Xuzhou Institute of Technology

About: Xuzhou Institute of Technology is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 1696 authors who have published 1521 publications receiving 13541 citations.

A study on the technical improvement and the crystalline quality optimization of columnar β-Ga2O3 crystal growth by an EFG method

Abstract: High quality columnar β-Ga2O3 crystals were successfully grown via an edge-defined film-fed growth (EFG) method equipped with columnar Ir die The effects of the pulling rate and die height on the structural quality and crystal shape of β-Ga2O3 are summarized and discussed in detail The main problems of spiral and polycrystal formation and incomplete columnar quality in the β-Ga2O3 crystal growth were solved successfully Suitable values for the growth parameters such as pulling rate and Ir height were determined to be in the range between 2–10 mm h−1 and 30–40 mm (06–08 mm longer than the crucible height), respectively Simultaneously, the die diameter should be 25 mm (05 mm wider than the crucible diameter) to obtain columnar β-Ga2O3 crystals in good quality Furthermore, the crystalline quality of the as-grown crystal was confirmed via high-resolution X-ray diffraction (HRXRD) with a full-width at half-maximum (FWHM) of 693 arcsec The defects of β-Ga2O3 were studied via a chemical etching method Two types of etch pits were observed: (1) a triangle-shaped etch pit and (2) a quadrangle-shaped etch pit The average defect density was estimated at a lower order of magnitude of −104 cm−2

Evaluation and selection strategy for green supply chain using interval-valued q-rung orthopair fuzzy combinative distance-based assessment

Abstract: Environmental deterioration and global warming has created a substantial impact on international companies to incorporate eco-friendly, green supply chain practices and remain competitive in the market. In food supply chain, the selection of the best supply chain management (SCM) strategy can improve the overall performance and can be crucial in managing the food supply chain challenges and achieving sustainable food supply chain. In this paper, different green SCM strategies were explored and evaluated for food supply chain using combinative distance-based assessment method under interval-valued q-rung orthopair fuzzy information as interval extension of q-rung orthopair fuzzy can effectively handle the ambiguity and uncertainty of preference given by experts in decision making. A generalized p-distance between two interval-valued q-rung orthopair fuzzy numbers is proposed in the current study and is used to formulate interval-valued q-rung orthopair fuzzy Hamming distance and interval-valued q-rung orthopair fuzzy Euclid distance. Five supply chain strategies, namely risk-based, efficiency-based, resource-based, innovation-based, and closed-loop strategy, are evaluated to select best strategy for food supply chain based on different attributes such as green manufacturing, green design and development, green management system, green procurement, and green marketing which contribute toward sustainability. It is found that closed-loop supply chain strategy is the best strategy for food supply chain to attain sustainability. A comparative analysis and sensitivity analysis are done to examine the reliability of results obtained from the proposed framework.

GRL: Knowledge graph completion with GAN-based reinforcement learning

Abstract: Knowledge graph completion intends to infer the entities that need to be queried through the entities and relations known in the knowledge graphs. It is used in many applications, such as question and answer systems, and searching engines. As the completion process can be represented as a Markov process, existing works would solve this problem with reinforcement learning. However, there are three issues blocking them from achieving high accuracy, which are reward sparsity, missing specific domain rules, and ignoring the generation of knowledge graphs. In this paper, we design a generative adversarial net (GAN)-based reinforcement learning model, named GRL, for knowledge graph completion. First, GRL employs the graph convolutional network to embed the knowledge graphs into the low-dimensional space. Second, GRL employs both GAN and long short-term memory (LSTM) to record trajectory sequences obtained by the agent from traversing the knowledge graph and generate new trajectory sequences if needed. At the same time, GRL applies domain-specific rules accordingly. Finally, GRL employs the deep deterministic policy gradient method to optimize both rewards and adversarial loss. The experiments show that GRL is able to both generate better policies and outperform traditional methods for several tasks.