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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.