955 resultados para Local solutions of partial differential equations
Resumo:
Local trajectories and arrangements play a significant role because the development of a research field, such as nanoscience and nanotechnology, requires substantial investments in human and instrumental resources. But why are there often concentrated in a limited number of places? What dynamics lead to such concentration? The hypothesis is that there is an assemblage of heterogeneous resources through the action of local actors. The chapter will explore, from an Actor Network Theory (ANT) perspective, how the local emergence of research dynamics from: the revival of local traditions, the local and national action of institutional entrepreneurs, controversial dynamics, and researchers' arrangements to involve other actors. It will examine how they connect up with each other and mutually commit themselves to the development of new technologies. It will focus on the role of narratives in this assembling: how were the local narratives of the past mobilized and to what effect.
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Iron is essential for retinal function but contributes to oxidative stress-mediated degeneration. Iron retinal homeostasis is highly regulated and transferrin (Tf), a potent iron chelator, is endogenously secreted by retinal cells. In this study, therapeutic potential of a local Tf delivery was evaluated in animal models of retinal degeneration. After intravitreal injection, Tf spread rapidly within the retina and accumulated in photoreceptors and retinal pigment epithelium, before reaching the blood circulation. Tf injected in the vitreous prior and, to a lesser extent, after light-induced retinal degeneration, efficiently protected the retina histology and function. We found an association between Tf treatment and the modulation of iron homeostasis resulting in a decrease of iron content and oxidative stress marker. The immunomodulation function of Tf could be seen through a reduction in macrophage/microglial activation as well as modulated inflammation responses. In a mouse model of hemochromatosis, Tf had the capacity to clear abnormal iron accumulation from retinas. And in the slow P23H rat model of retinal degeneration, a sustained release of Tf in the vitreous via non-viral gene therapy efficently slowed-down the photoreceptors death and preserved their function. These results clearly demonstrate the synergistic neuroprotective roles of Tf against retinal degeneration and allow identify Tf as an innovative and not toxic therapy for retinal diseases associated with oxidative stress.
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Partial-thickness tears of the supraspinatus tendon frequently occur at its insertion on the greater tubercule of the humerus, causing pain and reduced strength and range of motion. The goal of this work was to quantify the loss of loading capacity due to tendon tears at the insertion area. A finite element model of the supraspinatus tendon was developed using in vivo magnetic resonance images data. The tendon was represented by an anisotropic hyperelastic constitutive law identified with experimental measurements. A failure criterion was proposed and calibrated with experimental data. A partial-thickness tear was gradually increased, starting from the deep articular-sided fibres. For different values of tendon tear thickness, the tendon was mechanically loaded up to failure. The numerical model predicted a loss in loading capacity of the tendon as the tear thickness progressed. Tendon failure was more likely when the tendon tear exceeded 20%. The predictions of the model were consistent with experimental studies. Partial-thickness tears below 40% tear are sufficiently stable to persist physiotherapeutic exercises. Above 60% tear surgery should be considered to restore shoulder strength.
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In terms of the Fourier spectrum, a simple but general analytical expression is given for the evanescent field associated to a certain kind of non-paraxial exact solutions of the Maxwell equations. This expression enables one to compare the relative weight of the evanescent wave with regard to the propagating field. In addition, in those cases in which the evanescent term is significant, the magnitude of the field components across the transverse profile (including the evanescent features) can be determined. These results are applied to some illustrative examples.
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We show the existence of families of hip-hop solutions in the equal-mass 2N-body problem which are close to highly eccentric planar elliptic homographic motions of 2N bodies plus small perpendicular non-harmonic oscillations. By introducing a parameter ϵ, the homographic motion and the small amplitude oscillations can be uncoupled into a purely Keplerian homographic motion of fixed period and a vertical oscillation described by a Hill type equation. Small changes in the eccentricity induce large variations in the period of the perpendicular oscillation and give rise, via a Bolzano argument, to resonant periodic solutions of the uncoupled system in a rotating frame. For small ϵ ≠ 0, the topological transversality persists and Brouwer's fixed point theorem shows the existence of this kind of solutions in the full system
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Research on color difference evaluation has been active in recent thirty years. Several color difference formulas were developed for industrial applications. The aims of this thesis are to develop the color density which is denoted by comb g and to propose the color density based chromaticity difference formulas. Color density is derived from the discrimination ellipse parameters and color positions in the xy , xyY and CIELAB color spaces, and the color based chromaticity difference formulas are compared with the line element formulas and CIE 2000 color difference formulas. As a result of the thesis, color density represents the perceived color difference accurately, and it could be used to characterize a color by the attribute of perceived color difference from this color.
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In this work, we applied the free open source SCILAB software for the numerical integration of differential rate law equations to obtain the concentration profiles of chemical species involved in the kinetics of some complex reactions. An automated method was applied to construct the system of ordinary differential equations (ODE) from the postulated chemical models. The solutions of the ODEs were obtained numerically by standard SCILAB functions. We successfully simulated even complex chemical systems such as pH oscillators. This communication opens up the possibility of using SCILAB in simulations and modeling by our chemistry undergraduate students.
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The subject being analyzed of this Master’s Thesis is a development of a service that is used to define a current location of a mobile device. The service utilized data that is obtained from own GPS receiver in some possible cases and as well data from mobile devices which can be afforded for the current environment for acquisition of more precise position of the device. The computation environment is based on context of a mobile device. The service is implemented as an application for communicator series Nokia N8XX. The Master’s Thesis presents theoretical concept of the method and its practical implementation, architecture of the application, requirements and describes a process of its functionality. Also users’ work with application is presented and recommendations for possible future improvements are made.
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The arbitrary angular momentum solutions of the Schrödinger equation for a diatomic molecule with the general exponential screened coulomb potential of the form V(r) = (- a / r){1+ (1+ b )e-2b } has been presented. The energy eigenvalues and the corresponding eigenfunctions are calculated analytically by the use of Nikiforov-Uvarov (NU) method which is related to the solutions in terms of Jacobi polynomials. The bounded state eigenvalues are calculated numerically for the 1s state of N2 CO and NO
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ABSTRACT Knowledge of natural water availability, which is characterized by low flows, is essential for planning and management of water resources. One of the most widely used hydrological techniques to determine streamflow is regionalization, but the extrapolation of regionalization equations beyond the limits of sample data is not recommended. This paper proposes a new method for reducing overestimation errors associated with the extrapolation of regionalization equations for low flows. The method is based on the use of a threshold value for the maximum specific low flow discharge estimated at the gauging sites that are used in the regionalization. When a specific low flow, which has been estimated using the regionalization equation, exceeds the threshold value, the low flow can be obtained by multiplying the drainage area by the threshold value. This restriction imposes a physical limit to the low flow, which reduces the error of overestimating flows in regions of extrapolation. A case study was done in the Urucuia river basin, in Brazil, and the results showed the regionalization equation to perform positively in reducing the risk of extrapolation.
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OBJECTIVE: To verify whether the eradication of anal condylomata acuminata was effective for local control of HPV infection using anal colposcopy and anal brush cytology.METHODS: We evaluated 147 patients treated for anal margin and/or anal canal condyloma, with 108 HIV-positive and 39 HIV-negative individuals. The average age for males was 40 years for HIV-positive and 27.5 for HIV-negative. In females, the mean age was 37.5 years for HIV-positive and 31.5 for HIV-negative.RESULTS: Twenty-four patients (16.3%) had normal cytology and anal colposcopy, 16 (10.9%) normal cytology and altered anal colposcopy, 52 (35.4%) normal anal colposcopy and altered cytology, and 55 (37.4%) had altered cytology and anal colposcopy.CONCLUSION: the eradication of clinical lesions failed to locally control HPV infection.
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The inflammatory response elicited by various stimuli such as microbial products or cytokines is determined by differences in the pattern of cellular gene expression. We have used the differential display RT-PCR (DDRT-PCR) strategy to identify mRNAs that are differentially expressed in various murine cell types stimulated with pro-inflammatory cytokines, microbial products or anti-inflammatory drugs. Mouse embryonic fibroblasts (MEFs) were treated with IFNs, TNF, or sodium salicylate. Also, peritoneal macrophages from C3H/Hej mice were stimulated with T. cruzi-derived GPI-mucin and/or IFN-g. After DDRT-PCR, various cDNA fragments that were differentially represented on the sequencing gel were recovered, cloned and sequenced. Here, we describe a summary of several experiments and show that, when 16 of a total of 28 recovered fragments were tested for differential expression, 5 (31%) were found to represent mRNAs whose steady-state levels are indeed modulated by the original stimuli. Some of the identified cDNAs encode for known proteins that were not previously associated with the inflammatory process triggered by the original stimuli. Other cDNA fragments (8 of 21 sequences, or 38%) showed no significant homology with known sequences and represent new mouse genes whose characterization might contribute to our understanding of inflammation. In conclusion, DDRT-PCR has proven to be a potent technology that will allow us to identify genes that are differentially expressed when cells are subjected to changes in culture conditions or isolated from different organs.
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This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.
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A mathematical model to predict microbial growth in milk was developed and analyzed. The model consists of a system of two differential equations of first order. The equations are based on physical hypotheses of population growth. The model was applied to five different sets of data of microbial growth in dairy products selected from Combase, which is the most important database in the area with thousands of datasets from around the world, and the results showed a good fit. In addition, the model provides equations for the evaluation of the maximum specific growth rate and the duration of the lag phase which may provide useful information about microbial growth.