The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Estudo e implementação de testes de software em desenvolvimento ágil

Dissertação de mestrado em Engenharia de Sistemas

Frameworks no desenvolvimento de software: o caso da Elevation da PRIMAVERA BSS

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

Algorithms and computational tools for metabolic flux analysis

PhD thesis in Biomedical Engineering

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.

From software extensions to product lines of dataflow programs

Abstract Dataflow programs are widely used. Each program is a directed graph where nodes are computations and edges indicate the flow of data. In prior work, we reverse-engineered legacy dataflow programs by deriving their optimized implementations from a simple specification graph using graph transformations called refinements and optimizations. In MDE-speak, our derivations were PIM-to-PSM mappings. In this paper, we show how extensions complement refinements, optimizations, and PIM-to-PSM derivations to make the process of reverse engineering complex legacy dataflow programs tractable. We explain how optional functionality in transformations can be encoded, thereby enabling us to encode product lines of transformations as well as product lines of dataflow programs. We describe the implementation of extensions in the ReFlO tool and present two non-trivial case studies as evidence of our work’s generality

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.

Implementação de um sistema de previsão e controlo para operação otimizada de um sistema de recolha e armazenamento de água pluvial

Dissertação de mestrado integrado em Engenharia Civil

Recurrence of atrial septal defect in three generations

Beginning with a patient presenting with an atrial septal defect (ASD) of the secundum type, the genealogy was identified in four affected individuals who belonged to three successive generations of the same family. The defects were visually confirmed in all individuals and were found to be anatomically similar. No other congenital malformations were present in these individuals. The genealogy was identified in 1972, when ASD recurred in two generations, and it was concluded that the mechanism of transmission was autosomal recessive. The fifth individual, identified 21 years later, and having an anomaly identical to that of the others, was the child of a couple who had no consaguinity and whose mother was a member of the previously studied genealogy. Considering the absence of phenotype in the parents and the rarity of the ASD gene in the general population, the occurrence of the uniparental disomy for this family nucleus, and the same autosomal recessive mechanism of transmission by this affected individual is possible. This study reports the familial occurrence of ASD by genetic mechanisms of transmission, emphasizing the necessity for genetic-clinical studies in members of the familial nucleus in order to detect new carriers, who usually are asymptomatic, thereby allowing for early and adequate treatment of individuals who may be affected.

Double outlet right ventricle with anterior and left-sided aorta and subpulmonary ventricular septal defect

Double outlet right ventricle (DORV) is a heterogeneous group of abnormal ventriculoarterial connections where, by definition, both great arteries (pulmonary artery and aorta) arise primarily from the morphologically right ventricle. This condition affects 1-1.5% of the patients with congenital heart diseases, with a frequency of 1 in each 10,000 live births. We report the case of an 18-day-old infant with DORV and extremely rare anatomical features, such as anterior and left-sided aorta and subpulmonary ventricular septal defect (VSD). In addition to the anatomic features, the role of the echocardiogram for guiding the diagnosis and the surgical therapy of this congenital heart disease are discussed.

Surgical treatment of partial atrioventricular septal defect: functional analysis of the mitral valve in the postoperative period

OBJECTIVE: To study mitral valve function in the postoperative period after correction of the partial form of atrioventricular septal defect. METHODS: Fifty patients underwent surgical correction of the partial form of atrioventricular septal defect. Their mean age was 11.8 years and 62% of the patients were males. Preoperative echocardiography showed moderate and severe mitral insufficiency in 44% of the patients. The mitral valve cleft was sutured in 45 (90%) patients (group II - GII). Echocardiographies were performed in the early postoperative period, and 6 and 12 months after hospital discharge. RESULTS: The patients who had some type of arrhythmia in the postoperative period had ostium primum atrial septal defect of a larger size (2.74 x 2.08 cm). All 5 patients in group I (GI), who did not undergo closure of the cleft, had a competent mitral valve or mild mitral insufficiency in the preoperative period. One of these patients began to have moderate mitral insufficiency in the postoperative period. On the other hand, in GII, 88.8% and 82.2% of the patients had competent mitral valve or mild mitral insufficiency in the early and late postoperative periods, respectively. CONCLUSION: The mitral valve cleft was repaired in 90% of cases. Echocardiography revealed competent mitral valve or mild mitral insufficiency in 88.8% and 82.2% of GII patients in the early and late postoperative periods, respectively.