Volume 8, Issue 6

Although China has made great achievements in economic scale, the model of high-quality economic developments is still in the exploratory stage. A series problems, such as unreasonable industrial structure and low production efficiency, need to be resolved urgently. The transformation and upgrading of industrial structure is the inevitable choice. The analysis of industrial relations is an important method to reveal the internal mechanism of industrial structure change. Therefore, based on the concepts of sensitivity and influence coefficient, this paper proposed the concepts of driven and driving coefficient, and constructed the industrial complex networks of China from 1999 to 2013 according to the EORA input-output table. From the perspectives of the interaction power, industrial linkages and community structure, this paper systematically analyzes the evolution of China’s industrial structure. Results show that China’s industrial structure is undergoing a transformation from industry-oriented to industry-service-oriented. The interaction linkages between industries has formed a robust association structure. The traditional sectors such as infrastructure construction and industries are still the largest consumer group driving the development of other industries. Besides, light industry, infrastructure construction and finance have become increasingly important hub industries in the industrial system. However, some unbalanced community structures are formed in the economic system, which has resulted in a waste of resources and low production efficiency to a certain extent. Effectively grasping the relationship between China’s industrial structure evolution and its association is conducive to formulating economic restructuring policies, promoting the upgrading of China’s economic and, realizing the leap from quantitative change to qualitative change.

China’s grain science and technology policies have played an important role in the development of China’s food industry. This paper aims to examine the effects of China’s grain science and technology policies on food security. It quantitatively assesses China’s food security by analyzing the main contents and development trends of China’s food science technology policies through the text metrology method, and then investigates the effects of grain science and technology policies on food security by employing a provincial dynamic panel model. The results show that food security in China is all-round developed, and that the release frequency and cumulative effect of grain science and technology policies play a significant role in promoting food security. Powerful grain science and technology policies can effectively guarantee China’s food security.

In this paper, we investigate the multiple attribute decision making (MADM) problems in which the attribute values take the form of hesitant trapezoid fuzzy information. Firstly, inspired by the idea of hesitant fuzzy sets and trapezoid fuzzy numbers, the definition of hesitant trapezoid fuzzy set and some operational laws of hesitant trapezoid fuzzy elements are proposed. Then some hesitant trapezoid fuzzy aggregation operators based on Hamacher operation are developed, such as the hesitant trapezoid fuzzy Hamacher weighted average (HTrFHWA) operator, the hesitant trapezoid fuzzy Hamacher weighted geometric (HTrFHWG) operator, the hesitant trapezoid fuzzy Hamacher Choquet average (HTrFHCA), the hesitant trapezoid fuzzy Hamacher Choquet geometric (HTrFHCG), etc. Furthermore, an approach based on the hesitant trapezoid fuzzy Hamacher weighted average (HTrFHWA) operator and the hesitant trapezoid fuzzy Hamacher weighted geometric (HTrFHWG) operator is proposed for MADM problems under hesitant trapezoid fuzzy environment. Finally, a numerical example for supplier selection is given to illustrate the application of the proposed approach.

Detecting differential expression of genes in genom research (e.g., 2019-nCoV) is not uncommon, due to the cost only small sample is employed to estimate a large number of variances (or their inverse) of variables simultaneously. However, the commonly used approaches perform unreliable. Borrowing information across different variables or priori information of variables, shrinkage estimation approaches are proposed and some optimal shrinkage estimators are obtained in the sense of asymptotic. In this paper, we focus on the setting of small sample and a likelihood-unbiased estimator for power of variances is given under the assumption that the variances are chi-squared distribution. Simulation reports show that the likelihood-unbiased estimators for variances and their inverse perform very well. In addition, application comparison and real data analysis indicate that the proposed estimator also works well.

We propose a two-echelon remanufacturing model in the context of two periods in which the factor of patent protection of the original manufacturer is considered. Through the analysis of the Stackelberg game among the supplier, the original manufacturer and the remanufacturer we obtain the equilibrium solutions of the unit patent licensing fee of the original manufacturer, the recycling effort degree of the remanufacturer, the wholesale price of new and remanufactured core components and the retail price of new and remanufactured products in the second period which are all dependent on the retail price of new products in the first period in cases with patent protection and without patent protection. In the numerical study we analyze the impacts of the remanufacturing rate of used products and the retail price of new products in the first period on the equilibrium results of the second period, and make a comparison between the case with patent protection and the case without patent protection.

With the rapid development of cloud computing and other related services, higher requirements are put forward for network transmission and delay. Due to the inherent distributed characteristics of traditional networks, machine learning technology is difficult to be applied and deployed in network control. The emergence of SDN technology provides new opportunities and challenges for the application of machine learning technology in network management. A load balancing algorithm of Internet of things controller based on data center SDN architecture is proposed. The Bayesian network is used to predict the degree of load congestion, combining reinforcement learning algorithm to make optimal action decision, self-adjusting parameter weight to adjust the controller load congestion, to achieve load balance, improve network security and stability.