Feature Nets: behavioural modelling of software product lines

Software product lines (SPL) are diverse systems that are developed using a dual engineering process: (a)family engineering defines the commonality and variability among all members of the SPL, and (b) application engineering derives specific products based on the common foundation combined with a variable selection of features. The number of derivable products in an SPL can thus be exponential in the number of features. This inherent complexity poses two main challenges when it comes to modelling: Firstly, the formalism used for modelling SPLs needs to be modular and scalable. Secondly, it should ensure that all products behave correctly by providing the ability to analyse and verify complex models efficiently. In this paper we propose to integrate an established modelling formalism (Petri nets) with the domain of software product line engineering. To this end we extend Petri nets to Feature Nets. While Petri nets provide a framework for formally modelling and verifying single software systems, Feature Nets offer the same sort of benefits for software product lines. We show how SPLs can be modelled in an incremental, modular fashion using Feature Nets, provide a Feature Nets variant that supports modelling dynamic SPLs, and propose an analysis method for SPL modelled as Feature Nets. By facilitating the construction of a single model that includes the various behaviours exhibited by the products in an SPL, we make a significant step towards efficient and practical quality assurance methods for software product lines.

Modelação numérica de túneis com consideração explícita das heterogeneidades do maciço

Dissertação de mestrado integrado em Engenharia Civil

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.

Predicting SO2 pollution incidents by means of additive models with optimum variable selection

The aim of this paper is to predict time series of SO2 concentrations emitted by coal-fired power stations in order to estimate in advance emission episodes and analyze the influence of some meteorological variables in the prediction. An emission episode is said to occur when the series of bi-hourly means of SO2 is greater than a specific level. For coal-fired power stations it is essential to predict emission epi- sodes sufficiently in advance so appropriate preventive measures can be taken. We proposed a meth- odology to predict SO2 emission episodes based on using an additive model and an algorithm for variable selection. The methodology was applied to the estimation of SO2 emissions registered in sampling lo- cations near a coal-fired power station located in Northern Spain. The results obtained indicate a good performance of the model considering only two terms of the time series and that the inclusion of the meteorological variables in the model is not significant.

A critical evaluation of methods for the reconstruction of tissue-specific models

Under the framework of constraint based modeling, genome-scale metabolic models (GSMMs) have been used for several tasks, such as metabolic engineering and phenotype prediction. More recently, their application in health related research has spanned drug discovery, biomarker identification and host-pathogen interactions, targeting diseases such as cancer, Alzheimer, obesity or diabetes. In the last years, the development of novel techniques for genome sequencing and other high-throughput methods, together with advances in Bioinformatics, allowed the reconstruction of GSMMs for human cells. Considering the diversity of cell types and tissues present in the human body, it is imperative to develop tissue-specific metabolic models. Methods to automatically generate these models, based on generic human metabolic models and a plethora of omics data, have been proposed. However, their results have not yet been adequately and critically evaluated and compared. This work presents a survey of the most important tissue or cell type specific metabolic model reconstruction methods, which use literature, transcriptomics, proteomics and metabolomics data, together with a global template model. As a case study, we analyzed the consistency between several omics data sources and reconstructed distinct metabolic models of hepatocytes using different methods and data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are even contradictory. Additionally, the hepatocyte metabolic models generated are in many cases not able to perform metabolic functions known to be present in the liver tissue. We conclude that reliable methods for a priori omics data integration are required to support the reconstruction of complex models of human cells.

Introduction [to Contact force models for multibody dynamics]

"Series: Solid mechanics and its applications, vol. 226"

Pure elastic contact force models

"Series: Solid mechanics and its applications, vol. 226"

Dissipative contact force models

"Series: Solid mechanics and its applications, vol. 226"

Contact force models for multibody dynamics

"Series: Solid mechanics and its applications, vol. 226"

An evolutionary approach to the use of Petri net based models: from parallel controllers to HW/SW co-design

"A workshop within the 19th International Conference on Applications and Theory of Petri Nets - ICATPN’1998"

Sex differences in the fetal heart rate variability indices of twins

Aims: To evaluate the differences in linear and complex heart rate dynamics in twin pairs according to fetal sex combination [male-female (MF), male-male (MM), and female-female (FF)]. Methods: Fourteen twin pairs (6 MF, 3 MM, and 5 FF) were monitored between 31 and 36.4 weeks of gestation. Twenty-six fetal heart rate (FHR) recordings of both twins were simultaneously acquired and analyzed with a system for computerized analysis of cardiotocograms. Linear and nonlinear FHR indices were calculated. Results: Overall, MM twins presented higher intrapair average in linear indices than the other pairs, whereas FF twins showed higher sympathetic-vagal balance. MF twins exhibited higher intrapair average in entropy indices and MM twins presented lower entropy values than FF twins considering the (automatically selected) threshold rLu. MM twin pairs showed higher intrapair differences in linear heart rate indices than MF and FF twins, whereas FF twins exhibited lower intrapair differences in entropy indices. Conclusions: The results of this exploratory study suggest that twins have sex-specific differences in linear and nonlinear indices of FHR. MM twins expressed signs of a more active autonomic nervous system and MF twins showed the most active complexity control system. These results suggest that fetal sex combination should be taken into consideration when performing detailed evaluation of the FHR in twins.

Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.

Effect of oscillatory breathing on the variability of the RR Intervals and its prognostic importance in individuals with left ventricular global systolic dysfunction

OBJECTIVE: To assess the effect of the oscillatory breathing on the variability of RR intervals (VRR) and on prognostic significance after one year follow-up in subjects with left ventricular global systolic dysfunction. METHODS: We studied 76 subjects, whose age ranged from 40 to 80 years, paired for age and gender, divided into two groups: group I - 34 healthy subjects; group II - 42 subjects with left ventricular global systolic dysfunction (ejection fraction < 0.40). The ECG signals were acquired during 600s in supine position, and analyzed the variation of the thoracic amplitude and the VRR. Clinical and V-RR variables were applied into a logistic multivariate model to foretell survival after one year follow-up. RESULTS: Oscillatory breathing was detected in 35.7% of subjects in vigil state of group II, with a concentration of the spectral power in the very low frequency band, and was independent of the presence of diabetes, functional class, ejection fraction, cause of ventricular dysfunction and survival after one year follow-up. In the logistic regression model, ejection fraction was the only independent variable to predict survival. CONCLUSION: 1) Oscillatory breathing pattern is frequent during wakefulness in the left ventricular global systolic dysfunction and concentrates spectral power in the very low band of V-RR; 2) it does not relate to severity and cause of left ventricular dysfunction; 3) ejection fraction is the only independent predictive variable for survival in this group of subjects.

Modelos Mixtos-Aditivos Generalizados para el Estudio de Fenómenos Biológicos y Sociales Descriptos por Datos Agregados y/o Longitudinales. Generalized Mixed Additive Models for the Study of Biological and Social Phenomenons from Longitudinal and/or Aggregated Data.

Este proyecto propone extender y generalizar los procesos de estimación e inferencia de modelos aditivos generalizados multivariados para variables aleatorias no gaussianas, que describen comportamientos de fenómenos biológicos y sociales y cuyas representaciones originan series longitudinales y datos agregados (clusters). Se genera teniendo como objeto para las aplicaciones inmediatas, el desarrollo de metodología de modelación para la comprensión de procesos biológicos, ambientales y sociales de las áreas de Salud y las Ciencias Sociales, la condicionan la presencia de fenómenos específicos, como el de las enfermedades.Es así que el plan que se propone intenta estrechar la relación entre la Matemática Aplicada, desde un enfoque bajo incertidumbre y las Ciencias Biológicas y Sociales, en general, generando nuevas herramientas para poder analizar y explicar muchos problemas sobre los cuales tienen cada vez mas información experimental y/o observacional.Se propone, en forma secuencial, comenzando por variables aleatorias discretas (Yi, con función de varianza menor que una potencia par del valor esperado E(Y)) generar una clase unificada de modelos aditivos (paramétricos y no paramétricos) generalizados, la cual contenga como casos particulares a los modelos lineales generalizados, no lineales generalizados, los aditivos generalizados, los de media marginales generalizados (enfoques GEE1 -Liang y Zeger, 1986- y GEE2 -Zhao y Prentice, 1990; Zeger y Qaqish, 1992; Yan y Fine, 2004), iniciando una conexión con los modelos lineales mixtos generalizados para variables latentes (GLLAMM, Skrondal y Rabe-Hesketh, 2004), partiendo de estructuras de datos correlacionados. Esto permitirá definir distribuciones condicionales de las respuestas, dadas las covariables y las variables latentes y estimar ecuaciones estructurales para las VL, incluyendo regresiones de VL sobre las covariables y regresiones de VL sobre otras VL y modelos específicos para considerar jerarquías de variación ya reconocidas. Cómo definir modelos que consideren estructuras espaciales o temporales, de manera tal que permitan la presencia de factores jerárquicos, fijos o aleatorios, medidos con error como es el caso de las situaciones que se presentan en las Ciencias Sociales y en Epidemiología, es un desafío a nivel estadístico. Se proyecta esa forma secuencial para la construcción de metodología tanto de estimación como de inferencia, comenzando con variables aleatorias Poisson y Bernoulli, incluyendo los existentes MLG, hasta los actuales modelos generalizados jerárquicos, conextando con los GLLAMM, partiendo de estructuras de datos correlacionados. Esta familia de modelos se generará para estructuras de variables/vectores, covariables y componentes aleatorios jerárquicos que describan fenómenos de las Ciencias Sociales y la Epidemiología.

Variabilidad paleoambiental desde el Último Máximo Glacial en la región Centro-Sur de Argentina: Paleocirculación atmosférica, variabilidad hidroclimática y dimensión humana. Palaeoenvironmental variability from the Last Glacial Maximum in the Central-Southern region of Argentina: Atmospheric Palaeocirculation, Hydroclimatic Variability and Human Dimension.

El proyecto tiene como propósito caracterizar la variabilidad de la paleocirculación atmosférica en las latitudes medias de Sudamérica, su efecto sobre la fluctuación hidroclimática regional y la vulnerabilidad humana frente a los cambios ocurridos desde el Ultimo Máximo Glacial/Holoceno. El enfoque inter y multidisciplinaro aquí planteado para analizar la varibiliad hidroclimática pasada, sus causas y consecuencias, es inédito para esta región del país. El mismo contempla: a) análisis de archivos climáticos sedimentarios con una aproximación de multi-indicadores (sedimentología, geoquímica, isótopos estables y radiogénicos, mineralogía, ostrácodos y moluscos); b) determinación de la dinámica actual y pasada del polvo atmosférico (PA) combinando mediciones in situ y en registros sedimentarios y c) análisis de restos óseos humanos y malacológicos en sitios arqueológicos.Se contempla: a) Efectuar análisis de multi-indicadores de registros climáticos naturales almacenados en sistemas lacustres de la región Pampeana (S. Ambargasta, Mar Chiquita, Pocho, Melincué, Lagunas Encadenadas del Oeste de Buenos Aires) y en secuencias loessicas para inferir la variabilidad de la circulación atmosférica desde el UMG; b) Ampliar la resolución temporal de las reconstrucciones climáticas para ventanas de tiempo seleccionadas; c) Analizar la señal geoquímica del registro sedimentario de fases climáticas contrastantes; d) Identificar la variabilidad temporal de la procedencia y de los procesos actuantes mediante análisis mineralógicos y geoquímicos; e) Analizar el ambiente actual para calibrar indicadores ambientales o proxies (isótopos, flujo de sedimentos, geoquímica, moluscos y ostrácodos) con el escenario climático contemporáneo; f) Analizar en conjunto los archivos climáticos para inferir patrones de paleocirculación atmosférica regional y g) Dilucidar estrategias adaptativas y la historia biológica de poblaciones humanas en la región central de Argentina durante fases climáticas diversas.Este proyecto aborda uno de los aspectos menos conocidos de las reconstrucciones paleoambientales, que está relacionado con rol del material eólico derivado del Hemisferio Sur y el impacto que genera sobre el ciclo regional del Carbono. A pesar que el sur de Sudamérica es una de las áreas claves para entender este aspecto, no se conoce de forma acabada la incidencia de los cambios ambientales sobre el flujo de PA o el efecto de futuros cambios climáticos y/o uso de la tierra.La actividad planteada tiene implicancias directas sobre múltiples disciplinas como las ciencias atmosféricas, geoquímica, sedimentología, paleoclimatologia y bioarqueología. Nuestros resultados permitirán mejorar el entendimiento del cambio climático regional, la dinámica del polvo y su rol como forzante del sistema climático, la variabilidad hidrológica presente y pasada y la respuesta por parte de las poblaciones humanas. Profundizar el estudio de los cambios paleoclimáticos y bioarqueológicos en la región permitirá analizar la variabilidad hidroclimática y determinar su relación con las situaciones de crisis y vulnerabilidad del pobamiento humano. Asimismo, la inferencia de cambios para períodos con mínima o sin influencia humana es una herramienta clave para mejorar el conocimiento de las fluctuaciones climáticas del área extratropical Sudamericana. Estos resultados permitirán analizar no sólo los mecanismos operados en el sistema climático pasado sino también aquellos factores que explicarían el gran cambio hidroclimático registrado desde 1970 cuyos efectos han impactado claramente sobre las actividades socio-económicos en la región central Argentina.