Mathematical and Computer Modelling最新文献

Considering carbon dioxide (CO₂) emitters in different groups that may not have the same technology, this paper extends the Malmquist emission performance index (MCPI) for measuring changes in CO₂ emission performance using a nonparametric metafrontier. The MCPI can be decomposed into two components: efficiency change index and technological change index. Meanwhile, the interrelationships between two MCPIs, relative to the metafrontier and the group frontier respectively, are also examined by the key factor, technology gap ratio (TGR).

As city road congestion problems become more serious, many researchers have started to use the technique of vehicle ad hoc networks (VANETs) for road congestion detection. However, various on-board sensors equipped in vehicles may generate lots of atomic messages, which usually cause serious channel competition problems. In this paper, we propose a multilevel information fusion approach by combining the fuzzy clustering-based feature level information fusion (FCMA) and the modified Dempster–Shafer evidence reasoning-based decision level information fusion (D-SEMA). The FCMA can extract the key features from atomic messages, thereby greatly reducing the network traffic load. Furthermore, the D-SEMA mechanism is used to judge whether the road congestion event occurs. Performance analysis and simulation results under ONE simulator show that the proposed multilevel information fusion approach can detect road congestion efficiently with low bandwidth consumption.

This paper proposes a non-radial DEA approach consisting of both a static and a dynamic environmental performance index (EPI) for measuring environmental performance. The static EPI is defined as the ratio of a non-radial efficiency measure for reducing undesirable outputs to that for increasing desirable outputs. The proposed non-radial DEA approach has been applied to model the environmental performance of industrial sectors in different provinces of China from 1998 to 2009. It has been found that the static non-radial EPI has a higher discriminating power than the EPI derived from the non-radial undesirable outputs orientation DEA models. Our empirical results also show that the environmental performance of industrial sectors in China improves by 58% in 1998–2009 that is mainly driven by the technological change.

In recent years, two primary methods, the data envelopment analysis (DEA) and stochastic frontier analysis (SFA) have widely been applied to measure energy utilization efficiency as they both can estimate the energy efficiency appropriately. However, both of the methods have merits as well as drawbacks. In this paper we use the super efficiency of DEA (SEDEA) model and SFA to estimate the energy productive efficiency of 30 administrative regions of mainland China during the period 1998–2009. The comparative study of the two methods shows that despite the estimated values being quite different, the ranks of these two results are very similar. In addition, the results of the two methods suggest that energy efficiency in the eastern region of China possesses an advantage over the western regions. Further analysis indicates that SEDEA not only has the advantages of DEA and SFA, but also has the advantage of evaluation energy efficiency effectively. In addition, this paper decomposes the Malmquist index so as to explore energy efficiency comprehensively and dynamically.

Resource and information protection plays a relevant role in distributed systems like the ones present in the Internet of Things (IoT). Authorization frameworks like RBAC and ABAC do not provide scalable, manageable, effective, and efficient mechanisms to support distributed systems with many interacting services and are not able to effectively support the dynamicity and scaling needs of IoT contexts that envisage a potentially unbound number of sensors, actuators and related resources, services and subjects, as well as a more relevance of short-lived, unplanned and dynamic interaction patterns. Furthermore, as more end-users start using smart devices (e.g. smart phones, smart home appliances, etc.) the need to have more scalable, manageable, understandable and easy to use access control mechanisms increases. This paper describes a capability based access control system that enterprises, or even individuals, can use to manage their own access control processes to services and information. The proposed mechanism supports rights delegation and a more sophisticated access control customization. The proposed approach is being developed within the European FP7 IoT@Work project to manage access control to some of the project’s services deployed in the shop floor.

Future-Internet research tries to improve the Internet in various ways. Functional-block-based approaches use flexible network stacks called protocol graphs which are created from functional blocks which encapsulate network functionalities. This article presents a method for producing requirement-specific protocol graphs which organizes the problem domain into the separate concerns of modeling functional blocks with impact functions, using those impact functions to evaluate the impact of a protocol graph, searching through the set of possible protocol graphs to select those protocol graphs which fulfill the requirements, and then reducing that set of protocol graphs to the most suitable one. Algorithms for the impact evaluation, search and selection, and optimization methods along with their relations are presented. An example is also presented to further clarify and describe our approach.

In this study, the environmental efficiency of Beijing in the past five years is evaluated using a data envelopment analysis (DEA) model that considers undesirable outputs. We analyze the factors that influence the environmental efficiency of Beijing using a two-step method. In the first step, a DEA model is used to assess the environmental efficiency of Beijing. In the second step, the efficiency from the first step is used as a dependent variable and factors that impact the environmental efficiency are used as independent variables. The regression model and the coefficients of the independent variables determine which factors influence the environmental efficiency and the strength of the influence. Finally, we interpret the enlightenment to the reality and the future development based on its results. The conclusion shows that the environmental efficiency of Beijing over 2005–2009 has been rising. From 2005 to 2007, the environmental efficiency of Beijing continued to increase. In 2008, the environmental efficiency reached a maximum. In 2009, Beijing maintained a high environmental efficiency. The proportion of the tertiary industry to the whole industries is the most important factor that impacts Beijing’s environmental efficiency, that is, Beijing’s industrial structure has the most influence on the city’s environmental efficiency.

This paper has advanced a new method of maize yield prediction which is based on the spatio-temporal data mining. On the one hand, this method predicts the sequence of the target object itself by adopting statistics principles. On the other hand, the method calculates the influence of the spatial distribution of adjacent-area soil fertility on maize yield by neural network. Finally, the method obtains the integrated forecast outcome by taking linear regression. Taking the method to predict the maize yield of experimental area for six consecutive years is better than taking no account of the spatial influence. The errors are controlled by 5%. This paper provides a new method to solve the spatio-temporal data update of farmland affected on the maize yield.

Improved water quality prediction accuracy and reduced computational complexity are vital for ensuring a precise control over the water quality in intensive pearl breeding. This paper combined the wavelet transform with the BP neural network to build the short-term wavelet neural network water quality prediction model. The proposed model was used to predict the water quality of intensive freshwater pearl breeding ponds in Duchang county, Jiangxi province, China. Compared with prediction results achieved by the BP neural network and the Elman neural network, the mean absolute percentage error dropped from 17.464% and 8.438%, respectively, to 3.822%. The results show that the wavelet neural network is superior to the BP neural network and the Elman neural network. Furthermore, the proposed model features a high learning speed, improved predict accuracy, and strong robustness. The model can predict water quality effectively and can meet the management requirements in intensive freshwater pearl breeding.