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

Geography and similarity of regional cuisines in China

Food occupies a central position in every culture and it is therefore of great interest to understand the evolution of food culture. The advent of the World Wide Web and online recipe repositories has begun to provide unprecedented opportunities for data-driven, quantitative study of food culture. Here we harness an online database documenting recipes from various Chinese regional cuisines and investigate the similarity of regional cuisines in terms of geography and climate. We found that the geographical proximity, rather than climate proximity is a crucial factor that determines the similarity of regional cuisines. We develop a model of regional cuisine evolution that provides helpful clues to understand the evolution of cuisines and cultures.Comment: 13 pages, 11 figures and 2 table

High photo-excited carrier multiplication by charged InAs dots in AlAs/GaAs/AlAs resonant tunneling diode

We present an approach for the highly sensitive photon detection based on the quantum dots (QDs) operating at temperature of 77K. The detection structure is based on an AlAs/GaAs/AlAs double barrier resonant tunneling diode combined with a layer of self-assembled InAs QDs (QD-RTD). A photon rate of 115 photons per second had induced 10nA photocurrent in this structure, corresponding to the photo-excited carrier multiplication factor of 10^7. This high multiplication factor is achieved by the quantum dot induced memory effect and the resonant tunneling tuning effect of QD-RTD structure.Comment: 10 pages,5 figures. Submitted to Applied Physics Letter

Exploration of Ideological and Political Education in the Course of “Biopharmaceutical Testing and Testing Technology” under the Post Pandemic Situation

Motivated by curriculum reform and the traditional theoretical teaching content system of drug analysis and testing, we will strengthen experimental teaching in accordance with enterprise needs of vocational colleges. And then, under the direction of content and task driven, we will build an ACQUIN quality certification system for the Biopharmaceutical Testing and Testing Technology course to strengthen students’ independent participation, pique their interest in learning, and cultivate high-quality skilled talents who are capable of doing things and being good person, by thoroughly examining the ideological and political components of the course

Large-scale Multi-view Subspace Clustering in Linear Time

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the era of big data, the computational issue becomes critical. To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we first learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various large-scale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods.Comment: Accepted by AAAI 202

High glucose-induced Matrilin-2 expression in mouse mesangial cells was mediated by transforming growth factor beta 1 (TGF-β1)

This study aimed at evaluating the effect of high glucose on the expression of extracellular matrix (ECM) protein Matrilin-2 and the mechanism underlying this effect by using a mouse mesangial cell line. Mouse mesangial cells (MMCs) were cultured in media containing normal (5 mM d-glucose) or high concentrations of glucose (30 mM d-glucose). The expression of Matrilin-2 was assessed by either RT-PCR or western blot. Additionally, transforming growth factor beta 1 (TGF-β1) inhibitors and TGF-β1 were used to determine whether glucose-regulated Matrilin-2 expression was mediated by the TGF-β1/Smad3 signaling pathway. Our data demonstrated that Matrilin-2 expression was markedly induced by high glucose and TGF-β1. High glucose-induced Matrilin-2 expression was inhibited by TGF-β1/Smad3 inhibitors, indicating that Matrilin-2 was markedly induced by high glucose and this induction was mediated by the TGF-β1/Smad3 pathway. Taken together, our results showed that high-glucose-induced Matrilin-2 expression that was mediated by the TGF-β1/Smad3 signaling pathway might play a role in Diabetic nephropathy (DN) pathogenesis and our finding provided a potential diagnostic and/or therapeutic target for DN

Predicting multiple functions of sustainable flood retention basins under uncertainty via multi-instance multi-label learning

The ambiguity of diverse functions of sustainable flood retention basins (SFRBs) may lead to conflict and risk in water resources planning and management. How can someone provide an intuitive yet efficient strategy to uncover and distinguish the multiple potential functions of SFRBs under uncertainty? In this study, by exploiting both input and output uncertainties of SFRBs, the authors developed a new data-driven framework to automatically predict the multiple functions of SFRBs by using multi-instance multi-label (MIML) learning. A total of 372 sustainable flood retention basins, characterized by 40 variables associated with confidence levels, were surveyed in Scotland, UK. A Gaussian model with Monte Carlo sampling was used to capture the variability of variables (i.e., input uncertainty), and the MIML-support vector machine (SVM) algorithm was subsequently applied to predict the potential functions of SFRBs that have not yet been assessed, allowing for one basin belonging to different types (i.e., output uncertainty). Experiments demonstrated that the proposed approach enables effective automatic prediction of the potential functions of SFRBs (e.g., accuracy >93%). The findings suggest that the functional uncertainty of SFRBs under investigation can be better assessed in a more comprehensive and cost-effective way, and the proposed data-driven approach provides a promising method of doing so for water resources management