Ecole Nationale d'Ingénieurs de Monastir

Power costs increasing, environmental pollution and global warming are issues that we are dealing with in the present time. To reduce their effects, scientists are focusing on improving energy harvesting-based power generators. Thermoelectric generators (TEGs) have demonstrated their ability to directly convert thermal energy into an electrical one via the Seebeck effect. Also, they are environmentally friendly because they do not contain chemical products, they operate silently because they do not have mechanical structures and/or moving parts, and they can be fabricated on many types of substrates like silicon, polymers, and ceramics. Furthermore, TEGs are position-independent, present a long operating lifetime and are suitable for integration into bulk and flexible devices. This paper presents in-depth analysis of TEGs, starting by an extensive description of their working principle, types (planar, vertical and mixed), used materials, figure of merit, improvement techniques including different thermoelectric materials arrangement (conventional, segmented and cascaded), and used technologies and substrates types (silicon, ceramics and polymers). This manuscript also describes the exploitation of TEGs in various fields starting from low-power applications (medical and wearable devices, IoT: internet of things, and WSN: wireless sensor network) to high-power applications (industrial electronics, automotive engines, and aerospace).

Constructing large wind power plants (WPPs) and solar photovoltaic plants (SPVPs) is a significant long-term investment. Due to the various conflicting factors involved in the selection process, determining the most appropriate locations prior to deploying such facilities is of paramount importance. In this paper, we propose a preliminary assessment of the most promising sites in Tunisia to host large-scale WPPs and SPVPs using geographical information systems (GIS) and multi-criteria decision-making (MCDM). A study of this kind, focusing on both resources, has not been conducted in Tunisia. An in-depth literature review has been conducted in order to determine suitability criteria as well as constraint factors. The analytical hierarchy process (AHP) is used to assign weights to the considered criteria; then, the final suitability maps are generated using the weighted overlay tool under ArcGis 10.8 software. The study findings indicate that there are large areas that are suitable for the deployment of large-scale WPPs and SPVPs, covering 3335 km2 (2.15 % of the total area) and 3815 km2 (2.57 %), respectively. The most suitable wind sites are scattered northwest to southwest, southeast, and to a lesser extent north. In contrast, ideal solar sites are mainly distributed in the central and southern regions of the country. Furthermore, it has been demonstrated that these designated locations are capable of providing an estimated annual energy of 40.896 TWh and 781.83 TWh for wind and solar, respectively. As a result of the adopted model in this study, policymakers could be more proactive in developing solar and wind farms. This would increase the possibility of Tunisia achieving its 2030 target.

This articles deals with unsteady MHD free convection flow of blood with carbon nanotubes. The flow is over an oscillating vertical plate embedded in a porous medium. Both single-wall carbon nanotubes (SWCNTs) and multiple-wall carbon nanotubes (MWCNTs) are used with human blood as base fluid. The problem is modelled and then solved for exact solution using the Laplace transform technique. Expressions for velocity and temperature are determined and expressed in terms of complementary error functions. Results are plotted and discussed for embedded parameters. It is observed that velocity decreases with increasing CNTs volume fraction and an increase in CNTs volume fraction increases the blood temperature, which leads to an increase in the heat transfer rates. A validation of the present work is shown by comparing the current results with existing literature.

The random forest (RF) classifier, which is a combination of tree predictors, is one of the most powerful classification algorithms that has been recently applied for fault detection and diagnosis (FDD) of industrial processes. However, RF is still suffering from some limitations such as the noncorrelation between variables. These limitations are due to the direct use of variables measured at nodes and therefore the only use of static information from the process data. Thus, this article proposes two enhanced RF classifiers, namely the Euclidean distance based reduced kernel RF (RK-RF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ED</sub> ) and K-means clustering based reduced kernel RF (RK-RF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Kmeans</sub> ), for FDD. Based on the kernel principal component analysis, the proposed classifiers consist of two main stages: feature extraction and selection, and fault classification. In the first stage, the number of observations in the training data set is reduced using two methods: the first method consists of using the Euclidean distance as dissimilarity metric so that only one measurement is kept in case of redundancy between samples. The second method aims at reducing the amount of the training data based on the K-means clustering technique. Once the characteristics of the process are extracted, the most sensitive features are selected. During the second phase, the selected features are fed to an RF classifier. An emulated grid-connected PV system is used to validate the performance of the proposed RK-RF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ED</sub> and RK-RF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Kmeans</sub> classifiers. The presented results confirm the high classification accuracy of the developed techniques with low computation time.

Abstract Numerical solutions of the Benjamin‐Bona‐Mahony‐Burgers equation in one space dimension are considered using Crank‐Nicolson‐type finite difference method. Existence of solutions is shown by using the Brower's fixed point theorem. The stability and uniqueness of the corresponding methods are proved by the means of the discrete energy method. The convergence in L ∞ ‐norm of the difference solution is obtained. A conservative difference scheme is presented for the Benjamin‐Bona‐Mahony equation. Some numerical experiments have been conducted in order to validate the theoretical results.© 2007 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2007

Abstract In the present research, aluminum oxide- water (Al 2 O 3 -H 2 O) nanofluid free convection due to magnetic forces through a permeable cubic domain with ellipse shaped obstacle has been reported. Lattice Boltzmann approach is involved to depict the impacts of magnetic, buoyancy forces and permeability on nanoparticles migration. To predict properties of Al 2 O 3 - water nanofluid, Brownian motion impact has been involved. Outcomes revels that considering higher magnetic forces results in greater conduction mechanism. Permeability can enhance the temperature gradient.

Microparticles (MPs) are small fragments generated from the plasma membrane after cell stimulation or apoptosis. We have recently shown that MPs harboring the morphogen Sonic Hedgehog (MPs(Shh+)) correct endothelial injury by release of nitric oxide from endothelial cells [Agouni, Mostefai, Porro, Carusio, Favre, Richard, Henrion, Martínez and Andriantsitohaina (2007) FASEB J., 21, 2735-2741]. Here, we show that MPs(Shh+) induce the formation of capillary-like structures in an in vitro model using human endothelial cells, although they inhibited cell migration. Besides, MPs(Shh+) regulate cell proliferation. Both cell adhesion and expression of proteins involved in this process such as Rho A and phosphorylation of focal-activated kinase were increased by MPs(Shh+), via a Rho-associated coiled-coil-containing protein kinase inhibitor-sensitive pathway. We demonstrate that MPs(Shh+) increase messenger RNA and protein levels of proangiogenic factors as measured by quantitative reverse transcription-polymerase chain reaction and western blot. In spite of vascular endothelial growth factor expression, conditioned media from endothelial cells treated avec MPs(Shh+) reduces angiogenesis. Interestingly, the effects induced by MPs(Shh+) on the formation of capillary-like structures, expression of adhesion molecules and proangiogenic factors were reversed after silencing of the Shh receptor, using small interfering RNA or when Sonic Hedgehog (Shh) signaling was pharmacologically inhibited with cyclopamine. Taken together, we show that Shh carried by MPs(Shh+) regulate angiogenesis probably through both a direct and an indirect mechanisms, and we propose that MPs harboring Shh may contribute to the generation of a vascular network in pathologies associated with tumor growth.

In recent years, the world has witnessed a significant increase in the number of elderly who often suffer from chronic diseases, and has witnessed in recent months a major spread of the new coronavirus (COVID-19), which has led to thousands of deaths, especially among the elderly and people who suffer from chronic diseases. Coronavirus has also caused many problems in hospitals, where these are no longer able to accommodate a large number of patients. This virus has also begun to spread between medical and paramedical teams, and this causes a major risk to the health of patients staying in hospitals. To reduce the spread of the virus and maintain the health of patients who need a hospital stay, home hospitalization is one of the best possible solutions. This paper proposes a home hospitalization system based on the Internet of Things (IoT), Fog computing, and Cloud computing, which are among the most important technologies that have contributed to the development of the healthcare sector in a significant way. These systems allow patients to recover and receive treatment in their homes and among their families, where patient health and the hospitalization room environmental state are monitored, to enable doctors to follow the hospitalization process and make recommendations to patients and their supervisors, through monitoring units and mobile applications developed for this purpose. The results of evaluation have shown great acceptance of this system by patients and doctors alike.

Random Forest (RF) is one of the mostly used machine learning techniques in fault detection and diagnosis of industrial systems. However, its implementation suffers from certain drawbacks when considering the correlations between variables. In addition, to perform a fault detection and diagnosis, the classical RF only uses the raw data by the direct use of measured variables. The direct raw data could yield to poor performance due to the data redundancies and noises. Thus, this paper proposes four improved RF methods to overcome the above-mentioned limitations. The developed methods aim to reduce at first the amount of the training data and select the first kernel principal components (KPCs) using different kernel principal component analysis (PCA) based dimensionality reduction schemes. Then, the retained KPCs are fed to the RF classifier for fault diagnosis purposes. Finally, the proposed techniques are applied to a wind energy conversion (WEC) system. Different case studies were investigated in order to illustrate the effectiveness and robustness of the developed techniques compared to the state-of-the-art methods. The obtained results show the low computation time and high diagnosis accuracy of the proposed approaches (an average accuracy of 91%).

Surface treatment before adhesive bonding is vital for improving both strength and durability of adhesively bonded joints by modifying surface characteristics. This article reviews the effect of surface texture on the strength of adhesive-bonded joints. It starts with a presentation of different adhesion mechanisms. Afterwards, the surface texture is classified into stochastic and structured surfaces, the effect of these textures on the wettability is then discussed. The influence of surface texture on quasi-static strength and fatigue behaviour of adhesively bonded joints is reviewed with a focus on the effect of structured surface parameters. This paper provides also an overview of the manufacturing process of structured surface texture for adhesive bonding applications. Finally, future trends in this research direction are highlighted and fundamental conclusions are drawn.

BACKGROUND: Microparticles (MPs) are vesicles released from plasma membrane upon cell activation and during apoptosis. Human T lymphocytes undergoing activation and apoptosis generate MPs bearing morphogen Shh (MPs(Shh+)) that are able to regulate in vitro angiogenesis. METHODOLOGY/PRINCIPAL FINDINGS: Here, we investigated the ability of MPs(Shh+) to modulate neovascularization in a model of mouse hind limb ischemia. Mice were treated in vivo for 21 days with vehicle, MPs(Shh+), MPs(Shh+) plus cyclopamine or cyclopamine alone, an inhibitor of Shh signalling. Laser doppler analysis revealed that the recovery of the blood flow was 1.4 fold higher in MPs(Shh+)-treated mice than in controls, and this was associated with an activation of Shh pathway in muscles and an increase in NO production in both aorta and muscles. MPs(Shh+)-mediated effects on flow recovery and NO production were completely prevented when Shh signalling was inhibited by cyclopamine. In aorta, MPs(Shh+) increased activation of eNOS/Akt pathway, and VEGF expression, being inhibited by cyclopamine. By contrast, in muscles, MPs(Shh+) enhanced eNOS expression and phosphorylation and decreased caveolin-1 expression, but cyclopamine prevented only the effects of MPs(Shh+) on eNOS pathway. Quantitative RT-PCR revealed that MPs(Shh+) treatment increased FGF5, FGF2, VEGF A and C mRNA levels and decreased those of α5-integrin, FLT-4, HGF, IGF-1, KDR, MCP-1, MT1-MMP, MMP-2, TGFβ1, TGFβ2, TSP-1 and VCAM-1, in ischemic muscles. CONCLUSIONS/SIGNIFICANCE: These findings suggest that MPs(Shh+) may contribute to reparative neovascularization after ischemic injury by regulating NO pathway and genes involved in angiogenesis.

The sensitivity of various solar photovoltaic technologies to dust, temperature, and relative humidity is investigated for Doha's environment. Results obtained show that monocrystalline photovoltaics (PVs) have efficiencies as high as 85% compared to 70% for amorphous ones. Also, dust accumulation degrades more critically the efficiency of amorphous and monocrystalline silicon PVs than the panel's temperature or relative humidity. In addition, the results show that amorphous PVs are more affected by temperature and relative humidity than monocrystalline PVs. However, amorphous PVs prove to be more robust against dust settlement than monocrystalline PVs and hence are more suitable for implementation in desert climates like Doha unless cleaning strategies are devised. It was estimated that 100 days of dust accumulation over monocrystalline PV panels, caused the efficiency to decrease by around 10%. This limitation makes solar PVs to represent an unreliable source of power for unattended or remote devices and thus strongly suggests the challenge of cleaning the panel's surface regularly or injecting technical modifications. Furthermore, the study suggests operating solar PV plants in Doha from 11:00 am to 02:00 pm to optimize production.

The solar energy conversion into electricity is a very promising technique, knowing that the source is free, clean and abundant in several countries. However, the effect of the solar cells temperature on the photovoltaic panel performance and lifespan remains one of the major disadvantages of this technology. In this work, we present an experimental study of a particular photovoltaic panel. It is self-cooled due to its open design which facilitates natural ventilation helping to improve its performance mainly in hot hours of the day and to avoid dust accumulation on its surface. This solar system is tested for two soil natures, white and gray, and for two inclination angles, 0∘ and 30°. Results show that the photovoltaic panel performs better when it is inclined and placed on a white soil. A 3D CFD model describing the performance of this solar system is then developed and a good agreement between the numerical results and experimental data is found. Similarly, this CFD model was used to compare the thermal performance of this solar system to that of the flat PV system and to show that its lower temperature allows better electrical production.

The global need to solve pollution problems has conducted automotive engineers to promote the development and the use of electric vehicle technologies. This paper focuses on the fuel cell hybrid electric vehicle which uses a proton exchange membrane fuel cell as a main source associated to hybrid storage device: lithium ion battery and ultracapacitors. A common interest in such technology is to spread out the energy flow between its different sources in order to satisfy the power demand for any requested mission. However, the challenging task stills the optimization of this split to reduce hydrogen consumption and respect, at the same time, the system limitations such as admissible limits of storage system capacities and battery current variation. An adaptive filtering-based energy management strategy is proposed in this paper to ensure an optimum distribution of the energy between the sources taking into account dynamic and energetic constraints of each device. For more performance, a fuzzy logic system is used to adapt the frequency of separation with the system state evolution. A sliding mode control is applied to control electric characteristics (voltage and currents) in the considered hybrid power supply. Simulation results, obtained under MATLAB®/SimPowerSystems® for four driving cycles are presented. The proposed strategy achieved good performances by respecting the ultracapacitors state of charge while preserving the battery lifetime under various driving missions.

Adhesively bonded technology is now widely accepted as a valuable tool in mechanical design, allowing the production of connections with a very good strength‐to‐weight ratio. The bonding may be made between metal–metal, metal–composite or composite–composite. In the automotive industry, elastomeric adhesives such as polyurethanes are used in structural applications such as windshield bonding because they present important advantages in terms of damping, impact, fatigue and safety, which are critical factors. For efficient designs of adhesively bonded structures, the knowledge of the relationship between substrates and the adhesive layer is essential. The aim of this work, via an experimental study, is to carry out and quantify the various variables affecting the strength of single-lap joints (SLJs), especially the effect of the surface preparation and adhesive thickness. Aluminium SLJs were fabricated and tested to assess the adhesive performance in a joint. The effect of the bondline thickness on the lap-shear strength of the adhesives was studied. A decrease in surface roughness was found to increase the shear strength of the SLJs. Experimental results showed that rougher surfaces have less wettability which is coherent with shear strength tests. However, increasing the adhesive thickness decreased the shear strength of SLJs. Indeed, a numerical model was developed to search the impact of increasing adhesive thickness on the interface of the adherend.

The area of ensemble learning has gained a wide attention from the scientific research community. Ensemble methods are techniques that aim to improve the accuracy of results in models by combining multiple models instead of using a single model. The objective of this article is to develop intelligent fault detection and diagnosis (FDD) frameworks in order to ensure the high-performance operation of Grid-Connected Photovoltaic (PV) systems based on improved ensemble learning approaches. Therefore, three ensemble learning-based fault detection and diagnosis techniques for Grid-Connected PV systems are proposed. First, an ensemble learning (EL) technique that combines predictions from Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT) is presented. The developed method will contribute to the reduction of the overall diagnosis error and will have the ability to combine various models. However, classical ensemble models ignore the time-dependence of PV measurements. In addition, the PV system data are frequently time-correlated. Accordingly, in the current work, the dynamic and multivariate nature of the measurements will be considered when designing the prediction models by using multivariate and dynamic techniques. To do these, kernel PCA (KPCA)-based EL and reduced KPCA (RKPCA)-based EL classifiers are developed. The two proposed techniques are addressed so that the features extraction and selection phases are performed using the KPCA and RKPCA models and the sensitive and significant characteristics are transmitted to the EL model for classification purposes. The presented results prove that the proposed methods offer enhanced diagnosis performances when applied to PV systems.

This paper deals with dynamic simulation of a directly driven wind generator with a full scale converter as interface to the grid. Using the Permanent Magnet Synchronous Generator (PMSG), the system is controlled by two control strategies. I the first step, we have consider the vector (VC) strategy and in the second one, we have applied the sliding mode control (SMC) strategy. Simulation results investigate good performances of both proposed non linear approaches.

The mechanical, optical, thermoelectric and thermodynamic characteristics of Cesium based bromides are systematically studied using the most comprehensive DFT based Wien2k code for elucidating the energy renewable device applications. The mechanical, thermodynamic and structural stabilities of the studied perovskites are revealed in terms of Born mechanical stability criteria, enthalpy of formation and Goldschmidt's tolerance factor, respectively. The ductile/brittle nature, anisotropy, wave velocity and Debye temperatures are computed to illustrate the mechanical nature. The optical parameters have been found highly sensitive to the exhibited band gap lying within visible energy. The large thermoelectric efficiency has been found that is responsible to exhibit optimum values of the power factors and the thermal conductivity. Furthermore, the specific heat capacity, Hall coefficient, magnetic susceptibility and electron density are also described in detail. Hence, the studied perovskites showing band gap in visible region have been found suitable for commercial applications in Solar cell, optoelectronic and thermoelectric devices.

Thermophysical properties such as latent heat, viscosity and melting temperature could be changed for different physical properties of dispersed nanoparticle such as size, shape, and concentration. In this study, Nanocomposites-Enhanced Phase Change Materials NePCM are formed by dispersing Aluminium (Al) and Copper (Cu) nanoparticles into paraffin wax in various mass fractions (0.1, 0.3, 0.6, 1, 2.5 and 5%). The impact on the thermophysical properties of paraffin wax by the nanoparticles is also investigated. Heat conduction and differential scanning calorimeter experiments are used to investigate the effects of different nanoparticle concentrations on the melting point, solidification point, and latent capacity of nanocomposites. Experimental results show that the dispersion of nanoparticles of Al and Cu can decrease the melting temperature and increase the solidification temperature of PCM. this dispersion could also be limited due to increase in dynamic viscosity of the NePCM. Furthermore, Al and Cu nanocomposites with mass fractions of 2% and 1%, respectively, show better enhancements in the thermal storage characteristics of the paraffin compared to the next higher mass fraction.

The problem of efficiently scheduling production jobs on several machines is an important consideration when attempting to make effective use of a multimachines system such as a flexible job shop scheduling production system (FJSP). In most of its practical formulations, the FJSP is known to be NP-hard [8][9], so exact solution methods are unfeasible for most problem instances and heuristic approaches must therefore be employed to find good solutions with reasonable search time. In this paper, two closely related approaches to the resolution of the flexible job shop scheduling production system are described. These approaches combine the Ant system optimisation meta-heuristic (AS) with local search methods, including tabu search. The efficiency of the developed method is compared with others.