Abstract: Over the past decade, virtual reality (VR) technology has been increasingly used in educational settings. Its benefits and opportunities are opening up new avenues for learning. This research attempts to capture the strategic core factors involved in the evaluation of VR in education by implementing the A’WOT (AHP-SWOT) method, a combination of SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis and the Analytic Hierarchy Process (AHP). SWOT analysis presents a comprehensive summary of important forces and challenges that are necessary for the development of VR education. However, The SWOT analysis does not have a way of measuring weights analytically and determining the importance of the components. Therefore, AHP analysis was applied to quantify and rank the factors that affect the functioning of the system. The proposed framework analysis allows the priorities of the factors contained in the SWOT analysis to be accurately and analytically determined and measurable.

Abstract: In comparing the different electric motors used in electric vehicles, the Brushless motor achieved the highest efficiency, reaching 95%. After its success in taking over from DC motors, the Brushless motor has also become the main competitor to induction motors. This work consists of modeling and simulating the speed control of a BLDC motor for electric vehicles. First, the simulation of the global model was carried out under Matlab, and a PI controller ensured the speed control then, during the comparison, the PID type controller and the fuzzy logic were implemented to obtain a better performance according to the results obtained for each control.

Abstract: Abstract This study analyses the problem of multi‐tier supply chains, including suppliers, producers, wholesalers, and retailers. Decision‐makers should analyse the social, environmental, and economic constraints in a multi‐dimensional business context. We analyse these issues by considering the corporate social responsibility (CSR) concerns. A scorecard‐based mathematical model, consisting of mixed‐integer linear programming, is developed to assist fast‐fashion decision‐makers in supply chain policy formulation. The model is validated through a practical case study using IBM CPLEX Optimizer. The results indicate that involving the social aspect can increase the profit compared to considering only the economic impact, under high environmental costs with low return on investment. Furthermore, the mathematical model is able for the case study to optimise the distribution network of the entire multi‐tier supply chain, considering CSR concerns, in less than 5 s. This research has implications for the advancement of multi‐tier supply chain optimisation and provides a basis for future distribution decisions for firm stakeholders.

Abstract: In this paper, a stand-alone PV system for water pumping used in agricultural irrigation and water storage is proposed in a semiarid area of Morocco. The purpose of this work is to develop Moroccan agriculture, help local citizens and limit the use of traditional water pumping methods depending on fossil energy. In other parts, the photovoltaic pumping system performance is influenced by different meteorological conditions, such as solar irradiation and temperature. The considered place is located in Zagora, Morocco (30° 20′ 45.24″ Nord and 5° 50′ 26.3724″ West). The overall proposed system is used for the irrigation process, which is constructed by testing and evaluating its performance and real climate conditions and variable water demand. This paper proposes the results of one year from March 2021 to February 2022 of data operating system uses (seasonal and monthly results). According to the experimental results, the proposed system can extract an annual 17190 m3 of water, and it is remarked that the high pumped water produced in summer, 61,35%, is utilized for irrigation and the remaining 38,65% is used for other agriculture activities. The performance ratio value limits were recorded (Min.: 60% in June, Max.: 93% in December), the capacity factor values were (Max.: 24% in May, Min.: 17,4% in January), the reference yield limits were (Max.: 7 h/day in July, Min.: 6 h/day in January) and the final yield limit values were (Max.: 5,3 h/day in June, Min.: 4,6 h/day in February). In addition, the detailed results show that the proposed system has great performance in winter, spring and summer.

Abstract: Voltage stability is crucial for power system operation. STATCOM, an adaptable FACTS controller, has a linear PI control with a limited range for nonlinear processes. To overcome this problem, optimization methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used. In this paper, a hybrid GA-PSO was adopted to tune the PI controllers of the studied system. Simulation results are implemented in the MATLAB/Simulink architecture. These results show the effectiveness of the proposed optimization algorithm for minimizing the integral absolute error (IAE) of the grid voltage comparing to PI tuned by GA and PSO algorithms and the traditional PI.

Abstract: To stabilize the trajectory of an unmanned aerial vehicle (UAV) quadrotor system, a nonlinear adaptive integral sliding mode controller based on the Reaching law is proposed in this paper. First, the mathematical model of the quadrotor was developed using the Newton-Euler formalism, and then the design of an ISMC with three different types of reaching laws (reaching law with constant rate, exponential reaching law, and reaching law with power rate) and an analysis of their properties were investigated in this work. The proposed controllers’ performances were evaluated using the MATLAB/Simulation environment. The simulation results indicate that the ISMC-based exponential reaching law ensures fast convergence of the sliding manifold and attenuates the chattering phenomena in the sliding phase.

Abstract: Image restoration has been a longstanding challenge in image processing, involving the enhancement of image content to extract valuable information. Nonlinear models have demonstrated their efficacy in eliminating additive noise, which motivates our goal of investigating a novel nonlocal nonlinear reaction-diffusion model for noise removal. The proposed model integrates a fractional diffusion equation with a nonlinear nonlocal p-Laplace operator, with the fidelity term employing a weak norm to better capture oscillatory patterns and intricate details in textured images. Using the Schauder fixed point theorem, the well-posedness of the proposed model solution is established. The experimental results confirm the effectiveness and efficiency of the proposed model, providing validation for its practical utility.

Abstract: published or not.The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Abstract: ABSTRACT

Abstract: The quality of electrical energy is a critical element for all actors in the energy sector. Among all these actors, the network operator occupies a central position. Its responsibility is to implement the means to ensure the quality of electrical energy within the electrical networks. Among the reasons that define the great importance given by the actors, whether network managers, suppliers, producers, or consumers of electrical energy, are the electrical disturbances in the economic sector. This causes problems such as production stoppages, loss of raw materials, disruption of production quality, premature aging of equipment, etc. This work aims to design a system to evaluate the quality of electrical energy in the VHV(Very High Voltage) and HV (High Voltage) national power grid, to visualize in real time the results obtained, and to record all the parameters.

Abstract: The Principe of injecting photovoltaic energy into the power grid has been widely accepted in these times of renewable energy production. This paper explains a complete study for different components of a photovoltaic system connected to the, such as PV panels, DC/DC converters, PV inverters, transformers, filtering system and grid. Therefore, a general explanation of the different controls, using the incremental conductance method with the integral controller to control the maximum power point tracking (MPPT), as well as the VSC and boost converter controls. Then we used Simulink/MATLAB for simulated a model of grid connected PV system 100kW rated power.

Abstract: The range of electric vehicles is one of the main obstacles to their widespread adoption. Various charging methods are being used to address this issue and wireless charging technology is emerging as a promising solution. However, the misalignment between the transmitting and receiving coils affects the transfer efficiency. The geometry of the coils can also have an impact on the efficiency. This manuscript investigates the influence of misalignment on the main parameters of the WPT system and evaluates the Ansys Maxwell magnetic flux model under perfect alignment and in the case of misalignment along the X-axis for both the circular and the square design. The simulation results show that the circular design has a large coupling coefficient and a large mutual inductance compared to the square design, but the latter is less sensitive to misalignment. Under perfect alignment the magnetic field density is maximum at the center and decreases moving outwards, so the magnetic field becomes weak along the vertical Z direction.

Abstract: The ensuing specifications for the layout, setup, and operation of our facilities become essential for success. In the past, we have frequently made structures and control systems more sophisticated, which has led to rigid, monolithic production systems. However, the future must become “lean” in both organization and planning. technology, too! We must create the technological tools necessary to reduce planning work, hasten setup and planning, and quickly adjust to the product modifications while operations are in progress. We should incorporate smart technologies into our daily lives in order to solve these difficulties. The development of wireless communication will enable us to do away with cables. Numerous old control panels will be replaced by powerful computers or cellphones, and fragments will be replaced by abstract services byte controllable. These advances will lead not only to the mobility of people but also to the digitization of industrial systems which offers opportunities for innovation in terms of energy transition and cost reduction. To do this, autonomous systems will need to interact with their real environment, also known as a “digital twin”. The objective of this article is to evaluate the large increase in complexity to ensure the appropriate behavior of the autonomous system during production, in order to achieve the desired production goal. This goal can only be reasonably achieved through extensive use of model-based simulation, not only during design and planning but also during other phases of the life cycle, for purposes such as diagnosis and optimization of operations. This article presents the functioning of the four aspects that determine the future of manufacturing: Modularity - Connectivity - Autonomy - Digital pairing.

Abstract: Improving the planning and management of maritime operations is a key element in driving the maritime supply chain. Although technological innovations facilitate the management of maritime operations, they are still affected by disruptions and uncertainties related to the need for more reliable information. One of the most crucial pieces of information for steering the maritime supply chain is the estimated time of arrival (ETA) of vessels at their destination, as accurate ETA predictions can improve the determination of optimal vessel routes, the monitoring of maritime traffic and the planning of port operations.In this context, we propose to leverage three years of historical vessel trip data. The objective of this work is to present an approach based on deep learning models to predict the ETA of vessels in the Saint Lawrence River. The models were trained on different data sources, taking into account various types and characteristics of vessels and their respective itineraries. This study highlights the potential of deep learning-based methods to leverage maritime historical data to predict ETA, providing a valuable solution for managing maritime supply chains.

Abstract: This paper presents a discussion on the child marriage in Africa. It is a literature review paper which outlines the level of child marriage in Africa as it compares with other continents in the world. The paper explained concepts of marriage in general and child marriage specific. Different factors which are major causes of child marriage in Africa were discussed. Furthermore, effects of the child marriage on individual, society, State and globally have also highlighted. The paper concluded by giving several approaches/strategies which can be adopted in Africa to mitigate the effects of child marriage.

Abstract: Clinical decision-making for patients with cancer is often complex as many treatment combinations and sequences (e.g., surgery, chemotherapy, radiotherapy) can be considered, taking into account many clinical inputs. Multicriteria Group Decision-processes are usually considered to facilitate and improve this complex decision process for each cancer patient. Even though the literature includes several reviews on MCDA methods, reviews limited to the applications of MCDA specifically in clinical decision–making for oncology are not popular. In this work, we conduct a literature review with the objective to describe characteristic aspects and approaches, as well as the specifics of the various MCDA methods used in clinical decisions and structure. To this end, we focused on papers related to the use of MCDA methods while selecting the best treatment or diagnosis plan in oncology. Our initial literature search yielded 430 abstracts, 15 of which met the inclusion requirements and were taken into consideration for additional investigation. The majority of clinical decision-making (treatment, diagnostic) issues are solved using Value-Based Strategies. In fact AHP and ANP are the most used MCDA techniques, followed by Fuzzy PROMETHEE, EVIDEM, and TOPSIS. It’s interesting to note that practically reviewed paper adopted direct weighting, although the most popular methods for scoring and weighting are scales and AHP. Our findings can serve as a reference to develop strategies and frameworks that use MCDA to deal with clinical decision-making issues such as selecting the most efficient therapy strategy or method of diagnosis for a particular cancer patient

Abstract: The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph processing system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, graph processing systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and low-memory fast graph mutations. Existing graph structures offer a hard tradeoff between read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these tradeoffs and enables both fast read-only analytics and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists to achieve the best of both worlds. We compare CSR++ to CSR, adjacency lists from the Boost Graph Library, as well as state-of-the-art update-friendly graph structures: LLAMA, STINGER, GraphOne, and Teseo. In our evaluation, which is based on popular graph processing algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average), while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates. We also show that both CSR++’s update throughput and analytics performance exceed that of several state-of-the-art graph structures, while maintaining low memory consumption when the workload includes updates.

Abstract: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.

Abstract: Key Performance Indicators play a crucial role in monitoring and evaluating the performance of road safety systems. However, the process of selecting the most representative indicators can be challenging due to the multi-dimensional nature of road safety and the large number of available indicators. In this study, we propose a comprehensive approach for the effective ranking and prioritization of road safety indicators (RSIs) using the Analytical Hierarchy Process (AHP), a prominent method in Multi-Criteria Decision Analysis (MCDA). The application of MCDA methods, such as AHP, empowers Decision Makers (DMs) to make well-informed choices and select suitable options for their organizations. An application of the proposed approach using an established set of eight criteria and focusing on a specific subsection of RSIs is presented. The weights for the selected criteria are calculated and the priority ranking of RSIs are determined.

Abstract: Reinjection of produced water is a critical aspect of managing geothermal reservoirs. The sustainability of extracted thermal energy relies on the performance of well doublets, which in turn is linked to the hydrothermal properties of the reservoir, the thermal characteristics of confining layers, and operating conditions. This study presents an advanced mixed-hybrid finite element simulator that solves coupled hydrothermal equations on unstructured grids. The model is favorably compared with the conforming finite element method at a geothermal power plant site that exploits the Dogger reservoir in the Paris basin. The study also includes a parametric sensitivity analysis, using an ensemble simulation to assess doublet lifetime, thermal breakthrough, and their ratio as performance metrics. Our findings emphasize the significant influence of horizontal permeability anisotropy and production flow rate on doublet performance, with doublet orientation relative to the principal axes of the areal permeability tensor as a key control parameter. Other influential factors include the thermal conductivity of the confining beds, while longitudinal thermal dispersivity and the total productive thickness of the reservoir are less critical. We establish, for the first time, a simple relationship between doublet lifetime and thermal breakthrough, indicating that the former is twice as long as the latter. This underscores the importance of long-term monitoring to sustain geothermal exploitation. This relationship can inform enhanced management of the Dogger reservoir at the basin scale.

Abstract: In real-world settings, machines are not available all the time. They can undergo different collapses and malfunctions. This may increase costs and sometimes gravely threaten safety. To face this challenge, it is important to assess the availability based on the different dependencies between their components. The purpose of this paper is to compute exactly the availability of a two-component parallel system considering stochastic dependence. We propose an efficient and user-friendly model, based on Cox proportional hazards model using the generalised Weibull distribution. A calculation framework is presented to compute more realistic system availability even for real systems provided with a history of failures. A numerical example is given to assess the stochastic dependence effect on the availability of the system and to illustrate the model. A managerial insight is provided to allow the practitioners to better estimate this latter in order to develop adequate maintenance strategies.

Abstract: In our changing energy landscape, electricity is taking a major role in achieving decarbonization goals. Electricity can be a clean and efficient source of energy, and it is well-suited to help countries meet their climate goals. However, the electrical market is complex and constantly evolving, and it is important to carefully choose the design elements of the market to ensure that it is meeting its objectives. In this context, evaluating an electrical market's effectiveness requires a multifaceted approach that takes into account a range of elements, from environmental impact to economic viability. This paper provides an overview of several evaluation methods for different objectives to finally select the key criteria to consider in assisting decision-makers, regulators, and stakeholders in developing an electricity market that is not only effective but also reliable and sustainable.

Abstract: — This paper proposes a method for identifying isomorphisms between different kinematic chains that is highly efficient, reliable, and simple, with a short CPU running time (KC). In contrast to many methods proposed by researchers in this field, which require significant computing time, particularly in kinematic chains with a large number of bars. Isomorphism identification is critical for designers in order to avoid duplicate solutions and focus all of their energy and creativity on novel, independent kinematic chain solutions. The shortest path between non-binary bars is primarily used in this article to solve the problem of isomorphism identification. The computational complexity and efficiency of the method are evaluated and compared to existing published methods for a variety of cases, including 8-bar, 10-bar, 12-bar, three-complex 13-bar, 15-bar, 28-bar, and 42-bar single-joint kinematic chains. These comparisons demonstrate the superiority of the proposed method.