Nano-Wear Mechanism for Ultra-High Molecular Weight Polyethylene (UHMWPE) Sliding Against a Model Hard Asperity

This paper investigates the mechanism of wear-particle generation when a single asperity is passed over the surface of UHMWPE. A single asperity is modelled using a conical diamond-tip indenter attached to a nano-scratch tester. Scratches are produced by passing the indenter over the surface of the polymer in a single pass and multiple passes on a single track or on orthogonally intersecting tracks. The debris-generation process, as observed in the nano-scratch test, is complex and depends upon the direction of scratches. It is found that the rate of wear-debris generation is much higher when two consecutive scratches orthogonally intersect each other compared to when the scratches are made on the same track. Wall formation was observed between orthogonally intersecting scratches, and it is believed that this is central to the low-cycle wear mechanism in these systems.     

Mining Condensed Frequent-Pattern Bases

Frequent-pattern mining has been studied extensively and has many useful applications. However, frequent-pattern mining often generates too many patterns to be truly efficient or effective. In many applications, it is sufficient to generate and examine frequent patterns with a sufficiently good approximation of the support frequency instead of in full precision. Such a compact but close-enough frequent-pattern base is called a condensed frequent-pattern base.In this paper, we propose and examine several alternatives for the design, representation, and implementation of such condensed frequent-pattern bases. Several algorithms for computing such pattern bases are proposed. Their effectiveness at pattern compression and methods for efficiently computing them are investigated. A systematic performance study is conducted on different kinds of databases, and demonstrates the effectiveness and efficiency of our approach in handling frequent-pattern mining in large databases.     

Dissipation of fragrance materials in sludge-amended soils

A possible removal mechanism for fragrance materials (FMs) in wastewater is adsorption to sludge, and sludge application to land may be a route through which FMs are released to the soil environment. However, little is known about the concentrations and fate of FMs in soil receiving sludge application. This study was conducted to better understand the dissipation of FMs in sludge-amended soils. We first determined the spiking and extraction efficiencies for 22 FMs in soil and leachate samples. Nine FMs were detected in digested sludges from two wastewater treatment plants in Delaware using these methods. We conducted a 1-year die-away experiment which involved four different soils amended with sludge, with and without spiking of the 22 FMs. The initial dissipation of FMs in all spiked trays was rapid, and only seven FMs remained at concentrations above the quantification limits after 3 months: AHTN, HHCB, musk ketone, musk xylene, acetyl cedrene, OTNE, and DPMI. After 1 year, the only FMs remaining in all spiked trays were musk ketone and AHTN. DPMI was the only FM that leached significantly from the spiked trays, and no FMs were detected in leachate from any unspiked tray. While soil organic matter content affected the dissipation rate in general, different mechanisms (volatilization, transformation, leaching) appeared to be important for different FMs.     

From sequential pattern mining to structured pattern mining: A pattern-growth approach

Sequential pattern mining is an important data mining problem with broad applications. However,it is also a challenging problem since the mining may have to generate or examine a combinatorially explosivenumber of intermediate subsequences. Recent studies have developed two major classes of sequential patternmining methods: (1) a candidate generation-and-test approach, represented by (i) GSP, a horizontal format-basedsequential pattern mining method, and (ii) SPADE, a vertical format-based method; and (2) a pattern-growthmethod, represented by PrefixSpan and its further extensions, such as gSpan for mining structured patterns. In this study, we perform a systematic introduction and presentation of the pattern-growth methodologyand study its principles and extensions. We first introduce two interesting pattern-growth algorithms, FreeSpanand PrefixSpan, for efficient sequential pattern mining. Then we introduce gSpan for mining structured patternsusing the same methodology. Their relative performance in l     

分布开放式的入侵检测与响应架构--IDRA

提出了一个完整的新型入侵检测与响应架构,该系统架构采用分布式的结构,符合标准化的方向,具有极强的可扩展性,能够对系统检测到的网络安全事件做出迅速的响应,及时地遏制事态发展,首先从现有的入侵检测系统出发,分析其面临的主要问题,然后提出了分布开放式的入侵检测与响应架构IDRA,介绍其协议框架和系统架构,最后对IDRA的先进性以及可行性进行了分析。     

Mobility Management for Hierarchical Wireless Networks

In this paper, we consider the mobility management in large, hierarchically organized multihop wireless networks. The examples of such networks range from battlefield networks, emergency disaster relief and law enforcement etc. We present a novel network addressing architecture to accommodate mobility using a Home Agent concept akin to mobile IP. We distinguish between the physical routing hierarchy (dictated by geographical relationships between nodes) and logical hierarchy of subnets in which the members move as a group (e.g., company, brigade, battalion in the battlefield). The performance of the mobility management scheme is investigated through simulation.     

Individually addressable gel-integrated voltammetric microelectrode array for high-resolution measurement of concentration profiles at interfaces

The application of a novel voltammetric probe, based on an individually addressable gel-integrated microelectrode array (IA-GIME), for real-time, high-spatial resolution concentration profile measurements at interfaces is described. Reliability and validity of steep metal concentration gradients obtained with this novel system have been demonstrated by performing systematic tests at well-controlled liquid-liquid and liquid-solid interfaces. The liquid-liquid interface was formed by two layers of aqueous solutions with different components; only one layer contained trace metal ions (Pb(II) and Cd(II)); the individually addressable microelectrode array was placed at the interface of the liquid-liquid system; the concentration profiles were recorded as function of time; and the effective diffusion coefficients were calculated. The liquid-"solid" interface was formed from an aqueous solution layer overlying a bed of silica particles saturated with an aqueous solution. The sensor array has been used to monitor the diffusion processes of Tl(I) or Pb(II) from the liquid phase to the "solid" phase. The influences of porosity, geometry of the porous media, and complexation between metal ion and silica, on the diffusion processes, have been studied. All these results show that correct diffusion profiles of metal ions at interfaces can be obtained with 200-microm resolution with the IA-GIME. They also demonstrate that, for measurements in "solid" phase, the aforementioned factors must be considered carefully for correct calibration of any electrodes and the gel-integrated microelectrodes are unique tools to enable calibration of the sensors with synthetic solutions.     

对广告中女性形象的思考

广告在传播商业信息的同时,掩映着一个时代的文化。因而从对广告中出现频率较高的女性身上,可以一定程度的窥探企业产品的特征和社会文化立场,解读女性作为独特社会文化群体是如何传达企业和时代的声音的。     

信息化办公及其特征

本文从历史发展的现实,说明多年来办公室和办公活动有了很大变化,并可分为传统办公、机械化办公、、自动化办公和电子化办公(信息化办公)等几个阶段,并论述了信息化办公的几个特征(广义性、空间性、实时性、体系结构和与生活相结合等),还讨论了工作中应注意之点,例如系统安全、生命周期、节能和数字鸿沟等.     

Mining gene–sample–time microarray data: a coherent gene cluster discovery approach

Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene–sample–time microarray data sets that records the expression levels of various genes under a set of samples during a series of time points. In particular, we propose the mining of coherent gene clusters from such data sets. Each cluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. The coherent gene clusters may identify the samples corresponding to some phenotypes (e.g., diseases), and suggest the candidate genes correlated to the phenotypes. We present two efficient algorithms, namely the Sample-Gene Search and the Gene–Sample Search, to mine the complete set of coherent gene clusters. We empirically evaluate the performance of our approaches on both a real microarray data set and synthetic data sets. The test results have shown that our approaches are both efficient and effective to find meaningful coherent gene clusters. Daxin Jiang received the Ph.D. degree in computer science and engineering from the State University of New York at Buffalo in 2005. He received the B.S. degree in computer science from the University of Science and Technology of China. From 1998 to 2000, he was a M.S. student in Software Institute, Chinese Academy of Sciences. He is currently an assistant professor at the School of Computer Engineering, Nanyang Technology University, Singapore. His research interests include data mining, bioinformatics, machine learning, and information retrieval. Jian Pei received the Ph.D. degree in computing science from Simon Fraser University, Canada, in 2002, under Dr. Jiawei Han's supervision. He also received the B.Eng. and the M.Eng. degrees from Shanghai Jiao Tong University, China, in 1991 and 1993, respectively, both in Computer Science. He is currently an assistant professor of computing science at Simon Fraser University. His research interests include developing effective and efficient data analysis techniques for novel data intensive applications. He is currently interested in various techniques of data mining, data warehousing, online analytical processing, and database systems, as well as their applications in bioinformatics. His current research is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the National Science Foundation (NSF) of the United States. Since 2000, he has published over 70 research papers in refereed journals, conferences, and workshops, has served in the organization committees and the program committees of over 60 international conferences and workshops, and has been a reviewer for some leading academic journals. He is a member of the ACM, the ACM SIGMOD, and the ACM SIGKDD. Murali Ramanathan is an associate professor of pharmaceutical sciences and neurology. He received the B.Tech. (Honors) in chemical engineering from the Indian Institute of Technology, India, in 1983. After a 4-year stint in the chemical industry, he obtained the M.S. degree in chemical engineering from Iowa State University, Ames, IA, in 1987, and the Ph.D. degree in bioengineering from the University of California-San Francisco and University of California-Berkeley Joint Program in Bioengineering in 1994. Dr. Ramanathan research interests are primarily focused on the treatment of multiple sclerosis (MS), an inflammatory-demyelinating disease of the central nervous system that affects over 1 million patients worldwide. MS is a complex, variable disease that causes physical and cognitive disability and nearly 50% of patients diagnosed with MS are unable to walk after 15 years. The etiology and pathogenesis of MS remains poorly understood. Dr. Ramanathan's research interests include stochastic modeling of pharmaceutical systems and novel approaches to analyzing and using genetic and genomic data for improving patient care and optimizing therapy. Chuan Lin is currently a Ph.D. student in the Department of Computer Science and Engineering, State University of New York at Buffalo. She received the B.E. and the M.S. degrees in computer science and technology from Tsinghua University in China. Her research interests include bioinformatics, data mining, and machine learning. Chun Tang received the B.S. and M.S. degrees from Peking University, China, in 1996 and 1999, respectively, and the Ph.D. degree from State University of New York at Buffalo, USA, in 2005, all in computer science. Currently, she is a postdoctoral associate of Center for Medical Informatics, Yale University. Her research interests include bioinformatics, data mining, machine learning, database, and information retrieval. Aidong Zhang received the Ph.D. degree in computer science from Purdue University, West Lafayette, Indiana, in 1994. She was an assistant professor from 1994 to 1999, an associate professor from 1999 to 2002, and has been a professor since 2002 in the Department of Computer Science and Engineering at State University of New York at Buffalo. Her research interests include multimedia systems, content-based image retrieval, bioinformatics, and data mining. She is an author of over 140 research publications in these areas. Dr. Zhang's research has been funded by NSF, NIH, NIMA, and Xerox. Zhang serves on the editorial boards of International Journal of Bioinformatics Research and Applications (IJBRA), ACM Multimedia Systems, International Journal of Multimedia Tools and Applications, and International Journal of Distributed and Parallel Databases. She was the editor for ACM SIGMOD DiSC (Digital Symposium Collection) from 2001 to 2003. She was co-chair of the technical program committee for ACM Multimedia in 2001. She has also served on various conference program committees. Dr. Zhang is a recipient of the National Science Foundation CAREER award and SUNY Chancellor's Research Recognition award.