Market analysis on hybrid storage components

In recent years, the Energy Storage Systems field for all electric vehicle applications has attracted many researchers and academic institutions. The main challenge concerns the design in terms of weight, volume, and increasing as possible the hybrid source lifetime. Therefore, increasing the vehicle drive range is very important parameter to facilitate their commercialization.Several storage components can be used to achieve the needed performances as super capacitor, batteries and fuel cell systems. However, fuel cells are energetic components where it is needed to add a powerful element to ensure the power peaks during acceleration and deceleration phases. This paper deals with, a comparative study between fuel cell and battery based power trains for lightweight electric vehicles. A developed algorithm is used to obtain the optimal sizes and constraints of different Energy Storage Systems (ESS) based on fuel cells, batteries and supercapacitors. The comparison between the several systems is based on their costs, volumes, weights and constraints applied to cells. First, to confirm the hybridization benefits we present a comparative study between single and hybrid ESSs according to the desired drive range in terms of energy consumption, weight and cost. Furthermore, in the objective to improve the hybrid ESS performances, different technologies batteries and supercapacitors are tested and their performances will be evaluated. Simulation results show that the desired range influences the choice of storage system technologies for the hybrid enrgy storage system. This results confirm also the interest of hybrid storage system composed with fuel cells and battery for high drive range (>400km).

One of Saudi Arabia’s vision 2030 goals is to keep the unemployment rate at the lowest level to empower the economy. Research has shown that a rise in unemployment has a negative effect on any countries gross domestic product. Artificial Intelligence is the fastest developing technology these days. It has served in many specialties. Recently, Artificial Intelligence technology has shined in the field of recruiting. Researchers are working to invest its capabilities with many applications that help speed up the recruiting process. However, having an open labor market without a coherent data center makes it hard to monitor, integrate, analyze, and build an evaluation matrix that helps reach the best match of job candidate to job vacancy. A recruiter’s job is to assess a candidate’s data to build metrics that can make them choose a suitable candidate. Job seekers build themselves metrics to compare job offers to choose the best opportunity for their preferred choice. This paper address how Artificial Intelligence techniques can be effectively exploited to improve the current Saudi labor market. It aims to decrease the gap between recruiters and job seekers. This paper analyzes the current Saudi labor market, it then outlines an approach that proposes: 1) a new data storage technology approach, and 2) a new Artificial Intelligence architecture, with three layers to extract relevant information from data of both recruiters and job seekers by exploiting machine learning, in particular clustering algorithms, to group data points, natural language processing to convert text to numerical representations, and recurrent neural networks to produce matching keywords, and equations to generate a similarity score. We have completed the current Saudi labor market analysis, and a proposal for the Artificial Intelligence and data storage components is articulated in this paper. The proposed approach and technology will empower the Saudi government’s immediate and strategic decisions by having a comprehensive insight into the labor market.KeywordsRecruitingJob seekerArtificial IntelligenceNatural language processingClustering algorithmsRecurrent neural network

This paper is motivated by a global online satellite images distribution system operated at the Earth Resources Observation and Science (EROS) center of the U.S Geological Survey. Fundamental objectives of EROS include, but are not limited to, building high-speed and cost-effective massive data processing and storage systems to support online satellite images distribution. Hybrid storage systems -- containing solid-state drives (SSD), hard disks (HDD), and tapes -- can provide an ideal data storage solution for a wide variety of data processing centers like EROS. Large-scale hybrid storage systems will become increasingly popular in the next few years for the following two reasons. First, highly accessed storage objects in a hybrid storage system can be prefetched and cached to high-speed storage components such as solid-state drives. SSD-based hybrid storage system can provide large storage capacity, high I/O performance and data reliability. Second, hybrid storage systems are cost-effective, because inexpensive tapes help in increasing storage capacities at very low cost. Transferring data back and forth among SSDs, HDDs, and tapes plays a critical role in achieving high I/O performance. Thus, we proposed data mining algorithms that can judiciously prefetch data. Our analytical model and the experimental results reveals that our data mining prefetching algorithm increase the performance of the hybrid storage systems.