Selection of trees for rubbing by red and roe deer in forest plantations

Antler rubbing is a form of behaviour by which deer may damage and ultimately induce mortality of trees. Understanding factors affecting selection of trees for rubbing may contribute to mitigation of negative effects of such behaviour in plantations or woodlands. We analysed characteristics of trees rubbed by red and roe deer along transects established in plantations of Pinus pinaster (Aiton), Pseudotsuga menziesii (Mirbel) Franco, Betula alba L. and Quercus robur L. in Northeast Portugal. Transects were walked during five sampling periods covering mating seasons of red and roe deer. Red deer preferentially rubbed trees adjacent to the edge of plantations and large clearings whilst roe deer selected those inside plantations within small clearings. There was seasonal segregation in the number of trees rubbed by each deer species with red deer rubbing trees mainly between September and February and roe deer mainly between December and June. Both red and roe deer selected trees with smaller diameter than those of available trees although trees selected by red deer had larger diameters than those selected by roe deer. Roe, but not red deer, tended to avoid trees protected by shrubs. Overall, the selection of trees for rubbing was site-dependent suggesting that generalizations across sites should be made with caution. Mitigating measures, such as deer control, tree protection or provision of alternative rubbing posts should target stands of particular tree species, location of trees in relation to stand clearings and tree size classes.http://www.sciencedirect.com/science/article/B6T6X-4HGM78R-2/1/29fe58190c40581f0716e977b7847d3

Alternative models for the calculation of the RMR and Q indexes for granite rock masses

Empirical classification systems like the RMR and Q are often used in current practice of geotechnical structures design built in rock masses. They allow obtaining an overall description of the rock mass and the calculation, through analytical solutions, of strength and deformability parameters which are determinant in design. To be applied these systems need a set of geomechanical information that may not be available or can be difficult to obtain. In this work it is intended to develop new alternative regression models for the calculation of the RMR and Q indexes using less data than the original formulations and keeping a high accuracy level. It is also intended to have an insight of which parameters are the most important for the prediction of the indexes and in the rock masses behaviour. This study was carried out applying Data Mining techniques to a database of the empirical classification systems applications in a granite rock mass. Data Mining is a relatively new area of computer science which concerns with automatically find, simplify and summarize patterns and relationships within large databases. The used Data Mining techniques were the multiple regression and artificial neural networks. The developed models are able to predict the two geomechanical indexes using less information that in the original formulations with a good predictive capacity.Fundação para a Ciência e a Tecnologia (FCT) - projecto POCI/ECM/57495/2004 "Geotechnical Risk in Tunnels for High Speed Trains

Artificial intelligence approaches for the generation and assessment of believable human-like behaviour in virtual characters

Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) can not tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA-CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition [1], and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assess- ment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.MICINN -Ministerio de Ciencia e Innovación(FCT-13-7848

Uma abordagem computacional para segmentação das microestruturas do ferro fundido branco hipoeutético baseado em morfologia matemática

Este trabalho apresenta uma abordagem computacional para classicação automática das microestruturas de um ferro fundido branco hipoeutético usando morfologia matemática binária. Tal abordagem assume especial importância porque os softwares comerciais não segmentam corretamente essas microestruturas, que são: cementita, perlita e ledeburita. Para validar o algoritmo automático de segmentação proposto neste trabalho, são analisadas 30 amostras de ferro fundido branco hipoeutético, sendo binarizadas através de um limiar automático obtido usando o menor número de pixel em um histograma. Os resultados obtidos são semelhantes aos da examinação visual humana, segmentando ecientemente a cementita, perlita e ledeburita separadamente, diferentemente dos sistemas comerciais, que classicam a perlita e a ledeburita com sendo uma única microestrutura. Portanto, a abordagem computacional proposto neste trabalho, baseada nas técnicas da morfologia matemática com operações binárias, oferece aos estudantes, engenheiros, especialistas e outros da área das Ciências dos Materiais mais uma opção para uma análise microestrutural

Quantification of the hipoeutectic withe cast iron microstructure from images using mathematical morphology and an artificial neural network

This work presents a comparative analysis between two automatic methods to segment and quantify the microstructure of white cast iron from images. The comparative methods are the SVRNA (Microstructure Segmentation by Computational Vision using Artificial Neural Networks), developed during this work, and the Image Pro-Plus, a common tool used for material microstructure analysis.In our SVRNA system, mathematical morphology algorithms are used to segment the microstructure elements of the white cast iron, which are then identified and quantified by an artificial neural network. The development of a new computational system was necessary because the usual commercial software, like the Image Pro-Plus, does not segment correctly the microstructure elements of this cast iron, which are: cementite, pearlite and ledeburite.To validate our SVRNA system, 30 samples of white cast iron were analyzed. The results obtained are very similar to the ones accomplished by visual examination. In fact, the microstructure elements of the material in analysis were correctly segmented and quantified by our SVRNA system, what did not happened when we used the Image Pro-Plus system. Therefore, the proposed system, based on mathematical morphology operators and an artificial neural network, offers to researchers, engineers, specialists and others of the Material Sciences field, a valuable and adequate tool for automatic and efficient microstructural analysis from images

A framework for increasing the value of predictive data-driven models by enriching problem domain characterization with novel features

The need to leverage knowledge through data mining has driven enterprises in a demand for more data. However, there is a gap between the availability of data and the application of extracted knowledge for improving decision support. In fact, more data do not necessarily imply better predictive data-driven marketing models, since it is often the case that the problem domain requires a deeper characterization. Aiming at such characterization, we propose a framework drawn on three feature selection strategies, where the goal is to unveil novel features that can effectively increase the value of data by providing a richer characterization of the problem domain. Such strategies involve encompassing context (e.g., social and economic variables), evaluating past history, and disaggregate the main problem into smaller but interesting subproblems. The framework is evaluated through an empirical analysis for a real bank telemarketing application, with the results proving the benefits of such approach, as the area under the receiver operating characteristic curve increased with each stage, improving previous model in terms of predictive performance.The work of P. Cortez was supported by FCT within the Project Scope UID/CEC/00319/2013. The authors would like to thank the anonymous reviewers for their helpful comments.info:eu-repo/semantics/publishedVersio

Ferramenta de análise não destrutiva para obtenção de parâmetros microestruturais baseada em Visão Computacional

Este trabalho apresenta novos parâmetros de medida calculados por um Sistema de Visão Computacional desenvolvido para a Classificação de Microestruturas em Materiais Metálicos. Este sistema é uma ferramenta de análise de imagens adequada para a área de Ciência dos Materiais, permitindo realizar automaticamente a segmentação e quantificação de microestruturas em materiais metálicos. Como evolução deste sistema, este trabalho apresenta novos parâmetros de medida que possibilitam uma análise mais detalhada das microestruturas através de medidas de comprimento, área e perímetro. Para obter estas medidas, utiliza-se o algoritmo de crescimento de regiões e o filtro de Roberts. Após a calibração correta do microscópico óptico usado obtêm-se as fotomicrografias necessárias para a aplicação do sistema desenvolvido. Para validar os resultados obtidos é realizada uma comparação com a análise de microscopia convencional. Portanto, o sistema apresentado é capaz, para além de realizar segmentação e quantificação de microestruturas, de obter parâmetros importantes para uma análise mais detalhada das propriedades mecânica dos materiais baseados em ensaios não destrutivos

Antiphospholipid Syndrome Risk Evaluation

The antiphospholipid syndrome is an acquired autoimmune disorder produced by high titers of antiphospholipid antibodies that cause both arterial and veins thrombosis as well as pregnancy-related complications and morbidity, as clinical manifestations. This autoimmune hypercoagulable state, often associated with coronary artery disease and recurrent Acute Myocardium Infraction, has severe consequences for the patients, being one of the main causes of thrombotic disorders and death. Therefore, it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Despite the updated of the APS classification published as Sydney criteria, diagnosis of this syndrome remains challenging. Further research on clinically relevant antibodies and standardization of their quantification are required to improve clinical risk assessment in APS. This work will focus on the development of a diagnosis support system to antiphospholipid syndrome, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks. The proposed model allowed to improve the diagnosis, classifying properly the patients that really presented this pathology (sensitivity about 92%) as well as classifying the absence of APS (specificity ranging from 89% to 94%)

Multi-step time series prediction intervals using neuroevolution

Multi-step time series forecasting (TSF) is a crucial element to support tactical decisions (e.g., designing production or marketing plans several months in advance). While most TSF research addresses only single-point prediction, prediction intervals (PIs) are useful to reduce uncertainty related to important decision making variables. In this paper, we explore a large set of neural network methods for multi-step TSF and that directly optimize PIs. This includes multi-step adaptations of recently proposed PI methods, such as lower--upper bound estimation (LUBET), its ensemble extension (LUBEXT), a multi-objective evolutionary algorithm LUBE (MLUBET) and a two-phase learning multi-objective evolutionary algorithm (M2LUBET). We also explore two new ensemble variants for the evolutionary approaches based on two PI coverage--width split methods (radial slices and clustering), leading to the MLUBEXT, M2LUBEXT, MLUBEXT2 and M2LUBEXT2 methods. A robust comparison was held by considering the rolling window procedure, nine time series from several real-world domains and with different characteristics, two PI quality measures (coverage error and width) and the Wilcoxon statistic. Overall, the best results were achieved by the M2LUBET neuroevolution method, which requires a reasonable computational effort for time series with a few hundreds of observations.This article is a result of the project NORTE-01- 0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We would also like to thank the anonymous reviewers for their helpful suggestionsinfo:eu-repo/semantics/publishedVersio

Lamb meat quality assessment by support vector machines

The correct assessment of meat quality (i.e., to fulfill the consumer's needs) is crucial element within the meat industry. Although there are several factors that affect the perception of taste, tenderness is considered the most important characteristic. In this paper, a Feature Selection procedure, based on a Sensitivity Analysis, is combined with a Support Vector Machine, in order to predict lamb meat tenderness. This real-world problem is defined in terms of two difficult regression tasks, by modeling objective (e.g. Warner-Bratzler Shear force) and subjective (e.g. human taste panel) measurements. In both cases, the proposed solution is competitive when compared with other neural (e.g. Multilayer Perceptron) and Multiple Regression approaches