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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.     

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.