834 resultados para Network Analysis Methods


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Sequence analysis of the mitochondrial genome has become a routine method in the study of mitochondrial diseases. Quite often, the sequencing efforts in the search of pathogenic or disease-associated mutations are affected by technical and interpretive pr

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A series of new single-step methods and their corresponding algorithms with automatic step size adjustment for model equations of fiber Raman amplifiers are proposed and compared in this paper. On the basis of the Newton-Raphson method, multiple shooting algorithms for the two-point boundary value problems involved in solving Raman amplifier propagation equations are constructed. A verified example shows that, compared with the traditional Runge-Kutta methods, the proposed methods can increase the accuracy by more than two orders of magnitude under the same conditions. The simulations for Raman amplifier propagation equations demonstrate that our methods can increase the computing speed by more than 5 times, extend the step size significantly, and improve the stability in comparison with the Dormand-Prince method. The numerical results show that the combination of the multiple shooting algorithms and the proposed methods has the capacity to rapidly and effectively solve the model equations of multipump Raman amplifiers under various conditions such as co-, counter- and bi-directionally pumped schemes, as well as dual-order pumped schemes.

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Current based microscopic defect analysis methods such as current deep level transient spectroscopy (I-DLTS) and thermally stimulated current (TSC) have been further developed in accordance with the need for the defect analysis of highly irradiated (Phi(n) > 10(13) n/cm(2)) high resistivity silicon detectors. The new I-DLTS/TSC system has a temperature range of 8 K less than or equal to T less than or equal to 450 K and a high sensitivity that can detect a defect concentration of less than 10(10)/cm(3) (background noise as low as 10 fA). A new filling method using different wavelength laser illumination has been applied, which is more efficient and suitable than the traditional voltage pulse filling. It has been found that the filling of a defect level depends on such factors as the total concentration of free carriers generated or injected, the penetration length of the laser (laser wavelength), the temperature at which the filling is taking place, as well as the decay time after the filling (but before the measurement). The mechanism of the defect filling can be explained by the competition between trapping and detrapping of defect levels, possible capture cross section temperature dependence, and interaction among various defect levels in terms of charge transferring. Optimum defect filling conditions have been suggested for highly irradiated high resistivity silicon detectors.

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The successful design of biomaterial scaffolds for articular cartilage tissue engineering requires an understanding of the impact of combinations of material formulation parameters on diverse and competing functional outcomes of biomaterial performance. This study sought to explore the use of a type of unsupervised artificial network, a self-organizing map, to identify relationships between scaffold formulation parameters (crosslink density, molecular weight, and concentration) and 11 such outcomes (including mechanical properties, matrix accumulation, metabolite usage and production, and histological appearance) for scaffolds formed from crosslinked elastin-like polypeptide (ELP) hydrogels. The artificial neural network recognized patterns in functional outcomes and provided a set of relationships between ELP formulation parameters and measured outcomes. Mapping resulted in the best mean separation amongst neurons for mechanical properties and pointed to crosslink density as the strongest predictor of most outcomes, followed by ELP concentration. The map also grouped formulations together that simultaneously resulted in the highest values for matrix production, greatest changes in metabolite consumption or production, and highest histological scores, indicating that the network was able to recognize patterns amongst diverse measurement outcomes. These results demonstrated the utility of artificial neural network tools for recognizing relationships in systems with competing parameters, toward the goal of optimizing and accelerating the design of biomaterial scaffolds for articular cartilage tissue engineering.

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The introduction of advanced welding methods as an alternative joining process to riveting in the manufacture of primary aircraft structure has the potential to realize reductions in both manufacturing costs and structural weight. Current design and analysis methods for aircraft panels have been developed and validated for riveted fabrication. For welded panels, considering the buckling collapse design philosophy of aircraft stiffened panels, strength prediction methods considering welding process effects for both local-buckling and post-buckling behaviours must be developed and validated. This article reports on the work undertaken to develop analysis methods for the crippling failure of stiffened panels fabricated using laser beam and friction stir welding. The work assesses modifications to conventional analysis methods and finite-element analysis methods for strength prediction. The analysis work is validated experimentally with welded single stiffener crippling specimens. The experimental programme has demonstrated the potential static strength of laser beam and friction stir welded sheet-stiffener joints for post-buckling panel applications. The work undertaken has demonstrated that the crippling behaviour of welded stiffened panels may be analysed considering standard-buckling behaviour. However, stiffened panel buckling analysis procedures must be altered to account for the weld joint geometry and process altered material properties. © IMechE 2006.

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This paper considers invariant texture analysis. Texture analysis approaches whose performances are not affected by translation, rotation, affine, and perspective transform are addressed. Existing invariant texture analysis algorithms are carefully studied and classified into three categories: statistical methods, model based methods, and structural methods. The importance of invariant texture analysis is presented first. Each approach is reviewed according to its classification, and its merits and drawbacks are outlined. The focus of possible future work is also suggested.