3 resultados para generalized binary group
em AMS Tesi di Dottorato - Alm@DL - Università di Bologna
Resumo:
Traditional logic gates are rapidly reaching the limits of miniaturization. Overheating of these components is no longer negligible. A new physical approach to the machine was proposed by Prof. C S. Lent “Molecular Quantum cellular automata”. Indeed the quantum-dot cellular automata (QCA) approach offers an attractive alternative to diode or transistor devices. Th units encode binary information by two polarizations without corrent flow. The units for QCA theory are called QCA cells and can be realized in several way. Molecules can act as QCA cells at room temperature. In collaboration with STMicroelectronic, the group of Electrochemistry of Prof. Paolucci and the Nananotecnology laboratory from Lecce, we synthesized and studied with many techniques surface-active chiral bis-ferrocenes, conveniently designed in order to act as prototypical units for molecular computing devices. The chemistry of ferrocene has been studied thoroughly and found the opportunity to promote substitution reaction of a ferrocenyl alcohols with various nucleophiles without the aid of Lewis acid as catalysts. The only interaction between water and the two reagents is involve in the formation of a carbocation specie which is the true reactive species. We have generalized this concept to other benzyl alcohols which generating stabilized carbocations. Carbocation describe in Mayr’s scale were fondametal for our research. Finally, we used these alcohols to alkylate in enantioselective way aldehydes via organocatalysis.
Resumo:
Over the years the Differential Quadrature (DQ) method has distinguished because of its high accuracy, straightforward implementation and general ap- plication to a variety of problems. There has been an increase in this topic by several researchers who experienced significant development in the last years. DQ is essentially a generalization of the popular Gaussian Quadrature (GQ) used for numerical integration functions. GQ approximates a finite in- tegral as a weighted sum of integrand values at selected points in a problem domain whereas DQ approximate the derivatives of a smooth function at a point as a weighted sum of function values at selected nodes. A direct appli- cation of this elegant methodology is to solve ordinary and partial differential equations. Furthermore in recent years the DQ formulation has been gener- alized in the weighting coefficients computations to let the approach to be more flexible and accurate. As a result it has been indicated as Generalized Differential Quadrature (GDQ) method. However the applicability of GDQ in its original form is still limited. It has been proven to fail for problems with strong material discontinuities as well as problems involving singularities and irregularities. On the other hand the very well-known Finite Element (FE) method could overcome these issues because it subdivides the computational domain into a certain number of elements in which the solution is calculated. Recently, some researchers have been studying a numerical technique which could use the advantages of the GDQ method and the advantages of FE method. This methodology has got different names among each research group, it will be indicated here as Generalized Differential Quadrature Finite Element Method (GDQFEM).
Resumo:
Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.
