9 resultados para Best available techniques


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Thesis submitted to the Faculty of Sciences and Technology, New University of Lisbon, for the degree of Doctor of Philosophy in Environmental Sciences

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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Gestão e Sistemas Ambientais

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

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In the last few years, we have observed an exponential increasing of the information systems, and parking information is one more example of them. The needs of obtaining reliable and updated information of parking slots availability are very important in the goal of traffic reduction. Also parking slot prediction is a new topic that has already started to be applied. San Francisco in America and Santander in Spain are examples of such projects carried out to obtain this kind of information. The aim of this thesis is the study and evaluation of methodologies for parking slot prediction and the integration in a web application, where all kind of users will be able to know the current parking status and also future status according to parking model predictions. The source of the data is ancillary in this work but it needs to be understood anyway to understand the parking behaviour. Actually, there are many modelling techniques used for this purpose such as time series analysis, decision trees, neural networks and clustering. In this work, the author explains the best techniques at this work, analyzes the result and points out the advantages and disadvantages of each one. The model will learn the periodic and seasonal patterns of the parking status behaviour, and with this knowledge it can predict future status values given a date. The data used comes from the Smart Park Ontinyent and it is about parking occupancy status together with timestamps and it is stored in a database. After data acquisition, data analysis and pre-processing was needed for model implementations. The first test done was with the boosting ensemble classifier, employed over a set of decision trees, created with C5.0 algorithm from a set of training samples, to assign a prediction value to each object. In addition to the predictions, this work has got measurements error that indicates the reliability of the outcome predictions being correct. The second test was done using the function fitting seasonal exponential smoothing tbats model. Finally as the last test, it has been tried a model that is actually a combination of the previous two models, just to see the result of this combination. The results were quite good for all of them, having error averages of 6.2, 6.6 and 5.4 in vacancies predictions for the three models respectively. This means from a parking of 47 places a 10% average error in parking slot predictions. This result could be even better with longer data available. In order to make this kind of information visible and reachable from everyone having a device with internet connection, a web application was made for this purpose. Beside the data displaying, this application also offers different functions to improve the task of searching for parking. The new functions, apart from parking prediction, were: - Park distances from user location. It provides all the distances to user current location to the different parks in the city. - Geocoding. The service for matching a literal description or an address to a concrete location. - Geolocation. The service for positioning the user. - Parking list panel. This is not a service neither a function, is just a better visualization and better handling of the information.

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O projeto MEMORIAMEDIA tem como objetivos o estudo, a inventariação e divulgação de manifestações do património cultural imaterial: expressões orais; práticas performativas; celebrações; o saber-fazer de artes e ofícios e as práticas e conhecimentos relacionados com a natureza e o universo. O MEMORIAMEDIA iniciou em 2006, em pleno debate nacional e internacional das questões do património cultural imaterial. Este livro cruza essas discussões teóricas, metodológicas e técnicas com a caracterização do MEMORIAMEDIA. Os resultados do projeto, organizados num inventário nacional, estão publicados no site www.memoriamedia.net, onde se encontram disponíveis para consulta e partilha. Filomena Sousa é investigadora de pós-doutoramento em antropologia (FCSH/UNL) e doutorada em sociologia (ISCTE-IUL). Membro integrado no Instituto de Estudos de Literatura e Tradição - patrimónios, artes e culturas (IELT) da FCSH/UNL e consultora da Memória Imaterial CRL – organização não-governamental autora e gestora do projeto MEMORIAMEDIA. Desenvolve investigação no âmbito das políticas e instrumentos de identificação, documentação e salvaguarda do património cultural imaterial e realizou vários documentários sobre expressões culturais.

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Staphylococcus aureus is an important opportunistic pathogen that can cause a wide variety of diseases from mild to life-threatening conditions. S. aureus can colonize many parts of the human body but the anterior nares are the primary ecological niche. Its clinical importance is due to its ability to resist almost all classes of antibiotics available together with its large number of virulence factores. MRSA (Methicillin-Resistant S. aureus) strains are particularly important in the hospital settings, being the major cause of nosocomial infections worldwide. MRSA resistance to β-lactam antibiotics involves the acquisition of the exogenous mecA gene, part of the SCCmec cassette. Fast and reliable diagnostic techniques are needed to reduce the mortality and morbidity associated with MRSA infections, through the early identification of MRSA strains. The current identification techniques are time-consuming as they usually involves culturing steps, taking up to five days to determine the antibiotic resistance profile. Several amplification-based techniques have been developed to accelerate the diagnosis. The aim of this project was to develop an even faster methodology that bypasses the DNA amplification step. Gold-nanoprobes were developed and used to detect the presence of mecA gene in S. aureus genome, associated with resistance traits, for colorimetric assays based on non-crosslinking method. Our results showed that the mecA and mecA_V2 gold-nanoprobes were sensitive enough to discriminate the presence of mecA gene in PCR products and genomic DNA (gDNA) samples for target concentrations of 10 ng/μL and 20 ng/μL, respectively. As our main objective was to avoid the amplification step, we concluded that the best strategy for the early identification of MRSA infection relies on colorimetric assays based on non-crosslinking method with gDNA samples that can be extracted directly from blood samples.