Abstract:The fast-growing accumulation of electronic waste (e-waste) around the world is resulting in environmental pollution and adverse human health and, thus, has become a global area of concern. As the world's second largest producer of e-waste, China suffers significant pollution from e-waste because of inefficient recycling and management. This paper is a comparative systems dynamics study of two big home appliance manufacturers in China: Haier (Haier Group of Qingdao, China) and Gree (Gree Electric Appliances Inc. of Zhuhai, China). We used systems dynamics modeling to examine and compare the impact of closed-loop supply chain recycling and remanufacturing strategies on the total revenue of the supply chain and market shares of both manufacturers. Results show that an increase in third party service coverage and increased environmental awareness enhanced the total revenue of supply chain. Retailers showed more enthusiasm for recycling through contract development with manufacturers, thus resulting in reduced time of payment in closed-loop supply chains. We also found that an improvement of the recycling mechanism of retailers results in an increase in the share of the supply chain market. Hence, we propose a better supply chain mode.

China, as the largest electronic waste producer in the world, is facing a critical challenge to manage its negative impacts on the environment. Hence, e-waste management is crucial for sustainable Chinese economic development. In this paper, a system dynamics model is adopted to identify the effects of retailer-led recycling based on closed-loop dual chains competition. The influence of contracts made by manufacturers on different retail modes is also discussed. From the aspects of total revenue (TR), market share (MS) and market competitiveness (MC), this paper analyzes the impact of e-waste recycling coefficient (ERC) on supply chain and analyzes the equilibrium solution of total supply chain return. The research results show that the contract incentive mechanism can improve the retailer’s recycling enthusiasm, and the effect on the retail mode of executive shop is more obvious. When the ERC is adjusted to 44.3%, the TR of supply chain is optimal, and the MS and MC occupy an obvious advantage.

In order to solve the practical problem that the massive video data generated by monitoring equipment cannot be processed efficiently temporarily, this paper proposes a framework for face recognition of massive video data based on distributed environment. Combined with the application features of face recognition, this method designs a strategy for fast reading of massive video data and optimizes the feature data obtained by the cloud platform, so as to speed up the retrieval speed of face features. The results are as follows: the compression rate of the proposed compression method is higher than that of the traditional matrix triple and binary methods, which is increased by about 65%; the data optimization method in this paper greatly reduces the amount of feature data, which is 7.08 times less than that in the nonoptimization state. At the same time, the process of face recognition is reduced from 12.6 seconds to 2.73 seconds, and the time of feature decompression is only 0.75 seconds more than the original; the experiment shows that it takes 10180 seconds for the system to process 200 GB pictures with 9 computing nodes, and the total running time of the system is 4737 seconds longer than that of a single node, accounting for about 5.45% of the total time of a single node system. At the same time, the experimental data show that the system is 8.53 times faster than that of a single node with 9 computing nodes. It is proved that this framework has certain research significance in dealing with massive unstructured data. It not only provides theoretical reference value for the research of massive video processing but also makes a contribution to the actual industry.