943 resultados para Repeated Averages of Real-Valued Functions
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The optimal design of cold-formed steel columns is addressed in this paper, with two objectives: maximize the local-global buckling strength and maximize the distortional buckling strength. The design variables of the problem are the angles of orientation of cross-section wall elements the thickness and width of the steel sheet that forms the cross-section are fixed. The elastic local, distortional and global buckling loads are determined using Finite Strip Method (CUFSM) and the strength of cold-formed steel columns (with given length) is calculated using the Direct Strength Method (DSM). The bi-objective optimization problem is solved using the Direct MultiSearch (DMS) method, which does not use any derivatives of the objective functions. Trade-off Pareto optimal fronts are obtained separately for symmetric and anti-symmetric cross-section shapes. The results are analyzed and further discussed, and some interesting conclusions about the individual strengths (local-global and distortional) are found.
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The diagnostic value of real-time sonography in the study of portal hypertension was assessed in 66 patients with hepatosplenic schistosomiasis mansoni, all with Symmers's fibrosis and esophageal varices. Seventy-one individuals without schistosomiasis were selected as controls. The inner diameters of the portal vessels were measured by sonography in all patients and controls: splenoportography was also performed in the schistosomal group. Intra-splenic pressure was over 30 cm of water in 44 of 60 patients with schistosomiasis. The upper limit of normality for portal vessel diameters was set through receiver operating characteristic curve at 12 mm for portal vein, 9 mm for splenic vein at splenic hilus, and 9 mm for superior mesenteric vein. The best discriminative vein for the diagnosis of portal hypertension was the splenic vein followed by the portal vein. A direct correlation was observed between the diameter of the splenic vein, measured by sonography, and the intra-splenic pressure. Except for the paraumbilical and mesenteric veins, more frequently identified by sonography, there was no statistical difference in the frequency of visualization of splanchnic vessels by sonography or splenoportography.
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This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.
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The use of demand response programs enables the adequate use of resources of small and medium players, bringing high benefits to the smart grid, and increasing its efficiency. One of the difficulties to proceed with this paradigm is the lack of intelligence in the management of small and medium size players. In order to make demand response programs a feasible solution, it is essential that small and medium players have an efficient energy management and a fair optimization mechanism to decrease the consumption without heavy loss of comfort, making it acceptable for the users. This paper addresses the application of real-time pricing in a house that uses an intelligent optimization module involving artificial neural networks.
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The use of renewables have been increased I several countries around the world, namely in Europe. The wind power is generally the larger renewable resource with very specific characteristics in what concerns its variability and the inherent impacts in the power systems and electricity markets operation. This paper focuses on the Portuguese context of renewables use, including wind power. The work here presented includes the use of a real time pricing methodology developed by the authors aiming the reduction of electricity consumption in the moments of unexpected low wind power. A more specific example of application of real time pricing is demonstrated for the minimization of the operation costs in a distribution network. When facing lower wind power generation than expected from day ahead forecast, demand response is used in order to minimize the impacts of such wind availability change. In this way, consumers actively participate in regulation up and spinning reserve ancillary services through demand response programs.
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The use of Electric Vehicles (EVs) will change significantly the planning and management of power systems in a near future. This paper proposes a real-time tariff strategy for the charge process of the EVs. The main objective is to evaluate the influence of real-time tariffs in the EVs owners’ behaviour and also the impact in load diagram. The paper proposes the energy price variation according to the relation between wind generation and power consumption. The proposed strategy was tested in two different days in the Danish power system. January 31st and August 13th 2013 were selected because of the high quantities of wind generation. The main goal is to evaluate the changes in the EVs charging diagram with the energy price preventing wind curtailment.
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The dynamism and ongoing changes that the electricity markets sector is constantly suffering, enhanced by the huge increase in competitiveness, create the need of using simulation platforms to support operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents an enhanced electricity market simulator, based on multi-agent technology, which provides an advanced simulation framework for the study of real electricity markets operation, and the interactions between the involved players. MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) uses real data for the creation of realistic simulation scenarios, which allow the study of the impacts and implications that electricity markets transformations bring to different countries. Also, the development of an upper-ontology to support the communication between participating agents, provides the means for the integration of this simulator with other frameworks, such as MAN-REM (Multi-Agent Negotiation and Risk Management in Electricity Markets). A case study using the enhanced simulation platform that results from the integration of several systems and different tools is presented, with a scenario based on real data, simulating the MIBEL electricity market environment, and comparing the simulation performance with the real electricity market results.
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A thesis submitted in fulfilment of the requirements for the Degree of Doctor of Philosophy in Sanitary Engineering in the Faculty of Sciences and Technology of the New University of Lisbon
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Dissertation presented to obtain the Ph.D. degree in Biology at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa.
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Dissertation to Obtain the Degree of Master in Biomedical Engineering
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RESUMO:Os microrganismos reagem à súbita descida de temperatura através de uma resposta adaptativa específica que assegura a sua sobrevivência em condições desfavoráveis. Esta adaptação inclui alterações na composição da membrana, na maquinaria de tradução e transcrição. A resposta ao choque térmico pelo frio induz uma repressão da transcrição. No entanto, a descida de temperatura induz a produção de um grupo de proteínas específicas que ajudam a ajustar/re-ajustar o metabolismo celular às novas condições ambientais. Em E. coli o processo de adaptação demora apenas quatro horas, no qual um grupo de proteínas específicas são induzidas. Depois desde período recomeça lentamente a produção de proteínas.A ribonuclease R, uma das proteínas induzidas durante o choque térmico pelo frio, é uma das principais ribonucleases em E. coli envolvidas na degradação do RNA. É uma exoribonuclease que degrada RNA de cadeia dupla, possui funções importantes na maturação e “turnover” do RNA, libertação de ribossomas e controlo de qualidade de proteínas e RNAs. O nível celular desta enzima aumenta até dez vezes após exposição ao frio e estabiliza em células na fase estacionária. A capacidade de degradar RNA de dupla cadeia é importante a baixas temperaturas quando as estruturas de RNA estão mais estáveis. No entanto, este mecanismo é desconhecido. Embora a resposta específica ao “cold shock” tenha sido descoberta há mais de duas décadas e o número de proteínas envolvidas sugerirem que esta adaptação é rápida e simples, continuamos longe de compreender este processo. No nosso trabalho pretendemos descobrir proteínas que interactuem com a RNase R em condições ambientais diferentes através do método “TAP-tag” e espectrometria de massa. A informação obtida pode ser utilizada para deduzir algumas das novas funções da RNase R durante a adaptação bacteriana ao frio e durante a fase estacionária. Mais importante ainda, RNase R poderá ser recrutada para um complexo de proteínas de elevado peso molecular durante o “cold-shock”.------------ABSTRACT:Microorganisms react to the rapid temperature downshift with a specific adaptative response that ensures their survival in unfavorable conditions. Adaptation includes changes in membrane composition, in translation and transcription machinery. Cold shock response leads to overall repression of translation. However, temperature downshift induces production of a set of specific proteins that help to tune cell metabolism and readjust it to the new environmental conditions. For Escherichia coli the adaptation process takes only about four hours with a relatively small set of specifically induced proteins involved. After this time, protein production resumes, although at a slower rate. One of the cold inducible proteins is RNase R, one of the main E. coli ribonucleases involved in RNA degradation. RNase R is an exoribonuclease that digest double stranded RNA, serves important functions in RNA maturation and turnover, release of stalled ribosomes by trans-translation, and RNA and protein quality control. The level of this enzyme increases about ten-fold after cold induction, and it is also stabilised in cells growing in stationary phase. The RNase R ability to digest structured RNA is important at low temperatures where RNA structures are stabilized but the exact role of this mechanism remains unclear. Although specific bacterial cold shock response was discovered over two decades ago and the number of proteins involved suggests that this adaptation is fast and simple, we are still far from understanding this process. In our work we aimed to discover the proteins interacting with RNase R in different environmental conditions using TAP tag method and mass spectrometry analysis. The information obtained can be used to deduce some of the new functions of RNase R during adaptation of bacteria to cold and in stationary growth phase. Most importantly RNase R can be recruited into a high molecular mass complex of protein in cold shock.
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Many-core platforms are an emerging technology in the real-time embedded domain. These devices offer various options for power savings, cost reductions and contribute to the overall system flexibility, however, issues such as unpredictability, scalability and analysis pessimism are serious challenges to their integration into the aforementioned area. The focus of this work is on many-core platforms using a limited migrative model (LMM). LMM is an approach based on the fundamental concepts of the multi-kernel paradigm, which is a promising step towards scalable and predictable many-cores. In this work, we formulate the problem of real-time application mapping on a many-core platform using LMM, and propose a three-stage method to solve it. An extended version of the existing analysis is used to assure that derived mappings (i) guarantee the fulfilment of timing constraints posed on worst-case communication delays of individual applications, and (ii) provide an environment to perform load balancing for e.g. energy/thermal management, fault tolerance and/or performance reasons.
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The indiscriminate use of antibiotics in food-producing animals has received increasing attention as a contributory factor in the international emergence of antibiotic-resistant bacteria (Woodward in Pesticide, veterinary and other residues in food, CRC Press, Boca Raton, 2004). Numerous analytical methods for quantifying antibacterial residues in edible animal products have been developed over years (Woodward in Pesticide, veterinary and other residues in food, CRC Press, Boca Raton, 2004; Botsoglou and Fletouris in Handbook of food analysis, residues and other food component analysis, Marcel Dekker, Ghent, 2004). Being Amoxicillin (AMOX) one of those critical veterinary drugs, efforts have been made to develop simple and expeditious methods for its control in food samples. In literature, only one AMOX-selective electrode has been reported so far. In that work, phosphotungstate:amoxycillinium ion exchanger was used as electroactive material (Shoukry et al. in Electroanalysis 6:914–917, 1994). Designing new materials based on molecularly imprinted polymers (MIPs) which are complementary to the size and charge of AMOX could lead to very selective interactions, thus enhancing the selectivity of the sensing unit. AMOX-selective electrodes used imprinted polymers as electroactive materials having AMOX as target molecule to design a biomimetic imprinted cavity. Poly(vinyl chloride), sensors of methacrylic acid displayed Nernstian slopes (60.7 mV/decade) and low detection limits (2.9 × 10−5 mol/L). The potentiometric responses were not affected by pH within 4–5 and showed good selectivity. The electrodes were applied successfully to the analysis of real samples.
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Power laws, also known as Pareto-like laws or Zipf-like laws, are commonly used to explain a variety of real world distinct phenomena, often described merely by the produced signals. In this paper, we study twelve cases, namely worldwide technological accidents, the annual revenue of America׳s largest private companies, the number of inhabitants in America׳s largest cities, the magnitude of earthquakes with minimum moment magnitude equal to 4, the total burned area in forest fires occurred in Portugal, the net worth of the richer people in America, the frequency of occurrence of words in the novel Ulysses, by James Joyce, the total number of deaths in worldwide terrorist attacks, the number of linking root domains of the top internet domains, the number of linking root domains of the top internet pages, the total number of human victims of tornadoes occurred in the U.S., and the number of inhabitants in the 60 most populated countries. The results demonstrate the emergence of statistical characteristics, very close to a power law behavior. Furthermore, the parametric characterization reveals complex relationships present at higher level of description.
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We present stochastic dynamics on the production costs of Cournot competitions, based on perfect Nash equilibria of nonlinear R&D investment strategies to reduce the production costs of the firms at every period of the game. We analyse the effects that the R&D investment strategies can have in the profits of the firms along the time. We observe that, in certain cases, the uncertainty can improve the effects of the R&D strategies in the profits of the firms due to the non-linearity of the profit functions and also of the R&D parameters.