996 resultados para Hjelt, Sven-Erik
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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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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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Este trabalho é um estudo prospectivo e descritivo dos aspectos epidemiológicos e clínicos de 72 envenenamentos por escorpiões admitidos no Hospital Municipal de Santarém, Estado do Pará, Brasil, entre fevereiro de 2000 a fevereiro de 2001. Trouxeram o animal 8,3% das vítimas, os quais foram identificados como T. cambridgei. O sexo masculino foi acometido em 83,3%. A idade das vítimas e o tempo para o socorro médico foram respectivamente de 33,6±18,3 anos e 4,6±3,2 horas em média. Os membros superiores foram acometidos em 51,5% dos casos. As manifestações locais estiveram presentes em 91,7% e as sistêmicas em 98,6% dos envenenamentos. Entre os sintomas locais encontramos: parestesia em 79,2%, dor em 52,8%, e edema em 26,4% dos casos. Nas manifestações sistêmicas predominou as queixas neurológicas em 97,2% das vítimas, sendo o sintoma de sensação de "choque elétrico" pelo corpo (88,9%) o mais freqüente. No exame neurológico os sinais mais encontrados foram: mioclonias (93,0%), dismetria (86,1%), disartria (80,6%) e ataxia de marcha (70,8%). Classificou-se como moderados 76,4% dos envenenamentos, sem nenhum caso grave. Deixaram de realizar a soroterapia 32,7% dos casos moderados, por ausência de soro específico no momento do atendimento. O escorpionismo da região de Santarém mostra um comportamento clínico regional diferente daqueles descritos no Brasil e de outros locais da Amazônia e, apresenta uma clínica predominantemente neurológica, ainda não descrita na literatura brasileira.
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During drilling operation, cuttings are produced downhole and must be removed to avoid issues which can lead to Non Productive Time (NPT). Most of stuck pipe and then Bottom-Hole Assembly (BHA) lost events are hole cleaned related. There are many parameters which help determine hole cleaning conditions, but a proper selection of the key parameters will facilitate monitoring hole cleaning conditions and interventions. The aim of Hole Cleaning Monitoring is to keep track of borehole conditions including hole cleaning efficiency and wellbore stability issues during drilling operations. Adequate hole cleaning is the one of the main concerns in the underbalanced drilling operations especially for directional and horizontal wells. This dissertation addresses some hole cleaning fundamentals which will act as the basis for recommendation practice during drilling operations. Understand how parameters such as Flowrate, Rotation per Minute (RPM), Rate of Penetration (ROP) and Mud Weight are useful to improve the hole cleaning performance and how Equivalent Circulate Density (ECD), Torque & Drag (T&D) and Cuttings Volumes coming from downhole help to indicate how clean and stable the well is. For case study, hole cleaning performance or cuttings volume removal monitoring, will be based on real-time measurements of the cuttings volume removal from downhole at certain time, taking into account Flowrate, RPM, ROP and Drilling fluid or Mud properties, and then will be plotted and compared to the volume being drilled expected. ECD monitoring will dictate hole stability conditions and T&D and Cuttings Volume coming from downhole monitoring will dictate how clean the well is. T&D Modeling Software provide theoretical calculated T&D trends which will be plotted and compared to the real-time measurements. It will use the measured hookloads to perform a back-calculation of friction factors along the wellbore.
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Spatial analysis and social network analysis typically take into consideration social processes in specific contexts of geographical or network space. The research in political science increasingly strives to model heterogeneity and spatial dependence. To better understand and geographically model the relationship between “non-political” events, streaming data from social networks, and political climate was the primary objective of the current study. Geographic information systems (GIS) are useful tools in the organization and analysis of streaming data from social networks. In this study, geographical and statistical analysis were combined in order to define the temporal and spatial nature of the data eminating from the popular social network Twitter during the 2014 FIFA World Cup. The study spans the entire globe because Twitter’s geotagging function, the fundamental data that makes this study possible, is not limited to a geographic area. By examining the public reactions to an inherenlty non-political event, this study serves to illuminate broader questions about social behavior and spatial dependence. From a practical perspective, the analyses demonstrate how the discussion of political topics fluсtuate according to football matches. Tableau and Rapidminer, in addition to a set basic statistical methods, were applied to find patterns in the social behavior in space and time in different geographic regions. It was found some insight into the relationship between an ostensibly non-political event – the World Cup - and public opinion transmitted by social media. The methodology could serve as a prototype for future studies and guide policy makers in governmental and non-governmental organizations in gauging the public opinion in certain geographic locations.
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In the recent past, hardly anyone could predict this course of GIS development. GIS is moving from desktop to cloud. Web 2.0 enabled people to input data into web. These data are becoming increasingly geolocated. Big amounts of data formed something that is called "Big Data". Scientists still don't know how to deal with it completely. Different Data Mining tools are used for trying to extract some useful information from this Big Data. In our study, we also deal with one part of these data - User Generated Geographic Content (UGGC). The Panoramio initiative allows people to upload photos and describe them with tags. These photos are geolocated, which means that they have exact location on the Earth's surface according to a certain spatial reference system. By using Data Mining tools, we are trying to answer if it is possible to extract land use information from Panoramio photo tags. Also, we tried to answer to what extent this information could be accurate. At the end, we compared different Data Mining methods in order to distinguish which one has the most suited performances for this kind of data, which is text. Our answers are quite encouraging. With more than 70% of accuracy, we proved that extracting land use information is possible to some extent. Also, we found Memory Based Reasoning (MBR) method the most suitable method for this kind of data in all cases.
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The Keystone XL has a big role for transforming Canadian oil to the USA. The function of the pipeline is decreasing the dependency of the American oil industry on other countries and it will help to limit external debt. The proposed pipeline seeks the most suitable route which cannot damage agricultural and natural water recourses such as the Ogallala Aquifer. Using the Geographic Information System (GIS) techniques, the suggested path in this study got extremely high correct results that will help in the future to use the least cost analysis for similar studies. The route analysis contains different weighted overlay surfaces, each, was influenced by various criteria (slope, geology, population and land use). The resulted least cost path routes for each weighted overlay surface were compared with the original proposed pipeline and each displayed surface was more effective than the proposed Keystone XL pipeline.
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The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.
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Over a seven year period from 1991 to 1997, 22 species of tiger beetles, representing nine genera, were recorded near Manaus, Brazil. In the Whitewaterfloodplains along the Rio Solimões-Amazonas (Ilha de Marchantaria), three diurnal species inhabit inundation forests and six species (two diurnal, four nocturnal) live in open areas. Data on their natural history and adaptation to living conditions in floodplains are presented. Fifteen species were located on non-flooded uplands (Reserva Florestal A. Ducke). Five diurnal species inhabit the forest floor, two species are canopy dwellers, and eight species (seven diurnal, one nocturnal) live in open areas on whitesand or laterite. Only one species, Pentacomia lacordairei, was found in both floodplain and upland forests. A key to the larvae of tiger beetle genera located near Manaus is presented.
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The new species Notiobia glabrata, N. maxima and N. pseudolimbipennis are described. A key to the 11 Notiobia (s.str.) species known from Brazil, data about the distribution of each species and taxonomical remarks are provided. Notiobia parilis Bates, 1878 is a junior synonym of N. nebrioides Perty, 1830, and Notiobia umbrata Bates, 1882 is a junior synonym of N. jlavicinctus Erichson, 1847. The Brazilian Notiobia species belong to at least three different species groups, each distributed from Brazil over the North-Western part of South America, Central America to Mexico.
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Childhood is a central period for career and social-emotional development. However, the literature covering childhood career development and the role of emotions in careers is scarce. In this article, we advocate for the consideration of emotions in childhood career development. Emotional aspects of children’s career exploration, key-figures and interests, as well as of childhood antecedents of lifelong career processes are presented. Relations between childhood emotion, behavior, functioning and learning are also presented. Conclusions center on a call for focused study of the role of emotion in childhood career development and how such an agenda will advance the literature.
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The Closest Vector Problem (CVP) and the Shortest Vector Problem (SVP) are prime problems in lattice-based cryptanalysis, since they underpin the security of many lattice-based cryptosystems. Despite the importance of these problems, there are only a few CVP-solvers publicly available, and their scalability was never studied. This paper presents a scalable implementation of an enumeration-based CVP-solver for multi-cores, which can be easily adapted to solve the SVP. In particular, it achieves super-linear speedups in some instances on up to 8 cores and almost linear speedups on 16 cores when solving the CVP on a 50-dimensional lattice. Our results show that enumeration-based CVP-solvers can be parallelized as effectively as enumeration-based solvers for the SVP, based on a comparison with a state of the art SVP-solver. In addition, we show that we can optimize the SVP variant of our solver in such a way that it becomes 35%-60% faster than the fastest enumeration-based SVP-solver to date.
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Tese de Doutoramento em Ciência Política e Relações Internacionais