Stability of the approximation of a regular solution branch

In this paper we extend the so-called inf-sup conditions tononlinear problems having regular solution branches. The inf-supconditions for nonlinear problems have been introduced, forinstance, by Caloz and Rappaz in 1994. Here we present a moregeneral approach to include turning points on a solution branch.Our abstract results are applied to model examples. E-mail: Gabriel.Caloz{at}univ-rennesl.fr     

INTERACTION BETWEEN TYROSINE AND DIVALENT SULFUR IN FLUORESCENCE QUENCHING AND IN THE PHOTOCHEMISTRY OF RIBONUCLEASE

Abstract— –Ribonuclease is inactivated in aqueous solution by u.v. light through different mechanisms according to whether divalent sulfur or aromatic amino acids are the primary light absorbers. At 284 nm, absorbed mainly by tyrosine, the presence of O₂ inhibits photoinactivation and H₂S formation, but does less so at 254 or 313 nm. Based on data with model substances containing disulfide groups a mechanism is indicated in which excited tyrosine is quenched through electron transfer to adjacent divalent sulfur within the protein. Disulfide compounds are shown to be very efficient quenchers of tyrosine fluorescence.     

COMMUNICATION: Mild reaction of ethyl cellulose and N- phenylmaleamic acid in acetic anhydride

The reaction at room temperature of ethyl cellulose (EC) and N-phenylmaleamic acid (NPMA) (maleanilic acid) in acetic anhydride with a catalytic amount of anhydrous sodium acetate resulted in a Michael type of product whereby a cellulose ether was formed. The cyclisation product N-phenylmaleimide was minor. This revised version was published online in August 2006 with corrections to the Cover Date.     

Theoretical study of the Renner-Teller A 2A1 − X 2B1 system of NH2

(Molec. Phys., 1994, 81, 1445–1461)     

AN ADAPTIVE LEARNING FRAMEWORK FOR FORECASTING SEASONAL WATER ALLOCATIONS IN IRRIGATED CATCHMENTS

Abstract This paper describes an adaptive learning framework for forecasting end‐season water allocations using climate forecasts, historic allocation data, and results of other detailed hydrological models. The adaptive learning framework is based on artificial neural network (ANN) method, which can be trained using past data to predict future water allocations. Using this technique, it was possible to develop forecast models for end‐irrigation‐season water allocations from allocation data available from 1891 to 2005 based on the allocation level at the start of the irrigation season. The model forecasting skill was further improved by the incorporation of a set of correlating clusters of sea surface temperature (SST) and the Southern oscillation index (SOI) data. A key feature of the model is to include a risk factor for the end‐season water allocations based on the start of the season water allocation. The interactive ANN model works in a risk‐management context by providing probability of availability of water for allocation for the prediction month using historic data and/or with the incorporation of SST/SOI information from the previous months. All four developed ANN models (historic data only, SST incorporated, SOI incorporated, SST‐SOI incorporated) demonstrated ANN capability of forecasting end‐of‐season water allocation provided sufficient data on historic allocation are available. SOI incorporated ANN model was the most promising forecasting tool that showed good performance during the field testing of the model.     

COMMUNICATION: New derivatives of ethyl cellulose

Triethylamine-mediated carboxymethylation of ethyl cellulose using monochloroacetic acid produced a hydroxymethyl acetate derivative (I). The derivative (I) was subsequently converted to a phthalimidoethyl-ethyl cellulose ether derivative by reacting it with β-hydroxyethyl phthalimide using concentrated sulphuric acid as the catalyst. The elemental analysis agreed with the calculated values using an anhydroglucose unit formula that incorporates the degree of substitution. Mechanisms for the formation of these derivatives are proposed. This revised version was published online in August 2006 with corrections to the Cover Date.