Sol-gel preparation of a di-ureasil electrolyte doped with lithium perchlorate

Solid polymer electrolytes (SPEs) synthesized by the sol-gel process and designated as di-ureasils have been prepared through the incorporation of lithium perchlorate, LiClO₄, into the d-U(2000) organic-inorganic hybrid network. Electrolytes with lithium salt compositions of n (where n indicates the number of oxyethylene units per Li⁺ ion) between ∞ and 0.5 were characterized by conductivity measurements, cyclic voltammetry at a gold microelectrode, thermal analysis and Fourier transform Raman (FT-Raman) spectroscopy. The conductivity results obtained suggest that this system offers a quite significant improvement over previously characterized analogues doped with lithium triflate [S.C. Nunes, V. de Zea Bermudez, D. Ostrovskii, M.M. Silva, S. Barros, M.J. Smith, R.A. Sá Ferreira, L.D. Carlos, J. Rocha, E. Morales, J. Electrochem. Soc. 152 (2) (2005), A429]. “Free” perchlorate ions, detected in all the samples examined, are identified as the main charge carriers in the sample that yields the highest room temperature conductivity (n = 20). In the di-ureasils with n ≤ 10 ionic association is favoured and the ionic conductivity drops.     

Anthony D. Wagner: Award for Distinguished Scientific Early Career Contributions to Psychology.

Presents the citation for Anthony D. Wagner, who received the Award for Distinguished Scientific Early Career Contributions to Psychology (cognition and human learning) "for outstanding and innovative research on the neural basis of memory and executive control." A brief profile and a selected bibliography accompany the citation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

BP神经网络在致密砂岩储层识别中的应用

川西须家河组地层岩性复杂,属于超致密低孔渗储层,所以储层识别是该地层天然气勘探中所面临的关键问题和难点之一。针对常规储层识别准确率不高的状况,提出利用BP神经网络进行储层的气水干层识别,利用模糊聚类和产层测试结果标定建模样本,采取随机抽样形成建模集与测试集,建立BP神经网络模型对23口井的储层进行气水干层预测,正确率达77.9%以上,明显提高了该地区的测井解释精度,并提供了一种准确率较高的储层预测方法。     

A Review of Wavelets for Digital Wireless Communication

Wavelets have been favorably applied in almost all aspects of digital wireless communication systems including data compression, source and channel coding, signal denoising, channel modeling and design of transceivers. The main property of wavelets in these applications is in their flexibility and ability to characterize signals accurately. In this paper recent trends and developments in the use of wavelets in wireless communications are reviewed. Major applications of wavelets in wireless channel modeling, interference mitigation, denoising, OFDM modulation, multiple access, Ultra Wideband communications, cognitive radio and wireless networks are surveyed. The confluence of information and communication technologies and the possibility of ubiquitous connectivity have posed a challenge to developing technologies and architectures capable of handling large volumes of data under severe resource constraints such as power and bandwidth. Wavelets are uniquely qualified to address this challenge. The flexibility and adaptation provided by wavelets have made wavelet technology a strong candidate for future wireless communication. Madan Kumar Lakshmanan was born in Chennai, India, in 1979. He received the B.E. (with distinction) in electrical engineering from the University of Madras, Chennai, India, in 2000. He joined the Indian Software firm, Polaris Software Labs Ltd., in 2000 where he wrote software for Telecommunication applications. At Polaris, he was awarded the “On The Spot Of Excellence Award” for his efforts. In 2003, he moved to the Indian Institute of Technology-Madras, to develop and establish a wireless communications network for rural connectivity. In 2004, he was awarded the Royal Dutch/Shell Chevning scholarship to pursue a Master degree in Telecommunications at the Delft University of Technology (TUDelft). At TUDelft he is affiliated to the International Research Center for Telecommunications-Transmission and Radar (IRCTR) where he is undertaking research in the field of wavelets applications in Wireless Communications. Homayoun Nikookar received his Ph.D. in Electrical Engineering from Delft University of Technology (TUDelft), The Netherlands, in 1995. From 1995 to 1998 he was a postdoc researcher at the International Research Center for Telecommunications-Transmission and Radar, TUDelft, where since 1999 he has been an Assistant Professor. Dr. Nikookar has done research on different areas of wireless communications, including wireless channel modeling, UWB, MIMO, multicarrier transmission, Wavelet-based OFDM and CDMA. He is a senior member of the IEEE.     

Dental amalgam – the effect of the technology of alloy powder preparation on the corrosion behaviour and the release of mercury

Dental amalgams are based on a broad spectrum of materials differing in their chemical composition, metallurgical treatment, and in the way the initial alloys powders are prepared. In addition to their chemical composition, amalgams based on various powders differ in both their microstructure and the amount of mercury needed for preparation. All these facts may affect electrochemical processes occurring during their interaction with oral fluids, and also mercury release. While verifying the effect of the technology used for the preparation of the high‐copper ternary alloy powder on the properties of resulting amalgams, this study aimed at the mechanism of their interaction with a model saliva solution as well as mercury release was included. Measurements were done in a model saliva solution using standard electrochemical methods and exposition measurements. The interaction of individual types of amalgams with artificial saliva did not reveal any significant differences. The free corrosion potential of all these amalgams in an aerated solution settled in the range of values in which tin oxidation, resulting in a layer of insoluble corrosion producsts, turned out to be the dominant anodic process. The rate of mercury release was the lowest for amalgams based on a gas‐atomized alloy. The highest rate of mercury release, and also its dependence on time, was exhibited by lathe‐cut powder based amalgam. In addition to different volume fraction of the Ag‐Hg phase and the level of its tin alloying, this different behaviour may be explained by differences in the rate at which a layer of tin corrosion products acting as a barrier to mercury release is formed.     

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

A two‐dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5‐aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self‐organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM‐based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance. Copyright © 2005 Society of Chemical Industry     

Linearly many faults in Cayley graphs generated by transposition trees

We prove that when linearly many vertices are deleted in a Cayley graph generated by a transposition tree, the resulting graph has a large connected component containing almost all remaining vertices.     

Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.     

Evolving Fault-Tolerant Neural Networks

In this paper, genetic algorithm is used to help improve the tolerance of feedforward neural networks against an open fault. The proposed method does not explicitly add any redundancy to the network, nor does it modify the training algorithm. Experiments show that it may profit the fault tolerance as well as the generalisation ability of neural networks.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008