Data analysis in multiple-frequency bioelectrical impedance analysis

The performance of three analytical methods for multiple-frequency bioelectrical impedance analysis (MFBIA) data was assessed. The methods were the established method of Cole and Cole, the newly proposed method of Siconolfi and co-workers and a modification of this procedure. Method performance was assessed from the adequacy of the curve fitting techniques, as judged by the correlation coefficient and standard error of the estimate, and the accuracy of the different methods in determining the theoretical values of impedance parameters describing a set of model electrical circuits. The experimental data were well fitted by all curve-fitting procedures (r = 0.9 with SEE 0.3 to 3.5% or better for most circuit-procedure combinations). Cole-Cole modelling provided the most accurate estimates of circuit impedance values, generally within 1-2% of the theoretical values, followed by the Siconolfi procedure using a sixth-order polynomial regression (1-6% variation). None of the methods, however, accurately estimated circuit parameters when the measured impedances were low (<20 Omega) reflecting the electronic limits of the impedance meter used. These data suggest that Cole-Cole modelling remains the preferred method for the analysis of MFBIA data.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

Exploratory spatial data analysis techniques for examining urban crime

Regional planners, policy makers and policing agencies all recognize the importance of better understanding the dynamics of crime. Theoretical and application-oriented approaches which provide insights into why and where crimes take place are much sought after. Geographic information systems and spatial analysis techniques, in particular, are proving to be essential or studying criminal activity. However, the capabilities of these quantitative methods continue to evolve. This paper explores the use of geographic information systems and spatial analysis approaches for examining crime occurrence in Brisbane, Australia. The analysis highlights novel capabilities for the analysis of crime in urban regions.

Integrating technology to improve the efficiency of qualitative data analysis - A note on methods

Qualitative data analysis (QDA) is often a time-consuming and laborious process usually involving the management of large quantities of textual data. Recently developed computer programs offer great advances in the efficiency of the processes of QDA. In this paper we report on an innovative use of a combination of extant computer software technologies to further enhance and simplify QDA. Used in appropriate circumstances, we believe that this innovation greatly enhances the speed with which theoretical and descriptive ideas can be abstracted from rich, complex, and chaotic qualitative data. © 2001 Human Sciences Press, Inc.

Data analysis model selection for estimating local population size of the Risso’s dolphin (Grampus griseus) in the Azores

27th Annual Conference of the European Cetacean Society. Setúbal, Portugal, 8-10 April 2013.

Exploratory geospatial data analysis using self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Acquisition and data analysis related to changes in some forest soils properties after prescribed fire action

Controlled fires in forest areas are frequently used in most Mediterranean countries as a preventive technique to avoid severe wildfires in summer season. In Portugal, this forest management method of fuel mass availability is also used and has shown to be beneficial as annual statistical reports confirm that the decrease of wildfires occurrence have a direct relationship with the controlled fire practice. However prescribed fire can have serious side effects in some forest soil properties. This work shows the changes that occurred in some forest soils properties after a prescribed fire action. The experiments were carried out in soil cover over a natural site of Andaluzitic schist, in Gramelas, Caminha, Portugal, that had not been burn for four years. The composed soil samples were collected from five plots at three different layers (0-3cm, 3-6cm and 6-18cm) during a three-year monitoring period after the prescribed burning. Principal Component Analysis was used to reach the presented conclusions.

Acqusition and data analysis of the eco-efficiency for the establishment of indicators parameters for industrial environmental sustainability

The industrial activity is inevitably associated with a certain degradation of the environmental quality, because is not possible to guarantee that a manufacturing process can be totally innocuous. The eco-efficiency concept is globally accepted as a philosophy of entreprise management, that encourages the companies to become more competitive, innovative and environmentally responsible by promoting the link between its companies objectives for excellence and its objectives of environmental excellence issues. This link imposes the creation of an organizational methodology where the performance of the company is concordant with the sustainable development. The main propose of this project is to apply the concept of eco-efficiency to the particular case of the metallurgical and metal workshop industries through the development of the particular indicators needed and to produce a manual of procedures for implementation of the accurate solution.

Data analysis applied to the evaluation of a technological nature teaching model as a regional development tool

This paper presents the creation and development of technological schools directly linked to the business community and to higher public education. Establishing themselves as the key interface between the two sectors they make a signigicant contribution by having a greater competitive edge when faced with increasing competition in the tradional markets. The development of new business strategies supported by references of excellence, quality and competitiveness also provides a good link between the estalishment of partnerships aiming at the qualification of education boards at a medium level between the technological school and higher education with a technological foundation. We present a case study as an example depicting the success of Escola Tecnológica de Vale de Cambra.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Big data e data analysis: visualização de informação

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Visual data analysis in air traffic management

Visual data mining, multi-dimensional scaling, POLARMAP, Sammon's mapping, clustering, outlier detection

Real equilibrium exchange rates. A panel data approach for advanced and emerging economies.

Based on an behavioral equilibrium exchange rate model, this paper examines the determinants of the real effective exchange rate and evaluates the degree of misalignment of a group of currencies since 1980. Within a panel cointegration setting, we estimate the relationship between exchange rate and a set of economic fundamentals, such as traded-nontraded productivity differentials and the stock of foreign assets. Having ascertained the variables are integrated and cointegrated, the long-run equilibrium value of the fundamentals are estimated and used to derive equilibrium exchange rates and misalignments. Although there is statistical homogeneity, some structural differences were found to exist between advanced and emerging economies.

Multilayer perceptron with local constraint as an emerging method in spatial data analysis

The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.