Ab initio prediction of the infrared-absorption spectrum of the C2Cl radical

The three lowest (1(2)A('), 2(2)A('), and 1(2)A(')) potential-energy surfaces of the C2Cl radical, correlating at linear geometries with (2)Sigma(+) and (2)Pi states, have been studied ab initio using a large basis set and multireference configuration-interaction techniques. The electronic ground state is confirmed to be bent with a very low barrier to linearity, due to the strong nonadiabatic electronic interactions taking place in this system. The rovibronic energy levels of the (CCCl)-C-12-C-12-Cl-35 isotopomer and the absolute absorption intensities at a temperature of 5 K have been calculated, to an upper limit of 2000 cm(-1), using diabatic potential-energy and dipole moment surfaces and a recently developed variational method. The resulting vibronic states arise from a strong mixture of all the three electronic components and their assignments are intrinsically ambiguous. (c) 2005 American Institute of Physics.

Ab initio prediction of the infrared absorption spectrum of the C2Br radical

The first three electronic states (1(2)A', 2(2)A', 1(2)A '') of the C2Br radical, correlating at linear geometries with (2)Sigma(+) and (2)Pi states, have been studied ab initio, using Multi Reference Configuration Interaction techniques. The electronic ground state is found to have a bent equilibrium geometry, R-CC = 1.2621 angstrom, R-CBr = 1.7967 angstrom, < CCBr 156.1 degrees, with a very low barrier to linearity. Similarly to the valence isoelectronic radicals C2F and C2Cl, this anomalous behaviour is attributed to a strong three-state non-adiabatic electronic interaction. The Sigma, Pi(1/2), Pi(3/2) vibronic energy levels and their absolute infrared absorption intensities at a temperature of 5K have been calculated for the (CCBr)-C-12-C-12-Br-79 isotopomer, to an upper limit of 2000 cm(-1), using ab initio diabatic potential energy and dipole moment surfaces and a recently developed variational method.

Monodisperse binary nanocomposite in silica with enhanced magnetization for magnetic separation

The majority of research on magnetic nanoparticles has focused on optical, electrical, and magnetic storage areas. Recently, the application of magnetic nanoparticles as magnetically separable nanovehicles for chemical or biological species has become an area of intensive research but with rather different challenging criteria that are yet to be addressed. For example, the enhancement of intrinsically weak magnetic properties, avoidance of magnetic interactions among particles, and improvement of the stability of the nanoparticles remain key issues. Here, it is demonstrated using sequential nanochemistry preparation techniques that exchange-coupled nanomagnets, such as FePt-Fe3Pt or FePt-Fe3O4 with dramatically enhanced magnetization, can be placed inside a silica nanosphere. The advantages of enhanced magnetization and the provision of protective coating and anchored sites on the silica shell surface render these new coated particles suitable for use in magnetic separation.

Achieving quality through statistical prediction for building services systems

The application of prediction theories has been widely practised for many years in many industries such as manufacturing, defence and aerospace. Although these theories are not new, their application has not been widely used within the building services industry. Collectively, the building services industry should take a deeper look at these approaches in comparison with the traditional deterministic approaches currently being practised. By extending the application into this industry, this paper seeks to provide the industry with an overview of how simplified stochastic modelling coupled with availability and reliability predictions using historical data compiled from various sources could enhance the quality of building services systems.

A comparison of two time-domain analysis procedures in the determination of VO2 kinetics by pseudorandom binary sequence exercise testing

The purpose of this study was to apply and compare two time-domain analysis procedures in the determination of oxygen uptake (VO2) kinetics in response to a pseudorandom binary sequence (PRBS) exercise test. PRBS exercise tests have typically been analysed in the frequency domain. However, the complex interpretation of frequency responses may have limited the application of this procedure in both sporting and clinical contexts, where a single time measurement would facilitate subject comparison. The relative potential of both a mean response time (MRT) and a peak cross-correlation time (PCCT) was investigated. This study was divided into two parts: a test-retest reliability study (part A), in which 10 healthy male subjects completed two identical PRBS exercise tests, and a comparison of the VO2 kinetics of 12 elite endurance runners (ER) and 12 elite sprinters (SR; part B). In part A, 95% limits of agreement were calculated for comparison between MRT and PCCT. The results of part A showed no significant difference between test and retest as assessed by MRT [mean (SD) 42.2 (4.2) s and 43.8 (6.9) s] or by PCCT [21.8 (3.7) s and 22.7 (4.5) s]. Measurement error (%) was lower for MRT in comparison with PCCT (16% and 25%, respectively). In part B of the study, the VO2 kinetics of ER were significantly faster than those of SR, as assessed by MRT [33.4 (3.4) s and 39.9 (7.1) s, respectively; P<0.01] and PCCT [20.9 (3.8) s and 24.8 (4.5) s; P < 0.05]. It is possible that either analysis procedure could provide a single test measurement Of VO2 kinetics; however, the greater reliability of the MRT data suggests that this method has more potential for development in the assessment Of VO2 kinetics by PRBS exercise testing.

Miscibility studies in poly(methyl vinyl ether)/hydroxypropylcellulose binary system in aqueous solutions and solid state

The phase separation behaviour in aqueous mixtures of poly(methyl vinyl ether) and hydroxypropylcellulose has been studied by cloud points method and viscometric measurements. The miscibility of these blends in solid state has been assessed by infrared spectroscopy; methanol vapours sorption experiments and scanning electron microscopy. The values of Gibbs energy of mixing of the polymers and their blends with methanol as well as between each other were calculated. It was found that in solid state the polymers can interact with methanol very well but the polymer-polymer interactions are unfavourable. Although in aqueous solutions the polymers exhibit some intermolecular interactions their solid blends are not completely miscible. (C) 2005 Elsevier Ltd. All rights reserved.

Prediction of Parkinson's disease tremor onset using artificial neural networks

In this paper we present the initial results using an artificial neural network to predict the onset of Parkinson's Disease tremors in a human subject. Data for the network was obtained from implanted deep brain electrodes. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings.

OFDM joint data detection and phase noise cancellation based on minimum mean square prediction error

This paper proposes a new iterative algorithm for orthogonal frequency division multiplexing (OFDM) joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the relatively less studied problem of "overfitting" such that the iterative approach may converge to a trivial solution. Specifically, we apply a hard-decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the PHN, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical Simulations are also given to verify the proposed algorithm. (C) 2008 Elsevier B.V. All rights reserved.

Application of neural network to hybrid systems with binary inputs

Boolean input systems are in common used in the electric industry. Power supplies include such systems and the power converter represents these. For instance, in power electronics, the control variable are the switching ON and OFF of components as thyristors or transistors. The purpose of this paper is to use neural network (NN) to control continuous systems with Boolean inputs. This method is based on classification of system variations associated with input configurations. The classical supervised backpropagation algorithm is used to train the networks. The training of the artificial neural network and the control of Boolean input systems are presented. The design procedure of control systems is implemented on a nonlinear system. We apply those results to control an electrical system composed of an induction machine and its power converter.

A combined score test for binary and ordinal endpoints from clinical trials

There is growing interest, especially for trials in stroke, in combining multiple endpoints in a single clinical evaluation of an experimental treatment. The endpoints might be repeated evaluations of the same characteristic or alternative measures of progress on different scales. Often they will be binary or ordinal, and those are the cases studied here. In this paper we take a direct approach to combining the univariate score statistics for comparing treatments with respect to each endpoint. The correlations between the score statistics are derived and used to allow a valid combined score test to be applied. A sample size formula is deduced and application in sequential designs is discussed. The method is compared with an alternative approach based on generalized estimating equations in an illustrative analysis and replicated simulations, and the advantages and disadvantages of the two approaches are discussed.