O estatuto das pseudo relativas em português europeu

Dissertação apresentada para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Ciências da Linguagem

Reconstrução/processamento de imagem médica com GPU em tomossíntese

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Pseudo-outbreak of Clostridium difficile associated diarrhea (CDAD) in a tertiary-care hospital

The objective of this study was to describe a pseudo-outbreak of C. difficile in a hospital, following a change in the method used to detect the toxin. In February 2002, there were two cases of CDAD and in March 7 occurred, coinciding with a change of the test (from detection of toxin A to toxin A/B). An outbreak was suspected. Active surveillance and education of staff were started. A CDAD case was defined as a patient with acute onset of diarrhea (³ three episodes of liquid stools) and a positive stool test. They were classified as hospital or community-acquired. Stool samples were also collected for C. difficile culture and isolates were typed using AP-PCR. From March 2002 through December 2003 there were 138 cases of CDAD: 70% were hospital-acquired and among the 30% with CDAD present on admission, most (81%) came directly from the community (50% had no history of hospitalization). Fifty-two percent of hospital-acquired CDAD and 94% of cases on admission had already used antibiotics. The incidence of CDAD in hospitalized patients during surveillance was 3.3 per 1000 patient-admissions. The incidence of CDAD present on admission was 6.1/1000 patients. Sixteen isolates were typed and presented 13 different profiles. In conclusion, the CDAD increase in our study occurred due to change in diagnostic methods and not due to an outbreak, as suspected initially. The incidence in hospitalized patients was much lower than in reported outbreaks. There were 13 molecular types suggesting that an outbreak did not occur. CDAD was largely community-acquired.

Comparing the performance of geostatistical models with additional information from covariates for sewage plume characterization

In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the Mat`ern models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

Land cover dynamics in Savanna Ecosystem of Borena Ethiopia

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

Assessing the impacts of land use and land cover change on hydrology of watershed: a case study on Gigel-Abbay Watershed, Lake Tana Basin, Ethiopia

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

Instrumento de Avaliação de Repetição de Pseudo-palavras

Dissertação apresentada para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Desenvolvimento e Perturbações da Linguagem na Criança – Área de Especialização em Terapia da Fala e Perturbações da Linguagem,

Pseudo-Kaposi's Sarcoma Because of Suction-Socket Lower Limb Prosthesis

Pseudo-Kaposi sarcoma is a benign reactive vascular proliferation mainly involving the lower legs, which can be related to acquired chronic venous insufficiency or congenital arteriovenous malformations. In its most common presentation, acroangiodermatitis is seen in patients with chronic venous insufficiency of the lower limbs as an exaggeration of the stasis dermatitis. However, rare reports of acroangiodermatitis include descriptions in amputees (especially in those with poorly fitting suction-type devices), in patients undergoing hemodialysis (with lesions developing distally to arteriovenous shunts) and in patients with paralyzed legs. We report on a 28 year-old-male who presented pseudo-Kaposi's sarcoma in an amputation stump because of suction-socket lower limb prosthesis.

Bone marrow fibrosis (pseudo-myelofibrosis) in human kala-azar

Thirty cases of human kala-azar were diagnosed by iliac crest biopsy and myeloculture. Histological analysis of 12 patients showed diffuse thickening of reticulin fibers. To the best of our knowledge, this is the third report describing secondary bone marrow fibrosis (myelofibrosis-like) associated with kala-azar. Patients with positive bone marrow fibrosis (pbmf = 12) were compared to patients without detectable bone marrow fibrosis (wbmf = 18). There were no significant differences in clinical and blood parameters following treatment. All patients showed regression of hepatosplenomegaly.Our findings suggest that associated bone marrow fibrosis is transient and did not interfere in the evolution of treated patients.

Natural transovarial transmission of dengue virus 4 in Aedes aegypti from Cuiabá, State of Mato Grosso, Brazil

INTRODUCTION: Dengue is the most prevalent arboviral disease in tropical areas. In Mato Grosso, outbreaks are reported every year, but studies on dengue in this state are scarce. METHODS: Natural transovarial infection of Aedes aegypti by a flavivirus was investigated in the Jardim Industriário neighborhood of Cuiabá, Mato Grosso. Eggs were collected with ovitraps during the dry, intermediate, and rainy seasons of 2012. After the eggs hatched and the larvae developed to adulthood, mosquitoes (n = 758) were identified and allocated to pools of 1-10 specimens according to the collection location, sex, and climatic period. After RNA extraction, multiplex semi-nested RT-PCR was performed to detect the four dengue virus (DENV) serotypes, yellow fever virus, West Nile virus and Saint Louis encephalitis virus. RESULTS: DENV-4 was the only flavivirus detected, and it was found in 8/50 pools (16.0%). Three of the positive pools contained females, and five contained males. Their nucleotide sequences presented 96-100% similarity with DENV-4 genotype II strains from Manaus, Amazonas. The minimum infection rate was 10.5 per 1000 specimens, and the maximum likelihood estimator of the infection rate was 11.6 (95% confidence interval: 4.8; 23.3). CONCLUSIONS: This study provides the first evidence of natural transovarial infection by DENV-4 in Ae. Aegypti in Mato Grosso, suggesting that this type of infection might serve as a mechanism of virus maintenance during interepidemic periods in Cuiabá, a city where dengue epidemics are reported every year. These results emphasize the need for efficient vector population control measures to prevent arbovirus outbreaks in the state.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.