Simultaneous spectrophotometric determination of copper(II), lead(II) and cadrnium(II)

The full spectrum technique in the fourier domain was studied for simultaneous determination of Cu(II), Pb(II) and Cd(II) with 4-(2-pyridylazo) resorcinol (PAR). A program called SPGRFSQ. was designed to perform the calculations. Seven error functions were calculated for deducing the number of factors. Data reduction was performed using principal component analysis (PCA) algorithm. Experimental results showed the method to be successful even where there was severe overlap of spectra.     

Derivation and analysis of a filtered backprojection algorithm for cone beam projection data

The authors address the problem of three-dimensional image reconstruction from cone beam projections. Modifying a result due to A.A. Kirillov (Soviet Math. Dokl., vol. 2, p.268-9, 1961), the authors derive an inversion formula for the case where the cone vertices form an unbounded curve. For the special case in which the cone vertices form a circle, an approximate reconstruction formula is developed and shown to be essentially equivalent to the practical cone-beam algorithm of L.A. Feldkamp et al. (1984). For this approximate inverse, the authors derive the resulting spatially varying point spread function, examine the effect of bandlimiting due to sampling, and compare the resulting image quality as a function of the radius of the circle formed by the cone vertices.     

TEM IMAGING TECHNIQUE

This imaging technique is used for approximate interpretation of TEM data including ungrounded loopsource and grounded line source soundings.Loop source data can be collected as central-loop soundings or asout-of-loop soundings with a short or long offset configuration.The imaging method is based on the same princi-ple as that employed by Fullager(1989),with apparent resistivities computed directly from time-derivative data,db/d,and then associated with depths according to image theory.     

The fate of 14C-radiolabelled diclofenac and 4'-hydroxydiclofenac in membrane bioreactor treatment of wastewater

This study aimed at enhancing knowledge on the fate of diclofenac (DF), together with its main human metabolite 4'-hydroxydiclofenac (4'OHDF), during wastewater treatment by using a laboratory-scale membrane bioreactor (MBR). The reactor was fed continuously with non-radiolabelled diclofenac for a one month period prior to a single pulse of a 14C-radiolabelled solution of DF and 4'OHDF. The solution spike contained approximately 25% 4'OHDF and 65% DF, which corresponds to the ratio observed in municipal sewage, as well as traces of two other metabolites. The radioactivity was monitored for a total of twelve days in the various output streams. The calculation of the complete mass balance in the system demonstrated that the major part of the radioactivity left the reactor with the permeate (88.7%), while 2.1% was recovered in the excess sludge. Negligible amounts were recovered in the off-gas traps and on the membranes. Chromatographic analyses of effluent samples, by means of HPLC-MS coupled in parallel to a radiodetector, displayed a different pattern than the one of the spiked solution. It showed the occurrence of three additional metabolites.     

安钢SAP预留系统的开发与应用

本文简述了安钢信息化建设概况,介绍了安钢信息化建设中开发SAP预留系统的设计流程,阐述了预留系统实现的功能.     

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.     

Evaluation of tile–wall bonding integrity based on impact acoustics and support vector machine

It is well recognized that the impact-acoustic emissions contain information that can indicate the presence of the adhesive defects in the bonding structures. In our previous papers, artificial neural network (ANN) was adopted to assess the bonding integrity of the tile–walls with the feature extracted from the power spectral density (PSD) of the impact-acoustic signals acting as the input of classifier. However, in addition to the inconvenience posed by the general drawbacks such as long training time and large number of training samples needed, the performance of the classic ANN classifier is deteriorated by the similar spectral characteristics between different bonding status caused by abnormal impacts. In this paper our previous works was developed by the employment of the least-squares support vector machine (LS-SVM) classifier instead of the ANN to derive a bonding integrity recognition approach with better reliability and enhanced immunity to surface roughness. With the help of the specially designed artificial sample slabs, experiments results obtained with the proposed method are provided and compared with that using the ANN classifier, demonstrating the effectiveness of the present strategy.     

TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data

Statistical analysis of air mass back trajectories combined with long-term ambient air pollution measurements are useful tools for source identification. Using these methods, the geographic information system (GIS) based software, TrajStat, was developed to view, query, and cluster the trajectories and compute the potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses when measurement data are included.     

Computational simulation of Belousov–Zhabotinskii oscillating chemical reaction

The suitability of computational simulation of the Belousov–Zhabotinskii oscillating chemical reaction by differential kinetic methodology for resolving nonlinear multi-component system is demonstrated in this work. According to the Field–KÖrÖs–Noyes mechanism and the Oregonator model, the change of the concentrations of HBrO₂, bromide ion and cerium ion are simulated. The results of computational simulation are consistent with experimental results very well. At the same time, the effect of variables and parameters, especially the rate constant on the oscillation curve, are investigated deeply. A simple method of estimating rate constants is obtained through simulation the concentrations of key components of the system, and then comparison the simulation results with the experimental ones. The reasonable rate constant is also proposed.