Structures and Settlement Control of Yujingshan High-Speed Railway Tunnel Crossing Massive Rockfill in a Giant Karst Cave

The Yujingshan high-speed railway tunnel crosses a giant cavern system with a 108 × 104 m3 volume chamber and an 18 km long underground river. The massive project, which lasted three years, was eventually awarded the “Overcoming the Challenges” award by the International Tunneling and Underground Space Association (ITA) in 2020. However, since the cave chamber was filled with large-scale rockfill, structural settlement is a non-negligible problem. This paper presents the unique structures of a bridge supporting railway tracks wrapped by tunnel lining and the settlement control of the Yujingshan tunnel crossing massive rockfill in the giant cave. The geological characteristics and design considerations are systematically introduced. A three-dimensional coupling discrete element method (DEM) and finite difference method (FDM) numerical model and 13 months of long-term settlement monitoring were conducted to evaluate the settlement behavior. The results indicate that the morphology of cavern and internal deposits caused the whole rockfill to migrate to the lower left. The tunnel structure consequently developed a significant inclined settlement. The continuous construction load would increase the settlement value by 31.4%. The bottom reinforcement of steel-pipe pile with grouting could effectively inhibit settlement and differential settlement. Considering the simulation results, the tunnel bottom had greater settlement than the limit standard for high-speed railway embankment, which means this special structure form is reasonable for operation. Meanwhile, the monitoring results show that the tunnel bottom settlement in D3K279+891~D3K279+947 had not performed an apparent convergence trend after 13 months. Further structural monitoring and compensation grouting should be actively considered for operation maintenance.

Research on Industry Difference and Convergence of Green Innovation Efficiency of Manufacturing Industry in China Based on Super-SBM and Convergence Models

To accurately grasp the current situation of green innovation efficiency in the manufacturing industry in China, this paper analyzes the differences and convergence characteristics of green innovation efficiency in various industries. Based on the panel data of 29 manufacturing industries in China from 2010 to 2019, the super-slack-based measure (Super-SBM) model measures the green innovation efficiency of manufacturing industries whose evolution characteristics are classified and analyzed from the perspective of technical demand. The Dagum Gini coefficient decomposition method indicates the source of industry differences in green innovation efficiency of the manufacturing industry in China with its convergence characteristics analyzed from the time dimension by constructing σ and β convergence models. The results reveal the improvement of green innovation efficiency of the Chinese manufacturing industry with obvious distinctions among different sectors and the industries with high green innovation efficiency, mostly high-end technology ones. The narrowing overall difference of green innovation efficiency in the manufacturing industry is accompanied by the lowest contribution rate of super-variable density, with the disparities between groups being the main source. It also shows the fluctuation of the intermittent σ convergence characteristics of the national manufacturing industry as a whole and low-end and high-end technology industry groups. However, the entire manufacturing industry and the three groups witness the absolute β convergence trend, with an ununiform convergence rate. The research will provide a reference for further upgrading the efficiency of green innovation in the industry and help to achieve the goals of carbon emission reduction and neutrality with the policy implications for promoting high-quality development of the manufacturing industry.

Research on Carbon Productivity and It's Spatial Convergence of Steel Industry in China

The Global-Malmquist-Luenberger (GML) index was applied to analyse the carbon productivity in steel industry (SICP) of 29 provinces in China from 2006 to 2017, and then the SICP was decomposed into technical efficiency change index (TC) and technical progress index (EC). On this basis, the spatial effect is introduced into the traditional convergence model to investigate the spatial convergence of SICP. The empirical results show that: (1) The overall carbon productivity of China's steel industry is at a relatively low level, showing a slow growth trend. (2) The average value of the GML index of SICP is higher than 1, showing obvious inter-provincial and regional heterogeneity. Compared with EC, TC is the leading factor that promotes the increase of SICP. (3) The spatial absolute and condition β convergence of SICP exist in the whole country and the three major regions, but the σ convergence feature is not significant. The addition of spatial factors speeds up the convergence trend, and the speed of spatial absolute β convergence is about 3 times that of the classical convergence model. At the same time, the conditional convergence rate is significantly faster than the absolute convergence, which is closely related to the differences in influencing factors such as the industrial structure, economic development level, human capital, energy consumption intensity, and R&D investment among regions. There is still much room for improvement in carbon productivity in China's steel industry, and investment in scientific research must be increased in order to achieve the upgrading of the industrial structure and technological innovation. The existence of spatial convergence requires strengthening the joint reorganization of steel enterprises between provinces and regions, making full use of the spatial spillover effects of production technology, and realizing regional green and coordinated development.

Environmental Efficiency of Node Cities in Chinese Section of Silk Road Economic Zone and Its Influencing Factors

This paper adopts an environmental data envelopment analysis (DEA) model containing pollution emissions to measure the environmental efficiency of node cities in the Chinese section of Silk Road Economic Zone (SREZ) in 2011–2020 and verifies the convergence of the environmental efficiency. The results show that the ten node cities had an overall low environmental efficiency and a large gap in environmental efficiency, highlighting the necessity of cross-regional cooperation in emission reduction and the promotion of environmental technologies between regions; the environmental efficiency gaps between node cities and between the three regions started to narrow in 2016 and 2018, respectively, showing a certain convergence trend. In addition, the Tobit model was called to analyze the factors affecting environmental efficiency, revealing that per-capita gross domestic product (GDP), foreign trade, and population density promote environmental efficiency, while the proportion of the secondary industry, number of authorized patents, and regional feature significantly suppresses environmental efficiency. Finally, several suggestions were provided to reduce regional pollution emissions and increase China’s environmental efficiency, according to the results of empirical analysis.

Convergence Adjustment Method Based on Approximate Power Flow and Voltage Stability

Prone to problems flow calculation does not converge at the scheduled maximum power operating mode, the current operating personnel inability to visually observe the system state, convergence can only be adjusted by trial and error approach. Solving the problem of non convergence of power flow plays an important role in power system analysis. Based on the intermediate process of solving the Newton method, the concept of an approximate fashion, the relation between convergence and voltage stability is calculated by analyzing the trend of the voltage characteristics similar trend as the main basis of the judgment result in the trend does not converge, based on voltage sensitivity further, by improving the convergence of the method to boost the voltage level indirectly. Finally, after the New England 39-bus system to verify, by demonstrating the relevance and accuracy of the proposed method. When not solve the problem of the convergence trend is not impressive, it has a certain reference value for practical application. This study has a good application prospect in practical engineering application

CONVERGENCE ANALYSIS OF ENVIRONMENTAL EFFICIENCY FROM THE PERSPECTIVE OF ENVIRONMENTAL REGULATION: EVIDENCE FROM CHINA

The aim of this paper is to analyze the impact of environmental regulation on regional environmental efficiency convergence using the fixed effects model and threshold regression model. The results show that the differences in environmental efficiency have a convergence trend in China, as well as in the eastern, central and western regions. The effect of environmental regulation on regional environmental efficiency is inhibition first and then promotion, research and development investment and outward foreign direct investment have a positive transmission effect; when environmental regulation intensity exceeds a certain threshold, the growth rate of environmental efficiency in the central and western regions will be significantly higher than that in the eastern regions.

Dynamic OverCloud: Realizing Microservices-Based IoT-Cloud Service Composition over Multiple Clouds

With the expansion of cloud-leveraged Information and Communications Technology (ICT) convergence trend, cloud-native computing is starting to be the de-facto paradigm together with MSA(Microservices Architecture)-based service composition for agility and efficiency. Moreover, by bridging the Internet of Things (IoT) and cloud together, a variety of cloud applications are explosively emerging. As an example, the so-called IoT-Cloud services, which are cloud-leveraged inter-connected services with distributed IoT devices, dynamically utilize geographically-distributed multiple clouds since mobile IoT devices can selectively connect to the near-by cloud resources for low-latency and high-throughput connectivity. In comparison, most public cloud providers may cause vendor lock-in problems that limit the inter-operable service compositions. Thus, in this paper, we propose a new overlay approach to address the above limitations, denoted as Dynamic OverCloud, which is a specially-arranged razor-thin overlay layer that provides users with an inter-operable and visibility-supported environment for MSA-based IoT-Cloud service composition over the existing multiple clouds. Then, we design a software framework that dynamically builds the proposed concept. We also describe a detailed implementation of the software framework with workflows. Finally, we verify its feasibility by realizing a smart energy IoT-Cloud service with the suggested operation lifecycle.

A Study on Dynamic Patterns of Technology Convergence with IPC Co-Occurrence-Based Analysis: The Case of 3D Printing

Technology convergence has become a typical characteristic of innovation, which affects the evolution of industrial structures and the core competitiveness of organizations. However, the existing research has mainly focused on the development of core areas of convergence, ignoring the potential breakthroughs that emerging peripheral convergence may bring. Therefore, this research put forward a comprehensive methodology based on IPC (International Patent Classification) co-occurrence analysis to study the dynamic patterns of technology convergence from the perspectives of reinforcing convergence and novel convergence. For the former, convergence trends in each period were explored by using association rules, and the convergence degree was measured based on the number of patents containing different IPC codes. Then, the corresponding core technical fields were identified by using information entropy. For the latter, a community detection algorithm based on IPC co-occurrence network was adopted to investigate the convergence trend by period, and important technology fields were identified by the centrality indicators. The methodology proposed in this study is beneficial for firms to seize technological opportunities in technology convergence.

Big Data, Cloud Computing, and IoT (BCI) Amalgamation Model: The Art of “Reinventing Yourself” to Analysis the World in Which We Live

The spread of omnipresent sensing technology brings with it an increasing number of innovative models. The smart mobility initiatives offer new opportunities for Intelligent Systems to maximize the utilization of real-time data that are streaming out of different sensory resources. In recent years, the convergence trend of Big Data, Cloud and IoT has received considerable attention in industry and academia. A huge amount of data is generated every day from information systems and modern digital technologies such as the Internet of things (IoT) and cloud computing. The analysis of these massive data requires a lot of effort at multiple levels to extract knowledge to facilitate decision-making. Big data analysis is therefore a topical area of research and development. The main objective of this survey is to propose Big Data, Cloud Computing, and IoT (BCI) Amalgamation Model. Additionally, this paper explores the big data characteristics, challenges, analysis techniques, and various tools associated with it. The recommendation of the suitable analysis techniques of big data that could reduce the time and increase efficiency is discussed.