City Network Evolution Characteristics of Smart Industry: Evidence from Yangtze River Delta, China

The dual-path model of industrial evolution and spatial progression has been widely acknowledged and incorporated into the strategic planning to promote the development of urban industries and regional collaborations.However,current research on inter-enter-prise city networks mainly focuses on the single sector of flows on all enterprise branches,such as product value chains and production factors,but neglects that of particular industry department.Built upon the new economic geography and city networks theory,this paper develops a methodological framework that focuses on the analysis of city network evolution characteristics of smart industry.Particu-larly,a conceptual model of smart industry enterprise-industry-city is proposed and then applied to a case study of smart industry in the Yangtze River Delta Region,China.Using enterprise supplier-customer data,a city network of smart industry is constructed and sub-sequently analyzed with the proposed model.Findings indicate that the smart industry network in Yangtze River Delta Region exhibits a hierarchical structure and the expansion of the network presents a small-world network characteristic.The study not only makes a meth-odological contribution for revealing the industrial and spatial evolution path of the current smart industry,but also provides empirical support for the formulation of new economic development policies focused on smart industries,demonstrating the role of city clusters as carriers of regional synergistic development.

Data analytics and optimization for smart industry

Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries.Motivated by the major development strategies and needs of industrial intellectualization in China,this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization,as well as their application to smart industrial engineering.First,this study describes a general methodology for the fusion of data analytics and optimization.Then,it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing.Finally,it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization.The framework uses data analytics to perceive and analyze industrial production and logistics processes.It also demonstrates the intelligent capability of planning,scheduling,operation optimization,and optimal control.Data analytics and system optimization technologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing,resources and materials,energy,and logistics systems,such as high energy consumption,high costs,low energy efficiency,low resource utilization,and serious environmental pollution.The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency。Therefore,industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.

Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

SWOT Analysis of China’s Smart Pension Industry

China’s smart pension industry is a sunrise industry,is an organic combination of information technology and traditional service industry emerging industry.To analyze the strength,weakness,opportunities and threats of the industry is conducive to a comprehensive understanding of the development status of the industry and grasp the development trend of the industry.

Research and Practice of Smart Power Consumption

As a vital part of a smart grid, smart power consumption enables real-time interaction between consumers and the grid. With improved management of the demand side and energy efficiency, smart power consumption plays an important role in emission reduction as well as the economic operation of the power grid. Meanwhile, it enhances the power grid to a larger scale infrastructure by the two-way transmission of energy and information. This paper introduces the research, practice and vision of smart power consumption in China.

Impact of Industry 4.0 in service oriented firm

The first part of this study is devoted to concepts, approaches and some examples of the fourth industrial revolution. The basis of this revolution also well known as Industry 4.0 builds so called cyber-physical systems. They contain the integrated smart software systems including the internet address to enable the communication with envi- ronment as for product itself as for means of production and employees. All these enable the next level of efficiency and flexibility for both organizing and controlling of the value-creation chain over the whole lifecycle of products. In the first three chapters several Internet references and documents published by German Federal Ministry for Economic Affairs and Energy were used. Because of multiple cross references in documents, this report is written without detailed references in each paragraph of mentioned chapters. The last three chapters of the research presented undertake the short review of interdependencies between the Industry 4.0 and the well-known approach of computer-supported-cooperative-work established in the late 1980s. Long list of publications can be found in Wikipedia, and in many proceedings of ACM conferences on computer supported cooperative work （CSCW）.