The copolymerization of N-vinyl-2-pyrrolidone with 2-hydroxyethyl methacrylate

The bulk free radical copolymerization of 2-hydroxyethyl methacrylate (HEMA) with N-vinyl-2-pyrrolidone (VP) was carried out to low conversions at 50 degreesC, using benzoyl peroxide (BPO) as initiator. The compositions of the copolymers; were determined using C-13 NMR spectroscopy. The conversion of monomers to polymers was studied using FT-NIR spectroscopy in order to predict the extent of conversion of monomer to polymer. From model fits to the composition data, a statistical F-test revealed that die penultimate model describes die copolymerization better than die terminal model. Reactivity ratios were calculated by using a non-linear least squares analysis (NLLS) and r(H) = 8.18 and r(V) = 0.097 were found to be the best fit values of the reactivity ratios for the terminal model and r(HH) = 12.0, r(VH) = 2.20, r(VV) = 0.12 and r(HV) = 0.03 for the penultimate model. Predictions were made for changes in compositions as a function of conversion based upon the terminal and penultimate models.

A systematic study of the microwave and thermal cure kinetics of the DGEBA/DDS and DGEBA/DDM epoxy-amine resin systems

The microwave and thermal cure processes for the epoxy-amine systems (epoxy resin diglycidyl ether of bisphenol A, DGEBA) with 4,4'-diaminodiphenyl sulphone (DDS) and 4,4'-diaminodiphenyl methane (DDM) have been investigated for 1:1 stoichiometries by using fiber-optic FT-NIR spectroscopy. The DGEBA used was in the form of Ciba-Geigy GY260 resin. The DDM system was studied at a single cure temperature of 373 K and a single stoichiometry of 20.94 wt% and the DDS system was studied at a stoichiometry of 24.9 wt% and a range of temperatures between 393 and 443 K. The best values of the kinetic rate parameters for the consumption of amines have been determined by a least squares curve fit to a model for epoxy/amine cure. The activation energies for the polymerization of the DGEBA/DDS system were determined for both cure processes and found to be 66 and 69 kJ mol(-1) for the microwave and thermal cure processes, respectively. No evidence was found for any specific effect of the microwave radiation on the rate parameters, and the systems were both found to be characterized by a negative substitution effect. Copyright (C) 2002 John Wiley Sons, Ltd.

A monolithic smoothing-gap algorithm for contact-impact based on the signed distance function

A new algorithm has been developed for smoothing the surfaces in finite element formulations of contact-impact. A key feature of this method is that the smoothing is done implicitly by constructing smooth signed distance functions for the bodies. These functions are then employed for the computation of the gap and other variables needed for implementation of contact-impact. The smoothed signed distance functions are constructed by a moving least-squares approximation with a polynomial basis. Results show that when nodes are placed on a surface, the surface can be reproduced with an error of about one per cent or less with either a quadratic or a linear basis. With a quadratic basis, the method exactly reproduces a circle or a sphere even for coarse meshes. Results are presented for contact problems involving the contact of circular bodies. Copyright (C) 2002 John Wiley Sons, Ltd.

Overlarge sets of 2-(11, 5, 2) designs and related configurations

We consider the construction of several configurations, including: • overlarge sets of 2-(11,5,2) designs, that is, partitions of the set of all 5-subsets of a 12-set into 72 2-(11,5,2) designs; • an indecomposable doubly overlarge set of 2-(11,5,2) designs, that is, a partition of two copies of the set of all 5-subsets of a 12-set into 144 2-(11,5,2) designs, such that the 144 designs can be arranged into a 12 × 12 square with interesting row and column properties; • a partition of the Steiner system S(5,6,12) into 12 disjoint 2-(11,6,3) designs arising from the diagonal of the square; • bidistant permutation arrays and generalized Room squares arising from the doubly overlarge set, and their relation to some new strongly regular graphs.

Levene tests of homogeneity of variance for general block and treatment designs

This article develops a weighted least squares version of Levene's test of homogeneity of variance for a general design, available both for univariate and multivariate situations. When the design is balanced, the univariate and two common multivariate test statistics turn out to be proportional to the corresponding ordinary least squares test statistics obtained from an analysis of variance of the absolute values of the standardized mean-based residuals from the original analysis of the data. The constant of proportionality is simply a design-dependent multiplier (which does not necessarily tend to unity). Explicit results are presented for randomized block and Latin square designs and are illustrated for factorial treatment designs and split-plot experiments. The distribution of the univariate test statistic is close to a standard F-distribution, although it can be slightly underdispersed. For a complex design, the test assesses homogeneity of variance across blocks, treatments, or treatment factors and offers an objective interpretation of residual plot.

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

The expectation-maximization (EM) algorithm has been of considerable interest in recent years as the basis for various algorithms in application areas of neural networks such as pattern recognition. However, there exists some misconceptions concerning its application to neural networks. In this paper, we clarify these misconceptions and consider how the EM algorithm can be adopted to train multilayer perceptron (MLP) and mixture of experts (ME) networks in applications to multiclass classification. We identify some situations where the application of the EM algorithm to train MLP networks may be of limited value and discuss some ways of handling the difficulties. For ME networks, it is reported in the literature that networks trained by the EM algorithm using iteratively reweighted least squares (IRLS) algorithm in the inner loop of the M-step, often performed poorly in multiclass classification. However, we found that the convergence of the IRLS algorithm is stable and that the log likelihood is monotonic increasing when a learning rate smaller than one is adopted. Also, we propose the use of an expectation-conditional maximization (ECM) algorithm to train ME networks. Its performance is demonstrated to be superior to the IRLS algorithm on some simulated and real data sets.

Patterns of gene expression in the frontal cortex discriminate alcoholic from non-alcoholic individuals

Alcohol dependence is characterized by tolerance, physical dependence, and craving. The neuroadaptations underlying these effects of chronic alcohol abuse are likely due to altered gene expression. Previous gene expression studies using human post-mortem brain demonstrated that several gene families were altered by alcohol abuse. However, most of these changes in gene expression were small. It is not clear if gene expression profiles have sufficient power to discriminate control from alcoholic individuals and how consistent gene expression changes are when a relatively large sample size is examined. In the present study, microarray analysis (similar to 47 000 elements) was performed on the superior frontal cortex of 27 individual human cases ( 14 well characterized alcoholics and 13 matched controls). A partial least squares statistical procedure was applied to identify genes with altered expression levels in alcoholics. We found that genes involved in myelination, ubiquitination, apoptosis, cell adhesion, neurogenesis, and neural disease showed altered expression levels. Importantly, genes involved in neurodegenerative diseases such as Alzheimer's disease were significantly altered suggesting a link between alcoholism and other neurodegenerative conditions. A total of 27 genes identified in this study were previously shown to be changed by alcohol abuse in previous studies of human post-mortem brain. These results revealed a consistent re-programming of gene expression in alcohol abusers that reliably discriminates alcoholic from non-alcoholic individuals.

Enhanced detection-guided NLMS estimation of sparse FIR-modeled signal channels

In various signal-channel-estimation problems, the channel being estimated may be well approximated by a discrete finite impulse response (FIR) model with sparsely separated active or nonzero taps. A common approach to estimating such channels involves a discrete normalized least-mean-square (NLMS) adaptive FIR filter, every tap of which is adapted at each sample interval. Such an approach suffers from slow convergence rates and poor tracking when the required FIR filter is "long." Recently, NLMS-based algorithms have been proposed that employ least-squares-based structural detection techniques to exploit possible sparse channel structure and subsequently provide improved estimation performance. However, these algorithms perform poorly when there is a large dynamic range amongst the active taps. In this paper, we propose two modifications to the previous algorithms, which essentially remove this limitation. The modifications also significantly improve the applicability of the detection technique to structurally time varying channels. Importantly, for sparse channels, the computational cost of the newly proposed detection-guided NLMS estimator is only marginally greater than that of the standard NLMS estimator. Simulations demonstrate the favourable performance of the newly proposed algorithm. © 2006 IEEE.

Factors in the selection of patients for conditional release from their first psychiatric hospitalization

Objective: This study examined a sample of patients in Victoria, Australia, to identify factors in selection for conditional release from an initial hospitalization that occurred within 30 days of entry into the mental health system. Methods: Data were from the Victorian Psychiatric Case Register. All patients first hospitalized and conditionally released between 1990 and 2000 were identified (N = 8,879), and three comparison groups were created. Two groups were hospitalized within 30 days of entering the system: those who were given conditional release and those who were not. A third group was conditionally released from a hospitalization that occurred after or extended beyond 30 days after system entry. Logistic regression identified characteristics that distinguished the first group. Ordinary least-squares regression was used to evaluate the contribution of conditional release early in treatment to reducing inpatient episodes, inpatient days, days per episode, and inpatient days per 30 days in the system. Results: Conditional release early in treatment was used for 11 percent of the sample, or more than a third of those who were eligible for this intervention. Factors significantly associated with selection for early conditional release were those related to a better prognosis ( initial hospitalization at a later age and having greater than an 11th grade education), a lower likelihood of a diagnosis of dementia or schizophrenia, involuntary status at first inpatient admission, and greater community involvement ( being employed and being married). When the analyses controlled for these factors, use of conditional release early in treatment was significantly associated with a reduction in use of subsequent inpatient care.