Implications of labour market disruptions on subjective wellbeing during the COVID-19 pandemic in MENA countries.

COVID-19 has caused a global health crisis and severe economic and social consequences. Unprecedented economic disruption and high unemployment are the pronounced impacts of the pandemic. The current study is primarily concerned with the effects of COVID-19-induced labour market outcomes on workers' subjective wellbeing in four MENA countries using the Combined COVID-19 MENA Monitor Household Survey. The study documented that COVID-19-induced labour market changes negatively affected workers' subjective wellbeing after controlling for work characteristics, risks, social distancing, and socio-demographic variables. Job loss, income reduction, and wage delay were the most significant labour changes that deteriorated workers' subjective wellbeing. Our findings underscore the need for policy responses that reduce workers' vulnerability and sustain their livelihoods. Mental health services and income support policies are important tools to enhance subjective wellbeing of economically affected workers.

How to punish cyber criminals: A study to investigate the target and consequence based punishments for malware attacks in UK, USA, China, Ethiopia & Pakistan.

Numerous research studies have highlighted the exponential growth of malware attacks worldwide, posing a significant threat to society. Cybercriminals are becoming increasingly merciless and show no signs of pity towards individuals or organizations. It is evident that cyber criminals will stop at nothing to gain unauthorized access to confidential information. To effectively combat malware attacks, strict cyber laws are necessary, and the use of malware is punishable in many countries. However, the literature has not addressed whether these penalties create deterrence or not. This research article has addressed this gap. In this study, the effectiveness of criminal laws related to malware-related crimes in various jurisdictions was analyzed using the doctrinal research methodology. The cyber laws of the USA, UK, Ethiopia, Pakistan, and China were examined to determine whether the penalties imposed for these crimes are appropriate given the severity of the harm caused. The study concludes that malware penalties should take into account the creation or use of malicious code, targeting individuals or organizations, and the magnitude of consequences, regardless of whether mens rea is present or not.

Predictors of mental health problems during the COVID-19 outbreak in Egypt in 2021.

Background: With the widespread outbreak of the coronavirus (COVID-19) pandemic, many countries, including Egypt, have tried to restrict the virus by applying social distancing and precautionary measures. Understanding the impact of COVID-19-induced risks and social distancing measures on individuals' mental health will help mitigate the negative effects of crises by developing appropriate mental health services. This study aimed to investigate the most contributing factors that affected individuals' mental health and how individuals' mental health has changed over the lockdown period in Egypt in 2021. Methods: The study draws on a nationally representative sample from the combined COVID-19 MENA Monitor Household Survey conducted by the Economic Research Forum. The data were collected in Egypt by phone over two waves in February 2021 and June 2021. The total number of respondents is 4,007 individuals. The target population is mobile phone owners aged 18-64 years. The 5-item World Health Organization Well-Being Index (WHO-5) is used to assess the individuals' mental health over the past 2 weeks during the pandemic. Penalized models (ridge and LASSO regressions) are used to identify the key drivers of mental health status during the COVID-19 pandemic. Results: The mean value of mental health (MH) scores is 10.06 (95% CI: 9.90-10.23). The average MH score for men was significantly higher than for women by 0.87. Rural residents also had significantly higher MH scores than their urban counterparts (10.25 vs. 9.85). Middle-aged adults, the unemployed, and respondents in low-income households experienced the lowest MH scores (9.83, 9.29, and 9.23, respectively). Individuals' mental health has deteriorated due to the negative impacts of the COVID-19 pandemic. Regression analysis demonstrated that experiencing food insecurity and a decrease in household income were independent influencing factors for individuals' mental health (p < 0.001). Furthermore, anxiety about economic status and worrying about contracting the virus had greater negative impacts on mental health scores (p < 0.001). In addition, women, middle-aged adults, urban residents, and those belonging to low-income households were at increased risk of poor mental health (p < 0.05). Conclusion: The findings reveal the importance of providing mental health services to support these vulnerable groups during crises and activating social protection policies to protect their food security, incomes, and livelihoods. A gendered policy response to the pandemic is also required to address the mental pressures incurred by women.

Exploring the potential impact of group identity on post-traumatic growth in the aftermath of Corona outbreak: function of social-emotional competence as a mediator.

Background: This research endeavors to examine the potential effects of human and societal interactions on individuals' post-traumatic growth in the aftermath of the Corona outbreak. To achieve the aforementioned objective, the current research investigates the correlations between post-traumatic growth and group identity, while also examining the potential mediating function of social-emotional competence. Methods: A cross-sectional design included a representative sample of 2,637 high school students located in the capital territory of Pakistan using convenience sampling method. To explore the associations, correlation and mediation analyzes utilizing the group identification scale, the social-emotional competence scale, and the post-traumatic growth scale was performed with SPSS PROCESS 4 macro and AMOS. Results: The findings demonstrated that group identification emerged as a substantial predictor substantially associated with post-traumatic growth. Moreover, the relationship linking group identification and post-traumatic growth was found to be partially moderated by social-emotional competence. Conclusion: The phenomenon of group identification can exert influence on post-traumatic growth through both direct and mediating pathways, with the latter being essentially mediated by social-emotional competence. The aforementioned outcomes possess significant academic and practical implications concerning the promotion of post-traumatic growth and the improvement of psychological well-being after the Corona outbreak.

Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data.

As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations.

New evidence of gender inequality during COVID-19 outbreak in the Middle East and North Africa.

The COVID-19 pandemic has significantly altered employment and income distribution, impacting women and men differently. This study investigates the negative effects of COVID-19 on the labour market, focusing on the gender gap in five countries in the Middle East and North Africa (MENA) region. The study indicates whether women are more susceptible to losing their jobs, either temporarily or permanently, switching their primary occupation, and experiencing decreased working hours and income compared to men during the COVID-19 outbreak. The study utilizes a multivariate Probit model to estimate the relationship between gender and adverse labour outcomes controlling for correlations among outcomes. Data are obtained from the Combined COVID-19 MENA Monitor Household Survey, covering Egypt, Tunisia, Morocco, Jordan, and Sudan. The findings of this study offer empirical evidence of the gender gap in labour market outcomes during the pandemic. Women are more likely than men to experience negative work outcomes, such as permanent job loss and change in their main job. The increased childcare and housework responsibilities have significantly impacted women's labour market outcomes during the pandemic. However, the availability of telework has reduced the likelihood of job loss among women. The study's results contribute to a better understanding of the impact of COVID-19 on gender inequality in understudied MENA countries. Mitigation policies should focus on supporting vulnerable women who have experienced disproportionate negative effects of COVID-19.

Modified ridge-type for the Poisson regression model: simulation and application.

The Poisson regression model (PRM) is employed in modelling the relationship between a count variable (y) and one or more explanatory variables. The parameters of PRM are popularly estimated using the Poisson maximum likelihood estimator (PMLE). There is a tendency that the explanatory variables grow together, which results in the problem of multicollinearity. The variance of the PMLE becomes inflated in the presence of multicollinearity. The Poisson ridge regression (PRRE) and Liu estimator (PLE) have been suggested as an alternative to the PMLE. However, in this study, we propose a new estimator to estimate the regression coefficients for the PRM when multicollinearity is a challenge. We perform a simulation study under different specifications to assess the performance of the new estimator and the existing ones. The performance was evaluated using the scalar mean square error criterion and the mean squared error prediction error. The aircraft damage data was adopted for the application study and the estimators' performance judged by the SMSE and the mean squared prediction error. The theoretical comparison shows that the proposed estimator outperforms other estimators. This is further supported by the simulation study and the application result.

Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer's Disease-Based Neurodegenerative Disorders.

Alzheimer's disease is incurable at the moment. If it can be appropriately diagnosed, the correct treatment can postpone the patient's illness. To aid in the diagnosis of Alzheimer's disease and to minimize the time and expense associated with manual diagnosis, a machine learning technique is employed, and a transfer learning method based on 3D MRI data is proposed. Machine learning algorithms can dramatically reduce the time and effort required for human treatment of Alzheimer's disease. This approach extracts bottleneck features from the M-Net migration network and then adds a top layer to supervised training to further decrease the dimensionality and delete portions. As a consequence, the transfer network presented in this study has several advantages in terms of computational efficiency and training time savings when used as a machine learning approach for AD-assisted diagnosis. Finally, the properties of all subject slices are combined and trained in the classification layer, completing the categorization of Alzheimer's disease symptoms and standard control. The results show that this strategy has a 1.5 percentage point better classification accuracy than the one that relies exclusively on VGG16 to extract bottleneck features. This strategy could cut the time it takes for the network to learn and improve its ability to classify things. The experiment shows that the method works by using data from OASIS. A typical transfer learning network's classification accuracy is about 8% better with this method than with a typical network, and it takes about 1/60 of the time with this method.

Predictors of catastrophic out-of-pocket health expenditure in rural Egypt: application of the heteroskedastic probit model.

BACKGROUND: Out-of-pocket (OOP) health expenditure is a pressing issue in Egypt and far exceeds half of Egypt's total health spending, threatening the economic viability, and long-term sustainability of Egyptian households. Targeting households at risk of catastrophic health payments based on their characteristics is an obvious pathway to mitigate the impoverishing impacts of OOP health payments on livelihoods. This study was conducted to identify the risk factors of incurring catastrophic health payments hoping to formulate appropriate policies to protect households against financial catastrophes. METHODS: Using data derived from the Egyptian Household Income, Expenditure, and Consumption Survey (HIECS), a multiplicative heteroskedastic probit model is applied to account for heteroskedasticity and avoid biased and inconsistent estimates. RESULTS: Accounting for heteroskedasticity induces notable differences in marginal effects and demonstrates that the impact of some core variables is underestimated and insignificant and in the opposite direction in the homoscedastic probit model. Moreover, our results demonstrate the principal factors besides health status and socioeconomic characteristics responsible for incurring catastrophic health expenditure, such as the use of health services provided by the private sector, which has a dramatic effect on encountering catastrophic health payments. CONCLUSIONS: The marked differences between estimates of probit and heteroskedastic probit models emphasize the importance of investigating homoscedasticity assumption to avoid policies based on incorrect evidence. Many policies can be built upon our findings, such as enhancing the role of social health insurances in rural areas, expanding health coverage for poor households and chronically ill household heads, and providing adequate financial coverage for households with a high proportion of elderly, sick members, and females. Also, there is an urgent need to limit OOP health payments absorbed by private sector to achieve an acceptable level of fair financing.

Estimating equivalence scales and non-food needs in Egypt: Parametric and semiparametric regression modeling.

This paper investigated the appropriate specifications of Engel curves for non-food expenditure categories and estimated the deprivation indices of non-food needs in rural areas using a semi parametric examination of the presence of saturation points. The study used the extended partial linear model (EPLM) and adopted two estimation methods-the double residual estimator and differencing estimator-to obtain flexible shapes across different expenditure categories and estimate equivalence scales. We drew on data of the Egyptian Household Income, Expenditure, and Consumption Survey (HIEC). Our paper provides empirical evidence that the rankings of most non-food expenditure categories is of rank three at most. Rural households showed high economies of scale in non-food consumption, with child's needs accounting for only 10% of adult's non-food needs. Based on semi-parametrically estimated consumption behavior, the tendency of non-food expenditure categories to saturate did not emerge. While based on parametrically estimated consumption behavior, rural areas exhibited higher deprivation indices in terms of health and education expenditure categories, which indicates the need to design specific programs economically targeting such vulnerable households.

Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling.

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year's Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.