Showing papers by "Tunis University published in 2021"

A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa.

Abstract: The progression of the SARS-CoV-2 pandemic in Africa has so far been heterogeneous and the full impact is not yet well understood. Here, we describe the genomic epidemiology using a dataset of 8746 genomes from 33 African countries and two overseas territories. We show that the epidemics in most countries were initiated by importations predominantly from Europe, which diminished following the early introduction of international travel restrictions. As the pandemic progressed, ongoing transmission in many countries and increasing mobility led to the emergence and spread within the continent of many variants of concern and interest, such as B.1.351, B.1.525, A.23.1 and C.1.1. Although distorted by low sampling numbers and blind spots, the findings highlight that Africa must not be left behind in the global pandemic response, otherwise it could become a source for new variants.

Alarming coastal vulnerability of the deltaic and sandy beaches of North Africa.

Abstract: The arid coasts of North Africa, extending over 4633 km from the Gulf of Tunis to the Nile Delta, are undergoing pronounced shoreline retreats and coastal floodings that are reported as a consequence of the ongoing sea level rise resulting from global warming. Of particular interest are the abnormal shoreline dynamics for deltaic and sandy beaches, which are severely impacted by abrupt decadal variabilities in both climatic and anthropogenic drivers resulting in their increased vulnerability to disturbances from coastal hazards. Unfortunately, the evolution, distribution and impacts of these drivers remain largely unquantified, let alone understood, for these extensive arid coasts that harbor the major portion of North Africa’s population as well as unique and fragile marine ecosystems. To address this deficiency, we use GIS-based multi-criteria approaches combined with analytic hierarchy process to map the Coastal Vulnerability Index and the Socioeconomic Vulnerability Index along these coasts to investigate the amplitude and extent of shoreline deterioration resulting from sudden fluctuations in sediment transport to the coastline. We use the western bay of the Gulf of Tunis, the coasts of Tripoli and the Nile Delta as three validation sites for our vulnerability assessment. The statistical Integrated Coastal Vulnerability Index map reveals that 47% of arid North African coasts are characterized by high to very high vulnerability. In particular, we observe that the densely populated deltaic coasts in both Tunisia and Egypt are 70% more vulnerable than any others coast in the eastern Mediterranean Basin. These abnormally high-vulnerability extensive areas are also correlated with significant deterioration of coastal aquifers and hence in crop production, compromising local food security and resulting in increasing outflow migration trends. Both Tunisia and Egypt observed dramatic increases in the net population outflow migration by respectively 62% and 248% between 2000 and 2016, mostly from coastal areas. Our source analysis of the amplitude and extent of these high coastal vulnerabilities suggests that they result from the anthropogenic drivers of damming and rapid urban growth over the last few decades rather than the effects of global warming.

UVC disinfection robot.

Abstract: The aim of the present work is to contribute in the fight against the spread of Covid-19, a novel human coronavirus, in hospitals, public transport, airlines, and any enclosed areas. In this study, we have adopted the physical disinfection method by using UVC light as agent. The UVC devices are studied and classified according their disinfectant units, complementary devices, combined disinfection agents, mobilities, and order types. Our finding shows that a mobile robot is the most efficient device to inactivate microorganisms, so we have developed a robot called i-Robot UVC. The robot is equipped with eight UVC lamps around a central column and two lamps on the top. The column is fixed on a mobile base where several sensors are integrated to measure temperature and humidity on the one hand, and on the other, to detect motion plus position and to avoid obstacles. The robot can estimate automatically the disinfection time while monitored by Wi-Fi connection from a phone or a tablet. I-Robot UVC disinfects rooms and equipment with ultraviolet light, and shuts down when humans are around to keep them safe. The robot can kill 99,999% bacteria and various through UVC lamps led. The innovative robot UVC was patented under the number TN2020/0063.

Cancer in Africa: The Untold Story

Abstract: Background Despite rising incidence and mortality rates in Africa, cancer has been given low priority in the research field and in healthcare services. Indeed, 57% of all new cancer cases around the world occur in low income countries exacerbated by lack of awareness, lack of preventive strategies, and increased life expectancies. Despite recent efforts devoted to cancer epidemiology, statistics on cancer rates in Africa are often dispersed across different registries. In this study our goal included identifying the most promising prevention and treatment approaches available in Africa. To do this, we collated and analyzed the incidence and fatality rates for the 10 most common and fatal cancers in 56 African countries grouped into 5 different regions (North, West, East, Central and South) over 16-years (2002-2018). We examined temporal and regional trends by investigating the most important risk factors associated to each cancer type. Data were analyzed by cancer type, African region, gender, measures of socioeconomic status and the availability of medical devices. Results We observed that Northern and Southern Africa were most similar in their cancer incidences and fatality rates compared to other African regions. The most prevalent cancers are breast, bladder and liver cancers in Northern Africa; prostate, lung and colorectal cancers in Southern Africa; and esophageal and cervical cancer in East Africa. In Southern Africa, fatality rates from prostate cancer and cervical cancer have increased. In addition, these three cancers are less fatal in Northern and Southern Africa compared to other regions, which correlates with the Human Development Index and the availability of medical devices. With the exception of thyroid cancer, all other cancers have higher incidences in males than females. Conclusion Our results show that the African continent suffers from a shortage of medical equipment, research resources and epidemiological expertise. While recognizing that risk factors are interconnected, we focused on risk factors more or less specific to each cancer type. This helps identify specific preventive and therapeutic options in Africa. We see a need for implementing more accurate preventive strategies to tackle this disease as many cases are likely preventable. Opportunities exist for vaccination programs for cervical and liver cancer, genetic testing and use of new targeted therapies for breast and prostate cancer, and positive changes in lifestyle for lung, colorectal and bladder cancers. Such recommendations should be tailored for the different African regions depending on their disease profiles and specific needs.

Design of a PID optimized neural networks and PD fuzzy logic controllers for a two‐wheeled mobile robot

Abstract: In this paper, two intelligent techniques for a two‐wheeled differential mobile robot are designed and presented: A smart PID optimized neural networks based controller (SNNPIDC) and a PD ...

Acute respiratory distress syndrome: a life threatening associated complication of SARS-CoV-2 infection inducing COVID-19.

Abstract: Acute Respiratory Distress Syndrome (ARDS) is a form of respiratory failure in human. The number of deaths caused by SARS-CoV-2 infection inducing this severe pneumonia (ARDS) is relatively high. In fact, COVID-19 might get worsen in ARDS and provoke respiratory failure. A better understood of ARDS key features and the pathophysiological injuries of the pulmonary parenchyma are linked to lessons learned from previous severe diseases associated previous coronaviruses outbreaks (especially SARS-CoV and MERS-CoV) and more the ongoing SARS-CoV-2. The ARDS mechanism includes a diffuse alveolar damage associated disruption of alveolar capillary membrane, pulmonary edema, damaged endothelium and increased permeability. A diffuse inflammation, with acute onset, on the lung tissue accompanied by release of biochemical signal and inflammatory mediators (TNFα, IL-1 and IL-6) leading to hypoxemia, low PaO2/FiO2 ratio and the chest radiological expression of bilateral infiltrates in ARDS. The ongoing outbreak could lead to a better understood of ARDS pathophysiology and prognostic. An overview is also highlighted about the seven coronaviruses proved to infect human especially those having ability to cause severe disease SARS-CoV, MERS-CoV and SARS-CoV-2. In this review, we focused on the major pathological mechanisms leading to the ARDS development as a result of viral infection, severe COVID-19 worsening. Communicated by Ramaswamy H. Sarma.

Modeling of ZnO/MoS2/CZTS photovoltaic solar cell through window, buffer and absorber layers optimization

Abstract: In this work, we propose a feasible ZnO/MoS2/CZTS structure where the classical CdS buffer layer is replaced by a molybdenum disulfide buffer layer. This proposed solar cell is investigated thereafter using SCAPS code so as to determine the optimal value of each layer thickness constituting the cell of interest. The different photovoltaic parameters are determined versus each layer thickness. Our solar cell shows a conversion efficiency of about 23.69%. This corresponds to optimal thicknesses of 0.1 μm for ZnO window layer, 0.2 μm for MoS2 buffer layer and 1 μm for CZTS absorber layer and common doping concentrations of 1018 cm−3 for each layer. This conversion efficiency compares reasonably with those previously quoted in the literature.

Dieless Friction Stir Extrusion-Brazing (DFSE-B) of AA2024-T3 aluminum alloy to Copper with Zn interlayer

Abstract: The integration of flow-induced mechanical interlocking, interlayer-controlled atomic diffusion and self-reacting brazing benefit is employed to achieve an improved load-bearing performance of the dissimilar AA2024-T3/copper joint via the use of Dieless Friction Stir Extrusion-Brazing (DFSE-B). A comparative study of the Dieless Friction Stir Extrusion-Brazing (DFSE-B) and Friction Stir Spot Welding-Brazing (FSSW-B) of the dissimilar AA2024-T3/Cu joint was investigated by using a 100 µm thick Zn interlayer. The microstructure, tensile-shear load, and fracture modes of the respective joints were examined. The results show that Zn solidification-induced voids are inevitable at the brazed zones of both joints and the DFSE-B process suppresses flow-induced defect, unlike the FSSW-B process. An improved tensile-shear load with a significant toughness is achieved by the DFSE-B process due to the absence of tool-induced keyhole, and the intrinsic mechanical interlocking of the softer Al alloy into the harder Cu alloy. The DFSE-B is a suitable welding alternative for reactive metals.

Influence of COVID-19 on lifestyle behaviors in the Middle East and North Africa Region: a survey of 5896 individuals.

Abstract: Coronavirus disease (COVID-19) pandemic has affected health and lifestyle behaviors of people globally. This project aims to identify the impact of COVID-19 on lifestyle behavior of individuals in the Middle East and North Africa (MENA) region during confinement. We conducted an online survey in 17 countries (Egypt, Jordan, United Arab Emirates, Kuwait, Bahrain, Saudi Arabia, Oman, Qatar, Yemen, Syria, Palestine, Algeria, Morocco, Libya, Tunisia, Iraq, and Sudan) from the MENA region on August and September 2020. The questionnaire included self-reported information on lifestyle behaviors, including physical activity, eating habits, smoking, watching television, social media use and sleep before and during the pandemic. Logistic regression was performed to analyze the impact of COVID-19 on lifestyle behaviors. A total of 5896 participants were included in the final analysis and 62.8% were females. The BMI of the participants was 25.4 ± 5.8 kg/m2. Around 38.4% of the participants stopped practicing any physical activities during the confinement (P < 0.001), and 57.1% reported spending more than 2 h on social media (P < 0.001). There were no significant changes in smoking habits. Also, 30.9% reported an improvement in their eating habits compared with 24.8% reported worsening of their eating habits. Fast-food consumption decreased significantly in 48.8% of the study population. This direct/indirect exposure to COVID-19 was associated with an increased consumption of carbohydrates (OR = 1.09; 95% CI = 1.02–1.17; P = 0.01), egg (OR = 1.08; 95% CI = 1.02–1.16; P = 0.01), sugar (OR = 1.09; 95% CI = 1.02–1.16; P = 0.02), meat, and poultry (OR = 1.13; 95% CI = 1.06–1.20; P < 0.01). There was also associated increase in hours spent on watching television (OR = 1.07; 95% CI = 1.02–1.12; P < 0.01) and social media (OR = 1.09; 95% CI = 1.01–1.18; P = 0.03). However, our results showed a reduction in sleeping hours among those exposed to COVID-19 infection (OR = 0.85; 95% CI = 0.77–0.94; P < 0.01). The COVID-19 pandemic was associated with an increase in food consumption and sedentary life. Being exposed to COVID-19 by direct infection or through an infected household is a significant predictor of amplifying these changes. Public health interventions are needed to address healthy lifestyle behaviors during and after the COVID-19 pandemic.

Neuroprotective benefits of grape seed and skin extract in a mouse model of Parkinson's disease

Abstract: Parkinson's disease is a neurodegenerative disorder characterized by the progressive loss of midbrain dopaminergic (mDA) neurons in the substantia nigra pars compacta, and it involves oxidative stress. Our goal was to evaluate the neuroprotective effect of Vitis vinifera red grape seed and skin extract (GSSE) in a model of Parkinson's disease. GSSE is very rich in phenolic compounds, such as flavonoids, anthocyanins, catechins and stilbenes, which are present in the pulp, seeds, and leaves of the fruit. GSSE is known for its antioxidant properties and has shown beneficial effects against oxidative injury in different organs, such as the kidneys, liver, heart and brain. In this study, we revealed the neuroprotective effect of GSSE on midbrain dopaminergic neurons both in vitro and in vivo. We used the neurotoxin 6-hydroxydopamine (6-OHDA), which induces oxidative damage and mimics the degeneration of dopaminergic neurons observed in Parkinson's disease. We found that GSSE was effective in protecting dopamine neurons from 6-OHDA toxicity by reducing apoptosis, the level of reactive oxygen species (ROS) and inflammation. Furthermore, we found that GSSE treatment efficiently protected against neuronal loss and improved motor function in an in vivo 6-OHDA model of Parkinson's disease (PD). Altogether, our results show that GSSE acts at multiple levels to protect dopamine neurons from degeneration in a model of PD.

Service-oriented replication strategies for improving quality-of-service in cloud computing: a survey

Abstract: The recent years have witnessed significant interest in migrating different applications into the cloud platforms. In this context, one of the main challenges for cloud applications providers is how to ensure high availability of the delivered applications while meeting users’ QoS. In this respect, replication techniques are commonly applied to efficiently handle this issue. From the literature, according to the used granularity for replication there are two major approaches to achieve replication: either through replicating the service or the underlying data. The latter one is also known as Data-oriented Replication (DoR), while the former one is referred to as Service-oriented Replication (SoR). DoR is discussed extensively in the available literature and several surveys are already published. However, SoR is still at its infancy and there is a lack of research studies. Hence, in this paper we present a comprehensive survey of SoR strategies in cloud computing. We propose a classification of existing works based on the research methods they use. Then, we carried out an in-depth study and analysis of these works. In addition, a tabular representation of all relevant features is presented to facilitate the comparison of SoR techniques and the proposal of new enhanced strategies.

Convolutional neural network for smoke and fire semantic segmentation

Abstract: In recent decades, global warming has contributed to an increase in the number and intensity of wildfires destroying millions hectares of forest areas and causing many casualties each year. Firemen must therefore have the most effective means to prevent any wildfire from breaking out and to fight the blaze before being unable to contain and extinguish it. This article will present a new network architecture based on Convolutional Neural Network to detect and locate smoke and fire. This network generates fire and smoke masks in an RGB image by segmentation. The purpose of this work is to help firemen in assessing the extent of fire or monitor an incipient fire in real time with a camera embedded in a vehicle. To train this network, a database with the corresponding images and masks has been created. Such a database will allow to compare the performances of different networks. A comparison of this network with the best segmentation networks such as U-Net and Yuan networks has highlighted its efficiency in terms of location accuracy, reduction of false positive classifications such as clouds or haze. This architecture is also efficient in real time.

UTM-Chain: Blockchain-Based Secure Unmanned Traffic Management for Internet of Drones.

Abstract: Unmanned aerial systems (UAVs) are dramatically evolving and promoting several civil applications. However, they are still prone to many security issues that threaten public safety. Security becomes even more challenging when they are connected to the Internet as their data stream is exposed to attacks. Unmanned traffic management (UTM) represents one of the most important topics for small unmanned aerial systems for beyond-line-of-sight operations in controlled low-altitude airspace. However, without securing the flight path exchanges between drones and ground stations or control centers, serious security threats may lead to disastrous situations. For example, a predefined flight path could be easily altered to make the drone perform illegal operations. Motivated by these facts, this paper discusses the security issues for UTM’s components and addresses the security requirements for such systems. Moreover, we propose UTM-Chain, a lightweight blockchain-based security solution using hyperledger fabric for UTM of low-altitude UAVs which fits the computational and storage resources limitations of UAVs. Moreover, UTM-Chain provides secure and unalterable traffic data between the UAVs and their ground control stations. The performance of the proposed system related to transaction latency and resource utilization is analyzed by using cAdvisor. Finally, the analysis of security aspects demonstrates that the proposed UTM-Chain scheme is feasible and extensible for the secure sharing of UAV data.

A bilinear convolutional neural network for lung nodules classification on CT images.

Abstract: Lung cancer is the most frequent cancer worldwide and is the leading cause of cancer-related deaths. Its early detection and treatment at the stage of a lung nodule improve the prognosis. In this study was proposed a new classification approach named bilinear convolutional neural network (BCNN) for the classification of lung nodules on CT images. Convolutional neural network (CNN) is considered as the leading model in deep learning and is highly recommended for the design of computer-aided diagnosis systems thanks to its promising results on medical image analysis. The proposed BCNN scheme consists of two-stream CNNs (VGG16 and VGG19) as feature extractors followed by a support vector machine (SVM) classifier for false positive reduction. Series of experiments are performed by introducing the bilinear vector features extracted from three BCNN combinations into various types of SVMs that we adopted instead of the original softmax to determine the most suitable classifier for our study. The method performance was evaluated on 3186 images from the public LUNA16 database. We found that the BCNN [VGG16, VGG19] combination with and without SVM surpassed the [VGG16]2 and [VGG19]2 architectures, achieved an accuracy rate of 91.99% against 91.84% and 90.58%, respectively, and an area under the curve (AUC) rate of 95.9% against 94.8% and 94%, respectively. The proposed method improved the outcomes of conventional CNN-based architectures and showed promising and satisfying results, compared to other works, with an affordable complexity. We believe that the proposed BCNN can be used as an assessment tool for radiologists to make a precise analysis of lung nodules and an early diagnosis of lung cancers.

A Systematic Review of Systematic Reviews on Blended Learning: Trends, Gaps and Future Directions.

Abstract: Blended Learning (BL) is one of the most used methods in education to promote active learning and enhance students' learning outcomes. Although BL has existed for over a decade, there are still several challenges associated with it. For instance, the teachers' and students' individual differences, such as their behaviors and attitudes, might impact their adoption of BL. These challenges are further exacerbated by the COVID-19 pandemic, as schools and universities had to combine both online and offline courses to keep up with health regulations. This study conducts a systematic review of systematic reviews on BL, based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, to identify BL trends, gaps and future directions. The obtained findings highlight that BL was mostly investigated in higher education and targeted students in the first place. Additionally, most of the BL research is coming from developed countries, calling for cross-collaborations to facilitate BL adoption in developing countries in particular. Furthermore, a lack of ICT skills and infrastructure are the most encountered challenges by teachers, students and institutions. The findings of this study can create a roadmap to facilitate the adoption of BL. The findings of this study could facilitate the design and adoption of BL which is one of the possible solutions to face major health challenges, such as the COVID-19 pandemic.

Post-COVID-19 Syndrome: Nine Months after SARS-CoV-2 Infection in a Cohort of 354 Patients: Data from the First Wave of COVID-19 in Nord Franche-Comté Hospital, France.

Abstract: (1) Background. Post-COVID-19 syndrome is defined as the persistence of symptoms after confirmed SARS-CoV-2 infection. (2) Methods. ANOSVID is an observational retrospective study in Nord Franche-Comte Hospital in France that included adult COVID-19 patients confirmed by RT-PCR from 1 March 2020 to 31 May 2020. The aim was to describe patients with post-COVID-19 syndrome with persistent symptoms (PS group) and to compare them with the patients without persistent symptoms (no-PS group). (3) Results. Of the 354 COVID-19 patients, 35.9% (n = 127) reported persistence of at least one symptom after a mean of 289.1 ± 24.5 days after symptom onset. Moreover, 115 patients reported a recurrence of symptoms after recovery, and only 12 patients reported continuous symptoms. The mean age of patients was 48.6 years (19–93) ± 19.4, and 81 patients (63.8%) were female. Patients in the PS group had a longer duration of symptoms of initial acute SARS-CoV-2 infection than patients in the no-PS group (respectively, 57.1 ± 82.1 days versus 29.7 ± 42.1 days, p < 0.001). A majority of patients (n = 104, 81.9%) reported three or more symptoms. The most prevalent persistent symptoms were loss of smell (74.0%, n = 94), fatigue (53.5%, n = 68), loss of taste (31.5%, n = 40), and dyspnea (30.7%, n = 39). These were followed by pain symptoms (26.8% (n = 34), 26.0% (n = 33), 24.4% (n = 31); headache, arthralgia, and myalgia, respectively). More than half of patients reporting persistent symptoms (58%, n = 73) were healthcare workers (HCWs). Among outpatients, this population was more present in the PS group than the no-PS group ((86.6%) n = 71/82 versus (72.2%) n = 109/151, p = 0.012). Post-COVID-19 syndrome was more frequent in patients with a past history of chronic rhinosinusitis (8.7% (n = 11%) versus 1.3% (n = 3), p < 0.001). No significant difference was found regarding clinical characteristics and outcome, laboratory, imaging findings, and treatment received in the two groups. (4) Conclusions. More than a third of our COVID-19 patients presented persistent symptoms after SARS-CoV-2 infection, particularly through loss of smell, loss of taste, fatigue, and dyspnea, with a high prevalence in HCWs among COVID-19 outpatients.

Living With Bipolar Disorder in the Time of Covid-19: Biorhythms During the Severe Lockdown in Cagliari, Italy, and the Moderate Lockdown in Tunis, Tunisia

Abstract: Background: Restrictions during Covid-19 pandemic lockdown, in which rhythms of life have been compromised, can influence the course of bipolar disorder (BD). This study follows patients with bipolar disorder living in two geographically close cities (Cagliari and Tunis), but with different lockdown conditions: less severe in Tunis. Methods: Two cohorts were evaluated during lockdown (April 2020, t0) and 2 months later with lockdown lifted for a month (t1). Individuals were: over 18 years old without gender exclusion, BD I or II, in care for at least 1 year, received a clinical interview in the month before the start of the lockdown, stable clinically before the lockdown. The assessment was conducted by telephone by a psychiatrist or psychologist with good knowledge of patients. Diagnoses were made according to DSM-5 criteria. Depressive symptoms were collected through the Hamilton Rating Scale for Depression; cut-off 14 indicative of depressive episode. Circadian rhythms were measured using the BRIAN scale. Results: Forty individuals in Cagliari (70%female, age 48.57 ± 11.64) and 30 in Tunis (53.3% Female, age 41.8 ± 13.22) were recruited. In Cagliari at t0 45% had depressive episodes against none in Tunis, a similar difference appeared at t1. At t0 and t1 the Cagliari sample had more dysfunctional scores in the overall BRIAN scale and in the areas of sleep, activities and social rhythms; no differences were found in nutrition, both samples had predominantly nocturnal rhythm. In Cagliari at t0 and t1, the depressive sub-group showed more dysfunctional scores in the BRIAN areas sleep, activity, and nutrition. However, the differences in biological rhythms resulted, through ANCOVA analysis, independent of the co-presence of depressive symptoms. Discussion: A rigid lockdown could expose people with BD to depressive relapse through dysregulation of biological rhythms. The return to more functional rhythms did not appear 1 month after lockdown. The rekindling of the pandemic and the restoration of new restrictive measures will prevent, at least in the short term, the beneficial effect of a return to normality of the two cohorts. This was a limited exploratory study; future studies with larger samples and longer observational time are needed to verify the hypothesis.

Design of an Ultra-Compact and Highly-Sensitive Temperature Sensor Using Photonic Crystal Based Single Micro-Ring Resonator and Cascaded Micro-Ring Resonator

Abstract: In the present report, a photonic crystal based micro-ring resonator (MRR) structure is proposed which is very compact in size and has very fast response and is employed for temperature sensing purpose. Temperature sensing application for both the single MRR and cascaded MRR is illustrated in this paper. The sensitivity of the reported structure is increased from 2.9 nm/°C to 3.4 nm/°C by cascading two MRR. The refractive index of the material is subjected to change with the variation in temperature which results in the shift of the resonant wavelength of the proposed sensor. The finite difference time domain (FDTD) simulation is utilized to see the transmission spectrum of the proposed structure and analyzing the shift in the resonance wavelength the temperature is calculated. The proposed design is simple, reliable and may be integrated into different transducer and sensing applications.

Improvement of mechanical and wear behavior by the development of a new tool for the friction stir processing of Mg/B4C composite

Abstract: Large and irregular particle sizes of B4C in the Mg matrix are performance-impeding challenges of the as-cast Mg-B4C composites. In an attempt to overcome this, the secondary solid-state friction stir processing of the as-cast Mg-10%B4C composite with a flow-enhancing double-pin tool was carried out and the ensuing result was compared with that of a single-pin tool. The microstructure, hardness, tensile strength, wear, and the fractured surface of the processed composites were investigated and compared. The extra pin-shearing effect and the complex pin-induced interactive material flow of the double-pin tool induce better refinement of the B4C particles in the Mg-10%B4C composite. The use of a double-pin tool increases the stirred and recrystallized vortex/swirl width, kernel average misorientation (KAM) fraction, dislocation density, hardness value at the stirred center (117 HV), and tensile strength (194 MPa) of the Mg-10%B4C composite as compared to the single-pin tool. The double-pin tool changes the fracture path of the composite away from the stirred center owing to the improved material flow and properties of the stirred center. The tribological properties (weight loss, wear rate, and coefficient of friction) of the processed composites are equally improved by the double-pin tool. A double-pin tool is thus recommended for the improvement of material flow, particle-disintegration, mechanical and tribological properties of Mg-based metal matrix composite.