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.

According to a United Nations report, the proportion of the urban population living in informal houses worldwide had reached 23.5% in 2018, which means over 1 billion people have been living in slums, and an estimated 3 billion people will be requiring affordable housing by 2030. Slums are often linked to developing countries, where weak governance and misled socio-economic choices aggravate the situation and lead to continual conflict with the authorities. This research aims to model this conflict through Game Theory and shed the light on the different parameters that influence the decision-making process and finally propose a management model to limit the spread of this phenomenon as much as possible. This model will be applied in the city of Hassi Bahbah using ABM, the results of the game modeling allowed us to extract the delta threshold which will allow decision-makers to know when they have to make decisions. Our study aims to offer decision-makers a tool based on our model, allowing them to better manage and thus monitor the evolution of informal houses. The authors hope that the findings could provide means and insights for policies and strategic directions in land uses and planning systems.

Sedimentation of dam reservoirs is a complex problem with several dimensions, including filling rates and characteristics of accumulated sediments. Sediment supply from river basins is particularly high in this region because of its semi-arid climate and especially because of poor vegetation protection. The amount of silt accumulated annually since the construction of this dam is estimated at 330000 m³. This silt accumulation strongly limits its storage capacity and consequently its operating duration. The consequences of this serious problem have been catastrophic, including a considerable reduction of 43–84% of the storage capacity of the dams and a clear degradation of water quality that can cause the degradation of the ecosystem functioning and can lead to irreversible changes. The silt present in abundance in the Algerian dams can, thus, constitute a potential resource to be judiciously exploited towards the increase of the performances of the construction materials. The extraction of sediments accumulated in the dam reservoir is, therefore, imperative. These sediments have a great geotechnical value. The objective of this study is to assess the feasibility of the recovery of mud by studying the knowledge of the sediments of the dam of Koudiat Medouar. The results of the tests carried out in laboratory allowed us to identify the various sediments from a physical and geotechnical point of view. These materials must of course meet certain rigorous criteria in terms of mechanical strength and durability and environmental impact. The experimental approach that we adopted allowed us to determine the characteristics of the materials necessary for the realization of compressed earth bricks (BTC) in conformity with the recommendations of the technical guides of construction.

In this paper a H∞ control technique addresses the voltage regulation in distributed generation (DG) system connected to power converter under harmonic disturbances. The DG control technique combines a discrete sliding mode control (DSMC) in the current control and a Robust Servomechanism Problem (RSP) in the voltage control. Besides, a fractional Order Proportional-Integral-Derivative (FOPID) controller synthesized with an automatic calibration of adjustable fractional weights is formulated in this work. For performance and high robustness requirements, the parameters of FOPID are optimized through solving a multiobjective optimization problematic based on the automatic calibration of the weighted-mixed sensitivity problem. Furthermore, for ensuring an adequate calibration of parameters, the Integral of Time Weighted Absolute Error (ITAE) criterion with Genetic Algorithm (GA) are used to achieve better voltage regulation. The simulation results show that it can achieve trade-off between nominal performance (NP) and robust stability (RS) margins for the constrained uncertain plants in the large range frequencies. Also, the results validate the effectiveness of the proposed control at which both low total harmonic distortion (THD) and low tracking error.

The ongoing pandemic of COVID-19 is causing more health, economic and social issues worldwide. As of July 5, 2021, the world registered more than 184 million cases across 222 countries; more than 4 million have died from the deadly infection. The SARSCoV-2 continues spreading globally; new variants emerge randomly due to errors in the virus' gRNAs replication process. The present paper treats the new delta variant of concern, also known as B.1.617.2 lineage. The study highlights transmissibility, vaccine effectiveness, pathogenicity, and the likelihood of hospital admission related to delta variant infection based on a literature review of 10 indexed databases. The findings indicate high transmissibility of the B.1.617.2 lineage, approving it to be the dominant strain worldwide. Also, reduced vaccine effectiveness is confirmed. However, approved vaccines for emergency use remain valuable against COVID-19's delta variant. Finally, the risk of hospitalization seems to be twice in the case of delta variant infection. A combined approach of vaccination and nonpharmaceutical interventions is the leading way to contain the ongoing pandemic of COVID-19