Poly(sodium styrene sulfonate)-b-poly(methyl methacrylate) diblock copolymers through direct atom transfer radical polymerization: Influence of hydrophilic–hydrophobic balance on self-organization in aqueous solution

A series of poly(sodium styrene sulfonate)-b-poly(methyl methacrylate), PSSNa-b-PMMA, amphiphilic diblock copolymers have been synthesized through atom transfer radical polymerization (ATRP) of methyl methacrylate (MMA) in N,N-dimethylformamide/water mixtures, starting from a PSSNa macroinitiator. The kinetics of the polymerization was followed by ¹H NMR, while the chemical composition of the copolymers was verified by a variety of techniques, such as ¹H NMR, FTIR and TGA. The MMA content of the copolymers ranges from 0 up to 60 mol%, while the number–average molecular weight of the PSSNa macroinitiator was 9000 g/mol. The self-association of the diblock copolymers in aqueous solution was compared to the respective behavior of similar random P(SSNa-co-MMA) copolymers through optical density measurements, pyrene fluorescence probing, dynamic light scattering and surface tension measurements. It is shown that the diblock copolymers form micellar structures in water, characterized by an increasing hydrophobic character and a decreasing size as the length of the PMMA block increases. These micelle-like structures turn from surface inactive to surface active as the length of the PMMA block increases. Moreover, contrary to the MMA-rich random copolymers, the respective diblock copolymers form water insoluble polymer/surfactant complexes with cationic surfactants such as hexadecyltrimethyl ammonium bromide (HTAB), leading to materials with antimicrobial activity.     

Predictability and model selection in the context of ARCH models

Most of the methods used in the ARCH literature for selecting the appropriate model are based on evaluating the ability of the models to describe the data. An alternative model selection approach is examined based on the evaluation of the predictability of the models in terms of standardized prediction errors. Copyright © 2005 John Wiley & Sons, Ltd.     

Infinite divisibility,completeness and regression properties of the univariate generalized waring distribution

Summary This paper is concerned with properties of the univariate generalized Waring distribution such as infinite divisibility, discrete self-decomposability, completeness and regression.     

A novel method for urinary tract infection diagnosis and antibiogram using Raman spectroscopy

Urinary tract infection (UTI) diagnosis and antibiogram require a 48‐h waiting period using the standard conventional clinical methods. This long waiting period results in ineffective treatments, increased costs and, most importantly, in increased resistance to antibiotics. In this work, a novel method for classifying bacteria and determining their sensitivity to an antibiotic using Raman spectroscopy is described. Raman spectra belonging to three species of gram‐negative enterobacteria, which are most commonly responsible for UTIs, are classified with over 94% accuracy using novel feature extraction and discriminant analysis. Sensitivity to ciprofloxacin is also clearly evident as early as 2 h after treatment by differences in the Raman spectra of bacteria treated or not treated with this antibiotic. The proposed technique can become the basis for the development of new technology for UTI diagnosis and antibiogram with same day results, thus avoiding urine cultures and all undesirable consequences of current practice. Copyright © 2009 John Wiley & Sons, Ltd.     

On Testing for the Number of Components in a Mixed Poisson Model

Poisson mixtures are usually used to describe overdispersed data. Finite Poisson mixtures are used in many practical situations where often it is of interest to determine the number of components in the mixture. Identifying how many components comprise a mixture remains a difficult problem. The likelihood ratio test (LRT) is a general statistical procedure to use. Unfortunately, a number of specific problems arise and the classical theory fails to hold. In this paper a new procedure is proposed that is based on testing whether a new component can be added to a finite Poisson mixture which eventually leads to the number of components in the mixture. It is a sequential testing procedure based on the well known LRT that utilises a resampling technique to construct the distribution of the test statistic. The application of the procedure to real data reveals some interesting features of the distribution of the test statistic.     

Classification of Raman spectra using the correlation kernel

The classification of Raman spectra can be very useful in a wide range of diagnostic applications including bacterial identification. Before any form of classification can be carried out on the Raman spectra, some form of pre‐processing is commonly applied. This pre‐processing greatly affects the accuracy of the results and introduces user bias and over‐fitting effects. In this paper, we propose using support vector machines with the correlation kernel. The use of the correlation kernel on Raman spectra has not been presented before in any published work. Our results illustrate that the correlation kernel is ‘self‐normalizing’ and produces superior classification performance with minimal pre‐processing, even on highly noisy data obtained using inexpensive equipment. Such effective classification approaches can lead to clinically valuable diagnostic applications of Raman Spectroscopy. Copyright © 2010 John Wiley & Sons, Ltd.     

Towards a unification of certain characterizations by conditional expectations

The paper presents a characterization of a general family of distributions by the form of the expectation of an appropriately truncated function of the random variable involved. The obtained result unifies results existing in the literature for specific distributions as well as new results that appear for the first time in this paper. A discrete version is also provided unifying existing characterizations of known discrete distributions.     

Wire‐Active Microrheology to Differentiate Viscoelastic Liquids from Soft Solids

Viscoelastic liquids are characterized by a finite static viscosity and a yield stress of zero, whereas soft solids have an infinite viscosity and a non‐zero yield stress. The rheological nature of viscoelastic materials has long been a challenge and is still a matter of debate. Here, we provide for the first time the constitutive equations of linear viscoelasticity for magnetic wires in yield‐stress materials, together with experimental measurements by using magnetic rotational spectroscopy (MRS). In MRS, the wires were subjected to a rotational magnetic field as a function of frequency and the motion of the wire was monitored by using time‐lapse microscopy. The studied soft solids were aqueous dispersions of gel‐forming polysaccharide (gellan gum) at concentrations above the gelification point. It was found that soft solids exhibited a clear and distinctive signature compared with viscous and viscoelastic liquids. In particular, the average wire rotation velocity equaled zero over a broad frequency range. We also showed that the MRS technique is quantitative. The equilibrium elastic modulus was retrieved from the wire oscillation amplitudes, and agrees with polymer‐dynamics theory.     

An approach to group decision making under uncertainty with application to project selection

In this paper we present an approach and interactive procedure for group decision making under imprecision of expert judgements as applied to problems containing explicitly given resource constraints in particular to project selection. Alternatives (projects) are evaluated in a scale with a finite number of levels. Based on these estimates, fuzzy group preferences are determined and projects are divided into domination levels. Necessary quantities of resources are given in the form of intervals. Projects from successive levels are selected until maximum resource utilization is achieved. A numerical example is included to illustrate the proposed approach and procedure.     

On generalized binomial and multinomial distributions and their relation to generalized Poisson distributions

Summary The binomial and multinomial distributions are, probably, the best known distributions because of their vast number of applications. The present paper examines some generalizations of these distributions with many practical applications. Properties of these generalizations are studied and models giving rise to them are developed. Finally, their relationship to generalized Poisson distributions is examined and limiting cases are given.