Drilling process of composite laminates – a tool based analysis

“Drilling of polymeric matrix composites structures”

Using the TRAILER tool for managing informal learning in academic and professional contexts: the learner perspective

Informal Learning plays an important role in everyone's life and yet we often are unaware of it. The need to keep track of the knowledge acquired through informal learning is increasing as its sources become increasingly diverse. This paper presents a study on a tool developed to help keeping track of learners' informal learning, both within academic and professional contexts, This tool, developed within the European Commission funded TRAILER project, will further integrate the improvements suggested by users during the piloting phase. The two studied contexts were similar regarding the importance and perception of Informal Learning, but differed concerning tool usage. The overall idea of managing one's informal learning was well accepted and welcomed, which validated the emerging need for a tool with this purpose.

A Logic and tool for local reasoning about security protocols

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

National Immunization Program: Computerized System as a tool for new challenges

The scope and coverage of the Brazilian Immunization Program can be compared with those in developed countries because it provides a large number of vaccines and has a considerable coverage. The increasing complexity of the program brings challenges regarding its development, high coverage levels, access equality, and safety. The Immunization Information System, with nominal data, is an innovative tool that can more accurately monitor these indicators and allows the evaluation of the impact of new vaccination strategies. The main difficulties for such a system are in its implementation process, training of professionals, mastering its use, its constant maintenance needs and ensuring the information contained remain confidential. Therefore, encouraging the development of this tool should be part of public health policies and should also be involved in the three spheres of government as well as the public and private vaccination services.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.

Characterization of the volatile fraction emitted by phloems of four pinus species by solid-phase microextraction and gas chromatography–mass spectrometry

Pine forests constitute some of the most important renewable resources supplying timber, paper and chemical industries, among other functions. Characterization of the volatiles emitted by different Pinus species has proven to be an important tool to decode the process of host tree selection by herbivore insects, some of which cause serious economic damage to pines. Variations in the relative composition of the bouquet of semiochemicals are responsible for the outcome of different biological processes, such as mate finding, egg-laying site recognition and host selection. The volatiles present in phloem samples of four pine species, P. halepensis, P. sylvestris, P. pinaster and P. pinea, were identified and characterized with the aim of finding possible host-plant attractants for native pests, such as the bark beetle Tomicus piniperda. The volatile compounds emitted by phloem samples of pines were extracted by headspace solid-phase micro extraction, using a 2 cm 50/30 mm divinylbenzene/carboxen/polydimethylsiloxane table flex solid-phase microextraction fiber and its contents analyzed by high-resolution gas chromatography, using flame ionization and a non polar and chiral column phases. The components of the volatile fraction emitted by the phloem samples were identified by mass spectrometry using time-of-flight and quadrupole mass analyzers. The estimated relative composition was used to perform a discriminant analysis among pine species, by means of cluster and principal component analysis. It can be concluded that it is possible to discriminate pine species based on the monoterpenes emissions of phloem samples.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

A tool to aid institutions recognize their employees competences acquired by informal learning

People do not learn only in formal educational institutions, but also throughout their lives, from their experiences, conversations, observations of others, exploration of the Internet, meetings and conferences, and chance encounters etc. However this informal and non-formal learning can easily remain largely invisible, making it hard for peers and employers to recognize or act upon it. The TRAILER project aims to make this learning visible so that it can benefit both the individual and the organization. The proposed demonstration will show a software solution that (i) helps the learners to capture, organize and classify a wide range of ’informal’ learning taking place in their lives, and (ii) assists the organization in recognizing this learning and use it to help managing human resources (benefiting both parts). This software tool has recently been used in two phases of pilot studies, which have run in four different European countries.

Commercial agentes portfolio optimization in electricity markets

As it is well known, competitive electricity markets require new computing tools for power companies that operate in retail markets in order to enhance the management of its energy resources. During the last years there has been an increase of the renewable penetration into the micro-generation which begins to co-exist with the other existing power generation, giving rise to a new type of consumers. This paper develops a methodology to be applied to the management of the all the aggregators. The aggregator establishes bilateral contracts with its clients where the energy purchased and selling conditions are negotiated not only in terms of prices but also for other conditions that allow more flexibility in the way generation and consumption is addressed. The aggregator agent needs a tool to support the decision making in order to compose and select its customers' portfolio in an optimal way, for a given level of profitability and risk.

Tool geometry evaluation for carbon reinforced composite laminates

The use of composite laminates in complex structures has increased significantly. However, there are still some issues when considering their use, mainly related with machining, leading to some difficulties and lack of acceptance. In this work, a methodology to evaluate drill geometry and feed rate based on thrust force and delamination extension is presented.

Application of a control banding tool for risk level assessment and control of nanoparticles exposure in welding operations

Scope of study: welding operations result in harmful emissions of nanoparticles; the aim of emissions monitorisation is to evaluate exposure levels and to derive protection measures in order to protect exposed workers; however, the traditional approach of comparing measured concentrations with exposure limits cannot be used; but risk levels can be quantified by using Control Banding Strategies.

A behavioral analysis tool for models of software systems

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

A visão e a voz do diretor: a construção do seu projeto de Intervenção

Dissertação apresentada para obtenção do grau de Mestre em Ciências da Educação - Área de especialização em Administração Escolar

Space-time monitoring of prescribed burnt soils performance – an effective tool for forest management

Among the most important measures to prevent wild forest fires is the use of prescribed and controlled burning actions in order to reduce the availability of fuel mass. However, the impact of these activities on soil physical and chemical properties varies according to the type of both soil and vegetation and is not fully understood. Therefore, soil monitoring campaigns are often used to measure these impacts. In this paper we have successfully used three statistical data treatments - the Kolmogorov-Smirnov test followed by the ANOVA and the Kruskall-Wallis tests – to investigate the variability among the soil pH, soil moisture, soil organic matter and soil iron variables for different monitoring times and sampling procedures.