Equal treatment without large numbers

We provide a necessary and sufficient condition under which core allocations of arbitrary TU-games treat substitute players equally. The core satisfies the equal treatment property if and only if no player needs the participation of all of her substitutes to attain her core payoffs. We show how, without the requirement of a large number of players, this condition generalizes and unifies other sufficient conditions proposed in the literature (in the context of large games and economies) and it helps derive new results for particular classes of games.     

Optimization of spray drying conditions to microencapsulate cupuassu (Theobroma grandiflorum) seed by-product extract

Cupuassu (Theobroma grandiflorum Schum.) is a popular Amazonian fruit because of its intense aroma and nutritional value, whose lipid fraction is alternatively used in cosmetics. To preserve active principles and ensure their controlled release, extract was microencapsulated by spray drying. Influence of spray-drying conditions on microencapsulation of cupuassu seed by-product extract was investigated according to a 3³-Box Behnken factorial design, selecting inlet temperature, maltodextrin concentration and feed flowrate as independent variables, and total polyphenol and flavonoid contents, antiradical power, yields of drying and microencapsulation as responses. Fitting the results by second-order equations and modelling by Response Surface Methodology allowed predicting optimum conditions. Epicatechin and glycosylated quercetin were the major microencapsulated flavonoids. Microparticles showed satisfactory antiradical power and stability at 5 °C or under simulated gastrointestinal conditions, thus they may be used to formulate new foods or pharmaceuticals.     

Neural network solution to an inverse problem associated with the eigenvalues of the Stokes operator

A numerical method, based on the design of two artificial neural networks, is presented in order to approximate the viscosity and density features of fluids from the eigenvalues of the Stokes operator. The finite element method is used to solve the direct problem by training a first artificial neural network. A nonlinear map of eigenvalues of the Stokes operator as a function of the viscosity and density of the fluid under study is then obtained. This relationship is later inverted and refined by training a second artificial neural network, solving the aforementioned inverse problem. Numerical examples are presented in order to show the effectiveness and the limitations of this methodology.     

Implementation of a block-decomposition algorithm for solving large-scale conic semidefinite programming problems

In this paper, we consider block-decomposition first-order methods for solving large-scale conic semidefinite programming problems given in standard form. Several ingredients are introduced to speed-up the method in its pure form such as: an aggressive choice of stepsize for performing the extragradient step; use of scaled inner products; dynamic update of the scaled inner product for properly balancing the primal and dual relative residuals; and proper choices of the initial primal and dual iterates, as well as the initial parameter for the scaled inner product. Finally, we present computational results showing that our method outperforms the two most competitive codes for large-scale conic semidefinite programs, namely: the boundary-point method introduced by Povh et al. and the Newton-CG augmented Lagrangian method by Zhao et al.     

Structure and stability of self-assembled actin-lysozyme complexes in salty water

Interactions between actin, an anionic polyelectrolyte, and lysozyme, a cationic globular protein, have been examined using a combination of synchrotron small-angle x-ray scattering and molecular dynamics simulations. Lysozyme initially bridges pairs of actin filaments, which relax into hexagonally coordinated columnar complexes comprised of actin held together by incommensurate one-dimensional close-packed arrays of lysozyme macroions. These complexes are found to be stable even in the presence of significant concentrations of monovalent salt, which is quantitatively explained from a redistribution of salt between the condensed and the aqueous phases.     

Highly functionalized pyranopyrazoles: synthesis,antimicrobial activity,simulation studies and their structure activity relationships (SARs)

Research on Chemical Intermediates - Development of potent antibacterial and antifungal agents is a permanently new and unremitting investigation in the therapeutic field. Still, medicinal research...     

PCA and Kriging for the efficient exploration of consistency regions in Uncertainty Quantification

For stationary power sources such as utility boilers, it is useful to dispose of parametric models able to describe their behavior in a wide range of operating conditions, to predict some Quantities of Interest (QOIs) that need to be consistent with experimental observations. The development of predictive simulation tools for large scale systems cannot rely on full-order models, as the latter would lead to prohibitive costs when coupled to sampling techniques in the model parameter space. An alternative approach consists of using a Surrogate Model (SM). As the number of QOIs is often high and many SMs need to be trained, Principal Component Analysis (PCA) can be used to encode the set of QOIs in a much smaller set of scalars, called PCA scores. A SM is then built for each PCA score rather than for each QOI. The advantage of reducing the number of variables is twofold: computational costs are reduced (less SMs need to be trained) and information is preserved (correlation among the original variables).The strategy is applied to a CFD model simulating the Alstom 15 MW_th oxy-pilot Boiler Simulation Facility (BSF). In practice, experiments cannot provide full coverage of the pulverized-coal utility boiler due to both practicality and costs. Values of the model’s parameters which guarantee consistency with the experimental data of this test facility for 121 QOIs are found, by training a SM based on the combination of Kriging and PCA, using only 5 latent variables.