Conformal field theories (CFT) constitute an interesting class of twodimensionalquantum field theories, with applications in string theoryas well as condensed matter physics. The symmetries of a CFT can beencoded in the mathematical structure of a conformal vertex algebra.The rational CFT’s are distinguished by the property that the categoryof representations of the vertex algebra is a modular tensor category.The solution of a rational CFT can be split off into two separate tasks, apurely complex analytic and a purely algebraic part. The TFT-construction gives a solution to the second part of the problem.This construction gets its name from one of the crucial ingredients,a three-dimensional topological field theory (TFT). The correlators obtainedby the TFT-construction satisfy all consistency conditions of thetheory. Among them are the factorization constraints, whose implicationsfor boundary conditions are the main topic of this thesis. The main result reviewed in this thesis is that the factorization constraintsgive rise to a semisimple commutative associative complex algebrawhose irreducible representations are the so-called reflection coefficients.The reflection coefficients capture essential information aboutboundary conditions, such as ground-state degeneracies and Ramond-Ramond charges of string compactifications. We also show that the annuluspartition function can be derived fromthis classifying algebra andits representation theory.

This thesis demonstrates the application of biosensor technology for molecular biology investigations, utilizing a surface plasmon resonance based optical device for mass sensitive detection of biomolecular interactions at a chipsurface. Oligonucleotide model systems were designed for analysis of the action of DNA manipulating enzymes. DNA ligation, DNA cleavage and DNA synthesis could be quantitatively monitored in real-time. A protocol for DNA minisequencing was also established based on prevention of chain elongation by incorporation of chain-terminators. Determinations of affinities for short oligonucleotides hybridizing to an immobilized target were performed with various sequence content, length, temperature and degree of complementarity. The decrease in affinity for hybridizations involving mismatch situations was found to be strongly dependent on the relative position of the mismatch. Interestingly, also end-mismatches were clearly detectable. The stabilization effect achieved upon co-hybridization of two adjacently annealing short oligonucleotide modules (modular primer effect) was also investigated for different module combinations and hybridization situations. The modular concept of hybridizations was subsequently demonstrated to result in enhanced Capture of single stranded PCR products. The sequence based DNA analysis, first introduced with oligonucleotide modelsystems, was extended to the scanning and screening formutations in PCR amplified DNA from clinically relevant samples. Several different formats were investigated, eitherwith the PCR products immobilized on the chip and oligonucleotides injected or vice versa. Again, mismatch discrimination could be observed for wild type and mutant specific oligonucleotides hybridizing to the targets. The experimental set-up for mutation detection was further developed by the introduction of a subtractive mismatch sensitive hybridization outside the instrument and a subsequent determination of the relative amounts of remain ingoligonucleotides with analytical biosensor monitoring of hybridizations between fully complementary oligonucleotides. In conclusion, the applied technology was found to be a suitable tool for a wide range of molecular biology applications, with emphasis on hybridization analysis and mutation detection. / QC 20100611

This thesis makes a survey of converse theorems for Dirichlet series. A converse theo-rem gives sufficient conditions for a Dirichlet series to be the Dirichlet series attachedto a modular form. Such Dirichlet series have special properties, such as a functionalequation and an Euler product. Sometimes these properties characterize the modularform completely, i.e. they are sufficient to prove the proper transformation behaviourunder some discrete group. The problem dates back to Hecke and Weil, and has morerecently been treated by Conrey et.al. The articles surveyed are: "An extension of Hecke's converse theorem", by B. Conrey and D. Farmer "Converse theorems assuming a partial Euler product", by D. Farmer and K.Wilson "A converse theorem for ¡0(13)", by B. Conrey, D. Farmer, B. Odgers and N.Snaith The results and the proofs are described. The second article is found to contain anerror. Finally an alternative proof strategy is proposed.

This paper studies the possibility of building a modular multilevel converter (M2C) using silicon carbide (SiC) switches. The main focus is on a theoretical investigation of the conduction losses of such a converter and a comparison to a corresponding converter with silicon-insulated gate bipolar transistors. Both SiC BJTs and JFETs are considered and compared in order to choose the most suitable technology. One of the submodules of a down-scaled 3 kVA prototype M2C is replaced with a submodule with SiC JFETs without antiparallel diodes. It is shown that the diode-less operation is possible with the JFETs conducting in the negative direction, leaving the possibility to use the body diode during the switching transients. Experimental waveforms for the SiC submodule verify the feasibility during normal steady-state operation. The loss estimation shows that a 300 MW M2C for high-voltage direct current transmission would potentially have an efficiency of approximately 99.8% if equipped with future 3.3 kV 1.2 kA SiC JFETs. / © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.QC 20111220

Serial manipulators, which have large work space with respect to their own volume and occupied floor space, are the most common industrial robots by far. However, in many environments the situation is unstructured and less predictable, such as aboard a space station, a nuclear waste retrieval site, or a lunar base construction site. It is almost impossible to design a single robotic system which can meet all the requirements for every task. In these circumstances, it is important to deploy a modular reconfigurable robotic system, which is suitable to various task requirements. Modular reconfigurable robots have a variety of attributes that are well suited to for these conditions, including: the ability to serve as many different tools at once (saving weight), packing into compressed forms (saving space) and having high levels of redundany(increasing robustness). By easy disassembly and reassembly features, this serial modular robotic system will bring advantages to small and medium enterprise to save costs in the long term. This thesis focuses on developing such a serial reconfigurable modular robotic system with remote control functionality. The robotic arms are assembled by PowerCube Modules with cubic outward appearance. The control and power electronics are fully integrated on the connector block inside of the modules. Those modules are connected in series by looping through, and can work completely independently. The communication between robotic arms and PC controller is connected by the Control Area Network bus. CAN protocol detects and corrects transmission errors caused by electromagnetic interference. The local PC can directly control the robotic arm via Visual Basic code, and it can also be treated as server controller. Client PCs can access and control the robotic arm remotely through Socket communication mechanism with certain IP address and port number. A Java3D model is created on the client PC synchronously for customers online monitoring and control. The forward and inverse kinematic analysis is solved by Vector Algebraic Method. The Neutral Network Method is also introduced to improve the kinematic analysis. Multiple-layer networks are capable of approximating any function with finite number of discontinuities. For learning the inverse kinematics neural network needs information about coordinates, joint angles and actuator positions. The desired Cartesian coordinates are given as input to the neural network that returns actuator positions as output. The robot position is simulated using these actuator positions as reference values for each actuator.

Modular and Reconfigurable Robots (MRRs) are those designed to address the increasing demand for flexible and versatile manipulators in manufacturing facilities. The term, modularity, indicates that they are constructed by using a limited number of interchangeable standardized modules which can be assembled in different kinematic configurations. Thereby, a wide variety of specialized robots can be built from a set of standard components. The term, reconfigurability, implies that the robots can be disassembled and rearranged to accommodate different products or tasks rather than being replaced. A set of MRR modules may consist of joints, links, and end-effectors. Different kinematic configurations are achieved by using different joint, link, and end-effector modules and by changing their relative orientation. The number of distinct kinematic configurations, attainable by a set of modules, varies with respect to the size of the module set from several tens to several thousands. Although determining the most suitable configuration for a specific task from a predefined set of modules is a highly nonlinear optimization problem in a hybrid continuous and discrete search space, a solution to this problem is crucial to effectively utilize MRRs in manufacturing facilities. The objective of this thesis is to develop novel optimization methods that can effectively search the Kinematic Configuration (KC) space to identify the most suitable manipulator for any given task. In specific terms, the goal is to develop and synthesize fast and efficient algorithms for a Task-Based Configuration Optimization (TBCO) from a given set of constraints and optimization criteria. To achieve such efficiency, a TBCO solver, based on Memetic Algorithms (MA), is proposed. MAs are hybrids of Genetic Algorithms (GAs) and local search algorithms. MAs benefit from the exploration abilities of GAs and the exploitation abilities of local search methods simultaneously. Consequently, MAs can significantly enhance the search efficiency of a wide range of optimization problems, including the TBCO. To achieve more optimal solutions, the proposed TBCO utilizes all the solutions of the Inverse Kinematics(IK) problem. Another objective is to develop a method for incorporating the multiple solutions of the IK problem in a trajectory optimization framework. The output of the proposed trajectory optimization method consists of a sequence of desired tasks and a single IK solution to reach each task point. Moreover, the total cost of the optimized trajectory is utilized in the TBCO as a performance measure, providing a means to identify kinematic configurations with more efficient optimized trajectories. The final objective is to develop novel IK solvers which are both general and complete. Generality means that the solvers are applicable to all the kinematic configurations which can be assembled from the available module inventory. Completeness entails the algorithm can obtain all the possible IK solutions.

Understanding the origin of protein folds, and the mechanism by which evolution has generated them, is a critically important step on a path towards rational protein design. Modifying existing proteins and designing our own novel folds and functions is a lofty but achievable goal, for which there are many foreseeable rewards. It is believed that modern proteins may have arisen from a primordial set of peptide precursors, which were initially only pseudo-stable or stable only as complexes with RNA, and later were able to self-assemble into multimeric complexes that resembled modern folds. In order to experimentally examine the feasibility of this theory, an attempt was made at reconstructing the evolutionary path of a beta-trefoil. The beta-trefoil is a naturally abundant fold or superfold, possessing pseudo-threefold symmetry, and usually having a sugar-binding function. It has been proposed that such a fold could arise from the triplication of just one small peptide on the order of 40-50 amino acids in length. The evolutionary path of a ricin, a family within the beta-trefoils known to possess a carbohydrate binding function was the chosen template for evolutionary modelling. It was desirable to have a known function associated with this design, such that it would be possible to determine if not only the fold, but also the function, could be reconstructed. A small peptide of 47 amino acids was designed and expressed. This peptide not only trimerized as expected, but possessed the carbohydrate binding function it was predicted to have. In an evolutionary model of the early protein world, the gene for this peptide would undergo duplication and later, triplication, eventually resulting in a completely symmetrical beta-trefoil, which would represent the first modern beta-trefoil fold. Such a completely symmetrical protein was also designed and expressed by triplicating the gene for the aforementioned small peptide. This hypothetical first modern beta-trefoil is: well folded, stable, soluble, and appears to adopt a beta-trefoil fold. Together these results demonstrate that an evolutionary model of early life: that proteins first existed as self-assembling modular peptides, and subsequent to gene duplications or fusions, as what we now recognize as modern folds, is experimentally consistent and not only generates stable structures, but those with function, which of course is a prime requisite of evolution. Moreover the results show that it may be possible to use this modular nature of protein folding to design our own proteins and predict the structure of others.

Modular and Reconfigurable Robots (MRRs) are those designed to address the increasing demand for flexible and versatile manipulators in manufacturing facilities. The term, modularity, indicates that they are constructed by using a limited number of interchangeable standardized modules which can be assembled in different kinematic configurations. Thereby, a wide variety of specialized robots can be built from a set of standard components. The term, reconfigurability, implies that the robots can be disassembled and rearranged to accommodate different products or tasks rather than being replaced. A set of MRR modules may consist of joints, links, and end-effectors. Different kinematic configurations are achieved by using different joint, link, and end-effector modules and by changing their relative orientation. The number of distinct kinematic configurations, attainable by a set of modules, varies with respect to the size of the module set from several tens to several thousands. Although determining the most suitable configuration for a specific task from a predefined set of modules is a highly nonlinear optimization problem in a hybrid continuous and discrete search space, a solution to this problem is crucial to effectively utilize MRRs in manufacturing facilities. The objective of this thesis is to develop novel optimization methods that can effectively search the Kinematic Configuration (KC) space to identify the most suitable manipulator for any given task. In specific terms, the goal is to develop and synthesize fast and efficient algorithms for a Task-Based Configuration Optimization (TBCO) from a given set of constraints and optimization criteria. To achieve such efficiency, a TBCO solver, based on Memetic Algorithms (MA), is proposed. MAs are hybrids of Genetic Algorithms (GAs) and local search algorithms. MAs benefit from the exploration abilities of GAs and the exploitation abilities of local search methods simultaneously. Consequently, MAs can significantly enhance the search efficiency of a wide range of optimization problems, including the TBCO. To achieve more optimal solutions, the proposed TBCO utilizes all the solutions of the Inverse Kinematics(IK) problem. Another objective is to develop a method for incorporating the multiple solutions of the IK problem in a trajectory optimization framework. The output of the proposed trajectory optimization method consists of a sequence of desired tasks and a single IK solution to reach each task point. Moreover, the total cost of the optimized trajectory is utilized in the TBCO as a performance measure, providing a means to identify kinematic configurations with more efficient optimized trajectories. The final objective is to develop novel IK solvers which are both general and complete. Generality means that the solvers are applicable to all the kinematic configurations which can be assembled from the available module inventory. Completeness entails the algorithm can obtain all the possible IK solutions.

Understanding the origin of protein folds, and the mechanism by which evolution has generated them, is a critically important step on a path towards rational protein design. Modifying existing proteins and designing our own novel folds and functions is a lofty but achievable goal, for which there are many foreseeable rewards. It is believed that modern proteins may have arisen from a primordial set of peptide precursors, which were initially only pseudo-stable or stable only as complexes with RNA, and later were able to self-assemble into multimeric complexes that resembled modern folds. In order to experimentally examine the feasibility of this theory, an attempt was made at reconstructing the evolutionary path of a beta-trefoil. The beta-trefoil is a naturally abundant fold or superfold, possessing pseudo-threefold symmetry, and usually having a sugar-binding function. It has been proposed that such a fold could arise from the triplication of just one small peptide on the order of 40-50 amino acids in length. The evolutionary path of a ricin, a family within the beta-trefoils known to possess a carbohydrate binding function was the chosen template for evolutionary modelling. It was desirable to have a known function associated with this design, such that it would be possible to determine if not only the fold, but also the function, could be reconstructed. A small peptide of 47 amino acids was designed and expressed. This peptide not only trimerized as expected, but possessed the carbohydrate binding function it was predicted to have. In an evolutionary model of the early protein world, the gene for this peptide would undergo duplication and later, triplication, eventually resulting in a completely symmetrical beta-trefoil, which would represent the first modern beta-trefoil fold. Such a completely symmetrical protein was also designed and expressed by triplicating the gene for the aforementioned small peptide. This hypothetical first modern beta-trefoil is: well folded, stable, soluble, and appears to adopt a beta-trefoil fold. Together these results demonstrate that an evolutionary model of early life: that proteins first existed as self-assembling modular peptides, and subsequent to gene duplications or fusions, as what we now recognize as modern folds, is experimentally consistent and not only generates stable structures, but those with function, which of course is a prime requisite of evolution. Moreover the results show that it may be possible to use this modular nature of protein folding to design our own proteins and predict the structure of others.

In recent years, numerous methods to aid designers in conceptualizing new products have been developed. These methods intend to give structure to a process that was, at one time, considered to be a purely creative exercise. Resulting from the study, implementation, and refinement of design methodologies is the notion that both the structure of the development process and the structure of the developed product are key factors in creating value in a firm’s product line. With respect to the latter key factor, product architecture, but more specifically, modular product architecture has been the subject of much study. However, prior research in the area of modular product architecture has, with limited exception, focused on the construction of modules that are to be incorporated into a product before it becomes available to its end-users; that is, the modules are incorporated ‘pre-market.’ The research contained in this thesis is focused on two tasks: advancing the notion of a modular product architecture in which modules can be incorporated into a product ‘post-market,’ and creating a method that aids designers in synthesizing these post-market modules. Researchers have examined the idea of post-market modules; however, they do not fully formalize language used to describe these modules, and they also do not give the product space created by post-market modularization well-defined boundaries. Additionally, the prior work gives no method that can be used to create post-market modules. The research presented here addresses these shortcomings in the prior work by first, defining the terms ‘derivative product’ and ‘host product’ to describe the post-market module and the product that the module augments, respectively. Second, by establishing three guidelines that are used to assess the validity of potential derivative products, giving the newly termed host and derivative product space defined boundaries. And lastly, by developing a 7-step, biomimetic-based methodology that can be used to create derivative product concepts (post-market modules). This developed methodology is applied to four case studies in which it is used to create five derivative product concepts for a given host product. Thus, 20 derivative product concepts are developed in this study, demonstrating the qualitative effectiveness of the 7-step methodology.