Enhanced Biodecolorization of Reactive Dyes by Basidiomycetes Under Static Conditions

This study presents the biodecolorization potential of basidiomycete fungi Trametes hirsuta, Pycnoporus sp., and Irpex sp. for different reactive dyes viz. Reactive Red 120, Remazol Brilliant Blue R (RBBR), Reactive Orange G, and Reactive Orange 16 under static and shaking conditions. The screening trials revealed that T. hirsuta exhibited maximum potential (83.75 %) for biodecolorization of RBBR dye under static conditions after the fifth day of incubation. However, the rate of biodecolorization of RBBR dye by Pycnoporus sp. was much slow and reached maximum (81.25 %) after 15 days of incubation under shaking conditions. By process optimization, enhanced decolorization (91.2 %) of RBBR by T. hirsuta was achieved at pH 5.5 within 24 h using a defined salt medium amended with p-coumaric acid under static conditions. pH was found to be an important parameter for the enzymatic system involved in RBBR dye decolorization by T. hirsuta and Pycnoporus sp. Biodecolorization of RBBR dye was determined by a reduction in optical density at the wavelength of maximum absorbance (λ, 578 nm) by UV–vis spectrophotometer. The shift in maximum wavelength toward shorter/longer wavelength in UV–vis scanning spectrum revealed the degradation of RBBR dye into different transformation products.     

Asymptotic Properties of <Emphasis Type="Italic">QML</Emphasis> Estimation of Multivariate Periodic <Emphasis Type="Italic">CCC</Emphasis> − <Emphasis Type="Italic">GARCH</Emphasis> Models

In this paper, we explore some probabilistic and statistical properties of constant conditional correlation (CCC) multivariate periodic GARCH models (CCC ? PGARCH for short). These models which encompass some interesting classes having (locally) long memory property, play an outstanding role in modelling multivariate financial time series exhibiting certain heteroskedasticity. So, we give in the first part some basic structural properties of such models as conditions ensuring the existence of the strict stationary and geometric ergodic solution (in periodic sense). As a result, it is shown that the moments of some positive order for strictly stationary solution of CCC ? PGARCH models are finite.Upon this finding, we focus in the second part on the quasi-maximum likelihood (QML) estimator for estimating the unknown parameters involved in the models. So we establish strong consistency and asymptotic normality (CAN) of CCC ? PGARCH models.     

Stationnarité et β-mélange des processus bilinéaires généraux à changement de régime markovien

We study the class of general bilinear models in which the parameters are allowed to depend on an unobserved Markov chain. Necessary and sufficient conditions for strict and second-order stationarity, existence of higher-order moments and conditions ensuring the geometric ergodicity are proposed.     

An Improvement of Ostrowski’s and King’s Techniques with Optimal Convergence Order Eight

In this paper, we first establish a new class of three-point methods based on the two-point optimal method of Ostrowski. Analysis of convergence shows that any method of our class arrives at eighth order of convergence by using three evaluations of the function and one evaluation of the first derivative per iteration. Thus, this order agrees with the conjecture of Kung and Traub (J. ACM 643–651, 1974) for constructing multipoint optimal iterations without memory. We second present another optimal eighth-order class based on the King’s fourth-order family and the first attained class. To support the underlying theory developed in this work, we examine some methods of the proposed classes by comparison with some of the existing optimal eighth-order methods in literature. Numerical experience suggests that the new classes would be valuable alternatives for solving nonlinear equations.     

Consistent and asymptotically normal estimators for cyclically time-dependent linear models

We consider a general class of time series linear models where parameters switch according to a known fixed calendar. These parameters are estimated by means of quasi-generalized least squares estimators. conditions for strong consistency and asymptotic normality are given. Applications to cyclical ARMA models with non constant periods are considered.     

The role of ligands on protein retention in adsorption chromatography: A surface energetics approach

Protein adsorption onto hydrophobic chromatographic supports has been investigated using a colloid theory surface energetics approach. The surface properties of commercially available chromatographic beads, Toyopearl Phenyl 650‐C, and Toyopearl Butyl 650‐C, have been experimentally determined by contact angle and zeta potential measurements. The adsorption characteristics of these beads, which bear the same backbone matrix but harbor different ligands, have been studied toward selected model proteins, in the hydrated as well as dehydrated state. There were two prominent groups of proteins observed with respect to the chromatographic supports presented in this work: loosely retained proteins, which were expected to have lower average interaction energies, and the strongly retained proteins, which were expected to have higher average interaction energies. Results were also compared and contrasted with calculations derived from adsorbent surface energies determined by inverse liquid chromatography. These results showed a good qualitative agreement, and the interaction energy minima obtained from these extended Derjaguin, Landau, Verwey and Overbeek calculations were shown to correlate well with the experimentally determined adsorption behavior of each protein.     

On stationarity and second-order properties of bilinear random fields

One-dimensional indexed bilinear (BL) models are widely used for modeling non Gaussian time series. Extending BL models to multidimensional indexed (spatial) SBL one, yields a novel class of models which are capable of taking into account the important characteristic of non Gaussianity and spatiality behavior. Our main contribution here is to extend various results on BL processes to SBL one. Our attention is thus focussed on the probabilistic structure of some SBLmodels. So, we establish necessary and sufficient conditions for the existence of regular stationary and ergodic solutions in term of their transfer functions. As a consequence, we observe that the second order structure is similar to a spatial ARMA model with some uncorrelated white noise. So, it is necessary to look into higher-order moments in order to distinguish between linear and nonlinear random fields. Our study may be applied to GARCH or ARMA random fields.