4%) and Qatar (26.4%), whereas, during the lockdown, the percentage of obese smokers was highest in Bahrain (32.1%) followed by Qatar (31.3%) and Oman (25%). According to the logistic regression model, the odds ratio of smoking increased during the pandemic, whereas the odds ratio of TV watching decreased. This finding was statistically significant by age, gender, education level, country of residence, and work status. Conclusions: Although the overall rates of smoking among the studied countries decreased during the lockdown period, we cannot attribute this change in smoking behavior to the lockdown. Smoking cessation services need to anticipate that unexpected disruptions, such as pandemic lockdowns, may be associated with changes in daily tobacco consumption. Public health authorities should promote the adoption of healthy lifestyles to reduce the long-term negative effects of the lockdown.
Purpose Migrant status is a known risk factor for psychosis, but the underlying causes of this vulnerability are poorly understood. Recently, studies have begun to explore whether migrant status predicts transition to psychosis in individuals at clinical high risk (CHR) for psychosis. Results, however, have been inconclusive. The present study assessed the impact of migrant status on clinical symptoms and functional outcome in individuals at CHR for psychosis who took part in the NAPLS-3 study. Methods Participants’ migrant status was classified as native-born, first-generation, or second-generation migrant. Clinical symptoms were assessed using the Structured Interview for Psychosis-Risk Syndromes (SIPS); functional outcome was measured using the Global Functioning Scales:Social and Role (GF:S; GF:R). Assessments were conducted at baseline, 12-months, 18-months, and 24-months follow-up. Generalized linear mixed models for repeated measures were used to examine changes over time and differences between groups. Results The overall sample included 710 individuals at CHR for psychosis (54.2% males; Age: M = 18.19; SD = 4.04). A mixed model analysis was conducted, and no significant differences between groups in symptoms or functioning were observed at any time point. Over time, significant improvement in symptoms and functioning was observed within each group. Transition rates did not differ across groups. Conclusion We discuss potential factors that might explain the lack of group differences. Overall, migrants are a heterogeneous population. Discerning the impact of migration from that of neighborhood ethnic density, social disadvantage or socio-economic status of different ethnic groups could help better understand vulnerability and resilience to psychosis.
Confraternity studies is a vibrant field, but until now surveys of the Italian peninsula have given little attention to the south. This anthology is the first book in English to address this. Its case studies of confraternities and their artistic patronage make an important addition to studies not just of the Italian south but on early modern Italy in general.
In this article, I explore how English is used for Specific Business Purposes (ESBP) in the United Arab Emirates (UAE) with reference to the world Englishes framework. I discuss the current status of English in the Emirate and I speculate on what this might mean for the future. I provide an overview of the ways in which English is used in business, including the co‐construction of English in many of the interactions that take place and the characteristics of those interactions, as well as identifying the models of English that are referred to in a number of common business genres. I use Business English and Business English as a Lingua Franca as significant examples of English for Specific Purposes, as they have contributed greatly to the UAE's economic development specifically, as well as to the development of the GCC region as a whole.
The COVID-19 pandemic has affected all sectors of the economy resulting in unprecedented challenges for market participants, policymakers, and practitioners. This study envisages this issue from the perspective of real estate investment trusts (REITs), which is a relatively less analysed segment. We examine the impact of the COVID-19 pandemic on REIT returns for 12 top REIT regimes spread across America, Asia, and Europe under the bullish, bearish, and normal market conditions over the COVID-19 period (specifically from February 02, 2020, to January 24, 2022). We employ the quantile-on-quantile regression and causality-in-quantiles approach. We document a strong (weak) predictive power of COVID-19 cases on REIT returns within the lower (upper) conditioned quantiles. Our findings are of importance to market participants, practitioners, and regulators across REIT regimes.
This paper presents a trust-based evolutionary game model for managing Internet-of-Things (IoT) federations. The model adopts trust-based payoff to either reward or penalize things based on the behaviors they expose. The model also resorts to monitoring these behaviors to ensure that the share of untrustworthy things in a federation does not hinder the good functioning of trustworthy things in this federation. The trust scores are obtained using direct experience with things and feedback from other things and are integrated into game strategies. These strategies capture the dynamic nature of federations since the population of trustworthy versus untrustworthy things changes over time with the aim of retaining the trustworthy ones. To demonstrate the technical doability of the game strategies along with rewarding/penalizing things, a set of experiments were carried out and results were benchmarked as per the existing literature. The results show a better mitigation of attacks such as bad-mouthing and ballot-stuffing on trustworthy things.
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