This paper presents the optimization, elaboration and characterization of a new TCO (Transparent Conductive Oxides) electrode based on ITO/Ag/ITO multilayer design that enables overcoming the trade-off between the electrical and optical properties. A new hybrid approach combining the investigated design and Particle Swarm Optimization (PSO) technique is conducted with the aim of maximizing the Haacke Figures of merit (FoM). It is found that the optimized ITO/Ag/ITO tri-layered design paves a new path toward achieving a high FoM of 125 × 10-3Ω-1. Such improvement is attributed to the improved light management achieved by the efficient modulation of the Ag sub-layer geometry. Subsequently, the optimized multilayer design is fabricated using RF magnetron sputtering technique. The structural, optical and electrical properties associated with the deposited ITO/Ag/ITO multilayer structure are also analyzed. It is found that the fabricated TCO-based electrode shows a high transmittance over than 94.1% and a low sheet resistance of 4.5Ω × sq-1, which is in good agreement with the theoretical predictions. Therefore, the proposed design methodology based on experiments assisted by PSO metaheuristic approach offers exciting opportunities for bridging the gap between transparency and conductivity characteristics. This makes the elaborated ITO/Ag/ITO multilayer design suitable for high-performance optoelectronic applications.

With the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps. (i) image indexation step, in which we use MapReduce distributed model on Spark in order to speed up the indexation process. We also use a memory-centric distributed storage system, called Tachyon, to enhance the write operation (ii) image retrieving step which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on MapReduce model implemented under Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.

With the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps. (i) image indexation step, in which we use MapReduce distributed model on Spark in order to speed up the indexation process. We also use a memory-centric distributed storage system, called Tachyon, to enhance the write operation (ii) image retrieving step which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on MapReduce model implemented under Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.

With the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps. (i) image indexation step, in which we use MapReduce distributed model on Spark in order to speed up the indexation process. We also use a memory-centric distributed storage system, called Tachyon, to enhance the write operation (ii) image retrieving step which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on MapReduce model implemented under Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.

With the huge increase of large-scale multimedia over Internet, especially images, building Content-Based Image Retrieval (CBIR) systems for large-scale images has become a big challenge. One of the drawbacks associated with CBIR is the very long execution time. In this article, we propose a fast Content-Based Image Retrieval system using Spark (CBIR-S) targeting large-scale images. Our system is composed of two steps. (i) image indexation step, in which we use MapReduce distributed model on Spark in order to speed up the indexation process. We also use a memory-centric distributed storage system, called Tachyon, to enhance the write operation (ii) image retrieving step which we speed up by using a parallel k-Nearest Neighbors (k-NN) search method based on MapReduce model implemented under Apache Spark, in addition to exploiting the cache method of spark framework. We have showed, through a wide set of experiments, the effectiveness of our approach in terms of processing time.

Risk mapping is the foundation of the risk prevention strategy. It allows for the understanding of all factors that may affect the activities. It is a collective decision based on negotiation between the actors. Argument-based negotiation accelerates the negotiation process and converges it toward a final and common decision. It is in this context that this paper aims to illustrate the contribution of the improved PCA and AHP tools, considered as arguments, to the classification of major accident risks.

Risk mapping is the foundation of the risk prevention strategy. It allows for the understanding of all factors that may affect the activities. It is a collective decision based on negotiation between the actors. Argument-based negotiation accelerates the negotiation process and converges it toward a final and common decision. It is in this context that this paper aims to illustrate the contribution of the improved PCA and AHP tools, considered as arguments, to the classification of major accident risks.