Regional ecological security assessment is a significant methodology for environmental protection, land utilisation, and human development. This study aims to reveal the regional constraints of ecological resources to overcome the difficulties and complexities in quantification of current models used in land ecosystems. For this purpose, the technique for order preference by similarity to an ideal solution (TOPSIS) was linked to a grey relational analysis and integrated with a geographic information system. The obtained method was used to construct a land eco-security evaluation on a regional scale for application in a traditional mining city, Daye, in central China. Parameter analysis was introduced to the method to produce a more realistic spatial distribution of eco-security. Subsequently, based on the pressure–state–response framework, the eco-security index was calculated, and the carrying capacity of land resources and population for each sub-region were analysed. The results showed that: (i) very insecure and insecure classes comprised 5.65% and 18.2% of the total area, respectively, highlighting the vulnerable eco-environmental situation; (ii) moderate secure classes areas comprised a large amount of arable land, spanning an area of 494.5 km2; (iii) secure areas were distributed in the northwest, containing mostly water and wetland areas and accounting for 426.3 km2; and (iv) very secure areas were located on the southeastern region, involving traditional woodland with a better vegetation cover and an overall higher eco-environmental quality. In addition, for each sub-region, the extremely low and low ecological security areas were mainly arable and urban lands, which amounted to 305 and 190 km2, respectively. Under the current ecological constraints, sub-region 1 cannot continue supporting the population size in Daye City. The present results demonstrate the accuracy of our methodology, and our method may be used by local managers to make effective decisions for regional environment protection and sustainable use of land resources.
The superpoints are the sources (or the destinations) that connect with a great deal of destinations (or sources) during a measurement time interval, so detecting the superpoints in real time is very important to network security and management. Previous algorithms are not able to control the usage of the memory and to deliver the desired accuracy, so it is hard to detect the superpoints on a high speed link in real time. In this paper, we propose an adaptive sampling algorithm to detect the superpoints in real time, which uses a flow sample and hold module to reduce the detection of the non-superpoints and to improve the measurement accuracy of the superpoints. We also design a data stream structure to maintain the flow records, which compensates for the flow Hash collisions statistically. An adaptive process based on different sampling probabilities is used to maintain the recorded IP addresses in the limited memory. This algorithm is compared with the other algorithms by analyzing the real network trace data. Experiment results and mathematic analysis show that this algorithm has the advantages of both the limited memory requirement and high measurement accuracy.superpoints detection, adaptive process, flow sample and hold, collision compensation
The Counting Bloom Filter (CBF) is a space-efficient data structure that extends a Bloom filter so as to allow approximate multiplicity queries on a dynamic multi-set. An in-depth study of three existing CBF schemes is presented, that is, the Naïve Counting Bloom Filter (NCBF), the Space-Code Bloom Filter (SCBF) and the d-leftCounting Bloom Filter (dlCBF). Then, a CBF scheme called Binary Shrinking d-left Counting Bloom Filter (BSdlCBF) is proposed. A performance metrics named load adaptability for CBF schemes is also defined. The performance of the four CBF schemes is evaluated by using metrics of counting error, space complexity and load adaptability under both uniform and Zipfian multiplicity distributions. The experimental results show that the proposed BSdlCBF outperforms the other three in terms of accuracy, space-efficiency and load adaptability. The cost of such an advantage of BSdlCBF is a reasonable rise in computational and space complexity.
Magic matrix-based data hiding schemes are applied to transmit secret information through open communication channels safely. With the development of various magic matrices, some higher dimensional magic matrices are proposed for improving the security level. However, with the limitation of computing resource and the requirement of real time processing, these higher dimensional magic matrix-based methods are not advantageous. Hence, a kind of data hiding scheme based on a single or a group of multi-dimensional flexible magic matrices is proposed in this paper, whose magic matrix can be expanded to higher dimensional ones with less computing resource. Furthermore, an adaptive mechanism is proposed to reduce the embedding distortion. Adapting to the secret data, the magic matrix with least distortion is chosen to embed the data and a marker bit is exploited to record the choice. Experimental results confirm that the proposed scheme hides data with high security and a better visual quality.
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