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The collapse of the tailings pond of the Aznalcállar open pit mine (West of Seville, Spain) in April 1998 left more than 4000 ha of arable land and floodplains contaminated with heavy metal containing pyrite sludge. After a first remediation campaign a considerable contamination remained in the soil. The present study evaluates the possibilities of reflectance spectroscopy and airborne hyperspectral remote sensing for the qualitative and quantitative assessment of heavy metal contamination and the acidification risk related to the mining accident. Based on an extensive data set consisting of geochemical analyses and reflectance measurements of more than 300 soil samples different chemometrics methods (multiple linear regression, partial least squares and artificial neural networks) are tested for computation of concentrations of soil constituents on the basis of the spectral reflectance. Spectral mixture analysis is applied for the analysis of the spatial distribution of the contamination. The abundance information derived from spectral mixture analysis is turned into quantitative information incorporating an artificial mixture experiment. The results of this experiment provide a link between sludge abundance and sludge weight, allowing as a consequence calculation of the amount of residual sludge per pixel, the acidification potential and other parameters important for remediation planning. The application of laboratory, field and imaging spectroscopy for providing quantitative information about the contamination levels in their spatial context is a good complement to conventional methods. The advantage is the reduction of the time and labour-intensive geochemical analysis, because after the model calibration, further samples can be analysed directly with the chemometric models. Furthermore, the spatial distribution can be mapped with imaging spectroscopy data helping in a more precise remediation planning.
There is considerable evidence for an association between chronic dysregulation of the hypothalamus-pituitary adrenal (HPA) axis, atrophy of the hippocampus (HC) and cognitive and mood changes in clinical populations and in aging. The present thesis investigated this relationship in young healthy male subjects. Special emphasis was put on measures of HC volume and function derived from structural and functional magnetic resonance imaging (MRI). Higher cortisol levels after awakening were observed in subjects with higher levels of depressive symptomatology. Larger HC volume was associated with higher cortisol levels after awakening and in response to acute stress, whereas cognitive performance was impaired in subjects with larger HC volumes. Hippocampal activation during picture encoding was reduced after stress induction, and positive associations between activation and cognitive performance before stress were not present anymore afterwards. The present findings underscore the importance of structural and functional brain imaging for psychoneuroendocrinological research. The investigation of the association between cortisol levels and hippocampal integrity in young healthy subjects elicited unexpected results and adds to the understanding of HPA dysfunction and HC atrophy in clinical and aged populations.
Software and interactive systems that adapt their behavior to the user are often referred to as Adaptive Systems. These systems infer the user's goals, knowledge or preferences by observing the user's actions. A synposis of 43 published studies demonstrated that only few of the existing systems are evaluated empirically. Most studies failed to show an advantage of the user model. A new framework is proposed that categorizes existing studies and defines an evaluation procedure which is able to uncover failures and maladaptations in the user model. It consists of four layers: evaluation of input data, evaluation of inference, evaluation of adaptation decision and evaluation of total interaction. Exemplary, the framework has been applied to the HTML-Tutor, an online-course that adapts to the learners' knowledge. Several empirical studies are described that test the accuracy of the user models, and explore the effects of adaptation to knowledge respectively prior knowledge. Generalization issues of the approach are discussed.
Today obesity has been recognized as a disease. Evidence suggests that obesity often has Genetic, environmental, psychological and other factors. Growing evidence points to heredity as a strong determining factor of obesity. The characterization of uncoupling proteins (UCP) represents a major breakthrough of genetic factors towards understanding the molecular basis for energy expenditure and therefore likely to have important implication for the cause and treatment of human obesity. UCPs as mitochondrial anion carriers which creates a pathway that allows dissipation of the proton electrochemical gradient therefore which when deregulated are key risk factors in the development of obesity and other eating disorders. In order to better understand the roles of both UCP2 and UCP3 which considered as prime candidate genes involved in the pathogenesis of obesity, this study elucidate (1) Genomic organization: The human UCP2 (3) gene spans over 8.7 kb (7.5 kb) distributed on 8 (7) exons. Three UCP genes may have evolved from a common ancestor or are the result from gene duplication events. Two mRNA transcripts are generated from hUCP3 gene, the long and short form of hUCP3 is differing by the presence or absence of 37 amino acid residues at the C-terminus. (2) Mutational analysis revealed a mutation in exon 4 of hUCP2 resulting in the substitution of an alanine by a valine at codon 55 and an insertion polymorphism in exon 8 consisted of a 45 bp repeat located 150 bp downstream of the stop codon in the 3'-UTR. The allele frequencies of both polymorphisms were not significantly elevated in a subgroup of children characterized by low Resting Metabolic Rates (RMR). (3) Promoter Analysis showed that the promoter region of hUCP2 lacks a classical TATA or CAAT box. Functional characterization of hUCP2 promoter showed that minimal promoter activity was observed within 65 bp upstream of the transcriptional start site. 75 bp further upstream a strong cis-acting regulatory element was identified which significantly enhanced basal promoter activity. The regulation of human UCP2 gene expression involves complex interactions among positive and negative regulatory elements. the 5"-flanking region of the hUCP3 gene were characterized in which contains both TATA and CAAT boxes as well as consensus motifs for PPRE, TRE, CRE and muscle-specific MyoD and MEF2 sites. Functional characterization identified a cis-acting negative regulatory element between - 2983 and -982 while the region between -982 and -284 showed greatly increased basal promoter activity suggesting the presence of a strong enhancer element. Promoter activity was particularly enhanced in the murine skeletal muscle cell line C2C12 reflecting the tissue-selective expression pattern of UCP3.
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.
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.
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.
This work is concerned with arbitrage bounds for prices of contingent claims under transaction costs, but regardless of other conceivable market frictions. Assumptions on the underlying market are held as weak as convenient for the deduction of meaningful results that make good economic sense. In discrete time we also allow for underlying price processes with uncountable state space. In continuous time the underlying price process is modeled by a semimartingale. For the most part we could avoid any stronger assumptions. The main problems with which we deal in this work are the modelling of (proportional) transaction costs, Fundamental Theorems of Asset Pricing under transaction costs, dual characterizations of arbitrage bounds under transaction costs, Quantile-Hedging under transaction costs, alternatives to the Black-Scholes model in continuous time (under transaction costs). The results apply to stock and currency markets.
Hardware bugs can be extremely expensive, financially. Because microprocessors and integrated circuits have become omnipresent in our daily live and also because of their continously growing complexity, research is driven towards methods and tools that are supposed to provide higher reliability of hardware designs and their implementations. Over the last decade Ordered Binary Decision Diagrams (OBDDs) have been well proven to serve as a data structure for the representation of combinatorial or sequential circuits. Their conciseness and their efficient algorithmic properties are responsible for their huge success in formal verification. But, due to Shannon's counting argument, OBDDs can not always guarantee the concise representation of a given design. In this thesis, Parity Ordered Binary Decision Diagrams are presented, which are a true extension of OBDDs. In addition to the regular branching nodes of an OBDD, functional nodes representing a parity operation are integrated into the data structure, thus resulting in Parity-OBDDs. Parity-OBDDs are more powerful than OBDDs are, but, they are no longer a canonical representation. Besides theoretical aspects of Parity-OBDDs, algorithms for their efficient manipulation are the main focus of this thesis. Furthermore, an analysis on the factors that influence the Parity-OBDD representation size gives way for the development of heuristic algorithms for their minimization. The results of these analyses as well as the efficiency of the data structure are also supported by experiments. Finally, the algorithmic concept of Parity-OBDDs is extended to Mod-p-Decision Diagrams (Mod-p-DDs) for the representation of functions that are defined over an arbitrary finite domain.
Mobile computing poses different requirements on middleware than more traditional desktop systems interconnected by fixed networks. Not only the characteristics of mobile network technologies as for example lower bandwidth and unreliability demand for customized support. Moreover, the devices employed in mobile settings usually are less powerful than their desktop counterparts. Slow processors, a fairly limited amount of memory, and smaller displays are typical properties of mobile equipment, again requiring special treatment. Furthermore, user mobility results in additional requirements on appropriate middleware support. As opposed to the quite static environments dominating the world of desktop computing, dynamic aspects gain more importance. Suitable strategies and techniques for exploring the environment e.g. in order to discover services available locally are only one example. Managing resources in a fault-tolerant manner, reducing the impact ill-behaved clients have on system stability define yet another exemplary prerequisite. Most state of the art middleware has been designed for use in the realm of static, resource rich environments and hence is not immediately applicable in mobile settings as set forth above. The work described throughout this thesis aims at investigating the suitability of different middleware technologies with regard to application design, development, and deployment in the context of mobile networks. Mostly based upon prototypes, shortcomings of those technologies are identified and possible solutions are proposed and evaluated where appropriate. Besides tailoring middleware to specific communication and device characteristics, the cellular structure of current mobile networks may and shall be exploited in favor of more scalable and robust systems. Hence, an additional topic considered within this thesis is to point out and investigate suitable approaches permitting to benefit from such cellular infrastructures. In particular, a system architecture for the development of applications in the context of mobile networks will be proposed. An evaluation of this architecture employing mobile agents as flexible, network-side representatives for mobile terminals is performed, again based upon a prototype application. In summary, this thesis aims at providing several complementary approaches regarding middleware support tailored for mobile, cellular networks, a field considered to be of rising importance in a world where mobile communication and particularly data services emerge rapidly, augmenting the globally interconnecting, wired Internet.