Exact Search Directions for Optimization of Linear and Nonlinear Models Based on Generalized Orthonormal Functions

A novel technique for selecting the poles of orthonormal basis functions (OBF) in Volterra models of any order is presented. It is well-known that the usual large number of parameters required to describe the Volterra kernels can be significantly reduced by representing each kernel using an appropriate basis of orthonormal functions. Such a representation results in the so-called OBF Volterra model, which has a Wiener structure consisting of a linear dynamic generated by the orthonormal basis followed by a nonlinear static mapping given by the Volterra polynomial series. Aiming at optimizing the poles that fully parameterize the orthonormal bases, the exact gradients of the outputs of the orthonormal filters with respect to their poles are computed analytically by using a back-propagation-through-time technique. The expressions relative to the Kautz basis and to generalized orthonormal bases of functions (GOBF) are addressed; the ones related to the Laguerre basis follow straightforwardly as a particular case. The main innovation here is that the dynamic nature of the OBF filters is fully considered in the gradient computations. These gradients provide exact search directions for optimizing the poles of a given orthonormal basis. Such search directions can, in turn, be used as part of an optimization procedure to locate the minimum of a cost-function that takes into account the error of estimation of the system output. The Levenberg-Marquardt algorithm is adopted here as the optimization procedure. Unlike previous related work, the proposed approach relies solely on input-output data measured from the system to be modeled, i.e., no information about the Volterra kernels is required. Examples are presented to illustrate the application of this approach to the modeling of dynamic systems, including a real magnetic levitation system with nonlinear oscillatory behavior.

Numerical prediction of three-dimensional time-dependent viscoelastic extrudate swell using differential and algebraic models

This study investigates the numerical simulation of three-dimensional time-dependent viscoelastic free surface flows using the Upper-Convected Maxwell (UCM) constitutive equation and an algebraic explicit model. This investigation was carried out to develop a simplified approach that can be applied to the extrudate swell problem. The relevant physics of this flow phenomenon is discussed in the paper and an algebraic model to predict the extrudate swell problem is presented. It is based on an explicit algebraic representation of the non-Newtonian extra-stress through a kinematic tensor formed with the scaled dyadic product of the velocity field. The elasticity of the fluid is governed by a single transport equation for a scalar quantity which has dimension of strain rate. Mass and momentum conservations, and the constitutive equation (UCM and algebraic model) were solved by a three-dimensional time-dependent finite difference method. The free surface of the fluid was modeled using a marker-and-cell approach. The algebraic model was validated by comparing the numerical predictions with analytic solutions for pipe flow. In comparison with the classical UCM model, one advantage of this approach is that computational workload is substantially reduced: the UCM model employs six differential equations while the algebraic model uses only one. The results showed stable flows with very large extrudate growths beyond those usually obtained with standard differential viscoelastic models. (C) 2010 Elsevier Ltd. All rights reserved.

Continuum, Discrete, and Explicit Solvation Models for Describing the Low-Lying Absorption Spectrum of the Pterin Acid in Aqueous Environment

The absorption spectrum of the acid form of pterin in water was investigated theoretically. Different procedures using continuum, discrete, and explicit models were used to include the solvation effect on the absorption spectrum, characterized by two bands. The discrete and explicit models used Monte Carlo simulation to generate the liquid structure and time-dependent density functional theory (B3LYP/6-31G+(d)) to obtain the excitation energies. The discrete model failed to give the correct qualitative effect on the second absorption band. The continuum model, in turn, has given a correct qualitative picture and a semiquantitative description. The explicit use of 29 solvent molecules, forming a hydration shell of 6 angstrom, embedded in the electrostatic field of the remaining solvent molecules, gives absorption transitions at 3.67 and 4.59 eV in excellent agreement with the S(0)-S(1) and S(0)-S(2) absorption bands at of 3.66 and 4.59 eV, respectively, that characterize the experimental spectrum of pterin in water environment. (C) 2010 Wiley Periodicals, Inc. Int J Quantum Chem 110: 2371-2377, 2010

Equivalence between supersymmetric self-dual and Maxwell-Chern-Simons models coupled to a matter spinor superfield

We study the duality of the supersymmetric self-dual and Maxwell-Chern-Simons theories coupled to a fermionic matter superfield, using a master action. This approach evades the difficulties inherent to the quartic couplings that appear when matter is represented by a scalar superfield. The price is that the spinorial matter superfield represents a unusual supersymmetric multiplet, whose main physical properties we also discuss. (C) 2009 Elsevier B.V. All rights reserved.

The interaction of an antiparasitic peptide active against African Sleeping Sickness with cell membrane models

Zwitterionic peptides with trypanocidal activity are promising lead compounds for the treatment of African Sleeping Sickness, and have motivated research into the design of compounds capable of disrupting the protozoan membrane. In this study, we use the Langmuir monolayer technique to investigate the surface properties of an antiparasitic peptide, namely S-(2,4-dinitrophenyl)glutathione di-2-propyl ester, and its interaction with a model membrane comprising a phospholipid monolayer. The drug formed stable Langmuir monolayers. whose main feature was a phase transition accompanied by a negative surface elasticity. This was attributed to aggregation upon compression due to intermolecular bond associations of the molecules, inferred from surface pressure and surface potential isotherms. Brewster angle microscopy (BAM) images, infrared spectroscopy and dynamic elasticity measurements. When co-spread with dipalmitoyl phosphatidyl choline (DPPC). the drug affected both the surface pressure and the monolayer morphology, even at high surface pressures and with low amounts of the drug. The results were interpreted by assuming a repulsive, cooperative interaction between the drug and DPPC molecules. Such repulsive interaction and the large changes in fluidity arising from drug aggregation may be related to the disruption of the membrane, which is key for the parasite killing property. (C) 2009 Elsevier B.V. All rights reserved.

Chitosan as a removing agent of beta-lactoglobulin from membrane models

Many chitosan biological activities depend on the interaction with biomembranes, but so far it has not been possible to obtain molecular-level evidence of chitosan action. In this article, we employ Langmuir phospholipid monolayers as cell membrane models and show that chitosan is able to remove beta-lactoglobulin (BLG) from negatively charged dimyristoyl phosphatidic acid (DMPA) and dipalmitoyl phosphatidyl glycerol (DPPG). This was shown with surface pressure isotherms and elasticity and PM-IRRAS measurements in the Langmuir monolayers, in addition to quartz crystal microbalance and fluorescence spectroscopy measurements for Langmuir-Blodgett (LB) films transferred onto solid substrates. Some specificity was noted in the removal action because chitosan was unable to remove BLG incorporated into neutral dipalmitoyl phosphatidyl choline (DPPC) and cholesterol monolayers and had no effect on horseradish peroxidase and urease interacting with DMPA. An obvious biological implication of these findings is to offer reasons that chitosan can remove BLG from lipophilic environments, as reported in the recent literature.

Interaction of oligonucleotide-based amphiphilic block copolymers with cell membrane models

Oligonucleotides have unique molecular recognition properties, being involved in biological mechanisms such as cell-surface receptor recognition or gene silencing. For their use in human therapy for drug or gene delivery, the cell membrane remains a barrier, but this can be obviated by grafting a hydrophobic tail to the oligonucleotide. Here we demonstrate that two oligonucleotides, one consisting of 12 guanosine units (G(12)), and the other one consisting of five adenosine and seven guanosine (A(5)G(7)) units, when functionalized with poly(butadiene), namely PB-G(12) and PB-A(5)G(7), can be inserted into Langmuir monolayers of dipalmitoyl phosphatidyl choline (DPPC), which served as a cell membrane model. PB-G(12) and PB-A(5)G(7) were found to affect the DPPC monolayer even at high surface pressures. The effects from PB-G(12) were consistently stronger, particularly in reducing the elasticity of the DPPC monolayers, which may have important biological implications. Multilayers of DPPC and nucleotide-based copolymers could be adsorbed onto solid supports, in the form of Y-type LB films, in which the molecular-level interaction led to lower energies in the vibrational spectra of the nucleotide-based copolymers. This successful deposition of solid films opens the way for devices to be produced which exploit the molecular recognition properties of the nucleotides. (C) 2010 Elsevier Inc. All rights reserved.

Comparative study of liponucleosides in Langmuir monolayers as cell membrane models

Liponucleosides may assist the anchoring of nucleic acid nitrogen bases into biological membranes for tailored nanobiotechnological applications. To this end precise knowledge about the biophysical and chemical details at the membrane surface is required. In this paper, we used Langmuir monolayers as simplified cell membrane models and studied the insertion of five lipidated nucleosides. These molecules varied in the type of the covalently attached lipid group, the nucleobase, and the number of hydrophobic moieties attached to the nucleoside. All five lipidated nucleosides were found to be surface-active and capable of forming stable monolayers. They could also be incorporated into dipalmitoylphosphatidylcholine (DPPC) monolayers, four of which induced expansion in the surface pressure isotherm and a decrease in the surface compression modulus of DPPC. In contrast, one nucleoside possessing three alkyl chain modifications formed very condensed monolayers and induced film condensation and an increase in the compression modulus for the DPPC monolayer, thus reflecting the importance of the ability of the nucleoside molecules to be arranged in a closely packed manner. The implications of these results lie on the possibility of tuning nucleic acid pairing by modifying structural characteristics of the liponucleosides. (C) 2010 Elsevier B.V. All rights reserved.

Local Structure and Photochromic Response in Ormosils Containing Dodecatungstophosphoric Acid

Two hybrid materials based on dodecatungstophosphoric acid (HPW) dispersed in ormosils modified with 3-aminopropiltrietoxysilane (APTS) or with N-(3-(trimethoxysilyl)-propyl)-ethylene-diamine (TSPEN) show reversible photochromic response induced by irradiation in the 200-390 nm UV range. A set of solid-state nuclear magnetic resonance (NMR) techniques was used to analyze the structural properties of the main components of these hybrids (the HPW polyanion, the inorganic matrix, and the organic functionalities). For the ormosils, the use of (29)Si NMR, {(1)H}-(29)Si cross-polarization, and {(1)H}-(29)Si HETCOR revealed a homogeneous distribution of silicon species Q ``, T(2), and T(3) for the APTS hybrid, contrasting with the separation of T(3) species in the TSPEN hybrid. The combination of (31)P NMR, {(1)H}-(31)P cross-polarization and (31)P-{(1)H} spin-echo double resonance (SEDOR) revealed the dispersion of the HPW ions in the ormosil, occupying sites with a high number of close protons (>50). Differences in the molecular dynamics at room temperature, inferred from SEDOR experiments, indicate a state of restricted mobility of the HPW ion and the surrounding molecular groups in the TSPEN hybrid. This behavior is consistent with the presence of more amino groups in the TSPEN, acting as chelating groups to the HPW ion. This hybrid, with the strong chelate interaction of the diamine group, shows the most intense photochromic response, in agreement with the charge transfer models proposed to explain the photochromic effect. Electronic reflectance spectroscopy in irradiated samples revealed the presence of one-electron and two-electron reduced polyanions. The one-electron reduced species could be detected also by (31)P NMR spectroscopy immediately after UV irradiation.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

On Real-Time Monitoring And Control For Efficient Management Of Drinking Water Networks: Barcelona Case Study

Drinking water utilities in urban areas are focused on finding smart solutions facing new challenges in their real-time operation because of limited water resources, intensive energy requirements, a growing population, a costly and ageing infrastructure, increasingly stringent regulations, and increased attention towards the environmental impact of water use. Such challenges force water managers to monitor and control not only water supply and distribution, but also consumer demand. This paper presents and discusses novel methodologies and procedures towards an integrated water resource management system based on advanced ICT technologies of automation and telecommunications for largely improving the efficiency of drinking water networks (DWN) in terms of water use, energy consumption, water loss minimization, and water quality guarantees. In particular, the paper addresses the first results of the European project EFFINET (FP7-ICT2011-8-318556) devoted to the monitoring and control of the DWN in Barcelona (Spain). Results are split in two levels according to different management objectives: (i) the monitoring level is concerned with all the aspects involved in the observation of the current state of a system and the detection/diagnosis of abnormal situations. It is achieved through sensors and communications technology, together with mathematical models; (ii) the control level is concerned with computing the best suitable and admissible control strategies for network actuators as to optimize a given set of operational goals related to the performance of the overall system. This level covers the network control (optimal management of water and energy) and the demand management (smart metering, efficient supply). The consideration of the Barcelona DWN as the case study will allow to prove the general applicability of the proposed integrated ICT solutions and their effectiveness in the management of DWN, with considerable savings of electricity costs and reduced water loss while ensuring the high European standards of water quality to citizens.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

Parameterization Of Turbulence Models Using 3DVAR Data Assimilation

In this research the 3DVAR data assimilation scheme is implemented in the numerical model DIVAST in order to optimize the performance of the numerical model by selecting an appropriate turbulence scheme and tuning its parameters. Two turbulence closure schemes: the Prandtl mixing length model and the two-equation k-ε model were incorporated into DIVAST and examined with respect to their universality of application, complexity of solutions, computational efficiency and numerical stability. A square harbour with one symmetrical entrance subject to tide-induced flows was selected to investigate the structure of turbulent flows. The experimental part of the research was conducted in a tidal basin. A significant advantage of such laboratory experiment is a fully controlled environment where domain setup and forcing are user-defined. The research shows that the Prandtl mixing length model and the two-equation k-ε model, with default parameterization predefined according to literature recommendations, overestimate eddy viscosity which in turn results in a significant underestimation of velocity magnitudes in the harbour. The data assimilation of the model-predicted velocity and laboratory observations significantly improves model predictions for both turbulence models by adjusting modelled flows in the harbour to match de-errored observations. 3DVAR allows also to identify and quantify shortcomings of the numerical model. Such comprehensive analysis gives an optimal solution based on which numerical model parameters can be estimated. The process of turbulence model optimization by reparameterization and tuning towards optimal state led to new constants that may be potentially applied to complex turbulent flows, such as rapidly developing flows or recirculating flows.

Analysis Of Semi-Distributed And Global Hydrological Models In The Central Tropical Basins Of The Gulf Of Mexico To The Effects Of Extreme Hydrometeorological Phenomena

In the last years extreme hydrometeorological phenomena have increased in number and intensity affecting the inhabitants of various regions, an example of these effects are the central basins of the Gulf of Mexico (CBGM) that they have been affected by 55.2% with floods and especially the state of Veracruz (1999-2013), leaving economic, social and environmental losses. Mexico currently lacks sufficient hydrological studies for the measurement of volumes in rivers, since is convenient to create a hydrological model (HM) suited to the quality and quantity of the geographic and climatic information that is reliable and affordable. Therefore this research compares the semi-distributed hydrological model (SHM) and the global hydrological model (GHM), with respect to the volumes of runoff and achieve to predict flood areas, furthermore, were analyzed extreme hydrometeorological phenomena in the CBGM, by modeling the Hydrologic Modeling System (HEC-HMS) which is a SHM and the Modèle Hydrologique Simplifié à I'Extrême (MOHYSE) which is a GHM, to evaluate the results and compare which model is suitable for tropical conditions to propose public policies for integrated basins management and flood prevention. Thus it was determined the temporal and spatial framework of the analyzed basins according to hurricanes and floods. It were developed the SHM and GHM models, which were calibrated, validated and compared the results to identify the sensitivity to the real model. It was concluded that both models conform to tropical conditions of the CBGM, having MOHYSE further approximation to the real model. Worth mentioning that in Mexico there is not enough information, besides there are no records of MOHYSE use in Mexico, so it can be a useful tool for determining runoff volumes. Finally, with the SHM and the GHM were generated climate change scenarios to develop risk studies creating a risk map for urban planning, agro-hydrological and territorial organization.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)