Today, usage of complex circuit designs in computers, in multimedia applications and communication devices is widespread and still increasing. At the same time, due to Moore's Law we do not expect to see an end in the growth of the complexity of digital circuits. The decreasing ability of common validation techniques -- like simulation -- to assure correctness of a circuit design enlarges the need for formal verification techniques. Formal verification delivers a mathematical proof that a given implementation of a design fulfills its specification. One of the basic and during the last years widely used data structure in formal verification are the so called Ordered Binary Decision Diagrams (OBDDs) introduced by R. Bryant in 1986. The topic of this thesis is integration of structural high-level information in the OBDD-based formal verification of sequential systems. This work consist of three major parts, covering different layers of formal verification applications: At the application layer, an assertion checking methodology, integrated in the verification flow of the high-level design and verification tool Protocol Compiler is presented. At the algorithmic layer, new approaches for partitioning of transition relations of complex finite state machines, that significantly improve the performance of OBDD-based sequential verification are introduced. Finally, at the data structure level, dynamic variable reordering techniques that drastically reduce the time required for reordering without a trade-off in OBDD-size are described. Overall, this work demonstrates how a tighter integration of applications by using structural information can significantly improve the efficiency of formal verification applications in an industrial setting.

Mothers and Daughters: The Female English Bildungsroman, 1811-1915 This dissertation analyses the mother-daughter-relationship of five female apprenticeship novels. In the course of the study of Jane Austen's Sense and Sensibility (1811), Charlotte Brontë's Jane Eyre (1847), Elizabeth Gaskell's Wives and Daughters (1865), George Eliot's The Mill on the Floss (1860) and Virginia Woolf's The Voyage Out (1915) modern feminist, psychological, and psychoanalytical theories concerning the mother-daughter-conflict and female development are considered as well as autobio-graphic material and the authoresses' Œuvres. The historical context, the social and psychohistoric con-ditions, and changes in England during the 19th and beginning 20th century (especially concerning family, female socialisation and role training, motherhood, children's education) are studied and the features and achievements of the female Bildungsroman, that experiences an upswing during this time, emphasized. The dissertation shows the development of the female apprenticeship novel concerning its presentation of mother, daughter, and mother-daughter-relationship and also the enormous progressive-ness of this genre concerning the description of details of this relationship. The analysis demonstrates that all novels show complex and problematic mother-daughter-relationships, that for the daughters are on the one hand traumatic, but on the other hand lead to self-discovery and autonomy. The texts present the mother-daughter-relationship as highly ambivalent, oscillating between love, identification, aggression, rejection, rivalry, and rebellion. In this way they serve to correct the female doctrine and the ideological mother image of the Victorian period as much as the cliché of childhood as an idyllic condition without conflicts, and thus anticipate psychological discoveries and efforts of later periods. Furthermore, it becomes obvious that the authoresses put their own problematic mother-daughter-relationship into literary form and thus try to overcome it; that the fictitious mother-daughter-relation-ships often have a compensatory function. The fact that the analysed novels admit to the mother-daughter-relationship so early such an importance, constitutes their rank and justifies their place in the English literature and culture.

Improving market opportunities for local public transportation requires marketing strategies which put the customer in the centre of business considerations. The customer is the "source of wealth", for he or she provides for turnover, income and market dynamics. This context is the crucial market-strategy approach in the public transport sector. Therefore the most important element in the strategic marketing approach of customer orientation in public transportation is not only to design a self-contained system of marketing measures for convincing potential customers of the advantages of the product, but also to create "product quality" in order to gain "customer confidence". The key point of the concept of a customer-oriented "public transport/ation supply structure" is to develop a simplified method of estimating the market potential in order to confirm or verify sufficient demand effects that are predominantly based on traffic corridors burdened with heavy car traffic. Correspondingly, a public transport/ation supply structure is developed which will offer the customer extensive and therefore attractive access to public transport. Applying this attractive public transport/ation system enables the use of "economic potentials" for financing public transport outside urban agglomerations, which is only made possible by a consistent strategic marketing concept of customer orientation.

In this thesis, we study the convergence behavior of an efficient optimization method used for the identification of parameters for underdetermined systems. The research is motivated by optimization problems arising from the estimation of parameters in neural networks as well as in option pricing models. In the first application, we are concerned with neural networks used to forecasting stock market indices. Since neural networks are able to describe extremely complex nonlinear structures they are used to improve the modelling of the nonlinear dependencies occurring in the financial markets. Applying neural networks to the forecasting of economic indicators, we are confronted with a nonlinear least squares problem of large dimension. Furthermore, in this application the number of parameters of the neural network to be determined is usually much larger than the number of patterns which are available for the determination of the unknowns. Hence, the residual function of our least squares problem is underdetermined. In option pricing, an important but usually not known parameter is the volatility of the underlying asset of the option. Assuming that the underlying asset follows a one-factor continuous diffusion model with nonconstant drift and volatility term, the value of an European call option satisfies a parabolic initial value problem with the volatility function appearing in one of the coefficients of the parabolic differential equation. Using this system equation, the estimation of the volatility function is described by a nonlinear least squares problem. Since the adaption of the volatility function is based only on a small number of observed market data these problems are naturally ill-posed. For the solution of these large-scale underdetermined nonlinear least squares problems we use a fully iterative inexact Gauss-Newton algorithm. We show how the structure of a neural network as well as that of the European call price model can be exploited using iterative methods. Moreover, we present theoretical statements for the convergence of the inexact Gauss-Newton algorithm applied to the less examined case of underdetermined nonlinear least squares problems. Finally, we present numerical results for the application of neural networks to the forecasting of stock market indices as well as for the construction of the volatility function in European option pricing models. In case of the latter application, we discretize the parabolic differential equation using a finite difference scheme and we elucidate convergence problems of the discrete scheme when the initial condition is not everywhere differentiable.

In this thesis we focus on the development and investigation of methods for the computation of confluent hypergeometric functions. We point out the relations between these functions and parabolic boundary value problems and demonstrate applications to models of heat transfer and fluid dynamics. For the computation of confluent hypergeometric functions on compact (real or complex) intervals we consider a series expansion based on the Hadamard product of power series. It turnes out that the partial sums of this expansion are easily computable and provide a better rate of convergence in comparison to the partial sums of the Taylor series. Regarding the computational accuracy the problem of cancellation errors is reduced considerably. Another important tool for the computation of confluent hypergeometric functions are recurrence formulae. Although easy to implement, such recurrence relations are numerically unstable e.g. due to rounding errors. In order to circumvent these problems a method for computing recurrence relations in backward direction is applied. Furthermore, asymptotic expansions for large arguments in modulus are considered. From the numerical point of view the determination of the number of terms used for the approximation is a crucial point. As an application we consider initial-boundary value problems with partial differential equations of parabolic type, where we use the method of eigenfunction expansion in order to determine an explicit form of the solution. In this case the arising eigenfunctions depend directly on the geometry of the considered domain. For certain domains with some special geometry the eigenfunctions are of confluent hypergeometric type. Both a conductive heat transfer model and an application in fluid dynamics is considered. Finally, the application of several heat transfer models to certain sterilization processes in food industry is discussed.