914 resultados para Reactive Probabilistic Automata


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In developed countries, civil infrastructures are one of the most significant investments of governments, corporations, and individuals. Among these, transportation infrastructures, including highways, bridges, airports, and ports, are of huge importance, both economical and social. Most developed countries have built a fairly complete network of highways to fit their needs. As a result, the required investment in building new highways has diminished during the last decade, and should be further reduced in the following years. On the other hand, significant structural deteriorations have been detected in transportation networks, and a huge investment is necessary to keep these infrastructures safe and serviceable. Due to the significant importance of bridges in the serviceability of highway networks, maintenance of these structures plays a major role. In this paper, recent progress in probabilistic maintenance and optimization strategies for deteriorating civil infrastructures with emphasis on bridges is summarized. A novel model including interaction between structural safety analysis,through the safety index, and visual inspections and non destructive tests, through the condition index, is presented. Single objective optimization techniques leading to maintenance strategies associated with minimum expected cumulative cost and acceptable levels of condition and safety are presented. Furthermore, multi-objective optimization is used to simultaneously consider several performance indicators such as safety, condition, and cumulative cost. Realistic examples of the application of some of these techniques and strategies are also presented.

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Demo presented in 12th Workshop on Models and Algorithms for Planning and Scheduling Problems (MAPSP 2015). 8 to 12, Jun, 2015. La Roche-en-Ardenne, Belgium. Extended abstract.

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Proceedings of the International Conference on Computer Vision Theory and Applications, 361-365, 2013, Barcelona, Spain

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We present a novel approach of Stereo Visual Odometry for vehicles equipped with calibrated stereo cameras. We combine a dense probabilistic 5D egomotion estimation method with a sparse keypoint based stereo approach to provide high quality estimates of vehicle’s angular and linear velocities. To validate our approach, we perform two sets of experiments with a well known benchmarking dataset. First, we assess the quality of the raw velocity estimates in comparison to classical pose estimation algorithms. Second, we added to our method’s instantaneous velocity estimates a Kalman Filter and compare its performance with a well known open source stereo Visual Odometry library. The presented results compare favorably with state-of-the-art approaches, mainly in the estimation of the angular velocities, where significant improvements are achieved.

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In this paper we propose a novel fully probabilistic solution to the stereo egomotion estimation problem. We extend the notion of probabilistic correspondence to the stereo case which allow us to compute the whole 6D motion information in a probabilistic way. We compare the developed approach against other known state-of-the-art methods for stereo egomotion estimation, and the obtained results compare favorably both for the linear and angular velocities estimation.

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This study focus on the probabilistic modelling of mechanical properties of prestressing strands based on data collected from tensile tests carried out in Laboratório Nacional de Engenharia Civil (LNEC), Portugal, for certification purposes, and covers a period of about 9 years of production. The strands studied were produced by six manufacturers from four countries, namely Portugal, Spain, Italy and Thailand. Variability of the most important mechanicalproperties is examined and the results are compared with the recommendations of the ProbabilisticModel Code, as well as the Eurocodes and earlier studies. The obtained results show a very low variability which, of course, benefits structural safety. Based on those results, probabilistic modelsfor the most important mechanical properties of prestressing strands are proposed.

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In the traditional paradigm, the large power plants supply the reactive power required at a transmission level and the capacitors and transformer tap changer were also used at a distribution level. However, in a near future will be necessary to schedule both active and reactive power at a distribution level, due to the high number of resources connected in distribution levels. This paper proposes a new multi-objective methodology to deal with the optimal resource scheduling considering the distributed generation, electric vehicles and capacitor banks for the joint active and reactive power scheduling. The proposed methodology considers the minimization of the cost (economic perspective) of all distributed resources, and the minimization of the voltage magnitude difference (technical perspective) in all buses. The Pareto front is determined and a fuzzy-based mechanism is applied to present the best compromise solution. The proposed methodology has been tested in the 33-bus distribution network. The case study shows the results of three different scenarios for the economic, technical, and multi-objective perspectives, and the results demonstrated the importance of incorporating the reactive scheduling in the distribution network using the multi-objective perspective to obtain the best compromise solution for the economic and technical perspectives.

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It is unquestionable that an effective decision concerning the usage of a certain environmental clean-up technology should be conveniently supported. Significant amount of scientific work focussing on the reduction of nitrate concentration in drinking water by both metallic iron and nanomaterials and their usage in permeable reactive barriers has been worldwide published over the last two decades. This work aims to present in a systematic review of the most relevant research done on the removal of nitrate from groundwater using nanosized iron based permeable reactive barriers. The research was based on scientific papers published between 2004 and June 2014. It was performed using 16 combinations of keywords in 34 databases, according to PRISMA statement guidelines. Independent reviewers validated the selection criteria. From the 4161 records filtered, 45 met the selection criteria and were selected to be included in this review. This study's outcomes show that the permeable reactive barriers are, indeed, a suitable technology for denitrification and with good performance record but the long-term impact of the use of nanosized zero valent iron in this remediation process, in both on the environment and on the human health, is far to be conveniently known. As a consequence, further work is required on this matter, so that nanosized iron based permeable reactive barriers for the removal of nitrate from drinking water can be genuinely considered an eco-efficient technology.

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Introduction: C-reactive protein (CRP) and Bedside Index for Severity in Acute Pancreatitis (BISAP) have been used in early risk assessment of patients with AP. Objectives: We evaluated prognostic accuracy of CRP at 24 hours after hospital admission (CRP24) for in-hospital mortality (IM) in AP individually and with BISAP. Materials and Methods: This retrospective cohort study included 134 patients with AP from a Portuguese hospital in 2009---2010. Prognostic accuracy assessment used area under receiver---operating characteristic curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: Thirteen percent of patients had severe AP, 26% developed pancreatic necrosis, and 7% died during index hospital stay. AUCs for CRP24 and BISAP individually were 0.80 (95% confidence interval (CI) 0.65---0.95) and 0.77 (95% CI 0.59---0.95), respectively. No patients with CRP24 <60 mg/l died (P = 0.027; negative predictive value 100% (95% CI 92.3---100%)). AUC for BISAP plus CRP24 was 0.81 (95% CI 0.65---0.97). Change in NRI nonevents (42.4%; 95% CI, 24.9---59.9%) resulted in positive overall NRI (31.3%; 95% CI, − 36.4% to 98.9%), but IDI nonevents was negligible (0.004; 95% CI, − 0.007 to 0.014). Conclusions: CRP24 revealed good prognostic accuracy for IM in AP; its main role may be the selection of lowest risk patients.

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SUMMARY Chagas disease is a public health problem worldwide. The availability of diagnostic tools to predict the development of chronic Chagas cardiomyopathy is crucial to reduce morbidity and mortality. Here we analyze the prognostic value of adenosine deaminase serum activity (ADA) and C-reactive protein serum levels (CRP) in chagasic individuals. One hundred and ten individuals, 28 healthy and 82 chagasic patients were divided according to disease severity in phase I (n = 35), II (n = 29), and III (n = 18). A complete medical history, 12-lead electrocardiogram, chest X-ray, and M-mode echocardiogram were performed on each individual. Diagnosis of Chagas disease was confirmed by ELISA and MABA using recombinant antigens; ADA was determined spectrophotometrically and CRP by ELISA. The results have shown that CRP and ADA increased linearly in relation to disease phase, CRP being significantly higher in phase III and ADA at all phases. Also, CRP and ADA were positively correlated with echocardiographic parameters of cardiac remodeling and with electrocardiographic abnormalities, and negatively with ejection fraction. CRP and ADA were higher in patients with cardiothoracic index ≥ 50%, while ADA was higher in patients with ventricular repolarization disturbances. Finally, CRP was positively correlated with ADA. In conclusion, ADA and CRP are prognostic markers of cardiac dysfunction and remodeling in Chagas disease.

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Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Informática, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

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Dissertação para a obtenção de grau de doutor em Bioquímica pelo Instituto de Tecnologia Química e Biológica. Universidade Nova de Lisboa.

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ABSTRACT:C-reactive protein (CRP) has been widely used in the early risk assessment of patients with acute pancreatitis (AP), but unclear aspects about its prognostic accuracy in this setting persist. This project evaluated first CRP prognostic accuracy for severity, pancreatic necrosis (PNec), and in-hospital mortality (IM) in AP in terms of the best timing for CRP measurement and the optimal CRP cutoff points. Secondly it was evaluated the CRP measured at approximately 24 hours after hospital admission (CRP24) prognostic accuracy for IM in AP individually and in a combined model with a recent developed tool for the early risk assessment of patients with AP, the Bedside Index for Severity in AP (BISAP). Two single-centre retrospective cohort studies were held. The first study included 379 patients and the second study included 134 patients. Statistical methods such as the Hosmer-Lemeshow goodness-of-fit test, the area under the receiver-operating characteristic curve, the net reclassification improvement, and the integrated discrimination improvement were used. It was found that CRP measured at approximately 48 hours after hospital admission (CRP48) had a prognostic accuracy for severity, PNec, and IM in AP better than CRP measured at any other timing. It was observed that the optimal CRP48 cutoff points for severity, PNec, and IM in AP varied from 170mg/l to 190mg/l, values greater than the one most often recommended in the literature – 150mg/l. It was found that CRP24 had a good prognostic accuracy for IM in AP and that the cutoff point of 60mg/l had a negative predictive value of 100%. Finally it was observed that the prognostic accuracy of a combined model including BISAP and CRP24 for IM in AP could perform better than the BISAP alone model. These results might have a direct impact on the early risk assessment of patients with AP in the daily clinical practice.--------- RESUMO: A proteina c-reactiva (CRP) tem sido largamente usada na avaliação precoce do risco em doentes com pancreatite aguda (AP), mas aspectos duvidosos acerca do seu valor prognóstico neste contexto persistem. Este projecto avaliou primeiro o valor prognóstico da CRP para a gravidade, a necrose pancreática (PNec) e a mortalidade intra-hospitalar (IM) na AP em termos do melhor momento para efectuar a sua medição e dos seus pontos-de-corte óptimos. Em segundo lugar foi avaliado o valor prognóstico da proteína c-reactiva medida aproximadamente às 24 horas após a admissão hospitalar (CRP24) para a IM na AP isoladamente e num modelo combinado, que incluiu uma ferramenta de avaliação precoce do risco em doentes com AP recentemente desenvolvida, o Bedside Index for Severity in Acute Pancreatitis (BISAP). Dois estudos unicêntricos de coorte retrospectivo foram realizados. O primeiro estudo incluiu 379 doentes e o segundo estudo incluiu 134 doentes. Metodologias estatísticas como o teste de Hosmer-Lemeshow goodness-of-fit, a area under the receiver-operating characteristic curve, o net reclassification improvement e o integrated discrimination improvement foram usadas. Verificou-se que a CRP medida às 48 horas após a admissão hospitalar (CRP48) teve um valor prognóstico para a gravidade, a PNec e a IM na AP melhor do que a CRP medida em qualquer outro momento. Observou-se que os pontos de corte óptimos da CRP48 para a gravidade, a PNec e a IM na AP variaram entre 170mg/l e 190mg/l, valores acima do valor mais frequentemente recomendado na literatura – 150mg/l. Verificou-se que a CRP medida aproximadamente às 24 horas após a admissão hospitalar (CRP24) teve um bom valor prognóstico para a IM na AP e que o ponto de corte 60mg/l teve um valor preditivo negativo de 100%. Finalmente observou-se que o valor prognóstico de um modelo combinado incluindo o BISAP e a CRP24 para a IM na AP pode ter um desempenho melhor do que o do BISAP isoladamente. Estes resultados podem ter um impacto directo na avaliação precoce do risco em doentes com AP na prática clínica diária.

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Hybrid knowledge bases are knowledge bases that combine ontologies with non-monotonic rules, allowing to join the best of both open world ontologies and close world rules. Ontologies shape a good mechanism to share knowledge on theWeb that can be understood by both humans and machines, on the other hand rules can be used, e.g., to encode legal laws or to do a mapping between sources of information. Taking into account the dynamics present today on the Web, it is important for these hybrid knowledge bases to capture all these dynamics and thus adapt themselves. To achieve that, it is necessary to create mechanisms capable of monitoring the information flow present on theWeb. Up to today, there are no such mechanisms that allow for monitoring events and performing modifications of hybrid knowledge bases autonomously. The goal of this thesis is then to create a system that combine these hybrid knowledge bases with reactive rules, aiming to monitor events and perform actions over a knowledge base. To achieve this goal, a reactive system for the SemanticWeb is be developed in a logic-programming based approach accompanied with a language for heterogeneous rule base evolution having as its basis RIF Production Rule Dialect, which is a standard for exchanging rules over theWeb.

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This work studies the combination of safe and probabilistic reasoning through the hybridization of Monte Carlo integration techniques with continuous constraint programming. In continuous constraint programming there are variables ranging over continuous domains (represented as intervals) together with constraints over them (relations between variables) and the goal is to find values for those variables that satisfy all the constraints (consistent scenarios). Constraint programming “branch-and-prune” algorithms produce safe enclosures of all consistent scenarios. Special proposed algorithms for probabilistic constraint reasoning compute the probability of sets of consistent scenarios which imply the calculation of an integral over these sets (quadrature). In this work we propose to extend the “branch-and-prune” algorithms with Monte Carlo integration techniques to compute such probabilities. This approach can be useful in robotics for localization problems. Traditional approaches are based on probabilistic techniques that search the most likely scenario, which may not satisfy the model constraints. We show how to apply our approach in order to cope with this problem and provide functionality in real time.