906 resultados para Business planning -- Electronic data processing


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This paper addresses sensor network applications which need to obtain an accurate image of physical phenomena and do so with a high sampling rate in both time and space. We present a fast and scalable approach for obtaining an approximate representation of all sensor readings at high sampling rate for quickly reacting to critical events in a physical environment. This approach is an improvement on previous work in that after the new approach has undergone a startup phase then the new approach can use a very small sampling period.

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It has been shown that in reality at least two general scenarios of data structuring are possible: (a) a self-similar (SS) scenario when the measured data form an SS structure and (b) a quasi-periodic (QP) scenario when the repeated (strongly correlated) data form random sequences that are almost periodic with respect to each other. In the second case it becomes possible to describe their behavior and express a part of their randomness quantitatively in terms of the deterministic amplitude–frequency response belonging to the generalized Prony spectrum. This possibility allows us to re-examine the conventional concept of measurements and opens a new way for the description of a wide set of different data. In particular, it concerns different complex systems when the ‘best-fit’ model pretending to be the description of the data measured is absent but the barest necessity of description of these data in terms of the reduced number of quantitative parameters exists. The possibilities of the proposed approach and detection algorithm of the QP processes were demonstrated on actual data: spectroscopic data recorded for pure water and acoustic data for a test hole. The suggested methodology allows revising the accepted classification of different incommensurable and self-affine spatial structures and finding accurate interpretation of the generalized Prony spectroscopy that includes the Fourier spectroscopy as a partial case.

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Consider a wireless sensor network (WSN) where a broadcast from a sensor node does not reach all sensor nodes in the network; such networks are often called multihop networks. Sensor nodes take individual sensor readings, however, in many cases, it is relevant to compute aggregated quantities of these readings. In fact, the minimum and maximum of all sensor readings at an instant are often interesting because they indicate abnormal behavior, for example if the maximum temperature is very high then it may be that a fire has broken out. In this context, we propose an algorithm for computing the min or max of sensor readings in a multihop network. This algorithm has the particularly interesting property of having a time complexity that does not depend on the number of sensor nodes; only the network diameter and the range of the value domain of sensor readings matter.

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The availability of small inexpensive sensor elements enables the employment of large wired or wireless sensor networks for feeding control systems. Unfortunately, the need to transmit a large number of sensor measurements over a network negatively affects the timing parameters of the control loop. This paper presents a solution to this problem by representing sensor measurements with an approximate representation-an interpolation of sensor measurements as a function of space coordinates. A priority-based medium access control (MAC) protocol is used to select the sensor messages with high information content. Thus, the information from a large number of sensor measurements is conveyed within a few messages. This approach greatly reduces the time for obtaining a snapshot of the environment state and therefore supports the real-time requirements of feedback control loops.

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OBJECTIVE To analyze the access and utilization profile of biological medications for psoriasis provided by the judicial system in Brazil.METHODSThis is a cross-sectional study. We interviewed a total of 203 patients with psoriasis who were on biological medications obtained by the judicial system of the State of Sao Paulo, from 2004 to 2010. Sociodemographics, medical, and political-administrative characteristics were complemented with data obtained from dispensation orders that included biological medications to treat psoriasis and the legal actions involved. The data was analyzed using an electronic data base and shown as simple variable frequencies. The prescriptions contained in the lawsuits were analyzed according to legal provisions.RESULTS A total of 190 lawsuits requesting several biological drugs (adalimumab, efalizumab, etanercept, and infliximab) were analyzed. Patients obtained these medications as a result of injunctions (59.5%) or without having ever demanded biological medication from any health institution (86.2%), i.e., public or private health services. They used the prerogative of free legal aid (72.6%), even though they were represented by private lawyers (91.1%) and treated in private facilities (69.5%). Most of the patients used a biological medication for more than 13 months (66.0%), and some patients were undergoing treatment with this medication when interviewed (44.9%). Approximately one third of the patients discontinued treatment due to worsening of their illness (26.6%), adverse drug reactions (20.5%), lack of efficacy, or because the doctor discontinued this medication (13.8%). None of the analyzed medical prescriptions matched the legal prescribing requirements. Clinical monitoring results showed that 70.3% of the patients had not undergone laboratory examinations (blood work, liver and kidney function tests) for treatment control purposes.CONCLUSIONS The plaintiffs resorted to legal action to get access to biological medications because they were either unaware or had difficulty in accessing them through institutional public health system procedures. Access by means of legal action facilitated long-term use of this type of medication through irregular prescriptions and led to a high rate of adverse drug reactions as well as inappropriate clinical monitoring.

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Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde

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ABSTRACT OBJECTIVE To describe the prevalence of common mental disorders in Brazilian adolescent students, according to geographical macro-regions, school type, sex, and age. METHODS We evaluated 74,589 adolescents who participated in the Cardiovascular Risk Study in Adolescents (ERICA), a cross-sectional, national, school-based study conducted in 2013-2014 in cities with more than 100,000 inhabitants. A self-administered questionnaire and an electronic data collector were employed. The presence of common mental disorders was assessed using the General Health Questionnaire (GHQ-12). We estimated prevalence and 95% confidence intervals of common mental disorders by sex, age, and school type, in Brazil and in the macro-regions, considering the sample design. RESULTS The prevalence of common mental disorders was of 30.0% (95%CI 29.2-30.8), being higher among girls (38.4%; 95%CI 37.1-39.7) when compared to boys (21.6%; 95%CI 20.5-22.8), and among adolescents who were from 15 to 17 years old (33.6%; 95%CI 32.2-35.0) compared to those aged between 12 and 14 years (26.7%; 95%CI 25.8-27.6). The prevalence of common mental disorders increased with age for both sexes, always higher in girls (ranging from 28.1% at 12 years to 44.1% at 17 years) than in boys (ranging from 18.5% at 12 years to 27.7% at 17 years). We did not observe any significant difference by macro-region or school type. Stratified analyses showed higher prevalence of common mental disorders among girls aged from 15 to 17 years of private schools in the North region (53.1; 95%CI 46.8-59.4). CONCLUSIONS The high prevalence of common mental disorders among adolescents and the fact that the symptoms are often vague mean these disorders are not so easily identified by school administrators or even by health services. The results of this study can help the proposition of more specific prevention and control measures, focused on highest risk subgroups.

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Relatório Final apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e 2.º Ciclo do Ensino Básico

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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

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Environmental management is a complex task. The amount and heterogeneity of the data needed for an environmental decision making tool is overwhelming without adequate database systems and innovative methodologies. As far as data management, data interaction and data processing is concerned we here propose the use of a Geographical Information System (GIS) whilst for the decision making we suggest a Multi-Agent System (MAS) architecture. With the adoption of a GIS we hope to provide a complementary coexistence between heterogeneous data sets, a correct data structure, a good storage capacity and a friendly user’s interface. By choosing a distributed architecture such as a Multi-Agent System, where each agent is a semi-autonomous Expert System with the necessary skills to cooperate with the others in order to solve a given task, we hope to ensure a dynamic problem decomposition and to achieve a better performance compared with standard monolithical architectures. Finally, and in view of the partial, imprecise, and ever changing character of information available for decision making, Belief Revision capabilities are added to the system. Our aim is to present and discuss an intelligent environmental management system capable of suggesting the more appropriate land-use actions based on the existing spatial and non-spatial constraints.

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Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.

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Nos últimos anos, o processo de ensino e aprendizagem tem sofrido significativas alterações graças ao aparecimento da Internet. Novas ferramentas para apoio ao ensino têm surgido, nas quais se destacam os laboratórios remotos. Atualmente, muitas instituições de ensino disponibilizam laboratórios remotos nos seus cursos, que permitem, a professores e alunos, a realização de experiências reais através da Internet. Estes são implementados por diferentes arquiteturas e infraestruturas, suportados por vários módulos de laboratório acessíveis remotamente (e.g. instrumentos de medição). No entanto, a sua inclusão no ensino é ainda deficitária, devido: i) à falta de meios e competências técnicas das instituições de ensino para os desenvolverem, ii) à dificuldade na partilha dos módulos de laboratório por diferentes infraestruturas e, iii) à reduzida capacidade de os reconfigurar com esses módulos. Para ultrapassar estas limitações, foi idealizado e desenvolvido no âmbito de um trabalho de doutoramento [1] um protótipo, cuja arquitetura é baseada na norma IEEE 1451.0 e na tecnologia de FPGAs. Para além de garantir o desenvolvimento e o acesso de forma normalizada a um laboratório remoto, este protótipo promove ainda a partilha de módulos de laboratório por diferentes infraestruturas. Nesse trabalho explorou-se a capacidade de reconfiguração de FPGAs para embutir na infraestrutura do laboratório vários módulos, todos descritos em ficheiros, utilizando linguagens de descrição de hardware estruturados de acordo com a norma IEEE 1451.0. A definição desses módulos obriga à criação de estruturas de dados binárias (Transducer Electronic Data Sheets, TEDSs), bem como de outros ficheiros que possibilitam a sua interligação com a infraestrutura do laboratório. No entanto, a criação destes ficheiros é bastante complexa, uma vez que exige a realização de vários cálculos e conversões. Tendo em consideração essa mesma complexidade, esta dissertação descreve o desenvolvimento de uma aplicação Web para leitura e escrita dos TEDSs. Para além de um estudo sobre os laboratórios remotos, é efetuada uma descrição da norma IEEE 1451.0, com particular atenção para a sua arquitetura e para a estrutura dos diferentes TEDSs. Com o objetivo de enquadrar a aplicação desenvolvida, efetua-se ainda uma breve apresentação de um protótipo de um laboratório remoto reconfigurável, cuja reconfiguração é apoiada por esta aplicação. Por fim, é descrita a verificação da aplicação Web, de forma a tirar conclusões sobre o seu contributo para a simplificação dessa reconfiguração.

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Numa Estação de Tratamento de Águas Residuais (ETAR), são elevados os custos não só de tratamento das águas residuais como também de manutenção dos equipamentos lá existentes, nesse sentido procura-se utilizar processos capazes de transformar os resíduos em produtos úteis. A Digestão Anaeróbia (DA) é um processo atualmente disponível capaz de contribuir para a redução da poluição ambiental e ao mesmo tempo de valorizar os subprodutos gerados. Durante o processo de DA é produzido um gás, o biogás, que pode ser utilizado como fonte de energia, reduzindo assim a dependência energética da ETAR e a emissão de gases com efeito de estufa para a atmosfera. A otimização do processo de DA das lamas é essencial para o aumento da produção de biogás, mas a complexidade do processo constitui um obstáculo à sua otimização. Neste trabalho, aplicaram-se Redes Neuronais Artificiais (RNA) ao processo de DA de lamas de ETAR. RNA são modelos simplificados inspirados no funcionamento das células neuronais humanas e que adquirem conhecimento através da experiência. Quando a RNA é criada e treinada, produz valores de output aproximadamente corretos para os inputs fornecidos. Foi esse o motivo para recorrer a RNA na otimização da produção de biogás no digestor I da ETAR Norte da SIMRIA, usando o programa NeuralToolsTM da PalisadeTM para desenvolvimento das RNA. Para tal, efetuou-se uma análise e tratamento de dados referentes aos últimos quatro anos de funcionamento do digestor. Os resultados obtidos permitiram concluir que as RNA modeladas apresentam boa capacidade de generalização do processo de DA. Considera-se que este caso de estudo é promissor, fornecendo uma boa base para o desenvolvimento de modelos eventualmente mais gerais de RNA que, aplicado conjuntamente com as características de funcionamento de um digestor e o processo de DA, permitirá otimizar a produção de biogás em ETAR.

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Disaster management is one of the most relevant application fields of wireless sensor networks. In this application, the role of the sensor network usually consists of obtaining a representation or a model of a physical phenomenon spreading through the affected area. In this work we focus on forest firefighting operations, proposing three fully distributed ways for approximating the actual shape of the fire. In the simplest approach, a circular burnt area is assumed around each node that has detected the fire and the union of these circles gives the overall fire’s shape. However, as this approach makes an intensive use of the wireless sensor network resources, we have proposed to incorporate two in-network aggregation techniques, which do not require considering the complete set of fire detections. The first technique models the fire by means of a complex shape composed of multiple convex hulls representing different burning areas, while the second technique uses a set of arbitrary polygons. Performance evaluation of realistic fire models on computer simulations reveals that the method based on arbitrary polygons obtains an improvement of 20% in terms of accuracy of the fire shape approximation, reducing the overhead in-network resources to 10% in the best case.