914 resultados para units-invariant benchmarking
Resumo:
Sensitivity of output of a linear operator to its input can be quantified in various ways. In Control Theory, the input is usually interpreted as disturbance and the output is to be minimized in some sense. In stochastic worst-case design settings, the disturbance is considered random with imprecisely known probability distribution. The prior set of probability measures can be chosen so as to quantify how far the disturbance deviates from the white-noise hypothesis of Linear Quadratic Gaussian control. Such deviation can be measured by the minimal Kullback-Leibler informational divergence from the Gaussian distributions with zero mean and scalar covariance matrices. The resulting anisotropy functional is defined for finite power random vectors. Originally, anisotropy was introduced for directionally generic random vectors as the relative entropy of the normalized vector with respect to the uniform distribution on the unit sphere. The associated a-anisotropic norm of a matrix is then its maximum root mean square or average energy gain with respect to finite power or directionally generic inputs whose anisotropy is bounded above by a≥0. We give a systematic comparison of the anisotropy functionals and the associated norms. These are considered for unboundedly growing fragments of homogeneous Gaussian random fields on multidimensional integer lattice to yield mean anisotropy. Correspondingly, the anisotropic norms of finite matrices are extended to bounded linear translation invariant operators over such fields.
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The minimal supersymmetric standard model involves a rather restrictive Higgs potential with two Higgs fields. Recently, the full set of classes of symmetries allowed in the most general two-Higgs-doublet model was identified; these classes do not include the supersymmetric limit as a particular class. Thus, a physically meaningful definition of the supersymmetric limit must involve the interaction of the Higgs sector with other sectors of the theory. Here we show how one can construct basis invariant probes of supersymmetry involving both the Higgs sector and the gaugino-Higgsino-Higgs interactions.
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This paper is on the problem of short-term hydro scheduling (STHS), particularly concerning head-dependent reservoirs under competitive environment. We propose a novel method, based on mixed-integer nonlinear programming (MINLP), for optimising power generation efficiency. This method considers hydroelectric power generation as a nonlinear function of water discharge and of the head. The main contribution of this paper is that discharge ramping constraints and start/stop of units are also considered, in order to obtain more realistic and feasible results. The proposed method has been applied successfully to solve two case studies based on Portuguese cascaded hydro systems, providing a higher profit at an acceptable computation time in comparison with classical optimisation methods based on mixed-integer linear programming (MILP).
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Benchmarking is an important tool to organisations to improve their productivity, product quality, process efficiency or services. From Benchmarking the organisations could compare their performance with competitors and identify their strengths and weaknesses. This study intends to do a benchmarking analysis on the main Iberian Sea ports with a special focus on their container terminals efficiency. To attain this, the DEA (data envelopment analysis) is used since it is considered by several researchers as the most effective method to quantify a set of key performance indicators. In order to reach a more reliable diagnosis tool the DEA is used together with the data mining in comparing the sea ports operational data of container terminals during 2007.Taking into account that sea ports are global logistics networks the performance evaluation is essential to an effective decision making in order to improve their efficiency and, therefore, their competitiveness.
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A descriptive study was developed to compare air and surfaces fungal contamination in ten hospitals’ food units and two food units from companies. Fifty air samples of 250 litres through impaction method were collected from hospitals’ food units and 41 swab samples from surfaces were also collected, using a 10 by 10 cm square stencil. Regarding the two companies, ten air samples and eight surface samples were collected. Air and surface samples were collected in food storage facilities, kitchen, food plating and canteen. Outdoor air was also collected since this is the place regarded as a reference. Simultaneously, temperature, relative humidity and meal numbers were registered. Concerning air from hospitals’ food units, 32 fungal species were identified, being the two most commonly isolated genera Penicillium sp.
Resumo:
A descriptive study was developed to monitor air fungal contamination in ten food units from hospitals. Fifty air samples of 250 litres were collected through impaction method. Samples were collected in food storage facilities, kitchen, food plating, canteen and also, outside premises, since this is the place regarded as reference. Simultaneously, environmental parameters were also monitored, including temperature and relative humidity through the equipment Babouc, LSI Sistems and according to the International Standard ISO 7726.
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A descriptive study was developed in order to compare indoor and outdoor air contamination caused by fungi and particles in seven poultry units. Twenty eight air samples of 25 litters were collected through the impaction method on malt extract agar. Air sampling and particles concentration measurement were done in the interior and also outside premises of the poultries’ pavilions. Regarding the fungal load in the air, indoor concentration of mold was higher than outside air in six poultry units. Twenty eight species / genera of fungi were identified indoor, being Scopulariopsis brevicaulis (40.5%) the most commonly isolated species and Rhizopus sp. (30.0%) the most commonly isolated genus. Concerning outdoor, eighteen species/genera of fungi were isolated, being Scopulariopsis brevicaulis (62.6%) also the most isolated. All the poultry farms analyzed presented indoor fungi different from the ones identified outdoors. Regarding particles’ contamination, PM2.5, PM5.0 and PM10 had a statistically significant difference (Mann-Whitney U test) between the inside and outside of the pavilions, with the inside more contaminated (p=.006; p=.005; p=.005, respectively). The analyzed poultry units are potential reservoirs of substantial amounts of fungi and particles and could therefore free them in the atmospheric air. The developed study showed that indoor air was more contaminated than outdoors, and this can result in emission of potentially pathogenic fungi and particles via aerosols from poultry units to the environment, which may post a considerable risk to public health and contribute to environmental pollution.
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The best places to locate the Gas Supply Units (GSUs) on a natural gas systems and their optimal allocation to loads are the key factors to organize an efficient upstream gas infrastructure. The number of GSUs and their optimal location in a gas network is a decision problem that can be formulated as a linear programming problem. Our emphasis is on the formulation and use of a suitable location model, reflecting real-world operations and constraints of a natural gas system. This paper presents a heuristic model, based on lagrangean approach, developed for finding the optimal GSUs location on a natural gas network, minimizing expenses and maximizing throughput and security of supply.The location model is applied to the Iberian high pressure natural gas network, a system modelised with 65 demand nodes. These nodes are linked by physical and virtual pipelines – road trucks with gas in liquefied form. The location model result shows the best places to locate, with the optimal demand allocation and the most economical gas transport mode: by pipeline or by road truck.
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Natural gas industry has been confronted with big challenges: great growth in demand, investments on new GSUs – gas supply units, and efficient technical system management. The right number of GSUs, their best location on networks and the optimal allocation to loads is a decision problem that can be formulated as a combinatorial programming problem, with the objective of minimizing system expenses. Our emphasis is on the formulation, interpretation and development of a solution algorithm that will analyze the trade-off between infrastructure investment expenditure and operating system costs. The location model was applied to a 12 node natural gas network, and its effectiveness was tested in five different operating scenarios.
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OBJECTIVE: To identify potential prognostic factors for neonatal mortality among newborns referred to intensive care units. METHODS: A live-birth cohort study was carried out in Goiânia, Central Brazil, from November 1999 to October 2000. Linked birth and infant death certificates were used to ascertain the cohort of live born infants. An additional active surveillance system of neonatal-based mortality was implemented. Exposure variables were collected from birth and death certificates. The outcome was survivors (n=713) and deaths (n=162) in all intensive care units in the study period. Cox's proportional hazards model was applied and a Receiver Operating Characteristic curve was used to compare the performance of statistically significant variables in the multivariable model. Adjusted mortality rates by birth weight and 5-min Apgar score were calculated for each intensive care unit. RESULTS: Low birth weight and 5-min Apgar score remained independently associated to death. Birth weight equal to 2,500g had 0.71 accuracy (95% CI: 0.65-0.77) for predicting neonatal death (sensitivity =72.2%). A wide variation in the mortality rates was found among intensive care units (9.5-48.1%) and two of them remained with significant high mortality rates even after adjusting for birth weight and 5-min Apgar score. CONCLUSIONS: This study corroborates birth weight as a sensitive screening variable in surveillance programs for neonatal death and also to target intensive care units with high mortality rates for implementing preventive actions and interventions during the delivery period.
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In this paper a solution to an highly constrained and non-convex economical dispatch (ED) problem with a meta-heuristic technique named Sensing Cloud Optimization (SCO) is presented. The proposed meta-heuristic is based on a cloud of particles whose central point represents the objective function value and the remaining particles act as sensors "to fill" the search space and "guide" the central particle so it moves into the best direction. To demonstrate its performance, a case study with multi-fuel units and valve- point effects is presented.
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A mundividência contemporânea contextualiza a actividade empresarial num ambiente crescentemente competitivo e exigente em termos de eficiência das organizações. A inovação é um factor relevante para que as empresas vençam as concorrentes e se fortaleçamno mercado. Começamos, assim, por enquadrar este conceito de acordo com o que alguns dos autores mais conhecidos afirmam, explicando seguidamente quais os principais tipos de inovação e seu significado, dando relevo aos que aqui nos interessam. Posteriormente abordamos e explicamos o benchmarking, a sua importância como instrumento de gestão e as diversas tipologias de aplicação do mesmo. Fechamos o ciclo com a interligação desta ferramenta com a gestão da inovação, salientando os aspectos da utilização da primeira que facilitam a gestão da segunda. Abordamos de forma sintética a ligação do benchmarking estratégico com a gestão da inovação e apresentamos as nossas conclusões sabre a eficiência da parceria entre benchmarking e inovação.
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Dissertação de natureza científica realizada para obtenção do grau de Mestre em Engenharia de Redes de Computadores e Multimédia