Turbulent flows are characterized by the presence of "scales of fluctuations", or "structures" of varying magnitudes, the effects in which the mixing, transfer and dissipation of energy are preponderant. Most importantly, dissipation determines the depth profile of the flow. This contribution aims to implement a model able to predict unsteady turbulent flows generated by the presence of obstacles in a channel with complex geometry and to report, where the complexity of the phenomena are observed, such as: the separation of the boundary layer, the succession of vortices, local heat transfers, and the recirculation zones in the wake of obstacles and the oscillatory regime of the hydraulic jump for which this research is of exclusive interest. The current work therefore, presents the numerical simulation in unsteady turbulent regime based on the resolution of balance equations, using the RANS (Reynolds-Averaged Navier–Stokes) approach with an RNG k−ε closure model. To solve the incompressible Navier–Stokes equations governing these flows, we appealed to the motivated finite volume method, and its ability to process complex geometries. The simulation software FLUENT we used is based on the finite volume method. It allows to explore, the velocity and pressure fields in the digital channel of the studied flows.

This paper deals with multi-agent energy management and fault tolerant control of the micro-grid powered by wind farm based on two doubly fed induction generators. The stator flux orientation has used to eliminate the active and reactive power coupling. The proposed control scheme is based on two cascades closed loops. The inner controllers concern the rotor currents. The outer controllers have a parallel configuration with the stator voltage or the stator power control. Switching between these two controllers is realized by the synchronization mechanism. All controllers are designed with Lyapunov approach associated with sliding-mode control, this solution shows good robustness against parameter variations, measurement errors and faults. The global asymptotic stability of the overall system is proven. After that, a Multi-agent energy management was proposed and tested in order to satisfy some objectives and overcome some constraints. The advantages of the wind energy integration associated with multi-agent energy management are: production cost minimization, reduction of the carbon emissions, increasing the energy autonomy and he robustness against weather conditions and faults that may occur during operation. The results confirm the effectiveness of the proposed control.

Amniotic band syndrome (ABS) was first described by Montgomery in Montgomery (1832). It is a poorly known congenital malformation due to strangulation of the organs by an amniotic fibrous band. Several parts of the body can be affected: for instance, skull, face, neck, trunk and musculoskeletal system. It generally associates three types of anomalies namely, amputations, deformities, and malformations. There are two genuine theories covering this syndrome; the Intrinsic Theory associating the syndrome to a germline defect and the Purely Mechanical Extrinsic Theory related to the amniotic band. These theories have thoroughly tried to explain the disease and the organ involvement (Goldfarb et al., 2009). In the current study, we report a rare case of an open fracture of both leg bones with amniotic disease in a 10-day-old neonate who underwent surgical treatment. In our case, it is a surgical emergency where we try to explain its physiopathology and show how to operate it. We discuss likewise the appropriateness of using the expressions “leg fracture” and “congenital pseudarthrosis of the leg”. Finally, we describe a revised classification by Hall (1982) and Weinzweig (1994) of ABS incorporating a stage with bone involvement.

Hexavalent chromium (CrVI) compounds are potent toxicants commonly used in numerous industries. Thus, potential toxic effects and health hazards are of high relevance. Selenium (Se) and zinc (Zn) are known for their antioxidant and chemoprotective properties. However, little is known about their protective effects against CrVI-induced renal damage during pregnancy. In this context, the present study aimed to investigate the protective efficacy of these two essential elements against potassium dichromate-induced nephrotoxicity in pregnant Wistar Albino rats. Female rats were divided into control and four treated groups of six each receiving subcutaneously on the 3rd day of pregnancy, K₂Cr₂O₇ (10 mg/kg, s.c. single dose) alone, or in association with Se (0.3 mg/kg, s.c. single dose), ZnCl₂ (20 mg/kg, s.c. single dose) or both of them simultaneously. The nephrotoxic effects were monitored by the evaluation of plasma renal parameters, oxidative stress biomarkers, DNA damage, and renal Cr content. The obtained results showed that K₂Cr₂O₇ disturbed renal biochemical markers, induced oxidative stress and DNA fragmentation in kidney tissues, and altered renal histoarchitecture. The co-administration of Se and/or ZnCl₂ has exhibited pronounced chelative, antioxidant, and genoprotective effects against K₂Cr₂O₇-induced renal damage and attenuated partially the histopathological alterations. These results suggest that Se and Zn can be used as efficient nephroprotective agents against K₂Cr₂O₇-induced toxicity in pregnant Wistar Albino rats.

The aim of this study was to test the influence of nest site characteristics and food supplementation from rubbish dumps on reproductive parameters of white storks breeding in semi-arid habitats. A total of 148 nests were monitored in two colonies of white storks (control colony vs. colony that benefited from high food supply in rubbish dumps) in eastern Algeria over a six-year period (2011–2016) to measure nest characteristics and reproductive parameters (clutch size, number of hatchings, number of fledglings, breeding success). Results showed that pairs breeding at proximity from rubbish dumps had larger clutch sizes (5.1 ± 0.6 vs. 4.6 ± 0.6), hatched more chicks (4.7 ± 0.7 vs. 4.3 ± 0.7) and raised more fledglings (3.0 ± 0.9 vs. 2.6 ± 1.0) than pairs breeding far from rubbish dumps. Results also showed that clutch size was positively related to nest surface area, and that pairs nesting on electricity poles had a lower breeding success than those nesting in trees (48.9 ± 20.4% vs. 64.6 ± 17.6%). Our findings suggest that breeding outputs are strongly related to selective behavior in nest placement and food availability surrounding the nesting site.

This paper proposes a new approach for remaining useful life prediction that combines a bond graph, the Gaussian Mixture Model and similarity techniques to allow the use of both physical knowledge and the data available. The proposed method is based on the identification of relevant variables that carry information on degradation. To this end, the causal properties of the bond graph (BG) are first used to identify the relevant sensors through the fault observability. Then, a second stage of analysis based on statistical metrics is performed to reduce the number of sensors to only the ones carrying useful information for failure prognosis, thus, optimizing the data to be used in the prognosis phase. To generate data in the different system state, a simulator based on the developed BG is used. A Gaussian Mixture Model is then applied on the generated data for fault diagnosis and clustering. The Remaining Useful Life is estimated using a similarity technique. An application on a mechatronic system is considered for highlighting the effectiveness of the proposed approach.

In the last recent years, a large community of researchers and industrial practitioners has been attracted by combining different prognostics models as such strategy results in boosted accuracy and robust performance compared to the exploitation of single models. The present work is devoted to the investigation of three new fusion schemes for the remaining useful life forecast. These integrated frameworks are based on aggregating a set of Gaussian process regression models thanks to the Induced Ordered Weighted Averaging Operators. The combination procedure is built upon three proposed analytical weighting schemes including exponential, logarithmic and inverse functions. In addition, the uncertainty aspect is supported in this work, where the proposed functions are used to weighted average the variances released from competitive Gaussian process regression models. The training data are transformed into gradient values, which are adopted as new training data instead of the original observations. A lithium-ion battery data set is used as a benchmark to prove the efficiency of the proposed weighting schemes. The obtained results are promising and may provide some guidelines for future advances in performing robust fusion options to accurately estimate the remaining useful life.

This paper proposed a new strategy of sinusoidal pulse width modulation (SPWM) technique to control three-phase nine-level switched-capacitor inverter (9LSCI) in grid-connected PV systems. The main advantage of this inverter is high voltage gain, achieved by switching the capacitors in series and parallel to boost up the output voltage using low voltage input. To improve the quality of solar energy for injection into the electrical grid, a rooted tree optimization (RTO) algorithm is used to get optimum values of initial angles of multi carriers SPWM technique, giving the lowest possible values of the total harmonic distortion (THD). The design also can maximize the efficiency of the multi-level inverter by minimizing its size using fewer components and a single DC source and reducing the rate of THD. The higher effectiveness and accuracy of the suggested RTO-SPWM technique was tested and verified in comparison to existing classical SPWM technique from the performance of PV-grid systems that it can effectively reduce the total harmonic distortion to 0.16 %.

This paper gives and justifies a practical approach for solving fuzzy singular integro-differential equations. First, by using different techniques, we show that solutions to two types of fuzzy singular integro-differential equations exist and are unique: Picard’s theorem for logarithmic kernels and Arzelà–Ascoli theorem for Cauchy ones. Then, utilizing airfoil polynomials, we provide a collocation method to solve the current problems numerically. We also look at the approximate equations’ solutions, and we introduce the concept of error analysis. Using new procedures, we obtain two systems of linear equations. These are the problems to be examined. Eventually, we exhibit the precision of the proposed approach via numerical examples.

When designing tunnels, it is advisable to pre-estimate several tunnel parameters such as the depth (cover), the lining thickness, and the shape of the tunnel cross section. This condition is important in order to limit deformations during construction of the tunnel, and to ensure good tunnel resistance under seismic load conditions. In this context, the present paper is devoted to the analysis of the influence of some test parameters (the cover of the tunnel, the thickness of the lining, and the shape of the tunnel and the direction of the seismic waves) on the behaviour of the soil and the lining of a shallow tunnel built in soft ground subjected to seismic loading. The reference model for this parametric study is a real case, which happens to be the tunnel of Djebel El Ouahch (East-West motorway) in the province of Constantine/Algeria. The study is performed in three dimensions (3D) using a finite difference calculation method based on the FLAC3D calculation code. The results are presented in terms of shear strain induced in the soil around the tunnel, surface settlement, and vertical displacement of soil under the raft foundation, and also shear stress, bending moment, and shear strain, induced in the tunnel lining. The results show that the increase in thickness of the lining causes a reduction in shear force, and shear strain, while the circular or oval shape of the tunnel cross section results in low values of strain in the lining and ground displacement. It has been also pointed out that bending moment and shear strain induced in the lining are relatively low in comparison with the other forms. On the other hand, the direction of the seismic waves has a great influence on the behaviour of the lining and the surrounding soil. These results demonstrate that the strongest and most stable tunnel is the deepest tunnel with circular or oval section with a large thickness of the tunnel lining under the effect of compressive seismic waves. The results of the present study will be useful in the design of such a case by understanding the effects of various influencing parameters that control the stability of the tunnel in soil with bad characteristics.

In this study, we investigate a production planning problem in hybrid manufacturing remanufacturing production system. The objective is the determine the best mix between the manufacturing of new products, and the remanufacturing of recovered products, based on economic and environmental considerations. It consists to determine the best manufacturing and remanufacturing plans to minimising the total economic cost (start-up and production costs of new and remanufactured products, storage costs of new and returned products and disposal costs) and the carbon emissions (new products, remanufactured products and disposed products). The hybrid system consists of a set of machines used to produce new products and remanufactured products of different grades (qualities). We assume that remanufacturing is more environmentally efficient, because it allows to reduce the disposal of used products. A multi-objective mathematical model is developed, and a non dominated sorting genetic algorithm (NSGA-II) based approach is proposed. Numerical experience is presented to study the impact of carbon emissions generated by new, remanufactured and disposed products, over a production horizon of several periods.

Objective: The aim of our work is to study the links between anthropometric parameters and body composition obtained by bioelectric impedancemetry in case of obese women of peri- menopausal age. Method and Materials: 154 obese women were classified according to their degree of obesity according to WHO criteria. The analysis of body composition was performed by impedancemetry. Pearson’s (r) and Spearman’s (r2 ) correlations were calculated to check the relationships between age, weight, BMI, as well as total and segmental body fat composition. Results: 154 women of mean age 40.20 ± 13.13 years, obese, mean BMI 38.66 ± 6.56 Kg/m2 participated in our study. Impedance reduced an average total fat mass% (TFM%) of 45.39 ± 5.67%. BMI is strongly correlated with TFM% (r = 0.73; r2 = 0.82; p ≥ 0.05). For obesity stages 1-2, weight is correlated with BMI (r-r2 > 0.40; p ≤ 0.001). Likewise, a strong correlation exists between weight and TFM in Kg (r2 = 0.82; p ≥ 0.05). For a BMI ≥ 35 Kg/m2 , weight is inversely correlated with age [r2 ≥ (-0.36); p ≤ 0.003]. The FM of the trunk (Kg) is correlated with the weight for obesity grade 3 (r = 0.49; p = 0.0002) and whatever the stage of obesity at the BMI (r ≥ 0.32; p ≤ 0.02). Conclusion: The use of bioelectrical impedancemetry in the diagnostic management of obese people is quite useful. This tool gives us better information on the location and distribution of fatty tissue.

Wireless sensor networks (WSNs) have become an important component in the Internet of things (IoT) field. In WSNs, multi-channel protocols have been developed to overcome some limitations related to the throughput and delivery rate which have become necessary for many IoT applications that require sufficient bandwidth to transmit a large amount of data. However, the requirement of frequent negotiation for channel assignment in distributed multi-channel protocols incurs an extra-large communication overhead which results in a reduction of the network lifetime. To deal with this requirement in an energy-efficient way is a challenging task. Hence, the Reinforcement Learning (RL) approach for channel assignment is used to overcome this problem. Nevertheless, the use of the RL approach requires a number of iterations to obtain the best solution which in turn creates a communication overhead and time-wasting. In this paper, a Self-schedule based Cooperative multi-agent Reinforcement Learning for Channel Assignment (SCRL CA) approach is proposed to improve the network lifetime and performance. The proposal addresses both regular traffic scheduling and assignment of the available orthogonal channels in an energy-efficient way. We solve the cooperation between the RL agents problem by using the self-schedule method to accelerate the RL iterations, reduce the communication overhead and balance the energy consumption in the route selection process. Therefore, two algorithms are proposed, the first one is for the Static channel assignment (SSCRL CA) while the second one is for the Dynamic channel assignment (DSCRL CA). The results of extensive simulation experiments show the effectiveness of our approach in improving the network lifetime and performance through the two algorithms.

The security is so important for both storing and transmitting the digital data, the choice of parameters is critical for a security system, that is, a weak parameter will make the scheme very vulnerable to attacks, for example the use of supersingular curves or anomalous curves leads to weaknesses in elliptic curve cryptosystems, for RSA cryptosystem there are some attacks for low public exponent or small private exponent. In certain circumstances the secret sharing scheme is required to decentralize the risk. In the context of the security of secret sharing schemes, it is known that for the scheme of Shamir, an unqualified set of shares cannot leak any information about the secret. This paper aims to show that the well-known Shamir’s secret sharing is not always perfect and that the uniform randomization before sharing is insufficient to obtain a secure scheme. The second purpose of this paper is to give an explicit construction of weak polynomials for which the Shamir’s (k, n) threshold scheme is insecure in the sense that there exist a fewer than k shares which can reconstruct the secret. Particular attention is given to the scheme whose threshold is less than or equal to 6. It also showed that for certain threshold k, the secret can be calculated by a pair of shares with the probability of 1/2. Finally, in order to address the mentioned vulnerabilities, several classes of polynomials should be avoided.

Graph matching is a comparison process of two objects represented as graphs through finding a correspondence between vertices and edges. This process allows defining a similarity degree (or dissimilarity) between the graphs. Generally, graph matching is used for extracting, finding and retrieving any information or sub-information that can be represented by graphs. In this paper, a new consistency rule is proposed to tackle with various problems of graph matching. After, using the proposed rule as a necessary and sufficient condition for the graph isomorphism, we generalize it for subgraph isomorphism, homomorphism and for an example of inexact graph matching. To determine whether there is a matching or not, a backtracking algorithm called CRGI2 is presented who checks the consistency rule by exploring the overall search space. The tree-search is consolidated with a tree pruning technique that eliminates the unfruitful branches as early as possible. Experimental results show that our algorithm is efficient and applicable for a real case application in the information retrieval field. On the efficiency side, due to the ability of the proposed rule to eliminate as early as possible the incorrect solutions, our algorithm outperforms the existing algorithms in the literature. For the application side, the algorithm has been successfully tested for querying a real dataset that contains a large set of e-mail messages.

The purpose of this paper is to present a system reconfiguration for a three-phase induction motor (IM) in the event of an open-phase (OP) fault. After the occurrence of the fault, the challenge is how to ensure a safe operation when the IM is only supplied by two phases. The star point of stator is used to reconfigure the IM supply, and a fault tolerant rotor field-oriented control (FT-RFOC) is implemented. Consequently, an equivalent mathematical two-phase model is firstly calculated based on the two available currents. Modifications on the conventional space vector modulation (SVM) algorithm are also introduced in order to control the reconfigured inverter. This system reconfiguration is applied to achieve a safe post-operating after the occurrence of the OP fault. The implemented tests confirm the proposal and prove its effectiveness to compensate for the fault effect.

Three methods to solve two classes of integral equations of the second kind are introduced in

this paper. Firstly, two Kantorovich methods are proposed and examined to numerically solving an integral

equation appearing from mathematical modeling in biology. We use a sequence of orthogonal finite rank

projections. The first method is based on general grid projections. The second one is established by using

the shifted Legendre polynomials. We present a new convergence analysis results and we prove the associated

theorems. Secondly, a new Nystr¨om method is introduced for solving Fredholm integral equation of the second kind.

Many cloud providers offer very high precision services to exploit Optical Character Recognition (OCR). However, there is no provider offers Tifinagh Optical Character Recognition (OCR) as Web Services. Several works have been proposed to build powerful Tifinagh OCR. Unfortunately, there is no one developed as a Web Service. In this paper, we present a new architecture of Tifinagh Handwriting Recognition as a web service based on a deep learning model via Google Colab. For the implementation of our proposal, we used the new version of the TensorFlow library and a very large database of Tifinagh characters composed of 60,000 images from the Mohammed Vth University in Rabat. Experimental results show that the TensorFlow library based on a Tensor processing unit constitutes a very promising framework for developing fast and very precise Tifinagh OCR web services. The results show that our method based on convolutional neural network outperforms existing methods based on support vector machines and extreme learning machine.

This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. We study the traffic network in large scale routing problems as a field of application. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. Experimental analyses demonstrate the efficiency of our proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing technique in term of the optimal solution in every generation.

Automated plant classification using tree trunk has attracted increasing interest in the computer vision community as a contributed solution for the management of biodiversity. It is based on the description of the texture information of the bark surface. The multi-scale variants of the local binary patterns have achieved prominent performance in bark texture description. However, these approaches encode the scale levels of the macrostructure separately from each other. In this paper, a novel handcrafted texture descriptor termed multi-scale Statistical Macro Binary Patterns (ms-SMBP) is proposed to encode the characterizing macro pattern of different bark species. The proposed approach consists of defining a sampling scheme at high scale levels and summarizing the intensity distribution using statistical measures. The characterizing macro pattern is encoded by an in-depth gradient that describes the relationship between the scale levels and their adaptive statistical prototype. Besides this handcrafted feature descriptor, a learning-based description is performed with the ResNet34 model for bark classification. Extensive and comprehensive experiments on challenging and large-scale bark datasets demonstrate the effectiveness of ms-SMBP to identify bark species and outperforming different multi-scale LBP approaches. The tree trunk classification with ResNet34 shows interesting results on a very large-scale dataset.