A packed bed for solar energy storage in the form of sensible heat has been investigated using two-phase continuous model. The system contains Quartzite as the filler material and Palm oil as the heat transfer fluid. The aim of this work is to propose an eco-friendly storage system which uses natural concrete and certified sustainable oil for medium temperature thermal storage. Using the developed model, the performance of the Palm oil has been compared with two different synthetic oils which has shown that the Palm oil could efficiently be used as a heat transfer fluid for a working temperature below 300°C

For modern industry, data generated by machines and devices, product lifecycle management (PLM) solutions, production planning systems or quality and inventory management systems has reached a volume of more than a thousand Exabyte annually and is expected to increase in the next years. This has led to the need of “Big Data” which originally referred to the huge flood of data, has extended the scope of technological capability to store, manage, process, interpret, and visualize the amount of data. This paper outlines the value that Big data offers for supply chains that are increasingly complex. Indeed, Big data have the potential to revolutionize supply chain dynamics. In this article, we analyze Big data applications, their opportunities and challenges in the different supply chain processes.

Recommender Systems (RSs) are valuable and practical tools that help users to find interesting products in a large space of possible options. Many hybrid recommender systems combine collaborative filtering and content-based approach to build a more robust system. This paper aims to propose a new deep learning based recommender system to enhance recommendation performance and to overcome the limitations of existing approaches, especially when dealing with the cold start problem. So, a hybrid model based on Deep Belief Networks and item-based collaborative filtering is proposed. We conducted experiments on MovieLens 100K dataset. The results showed that our method outperforms existing hybrid recommender systems.