ZnO／Ag纳米复合材料光催化性能研究

通过XRD、TEM测试研究了自制ZnO／Ag纳米复合材料的结构和形貌，通过UV检测确定了以该纳米复合材料为光催化剂，在不同条件下对甲基橙的光催化降解率。结果表明：与空气煅烧相比，真空煅烧所得纳米复合材料的光催化降解效果更好，且光催化降解率随纳米复合材料用量增加而增大：甲基橙溶液的pH在5左右时，光催化降解率最高：H2O2浓度为0．9g／L时，光催化降解率可达100％。     

Impact of transient chemistry on the corrosion potential of a steam turbine disc steel

The adoption of two-shifting in coal-fired power generation (switching from on-load to off-load conditions on a daily basis and off-load for the weekend) results in regular cycling of the solution chemistry of the condensed steam on the turbine discs. A first stage investigation has been made of the effect of such chemistry cycling on the corrosion potential of the disc steel. The corrosion potential is typically about +0.05 V (SCE) in off-load chemistry (aerated pure water) and below −0.6 V (SCE) in on-load chemistry (deaerated solution containing chloride or chloride and sulphate anions). The critical observation was the sluggish response of the corrosion potential upon restoration of deaerated on-load conditions with the implication of a likely increase in pit and stress corrosion crack development.     

工艺参数对合成ZnO／Ag纳米复合抗菌剂产率的影响

通过正交实验，研究了反应温度、反应时间、反应物配比对ZnO／Ag纳米复合抗菌剂合成产率的影响规律。获得了高产率合成ZnO／Ag纳米复合抗菌剂的优选工艺条件：反应温度为10～25℃，ZnSO4与NH4HCO3的摩尔比为1：2-2．5，ZnSO4与NH4HCO3的反应时间为2．5～3h，AgNO3与NH4HCO3的摩尔比为1：1．5～2，反应时间为0．5h，ZnO／Ag的产率超过98％。XRD、TEM和SAED结果表明：ZnO／Ag纳米复合抗菌剂为具有纤锌矿结构的ZnO和立方结构的Ag组成。粒径为15～25nm，分散性较好。     

It's the Thought that Counts: The Mediating Effects of Information Processing in Virtual Team Decision Making

Abstract

Previous research shows that although collaboration technology can improve the exchange of information during team decision making, one of the challenges is that this better discussion often does not lead to better decisions. In this paper, we investigate the impact of individual information processing on team decision making. We investigated the effects of two types of collaboration technology when participants were either the member of a team majority or a team minority. There was little impact due to the majority/minority, but the use of collaboration technology that required users to categorize the comments they received from others resulted in increased information processing, which in turn led to better decisions and more satisfied participants.     

客厅电脑，一定要小！——7款小型HTPC机箱实战客厅电脑

给客厅里的大屏幕平板电视机搭配一台电脑是许多玩家的愿望。既然如此,《微型计算机》就收集了本年度最具代表性的7款小型HTPC机箱并进行了测试和体验,看看谁更适合安装客厅电脑。     

Development of an operational fitness test for the Royal Air Force

Since 2002, the Royal Air Force (RAF) has been working towards developing role-related physical tests for use as an operational fitness test (OFT). The purpose of this study was to establish reliability of the OFT (comprising four tests), investigate gym-based tests as predictors of performance and establish performance standards. Fifty-eight RAF personnel performed the OFT on three occasions. A separate cohort carried out fitness and anthropometric tests before performing the OFT, by way of establishing performance predictors. Documented evidence and views of an expert panel were used to determine OFT standards. Reliability ranged from moderate to good for three tests, with one test (Dig) showing poor reliability. The 95% limits of agreement for the prediction models ranged from good to poor (6.7-34.2%). The prediction models were not sufficiently accurate to estimate confidently OFT performance, but could be used as a guide to quantify likely outcome and training needs.     

Performance analysis challenges and framework for high-performance reconfigurable computing

Reconfigurable computing (RC) applications employing both microprocessors and FPGAs have potential for large speedup when compared with traditional (software) parallel applications. However, this potential is marred by the additional complexity of these dual-paradigm systems, making it difficult to identify performance bottlenecks and achieve desired performance. Performance analysis concepts and tools are well researched and widely available for traditional parallel applications but are lacking in RC, despite being of great importance due to the applications’ increased complexity. In this paper, we explore challenges and present new techniques in automated instrumentation, runtime measurement, and visualization of RC application behavior. We also present ideas for integration with conventional performance analysis tools to create a unified tool for RC applications as well as our initial framework for FPGA instrumentation and measurement. Results from a case study are provided using a prototype of this new tool.     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

Siting park-and-ride facilities using a multi-objective spatial optimization model

Park-and-ride services are an important component of many public transportation systems in the United States. Locating park-and-ride facilities is an essential step when planning for these services. In this research we focus on three major siting/modeling concerns that need to be addressed when siting park-and-ride facilities: covering as much potential demand as possible, locating park-and-ride facilities as close as possible to major roadways, and siting such facilities in the context of an existing system. Unfortunately, existing models do not enable each of these concerns to be simultaneously addressed. This paper presents a multi-objective spatial optimization model for integrating these considerations. This model is applied for siting park-and-ride facilities in Columbus, Ohio. Application results show the usefulness of the developed model in supporting transit planning in an urban region.