<p style="text-align: justify;"><span style="font-family: 'times new roman', times, serif; font-size: 14pt;">In this work we aim to identify potential determinants and seek to predict terrorism attack. Thus, to eliminate uncertainty linked to explanatory variables we used the BMA method. We show that, contrary to expectations terrorism in MENA region is no longer purely of economic origin but mainly due to political problems, education, financial development and countries’ demographic characteristics. Likewise, we find that national, international and global terrorism are not of same origins even they present many common roots. In the end, we show that it is possible to predict majority of attacks based on a small number of indicators measuring political risk, financial development and income inequalities.</span></p>

Since artificial intelligence is still a relatively new technology, research on the effects of artificial intelligence on work performance in developing countries is still limited. Thus, the chapter aims to examine and analyze the effect of artificial intelligence on job performance among Tunisian employees, with gender as the moderator variable and experience as the mediating variable. A questionnaire was developed to test the model based on a dataset of 350 employees in different sectors. The generated data were analyzed using IBM SPSS.26 and IBM SPSS AMOS.26. The results of exploratory and confirmatory factor analyses showed the absence of an impact of artificial intelligence on job performance. In addition, the authors found no moderating effect of gender or mediating effect of experience on the relationship between artificial intelligence and job performance. However, the experience of the employee has a positive and significant impact on job performance.

Smart farming aims to ameliorate the quantity and quality of farms products using modern information and communication technologies. Agriculture has always used cutting-edge technology to improve profitability, efficiency, and safety as well as creating the opportunity for more environmentally friendly operations. In smart agriculture network communication between smart devices is necessary to managing the agriculture process. Consequently, the possibility of information being exposed to hacking is very likely, which makes the information security system extremely important and cannot be neglected. Even though many solutions have been developed to overcome cyberattacks in smart networks, some limitations are observed, such as solving multi-class problems, high feature dimensions, and overhead computation. To address the aforementioned issues, an intelligent Intrusion Detection System (IDS) for identifying cyberattacks in the Internet of Agriculture Things (IoAT) is proposed. The developed model uses a proposed reduced kernel method, the Downsized Kernel Partial Least Square (DKPLS) to extract and reduce data feature dimension to improve detection performance. The DRKPLS approach is used to reduce the dimension of a kernel matrix generated using the Kernel Partial Least Square (KPLS) technique by selecting the most important features. In classification phase, the Kernel Extreme Learning Machine (KELM) is used to classify data for binary and multi-class classification. The proposed IDS is evaluated on a new Industrial IoT Dataset, X-IIoTID. The developed approach achieved higher performances for binary classification and for multi–class classification compared to others machine learning and deep learning approaches. The proposed approach achieved an accuracy rate of 99.92% for binary classification and 99.99% for multi–class classification. Furthermore, the higher percentage in multi-class classification is obtained for the two types of attacks Ransom Denial of Service (RDoS) and Command_Control respectively.

Abstract The rapid proliferation of Internet of Things (IoT) devices has transformed our daily lives, introducing innovations like smart homes, wearables, and advanced industrial automation. While these interconnected systems offer convenience and efficiency, they also present significant security challenges. With the expansion of the IoT network comes an increased risk of malicious attacks, making safeguarding these networks a pressing concern. Intrusion detection serves as a crucial defense mechanism, detecting abnormal activities and triggering appropriate responses. In our study, we harness the power of ensemble learning through a technique known as bagging. By combining the strengths of Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), we aim to capitalize on their unique advantages and enhance the overall capability of intrusion detection systems.

The global evolution of pre-hospital care systems faces dynamic challenges, particularly in multinational settings. Machine learning (ML) techniques enable the exploration of deeply embedded data patterns for improved patient care and resource optimisation. This study's objective was to accurately predict cases that necessitated transportation versus those that did not, using ML techniques, thereby facilitating efficient resource allocation. ML algorithms were utilised to predict patient transport decisions in a Middle Eastern national pre-hospital emergency medical care provider. A comprehensive dataset comprising 93,712 emergency calls from the 999-call centre was analysed using R programming language. Demographic and clinical variables were incorporated to enhance predictive accuracy. Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) algorithms were trained and validated. All the trained algorithm models, particularly XGBoost (Accuracy = 83.1%), correctly predicted patients' transportation decisions. Further, they indicated statistically significant patterns that could be leveraged for targeted resource deployment. Moreover, the specificity rates were high; 97.96% in RF and 95.39% in XGBoost, minimising the incidence of incorrectly identified "Transported" cases (False Positive). The study identified the transformative potential of ML algorithms in enhancing the quality of pre-hospital care in Qatar. The high predictive accuracy of the employed models suggested actionable avenues for day and time-specific resource planning and patient triaging, thereby having potential to contribute to pre-hospital quality, safety, and value improvement. These findings pave the way for more nuanced, data-driven quality improvement interventions with significant implications for future operational strategies.

Abstract The Internet of Things (IoT) landscape is fraught with vulnerabilities, making it a prime target for various types of attacks. While existing literature has extensively explored IoT attacks through studies and simulations, this paper introduces a fresh perspective by proposing a new methodology for testing attacks in IoT environments. Focusing on six prominent attack vectors, we conduct comprehensive tests using both the Cooja and OMNET + + simulators. Our research delves into the underlying factors driving these attacks, analyzing data based on the attackers' chosen target addresses. Through our novel approach, we aim to deepen the understanding of IoT vulnerabilities and provide insights into the behavior of attackers, ultimately paving the way for more effective defense mechanisms in IoT ecosystems.

In this paper, using operational rules, we derive some explicit expansion formulas associated with Brenke type polynomials. In particular, the corresponding connection and linearization coefficients are expressed either in terms of double sums or integral representations or sometimes in closed hypergeometric terms. The derived results are applied to Generalized Gould–Hopper and q-Laguerre polynomials. Some well-known duplication and convolution formulas are deduced as particular cases.

The magneto-hydrodynamics (MHD) double-diffusive mixed convection and heat generation/absorption in a lid-driven inclined wavy enclosure filled with (Fe3O4/ water) ferrofluid is quantitatively investigated in this paper. The present study focuses on improving the efficiency of mass and thermal performance of a system. Both the left and right sidewalls of the cavity are allowed to move with a constant velocity in the upward and downward directions, respectively. The finite difference approach was applied to discretize the subsequent governing equations followed by the Bi-Conjugate Gradient Stabilized (Bi-CGStab) method to solve them. The numerical simulation was performed for a variety of parameters, including the Hartmann number varied as 0 ≤ Ha ≤ 45, the inclination angle of the enclosure varied as 0° ≤ δ ≤ 180°, the buoyancy ratio varied as -2 ≤ N ≤ 2, heat generation or absorption parameter varied as −10 ≤ Qo ≤ 10, Richardson number varied as 0.01 ≤ Ri ≤ 10, and solid volume fraction varied as 0 ≤ ϕ ≤ 0.06. The numerical simulation results were presented in terms of streamlines, isotherms, isoconcentrations, average Nusselt number, and average Sherwood number. The heat and mass transfer rates were found to decrease with the increase in Ha but increase with N, Ri, and Φ. Also, both of them reach their peak values at Ri = 10. In addition, the heat generation parameter enhances both thermal and mass performance as they reach their maximum values at Qo = 10. Increasing the heat generation factor from Qo = 5 to Qo = 10 increases the Nusselt number by 3.5 times. The outcomes of the study have significant importance for modern industrial applications specifically in the discipline of electronic device cooling.

Objective structured clinical examination (OSCE) is well-established and designed to evaluate students' clinical competence and practical skills in a standardized and objective manner. While OSCEs are widespread in higher-income countries, their implementation in low-resource settings presents unique challenges that warrant further investigation. This study aims to evaluate the perception of the health sciences students and their educators regarding deploying OSCEs within the School of Health Sciences and Techniques of Sousse (SHSTS) in Tunisia and their efficacity in healthcare education compared to traditional practical examination methods. This cross-sectional study was conducted in June 2022, focusing on final-year Health Sciences students at the SHSTS in Tunisia. The study participants were students and their educators involved in the OSCEs from June 6th to June 11th, 2022. Anonymous paper-based 5-point Likert scale satisfaction surveys were distributed to the students and their educators, with a separate set of questions for each. Spearman, Mann-Whitney U and Krusakll-Wallis tests were utilized to test the differences in satisfaction with the OSCEs among the students and educators. The Wilcoxon Rank test was utilized to examine the differences in students' assessment scores in the OSCEs and the traditional practical examination methods. The satisfaction scores were high among health sciences educators and above average for students, with means of 3.82 ± 1.29 and 3.15 ± 0.56, respectively. The bivariate and multivariate analyzes indicated a significant difference in the satisfaction between the students' specialities. Further, a significant difference in their assessment scores distribution in the practical examinations and OSCEs was also demonstrated, with better performance in the OSCEs. Our study provides evidence of the relatively high level of satisfaction with the OSCEs and better performance compared to the traditional practical examinations. These findings advocate for the efficacy of OSCEs in low-income countries and the need to sustain them.

The COVID-19 pandemic is a real global health crisis. Its clinical presentation has evolved over time with an increasing number of symptoms. Olfactory dysfunction (OD) has recently been recognized as a frequent symptom relevant to screening for COVID-19, especially in pauci-asymptomatic forms. However, the underlying mechanisms of OD are not yet fully understood. To determine the prevalence of OD in healthcare workers with SARS-CoV-2 and to identify its associated factors. This is a cross-sectional, analytical study, carried out during a period of six months and including all healthcare workers at Farhat Hached Academic Hospital (Tunisia) who were diagnosed with SARS-CoV-2 by PCR, RAT, or chest CT scan. A total of 474 healthcare workers were included, representing a participation rate of 85.4%. The mean age was 41.02±10.67 years with a sex ratio of 0.2. The distribution of this population by department noted that it was mainly maternity (13.9%). The most presented workstation was nursing (31.4%). OD represented 39.2% of the reasons for consultation. Hospitalization was indicated in 16 patients (3.4%). The average duration of hospitalization was 8.87 ± 7.8 days. The average time off work was 17.04 ± 11.6 days. OD persisted for more than 90 days in 35 patients (7.4%). After multiple binary logistic regression, OD was statistically associated with female gender (p =0.001; OR 95% CI: 2.46 [1.4-4.2]) and blue-collar occupational category (p =0.002; OR IC95%:3.1 [1.5-6.5]). A significant association was also noted between OD and professional seniority and absence from work duration (p =0.019; OR 95% CI: 0.97 [0.95-0.99] and p =0.03; OR 95% CI: 0.97 [0.95-0.99]) respectively. OD is common in COVID-19 patients. The identification of its associated factors may contribute to enhancing the understanding of its mechanism and drive therapeutic options.