Neural networks for variational problems in engineering

In this work a conceptual theory of neural networks (NNs) from the perspective of functional analysis and variational calculus is presented. Within this formulation, the learning problem for the multilayer perceptron lies in terms of finding a function, which is an extremal for some functional. Therefore, a variational formulation for NNs provides a direct method for the solution of variational problems. This proposed method is then applied to distinct types of engineering problems. In particular a shape design, an optimal control and an inverse problem are considered. The selected examples can be solved analytically, which enables a fair comparison with the NN results. Copyright © 2008 John Wiley & Sons, Ltd

Tobacco control campaign in Uruguay: Impact on smoking cessation during pregnancy and birth weight

We analyzed a nationwide registry of all pregnancies in Uruguay during 2007-2013 to assess the impact of three types of tobacco control policies: (1) provider-level interventions aimed at the treatment of nicotine dependence, (2) national-level increases in cigarette taxes, and (3) national-level non-price regulation of cigarette packaging and marketing. We estimated models of smoking cessation during pregnancy at the individual, provider and national levels. The rate of smoking cessation during pregnancy increased from 15.4% in 2007 to 42.7% in 2013. National-level non-price policies had the largest estimated impact on cessation. The price response of the tobacco industry attenuated the effects of tax increases. While provider-level interventions had a significant effect, they were adopted by relatively few health centers. Quitting during pregnancy increased birth weight by an estimated 188. g. Tobacco control measures had no effect on the birth weight of newborns of non-smoking women. Keywords: Economic evaluation; Cigarette taxes; Package warnings; Advertising bans; Tobacco contro

An efficient ‘a priori’ model reduction for boundary element models

The Boundary Element Method (BEM) is a discretisation technique for solving partial differential equations, which offers, for certain problems, important advantages over domain techniques. Despite the high CPU time reduction that can be achieved, some 3D problems remain today untreatable because the extremely large number of degrees of freedom—dof—involved in the boundary description. Model reduction seems to be an appealing choice for both, accurate and efficient numerical simulations. However, in the BEM the reduction in the number of degrees of freedom does not imply a significant reduction in the CPU time, because in this technique the more important part of the computing time is spent in the construction of the discrete system of equations. In this way, a reduction also in the number of weighting functions, seems to be a key point to render efficient boundary element simulations

Global search methods for nonlinear optimisation: a new probabilistic-stochastic approach

In this work the problem of overcoming local minima in the solution of nonlinear optimisation problems is addressed. As a first step, the existing nonlinear local and global optimisation methods are reviewed so as to identify their advantages and disadvantages. Then, the major capabilities of a number of successful methods such as genetic, deterministic global optimisation methods and simmulated annealing, are combined to develop an alternative global optimisation approach based on a Stochastic-Probabilistic heuristic. The capabilities, in terms of robustness and efficiency, of this new approach are validated through the solution of a number of nonlinear optimisation problems. A well know evolutionary technique (Differential Evolution) is also considered for the solution of these case studies offering a better insight of the possibilities of the method proposed here.Postprint (published version

An iterative identification procedure for dynamic modeling of biochemical networks

<p>Abstract</p> <p>Background</p> <p>Mathematical models provide abstract representations of the information gained from experimental observations on the structure and function of a particular biological system. Conferring a predictive character on a given mathematical formulation often relies on determining a number of non-measurable parameters that largely condition the model's response. These parameters can be identified by fitting the model to experimental data. However, this fit can only be accomplished when identifiability can be guaranteed.</p> <p>Results</p> <p>We propose a novel iterative identification procedure for detecting and dealing with the lack of identifiability. The procedure involves the following steps: 1) performing a structural identifiability analysis to detect identifiable parameters; 2) globally ranking the parameters to assist in the selection of the most relevant parameters; 3) calibrating the model using global optimization methods; 4) conducting a practical identifiability analysis consisting of two (<it>a priori </it>and <it>a posteriori</it>) phases aimed at evaluating the quality of given experimental designs and of the parameter estimates, respectively and 5) optimal experimental design so as to compute the scheme of experiments that maximizes the quality and quantity of information for fitting the model.</p> <p>Conclusions</p> <p>The presented procedure was used to iteratively identify a mathematical model that describes the NF-<it>κ</it>B regulatory module involving several unknown parameters. We demonstrated the lack of identifiability of the model under typical experimental conditions and computed optimal dynamic experiments that largely improved identifiability properties.</p

Mononuclear Pd(II) and Pt(II) complexes with an α-N-heterocyclic thiosemicarbazone: Cytotoxicity, solution behaviour and interaction: Versus proven models from biological media

Two Pd(ii) and Pt(ii) complexes with two pyrrol-2-carbaldehyde N-p-chlorophenylthiosemicarbazone ligands are designed and characterized showing mononuclear structures. An important pharmacological property for both compounds is the high selectivity for tumor cells and a lack of activity in healthy cells. The Pd(ii) compound shows a higher antitumor activity and selectivity than the Pt(ii) compound. Both complexes present a variety of biological interactions: with DNA models (pBR322 and CT DNA), proteins (lysozyme and RNase) and other biological targets like proteosome. Our results show that the Pd(ii) complex is a more interesting candidate for potential anticancer therapies than the Pt(ii) complex, and we provide new insight into the design and synthesis of palladium compounds as potential antitumor agents.This work was supported by the following grants for the Spanish MINECO: SAF-2012-34424, CTQ2015-68779R and CTQ2015-70371-RED