898 resultados para Flail space model
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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The study compared the host response to a human and a porcine acellular dermal tissue implanted in the subcutaneous space of a rat model. The human and porcine acellular grafts were surgically implanted in the subcutaneous tissue of rats (5 rats/group) and the materials were evaluated at 7, 15, 30, 60 and 180 postoperative days (PO). The histological immune response was quantified using a digital image analysis system, which evaluated the number of vessels present in the implants and in the surrounding soft tissue, the area of inflammatory cell infiltration in the grafts, the width of the capsular formation present around the tissues and the area of implants absorbed. The data were submitted to statistical analysis. Light microscopy showed mononuclear cellular infiltration, the presence of a capsular formation surrounding the grafts and the presence of vacuolar structures (optically empty spaces) inside the implants. The image analysis comparing both materials showed significant inflammatory cells in the human graft at 15 and 30 PO, thicker capsular formation in the porcine tissue at 60 PO, increased number of vessels inside the implants and in the surrounding tissues in the porcine graft and a similar absorption pattern in both materials at 180 PO. The histological findings showed that both tissues were well-tolerated when implanted in the subcutaneous tissue of rats, allowing us to consider the porcine acellular dermal graft as a provisional alternative material for reconstructive plastic surgery. Copyright © 2005 Taylor & Francis LLC.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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In this work we present an agent-based model for the spread of tuberculosis where the individuals can be infected with either drug-susceptible or drug-resistant strains and can also receive a treatment. The dynamics of the model and the role of each one of the parameters are explained. The whole set of parameters is explored to check their importance in the numerical simulation results. The model captures the beneficial impact of the adequate treatment on the prevalence of tuberculosis. Nevertheless, depending on the treatment parameters range, it also captures the emergence of drug resistance. Drug resistance emergence is particularly likely to occur for parameter values corresponding to less efficacious treatment, as usually found in developing countries.
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In questa tesi sono state applicate le tecniche del gruppo di rinormalizzazione funzionale allo studio della teoria quantistica di campo scalare con simmetria O(N) sia in uno spaziotempo piatto (Euclideo) che nel caso di accoppiamento ad un campo gravitazionale nel paradigma dell'asymptotic safety. Nel primo capitolo vengono esposti in breve alcuni concetti basilari della teoria dei campi in uno spazio euclideo a dimensione arbitraria. Nel secondo capitolo si discute estensivamente il metodo di rinormalizzazione funzionale ideato da Wetterich e si fornisce un primo semplice esempio di applicazione, il modello scalare. Nel terzo capitolo è stato studiato in dettaglio il modello O(N) in uno spaziotempo piatto, ricavando analiticamente le equazioni di evoluzione delle quantità rilevanti del modello. Quindi ci si è specializzati sul caso N infinito. Nel quarto capitolo viene iniziata l'analisi delle equazioni di punto fisso nel limite N infinito, a partire dal caso di dimensione anomala nulla e rinormalizzazione della funzione d'onda costante (approssimazione LPA), già studiato in letteratura. Viene poi considerato il caso NLO nella derivative expansion. Nel quinto capitolo si è introdotto l'accoppiamento non minimale con un campo gravitazionale, la cui natura quantistica è considerata a livello di QFT secondo il paradigma di rinormalizzabilità dell'asymptotic safety. Per questo modello si sono ricavate le equazioni di punto fisso per le principali osservabili e se ne è studiato il comportamento per diversi valori di N.
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Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.
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Stem cell regeneration of damaged tissue has recently been reported in many different organs. Since the loss of retinal pigment epithelium (RPE) in the eye is associated with a major cause of visual loss - specifically, age-related macular degeneration - we investigated whether hematopoietic stem cells (HSC) given systemically can home to the damaged subretinal space and express markers of RPE lineage. Green fluorescent protein (GFP) cells of bone marrow origin were used in a sodium iodate (NaIO(3)) model of RPE damage in the mouse. The optimal time for adoptive transfer of bone marrow-derived stem cells relative to the time of injury and the optimal cell type [whole bone marrow, mobilized peripheral blood, HSC, facilitating cells (FC)] were determined by counting the number of GFP(+) cells in whole eye flat mounts. Immunocytochemistry was performed to identify the bone marrow origin of the cells in the RPE using antibodies for CD45, Sca-1, and c-kit, as well as the expression of the RPE-specific marker, RPE-65. The time at which bone marrow-derived cells were adoptively transferred relative to the time of NaIO(3) injection did not significantly influence the number of cells that homed to the subretinal space. At both one and two weeks after intravenous (i.v.) injection, GFP(+) cells of bone marrow origin were observed in the damaged subretinal space, at sites of RPE loss, but not in the normal subretinal space. The combined transplantation of HSC+FC cells appeared to favor the survival of the homed stem cells at two weeks, and RPE-65 was expressed by adoptively transferred HSC by four weeks. We have shown that systemically injected HSC homed to the subretinal space in the presence of RPE damage and that FC promoted survival of these cells. Furthermore, the RPE-specific marker RPE-65 was expressed on adoptively transferred HSC in the denuded areas.
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Turbulence affects traditional free space optical communication by causing speckle to appear in the received beam profile. This occurs due to changes in the refractive index of the atmosphere that are caused by fluctuations in temperature and pressure, resulting in an inhomogeneous medium. The Gaussian-Schell model of partial coherence has been suggested as a means of mitigating these atmospheric inhomogeneities on the transmission side. This dissertation analyzed the Gaussian-Schell model of partial coherence by verifying the Gaussian-Schell model in the far-field, investigated the number of independent phase control screens necessary to approach the ideal Gaussian-Schell model, and showed experimentally that the Gaussian-Schell model of partial coherence is achievable in the far-field using a liquid crystal spatial light modulator. A method for optimizing the statistical properties of the Gaussian-Schell model was developed to maximize the coherence of the field while ensuring that it does not exhibit the same statistics as a fully coherent source. Finally a technique to estimate the minimum spatial resolution necessary in a spatial light modulator was developed to effectively propagate the Gaussian-Schell model through a range of atmospheric turbulence strengths. This work showed that regardless of turbulence strength or receiver aperture, transmitting the Gaussian-Schell model of partial coherence instead of a fully coherent source will yield a reduction in the intensity fluctuations of the received field. By measuring the variance of the intensity fluctuations and the received mean, it is shown through the scintillation index that using the Gaussian-Schell model of partial coherence is a simple and straight forward method to mitigate atmospheric turbulence instead of traditional adaptive optics in free space optical communications.
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In the context of expensive numerical experiments, a promising solution for alleviating the computational costs consists of using partially converged simulations instead of exact solutions. The gain in computational time is at the price of precision in the response. This work addresses the issue of fitting a Gaussian process model to partially converged simulation data for further use in prediction. The main challenge consists of the adequate approximation of the error due to partial convergence, which is correlated in both design variables and time directions. Here, we propose fitting a Gaussian process in the joint space of design parameters and computational time. The model is constructed by building a nonstationary covariance kernel that reflects accurately the actual structure of the error. Practical solutions are proposed for solving parameter estimation issues associated with the proposed model. The method is applied to a computational fluid dynamics test case and shows significant improvement in prediction compared to a classical kriging model.
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"June 1969."
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Includes index.
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"June 1969."
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"August 1976."