scholarly journals Research on the Technological Innovation Efficiency of China’s Strategic Emerging Industries Based on SBM: NDEA Model and Big Data

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zuchang Zhong ◽  
Fanchao Meng ◽  
Yuanbing Zhu ◽  
Gang Wang
Keyword(s):  
Big Data ◽  
Technological Innovation ◽  
Large Scale ◽  
Scale Efficiency ◽  
Eastern Region ◽  
Network Dea ◽  
Emerging Industries ◽  
Knowledge Innovation ◽  
Technological Efficiency ◽  
Level 2

Characterized by large scale, variety, fast generation, and extremely high value but low density, big data can be used to mine effective information, provide users with auxiliary decision-making, and realize its own value. Based on the nonoriented SBM and the network DEA model, this paper systematically and objectively evaluates the technological innovation efficiency of strategic emerging industries in all provinces of China in 2002–2013. The study found the following. (1) The overall technological efficiency of China’s strategic emerging industries is low. The average of comprehensive efficiency is 0.278; of 26 provinces, only 8 are above the average level. (2) The efficiency in the commercialization stage of scientific and technological achievements of strategic emerging industries in the whole country and most of the provinces is higher than that in the stage of knowledge innovation. The inefficiency of the knowledge innovation stage restricts the efficiency promotion of China’s strategic emerging industries. (3) The overall innovation efficiency of strategic emerging industries has been increasing from 2002 to 2013. In comparison, the growth rate of pure technical efficiency is larger than that of scale efficiency. (4) The overall efficiency, the efficiency in the knowledge innovation stage, and the efficiency in the commercialization stage of scientific and technological achievements of the eastern region are higher than those of the central and western regions.

scholarly journals The Analysis for the Scale and Efficiency of China’s Major Automotive Enterprises Based on DEA Model

2018 ◽  
Vol 10 (2) ◽  
pp. 129
Author(s):  
Zheng Fei ◽  
Li L. Z
Keyword(s):  
Technical Efficiency ◽  
Technological Innovation ◽  
Automotive Industry ◽  
Large Scale ◽  
Economies Of Scale ◽  
Scale Efficiency ◽  
Small Scale ◽  
Production Scale ◽  
Exit Costs ◽  
Overall Efficiency

This paper uses Data Envelopment Analysis (DEA) to measure the scale and efficiency of 28 major automotive enterprises in Chinese, and the results show that at this stage, large automobile manufacturers of China are under-produced and the production is too scattered, and the overall efficiency of automobile manufacturers is low. One of the main reasons is that because of the low technical efficiency value, the technological innovation capability of enterprises needs to be strengthened. The other reason is that the low efficiency of a large number of enterprises lowers the overall efficiency level. There is a positive correlation between the scale and efficiency of automobile manufacturers. Whether it is the horizontal comparison between different enterprises (nature) or the vertical comparison between the same enterprises, all show that compared with small-scale enterprises, large-scale manufacturing enterprises not only have higher scale efficiency but also have higher technical efficiency. With the expansion of production scale, the scale of enterprises and technical efficiency have improved, which shows that for the automotive industry, compared with other factors, economies of scale is the main factor that affects the automotive industry, and not only is it reflected in the scale but also in technological innovation. Therefore, when formulating policies, the relevant departments should support the development of large-scale enterprises, encourage mergers and acquisitions among enterprises, increase R&D investment, support technological innovation, and set up a scientific market exit mechanism to reduce exit costs, such as guiding the transformation of enterprises and establish a competition mechanism for the survival of the fittest.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

How tourism industry agglomeration improves tourism economic efficiency?

2021 ◽  
pp. 135481662110091
Author(s):  
Zhoufei Li ◽  
Huiyue Liu
Keyword(s):  
Positive Impact ◽  
Regional Analysis ◽  
Tourism Industry ◽  
Scale Efficiency ◽  
Eastern Region ◽  
Location Quotient ◽  
Industry Agglomeration ◽  
Central Regions ◽  
National Perspective ◽  
The Impact

The agglomeration of the tourism industry has important effects on its efficiency. This article used panel data on the Chinese provincial tourism industry for the 2011–2016 period, applied the location quotient index and three-stage data envelopment analysis method to, respectively, measure the degree of agglomeration and efficiency, and explained the impact of agglomeration on tourism efficiency. The empirical results of this study indicate the following. (1) China’s tourism industry shows a trend towards agglomeration, revealing gradient differences where the highest degree of agglomeration is in the eastern region, followed by the western and central regions. (2) After eliminating random and environmental factors, the adjusted efficiencies are lower than the unadjusted efficiencies. The average overall tourism efficiency is higher in the eastern region than in the central and western regions. (3) From the national perspective, industrial agglomeration can significantly improve the overall efficiency (TE), pure technical efficiency (PTE), and scale efficiency of the tourism industry. (4) Based on regional analysis, the agglomeration of the eastern tourism industry can significantly enhance its TE and PTE. Agglomeration for the western area has a significant positive impact on PTE. There is no significant relationship between agglomeration and efficiency in the central region.

scholarly journals Modelling Reliability Characteristics of Technical Equipment of Local Area Computer Networks

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 955
Author(s):  
Vasyl Teslyuk ◽  
Andriy Sydor ◽  
Vincent Karovič ◽  
Olena Pavliuk ◽  
Iryna Kazymyra
Keyword(s):  
Large Scale ◽  
Computer Network ◽  
Network Reliability ◽  
Local Area ◽  
Technical Equipment ◽  
Time To Failure ◽  
Reliability Parameters ◽  
Reliability Characteristics ◽  
Global World ◽  
Level 2

Technical systems in the modern global world are rapidly evolving and improving. In most cases, these are large-scale multi-level systems and one of the problems that arises in the design process of such systems is to determine their reliability. Accordingly, in the paper, a mathematical model based on the Weibull distribution has been developed for determining a computer network reliability. In order to simplify calculating the reliability characteristics, the system is considered to be a hierarchical one, ramified to level 2, with bypass through the level. The developed model allows us to define the following parameters: the probability distribution of the count of working output elements, the availability function of the system, the duration of the system’s stay in each of its working states, and the duration of the system’s stay in the prescribed availability condition. The accuracy of the developed model is high. It can be used to determine the reliability parameters of the large, hierarchical, ramified systems. The research results of modelling a local area computer network are presented. In particular, we obtained the following best option for connecting workstations: 4 of them are connected to the main hub, and the rest (16) are connected to the second level hub, with a time to failure of 4818 h.

scholarly journals Using Participatory Spatial Tools to Unravel Community Perceptions of Land-Use Dynamics in a Mine-Expanding Landscape in Ghana

2021 ◽  
Author(s):  
Jane J. Aggrey ◽  
Mirjam A. F. Ros-Tonen ◽  
Kwabena O. Asubonteng
Keyword(s):  
Land Use ◽  
Adverse Effects ◽  
Large Scale ◽  
Sub Saharan Africa ◽  
Small Scale ◽  
Eastern Region ◽  
Food Crop ◽  
Mosaic Landscape ◽  
Rural Landscapes ◽  
The Future

AbstractArtisanal and small-scale mining (ASM) in sub-Saharan Africa creates considerable dynamics in rural landscapes. Many studies addressed the adverse effects of mining, but few studies use participatory spatial tools to assess the effects on land use. Hence, this paper takes an actor perspective to analyze how communities in a mixed farming-mining area in Ghana’s Eastern Region perceive the spatial dynamics of ASM and its effects on land for farming and food production from past (1986) to present (2018) and toward the future (2035). Participatory maps show how participants visualize the transformation of food-crop areas into small- and large-scale mining, tree crops, and settlement in all the communities between 1986 and 2018 and foresee these trends to continue in the future (2035). Participants also observe how a mosaic landscape shifts toward a segregated landscape, with simultaneous fragmentation of their farming land due to ASM. Further segregation is expected in the future, with attribution to the expansion of settlements being an unexpected outcome. Although participants expect adverse effects on the future availability of food-crop land, no firm conclusions can be drawn about the anticipated effect on food availability. The paper argues that, if responsibly applied and used to reveal community perspectives and concerns about landscape dynamics, participatory mapping can help raise awareness of the need for collective action and contribute to more inclusive landscape governance. These findings contribute to debates on the operationalization of integrated and inclusive landscape approaches and governance, particularly in areas with pervasive impacts of ASM.

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