Acquisition and data analysis related to changes in some forest soils properties after prescribed fire action

Controlled fires in forest areas are frequently used in most Mediterranean countries as a preventive technique to avoid severe wildfires in summer season. In Portugal, this forest management method of fuel mass availability is also used and has shown to be beneficial as annual statistical reports confirm that the decrease of wildfires occurrence have a direct relationship with the controlled fire practice. However prescribed fire can have serious side effects in some forest soil properties. This work shows the changes that occurred in some forest soils properties after a prescribed fire action. The experiments were carried out in soil cover over a natural site of Andaluzitic schist, in Gramelas, Caminha, Portugal, that had not been burn for four years. The composed soil samples were collected from five plots at three different layers (0-3cm, 3-6cm and 6-18cm) during a three-year monitoring period after the prescribed burning. Principal Component Analysis was used to reach the presented conclusions.

Acqusition and data analysis of the eco-efficiency for the establishment of indicators parameters for industrial environmental sustainability

The industrial activity is inevitably associated with a certain degradation of the environmental quality, because is not possible to guarantee that a manufacturing process can be totally innocuous. The eco-efficiency concept is globally accepted as a philosophy of entreprise management, that encourages the companies to become more competitive, innovative and environmentally responsible by promoting the link between its companies objectives for excellence and its objectives of environmental excellence issues. This link imposes the creation of an organizational methodology where the performance of the company is concordant with the sustainable development. The main propose of this project is to apply the concept of eco-efficiency to the particular case of the metallurgical and metal workshop industries through the development of the particular indicators needed and to produce a manual of procedures for implementation of the accurate solution.

Data analysis applied to the evaluation of a technological nature teaching model as a regional development tool

This paper presents the creation and development of technological schools directly linked to the business community and to higher public education. Establishing themselves as the key interface between the two sectors they make a signigicant contribution by having a greater competitive edge when faced with increasing competition in the tradional markets. The development of new business strategies supported by references of excellence, quality and competitiveness also provides a good link between the estalishment of partnerships aiming at the qualification of education boards at a medium level between the technological school and higher education with a technological foundation. We present a case study as an example depicting the success of Escola Tecnológica de Vale de Cambra.

Amygdala activation in response to 2D and 3D emotion-inducing stimuli

Studying changes in brain activation according to the valence of emotion-inducing stimuli is essential in the research on emotions. Due to the ecological potential of virtual reality, it is also important to examine whether brain activation in response to emotional stimuli can be modulated by the three-dimensional (3D) properties of the images. This study uses functional Magnetic Resonance Imaging to compare differences between 3D and standard (2D) visual stimuli in the activation of emotion-related brain areas. The stimuli were organized in three virtual-reality scenarios, each with a different emotional valence (pleasant, unpleasant and neutral). The scenarios were presented in a pseudo-randomized order in the two visualization modes to twelve healthy males. Data were analyzed through a GLM-based fixed effects procedure. Unpleasant and neutral stimuli activated the right amygdala more strongly when presented in 3D than in 2D. These results suggest that 3D stimuli, when used as “building blocks” for virtual environments, can induce increased emotional loading, as shown here through neuroimaging.

A Distributed System for the Dissemination of DGPS Data through the Internet

In this paper we describe a low cost distributed system intended to increase the positioning accuracy of outdoor navigation systems based on the Global Positioning System (GPS). Since the accuracy of absolute GPS positioning is insufficient for many outdoor navigation tasks, another GPS based methodology – the Differential GPS (DGPS) – was developed in the nineties. The differential or relative positioning approach is based on the calculation and dissemination of the range errors of the received GPS satellites. GPS/DGPS receivers correlate the broadcasted GPS data with the DGPS corrections, granting users increased accuracy. DGPS data can be disseminated using terrestrial radio beacons, satellites and, more recently, the Internet. Our goal is to provide mobile platforms within our campus with DGPS data for precise outdoor navigation. To achieve this objective, we designed and implemented a three-tier client/server distributed system that, first, establishes Internet links with remote DGPS sources and, then, performs campus-wide dissemination of the obtained data. The Internet links are established between data servers connected to remote DGPS sources and the client, which is the data input module of the campus-wide DGPS data provider. The campus DGPS data provider allows the establishment of both Intranet and wireless links within the campus. This distributed system is expected to provide adequate support for accurate outdoor navigation tasks.

Implementation of a Campus wide DGPS Data Server

Although the Navigation Satellite Timing and Ranging (NAVSTAR) Global Positioning System (GPS) is, de facto, the standard positioning system used in outdoor navigation, it does not provide, per se, all the features required to perform many outdoor navigational tasks. The accuracy of the GPS measurements is the most critical issue. The quest for higher position readings accuracy led to the development, in the late nineties, of the Differential Global Positioning System (DGPS). The differential GPS method detects the range errors of the GPS satellites received and broadcasts them. The DGPS/GPS receivers correlate the DGPS data with the GPS satellite data they are receiving, granting users increased accuracy. DGPS data is broadcasted using terrestrial radio beacons, satellites and, more recently, the Internet. Our goal is to have access, within the ISEP campus, to DGPS correction data. To achieve this objective we designed and implemented a distributed system composed of two main modules which are interconnected: a distributed application responsible for the establishment of the data link over the Internet between the remote DGPS stations and the campus, and the campus-wide DGPS data server application. The DGPS data Internet link is provided by a two-tier client/server distributed application where the server-side is connected to the DGPS station and the client-side is located at the campus. The second unit, the campus DGPS data server application, diffuses DGPS data received at the campus via the Intranet and via a wireless data link. The wireless broadcast is intended for DGPS/GPS portable receivers equipped with an air interface and the Intranet link is provided for DGPS/GPS receivers with just a RS232 DGPS data interface. While the DGPS data Internet link servers receive the DGPS data from the DGPS base stations and forward it to the DGPS data Internet link client, the DGPS data Internet link client outputs the received DGPS data to the campus DGPS data server application. The distributed system is expected to provide adequate support for accurate (sub-metric) outdoor campus navigation tasks. This paper describes in detail the overall distributed application.

Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical, Bacteriological and Ecotoxicological Data

This study aims to optimize the water quality monitoring of a polluted watercourse (Leça River, Portugal) through the principal component analysis (PCA) and cluster analysis (CA). These statistical methodologies were applied to physicochemical, bacteriological and ecotoxicological data (with the marine bacterium Vibrio fischeri and the green alga Chlorella vulgaris) obtained with the analysis of water samples monthly collected at seven monitoring sites and during five campaigns (February, May, June, August, and September 2006). The results of some variables were assigned to water quality classes according to national guidelines. Chemical and bacteriological quality data led to classify Leça River water quality as “bad” or “very bad”. PCA and CA identified monitoring sites with similar pollution pattern, giving to site 1 (located in the upstream stretch of the river) a distinct feature from all other sampling sites downstream. Ecotoxicity results corroborated this classification thus revealing differences in space and time. The present study includes not only physical, chemical and bacteriological but also ecotoxicological parameters, which broadens new perspectives in river water characterization. Moreover, the application of PCA and CA is very useful to optimize water quality monitoring networks, defining the minimum number of sites and their location. Thus, these tools can support appropriate management decisions.

Adherend thickness effect on the tensile fracture toughness of a structural adhesive using an optical data acquisition method

Adhesive bonding is nowadays a serious candidate to replace methods such as fastening or riveting, because of attractive mechanical properties. As a result, adhesives are being increasingly used in industries such as the automotive, aerospace and construction. Thus, it is highly important to predict the strength of bonded joints to assess the feasibility of joining during the fabrication process of components (e.g. due to complex geometries) or for repairing purposes. This work studies the tensile behaviour of adhesive joints between aluminium adherends considering different values of adherend thickness (h) and the double-cantilever beam (DCB) test. The experimental work consists of the definition of the tensile fracture toughness (GIC) for the different joint configurations. A conventional fracture characterization method was used, together with a J-integral approach, that take into account the plasticity effects occurring in the adhesive layer. An optical measurement method is used for the evaluation of crack tip opening and adherends rotation at the crack tip during the test, supported by a Matlab® sub-routine for the automated extraction of these quantities. As output of this work, a comparative evaluation between bonded systems with different values of adherend thickness is carried out and complete fracture data is provided in tension for the subsequent strength prediction of joints with identical conditions.

Analysis of Forest Fires by means of Pseudo Phase Plane and Multidimensional Scaling Methods

Forest fires dynamics is often characterized by the absence of a characteristic length-scale, long range correlations in space and time, and long memory, which are features also associated with fractional order systems. In this paper a public domain forest fires catalogue, containing information of events for Portugal, covering the period from 1980 up to 2012, is tackled. The events are modelled as time series of Dirac impulses with amplitude proportional to the burnt area. The time series are viewed as the system output and are interpreted as a manifestation of the system dynamics. In the first phase we use the pseudo phase plane (PPP) technique to describe forest fires dynamics. In the second phase we use multidimensional scaling (MDS) visualization tools. The PPP allows the representation of forest fires dynamics in two-dimensional space, by taking time series representative of the phenomena. The MDS approach generates maps where objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to better understand forest fires behaviour.

Quality of Life Predictors and Normative Data

This study identifies predictors and normative data for quality of life (QOL) in a sample of Portuguese adults from general population. A cross-sectional correlational study was undertaken with two hundred and fifty-five (N = 255) individuals from Portuguese general population (mean age 43 years, range 25–84 years; 148 females, 107 males). Participants completed the European Portuguese version of the World Health Organization Quality of Life short-form instrument and the European Portuguese version of the Center for Epidemiologic Studies Depression Scale. Demographic information was also collected. Portuguese adults reported their QOL as good. The physical, psychological and environmental domains predicted 44 % of the variance of QOL. The strongest predictor was the physical domain and the weakest was social relationships. Age, educational level, socioeconomic status and emotional status were significantly correlated with QOL and explained 25 % of the variance of QOL. The strongest predictor of QOL was emotional status followed by education and age. QOL was significantly different according to: marital status; living place (mainland or islands); type of cohabitants; occupation; health. The sample of adults from general Portuguese population reported high levels of QOL. The life domain that better explained QOL was the physical domain. Among other variables, emotional status best predicted QOL. Further variables influenced overall QOL. These findings inform our understanding on adults from Portuguese general population QOL and can be helpful for researchers and practitioners using this assessment tool to compare their results with normative data

An Approach To Publish a Data Warehouse Content as Linked Data

Mestrado em Engenharia Informática - Área de Especialização em Tecnologias do Conhecimento e Decisão

Development of a system to monitor surfer’s balance and plantar pressure and surfboard’s movement

O uso da tecnologia tem crescido nas últimas décadas nas mais diversas áreas, seja na indústria ou no dia-a-dia, e é cada vez mais evidente os benefícios que traz. No desporto não é diferente. Cada dia surgem novos desenvolvimentos objetivando a melhoria do desempenho dos praticantes de atividades físicas, possibilitando atingir resultados nunca antes pensados. Além disto, a utilização da tecnologia no desporto permite a obtenção de dados biomecânicos que podem ser utilizados tanto no treinamento quando na melhoria da qualidade de vida dos atletas auxiliando na prevenção de lesões, por exemplo. Deste modo, o presente projeto se aplica na área do desporto, nomeadamente, na modalidade do surfe, onde a ausência de trabalhos científicos ainda é elevada, aliando a tecnologia eletrônica ao desporto para quantificar informações até então desconhecidas. Três fatores básicos de desempenho foram levantados, sendo eles: equilíbrio, posicionamento dos pés e movimentação da prancha de surfe. Estes fatores levaram ao desenvolvimento de um sistema capaz de medi-los dinamicamente através da medição das forças plantares e da rotação da prancha de surfe. Além da medição dos fatores, o sistema é capaz de armazenar os dados adquiridos localmente através de um cartão de memória, para posterior análise; e também enviá-los através de uma comunicação sem fio, permitindo a visualização do centro de pressões plantares; dos ângulos de rotação da prancha de surfe; e da ativação dos sensores; em tempo real. O dispositivo consiste em um sistema eletrônico embarcado composto por um microcontrolador ATMEGA1280; um circuito de aquisição e condicionamento de sinal analógico; uma central inercial; um módulo de comunicação sem fio RN131; e um conjunto de sensores de força Flexiforce. O firmware embarcado foi desenvolvido em linguagem C. O software Matlab foi utilizado para receção de dados e visualização em tempo real. Os testes realizados demostraram que o funcionamento do sistema atende aos requisitos propostos, fornecendo informação acerca do equilíbrio, através do centro de pressões; do posicionamento dos pés, através da distribuição das pressões plantares; e do movimento da prancha nos eixos pitch e roll, através da central inercial. O erro médio de medição de força verificado foi de -0.0012 ± 0.0064 N, enquanto a mínima distância alcançada na transmissão sem fios foi de 100 m. A potência medida do sistema foi de 330 mW.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.