With the development of industrialization and urbanization, cities have become the main carriers of economic activities. However, the long-term development of cities has also caused damage to resources and the environment. Hence, objective and scientific evaluation of urban low-carbon sustainable development capacity is very important. An index system of urban low-carbon sustainable development capability is constructed in this paper, and a TOPSIS-BP neural network model is established to evaluate the low-carbon sustainable development capability of Beijing, Shanghai, Shenzhen, and Guangzhou in (...) China. At the same time, the difference degree of low-carbon sustainable development level in these four cities is analyzed by standard deviation and coefficient of variation, and the influencing factors of urban low-carbon sustainable development ability are extracted by grey correlation analysis. The results show that the capability of low-carbon sustainable development in four cities is rising and the difference of low-carbon sustainable development capability is decreasing; the general view that the higher the general investment in low-carbon sustainable development, the higher the level of low-carbon sustainable development in cities has not been verified; with the change of time series, the factors affecting the capability of low-carbon sustainable development in the same city are different and the influence of the same factor on the capability of low-carbon sustainable development in different cities is different. (shrink)

In the context of China’s supply-side structural reform and the concept of green development, we introduce the energy and environmental factors into the analytical framework of the total factor productivity of the logistics industry and use the Global Malmquist–Luenberger index method to analyze the evolution trend and heterogeneity of green TFP in the logistics industry of 30 provinces in China from 2003 to 2017. The results show that, firstly, the green TFP and traditional TFP of China’s logistics industry are both (...) on the rise and the absence of energy and environmental factors will lead to the overestimation of TFP of the logistics industry. Whether it is green TFP or traditional TFP, the main source of its growth is technical progress. Secondly, there is obvious regional heterogeneity in green TFP of logistics industry. Under the three regional division standards, the average annual growth rate of green TFP is from high to low in the order of eastern, western, and central regions. Under the eight regional classification standards, the eastern coastal economic zone, the southern coastal zone, the northern coastal zone, the northeast region, the middle reaches of the Yellow River, the southwest region, the middle reaches of the Yangtze River, and the northwest region are in order from high to low. Thirdly, there is obvious interprovincial heterogeneity in green TFP of the logistics industry. The highest growth rate of green TFP is in Zhejiang, followed by Jiangsu and Guangdong, and the slowest growth rate is in Chongqing. The technical progress of logistics industry in most provinces contributes more to the growth of green TFP. Fourthly, the differences of green TFP in the three regions of the east, center, and west are shrinking, which may be σ convergence, but the differences among the three regions are expanding. Compared with the existing literature, this paper applies the measurement framework of green TFP to China’s logistics industry and investigates the regional and provincial heterogeneity of green TFP in logistics industry. The conclusions are significant to understand and grasp the heterogeneity of green TFP growth in China’s logistics industry under environmental constraints and how to promote the development of green logistics in China. (shrink)

The infamous “Sydney Tar Ponds” are well known as one of the largest toxic waste sites of Canada, due to almost 100 years of steelmaking in Sydney, a once beautiful and peaceful city located on the east side of Cape Breton Island, Nova Scotia. This article begins with a contextual overview of the Tar Ponds issue including a brief introduction and history and summaries of the effects on the earth, the people, and the biotic community (animals and vegetation). Then the (...) authors talk about the STS analysis approach, namely, a discussion of six systems to indicate what has been brought to the earth and mankind by technology and modern industry. The remaining part of the article describes the difficulties confronting China, some of which are similar to the ones Canada faces as a result of the Tar Ponds contamination, and summarizes some of the experiences at Tar Ponds and the lessons China can learn from Tar Ponds. (shrink)

This paper studies the adaptive event-triggered finite-time tracking of output-constrained high-order nonlinear systems with time-varying powers. Due to the presence of multiple unknown powers and the consideration of event-triggered control, all the existing control methods of output-constrained nonlinear systems are inapplicable. By introducing nonlinear mappings, finite-time performance functions, and low-power and high-power terms into adding a power integrator technique and the relative threshold strategy, an adaptive state-feedback controller is designed to eliminate the effects caused by the output constraint and time-varying (...) powers. It is proved that all the closed-loop signals are bounded, the asymmetric time-varying output constraint is not violated, and the tracking error converges to a prescribed arbitrarily small region around zero in a preassigned finite time. Furthermore, the Zeno phenomenon can be avoided. Two simulation examples demonstrate the effectiveness of this control scheme. (shrink)

In recent years, the distributed architecture has been widely adopted by security companies with the rapid expansion of their business. A distributed system is comprised of many computing nodes of different components which are connected by high-speed communication networks. With the increasing functionality and complexity of the systems, failures of nodes are inevitable which may result in considerable loss. In order to identify anomalies of the possible failures and enable DevOps engineers to operate in advance, this paper proposes a two-layer (...) prediction architecture based on the monitoring sequences of nodes status. Generally speaking, in the first layer, we make use of EXPoSE anomaly detection technique to derive anomaly scores in constant time which are then used as input data for ensemble learning in the second layer. Experiments are conducted on the data provided by one of the largest security companies, and the results demonstrate the predictability of the proposed approach. (shrink)