Scaling behavior of online human activity

The rapid development of Internet technology enables human explore the web and record the traces of online activities. From the analysis of these large-scale data sets (i.e. traces), we can get insights about dynamic behavior of human activity. In this letter, the scaling behavior and complexity of human activity in the e-commerce, such as music, book, and movie rating, are comprehensively investigated by using detrended fluctuation analysis technique and multiscale entropy method. Firstly, the interevent time series of rating behaviors of these three type medias show the similar scaling property with exponents ranging from 0.53 to 0.58, which implies that the collective behaviors of rating media follow a process embodying self-similarity and long-range correlation. Meanwhile, by dividing the users into three groups based their activities (i.e., rating per unit time), we find that the scaling exponents of interevent time series in three groups are different. Hence, these results suggest the stronger long-range correlations exist in these collective behaviors. Furthermore, their information complexities vary from three groups. To explain the differences of the collective behaviors restricted to three groups, we study the dynamic behavior of human activity at individual level, and find that the dynamic behaviors of a few users have extremely small scaling exponents associating with long-range anticorrelations. By comparing with the interevent time distributions of four representative users, we can find that the bimodal distributions may bring the extraordinary scaling behaviors. These results of analyzing the online human activity in the e-commerce may not only provide insights to understand its dynamic behaviors but also be applied to acquire the potential economic interest

Stability and Persistence of an Avian Influenza Epidemic Model with Impacts of Climate Change

The growing number of reported avian influenza cases has prompted awareness of the importance of research methods to control the spread of the disease. Seasonal variation is one of the important factors that affect the spread of avian influenza. This paper presents a “nonautonomous” model to analyze the transmission dynamics of avian influenza with the effects of climate change. We obtain and discuss the global stability conditions of the disease-free equilibrium; the threshold conditions for persistence, permanence, and extinction of the disease; and the parameters with periodicity for controlling and eliminating the avian influenza

Hierarchical visual perception and two-dimensional compressive sensing for effective content-based color image retrieval

Content-based image retrieval (CBIR) has been an active research theme in the computer vision community for over two decades. While the field is relatively mature, significant research is still required in this area to develop solutions for practical applications. One reason that practical solutions have not yet been realized could be due to a limited understanding of the cognitive aspects of the human vision system. Inspired by three cognitive properties of human vision, namely, hierarchical structuring, color perception and embedded compressive sensing, a new CBIR approach is proposed. In the proposed approach, the Hue, Saturation and Value (HSV) color model and the Similar Gray Level Co-occurrence Matrix (SGLCM) texture descriptors are used to generate elementary features. These features then form a hierarchical representation of the data to which a two-dimensional compressive sensing (2D CS) feature mining algorithm is applied. Finally, a weighted feature matching method is used to perform image retrieval. We present a comprehensive set of results of applying our proposed Hierarchical Visual Perception Enabled 2D CS approach using publicly available datasets and demonstrate the efficacy of our techniques when compared with other recently published, state-of-the-art approaches

Steam explosion pretreatment enhancing enzymatic digestibility of overground tubers of tiger nut (Cyperus esculentus L.)

IntroductionTiger nut (TN) is recognized as a high potential plant which can grow in well-drained sandy or loamy soils and provide food nutrients. However, the overground tubers of TN remain unutilized currently, which limits the value-added utilization and large-area cultivation of this plant.MethodsIn the present study, the overground tubers of TN were subjected to enzymatic hydrolysis to produce fermentable sugars for biofuels production. Steam explosion (SE) was applied to modify the physical-chemical properties of the overground tubers of TN for enhancing its saccharification.Results and discussionResults showed that SE broke the linkages of hemicellulose and lignin in the TN substrates and increased cellulose content through removal of hemicellulose. Meanwhile, SE cleaved inner linkages within cellulose molecules, reducing the degree of polymerization by 32.13–77.84%. Cellulose accessibility was significantly improved after SE, which was revealed visibly by the confocal laser scanning microscopy imaging techniques. As a result, enzymatic digestibility of the overground tubers of TN was dramatically enhanced. The cellulose conversion of the SE treated TN substrates reached 38.18–63.97%, which was 2.5–4.2 times higher than that without a SE treatment.ConclusionTherefore, SE pretreatment promoted saccharification of the overground tubers of TN, which paves the way for value-added valorization of the TN plants