Eco-industrial park (EIP) projects have become more prevalent in China. In order to evaluate the performance of such innovative projects, the State Environmental Protection Administration (SEPA) has set up a new national standard for EIPs, the first of its kind globally. This article examines the applicability and feasibility of the indicator system established in the standard. It first presents the details of this new standard. Then benefits and challenges in the standard's application are analyzed. The analysis shows that the new indicators are ecoefficiency-oriented and do not address the essence of the EIP. In the future, there will be a need to revise this set of indicators by considering the principles of eco-industrial development and local realities in order to ensure that the indicators are indeed used to promote sustainable development of industrial parks.
The Normalized Mutual Information (NMI) has been widely used to evaluate the accuracy of community detection algorithms. However in this article we show that the NMI is seriously affected by systematic errors due to finite size of networks, and may give a wrong estimate of performance of algorithms in some cases. We give a simple theory to the finite-size effect of NMI and test our theory numerically. Then we propose a new metric for the accuracy of community detection, namely the relative Normalized Mutual Information (rNMI), which considers statistical significance of the NMI by comparing it with the expected NMI of random partitions. Our numerical experiments show that the rNMI overcomes the finite-size effect of the NMI.Detection of community structures, which asks to group nodes in a network into groups, is a key problem in network science, computer science, sociology and biology. Many algorithms have been proposed for this problem, see ref.[1] for a review. However on a given network, different algorithms usually give different results. Thus evaluating performance of these algorithms and finding the best ones are of great importance.Usually the evaluations are performed on benchmark networks each of which has a reference partition. These benchmarks include networks generated by generative models, like Stochastic Block Model [2] and LFR model [3], with a planted partition as the reference partition; and some real-world networks, like the famous Karate Club network [4] and the Political Blog network [5], with a partition annotated by domain experts as the reference partition. The accuracy of a community detection algorithm is usually represented using similarity between the reference partition and partition found by the algorithm -the larger similarity, the better performance the algorithm has on the benchmark.Without losing generality, in what follows we call the reference partition A and the detected partition B, and our task is to study the measure of similarity between partition A and partition B.When the number of groups are identical, q A = q B = q, the similarity can be easily defined by the overlap, which is the number of identical group labels in A and B maximized over all possible permutations:
Water rotational dynamics in NaSCN and KSCN solutions at a series of concentrations are investigated using femtosecond infrared spectroscopy and theory.
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