Mathematical and Computer Modelling最新文献

Ranking DMUs is an important issue in DEA studies. Traditional DEA model usually leads to the failure to ranking all DMUs when DMUs get the same efficiency score, such as efficient DMUs or inefficient DMUs with the same efficiency score. For the sake of comparing and improving capabilities of DMUs, many researchers have sought to fully rank both efficient, as well as inefficient DMUs. In this paper, an extended DEA model considering undesirable outputs using restrictions is presented to realize a unique ranking of DMUs through the new “Maximal Balance Index” based on the optimal shadow prices. Meanwhile, a numerical example and an empirical example are provided to illustrate the difference from previous work and the application of the extended model. In the empirical study, the complete ranking of DMUs incorporating undesirable outputs is proved to be useful and unique.

The introduction of a precise and effective production efficiency evaluative model has vital theoretical importance. It can promote the improvement of production efficiency in energy companies and the enhancement of China’s energy supply. Data envelopment analysis (DEA) is a nonparametric method to evaluate the relative effectiveness of decision-making units (DMU). While DEA has many theoretical advantages, it is also very sensitive to the number of decision-making units being evaluated as well as the accuracy of the data. Super-efficiency DEA can make up this limitation. However, this model has several shortcomings, like the possible exaggeration of the efficiency value and the variety of the evaluating benchmarks. Integrating the measurement of undesirable outputs, this paper combined the traditional CCR model, super-efficiency DEA model and ideal-DMU-based benchmark sorting model to get an improved super-efficiency DEA model. Then, we applied this method to 10 subsidiaries of a well-known domestic energy corporation to testify to the feasibility of it.

Recently, environmental pollution has attracted a lot of attention domestically and internationally. It is known that energy utilization is a major source of China’s environmental pollution. Measuring environmental efficiency of energy utilization in Chinese provinces first, the paper then offers decision supports for relevant departments in order to achieve sustainable development of energy and environment in China. This research chooses CO₂ emission as a substitute variable for environmental effects of energy utilization and puts it into the study framework based on total-factor production theory to measure the environmental efficiency of energy utilization of each province and three major regions. On the basis of the above results this research further measures the cost of environmental regulation for each province. Provincial panel data during the period of 2000–2007 are used to carry out empirical study. The analysis indicates that the environmental efficiency of energy utilization in Chinese provinces varies greatly from each other, meaning that both economic output and CO₂ emissions need considerable improvement. In addition, the results of empirical study also show that the environmental efficiency of energy utilization in China is quite low. Therefore, to ensure sustainable development of China’s energy, economy and environment, we should resort to advanced technology as the support to promote the efficient and clean utilization of energy resources. Besides, most Chinese provinces have spent quite a lot on environmental regulation, implying that in these provinces development of the economy and the environment are not in harmony. The contributions of this research are as follows: it constructs the models for measuring environmental efficiency of energy utilization in Chinese provinces; it introduces the idea of “environmental regulation” into research system and measures the environmental regulation cost of each Chinese province.

There are 16 cities in the Yangtze River Delta and this region plays a leading role in Chinese economic growth. A great deal of power is needed for the economic growth and social development in the Yangtze River Delta, but the local firms’ production and people’s living are often disturbed by shortages of power. From 2000 to 2010, the power output of the regional electric power industry was accompanied by a large amount of undesirable outputs. Environmental Data Envelopment Analysis (DEA) technology is applied. Two DEA models incorporating undesirable outputs are constructed and used to measure the environmental efficiency of the electric power industry in the Yangtze River Delta from 2000 to 2010. Both constant return of scale and variable return of scale are considered. Some meaningful findings are obtained from the measurement results of the 16 cities and of the whole region.

Modern urban areas are influenced by numerous inter-related human and natural factors and present extremely complicated non-linear and dynamic properties. As a bridge between the physical world and the cyber space, pervasive sensing technologies make it possible to have a deep understanding of urban environments. There are two inter-related technical planes to achieve it, namely, sensing and computing. In this paper, we introduce our work progress on the urban dynamics study for each technical plane, respectively. First, we design and implement a prototype urban sensing system based on automobiles as mobile agents, performing information-rich data collection. Then, we propose a Spatial–Temporal-Entangled Analysis (STEA) algorithm based on semi-supervised manifold learning and the regularized optimization to extract semantic information from incomplete sensing data, aiming for better understanding of the spatial–temporal correlation of a complex physical process and the correlation between human activities and environmental changes via incomplete sensing data. Finally, we evaluate STEA in a real urban sensing application. Specifically, the proposed prototype is used to collect traffic and air pollution data in Beijing, and such real-world datasets are used to evaluate the effectiveness of STEA. The results obtained are very promising and show an implicit correlation between the traffic density and the air pollution, demonstrating the potential of this technique in environmental studies for urban areas.

In this paper the authors present the idea of a system for cognitive analysis of coronary vascularisation reconstructions acquired by a 128-slice spiral CT apparatus. Unlike previous authors’ solutions this paper presents a multi-stage analysis system, which utilises cognitive analysis for description and recognition of pathological changes in coronary arteries structure. Such systems can be built for various types of data, but the main subject of this paper is a presentation of the cognitive system for diagnostic and therapeutic purposes. The main idea of such a system consists in gathering information from several stages of analysis, which allows a comprehensive assessment of the analysed image and formulation of the appropriate diagnosis. The proposed methods although they are mainly predestined for medical applications can also provide a base for other solutions, particularly for the acquisition and sophisticated semantic analysis of complex image patterns. Such techniques can be of great significance and find a number of uses in image recognition.

Malware obfuscation obscures malware into different versions, making traditional syntactic nature based detection ineffective. Furthermore, with the huge and exponentially growing number of malware samples, existing malware detection systems are either evaded by malware obfuscation, or overwhelmed by numerous malware samples. This paper proposes an anti-obfuscation, scalable and collaborative malware detection system—ENDMal. ENDMal identifies the program that behaves suspiciously in end-hosts and similarly between a group of suspicious programs in a wide area as malicious. We present the Iterative Sequence Alignment (ISA) method to defeat malware obfuscation. Instead of using complex behavior graph, we propose the Handle dependences and Probabilistic Ordering Dependence (HPOD) technology to represent the program behaviors. In addition, we design a novel information sharing infrastructure, RENShare, to collaboratively congregate the group characteristics of programs spreading over different network areas. Our experimental results show that ENDMal can detect unknown malwares much faster than the centralized detection system and is more effective than the existing distributed detection system.

In this paper, the environmental efficiencies of 39 industrial sectors are measured using a non-radical, non-oriented slacks-based measure (SBM) model that considers the environmental impact of each industry. The economic efficiency of each industrial sector is measured using traditional data envelope analysis (DEA) methods and the differences between environmental efficiency and economic efficiency are analyzed using the transverse analysis method. A total of 39 industrial sectors are examined within three main industrial sectors (mining, manufacturing, and supply). Using the longitudinal analysis method, the environmental efficiencies of the three main industrial sectors from 2002 to 2009 are analyzed using the average value of their sub-industries’ environmental efficiencies. The time regression equation of the three industrial fields’ environmental efficiencies is examined using a regression analysis method. Based on these results, tactics for enhancing the environmental efficiency of Chinese industries are suggested in accordance with the characteristics of each industrial sector’s environmental efficiency.

Data envelopment analysis (DEA) has recently become a popular approach in measuring the energy and environmental performance at the macro-economy level. A common limitation of several previous studies is that they ignored the undesirable outputs and did not consider the separation of inputs into energy resources and non-energy resources under the DEA framework. Thus, within a joint production framework of considering both desirable and undesirable outputs, as well as energy and non-energy inputs, this study analyzes China’s regional total-factor energy and environmental efficiency. This paper utilizes improved DEA models to measure the energy and environmental efficiency of 29 administrative regions of China during the period of 2000–2008. In addition, the DEA window analysis technique is applied to measure the efficiency in cross-sectional and time-varying data. The empirical results show that the east area of China has the highest energy and environmental efficiency, while the efficiency of the west area is worst. All three areas of China have similar trends in the variation of efficiency and in general the energy and environmental efficiency of China slightly increased from 2000 to 2008. The regions of the east area have a more balanced development than the regions of the central area and west area according to energy and environmental efficiency.

Utilizing undesirable outputs, a weighted SBM model was proposed in our study. Weights of undesirable outputs and weights of fossil energy were established, based on energy scarcity and the effect on the environment caused by undesirable outputs respectively. Then the environmental efficiency of industrial sectors in China are assessed with the improved weighted SMB. To eliminate the difference in energy structure and undesirable outputs structure, energy inputs and undesirable outputs were employed as classification indicators. Industrial sectors were classified into four groups. Results of the environmental efficiency assessed by three methods showed that the improved SBM model has stronger discrimination power than the previous SBM. The efficiency score measured by the improved SBM is close to the score measured by the SBM model. However, there is an obvious gap between the CCR and the improved SBM. Finally, it is easy to find key indices that have a great impact on the environmental efficiency with the improved SBM.