PRODUTOS FLORESTAIS NÃO MADEIREIROS E VALOR POTENCIAL DE EXPLORAÇÃO SUSTENTÁVEL DA FLORESTA ATLÂNTICA NO SUL DE SANTA CATARINA

Este estudo indicou os produtos florestais não madeireiros (PFNM) associados a espécies arbóreas dos remanescentes de Floresta Ombrófila Densa estudados no Sul de Santa Catarina. As espécies analisadas foram hierarquizadas com base na análise de valor potencial de exploração sustentável (VPES) dos PFNM, integrando-se às seguintes informações obtidas a partir de revisão bibliográfica: densidade, parte usada, capacidade de regeneração natural e crescimento, conhecimento sobre biologia reprodutiva e dinâmica populacional, processamento requerido e nível de injúria ou toxicidade. Os resultados obtidos evidenciam ser possível a conservação e desenvolvimento regional, a partir de alternativas sustentáveis de uso dos recursos florestais. Das 79 espécies observadas, 38 atingiram VPES igual ou superior a 10, sendo consideradas de elevado potencial de exploração sustentável de PFNM.

[France's margins and the necessary territorial attractiveness].

International audience

Robust Stereo Visual Odometry through a Probabilistic Combination of Points and Line Segments

Most approaches to stereo visual odometry reconstruct the motion based on the tracking of point features along a sequence of images. However, in low-textured scenes it is often difficult to encounter a large set of point features, or it may happen that they are not well distributed over the image, so that the behavior of these algorithms deteriorates. This paper proposes a probabilistic approach to stereo visual odometry based on the combination of both point and line segment that works robustly in a wide variety of scenarios. The camera motion is recovered through non-linear minimization of the projection errors of both point and line segment features. In order to effectively combine both types of features, their associated errors are weighted according to their covariance matrices, computed from the propagation of Gaussian distribution errors in the sensor measurements. The method, of course, is computationally more expensive that using only one type of feature, but still can run in real-time on a standard computer and provides interesting advantages, including a straightforward integration into any probabilistic framework commonly employed in mobile robotics.

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.

Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of scoring functions used in most VS methods we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, this information being exploited afterwards to improve VS predictions.

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.

Information and communication technologies in tourism: challenges and trends

The integration of Information and Communication Technologies (ICT) in the tourism industry is an essential element for the success of any tourism enterprise. ICTs provide access to information of tourism products from anywhere and at any time. Tour companies may also reach out to target customers around the world through a series of emerging technologies. This paper aims to make a review of the main key factors of ICT in Tourism. Aspects such as the quality of the website, Digital Marketing, Social Networking, Multimedia, Mobile Technologies and Intelligent Environments are discussed.

SQMS – A prototype for a supplier quality management system

Within the supply chain, the customer does not simply buy parts or services from suppliers, but also buys supplier capabilities, which results in quality products and services. Having a tool that handles the Inspection as well as the Nonconformance, Complaint, Corrective Action and Concession processes is key to successfully track the supplier performance. Taking as a case study a Supplier Quality Management (SQM) currently in operation in an Original Equipment Manufacturer (OEM) for automotive industry, this paper presents a platform to support a Supplier Quality Management System (SQMS), that fits the technical specification ISO/TS 16949 requirements. This prototype is composed by a web platform and a mobile App, having flexibility and mobility as key main characteristics.

A implantação de hidrelétricas na Amazônia brasileira, impactos socioambientais e à saúde com as transformações no território : o caso da UHE de Belo Monte

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Pós-Graduação em Geografia, 2016.

Distribution and at-sea activity of a nocturnal seabird, the Bulwer's petrel Bulweria bulwerii, during the incubation period

Bulwer'spetrelsarenocturnalseabirdsthatmostlypreyonmesopelagicfauna.Asaerialforagersand shallowdivers,theirfeedingopportunitiesarelimitedbynear-surfaceavailabilityoftheirprey,whichis highlyvariablebothtemporally(reflectingdiurnalandlunarcycles)andspatially.Herewestudiedhow Bulwer'spetrelscopewiththeseconstraintsbyanalysingtheirat-seadistributionandactivityduringthe incubationperiod.Wetrackedthemovementsof20birdsfromSelvagemGrande(NEAtlantic)duringa completelunarcycle,andrecorded30foragingtripsthatlasted11daysonaverage.Birdswereboth distributedaroundthecolonyandinwatersclosetotheAzoreanarchipelago(mid-Atlantic)located 1700kmaway,andweresignificantlymoreactiveatnight(especiallyjustaftersunsetandbeforesunrise), whenmesopelagicfaunaisalsoclosertotheseasurfaceduetotheirdielverticalmigrations.Bulwer's petrelsspentsignificantlymoretime flyingduringmoonlight,althoughtheeffectofthemoonwasrela- tivelyweak(ca.10–15%differencebetweenmoonlitanddarkperiodsofthenight),andnotobviouswhen birdswereforaginginmid-Atlanticwaters,whichwerealsotargetedmoreoftenduringfull-moon.These resultsrevealkeyadaptationsoftheBulwer'spetreltothehighlydynamicecologyofitsmesopelagicprey.

Entrepreneurial Orientation: do we actually know as much as we think we do?

International audience

Genes found in partial sequencing od Phytophthora cinnamomi

Members of the oomycete cause extensive losses in agriculture and widespread degradation in natural plant communities, being responsible for the death of thousands of trees every year. Two of the representative species are Phytophthora infestans, which causes late blight of potato, and Phytophthora cinnamomi, which causes chestnut ink disease, responsible for losses on sweet chestnut production in Europe. Genome sequencing efforts have been focused on the study of three species: P. infestans, P. sojae and P. ramorum. Phytophthora infestans has been developed as the model specie for the genus, possessing excellent genetic and genomics resources including genetic maps, BAC libraries, and EST sequences. Our research team is trying to sequence the genome of P. cinnamomi in order to gain a better understanding of this oomycete, to study changes in plant-pathogen relationships including those resulting from climate change and trying to decrease the pathogen’s impact on crops and plants in natural ecosystems worldwide. We present here a preliminary report of partially sequenced genomic DNA from P. cinnamomi encoding putative protein-coding sequences and tRNAs. Database analysis reveals the presence of genes conserved in oomycetes.

Branch and Bound algorithms in greenhouse climate control

The horticultural sector has become an increasingly important sector of food production, for which greenhouse climate control plays a vital role in improving its sustainability. One of the methods to control the greenhouse climate is Model Predictive Control, which can be optimized through a branch and bound algorithm. The application of the algorithm in literature is examined and analyzed through small examples, and later extended to greenhouse climate simulation. A comparison is made of various alternative objective functions available in literature. Subsequently, a modidified version of the B&B algorithm is presented, which reduces the number of node evaluations required for optimization. Finally, three alternative algorithms are developed and compared to consider the optimization problem from a discrete to a continuous control space.

(N)um lugar ao sol: contributos do turismo para as dinâmicas criativas e para a atração da classe criativa no Algarve

Tese de Doutoramento, Turismo, Faculdade de Economia, Universidade do Algarve, 2016

Descubrimiento de fármacos basado en cribado virtual refinado con enfoques neuronales paralelos

En el campo de la medicina clínica es crucial poder determinar la seguridad y la eficacia de los fármacos actuales y además acelerar el descubrimiento de nuevos compuestos activos. Para ello se llevan a cabo ensayos de laboratorio, que son métodos muy costosos y que requieren mucho tiempo. Sin embargo, la bioinformática puede facilitar enormemente la investigación clínica para los fines mencionados, ya que proporciona la predicción de la toxicidad de los fármacos y su actividad en enfermedades nuevas, así como la evolución de los compuestos activos descubiertos en ensayos clínicos. Esto se puede lograr gracias a la disponibilidad de herramientas de bioinformática y métodos de cribado virtual por ordenador (CV) que permitan probar todas las hipótesis necesarias antes de realizar los ensayos clínicos, tales como el docking estructural, mediante el programa BINDSURF. Sin embargo, la precisión de la mayoría de los métodos de CV se ve muy restringida a causa de las limitaciones presentes en las funciones de afinidad o scoring que describen las interacciones biomoleculares, e incluso hoy en día estas incertidumbres no se conocen completamente. En este trabajo abordamos este problema, proponiendo un nuevo enfoque en el que las redes neuronales se entrenan con información relativa a bases de datos de compuestos conocidos (proteínas diana y fármacos), y se aprovecha después el método para incrementar la precisión de las predicciones de afinidad del método de CV BINDSURF.