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Due to the strategic emerging industrial cluster includes different types of subjects, their interaction will affect the industrial economy and innovation capacity. According lo the data of strategic emerging enterprises, this paper constructs the economic 2-mode network and innovative 2-mode network composed of the strategic emerging industries and regions, respectively, according to the main business income and the number of effective patents of enterprises. Using the centrality of social network analysis to measure the structural characteristics of the 2-mode network, this paper analyzes the development status and evolution trend of strategic emerging industrial clusters. Then the three-dimensional grey correlation model is used to evaluate the innovation ability of each region with strategic emerging industrial clusters. Finally, an empirical study is carried out with Jiangsu province's strategic emerging industrial cluster as a typical case. The results show that: (1)The new generation of informational technology, biological industry, and high-end equipment manufacturing industry all have high centrality in the network, and they are located in an important position in the network. It shows that these three industries have strong economic effects and innovation ability and have a strong ability to control inter-enterprise and inter-regional informational transfer, while the digital creative industry has low centrality and is still in its initial stage located at the edge of the network. (2) The network centrality of Nanjing and Suzhou has always ranked first and second, indicating that these two regions have the highest influence on the network. The strategic emerging industrial clusters of Nanjing and Suzhou are relatively well-developed, and the innovation and economy have formed a positive interaction effect in these industrial clusters. (3) The innovation ability Of strategic emerging industrial clusters in Suzhou, Wuxi, and Nanjing is relatively strong, while the innovation ability of strategic emerging industrial clusters iii Taizhou and Yancheng is relatively weak. There is a great difference between southern Jiangsui and northern Jiangsu. [ABSTRACT FROM AUTHOR]

This paper studies the evolutionary development and influencing factors of service outsourcing input-output efficiency in Jiangsu Province. First, four input-output indicators for service outsourcing are selected, and the evolutionary trend of service outsourcing efficiency in Jiangsu Province from 2007 to 2016, which can be divided into three phases, is calculated by using the Data Envelopment Analysis model. Then this paper selects 11 economic indicators that affect the evolutionary development of service outsourcing input-output efficiency in Jiangsu Province, and uses Grey Relation Entropy analysis to calculate the grey correlation degree between service outsourcing efficiency and its influencing factors. The main conclusions of this paper include: the rank of the influencing factors of the service outsourcing efficiency in Jiangsu Province, the analysis of the status quo of the development of service outsourcing industry, the existing problems, and relevant recommendations. [ABSTRACT FROM AUTHOR]

Industrial structure is the result of the allocation of economic resources of a country or a region, and to a large extent determines the efficiency of the use of economic resources. The process of national or regional economic growth is the dynamic evolution process of industrial structure from uncoordinated to coordinated, from lower-level coordination to higher-level coordination, that is, the process of continuous optimization of industrial structure. The concepts of industrial structure deviation and industrial structure coordination degree are proposed in this paper. The weighted average values of the industrial structures of the developed countries such as the USA, UK, France, Germany and Japan in the year when their per capita GDP reaches US $10000, US $20000 and US $30000 was calculated respectively. Then, the standard industrial structure was calculated with the weighted average values of the industrial structures of the five developed countries as reference frame. And the definition of industrial structure deviation and industrial structure coordination degree is put forward accordingly. At last, the coordination degree of industrial structure in China. Jiangsu Province, Southern Jiangsu, Central Jiangsu and Northern Jiangsu is calculated according to the data oj actual industrial structure in 2019. And the industrial structure optimization and adjustment plan of China and Jiangsu Province is worked out according to the set target structure. [ABSTRACT FROM AUTHOR]

To ensure smart elderly care service quality, this paper explores its multi-dimensional factors, and constructs an evaluation indicator system of smart community elderly care service quality based on intelligence, tangibles, responsiveness, security, reliability, and empathy. Aiming at the problem of the fuzziness of elderly's perception and the hesitation of experts' evaluation of service quality in multi-attribute decision-making, the interval-valued intuitionistic fuzzy entropy (IVIFE) is introduced, and the evaluation method of the smart community elderly care service quality by combining the IVIFE and a two-stage decision model with grey synthetic measure is established. Four typical cities in Jiangsu Province were taken as examples to verify the feasibility of the proposed method, in order to provide a reference for promoting service standardization and reducing the difference in the level of smart community elderly care services between regions. [ABSTRACT FROM AUTHOR]

Abstract - Due to the operation mechanism of R&D input-output system that is imprecise, it is hard to predict the structure optimization of R&D funding investment. This paper confirms contribution degree of scientific and technological output from R&D funding by grey relational analysis, and establishes objective function. At last, we predict and optimize the R&D funding investment of Jiangsu Province in the eleventh five-year plan, and the best allocation program is given, which verifies the feasibility and effectiveness of the method. [ABSTRACT FROM AUTHOR]

This paper proposes an approximation procedure of whitening response in GM(1, 1), GM(1, 1) procedure for short, by using a technique that combines the improved initial value with the optimized background value. The procedure is constructed and compared with the existing three kinds of GM (1, 1) models in modeling precision. We use the financial investments to science and technology forecasting of Jiangsu province of China as a case study to test the efficiency and accuracy of the proposed procedure. According to the experimental results, this procedure obviously can improve the prediction accuracy of the original grey model. Compared with the other three GM (1, 1) models, the modeling precision (simulation precision and forecast precision) of the GM (1, 1) procedure (model) is the highest. It is not only suitable for the low growth sequence modeling, but also suitable for the high growth sequence modeling, particularly to the high growth sequence, the simulation precision and the forecast precision of the GM (1, 1) procedure is still high even if the development coefficient is bigger than 2. [ABSTRACT FROM AUTHOR]