用户身份管理策略研究

In recent years there has been a fast grow of various electronic services.Each service requires specif-ic credentials for authenticating and authorizing user ac-cess or specific subset of user's personal information for transactions the service offers.Such a situation causes problems with remembering multiple,service-specific authentication data,with controlling user's personal data spread across different services-problems with managing user's identities.This paper is an overview on Identity Management(IdM...     

Unsupervised classification methods in food sciences: discussion and outlook

This paper reviews three unsupervised multivariate classification methods: principal component analysis, principal component similarity analysis and heuristic cluster analysis. The theoretical basis of each method is presented in brief, and assumptions inherent to the methods are highlighted. A literature review shows that these methods have sometimes been used inappropriately or without referencing all essential parameters. The paper also brings to the attention of the reader a relatively unknown method: probabilistic or model‐based cluster analysis. The goal of this method is to uncover the true classification of objects rather than a convenient classification provided by the other methods. For this reason it is felt that model‐based cluster analysis will have broad application in the future. Copyright © 2008 Society of Chemical Industry     

An electron microscopy three‐dimensional characterization of titania nanotubes

To characterize complex, three‐dimensional nanostructures, modern microscopy techniques are needed, such as electron tomography and focused ion beam (FIB) sectioning. The aim of this study was to apply these two techniques to characterize TiO₂ nanotubes in terms of their size, shape, volume, porosity, geometric surface area, and specific surface area (SSA). For these experiments, titania nanotubes were fabricated by means of the electrochemical oxidation of titanium at a voltage of 20 V for 2 hr followed by heat treatment at 450°C for 3 hr to change the amorphous structure into a crystalline anatase structure. The quantitative data obtained from the FIB and electron tomography reconstructions show a high similarity in porosity and some differences in SSA. These might be the result of differences in resolution between the two reconstruction techniques.     

A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction,segmentation, classification and detection approaches

With the development of Computer-aided Diagnosis (CAD) and image scanning techniques, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital histopathology. Since 2004, WSI has been used widely in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computer algorithms, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists to obtain more stable and quantitative results with minimum labor costs and improved diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning techniques in WSI segmentation, classification, and detection are reviewed. Finally, the existing methods are studied, and the application prospects of the methods in this field are forecasted.

     

An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things

Mobile Networks and Applications - The application of the traditional single frame character image super-resolution reconstruction method has some problems, such as noise can not be removed...     

Bacteriophages vB_Sen-TO17 and vB_Sen-E22, Newly Isolated Viruses from Chicken Feces,Specific for Several Salmonella enterica Strains

Two newly discovered bacteriophages, isolated from chicken feces and infecting Salmonella enterica strains, are described in this report. These phages have been named vB_Sen-TO17 and vB_Sen-E22, and we present their molecular and functional characterization. Both studied viruses are able to infect several S. enterica strains and develop lytically, but their specific host ranges differ significantly. Electron microscopic analyses of virions have been performed, and full genome sequences were determined and characterized, along with molecular phylogenetic studies. Genomes of vB_Sen-TO17 (ds DNA of 41,658 bp) and vB_Sen-E22 (dsDNA of 108,987 bp) are devoid of homologs of any known or putative gene coding for toxins or any other proteins potentially deleterious for eukaryotic cells. Both phages adsorbed efficiently (>95% adsorbed virions) within 10 min at 42 °C (resembling chicken body temperature) on cells of most tested host strains. Kinetics of lytic development of vB_Sen-TO17 and vB_Sen-E22, determined in one-step growth experiments, indicated that development is complete within 30–40 min at 42 °C, whereas burst sizes vary from 9 to 79 progeny phages per cell for vB_Sen-TO17 and from 18 to 64 for vB_Sen-E22, depending on the host strain. Virions of both phages were relatively stable (from several percent to almost 100% survivability) under various conditions, including acidic and alkaline pH values (from 3 to 12), temperatures from −80 °C to 60 °C, 70% ethanol, chloroform, and 10% DMSO. These characteristics of vB_Sen-TO17 and vB_Sen-E22 indicate that these phages might be considered in further studies on phage therapy, particularly in attempts to eliminate S. enterica from chicken intestine.     

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

     

Training neural networks on high-dimensional data using random projection

Pattern Analysis and Applications - Training deep neural networks (DNNs) on high-dimensional data with no spatial structure poses a major computational problem. It implies a network architecture...     

A quadratic boundedness approach to a neural network-based simultaneous estimation of actuator and sensor faults

Neural Computing and Applications - The paper is devoted to the problem of a neural network-based robust simultaneous actuator and sensor faults estimator design for the purpose of the fault...     

Retracted papers by Iranian authors: causes,journals, time lags,affiliations, collaborations

Scientometrics - This study aims to analyze 343 retraction notices indexed in the Scopus database, published in 2001–2019, related to scientific articles (co-)written by at least one author...