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.

This work deals with the study and performance improvement of the direct power control of DFIG based on backstepping Controller. Direct power control using hysteresis regulator has certain disadvantages such as significant powers ripples, variable switching frequency and sensitivity to parametric variations. To surmount these disadvantages, we present a robust controller such as the backstepping-based direct power control using SVM. A comparison study was made between the classic direct power control and the backstepping controller. The simulation results show that the backstepping controller provides good results reduces powers ripples.

In this investigation, state vector estimation of the Permanent Magnet Synchronous machine (PMSM) using the nonlinear Kalman estimator (Extended Kalman Filter) is considered. The considered states are the speed of the rotor, its angular position, the torque of the load and the resistance of the stator. Since the extended Kalman filter contains some free parameters, it will be necessary to optimize them in order to obtain a better efficiency. The free parameters of EKF are the covariance matrices of state noise and measurement noise. These later will be auto adjusted by a new metaheuristic optimization technique called Biogeographical-based optimization (BBO). As far as we know, BBO–EKF optimization for PMSM state was not treated in the literature. The suggested estimation tuning approach is demonstrated using a computer simulation of a PMSM. Simulated experimentations show the robustness and effectiveness of the proposed scheme. In addition, a detailed comparative study with conventional methods like Particle Swarm Optimization and Genetic Algorithms will be given.

Geotechnical engineers often deal with layered foundation soils. In this case, the soil bearing capacity assessment using the conventional bearing capacity theory based on the upper layer properties introduces significant inaccuracies if the top layer thickness is comparable to the rigid footing width placed on the soil surface. Under undrained conditions the cohesion increases almost linearly with depth. A few theoretical studies have been proposed in the literature in order to incorporate the cohesion variation with depth in the computation of the ultimate bearing capacity of the strip and circular footings. Rigorous solutions to the problem of circular footings resting on layered clays with linear increase of cohesion do not appear to exist. In this paper, numerical computations using FLAC code are carried out to assess the vertical bearing capacity beneath rough rigid circular footing resting on two-layered clays of both homogeneous and linearly increasing shear strength profiles. The bearing capacity calculation results which depend on the top layer thickness, the two-layered clays strength ratio and the cohesion increase rates with depth are presented in both tables and graphs, and compared with previously published results available in the literature. The critical depth for circular footing is found significantly less than for strip footing.

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This paper investigates capacity planning with outsourcing under reliability-maintenance constraints. The considered supply-chain consists of a single-manufacturer and multiple-subcontractors. The manufacturer's company is composed of a single unit subject to random failures. Corrective maintenance is endorsed when failures occur, and preventive maintenance can be carried out to reduce the degradation. The high in-house costs and the incapacity motivate the manufacturer outsourcing to independent subcontractors. In addition, based on the principle of comparative advantage, the manufacturer balances between in-house capacities and outsourcing services, which minimises the total cost. The aim is to propose a new policy based on the combination between integrated-maintenance and outsourcing policies. A mathematical model and an optimisation procedure have been developed in order to determine the best in-house production-maintenance and outsourcing plans for the manufacturer while minimising the total cost. In order to show the applicability of our approach, we conduct experimentations to study the management insights.

Antibiotic resistance is one of the greatest public-health challenges worldwide, especially with regard to Gram-negative bacteria (GNB). Carbapenems are the β-lactam antibiotics of choice with the broadest spectrum of activity and, in many cases, are the last-resort treatment for several bacterial infections. Carbapenemase-encoding genes, mainly carried by mobile genetic elements, are the main mechanism of resistance against carbapenems in GNB. These enzymes exhibit a versatile hydrolytic capacity and confer resistance to most β-lactam antibiotics. After being considered a clinical issue, increasing attention is being giving to the dissemination of such resistance mechanisms in the environment and especially through water. Aquatic environments are among the most significant microbial habitats on our planet, known as a favourable medium for antibiotic gene transfer, and they play a crucial role in the huge spread of drug resistance in the environment and the community. In this review, we present current knowledge regarding the spread of carbapenemase-producing isolates in different aquatic environments, which may help the implementation of control and prevention strategies against the spread of such dangerous resistant agents in the environment.

The Reynolds–averaged Navier–Stokes (RANS) equations were solved along with Reynolds stress model (RSM), to study the fully-developed unsteady and anisotropic single-phase turbulent flow in 90° bend pipe with circular cross-section. Two flow configurations are considered the first is without ribs and the second is with ribs attached to solid walls. The number of ribs is 14 ribs regularly placed along the straight pipe. The pitch ratios is 40 and the rib height e (mm) is 10% of the pipe diameter. Both bends have a curvature radius ratio, of 2.0. The solutions of these flows were obtained using the commercial CFD software Fluent at a Dean number range from 5000 to 40000. In order to validate the turbulence model, numerical simulations were compared with the existing experimental data. The results are found in good agreement with the literature data. After validation of the numerical strategy, the axial velocity distribution and the anisotropy of the Reynolds stresses at several downstream longitudinal locations were obtained in order to investigate the hydrodynamic developments of the analyzed flow. The results show that in the ribbed bend pipe, the maximum velocity value is approximately 47% higher than the corresponding upstream value but it is 9% higher in the case of the bend pipe without ribs. It was also found for both cases that the distribution of the mean axial velocity depends faintly on the Dean number. Finally, it can be seen that the analyzed flow in the bend pipe without ribs appears more anisotropic than in bend pipe with ribs.

In this study, an efficient full-wave method is developed for characterizing the resonant frequencies, bandwidths, and quality factors of an inverted circular superconducting patch antenna. Our technique is based on the Galerkin procedure in the Hankel transform domain (HTD) combined with the complex resistive boundary conditions. With the use of suitable Green’s functions in the HTD, the analysis is performed for the case where the superconducting circular patches is printed on an anisotropic substrate. The numerical results obtained using this approach are compared with the experimental results. These comparisons were very good, which proves the correctness and the validity of the method. It is found that the optical properties combined with optimally-chosen structural parameters of anisotropic materials can maintain control of the resonant frequency and exhibit wider bandwidth characteristics.

This paper presents an experimental study to evaluate the effect of local mineral additions (pozzolan, slag and limestone) on the rheological behaviour of based cement binder’s pastes. The binary, ternary and quaternary binder pastes were prepared with the partial clinker cement replacement limited up to 20%, according with type CEM II specifications. The cements were characterized by their geometric shapes, the reactivity and the chemical composition. An experimental design plan was used to modelling the rheological behaviour of pastes. The relatives yield stress and plastic viscosity of binder’s pastes, with normal consistency, were determined. The results showed that all the tested compositions with additions follow the same rheological behaviour law according to the Bingham model. The binder pastes rheological parameters (yield stress and viscosity) are affected by mineral additions. The highest values of the rheological parameters were measured in binary and ternary cements with limestone and pozzolan. On the other hand, the lower viscosity among the tested pastes was obtained with slag addition. The statistical approach allowed us to obtain a satisfactory modelling of viscosity and yield stress with a coefficient of determination R² = 0.91 and 0.92, respectively and a satisfactory correlation between the viscosity and the water/binder ratio (W/B) for a normal consistency with a coefficient of determination R² = 0.91.

The current study focuses on the chemical composition, and evaluation of antioxidant and antibacterial activity of the aerial parts of Centaurea resupinata subsp. dufourii. Using different chromatographic methods nine compounds 1–9 were isolated. The structural identification of isolated compounds was achieved using several spectroscopic methods NMR techniques (1H NMR, 13C NMR, COSY, HSQC, HMBC) and mass spectroscopy (ESI-MS) and by comparison with literature data. The structures of these compounds were identified as nicotiflorin (1), apigetrin (2), chrysoeriol (3), apigenin (4), chrysin (5), daucosterol (6), β-sitosterol (7), taraxastrerol (8) and lupeol (9). The antibacterial and antioxidant activities of ethyl acetate and n-butanol extracts have been evaluated. The antioxidant activity was assessed in vitro using DPPH radical scavenging method, which showed that ethyl acetate extract possessed an interesting antioxidant potential (IC50 = 36.263 ± 0.005 μg/mL).  

The phytochemical profile obtained from LC-ESI-MS/MS analysis of the n-butanol extract (BEHL) from the North African endemic plant Hyacinthoides lingulata (Poir.) Rothm. brought about the identification of ten glycosylated derivatives of apigenin and luteolin flavones. For the same plant extract, in vitro anti-inflammatory (hypotonic induced hemolysis and heat induced haemolysis assay) and antioxidant (DPPH and β-Carotene) activities were evaluated observing high inflammatory inhibition by protecting membrane stability of erythrocyte in both heat (84.70 ± 0.24%) and hypotonic induced hemolysis (79.45 ± 0.12%). A remarkable hemostatic effect was also established by measuring the coagulation time (15.95 ± 1.05 s at a dose of 1 mg/mL) of decalcified plasma related to its phytochemical content. It is the first report on combined chemical components and biological evaluation of this specific plant.

A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed with breast cancer. Currently, mammography, magnetic resonance imaging, ultrasound, and biopsies are the main screening techniques, which require either, expensive devices or personal qualified; but some countries still lack access due to economic, social, or cultural issues. As an alternative diagnosis methodology for breast cancer, this study presents a computer-aided diagnosis system based on convolutional neural networks (CNN) using thermal images. We demonstrate that CNNs are faster, reliable and robust when compared with different techniques. We study the influence of data pre-processing, data augmentation and database size on several CAD models. Among the 57 patients database, our CNN models obtained a higher accuracy (92%) and F1-score (92%) that outperforms several state-of-the-art architectures such as ResNet50, SeResNet50, and Inception. This study exhibits that a CAD system that implements data-augmentation techniques reach identical performance metrics in comparison with a system that uses a bigger database (up to 33%) but without data-augmentation. Finally, this study proposes a computer-aided system for breast cancer diagnosis but also, it stands as baseline research on the influence of data-augmentation and database size for breast cancer diagnosis from thermal images with CNNs

Cet article est consacré à l’étude des performances de la génératrice asynchrone à cage double étoile (GASDE) en site isolé. Le système de commande est composé d’une GASDE raccordé à un bus continu et une charge en sortie de deux redresseurs à commande MLI. Une étude comparative entre la technique de commande conventionnelle et la commande adaptée basée sur l’introduction de la SVM-PI-flou et un nouvel estimateur de flux (flux virtuel statorique) afin d’améliorer la qualité d’énergie et d’atténuer les harmoniques du courant.

Background: Coronavirus disease has become a worldwide threat affecting almost every country in the world. The spread of the virus is likely to continue unabated. The aim of this study is to compare between Autoregressive Integrated Moving Average (ARIMA) model, Fuzzy time series and Holt-Winters – No seasonal for forecasting the COVID-19 new cases in Algeria. 

Methods: Three different models to predict the number of Covid-19 new cases in Algeria were used. The number of new cases of COVID-19 in Algeria during the period from 24^th February 2020 to 31th July 2021 was modeled according to ARIMA(4,1,2) model, Five based Fuzzy time series models including the Chen model, Heuristic Huareng model, Singh model, Abbasov-Manedova model and NFTS model, and Holt-Winters – No seasonal. 

Results: The predictive values were obtained from the 1^st August 2021 to 31th December 2021. According to a set of criteria (ME, MAE, MSE, RMSE, U), we found that the FTNS model is the most accurate and best generating model for the values of the number of new cases of Covid-19. 

Conclusion: To the best of our knowledge, this is the first comparative study of three models of forecasting of Covid-19 new cases in Algeria. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria. Moreover, this forecast will help the Health authorities to be better prepared to fight the epidemic by engaging their healthcare facilities.

A comparative study between a set of chosen machine learning tools for direct remaining useful life prediction is presented in this work. The main objective of this study is to select the appropriate prediction tool for health estimation of aircraft engines for future uses. The training algorithms are evaluated using “time-varying” data retrieved from Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) developed by NASA. The training and testing processes of each algorithm are carried out under the same circumstances using the similar initial condition and evaluation sets. The results prove that among the studied training tools, Support vector machine (SVM) achieved the best results.