Spectroscopic Multicomponent Analysis Using Multi-objective Optimization for Variable Selection

The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.     

Fix-and-Relax-Coordination for a multi-period location–allocation problem under uncertainty

A multi-period discrete facility location problem is introduced for a risk neutral strategy with uncertainty in the costs and some of the requirements along the planning horizon. A compact 0–1 formulation for the Deterministic Equivalent Model of the problem under two alternative strategies for the location decisions is presented. Furthermore, a new algorithmic matheuristic, Fix-and-Relax-Coordination, is introduced. This solution scheme is based on a specialization of the Branch-and-Fix Coordination methodology, which exploits the Nonanticipativity Constraints and uses the Twin Node Family concept. The results of an extensive computational experience allow to compare the alternative modeling strategies and assess the effectiveness of the proposed approach versus the plain use of a state-of-the-art MIP solver.     

An Object-oriented Interpretation of the EAT System

In a previous paper we characterized, in the Category Theory setting, a class of implementations of Abstract Data Types, which has been suggested by the way of programming in the EAT system. (EAT, Effective Algebraic Topology, is one of Sergeraerts systems for effective homology and homotopy computation.) This characterization was established using classical tools, in an unrelated way to the current mainstream topics in the field of Algebraic Specifications. Looking for a connection with these topics, we have found, rather unexpectedly, that our approach is related to some object-oriented formalisms, namely hidden specifications and the coalgebraic view. In this paper, we explore these relations making explicit the implicit object-oriented features of the EAT system and generalizing the data structure analysis we had previously done.     

Using implicit equations of parametric curves and surfaces without computing them: Polynomial algebra by values

The availability of the implicit equation of a plane curve or of a 3D surface can be very useful in order to solve many geometric problems involving the considered curve or surface: for example, when dealing with the point position problem or answering intersection questions. On the other hand, it is well known that in most cases, even for moderate degrees, the implicit equation is either difficult to compute or, if computed, the high degree and the big size of the coefficients makes extremely difficult its use in practice.We will show that, for several problems involving plane curves, 3D surfaces and some of their constructions (for example, offsets), it is possible to use the implicit equation (or, more precisely, its properties) without needing to explicitly determine it. We replace the computation of the implicit equation with the evaluation of the considered parameterizations in a set of points. We then translate the geometric problem in hand, into one or several generalized eigenvalue problems on matrix pencils (depending again on several evaluations of the considered parameterizations).This is the so-called “polynomial algebra by values” approach where the huge polynomial equations coming from Elimination Theory (e.g., using resultants) are replaced by big structured and sparse numerical matrices. For these matrices there are well-known numerical techniques allowing to provide the results we need to answer the geometric questions on the considered curves and surfaces.     

Bubble Dynamics in Turbulent Duct Flows: Lattice-Boltzmann Simulations and Drop Tower Experiments

Lattice-Boltzmann simulations of a turbulent duct flow have been carried out to obtain trajectories of passive tracers in the conditions of a series of microgravity experiments of turbulent bubble suspensions. The statistics of these passive tracers are compared to the corresponding measurements for single-bubble and bubble-pair statistics obtained from particle tracking techniques after the high-speed camera recordings from drop-towers experiments. In the conditions of the present experiments, comparisons indicate that experimental results on bubble velocity fluctuations are not consistent with simulations of passive tracers, which points in the direction of an active role of bubbles. The present analysis illustrates the utility of a recently introduced experimental setup to generate controlled turbulent bubble suspensions in microgravity.     

Implicit Algorithm for Modeling Unsaturated Soil Response in Three-Invariant Stress Space

An implicit integration algorithm has been refined to predict the stress–strain–strength response of unsaturated soil under suction-controlled, multiaxial stress paths that are not achievable in a conventional cylindrical cell. The algorithm supports numerical analyses in a deviatoric plane by using a mixed control constitutive driver, in conjunction with a generalized Cam-Clay model that also incorporates the influence of a third stress invariant, or Lode-angle θ, within a constant-suction scheme. True triaxial data from a previously accomplished series of suction-controlled triaxial compression, triaxial extension, and simple shear tests on 10-cm cubical specimens of silty sand, were used for the tuning and validation of the refined algorithm. The elliptical Willam–Warnke surface was adopted for simulation of unsaturated soil response in three-invariant stress space. Reasonably satisfactory agreement was observed between experimental and predicted deviatoric stress versus principal strain response for different suction states, as well as between experimental and predicted strength loci in a deviatoric plane.     

A systematic tool for the minimization of the life cycle impact of solar assisted absorption cooling systems

In recent years, there has been a growing increase of the cooling demand in many parts of the world, which has led to major energy problems. In this context, solar assisted absorption cooling systems have emerged as a promising alternative to conventional vapor compression air conditioning systems, given the fact that in many cases the cooling demand coincide with the availability of solar radiation. In this work, we present a decision-support tool based on mathematical programming for the design of solar assisted absorption cooling systems. The design task is formulated as a bi-criteria mixed-integer nonlinear programming (MINLP) optimization problem that accounts for the minimization of the total cost of the cooling system and the associated environmental impact measured over its entire life cycle. The capabilities of the proposed method are illustrated in a case study that addresses the design of a solar assisted ammonia-water absorption cycle considering weather data of Barcelona (Spain).