Abstract

Abstract
This paper explores a technique denoted LASCA to solve large scale optimization problems with metaheuristics by reducing the search space dimension with autoassociative neural networks. The technique applies autoencoders as a reversible mapping between the original problem space and a reduced space. A metaheuristic then evolves in the latter, having its objective function assessed in the original space. The technique is illustrated with an application of an Evolutionary Particle Swarm Optimization (EPSO) algorithm to four benchmarking unconstrained optimization functions and to a wind-hydro constrained coordination problem. The new technique allows an improvement in the quality of the solutions attained.

Abstract

Abstract
This work addresses the joint scheduling of continuous caster and hot strip mill processes in the steel industry. Traditionally, slab yards are used to decouple these two stages. However, the rising importance of energy costs and reduced logistic effort gives motivation for a combined scheduling. For each of the processes, a mixed-integer linear optimisation model based on the block planning principle is presented. This approach develops production schedules that take technological sequences of steel grades and milling programmes into account. We consider the integrated steel plant of an international steel company as a case study. Numerical results demonstrate the practicability of this approach under experimental conditions, which reflect typical settings from an industrial application in the steel industry.

Abstract
Background: Hydroxyethyl starch (HES) is one of the most used colloids for intravascular volume replacement during anesthesia. Aim: To investigate the existence of a chemical interaction between HES and the anesthetic propofol by in vitro propofol dosing, computational docking, and examination of a complex between propofol and HES by infrared (IR), ultraviolet (UV), and H-1 and C-13 nuclear magnetic resonance (NMR) spectroscopy. Methods: Ten samples with human plasma mixed with HES or lactated Ringers (n = 5 for each fluid) were prepared, and the propofol free fraction was quantified until 50 min, using gas chromatography-mass spectrometry. The docking study was performed between HES and propofol and compared with controls. The binding affinities between HES and the small molecules were evaluated by binding free energy approximation (Delta Gb, kJ mol(-1)). The IR, UV, and NMR spectra were measured for propofol, HES, and a mixture of both obtained by the kneading method. Results: Propofol concentrations were significantly lower in the HES samples than in the LR samples (p = .021). The spectroscopic characterization of propofol combined with HES revealed differences in spectra and docking studies reinforced a potential interaction between propofol and HES. Conclusions: Propofol and HES form a complex with different physical-bio-chemical behavior than the single drugs, which may be an important drug interaction. Further studies should evaluate its clinical effects.

Abstract
This paper shows how bibliometric models can be used to assist peers in selecting candidates for academic openings. Several studies have demonstrated that a relationship exists between results from peer-review evaluations and results obtained with certain bibliometric indicators. However, very little has been done to analyse the predictive power of models based on bibliometric indicators. Indicators with high predictive power will be seen as good instruments to support peer evaluations. The goal of this study is to assess the predictive power of a model based on bibliometric indicators for the results of academic openings at the level of Associado and Catedratico at Portuguese universities. Our results suggest that the model can predict the results of peer-review at this level with a reasonable degree of accuracy. This predictive power is better when only the scientific performance is assessed by peers.