This paper is about the study of the well-posedness of a stochastic Cahn-Hilliard equation driven by white noise induced by a Q-Brownian motion. The proof of the existence of a unique global solution relies on the Galerkin method together with a monotonicity method.

Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is considered as one of the most critical issues in Intelligent Transportation Systems (ITS). Among the proposed AI-based approaches, Deep Reinforcement Learning (DRL) has been largely applied while showing better performances. This paper proposes a new DRL-based cooperative approach for controlling multiple intersections. The problem is modelled as a Multi-Agent Reinforcement Learning (MARL) system, while each agent is trained to select the best action to control an intersection by obtaining information about its local lanes state. The cooperation aspect is manifested in this approach by considering the effect of the state, action and reward of neighbour agents in the process of policy learning. An intersection controller applies a Deep Q-Network (DQN) method, while transferring state, action and reward received from their neighbour agents to its own loss function during the learning process. The experimental results under different scenarios shows that the proposed approach outperforms many state-of-the-art approaches in terms of three metrics: Average Waiting Time (AWT), Average Queue Length (AQL) and Average Emission CO₂ (AEC). In addition, the cooperation between the different trained DRL-based controllers allows the system to continuously learn and improve its performance by interacting with the environment, particularly when the traffic is congested.

Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is considered as one of the most critical issues in Intelligent Transportation Systems (ITS). Among the proposed AI-based approaches, Deep Reinforcement Learning (DRL) has been largely applied while showing better performances. This paper proposes a new DRL-based cooperative approach for controlling multiple intersections. The problem is modelled as a Multi-Agent Reinforcement Learning (MARL) system, while each agent is trained to select the best action to control an intersection by obtaining information about its local lanes state. The cooperation aspect is manifested in this approach by considering the effect of the state, action and reward of neighbour agents in the process of policy learning. An intersection controller applies a Deep Q-Network (DQN) method, while transferring state, action and reward received from their neighbour agents to its own loss function during the learning process. The experimental results under different scenarios shows that the proposed approach outperforms many state-of-the-art approaches in terms of three metrics: Average Waiting Time (AWT), Average Queue Length (AQL) and Average Emission CO₂ (AEC). In addition, the cooperation between the different trained DRL-based controllers allows the system to continuously learn and improve its performance by interacting with the environment, particularly when the traffic is congested.

Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is considered as one of the most critical issues in Intelligent Transportation Systems (ITS). Among the proposed AI-based approaches, Deep Reinforcement Learning (DRL) has been largely applied while showing better performances. This paper proposes a new DRL-based cooperative approach for controlling multiple intersections. The problem is modelled as a Multi-Agent Reinforcement Learning (MARL) system, while each agent is trained to select the best action to control an intersection by obtaining information about its local lanes state. The cooperation aspect is manifested in this approach by considering the effect of the state, action and reward of neighbour agents in the process of policy learning. An intersection controller applies a Deep Q-Network (DQN) method, while transferring state, action and reward received from their neighbour agents to its own loss function during the learning process. The experimental results under different scenarios shows that the proposed approach outperforms many state-of-the-art approaches in terms of three metrics: Average Waiting Time (AWT), Average Queue Length (AQL) and Average Emission CO₂ (AEC). In addition, the cooperation between the different trained DRL-based controllers allows the system to continuously learn and improve its performance by interacting with the environment, particularly when the traffic is congested.

In this paper, we consider the stationary problem in three dimensional thin domain ΩεΩε with maximal monotone graph and Tresca conditions. In the first step, we present the problem statement and give the variational formulation. We then study the asymptotic behavior when one dimension of the domain tends to zero. In the latter case a specific Reynolds limit equation is obtained and the uniqueness of the displacement of the limit problem are proved.

In this paper, we consider the stationary problem in three dimensional thin domain ΩεΩε with maximal monotone graph and Tresca conditions. In the first step, we present the problem statement and give the variational formulation. We then study the asymptotic behavior when one dimension of the domain tends to zero. In the latter case a specific Reynolds limit equation is obtained and the uniqueness of the displacement of the limit problem are proved.

In most industrial environments, vibration analysis is widely used for fault diagnosis of rolling bearings. The vibration signal measured from a bearing represents a mixture of motor vibration, rolling vibration, noise, and other sources. Due to the high cost of devices and limited space, only one sensor can be installed to measure this signal. In this paper, a feature extraction method based on Single Channel Blind Source Separation (SCBSS) and Normal Distribution (ND) is proposed for vibration monitoring of rolling element bearings. To decompose the bearing signal, SCBSS is applied for separating the different sources. Because ND is sensitive to the type of fault, it is used as criterion to find an output that contains the maximum information about the fault by removing the other sources. In fact, the obtained signal contains other vibrations which affect the correct source of fault. A second SCBSS filter is, therefore, proposed to decompose the selected source and thus improves the performance of fault diagnosis. The application of the proposed method is carried out on a deep groove ball bearing with outer race fault, ball fault, and inner race fault in order to better validate the diagnosis results.

In most industrial environments, vibration analysis is widely used for fault diagnosis of rolling bearings. The vibration signal measured from a bearing represents a mixture of motor vibration, rolling vibration, noise, and other sources. Due to the high cost of devices and limited space, only one sensor can be installed to measure this signal. In this paper, a feature extraction method based on Single Channel Blind Source Separation (SCBSS) and Normal Distribution (ND) is proposed for vibration monitoring of rolling element bearings. To decompose the bearing signal, SCBSS is applied for separating the different sources. Because ND is sensitive to the type of fault, it is used as criterion to find an output that contains the maximum information about the fault by removing the other sources. In fact, the obtained signal contains other vibrations which affect the correct source of fault. A second SCBSS filter is, therefore, proposed to decompose the selected source and thus improves the performance of fault diagnosis. The application of the proposed method is carried out on a deep groove ball bearing with outer race fault, ball fault, and inner race fault in order to better validate the diagnosis results.

In most industrial environments, vibration analysis is widely used for fault diagnosis of rolling bearings. The vibration signal measured from a bearing represents a mixture of motor vibration, rolling vibration, noise, and other sources. Due to the high cost of devices and limited space, only one sensor can be installed to measure this signal. In this paper, a feature extraction method based on Single Channel Blind Source Separation (SCBSS) and Normal Distribution (ND) is proposed for vibration monitoring of rolling element bearings. To decompose the bearing signal, SCBSS is applied for separating the different sources. Because ND is sensitive to the type of fault, it is used as criterion to find an output that contains the maximum information about the fault by removing the other sources. In fact, the obtained signal contains other vibrations which affect the correct source of fault. A second SCBSS filter is, therefore, proposed to decompose the selected source and thus improves the performance of fault diagnosis. The application of the proposed method is carried out on a deep groove ball bearing with outer race fault, ball fault, and inner race fault in order to better validate the diagnosis results.

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 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 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 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 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 article deals with the problem of blind separation of statistically independent sources from the instantaneous linear model (n × n). When the observation signals are affected by the additive white gaussian noise (AWGN), the implementation of the proposed solution is performed by following three steps. The first step is a whitening process. The second step aims to convert the uncorrelated signals into statistically independent signals. The last step consists in reducing the noise existing in the noisy estimations. The main part of the proposed solution is to determine the adequate rotating angle (θ) that maximizes the kurtosis of the whitened signals. This rotating angle is obtained through the use of optimization techniques by applying a genetic algorithm. The proposed solution has the advantage of not converging to a local maximum, and also the separation method can be easily generalized to converge directly towards the global maximum for the case of several sources. The results obtained by applying many simulations, prove the effectiveness and the performance of the proposed method even in the noisy case and whatever the type of the signals (stationary or non-stationary).

This article deals with the problem of blind separation of statistically independent sources from the instantaneous linear model (n × n). When the observation signals are affected by the additive white gaussian noise (AWGN), the implementation of the proposed solution is performed by following three steps. The first step is a whitening process. The second step aims to convert the uncorrelated signals into statistically independent signals. The last step consists in reducing the noise existing in the noisy estimations. The main part of the proposed solution is to determine the adequate rotating angle (θ) that maximizes the kurtosis of the whitened signals. This rotating angle is obtained through the use of optimization techniques by applying a genetic algorithm. The proposed solution has the advantage of not converging to a local maximum, and also the separation method can be easily generalized to converge directly towards the global maximum for the case of several sources. The results obtained by applying many simulations, prove the effectiveness and the performance of the proposed method even in the noisy case and whatever the type of the signals (stationary or non-stationary).

This paper seeks to explore the Algerian EFL learners’ beliefs about, and attitudes towards, promoting Learner Autonomy (LA) via the implementation of Collaborative Learning (CL) as a teaching method. The study was carried out at the Department of English of Mostefa Benboulaid Batna 2 University during the academic year 2018-2019. To meet the paper’s objectives, a quantitative approach was opted for where an adapted questionnaire was administered to the first-year students. The overall results revealed the positive disposition of the Algerian EFL learners towards the use of the CL teaching method as a channel to develop their autonomy.

This paper seeks to explore the Algerian EFL learners’ beliefs about, and attitudes towards, promoting Learner Autonomy (LA) via the implementation of Collaborative Learning (CL) as a teaching method. The study was carried out at the Department of English of Mostefa Benboulaid Batna 2 University during the academic year 2018-2019. To meet the paper’s objectives, a quantitative approach was opted for where an adapted questionnaire was administered to the first-year students. The overall results revealed the positive disposition of the Algerian EFL learners towards the use of the CL teaching method as a channel to develop their autonomy.

According to several American and European scholars, intellectuals, and media experts in U.S. foreign policy, the American national security strategy had witnessed a reformulation in the 21st century after the 9/11 attacks. In reality, never after Pearl Harbor, America has experienced such a dramatic security event. For which the obvious question remains posed: President Bush’s National Security Strategy marked a new path to a universal American security measure? Did it develop a new policy process with new norms to fit the modern era? Accordingly, U.S. military intervention in Afghanistan aims to preserve; freedom, liberal values, deter terrorism, and protect the threatened American security; hence, we have come with this study to evidence that by the fact that Bush's unilateral preventive war strategy, which witnessed a blatant violation of International Law, Human Rights, and the United Nations Charter of State’s sovereignty, was no more than the natural reaction to their foreign policy adoption of duplicity. The American National Security Policy is, in reality, an overtly way of military expression policy to enforce duplicity.

According to several American and European scholars, intellectuals, and media experts in U.S. foreign policy, the American national security strategy had witnessed a reformulation in the 21st century after the 9/11 attacks. In reality, never after Pearl Harbor, America has experienced such a dramatic security event. For which the obvious question remains posed: President Bush’s National Security Strategy marked a new path to a universal American security measure? Did it develop a new policy process with new norms to fit the modern era? Accordingly, U.S. military intervention in Afghanistan aims to preserve; freedom, liberal values, deter terrorism, and protect the threatened American security; hence, we have come with this study to evidence that by the fact that Bush's unilateral preventive war strategy, which witnessed a blatant violation of International Law, Human Rights, and the United Nations Charter of State’s sovereignty, was no more than the natural reaction to their foreign policy adoption of duplicity. The American National Security Policy is, in reality, an overtly way of military expression policy to enforce duplicity.