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Abstract: This study presents three main Topics; Taking into consideration the analysis of the status of the international competition situation, which is one of the priorities of the major powers in the process of winning this region. One of the priorities of this study is to identify the geopolitical dimension of the African Sahara, The importance of this region geographically and economically, which form the basis of the political dimension, and how these great powers struggle to obtain these resources, thus identifying the strategic importance of this geographic region, which has become the focus of the international doer and his attempt to introduce in the focus of regional conflicts and to international ones, in order to secure their areas of influence; The thing prompting us to discuss these facts. What we would like to clarify through this study is to touch the concept of the geopolitical dimension and its various concepts, and show the international conflict and its repercussions on the African Desert, and Find out about the reality of international competition and its future vision for the African Desert. Keywords: African Desert, African Coast, Geopolitical Dimension, International Doer, International Competition.

International audience ; Targets recognition in radar images presents an essential task for monitoring and surveillance of sensitive areas such as military zones. The fundamental problem in radar imaging is related to the recognition of objects in radar images, that its needs a whole chain of treatment. To classify radar images a feature extraction method is used to detect an appropriate subspace in the original feature space, which is based on transformation of the original feature. This subspace should be big enough to maintain minimal loss of information and small enough to minimize the complexity of classifier. Since the feature extractor is difficult to build in manual mode and needs to be redesigned for each application, a Deep Learning in automatic mode is used with a training process subdivided into several modules. In this paper, we lay out an approach to classify Synthetic aperture radar (SAR) and Inverse Synthetic aperture radar (ISAR) images using Deep learning techniques. At first, in order to evaluate the effect of convolution layers and the number of hidden layers of the perceptron we thought of implementing 4 configurations of CNN (Convolutional neural network). In the second time, we use the CAE (Convolutional auto-encoder) to learn the optimal filters that minimize the reconstruction error, after we use these filters to feed the CNN retained and evaluate the effect on performance's system.

International audience ; Targets recognition in radar images presents an essential task for monitoring and surveillance of sensitive areas such as military zones. The fundamental problem in radar imaging is related to the recognition of objects in radar images, that its needs a whole chain of treatment. To classify radar images a feature extraction method is used to detect an appropriate subspace in the original feature space, which is based on transformation of the original feature. This subspace should be big enough to maintain minimal loss of information and small enough to minimize the complexity of classifier. Since the feature extractor is difficult to build in manual mode and needs to be redesigned for each application, a Deep Learning in automatic mode is used with a training process subdivided into several modules. In this paper, we lay out an approach to classify Synthetic aperture radar (SAR) and Inverse Synthetic aperture radar (ISAR) images using Deep learning techniques. At first, in order to evaluate the effect of convolution layers and the number of hidden layers of the perceptron we thought of implementing 4 configurations of CNN (Convolutional neural network). In the second time, we use the CAE (Convolutional auto-encoder) to learn the optimal filters that minimize the reconstruction error, after we use these filters to feed the CNN retained and evaluate the effect on performance's system.

International audience ; Targets recognition in radar images presents an essential task for monitoring and surveillance of sensitive areas such as military zones. The fundamental problem in radar imaging is related to the recognition of objects in radar images, that its needs a whole chain of treatment. To classify radar images a feature extraction method is used to detect an appropriate subspace in the original feature space, which is based on transformation of the original feature. This subspace should be big enough to maintain minimal loss of information and small enough to minimize the complexity of classifier. Since the feature extractor is difficult to build in manual mode and needs to be redesigned for each application, a Deep Learning in automatic mode is used with a training process subdivided into several modules. In this paper, we lay out an approach to classify Synthetic aperture radar (SAR) and Inverse Synthetic aperture radar (ISAR) images using Deep learning techniques. At first, in order to evaluate the effect of convolution layers and the number of hidden layers of the perceptron we thought of implementing 4 configurations of CNN (Convolutional neural network). In the second time, we use the CAE (Convolutional auto-encoder) to learn the optimal filters that minimize the reconstruction error, after we use these filters to feed the CNN retained and evaluate the effect on performance's system.

The strategic goal of this paper is to study the effects of the prevention policies against money laundering on growth in the gulf countries (Saudi Arabia, Kuwait, Qatar, Bahrain, UAE and Oman) from 1980 to 2014. Thus, the logistic regression (logit model) had given three fundamental results. The first had shown that the main policies in matter of fight against money laundering (anti money laundering law AMLL, suspicious transaction reporting STR, the criminalizing of terrorist financing CTF) have had positive effects on the increasing of probabilities to realize more growth. The second is that the said policies have had positive effects on the increasing of the degree of openness of the whole sample. The third is that the variable (proximity) had a positive and significant effect on anti-money laundering policies.

Purpose
This paper aims to investigate board director disciplinary and cognitive influence on corporate value creation.


Design/methodology/approach
Fixed-effect regressions are used to check whether gender diversity, education, independence and size of the board of directors affect measures of corporate value creation.


Findings
The empirical results show that corporate value creation is positively influenced by the cross effect of the board independence and the presence of women. They also point out a positive impact of the cross effect of board independence and management education. They reveal that the board of directors contributes significantly to corporate value creation, particularly when there is a mix of independent, female and management-qualified directors.


Originality/value
The evidence presented and discussed in this paper should be of interest to managers and regulators. The methodological approach and the empirical results extend the existing literature. They enrich the limited empirical research devoted to this theme, especially in a continental European context, i.e. France. They shed light on the effect of board of directors' disciplinary and cognitive influence on corporate value creation.

Several standards exist for the design and safety regulations for vehicles carrying dangerous goods. These standards apply to cargo tanks used for highway transportation. The increase in the reenhouse gases emission and governmental restrictions, research takes towards the use of lighter materials (Al, Mg, plastics, composites, etc.,) to reduce weight, fuel consumption and CO2 emission.These standards describe basic requirements for design, construction, testing, inspection, re-testing, qualification and maintenance, and identification aspects of such tanks. The container shapes most commonly associated with road tankers are the rectangular tank, the horizontal cylinder, the sphere, the cylindrical tank of trapezoidal cross section, the paraboloid and the conical tank for special vehicles. The standards also address the design requirements for joints, manholes, openings, piping, valves and fittings, supports, circumferential reinforcements and accident damage protections. However, the standards do not address the adverse influence of liquid sloshing forces in partially-filled tanks on the stability and handling of tank vehicles. In general, the tanks are designed based on their structural integrity rather than on vehicle system stability considerations. The forces and the moments resulting from the interactions between the liquid and the vehicle, in several maneuver situations as turning and braking in turning can make considerable variations of the liquid load shift and will cause high local pressures and dangerous stress on the tank structure.In this study, analytical and numerical liquid models are formulated based on the Navier-Stokes equations with some assumptions for the analytical model. The pressure forces will be calculated and compared to tensile strength for several materials proprieties. The configuration of the free surface of the liquid used in this study is illustrated in Figure 1. The Figure 2 highlights the numerical modeling of the free surface subject to lateral acceleration and longitudinal acceleration respectively.

Abstract: Autonomous Underwater Vehicles (AUVs) are used in many applications such as the exploration of oceans, scientific and military missions, etc. Developing control schemes for AUVs is considered to be a very challenging task due to the complexity of the AUV model, the unmodeled dynamics, the uncertainties and the environmental disturbances. This paper develops a robust control scheme for the dynamic positioning and way-point tracking of underactuated autonomous underwater vehicles. In order to insure the robustness of the proposed controllers, the sliding mode control technique is adopted in the design process. Simulation results are given to validate the proposed controllers. Moreover, studies are presented to evaluate the robustness of the developed controllers with model uncertainties and under different types of disturbances including unknown currents. ; Kuwait Foundation for the Advancement of Science (KFAS 2013-5505-01)

International audience ; Highlights We identify the displaced commercial risk DCR exposure of Islamic banks. We identify the scenarios of displaced commercial risk exposure to compute the DCR Profits and Losses to Islamic banks shareholders. Scenarios of risk depend on the actual rate of return on investment accounts, the benchmark rate of return and level of existing reserves to mitigate the DCR. We assess the capital charge needed to cover the displaced commercial risk using the Value-at-risk measure of risk, DCR-VaR. We assess the coefficient alpha α CAR-VaR for the capital adequacy ratio for Islamic banks. We consider three methods, the Historical non-parametric VaR, the parametric-VaR and the Extreme Value Theory-VaR. Abstract The objective of the research is to quantify the displaced commercial risk (DCR) based on quantitative finance techniques. We develop an internal model based on the Value-at-risk (VaR) measure of risk to assess the DCR-VaR and the alpha coefficient in the capital adequacy ratio of Islamic banks. We identify first the scenarios of exposure of Islamic banks to DCR that depend on the actual return on unrestricted profit sharing investment accounts (PSIA U), the benchmark return as well as the level of the existing profit equalization reserve (PER) and investment risk reserve (IRR). Second, we quantify the DCR-VaR and the alpha coefficient for a given holding period and for given confidence level. We illustrate the DCR-VaR model on selected Islamic banks from Bahrain. Our model helps to better assess the needed equity to cover the DCR and an accurate capital adequacy ratio for Islamic banks. The model has also policy implications for regulators and the IFSB to develop better guidance on good practices in managing this risk.

International audience ; Highlights We identify the displaced commercial risk DCR exposure of Islamic banks. We identify the scenarios of displaced commercial risk exposure to compute the DCR Profits and Losses to Islamic banks shareholders. Scenarios of risk depend on the actual rate of return on investment accounts, the benchmark rate of return and level of existing reserves to mitigate the DCR. We assess the capital charge needed to cover the displaced commercial risk using the Value-at-risk measure of risk, DCR-VaR. We assess the coefficient alpha α CAR-VaR for the capital adequacy ratio for Islamic banks. We consider three methods, the Historical non-parametric VaR, the parametric-VaR and the Extreme Value Theory-VaR. Abstract The objective of the research is to quantify the displaced commercial risk (DCR) based on quantitative finance techniques. We develop an internal model based on the Value-at-risk (VaR) measure of risk to assess the DCR-VaR and the alpha coefficient in the capital adequacy ratio of Islamic banks. We identify first the scenarios of exposure of Islamic banks to DCR that depend on the actual return on unrestricted profit sharing investment accounts (PSIA U), the benchmark return as well as the level of the existing profit equalization reserve (PER) and investment risk reserve (IRR). Second, we quantify the DCR-VaR and the alpha coefficient for a given holding period and for given confidence level. We illustrate the DCR-VaR model on selected Islamic banks from Bahrain. Our model helps to better assess the needed equity to cover the DCR and an accurate capital adequacy ratio for Islamic banks. The model has also policy implications for regulators and the IFSB to develop better guidance on good practices in managing this risk.

International audience ; Highlights We identify the displaced commercial risk DCR exposure of Islamic banks. We identify the scenarios of displaced commercial risk exposure to compute the DCR Profits and Losses to Islamic banks shareholders. Scenarios of risk depend on the actual rate of return on investment accounts, the benchmark rate of return and level of existing reserves to mitigate the DCR. We assess the capital charge needed to cover the displaced commercial risk using the Value-at-risk measure of risk, DCR-VaR. We assess the coefficient alpha α CAR-VaR for the capital adequacy ratio for Islamic banks. We consider three methods, the Historical non-parametric VaR, the parametric-VaR and the Extreme Value Theory-VaR. Abstract The objective of the research is to quantify the displaced commercial risk (DCR) based on quantitative finance techniques. We develop an internal model based on the Value-at-risk (VaR) measure of risk to assess the DCR-VaR and the alpha coefficient in the capital adequacy ratio of Islamic banks. We identify first the scenarios of exposure of Islamic banks to DCR that depend on the actual return on unrestricted profit sharing investment accounts (PSIA U), the benchmark return as well as the level of the existing profit equalization reserve (PER) and investment risk reserve (IRR). Second, we quantify the DCR-VaR and the alpha coefficient for a given holding period and for given confidence level. We illustrate the DCR-VaR model on selected Islamic banks from Bahrain. Our model helps to better assess the needed equity to cover the DCR and an accurate capital adequacy ratio for Islamic banks. The model has also policy implications for regulators and the IFSB to develop better guidance on good practices in managing this risk.

International audience ; Highlights We identify the displaced commercial risk DCR exposure of Islamic banks. We identify the scenarios of displaced commercial risk exposure to compute the DCR Profits and Losses to Islamic banks shareholders. Scenarios of risk depend on the actual rate of return on investment accounts, the benchmark rate of return and level of existing reserves to mitigate the DCR. We assess the capital charge needed to cover the displaced commercial risk using the Value-at-risk measure of risk, DCR-VaR. We assess the coefficient alpha α CAR-VaR for the capital adequacy ratio for Islamic banks. We consider three methods, the Historical non-parametric VaR, the parametric-VaR and the Extreme Value Theory-VaR. Abstract The objective of the research is to quantify the displaced commercial risk (DCR) based on quantitative finance techniques. We develop an internal model based on the Value-at-risk (VaR) measure of risk to assess the DCR-VaR and the alpha coefficient in the capital adequacy ratio of Islamic banks. We identify first the scenarios of exposure of Islamic banks to DCR that depend on the actual return on unrestricted profit sharing investment accounts (PSIA U), the benchmark return as well as the level of the existing profit equalization reserve (PER) and investment risk reserve (IRR). Second, we quantify the DCR-VaR and the alpha coefficient for a given holding period and for given confidence level. We illustrate the DCR-VaR model on selected Islamic banks from Bahrain. Our model helps to better assess the needed equity to cover the DCR and an accurate capital adequacy ratio for Islamic banks. The model has also policy implications for regulators and the IFSB to develop better guidance on good practices in managing this risk.