A bayesian approach for NDT data fusion: The Saint Torcato Church case study

This paper presents a methodology based on the Bayesian data fusion techniques applied to non-destructive and destructive tests for the structural assessment of historical constructions. The aim of the methodology is to reduce the uncertainties of the parameter estimation. The Young's modulus of granite stones was chosen as an example for the present paper. The methodology considers several levels of uncertainty since the parameters of interest are considered random variables with random moments. A new concept of Trust Factor was introduced to affect the uncertainty related to each test results, translated by their standard deviation, depending on the higher or lower reliability of each test to predict a certain parameter.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

New integrative computational approaches unveil the Saccharomyces cerevisiae pheno-metabolomic fermentative profile and allow strain selection for winemaking

During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds are formed. The objective of the present work was to adapt computational approaches to analyze pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and genetic characterization together with individual must fermentations were performed, and metabolites relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of most-correlated features of noteworthy biological importance. The present method allowed, as a breakthrough, to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties in comparing different types of data. Therefore, the proposed computational approach revealed as successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative conditions. This will allow the identification of combined relevant features with application in selection of good winemaking strains.

Implementation of a probabilistic based framework for safety assessment of existing timber elements

The assessment of existing timber structures is often limited to information obtained from non or semi destructive testing, as mechanical testing is in many cases not possible due to its destructive nature. Therefore, the available data provides only an indirect measurement of the reference mechanical properties of timber elements, often obtained through empirical based correlations. Moreover, the data must result from the combination of different tests, as to provide a reliable source of information for a structural analysis. Even if general guidelines are available for each typology of testing, there is still a need for a global methodology allowing to combine information from different sources and infer upon that information in a decision process. In this scope, the present work presents the implementation of a probabilistic based framework for safety assessment of existing timber elements. This methodology combines information gathered in different scales and follows a probabilistic framework allowing for the structural assessment of existing timber elements with possibility of inference and updating of its mechanical properties, through Bayesian methods. The probabilistic based framework is based in four main steps: (i) scale of information; (ii) measurement data; (iii) probability assignment; and (iv) structural analysis. In this work, the proposed methodology is implemented in a case study. Data was obtained through a multi-scale experimental campaign made to old chestnut timber beams accounting correlations of non and semi-destructive tests with mechanical properties. Finally, different inference scenarios are discussed aiming at the characterization of the safety level of the elements.

Industrial engineering and management curriculum profile: Developing a framework of competences

This paper presents a framework of competences developed for Industrial Engineering and Management that can be used as a tool for curriculum analysis and design, including the teaching and learning processes as well as the alignment of the curriculum with the professional profile. The framework was applied to the Industrial Engineering and Management program at University of Minho (UMinho), Portugal, and it provides an overview of the connection between IEM knowledge areas and the competences defined in its curriculum. The framework of competences was developed through a process of analysis using a combination of methods and sources for data collection. The framework was developed according to four main steps: 1) characterization of IEM knowledge areas; 2) definition of IEM competences; 3) survey; 4) application of the framework at the IEM curriculum. The findings showed that the framework is useful to build an integrated vision of the curriculum. The most visible aspect in the learning outcomes of IEM program is the lack of balance between technical and transversal competences. There was not almost any reference to the transversal competences and it is fundamentally concentrated on Project-Based Learning courses. The framework presented in this paper provides a contribution to the definition of IEM professional profile through a set of competences which need to be explored further. In addition, it may be a relevant tool for IEM curriculum analysis and a contribution for bridging the gap between universities and companies.

Step towards multiplatform framework for supporting pediatric and neonatology care unit decision process

Children are an especially vulnerable population, particularly in respect to drug administration. It is estimated that neonatal and pediatric patients are at least three times more vulnerable to damage due to adverse events and medication errors than adults are. With the development of this framework, it is intended the provision of a Clinical Decision Support System based on a prototype already tested in a real environment. The framework will include features such as preparation of Total Parenteral Nutrition prescriptions, table pediatric and neonatal emergency drugs, medical scales of morbidity and mortality, anthropometry percentiles (weight, length/height, head circumference and BMI), utilities for supporting medical decision on the treatment of neonatal jaundice and anemia and support for technical procedures and other calculators and widespread use tools. The solution in development means an extension of INTCare project. The main goal is to provide an approach to get the functionality at all times of clinical practice and outside the hospital environment for dissemination, education and simulation of hypothetical situations. The aim is also to develop an area for the study and analysis of information and extraction of knowledge from the data collected by the use of the system. This paper presents the architecture, their requirements and functionalities and a SWOT analysis of the solution proposed.

Search for a Charged Higgs Boson Produced in the Vector-boson Fusion Mode with Decay H±→W±Z using pp Collisions at s√=8 TeV with the ATLAS Experiment

A search for a charged Higgs boson, H±, decaying to a W± boson and a Z boson is presented. The search is based on 20.3 fb−1 of proton-proton collision data at a center-of-mass energy of 8 TeV recorded with the ATLAS detector at the LHC. The H± boson is assumed to be produced via vector-boson fusion and the decays W±→qq′¯ and Z→e+e−/μ+μ− are considered. The search is performed in a range of charged Higgs boson masses from 200 to 1000 GeV. No evidence for the production of an H± boson is observed. Upper limits of 31--1020 fb at 95% CL are placed on the cross section for vector-boson fusion production of an H± boson times its branching fraction to W±Z. The limits are compared with predictions from the Georgi-Machacek Higgs Triplet Model.

An innovative framework for probabilistic-based structural assessment with an application to existing reinforced concrete structures

A novel framework for probabilistic-based structural assessment of existing structures, which combines model identification and reliability assessment procedures, considering in an objective way different sources of uncertainty, is presented in this paper. A short description of structural assessment applications, provided in literature, is initially given. Then, the developed model identification procedure, supported in a robust optimization algorithm, is presented. Special attention is given to both experimental and numerical errors, to be considered in this algorithm convergence criterion. An updated numerical model is obtained from this process. The reliability assessment procedure, which considers a probabilistic model for the structure in analysis, is then introduced, incorporating the results of the model identification procedure. The developed model is then updated, as new data is acquired, through a Bayesian inference algorithm, explicitly addressing statistical uncertainty. Finally, the developed framework is validated with a set of reinforced concrete beams, which were loaded up to failure in laboratory.

Caso de demonstração de uma framework para automatização do teste de APIs de aplicações SaaS

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

Text mining na análise de sentimentos em contextos de big data

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

Enquadramento para a compreensão dos impactos do Open Government Data

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

Data analytics: abordagem para visualização da informação

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

Pervasive business intelligence: a marketing intelligence framework proposal

Doctoral Thesis in Information Systems and Technologies Area of Information Systems and Technology