Study on the Preparation of Solar Grade Silicon by Metallurgical Method

The global PV industry has rapidly developed over the past decade, which has led to a large demand for silicon materials. Solar cells are currently fabricated from a variety of silicon‐based materials. However, current market is difficult to ensure a steady supply for this material. Development of a new process to produce silicon at low cost is definitely necessary. Metallurgical grade silicon (MG‐Si) with the purity of 98%, which is produced by carbothermic reduction in electric arc furnaces, has been considered as a cheap starting material for conversion to purity of 99.99%. Many alternative methods for purifying MG‐Si to Solar grade silicon (SoG‐Si) have been explored, for example, (1) pyro metallurgical processes, (2) hydrometallurgical processes and (3) electrochemical methods. Metallurgical route is recognized as a promising process to meet market demand for solar energy silicon material, which is different from the traditional Siemens process. This chapter focuses on the introduction of three kinds of typical impurity removal methods in metallurgical process, and the impurity removal effect of different processes was analysed and discussed

Blockchain architecture and its applications in a bank risk mitigation framework

This study proposes a simple two-period model to consider consumers’ borrowing behaviour in a decentralised consensus and information distribution platform. Based on this model, we develop a bank risk mitigation framework and find that decentralised digital identity and encryption technology are the most important factors for attaining market equilibrium between decentralised consensus and information distribution. Specifically, the greater the scope of digital identity construction and the more blockchain consensus records there are, the less likely the borrower will default. Our study provides meaningful practical implications for bankers and policy regulators to help them better understand consumers’ borrowing behaviour and decisions to default

A words-of-interest model of sketch representation for image retrieval

In this paper we propose a method for sketch-based image retrieval. Sketch is a magical medium which is capable of conveying semantic messages for user. It’s in accordance with user’s cognitive psychology to retrieve images with sketch. In order to narrow down the semantic gap between the user and the images in database, we preprocess all the images into sketches by the coherent line drawing algorithm. During the process of sketches extraction, saliency maps are used to filter out the redundant background information, while preserve the important semantic information. We use a variant of Words-of-Interest model to retrieve relevant images for the user according to the query. Words-of-Interest (WoI) model is based on Bag-ofvisual Words (BoW) model, which has been proven successfully for information retrieval. Bag-of-Words ignores the spatial relationships among visual words, which are important for sketch representation. Our method takes advantage of the spatial information of the query to select words of interest. Experimental results demonstrate that our sketch-based retrieval method achieves a good tradeoff between retrieval accuracy and semantic representation of users’ query