This paper deals with the robust stabilization of infinite dimensional systems subjected to stochastic and deterministic perturbations. First, we give conditions providing the stability of the parameterized system. Then, we investigate the maximization of the stability radius by state feedback. We establish conditions for the existence of suboptimal controllers. Using these conditions we characterize the supreme achievable stability radius via an infinite dimensional Riccati equation.

Recently deep learning has shown significant achievement in the performance of many tasks, like natural language processing, image and speech recognition. Also, this improvement concerns multiple biometrics recognition systems. In this work, we focus on biometrics recognition, we present a stacked auto-encoder-based approach for various biometrics recognition, including Iris, Ear, palm-print, and face recognition. The proposed method allows training a neural network that includes two hidden layers for biometrics tasks. It runs in two steps, in the first one, each layer is trained individually in an unsupervised manner by auto-encoders, then the layers are stacked and trained in a supervised way. Experimental results on images, obtained from publicly available biometrics databases clearly demonstrate the benefit of using stacked auto-encoders as feature extraction and dimension reduction tools for biometrics recognition, as significant high accuracy rates are obtained over the four databases.

In this work, we studied an L-shaped frequency selective surface (FSS) by a method called Wave Concept Iterative Procedure (WCIP), this method developed from the Modal Fast Transformation (FMT) is based on the cross- formulation. wave and the solution obtained by an iterative procedure does not use the matrix to ensure convergence and the procedure is stopped when it arrives at convergence, for this geometry the results of a single resonance obtained by the WCIP method have a resonant frequency of 5.35 GHz with a band bandwidth of 2.3 GHz, when the structure is excited in the X direction, a frequency at 10.35 GHz with a bandwidth of 0.44 GHz when the structure is excited in the Y direction. The simulation of the results obtained by the WCIP method is compared with the results of the software HFSS 13.0 (High Frequency Structure Simulator), we find a good agreement.

Dans cette thèse, nous nous sommes intéressés à un modèle de gestion des connaissances des entreprises industrielles. Certaines tâches manufacturières impliquent un niveau élevé de connaissance tacite des opérateurs qualifiés. L'industrie a besoin des méthodes fiables pour la capture et l'analyse de ces connaissances tacites afin qu'elles puissent être partagées et sans aucune perte. Nous proposons, un modèle de gestion contenant deux processus de gestion, le premier processus est la capitalisation des connaissances basée sur une tâche industrielle. Nous avons utilisé une combinaison de deux méthodologies : une méthodologie d’ingénierie de connaissances CommonKADS et une méthodologie d’élicitation des connaissances MACTAK. Dans la phase de modélisation, nous avons utilisé deux différentes techniques de modélisation, une modélisation basée sur les connaissances d’expert et la deuxième une représentation ontologique. Ce modèle facilite la capture des connaissances d’experts et transforme les connaissances tacites en explicites avec une maximisation des règles de production. Le deuxième processus concerne le partage des connaissances à base d’une ontologie des Tâches Manufacturières MATO en identifiant un ensemble des concepts de fabrication et leurs relations, cette ontologie proposée facilite le partage des connaissances entre les tâches de fabrication et aide à partager et à réutiliser les connaissances durant l'exécution des tâches. Ensuite, une application proposée pour le diagnostic de système d’alarme dans une centrale thermique a été présentée pour démontrer l’importance et l’apport de l’ontologie.

In recent decades, vascular surgery has seen the arrival of endovascular techniques for the treatment of vascular diseases such as aortic diseases (aneurysms, dissections, and atherosclerosis). The 3D printing process by addition of material gives an effector of choice to the digital chain, opening the way to the manufacture of shapes and complex geometries, impossible to achieve before with conventional methods. This chapter focuses on the bio-design study of the thoracic aorta in adults. A bio-design protocol was established based on medical imaging, extraction of the shape, and finally, the 3D modeling of the aorta; secondly, a bio-printing method based on 3D printing that could serve as regenerative medicine has been proposed. A simulation of the bio-printing process was carried out under the software Simufact Additive whose purpose is to predict the distortion and residual stress of the printed model. The binder injection printing technique in a Powder Bed Printer (PBP) bed is used. The results obtained are very acceptable compared with the results of the error elements found.

Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a stand-alone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation artificial neural network the efficiency and the quality of the production of energy from photovoltaic is increased. Practical value. The proposed algorithm is validated by a dSPACE DS1104 for different operating conditions. All practice results confirm the effectiveness of our proposed algorithm.

Feed for word neural networks (FFNN) have attracted a great attention, in digital communication area. Especially they are investigated as nonlinear equalizers at the receiver, to mitigate channel distortions and additive noise. The major drawback of the FFNN is their extensive training. We present a new approach to enhance their training efficiency by adapting the activation function. Adapting procedure for activation function extensively increases the flexibility and the nonlinear approximation capability of FFNN. Consequently, the learning process presents better performances, offers more flexibility and enhances nonlinear capability of NN structure thus the final state kept away from undesired saturation regions. The effectiveness of the proposed method is demonstrated through different challenging channel models, it performs quite well for nonlinear channels which are severe and hard to equalize. The performance is measured throughout, convergence properties, minimum bit error achieved. The proposed algorithm was found to converge rapidly, and accomplish the minimum steady state value. All simulation shows that the proposed method improves significantly the training efficiency of FFNN based equalizer compared to the standard training one.

In this digital age embracing robotics across various areas of life, especially intellectual ones, have reaped great benefits owing to this modern technology. Therefore, the learning field has not remained unchanged given current evolutions as the schooling conditions have been improved through these smart devices. However, teachers still face some difficulties when choosing pedagogical methods and means for effective language learning for children. Thus, this paper aims to measure the effectiveness of social robots in facilitating children’s learning of a second language (L2). For this purpose, the term L2 learning and its subordinate concepts have been distinguished, and then the different methods of language learning were discussed. The latest research regarding social robots in the educational context was also discussed when reviewing the literature. An experimental study conducted on a sample of children illustrated that the use of the social robot significantly helped them in the L2 learning regarding the assimilation fast, retention, and correct pronunciation of its vocabulary. Finally, this study concludes that the social robot would be a good solution and recommends their widespread use in education given its role in improving the schooling conditions of children.

The aim of the present study was to evaluate age related changes in testicular histomorphometry and spermatogenic activity of bulls during their sexual development. A total of 36 bulls were selected and divided into four groups (n=9 in each) according to their age. Bulls included in Groups I, II, III and IV were 10, 12, 14 and 16 months old respectively. Left testes of bulls were subjected to histomorphometry after slaughter. Statistical analysis revealed that the secondary spermatocytes, round and elongated spermatids increased significantly (P˂0.05) with the age of bulls. Likewise, both sertoli and leydig cell numbers increased significantly (P˂0.05) with the age of bulls. However, the number of spermatogonia and primary spermatocytes did not change (P>0.05) due to age. The mean tubular diameter increased from 200.70±5.45 μm (10 months of age) to 227.30±9.16 μm (16 months of age) and the total volume of seminiferous tubule per testis from 69.63±1.50 % (10 months of age) to 84.64±2.53 % (16 months of age). A positive linear relationship (P<0.05) was found between meiotic index (Y) and the age (X, in month), which was characterized by the equation 0.048X+3.135 and a coefficient of correlation (R) of 0.396. The correlation between age and sertoli cell efficiency was 0.482 with a regression equation Y= 0.141X+7.696. It is concluded that histomorphometric parameters of the bulls’ testes and spermatogenic activity are correlated with the age, so these parameters provide a reliable tool for the assessment of the reproductive state and sperm production capacity of a bull in a breeding program.

This work is a study of the elastic fields’ effect (stresses and displacements) caused by dislocations networks at a heterostructure interface of a InAs / GaAs semiconductors thin system in the cases of isotropic and anisotropic elasticity. The numerical study of this type of heterostructure aims to predict the behavior of the interface with respect to these elastic fields satisfying the boundary conditions. The method used is based on a development in Fourier series. The deformation near the dislocation is greater than the other locations far from the dislocation.     

Mastitis in cows is a major problem in dairy farms leading to a decrease in the quantity and quality of milk. The aim of the present study was to examine the association between the presence of Escherichia coli (E. coli) in milk and the subclinical mastitis, and to characterize the antibiotic resistance profiles of the isolated E. coli. In the current study, a total of 360 cow raw milk samples from three dairy farms of the region of Algiers were analyzed. The analysis period lasted from Spring 2017 to Winter 2019. The California Mastitis Test (CMT) was applied to detect subclinical mastitis. The E. coli strains were isolated from milk using conventional bacteriological methods. The antibiotic resistance profile of the isolated E. coli strains to 12 different antibiotics was tested using the disk diffusion method. On β-lactamase-producing strains, a double diffusion test was applied to identify the Extendedspectrum β-lactamase (ESBL) phenotype. Finally, the ctXx-M genes were amplified by PCR. Two-thirds (66.4%) of the milk samples were positive for the CMT test. A total of 97 E. coli strains were isolated from the milk samples, their resistance to antibiotics was tested, and 3.1% of the strains were resistant to trimethoprim-sulfamethoxazole, 6.2% to chloramphenicol, 12.3% to gentamicin, 13.4% to colistin, 23.3% to amoxicillin/clavulanate, 31.9% to kanamycin, 39.2% to enrofloxacin, 51.5% to cefotaxime, 52% to tetracycline, 57.7% to ampicillin, 74.3% to nalidixic acid, and 75.3% to amoxicillin. Furthermore, most of the E. coli strains (92.8%) were resistant to more than one antibiotic with a Multiple Antibiotic Resistance index ranging from 0 to 0.8. The 50 strains resistant to cefotaxime were analyzed for an ESBL phenotype. 39 of them (78%) were positive to the double-disk synergy test. Among the 39 ESBL positive strains, 27 (69.2%) were confirmed for the presence of a CTX-M gene by PCR. The present study showed that multiple drug-resistant E. coli, including ESBL-carriers, were frequently isolated from the milk of dairy cows in Algeria. The results underlined that the use of antibiotics on farms must be reasoned to avoid the spread of resistant strains in animals and human populations.

The work presented here was aimed to investigate the in vivo anti-inflammatory and in vitro hemostatic activities of Linaria reflexa extract and to establish the relationship between its bioactivity and chemical composition. Twenty-three secondary metabolites were identified, most of them are good anti-inflammatory agents, in line with data by carrageenin-induced rat paw edema assays of the n-butanol extract showing high anti-inflammatory inhibition (63.90%) of edema swelling in the rat paw at the dose 200 mg/kg after 4 h. Furthermore, both extent of inflammatory response and tissue injury were prevented keeping the levels of rate myeloperoxidase (60.16%) and of malondialdehyde, which is the final product of lipid peroxidation generated by free radicals (58.58%). The same extract showed also a remarkable hemostatic effect established by measuring the coagulation time of decalcified plasma (45 s), related to its flavonoid glycosides content.

The objective of the present study was to determine the pharmacological properties of the methanolic (MeOH) extract of Marrubium deserti leaves. For this purpose, antioxidant activity was carried out by DPPH and Ferric reducing power (FRAP) assays respectivelywhile In vivoanti-inflammatory activity was tested by carrageenan-induced paw edema model. The Phytochemical investigation revealed the presence of several biocompounds, and total phenolic and flavonoidcontents were also determined to support our results and revealed a high proportions of polyphenols (184 ± 0.78 mg GAE/g extract) and flavonoids (28.48 ± 0.40 mg QE/g extract). The MeOH extract demonstrated great pharmacological properties with a dose-effect relationship. Thus, a great antioxidant effect was recorded in both DPPH and FRAP assayswith a respective IC50 of (15.1 μg/ml) and (80.01 ± 1 μg EAA/g of extract) and were considered significant (P<0.05) when compared to respective standards. On the other hand, anti-inflammatory results suggested that the plant extract could effectively oppose the inflammation caused by carrageenan at the dose of 200 mg/kg with significant decrease (84.1 %) of inflammation. These encouraging results suggest that our plant could be a good candidate to treat more effectively pathologies related to oxidative stress and inflammation.

This experimental study aims to investigate the effects of the strengthening arrangement on the behavior and the performance of strengthened beams, according to the Near-Surface Mounted (NSM) and the Externally Bonded Reinforcement (EBR) techniques. In total five rectangular beams including a Control Beam (CB) and four Carbon Fiber Reinforced Polymer (CFRP) strengthened beams with NSM and EBR techniques. The beams were tested to failure in Four-Point Bending (FPB) test. The experimental program comprises two beams strengthened by one and two strips according to the NSM technique. Two other beams were strengthened by the same configuration with the EBR, whereas the last beam was un-strengthened and considered as the CB. The responses of control and strengthened beams were compared. The efficiency and the effectiveness of different CFRP configurations were evaluated. The test results showed that the flexural load capacity, the deflection, the ductility and the stiffness of strengthened beams increased with increasing of plates distribution. This increase was more significant for the EBR technique than the NSM. This paper also highlights the beams failure modes due to the different configurations of strengthening. The obtained results revealed that the crack patterns were affected by the arrangement of the strips.

Seismic fragility curves are considered an effective tool for the evaluation of the behavior of interaction of the soil-pile-structure (ISPS) subjected to earthquake loading. In this research, in order to better understand the ISPS effect, a nonlinear static analysis is applied with a variation of the vertical load, the diameter of pile, and finally the longitudinal steel ratio of the pile in different types of sand (loose, medium, dense) to obtain the capacity curves of each parameter for elaborating the curves of fragility. After a comparison of fragility curves of these parameters, it appears that the effect of the ISPS system is advantageous with respect to the vertical axial load and the diameter of pile, while the longitudinal ratio of the pile depending on the ductility and the lateral resistance of the ISPS system. The proposed equation is intended to help engineers in the design and performance of the soil-pile-structure interaction. The results of this equation provided a convergence with the results of the fragility curves.

History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT image segmentation is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art segmentation approaches, namely GraphCut, Medical Image Segmentation (MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98, 0.73, 0.71, 0.73, 0.71, 0.71, 0.57, 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward.

Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.