960 resultados para Homogenization Schemes


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Advanced optical biosensor platforms exploiting long range surface plasmons (LRSPs) and responsive N-isopropylacrylamide (NIPAAm) hydrogel binding matrix for the detection of protein and bacterial pathogen analytes were carried out. LRSPs are optical waves that originate from coupling of surface plasmons on the opposite sites of a thin metallic film embedded between two dielectrics with similar refractive indices. LRSPs exhibit orders of magnitude lower damping and more extended profile of field compared to regular surface plasmons (SPs). Their excitation is accompanied with narrow resonance and provides stronger enhancement of electromagnetic field intensity that can advance the sensitivity of surface plasmon resonance (SPR) and surface plasmon-enhanced fluorescence spectroscopy (SPFS) biosensors. Firstly, we investigated thin gold layers deposited on fluoropolymer surface for the excitation of LRSPs. The study indicates that the morphological, optical and electrical properties of gold film can be changed by the surface energy of fluoropolymer and affect the performance of a SPFS biosensor. A photo-crosslinkable NIPAAm hydrogel was grafted to the sensor surface in order to serve as a binding matrix. It was modified with bio-recognition elements (BREs) via amine coupling chemistry and offered the advantage of large binding capacity, stimuli responsive properties and good biocompatibility. Through experimental observations supported by numerical simulations describing diffusion mass transfer and affinity binding of target molecules in the hydrogel, the hydrogel binding matrix thickness, concentration of BREs and the profile of the probing evanescent field was optimized. Hydrogel with a up to micrometer thickness was shown to support additional hydrogel optical waveguide (HOW) mode which was employed for probing affinity binding events in the gel by means of refractometric and fluorescence measurements. These schemes allow to reach limits of detection (LODs) at picomolar and femtomolar levels, respectively. Besides hydrogel based experiments for detection of molecular analytes, long range surface plasmon-enhanced fluorescence spectroscopy (LRSP-FS) was employed for detection of bacterial pathogens. The influence of capture efficiency of bacteria on surfaces and the profile of the probing field on sensor response were investigated. The potential of LRSP-FS with extended evanescent field is demonstrated for detection of pathogenic E. coli O157:H7 on sandwich immunoassays . LOD as low as 6 cfu mL-1 with a detection time of 40 minutes was achieved.rn

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To determine the potential benefit of combined respiratory-cardiac triggering for diffusion-weighted imaging (DWI) of kidneys compared to respiratory triggering alone (RT).

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Self-monitoring of blood glucose (SMBG) in type 2 diabetes has increasingly been shown to display beneficial effects on glycemic control. SMBG is not only associated with a reduction of hemoglobin A1c but has also been demonstrated to increase patients' awareness of the disease. SMBG has also the potential to visualize and predict hypoglycemic episodes. International guidelines by the International Diabetes Federation, the European Society of Cardiology, and the European Association for the Study of Diabetes and also the International Society for Pediatric and Adolescent Diabetes emphasize that SMBG is an integral part of self-management. More recently, two European consensus documents have been published to give recommendations for frequency and timing of SMBG also for various clinical scenarios. Recently, a European expert panel was held to further facilitate and enhance standardized approaches to SMBG. The aim was to present simple, clinically meaningful, and standardized SMBG strategies for type 2 diabetes. The panel recommended a less intensive and an intensive scheme for SMBG across the type 2 diabetes continuum. The length and frequency of SMBG performance depend on the clinical circumstances and the quality of glycemic control. The expert panel also recommended further evaluation of various schemes for SMBG in type 2 diabetes in clinical studies.

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Data gathering, either for event recognition or for monitoring applications is the primary intention for sensor network deployments. In many cases, data is acquired periodically and autonomously, and simply logged onto secondary storage (e.g. flash memory) either for delayed offline analysis or for on demand burst transfer. Moreover, operational data such as connectivity information, node and network state is typically kept as well. Naturally, measurement and/or connectivity logging comes at a cost. Space for doing so is limited. Finding a good representative model for the data and providing clever coding of information, thus data compression, may be a means to use the available space to its best. In this paper, we explore the design space for data compression for wireless sensor and mesh networks by profiling common, publicly available algorithms. Several goals such as a low overhead in terms of utilized memory and compression time as well as a decent compression ratio have to be well balanced in order to find a simple, yet effective compression scheme.

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Instrumental daily series of temperature are often affected by inhomogeneities. Several methods are available for their correction at monthly and annual scales, whereas few exist for daily data. Here, an improved version of the higher-order moments (HOM) method, the higher-order moments for autocorrelated data (HOMAD), is proposed. HOMAD addresses the main weaknesses of HOM, namely, data autocorrelation and the subjective choice of regression parameters. Simulated series are used for the comparison of both methodologies. The results highlight and reveal that HOMAD outperforms HOM for small samples. Additionally, three daily temperature time series from stations in the eastern Mediterranean are used to show the impact of homogenization procedures on trend estimation and the assessment of extremes. HOMAD provides an improved correction of daily temperature time series and further supports the use of corrected daily temperature time series prior to climate change assessment.

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We derive a new class of iterative schemes for accelerating the convergence of the EM algorithm, by exploiting the connection between fixed point iterations and extrapolation methods. First, we present a general formulation of one-step iterative schemes, which are obtained by cycling with the extrapolation methods. We, then square the one-step schemes to obtain the new class of methods, which we call SQUAREM. Squaring a one-step iterative scheme is simply applying it twice within each cycle of the extrapolation method. Here we focus on the first order or rank-one extrapolation methods for two reasons, (1) simplicity, and (2) computational efficiency. In particular, we study two first order extrapolation methods, the reduced rank extrapolation (RRE1) and minimal polynomial extrapolation (MPE1). The convergence of the new schemes, both one-step and squared, is non-monotonic with respect to the residual norm. The first order one-step and SQUAREM schemes are linearly convergent, like the EM algorithm but they have a faster rate of convergence. We demonstrate, through five different examples, the effectiveness of the first order SQUAREM schemes, SqRRE1 and SqMPE1, in accelerating the EM algorithm. The SQUAREM schemes are also shown to be vastly superior to their one-step counterparts, RRE1 and MPE1, in terms of computational efficiency. The proposed extrapolation schemes can fail due to the numerical problems of stagnation and near breakdown. We have developed a new hybrid iterative scheme that combines the RRE1 and MPE1 schemes in such a manner that it overcomes both stagnation and near breakdown. The squared first order hybrid scheme, SqHyb1, emerges as the iterative scheme of choice based on our numerical experiments. It combines the fast convergence of the SqMPE1, while avoiding near breakdowns, with the stability of SqRRE1, while avoiding stagnations. The SQUAREM methods can be incorporated very easily into an existing EM algorithm. They only require the basic EM step for their implementation and do not require any other auxiliary quantities such as the complete data log likelihood, and its gradient or hessian. They are an attractive option in problems with a very large number of parameters, and in problems where the statistical model is complex, the EM algorithm is slow and each EM step is computationally demanding.

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PURPOSE: The surgical treatment of oral cancer results in functional and aesthetical impairments. Patients' quality of life is considerably impaired by oral symptoms resulting from therapy of oral cancer. In many cases the inevitable resection of the tumor, as well as the adjuvant radiochemotherapy will cause the destruction of physiologically and anatomically important structures. One focus of research was the specific rehabilitation of dental loss by functional dentures. Another was the course of 19 impairments (comprehension of speech for unknown others, comprehension of speech for familiar others, eating/swallowing, mobility of the tongue, opening range of the mouth, mobility of lower jaw, mobility of neck, mobility of arms and shoulders, sense of taste, sense of smell, appearance, strength, appetite, respiration, pain, swelling, xerostomia, halitosis). METHODS: Commissioned by the German, Austrian and Swiss cooperative group on tumors of the maxillofacial region (DOSAK), data were collected in 3.894 questionnaires at 43 hospitals in Germany, Austria and Switzerland. The catalogue comprised 147 items in 9 chapters. At the end of the enquiry, 1.761 anonymous questionnaires were returned by 38 hospitals. 1.652 of these could be evaluated regarding the question. RESULTS: The sum score of the 19 impairments was highly increased immediately after the operation and recovered over the next 6 months, without, however, reaching the pre-surgery level. Of 1.652 patients, only 35% did not lose any teeth during therapy. 23% lost up to 5, 17% up to 10 teeth. A quarter of the patients lost more than 10 teeth. The more teeth were lost, the greater the decline of quality of life (p < or = 0.001), although this could be allayed by the functionality of the dentures (p < or = 0.001). There is a reciprocal dependence between the functionality of dental prosthetics and impairment by eating/swallowing (p < or = 0.001). CONCLUSIONS: Patients' quality of life after radical surgery of a carcinoma of the oral cavity depends not only on the functionality of dentures and the specificity of rehabilitation, but also from the initial findings, the extent and location of the resection, the chosen therapy, the general circumstances of the patient's life as well as their strategies of coping. These factors, however, unlike those of functionality of dental prosthesis and rehabilitation, are not modifiable.

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The maximum principle is an important property of solutions to PDE. Correspondingly, it's of great interest for people to design a high order numerical scheme solving PDE with this property maintained. In this thesis, our particular interest is solving convection-dominated diffusion equation. We first review a nonconventional maximum principle preserving(MPP) high order finite volume(FV) WENO scheme, and then propose a new parametrized MPP high order finite difference(FD) WENO framework, which is generalized from the one solving hyperbolic conservation laws. A formal analysis is presented to show that a third order finite difference scheme with this parametrized MPP flux limiters maintains the third order accuracy without extra CFL constraint when the low order monotone flux is chosen appropriately. Numerical tests in both one and two dimensional cases are performed on the simulation of the incompressible Navier-Stokes equations in vorticity stream-function formulation and several other problems to show the effectiveness of the proposed method.