Modelling tyre-road noise with data mining techniques

The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and uneven- ness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.

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

Big data e data analysis: visualização de informação

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 data mining engine

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

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

Couple's willingness to donate embryos for research: a longitudinal study

Introduction. Decision-making on embryo disposition is a source of distress and is subject to change over time. This paper analyses the willingness of couples undergoing in vitro fertilization to donate cryopreserved embryos for research from 15 days after embryo transfer to 12 months later, taking into account the influence of psychosocial, demographic, and reproductive factors. Materials and methods. Prospective longitudinal study, with 74 heterosexual couples undergoing in vitro fertilization in a public fertility centre in Portugal, recruited between 2011 and 2012. Participants were evaluated twice: 15 days after embryo transfer and 12 months later. Results. A significant decrease in patients’ willingness to donate embryos for research over time was observed [86.5% to 73.6%; relative risk (RR) = 0.85; 95% CI 0.76–0.95]. A higher education level (>12 years) [adjusted RR (RRadj) = 0.79; 95% CI 0.64–0.96], considering research on human embryos to be important (vs. very important) (RRadj = 0.59; 95% CI 0.39–0.85) and practicing a religion less than once a month (vs. at least once a month) (RRadj = 0.73; 95% CI 0.53–1.00) seemed associated with unwillingness to donate embryos for research over time. Change towards non-donation happened mainly among couples who first considered that it was better to donate than wasting the embryos. Change towards donation occurred mostly among those stating that their priority at time 1 was to have a baby and who became pregnant in the meantime. Conclusions. Quality of care guided by patients’ characteristics, values, preferences, and needs calls for considering the factors and reasons underlying couples’ willingness to donate embryos for research over time as a topic in psychosocial guidelines for infertility and medically assisted reproductive care.

Factors associated with the donation and non-donation of embryos for research: a systematic review

Background: Systematic knowledge on the factors that influence the decisions of IVF users regarding embryo donation for research is a core need for patient-centred policies and ethics in clinical practice. However, no systematic review has been provided on the motivations of patients who must decide embryo disposition. This paper fills this gap, presenting a systematic review of quantitative and qualitative studies, which synthesizes the current body of knowledge on the factors and reasons associated with IVF patients’ decisions to donate or not to donate embryos for research. Methods: A systematic search of studies indexed in PubMed, ISIWoK and PsycINFO, published before November 2013, was conducted. Only empirical, peer-reviewed, full-length, original studies reporting data on factors and reasons associated with the decision concerning donation or non-donation of embryos for research were included. Eligibility and data extraction were performed by two independent researchers and disagreements were resolved by discussion or a third reviewer, if required. The main quantitative findings were extracted and synthesized and qualitative data were assessed by thematic content analysis. Results: A total of 39 studies met the inclusion criteria and were included in the review. More than half of the studies (n ¼ 21) used a quantitative methodology, and the remaining were qualitative (n ¼ 15) or mixed-methods (n ¼ 3) studies. The studies were derived mainly from European countries (n ¼ 18) and the USA(n ¼ 11). The proportion of IVF users who donated embryos for research varied from 7% in a study in France to 73% in a Swiss study. Those who donate embryos for research reported feelings of reciprocity towards science and medicine, positive views of research and high levels of trust in the medical system. They described their decision as better than the destruction of embryos and as an opportunity to help others or to improve health and IVF treatments. The perception of risks, the lack of information concerning research projects and the medical system and the conceptualization of embryos in terms of personhood were the most relevant motives for not donating embryos for research. Results relating to the influence of sociodemographic characteristics and reproductive and gynaecological history were mostly inconclusive. Conclusions: Three iterative and dynamic dimensions of the IVF patients’ decision to donate or not to donate embryos for research emerged from this review: the hierarquization of the possible options regarding embryo disposition, according to the moral, social and instrumental status attributed to embryos; patients’ understanding of expectations and risks of the research on human embryos; and patients’ experiences of information exchange and levels of trust in the medical-scientific institutions.

Pervasive decision support to predict football corners and goals by means of data mining

Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.

From perfect strangers to research partners: a study of relationships, resource sharing and perceived benefits within R&D cooperation networks

Doctoral thesis in Marketing and Strategy.