Analysis of the respiratory dynamics during normal breathing by means of pseudo phase plots and pressure-volume loops

This paper reports on the analysis of tidal breathing patterns measured during noninvasive forced oscillation lung function tests in six individual groups. The three adult groups were healthy, with prediagnosed chronic obstructive pulmonary disease, and with prediagnosed kyphoscoliosis, respectively. The three children groups were healthy, with prediagnosed asthma, and with prediagnosed cystic fibrosis, respectively. The analysis is applied to the pressure–volume curves and the pseudophaseplane loop by means of the box-counting method, which gives a measure of the area within each loop. The objective was to verify if there exists a link between the area of the loops, power-law patterns, and alterations in the respiratory structure with disease. We obtained statistically significant variations between the data sets corresponding to the six groups of patients, showing also the existence of power-law patterns. Our findings support the idea that the respiratory system changes with disease in terms of airway geometry and tissue parameters, leading, in turn, to variations in the fractal dimension of the respiratory tree and its dynamics.

Analysis of the Respiratory Dynamics During Normal Breathing by Means of Pseudophase Plots and Pressure–Volume Loops

This paper reports on the analysis of tidal breathing patterns measured during noninvasive forced oscillation lung function tests in six individual groups. The three adult groups were healthy, with prediagnosed chronic obstructive pulmonary disease, and with prediagnosed kyphoscoliosis, respectively. The three children groups were healthy, with prediagnosed asthma, and with prediagnosed cystic fibrosis, respectively. The analysis is applied to the pressure-volume curves and the pseudophase-plane loop by means of the box-counting method, which gives a measure of the area within each loop. The objective was to verify if there exists a link between the area of the loops, power-law patterns, and alterations in the respiratory structure with disease. We obtained statistically significant variations between the data sets corresponding to the six groups of patients, showing also the existence of power-law patterns. Our findings support the idea that the respiratory system changes with disease in terms of airway geometry and tissue parameters, leading, in turn, to variations in the fractal dimension of the respiratory tree and its dynamics.

A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of São Paulo, Brazil

INTRODUCTION: Forecasting dengue cases in a population by using time-series models can provide useful information that can be used to facilitate the planning of public health interventions. The objective of this article was to develop a forecasting model for dengue incidence in Campinas, southeast Brazil, considering the Box-Jenkins modeling approach. METHODS: The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. We fitted a model based on the reported monthly incidence of dengue from 1998 to 2008, and we validated the model using the data collected between January and December of 2009. RESULTS: SARIMA (2,1,2) (1,1,1)12 was the model with the best fit for data. This model indicated that the number of dengue cases in a given month can be estimated by the number of dengue cases occurring one, two and twelve months prior. The predicted values for 2009 are relatively close to the observed values. CONCLUSIONS: The results of this article indicate that SARIMA models are useful tools for monitoring dengue incidence. We also observe that the SARIMA model is capable of representing with relative precision the number of cases in a next year.

Previsão de produção de energia elétrica através de fontes de energia renováveis

A capacidade de prever precisamente a produção de energia renovável é extremamente relevante tanto do ponto de vista económico como para controlo da estabilidade da rede elétrica. Para tal, é necessário realizar uma previsão das condições meteorológicas adjacentes à produção de energia a partir de fontes de energia renovável. Vários modelos de previsão têm sido utilizados para este fim, desde modelos atmosféricos a modelos estatísticos, onde se destacam métodos como Redes Neuronais Artificiais ou a Metodologia de Box & Jenkins. Lidar com dados meteo-rológicos pode revelar algumas complicações devido à possível instabilidade das medições, com-plicando o desenvolvimento de um modelo de previsão adequado. Neste trabalho pretende-se realizar a previsão de produção a partir de uma instalação fotovoltaica e um gerador eólico através do uso da Metodologia de Box & Jenkins para desenvolver um modelo capaz de realizar a previsão das condições meteorológicas para diferentes horizontes temporais medidos no topo do edifício do Departamento de Engenharia Eletrotécnica (DEE) da Faculdade de Ciências e Tecnologia (FCT), Universidade Nova de Lisboa (UNL), e usando esses valores para calcular a produção de energia. Os resultados obtidos revelaram um bom desempenho quando comparados os resultados previstos com os resultados reais para o mesmo período de tempo, garantindo que podem ser utilizados para calcular a previsão de potência produzida através das instalações presentes no local e encorajando novos estudos no tema.

Fractal Dimension in Quantifying Experimental-Pulmonary-Hypertension-Induced Cardiac Dysfunction in Rats

Abstract Background: Right-sided heart failure has high morbidity and mortality, and may be caused by pulmonary arterial hypertension. Fractal dimension is a differentiated and innovative method used in histological evaluations that allows the characterization of irregular and complex structures and the quantification of structural tissue changes. Objective: To assess the use of fractal dimension in cardiomyocytes of rats with monocrotaline-induced pulmonary arterial hypertension, in addition to providing histological and functional analysis. Methods: Male Wistar rats were divided into 2 groups: control (C; n = 8) and monocrotaline-induced pulmonary arterial hypertension (M; n = 8). Five weeks after pulmonary arterial hypertension induction with monocrotaline, echocardiography was performed and the animals were euthanized. The heart was dissected, the ventricles weighed to assess anatomical parameters, and histological slides were prepared and stained with hematoxylin/eosin for fractal dimension analysis, performed using box-counting method. Data normality was tested (Shapiro-Wilk test), and the groups were compared with non-paired Student t test or Mann Whitney test (p < 0.05). Results: Higher fractal dimension values were observed in group M as compared to group C (1.39 ± 0.05 vs. 1.37 ± 0.04; p < 0.05). Echocardiography showed lower pulmonary artery flow velocity, pulmonary acceleration time and ejection time values in group M, suggesting function worsening in those animals. Conclusion: The changes observed confirm pulmonary-arterial-hypertension-induced cardiac dysfunction, and point to fractal dimension as an effective method to evaluate cardiac morphological changes induced by ventricular dysfunction.

Analysis of outliers in electricity spot prices with exampleof New England and New Zealand markets

Electricity spot prices have always been a demanding data set for time series analysis, mostly because of the non-storability of electricity. This feature, making electric power unlike the other commodities, causes outstanding price spikes. Moreover, the last several years in financial world seem to show that ’spiky’ behaviour of time series is no longer an exception, but rather a regular phenomenon. The purpose of this paper is to seek patterns and relations within electricity price outliers and verify how they affect the overall statistics of the data. For the study techniques like classical Box-Jenkins approach, series DFT smoothing and GARCH models are used. The results obtained for two geographically different price series show that patterns in outliers’ occurrence are not straightforward. Additionally, there seems to be no rule that would predict the appearance of a spike from volatility, while the reverse effect is quite prominent. It is concluded that spikes cannot be predicted based only on the price series; probably some geographical and meteorological variables need to be included in modeling.

Enhanced Vegetation Index (EVI) na análise da dinâmica da vegetação da reserva biológica de Sooretama, ES

Técnicas de análises de séries temporais são utilizadas para caracterizar o comportamento de fenômenos naturais no domínio do tempo. Neste artigo, segundo a metodologia proposta por Box et al. (1994), 125 observações do Enhanced Vegetation Index (EVI) foram analisadas. Os valores modelados correspondem às variações temporais ocorridas no dossel florestal da reserva biológica de Sooretama, localizada ao Norte do Estado do Espírito Santo, no Município de Linhares. Os resultados indicaram que a metodologia foi adequada. Os resíduos do modelo ajustado são não correlacionados com distribuição normal, média zero e variância s². Com o menor valor do Critério de Informação de Akaike (AIC) -570,51, o modelo ajustado foi o Sazonal Auto-Regressivo Integrado de Médias Móveis (1,0,1)(1,0,1)12.

Analysis of Stochastic Volatility Sequences Generated by Product Autoregressive Models

The classical methods of analysing time series by Box-Jenkins approach assume that the observed series uctuates around changing levels with constant variance. That is, the time series is assumed to be of homoscedastic nature. However, the nancial time series exhibits the presence of heteroscedasticity in the sense that, it possesses non-constant conditional variance given the past observations. So, the analysis of nancial time series, requires the modelling of such variances, which may depend on some time dependent factors or its own past values. This lead to introduction of several classes of models to study the behaviour of nancial time series. See Taylor (1986), Tsay (2005), Rachev et al. (2007). The class of models, used to describe the evolution of conditional variances is referred to as stochastic volatility modelsThe stochastic models available to analyse the conditional variances, are based on either normal or log-normal distributions. One of the objectives of the present study is to explore the possibility of employing some non-Gaussian distributions to model the volatility sequences and then study the behaviour of the resulting return series. This lead us to work on the related problem of statistical inference, which is the main contribution of the thesis

Medidas fractales de radiografías de tórax de pacientes con diferentes patologías

Introducción: La geometría fractal permite la descripción objetiva de objetos irregulares tales como las estructuras del cuerpo humano: Por ello, en este caso, se aplicó al desarrollo de una nueva metodología de caracterización de la cavidad cardiotorácica.Material y métodos: Estudio exploratorio descriptivo en el que se desarrolló una metodología de medición basada en la geometría fractal aplicada a 14 radiografías de tórax de sujetos con diferentes patologías. Se calcularon las dimensiones fractales de la cavidad torácica, la silueta cardíaca y la superposición de estas partes con el método de Box-Counting.Resultados: Se obtuvieron nuevas medidas morfométricas objetivas y reproducibles de placas de tórax a partir de dimensiones fractales.Conclusiones: La geometría fractal permite la caracterización matemática de placas de tórax de pacientes con diferentes patologías. Es posible que el desarrollo de esta metodología en posteriores investigaciones permita generar parámetros útiles de aplicación clínica, independientes de la experiencia del médico y de su observación subjetiva, de modo que garantice la reproducibilidad y objetividad de las medidas.

Descripción matemática con dimensiones fractales de células normales y con anormalidades citológicas de cuello uterino

Introducción. La geometría fractal ha mostrado ser adecuada en la descripción matemática de objetos irregulares; esta medida se ha denominado dimensión fractal. La aplicación del análisis fractal para medir los contornos de las células normales así como aquellas que presentan algún tipo de anormalidad, ha mostrado la posibilidad de caracterización matemática de su irregularidad. Objetivos. Medir, a partir de la geometría fractal células del epitelio escamoso de cuello uterino clasificadas como normales, atipias escamosas de significado indeterminado (ASC-US) y lesiones intraepiteliales escamosas de bajo grado (LEIBG), diagnosticadas mediante observación microscópica, en busca de mediciones matemáticas que las distingan. Metodología. Este es un estudio exploratorio descriptivo en el que se calcularon las dimensiones fractales, con el método de box counting simplificado y convencional, de los contornos celular y nuclear de 13 células del epitelio escamoso de cuello uterino normales y con anormalidades como ASC-US y lesiones intraepiteliales de bajo grado (LEI BG), a partir de fotografías digitales de 7 células normales, 2 ASCUS y 4 LEI BG diagnosticadas con criterios citomorfológicos mediante observación microscópica convencional. Resultados. Se desarrolló una medida cuantitativa, objetiva y reproducible del grado de irregularidad en las células del epitelio escamoso de cuello uterino identificadas microscópicamente como normales, ASC-US y LEI BG. Conclusiones Se evidenció una organización fractal en la arquitectura celular normal, así como en células ASC-US y las lesiones intraepiteliales de bajo grado (LEI BG). No se encontraron diferencias entre los tipos celulares estudiados.

Impacto de la innovación en el crecimiento de la oferta exportable de bienes no tradicionales en Colombia

El objetivo de este paper es avanzar en la comprensión existente acerca del impacto de la innovación (en este caso entendida como la inversión en actividades de innovación) en las exportaciones no tradicionales. El estudio analiza un conjunto de datos de empresas colombianas que desempeñan sus actividades en los sectores de la Clasificación Industrial Internacional Uniforme – CIIU - durante el periodo del 2005 al 2012. Para esto se usó un modelo de datos panel en el cual a través de la teoría Box Jenkins, se lograron identificar las variables estadísticamente significativas en el desempeño de las exportaciones. Los hallazgos permiten comprobar las teorías acerca de la relación positiva entre estas variables, y en nuestro caso particular demostrar el impacto que tienen las actividades de innovación en el desarrollo de las exportaciones. Finalmente los resultados sugieren que el estímulo de la innovación y políticas que la promuevan es esencial para el crecimiento de las exportaciones.

Statistical analysis of eight surface ozone measurement series for various sites in Ireland

Data from various stations having different measurement record periods between 1988 and 2007 are analyzed to investigate the surface ozone concentration, long-term trends, and seasonal changes in and around Ireland. Time series statistical analysis is performed on the monthly mean data using seasonal and trend decomposition procedures and the Box-Jenkins approach (autoregressive integrated moving average). In general, ozone concentrations in the Irish region are found to have a negative trend at all sites except at the coastal sites of Mace Head and Valentia. Data from the most polluted Dublin city site have shown a very strong negative trend of −0.33 ppb/yr with a 95% confidence limit of 0.17 ppb/yr (i.e., −0.33 ± 0.17) for the period 2002−2007, and for the site near the city of Cork, the trend is found to be −0.20 ± 0.11 ppb/yr over the same period. The negative trend for other sites is more pronounced when the data span is considered from around the year 2000 to 2007. Rural sites of Wexford and Monaghan have also shown a very strong negative trend of −0.99 ± 0.13 and −0.58 ± 0.12, respectively, for the period 2000−2007. Mace Head, a site that is representative of ozone changes in the air advected from the Atlantic to Europe in the marine planetary boundary layer, has shown a positive trend of about +0.16 ± 0.04 ppb per annum over the entire period 1988−2007, but this positive trend has reduced during recent years (e.g., in the period 2001−2007). Cluster analysis for back trajectories are performed for the stations having a long record of data, Mace Head and Lough Navar. For Mace Head, the northern and western clean air sectors have shown a similar positive trend (+0.17 ± 0.02 ppb/yr for the northern sector and +0.18 ± 0.02 ppb/yr for the western sector) for the whole period, but partial analysis for the clean western sector at Mace Head shows different trends during different time periods with a decrease in the positive trend since 1988 indicating a deceleration in the ozone trend for Atlantic air masses entering Europe.

Assessing macro uncertainty in real-time when data are subject to revision

Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box-Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically-estimated model of data revisions for US output growth is used to investigate small-sample properties.

MODELLING AND FORECASTING INFLATION RATES IN GHANA: AN APPLICATION OF SARIMA MODELS

Ghana faces a macroeconomic problem of inflation for a long period of time. The problem in somehow slows the economic growth in this country. As we all know, inflation is one of the major economic challenges facing most countries in the world especially those in African including Ghana. Therefore, forecasting inflation rates in Ghana becomes very important for its government to design economic strategies or effective monetary policies to combat any unexpected high inflation in this country. This paper studies seasonal autoregressive integrated moving average model to forecast inflation rates in Ghana. Using monthly inflation data from July 1991 to December 2009, we find that ARIMA (1,1,1)(0,0,1)12 can represent the data behavior of inflation rate in Ghana well. Based on the selected model, we forecast seven (7) months inflation rates of Ghana outside the sample period (i.e. from January 2010 to July 2010). The observed inflation rate from January to April which was published by Ghana Statistical Service Department fall within the 95% confidence interval obtained from the designed model. The forecasted results show a decreasing pattern and a turning point of Ghana inflation in the month of July.