Demonstration Analysis of Relationship Between R&D Investment and GDP

To reveal the quantitative relationship between research and development (R&D) investment and gross domestic product (GDP) in China, we have demonstrated and analyzed the relationship between R&D investment and science and technology (S&T) progress, and based on a mount of S&T statistical data, have proceeded demonstration research of the relationship between R&D investment and GDP in China with Solow and vector auto regression (VAR) models. Cubic curve fitting and cross-correlation analysis of them with SPSS have shown that there is a strong synchronic relationship between R&D investment and GDP.

Analysis of the current situation and demand of green food industry in China

With the high-speed development of economy in China, people require higher and higher quality of food, and accidents of food safety in recent years have been reported, causing the promotion of consumption demand on green food. The paper firstly investigates the current situation of green food industry at home and abroad, then focuses on the analysis of the demand of green food market. We study the balance between the green food and the per capita disposable income in short and long term, through vector auto regression model and co-integration analysis on the income elasticity of demand. The paper shows that, the relationship between green food consumption and per capita disposable income is "bullwhip effect", which means that the per capita disposable income have a significant role to the green food sales in the short term, but no stable co-integration relationship in the long term.

Edge computing-Based mobile object tracking in internet of things

Mobile object tracking,which has broad applications,utilizes a large number of Internet of Things(IoT)devices to identify,record,and share the trajectory information of physical objects.Nonetheless,IoT devices are energy con-strained and not feasible for deploying advanced tracking techniques due to significant computing requirements.To address these issues,in this paper,we develop an edge computing-based multivariate time series(EC-MTS)framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks.Specifically,EC-MTS leverages statistical technique(i.e.,vector auto regression(VAR))to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction.Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure.We have validated the efficacy of EC-MTS and our experimental results demon-strate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects.In addition,we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.