Comparing different methods to measure biofilm thickness: the techniques are: optical coherence tomography, confocal laser scanning microscopy and low load compression test

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Engenharia Clínica)

Poker learner: modelação de jogadores através de data mining

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

Implementation and validation of a strain rate dependent anisotropic continuum model for masonry

A newly developed strain rate dependent anisotropic continuum model is proposed for impact and blast applications in masonry. The present model adopted the usual approach of considering different yield criteria in tension and compression. The analysis of unreinforced block work masonry walls subjected to impact is carried out to validate the capability of the model. Comparison of the numerical predictions and test data revealed good agreement. Next, a parametric study is conducted to evaluate the influence of the tensile strengths along the three orthogonal directions and of the wall thickness on the global behavior of masonry walls.

Data supporting the role of enzymes and polysaccharides during cassava postharvest physiological deterioration

This data article is referred to the research article entitled The role of ascorbate peroxidase, guaiacol peroxidase, and polysaccharides in cassava (Manihot esculenta Crantz) roots under postharvest physiological deterioration by Uarrota et al. (2015). Food Chemistry 197, Part A, 737746. The stress duo to PPD of cassava roots leads to the formation of ROS which are extremely harmful and accelerates cassava spoiling. To prevent or alleviate injuries from ROS, plants have evolved antioxidant systems that include non-enzymatic and enzymatic defence systems such as ascorbate peroxidase, guaiacol peroxidase and polysaccharides. In this data article can be found a dataset called newdata, in RData format, with 60 observations and 06 variables. The first 02 variables (Samples and Cultivars) and the last 04, spectrophotometric data of ascorbate peroxidase, guaiacol peroxidase, tocopherol, total proteins and arcsined data of cassava PPD scoring. For further interpretation and analysis in R software, a report is also provided. Means of all variables and standard deviations are also provided in the Supplementary tables (data.long3.RData, data.long4.RData and meansEnzymes.RData), raw data of PPD scoring without transformation (PPDmeans.RData) and days of storage (days.RData) are also provided for data analysis reproducibility in R software.

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.

Characterization of dry-stack interlocking compressed earth blocks

Earth has been a traditional building material to construct houses in Africa. One of the most common techniques is the use of sun dried or kiln fired adobe bricks with mud mortar. Fired bricks are the main cause for deforestation in countries like Malawi. Although this technique is low-cost, the bricks vary largely in shape, strength and durability. This leads to weak houses which suffer considerable damage during floods and seismic events. One solution is the use of dry-stack masonry with stabilized interlocking compressed earth blocks (ICEB). This technology has the potential of substituting the current bricks by a more sustainable kind of block. This study was made in the context of the HiLoTec project, which focuses on houses in rural areas of developing countries. For this study, Malawi was chosen for a case study. This paper presents the experimental results of tests made with dry-stack ICEBs. Soil samples from Malawi were taken and studied. Since the experimental campaign could not be carried out in Malawi, a homogenization process of Portuguese soil was made to produce ICEBs at the University of Minho, Portugal. Then, the compression and tensile strength of the materials was determined via small cylinder samples. Subsequently, the compression and flexural strength of units were determined. Finally, tests to determine the compressive strength of both prisms and masonry wallets and to determine the initial shear strength of the dry interfaces were carried out. This work provides valuable data for low-cost eco-efficient housing

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

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Erratum to: Measurement of the inclusive jet cross-section in proton-proton collisions at s√=7 TeV using 4.5 fb−1 of data with the ATLAS detector

It was found that the non-perturbative corrections calculated using Pythia with the Perugia 2011 tune did not include the effect of the underlying event. The affected correction factors were recomputed using the Pythia 6.427 generator. These corrections are applied as baseline to the NLO pQCD calculations and thus the central values of the theoretical predictions have changed by a few percent with the new corrections. This has a minor impact on the agreement between the data and the theoretical predictions. Figures 2 and 6 to 13, and all the tables have been updated with the new values. A few sentences in the discussion in sections 5.2 and 9 were altered or removed.

Modelling Z→ττ processes in ATLAS with τ-embedded Z→μμ data

This paper describes the concept, technical realisation and validation of a largely data-driven method to model events with Z→ττ decays. In Z→μμ events selected from proton-proton collision data recorded at s√=8 TeV with the ATLAS experiment at the LHC in 2012, the Z decay muons are replaced by τ leptons from simulated Z→ττ decays at the level of reconstructed tracks and calorimeter cells. The τ lepton kinematics are derived from the kinematics of the original muons. Thus, only the well-understood decays of the Z boson and τ leptons as well as the detector response to the τ decay products are obtained from simulation. All other aspects of the event, such as the Z boson and jet kinematics as well as effects from multiple interactions, are given by the actual data. This so-called τ-embedding method is particularly relevant for Higgs boson searches and analyses in ττ final states, where Z→ττ decays constitute a large irreducible background that cannot be obtained directly from data control samples.

Text mining na análise de sentimentos em contextos de big data

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

Big data e data analysis: visualização de informação

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

Integração da área de negócio de retalho do grupo Riopele no Data Warehouse corporativo

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

Forecasting human entrances at a commercial store using facial recognition data

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