Application of liquid-phase microextraction to the analysis of trihalomethanes in water

A liquid-phase microextraction method for the determination of trihalomethanes (THMs) including chloroform (CHCl₃), bromodichloromethane (CHBrCl₂), dibromochloromethane (CHBr₂Cl) and bromoform (CHBr₃) in water samples was developed, with analysis by gas chromatography-electron capture detection (GC-ECD). After the determination of the most suitable solvent and stirring rate for the extraction, several other parameters (solvent drop volume, extraction time and ionic strength of the sample) were optimized using a factorial design to obtain the most relevant variables. The optimized extraction conditions for 5 mL of sample volume in a 10 mL vial were as follows: n-hexane an organic solvent; a solvent drop volume of 2 μL; an extraction time of 5.0 min; a stirring rate of 600 rpm at 25 °C; sample ionic strength of 3 M sodium chloride. The linear range was 1-75 μg L⁻¹ for the studied THMs. The limits of detection (LODs) ranged from 0.23 μg L⁻¹ (for CHBr₂Cl) to 0.45 μg L⁻¹ (for CHCl₃). Recoveries of THMs from fortified distilled water were over 70% for a fortification level of 15 μg L⁻¹, and relative standard deviations of the recoveries were below 5%. Real samples collected from tap water and well water were successfully analyzed using the proposed method. The recovery of spiked water samples was from 73% to 78% with relative standard deviations below 7%.     

On the evolution law for the frozen fraction in linear theories of shape memory polymers

Shape memory properties of thermally responsive polymeric materials are due mainly to a phase transition from the rubbery phase above the transition temperature (glass transition or melting temperature) to the glassy or semicrystalline phase below this temperature. Within constitutive models of shape memory polymers (SMPs), this phase transition is mathematically accounted for by the frozen volume fraction for which a suitable evolution law must be postulated or derived. In this paper, the evolution laws that have been proposed in the literature are examined both from the experimental and from the theoretical point of view. It is found that the predictive capabilities of the phenomenological laws may be improved by admitting involved material constants to depend on parameters such as pre-strain, rate of heating and cooling, and other quantities characterizing thermomechanical cyclic tests. It is next shown that for a wide class of linear constitutive models of SMPs, the evolution law for the frozen volume fraction may be derived in a systematic way from strain and stress profiles experimentally obtained in the standard thermomechanical test.     

A generalization of translation surfaces with constant curvature in the isotropic space

In this paper we study the translation surfaces generated by a space curve and a planar curve in the isotropic 3-space \({\mathbb{I}^{3}}\). We completely classify such surfaces in \({\mathbb{I}^{3}}\) with constant curvature. Several examples are also given by figures.     

A Distributed Evolutionary Simulated Annealing Algorithm for Combinatorial Optimisation Problems

In this paper, the Evolutionary Simulated Annealing (ESA) algorithm, its distributed implementation (dESA) and its application to two combinatorial problems are presented. ESA consists of a population, a simulated annealing operator, instead of the more usual reproduction operators used in evolutionary algorithms, and a selection operator. The implementation is based on a multi island (agent) system running on the Distributed Resource Machine (DRM), which is a novel, scalable, distributed virtual machine based on Java technology. As WAN/LAN systems are the most common multi-machine systems, dESA implementation is based on them rather than any other parallel machine. The problems tackled are well-known combinatorial optimisation problems, namely, the classical job-shop scheduling problem and the uncapacitated facility location problem. They are difficult benchmarks, widely used to measure the efficiency of metaheuristics with respect to both the quality of the solutions and the central processing unit (CPU) time spent. Both applications show that dESA solves problems finding either the optimum or a very near optimum solution within a reasonable time outperforming the recent reported approaches for each one allowing the faster solution of existing problems and the solution of larger problems.