Modeling electrocortical activity through improved local approximations of integral neural field equations

Neural field models of firing rate activity typically take the form of integral equations with space-dependent axonal delays. Under natural assumptions on the synaptic connectivity we show how one can derive an equivalent partial differential equation (PDE) model that properly treats the axonal delay terms of the integral formulation. Our analysis avoids the so-called long-wavelength approximation that has previously been used to formulate PDE models for neural activity in two spatial dimensions. Direct numerical simulations of this PDE model show instabilities of the homogeneous steady state that are in full agreement with a Turing instability analysis of the original integral model. We discuss the benefits of such a local model and its usefulness in modeling electrocortical activity. In particular we are able to treat "patchy'" connections, whereby a homogeneous and isotropic system is modulated in a spatially periodic fashion. In this case the emergence of a "lattice-directed" traveling wave predicted by a linear instability analysis is confirmed by the numerical simulation of an appropriate set of coupled PDEs. Article published and (c) American Physical Society 2007

Photovoltaic power forecast modeling with artificial neural networks

Dissertação de Mestrado, Engenharia Eletrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2016

Graded Meshes In Bio-thermal Problems With Transmission-line Modeling Method.

In this study, the transmission-line modeling (TLM) applied to bio-thermal problems was improved by incorporating several novel computational techniques, which include application of graded meshes which resulted in 9 times faster in computational time and uses only a fraction (16%) of the computational resources used by regular meshes in analyzing heat flow through heterogeneous media. Graded meshes, unlike regular meshes, allow heat sources to be modeled in all segments of the mesh. A new boundary condition that considers thermal properties and thus resulting in a more realistic modeling of complex problems is introduced. Also, a new way of calculating an error parameter is introduced. The calculated temperatures between nodes were compared against the results obtained from the literature and agreed within less than 1% difference. It is reasonable, therefore, to conclude that the improved TLM model described herein has great potential in heat transfer of biological systems.

Adaptive Approach for a Maximum Entropy Algorithm in Ecological Niche Modeling

This paper presents an Adaptive Maximum Entropy (AME) approach for modeling biological species. The Maximum Entropy algorithm (MaxEnt) is one of the most used methods in modeling biological species geographical distribution. The approach presented here is an alternative to the classical algorithm. Instead of using the same set features in the training, the AME approach tries to insert or to remove a single feature at each iteration. The aim is to reach the convergence faster without affect the performance of the generated models. The preliminary experiments were well performed. They showed an increasing on performance both in accuracy and in execution time. Comparisons with other algorithms are beyond the scope of this paper. Some important researches are proposed as future works.

Evaluation of Adaptive Genetic Algorithm to Environmental Modeling of Peponapis and Cucurbita

This paper presents a free software tool that supports the next-generation Mobile Communications, through the automatic generation of models of components and electronic devices based on neural networks. This tool enables the creation, training, validation and simulation of the model directly from measurements made on devices of interest, using an interface totally oriented to non-experts in neural models. The resulting model can be exported automatically to a traditional circuit simulator to test different scenarios.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

MODELING ELECTRODIALYSIS AND A PHOTOCHEMICAL PROCESS FOR THEIR INTEGRATION IN SALINE WASTEWATER TREATMENT

Oxidation processes can be used to treat industrial wastewater containing non-biodegradable organic compounds. However, the presence of dissolved salts may inhibit or retard the treatment process. In this study, wastewater desalination by electrodialysis (ED) associated with an advanced oxidation process (photo-Fenton) was applied to an aqueous NaCl solution containing phenol. The influence of process variables on the demineralization factor was investigated for ED in pilot scale and a correlation was obtained between the phenol, salt and water fluxes with the driving force. The oxidation process was investigated in a laboratory batch reactor and a model based on artificial neural networks was developed by fitting the experimental data describing the reaction rate as a function of the input variables. With the experimental parameters of both processes, a dynamic model was developed for ED and a continuous model, using a plug flow reactor approach, for the oxidation process. Finally, the hybrid model simulation could validate different scenarios of the integrated system and can be used for process optimization.

On Building a Memory Evolutive System for Application to Learning and Cognition Modeling

We address here aspects of the implementation of a memory evolutive system (MES), based on the model proposed by A. Ehresmann and J. Vanbremeersch (2007), by means of a simulated network of spiking neurons with time dependent plasticity. We point out the advantages and challenges of applying category theory for the representation of cognition, by using the MES architecture. Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich`s formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Reactivity of chars and carbons: New insights through molecular modeling

A new model proposed for the gasification of chars and carbons incorporates features of the turbostratic nanoscale structure that exists in such materials. The model also considers the effect of initial surface chemistry and different reactivities perpendicular to the edges and to the faces of the underlying crystallite planes comprising the turbostratic structure. It may be more realistic than earlier models based on pore or grain structure idealizations when the carbon contains large amounts of crystallite matter. Shrinkage of the carbon particles in the chemically controlled regime is also possible due to the random complete gasification of crystallitic planes. This mechanism can explain observations in the literature of particle size reduction. Based on the model predictions, both initial surface chemistry and the number of stacked planes in the crystallites strongly influence the reactivity and particle shrinkage. Its test results agree well with literature data on the air-oxidation of Spherocarb and show that it accurately predicts the variation of particle size with conversion. Model parameters are determined entirely from rate measurements.

Mantle convection modeling with viscoelastic/brittle lithosphere: Numerical methodology and plate tectonic modeling

The earth's tectonic plates are strong, viscoelastic shells which make up the outermost part of a thermally convecting, predominantly viscous layer. Brittle failure of the lithosphere occurs when stresses are high. In order to build a realistic simulation of the planet's evolution, the complete viscoelastic/brittle convection system needs to be considered. A particle-in-cell finite element method is demonstrated which can simulate very large deformation viscoelasticity with a strain-dependent yield stress. This is applied to a plate-deformation problem. Numerical accuracy is demonstrated relative to analytic benchmarks, and the characteristics of the method are discussed.

A Dynamical Modeling to Study the Adaptive Immune System and the Influence of Antibodies in the Immune Memory

Immunological systems have been an abundant inspiration to contemporary computer scientists. Problem solving strategies, stemming from known immune system phenomena, have been successfully applied to chall enging problems of modem computing. Simulation systems and mathematical modeling are also beginning use to answer more complex immunological questions as immune memory process and duration of vaccines, where the regulation mechanisms are not still known sufficiently (Lundegaard, Lund, Kesmir, Brunak, Nielsen, 2007). In this article we studied in machina a approach to simulate the process of antigenic mutation and its implications for the process of memory. Our results have suggested that the durability of the immune memory is affected by the process of antigenic mutation.and by populations of soluble antibodies in the blood. The results also strongly suggest that the decrease of the production of antibodies favors the global maintenance of immune memory.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

Influence of magnetically-induced E-fields on cardiac electric activity during MRI: A modeling study

In modern magnetic resonance imaging (MRI), patients are exposed to strong, time-varying gradient magnetic fields that may be able to induce electric fields (E-fields)/currents in tissues approaching the level of physiological significance. In this work we present theoretical investigations into induced E-fields in the thorax, and evaluate their potential influence on cardiac electric activity under the assumption that the sites of maximum E-field correspond to the myocardial stimulation threshold (an abnormal circumstance). Whole-body cylindrical and planar gradient coils were included in the model. The calculations of the induced fields are based on an efficient, quasi-static, finite-difference scheme and an anatomically realistic, whole-body model. The potential for cardiac stimulation was evaluated using an electrical model of the heart. Twelve-lead electrocardiogram (ECG) signals were simulated and inspected for arrhythmias caused by the applied fields for both healthy and diseased hearts. The simulations show that the shape of the thorax and the conductive paths significantly influence induced E-fields. In healthy patients, these fields are not sufficient to elicit serious arrhythmias with the use of contemporary gradient sets. However, raising the strength and number of repeated switching episodes of gradients, as is certainly possible in local chest gradient sets, could expose patients to increased risk. For patients with cardiac disease, the risk factors are elevated. By the use of this model, the sensitivity of cardiac pathologies, such as abnormal conductive pathways, to the induced fields generated by an MRI sequence can be investigated. (C) 2003 Wiley-Liss, Inc.