Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

Optoelectronic Properties of InAlN/GaN Distributed Bragg Reflector Heterostructure Examined by Valence

High-resolution monochromated electron energy loss spectroscopy (EELS) at subnanometric spatial resolution and <200 meV energy resolution has been used to assess the valence band properties of a distributed Bragg reflector multilayer heterostructure composed of InAlN lattice matched to GaN. This work thoroughly presents the collection of methods and computational tools put together for this task. Among these are zero-loss-peak subtraction and nonlinear fitting tools, and theoretical modeling of the electron scattering distribution. EELS analysis allows retrieval of a great amount of information: indium concentration in the InAlN layers is monitored through the local plasmon energy position and calculated using a bowing parameter version of Vegard Law. Also a dielectric characterization of the InAlN and GaN layers has been performed through Kramers-Kronig analysis of the Valence-EELS data, allowing band gap energy to be measured and an insight on the polytypism of the GaN layers.

A large strain anisotropic elastoplastic continuum theory for nonlinear kinematic hardening and texture evolution

In this paper we present a continuum theory for large strain anisotropic elastoplasticity based on a decomposition of the modified plastic velocity gradient into energetic and dissipative parts. The theory includes the Armstrong and Frederick hardening rule as well as multilayer models as special cases even for large strain anisotropic elastoplasticity. Texture evolution may also be modelled by the formulation, which allows for a meaningful interpretation of the terms of the dissipation equation

Surface Impedance Characterization for UTD Based Solution with IBC for Surface Fields on a Dielectric-Coated PEC Circular Cylinder

A novel formulation for the surface impedance characterization is introduced for the canonical problem of surface fields on a perfect electric conductor (PEC) circular cylinder with a dielectric coating due to a electric current source using the Uniform Theory of Diffraction (UTD) with an Impedance Boundary Condition (IBC). The approach is based on a TE/TM assumption of the surface fields from the original problem. Where this surface impedance fails, an optimization is performed to minimize the error in the SD Green?s function between the original problem and the equivalent one with the IBC. This new approach requires small changes in the available UTD based solution with IBC to include the geodesic ray angle and length dependence in the surface impedance formulas. This asymptotic method, accurate for large separations between source and observer points, in combination with spectral domain (SD) Green?s functions for multidielectric coatings leads to a new hybrid SD-UTD with IBC to calculate mutual coupling among microstrip patches on a multilayer dielectric-coated PEC circular cylinder. Results are compared with the eigenfunction solution in SD, where a very good agreement is met.

Surface Impedance Characterization for Mutual Coupling Calculation of Patches on a Dielectric-Coated PEC Circular Cylinder

A novel formulation for the surface impedance characterization is introduced for the canonical problem of surface fields on a perfect electric conductor (PEC) circular cylinder with a dielectric coating due to a electric current source using the Uniform Theory of Diffraction (UTD) with an Impedance Boundary Condition (IBC). The approach is based on a TE/TM assumption of the surface fields from the original problem. Where this surface impedance fails, an optimization is performed to minimize the error in the SD Green?s function between the original problem and the equivalent one with the IBC. This asymptotic method, accurate for large separations between source and observer points, in combination with spectral domain (SD) Green?s functions for multidielectric coatings leads to a new hybrid SD-UTD with IBC to calculate mutual coupling among microstrip patches on a multilayer dielectric-coated PEC circular cylinder. Results are compared with the eigenfunction solution in SD, where a very good agreement is met.

Data mining for the diagnosis of type 2 diabetes

Diabetes is the most common disease nowadays in all populations and in all age groups. diabetes contributing to heart disease, increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. Different techniques of artificial intelligence has been applied to diabetes problem. The purpose of this study is apply the artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining (DM) technique for the diabetes disease diagnosis. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with decision tree (DT), Bayesian classifier (BC) and other algorithms, recently proposed by other researchers, that were applied to the same database. The robustness of the algorithms are examined using classification accuracy, analysis of sensitivity and specificity, confusion matrix. The results obtained by AMMLP are superior to obtained by DT and BC.

ANN and Fuzzy c-Means applied to environmental pollution prediction

Salamanca, situated in center of Mexico is among the cities which suffer most from the air pollution in Mexico. The vehicular park and the industry, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Sulphur Dioxide (SO2). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables and air pollutant concentrations of SO2. Before the prediction, Fuzzy c-Means and K-means clustering algorithms have been implemented in order to find relationship among pollutant and meteorological variables. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of SO2 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours.

Caracterización de la impedancia de superficie para el cálculo del acoplo mutuo en un cilindro circular conductor cubierto por una dieléctrico multicapa

A novel formulation for the surface impedance characterization is introduced for the canonical problem of surface fields on a perfect electric conductor (PEC) circular cylinder with a dielectric coating due to a electric current source using the Uniform Theory of Diffraction (UTD) with an Impedance Boundary Condition (IBC). The approach is based on a TE/TM assumption of the surface fields from the original problem. Where this surface impedance fails, an optimization is performed to minimize the error in the SD Green's function between the original problem and the equivalent one with the IBC. This new approach requires small changes in the available UTD based solution with IBC to include the geodesic ray angle and length dependence in the surface impedance formulas. This asymptotic method, accurate for large separations between source and observer points, in combination with spectral domain (SD) Green's functions for multidielectric coatings leads to a new hybrid SD-UTD with IBC to calculate mutual coupling among microstrip patches on a multilayer dielectric-coated PEC circular cylinder. Results are compared with the eigenfunction solution in SD, where a very good agreement is met.

DIMETER: a haptic master device for tremor diagnosis in neurodegenerative diseases

In this study, a device based on patient motion capture is developed for the reliable and non-invasive diagnosis of neurodegenerative diseases. The primary objective of this study is the classification of differential diagnosis between Parkinson's disease (PD) and essential tremor (ET). The DIMETER system has been used in the diagnoses of a significant number of patients at two medical centers in Spain. Research studies on classification have primarily focused on the use of well-known and reliable diagnosis criteria developed by qualified personnel. Here, we first present a literature review of the methods used to detect and evaluate tremor; then, we describe the DIMETER device in terms of the software and hardware used and the battery of tests developed to obtain the best diagnoses. All of the tests are classified and described in terms of the characteristics of the data obtained. A list of parameters obtained from the tests is provided, and the results obtained using multilayer perceptron (MLP) neural networks are presented and analyzed.

Tunable Fano resonance in symmetric multilayered gold nanoshells

We have studied the evolution of dipole–dipole all-plasmonic Fano resonances (FRs) in symmetric multilayered nanoshells as a function of their geometrical parameters. We demonstrate that symmetry breaking is not mandatory for controlling the Fano resonance in such multilayer structures. By carefully selecting the geometrical parameters, the position of the FR can be tuned between 600 and 950 nm and its intensity can be increased up to four fold with respect to the non-optimized structures. Generation of FRs in such symmetric nanostructures presents clear advantages over their asymmetric counterparts, as they are easier to fabricate and can be used in a wider range of technological applications.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

An Architecture forRepresenting Biological Processes based on Networks of Bio-inspired Processors

n this paper we propose the use of Networks of Bio-inspired Processors (NBP) to model some biological phenomena within a computational framework. In particular, we propose the use of an extension of NBP named Network Evolutionary Processors Transducers to simulate chemical transformations of substances. Within a biological process, chemical transformations of substances are basic operations in the change of the state of the cell. Previously, it has been proved that NBP are computationally complete, that is, they are able to solve NP complete problems in linear time, using massively parallel computations. In addition, we propose a multilayer architecture that will allow us to design models of biological processes related to cellular communication as well as their implications in the metabolic pathways. Subsequently, these models can be applied not only to biological-cellular instances but, possibly, also to configure instances of interactive processes in many other fields like population interactions, ecological trophic networks, in dustrial ecosystems, etc.

Data Mining with Enhanced Neural Networks-CMMSE

Abstract This paper presents a new method to extract knowledge from existing data sets, that is, to extract symbolic rules using the weights of an Artificial Neural Network. The method has been applied to a neural network with special architecture named Enhanced Neural Network (ENN). This architecture improves the results that have been obtained with multilayer perceptron (MLP). The relationship among the knowledge stored in the weights, the performance of the network and the new implemented algorithm to acquire rules from the weights is explained. The method itself gives a model to follow in the knowledge acquisition with ENN.

Parametric analysis on bridge reinforced concrete elevated pile-cap foundations

Current design practices recommend to comply with the capacity protection principle, which pays special attention to ensuring an elastic response of the foundations under ground motion events. However, in cases such as elevated reinforced concrete (RC) pile-cap foundation typologies, this design criterion may lead to conservative designs, with excessively high construction costs. Reinforced concrete elevated pile-cap foundations is a system formed by a group of partially embedded piles connected through an aboveground stayed cap and embedded in soil. In the cases when they are subjected to ground motions, the piles suffer large bending moments that make it difficult to maintain their behavior within the elastic range of deformations. Aiming to make an in-depth analysis of the nonlinear behavior of elevated pile-cap foundations, a cyclic loading test was performed on a concrete 2x3 pile configuration specimen of elevated pile-cap foundation. Two results of this test, the failure mechanism and the ductile behavior, were used for the calibration of a numerical model built in OpenSees framework, by using a pushover analysis. The calibration of the numerical model enabled an in-depth study of the seismic nonlinear response of this kind of foundations. A parametric analysis was carried for this purpose, aiming to study how sensitive RC elevated pile-cap foundations are, when subjected to variations in the diameter of piles, reinforcement ratios, external loads, soil density or multilayer configurations. This analysis provided a set of ductility factors that can be used as a reference for design practices and which correspond to each of the cases analyzed.

Influencia de la temperatura en la deflexión de firmes flexibles

En esta investigación se ha estudiado el efecto de la variación de la temperatura en la deflexión de firmes flexibles. En primer lugar se han recopilado los criterios existentes de ajuste de la deflexión por efecto de la temperatura. Posteriormente, se ha llevado a cabo un estudio empírico mediante la auscultación de las deflexiones en cinco tramos de carretera con firme flexible y con diferentes espesores de mezclas bituminosas (entre 10 y 30 cm). Las medidas se han efectuado en dos campañas (verano e invierno), tratando de abarcar un amplio rango de temperaturas. En cada campaña, se han llevado a cabo distintas auscultaciones a diferentes temperaturas. Las medidas de cada campaña se han realizado el mismo día. Se han obtenido los coeficientes empíricos de ajuste por temperatura para cada tramo analizado. Además, se ha realizado un estudio teórico mediante la elaboración de diferentes modelos (multicapa elástico lineal, multicapa visco-elástico lineal y elementos finitos) que reproducen la respuesta estructural de los firmes flexibles auscultados. La caracterización mecánica de las mezclas bituminosas se ha realizado mediante ensayos de módulo complejo en laboratorio, a diferentes temperaturas y frecuencias, sobre testigos extraídos en las carreteras estudiadas. Se han calculado los coeficientes teóricos de ajuste por temperatura para cada modelo elaborado y tramo analizado. Finalmente, se ha realizado un estudio comparativo entre los distintos coeficientes de ajuste (existentes, empíricos y teóricos), que ha puesto de manifiesto que, en todos los casos analizados, los coeficientes obtenidos en el modelo de elementos finitos son los que más se aproximan a los coeficientes empíricos (valor de referencia para los tramos analizados). El modelo desarrollado de elementos finitos permite reproducir el comportamiento visco-elástico de las mezclas bituminosas y el carácter dinámico de las cargas aplicadas. Se han utilizado elementos tipo tetraedro isoparamétrico lineal (C3D8R) para el firme y la parte superior del cimiento, mientras que para la parte inferior se han empleado elementos infinitos (CIN3D8). In this research the effect produced by the temperature change on flexible pavements deflection is analysed. First, the existing criteria of deflection adjustment by temperature were collected. Additionally, an empirical analysis was carried out, consisting on deflection tests in five flexible-pavement road sections with different asphalt mix thickness (from 10 to 30 cm). The measures were taken in two seasons (summer and winter) in an effort to register a wide range of temperatures. Different surveys were carried out at different temperatures in each season. The tests of each season were done at the same day. The empirical temperature adjustment factors for every analysed section were obtained. A theoretical study was carried out by developing different models (linear elastic multilayer, linear visco-elastic multilayer and finite elements) that reproduce the structural response of the tested flexible pavements. The mechanical characterization of the asphalt mixes was achieved through laboratory complex-modulus tests at different temperatures and frequencies, using pavement cores from the surveyed roads. The theoretical temperature adjustment factors for each model developed and each section analysed were calculated. Finally, a comparative study among the different adjustment factors (existing, empirical and theoretical) was carried out. It has shown that, in all analysed cases, the factors obtained with the finite elements model are the closest to the empirical factors (reference value for the analysed sections). The finite elements model developed makes it possible to reproduce the visco-elastic behavior of the asphalt mixes and the dynamic nature of the applied loads. Linear isoparametric tetrahedral elements (C3D8R) have been used for the pavement and the subgrade, while infinite elements (CIN3D8) have been used for the foundations.