Modern Internet technologies open new possibilities in a wide spectrum of traditional methods, used in mathematical education. One of the areas, where these technologies can be efficiently used, is an organization of mathematical competitions. Contestants can stay in their schools or universities in different cities and even different countries and try to solve as many mathematical problems as possible and then submit their solutions to organizers through the Internet. Simple Internet technologies supply audio and video connection between participants and organizers in a time of the competitions.

Das praxisorientierte Forschungsprojekt widmet sich den Grenzen und Potenzialen der Wiederaufarbeitung von Bekleidung – einer Nischenlösung der Circular Economy. Im Mittelpunkt der Forschungsarbeit steht die Frage, wie die Wiederverwertung von Abfallmaterialien aus der Textilindustrie als Teilflächen mit Hilfe von digitalen Technologien nach-haltig und effektiv gestaltet werden kann. Zudem wird erforscht, welche damit verbundenen ästhetischen Potenziale für den Entwurf neuer Bekleidung entstehen.

Die Entdeckung von neuen Materialien mit speziellen funktionalen Eigenschaften ist eins der wichtigsten Ziele in den Materialwissenschaften. Das Screening des strukturellen und chemischen Phasenraums nach potentiellen neuen Materialkandidaten wird häufig durch den Einsatz von Hochdurchsatzmethoden erleichtert. Schnelle und genaue Berechnungen sind eins der Hauptwerkzeuge solcher Screenings, deren erster Schritt oft Geometrierelaxationen sind. In Teil I dieser Arbeit wird eine neue Methode der eingeschränkten Geometrierelaxation vorgestellt, welche die perfekte Symmetrie des Kristalls erhält, Resourcen spart sowie Relaxationen von metastabilen Phasen und Systemen mit lokalen Symmetrien und Verzerrungen erlaubt. Neben der Verbesserung solcher Berechnungen um den Materialraum schneller zu durchleuchten ist auch eine bessere Nutzung vorhandener Daten ein wichtiger Pfeiler zur Beschleunigung der Entdeckung neuer Materialien. Obwohl schon viele verschiedene Datenbanken für computerbasierte Materialdaten existieren ist die Nutzbarkeit abhängig von der Darstellung dieser Daten. Hier untersuchen wir inwiefern semantische Technologien und Graphdarstellungen die Annotation von Daten verbessern können. Verschiedene Ontologien und Wissensgraphen werden entwickelt anhand derer die semantische Darstellung von Kristallstrukturen, Materialeigenschaften sowie experimentellen Ergebenissen im Gebiet der heterogenen Katalyse ermöglicht werden. Wir diskutieren, wie der Ansatz Ontologien und Wissensgraphen zu separieren, zusammenbricht wenn neues Wissen mit künstlicher Intelligenz involviert ist. Eine Zwischenebene wird als Lösung vorgeschlagen. Die Ontologien bilden das Hintergrundwissen, welches als Grundlage von zukünftigen autonomen Agenten verwendet werden kann. Zusammenfassend ist es noch ein langer Weg bis Materialdaten für Maschinen verständlich gemacht werden können, so das der direkte Nutzen semantischer Technologien nach aktuellem Stand in den Materialwissenschaften sehr limitiert ist. / The discovery of novel materials with specific functional properties is one of the highest goals in materials science. Screening the structural and chemical space for potential new material candidates is often facilitated by high-throughput methods. Fast and still precise computations are a main tool for such screenings and often start with a geometry relaxation to find the nearest low-energy configuration relative to the input structure. In part I of this work, a new constrained geometry relaxation is presented which maintains the perfect symmetry of a crystal, saves time and resources as well as enables relaxations of meta-stable phases and systems with local symmetries or distortions. Apart from improving such computations for a quicker screening of the materials space, better usage of existing data is another pillar that can accelerate novel materials discovery. While many different databases exists that make computational results accessible, their usability depends largely on how the data is presented. We here investigate how semantic technologies and graph representations can improve data annotation. A number of different ontologies and knowledge graphs are developed enabling the semantic representation of crystal structures, materials properties as well experimental results in the field of heterogeneous catalysis. We discuss the breakdown of the knowledge-graph approach when knowledge is created using artificial intelligence and propose an intermediate information layer. The underlying ontologies can provide background knowledge for possible autonomous intelligent agents in the future. We conclude that making materials science data understandable to machines is still a long way to go and the usefulness of semantic technologies in the domain of materials science is at the moment very limited.

With the rise of computationally expensive application domains such as machine learning, genomics, and fluids simulation, the quest for performance and energy-efficient computing has gained unprecedented momentum. The significant increase in computing and memory devices in modern systems has resulted in an unsustainable surge in energy consumption, a substantial portion of which is attributed to the memory system. The scaling of conventional memory technologies and their suitability for the next-generation system is also questionable. This has led to the emergence and rise of nonvolatile memory ( NVM ) technologies. Today, in different development stages, several NVM technologies are competing for their rapid access to the market. Racetrack memory ( RTM ) is one such nonvolatile memory technology that promises SRAM -comparable latency, reduced energy consumption, and unprecedented density compared to other technologies. However, racetrack memory ( RTM ) is sequential in nature, i.e., data in an RTM cell needs to be shifted to an access port before it can be accessed. These shift operations incur performance and energy penalties. An ideal RTM , requiring at most one shift per access, can easily outperform SRAM . However, in the worst-cast shifting scenario, RTM can be an order of magnitude slower than SRAM . This thesis presents an overview of the RTM device physics, its evolution, strengths and challenges, and its application in the memory subsystem. We develop tools that allow the programmability and modeling of RTM -based systems. For shifts minimization, we propose a set of techniques including optimal, near-optimal, and evolutionary algorithms for efficient scalar and instruction placement in RTMs . For array accesses, we explore schedule and layout transformations that eliminate the longer overhead shifts in RTMs . We present an automatic compilation framework that analyzes static control flow programs and transforms the loop traversal order and memory layout to maximize accesses to consecutive RTM locations and minimize shifts. We develop a simulation framework called RTSim that models various RTM parameters and enables accurate architectural level simulation. Finally, to demonstrate the RTM potential in non-Von-Neumann in-memory computing paradigms, we exploit its device attributes to implement logic and arithmetic operations. As a concrete use-case, we implement an entire hyperdimensional computing framework in RTM to accelerate the language recognition problem. Our evaluation shows considerable performance and energy improvements compared to conventional Von-Neumann models and state-of-the-art accelerators.

Die vorliegende Dissertation beschäftigt sich hauptsächlich mit zwei Fragen: Erstens, welche Faktoren beein-flussen den Prozess, durch den sich neue Technologien unter Firmen verbreiten? Zweitens, welche Konsequen-zen ergeben sich aus der Verbreitung neuer Technologien? Beide Fragen beschäftigen sich mit der Dynamik des technologischen Wandels. Die Analyse wird am konkreten Beispiel von e-Business Technologien durchgeführt. Dabei werden insbesondere die Konsequenzen von interdependenten Technologien untersucht. Es wird ge-zeigt, dass es zu steigenden Erträgen der Adoption kommen kann, wenn verwandte Technologien sich nicht in ih-ren Funktionalitäten substituieren. Dies kann zu einer endogenen Beschleunigung der technologischen Entwick-lung führen. Dies bedeutet, dass die Wahrscheinlichkeit der Adoption einer Technologie mit der Anzahl der zu-vor adoptierten, verwandten Technologien ansteigt. Diese Theorie wird empirisch getestet und in vier verschie-denen Untersuchungen mit zwei verschiedenen, großen Datensätzen bestätigt. Die Existenz einer wachsenden di-gitalen Kluft in der e-Business Technologie-Ausstattung der Unternehmen wird für den Zeitraum von 1994-2002 nachgewiesen. Außerdem wird argumentiert, dass die Adoption neuer Technologien in Firmen strategische Bedeutung hat da sich daraus Möglichkeiten zur Durchführung von Innovationen ergeben. Diese können sich entweder durch redu-zierte Produktionkosten für bestehende Produkte, neue Produkte und Dienstleistungen, oder neue Distributions-kanäle manifestieren. Empirische Evidenz zeigt, dass e-Business Technologien derzeit wichtige Enabler von In-novationen sind und dass innovative Firmen mit höherer Wahrscheinlichkeit wachsen. Außerdem wird gezeigt, dass durch e-Business Technologien induzierte Innovationen gegenüber anderen Innovationsarten nicht inferior sind in Bezug auf deren gleichzeitiges Auftreten mit finanziellen Leistungsindikatoren. Die Arbeit diskutiert die Implikationen dieser Ergebnisse aus volks- und betriebswirtschaftlicher Perspektive. / This dissertation primarily deals with two questions: First, what determines the process by which new tech-nologies spread among enterprises over time? Second, what are the consequences of the spread of new technolo-gies? Both questions concern the dynamics of technological change. They are analyzed considering the diffusion and implications of e-business technologies as a concrete example. Particular attention is given to technological interdependencies. It is shown that increasing returns to adoption can arise if related technologies do not substitute each other in their functionalities. This can lead to an endoge-nous acceleration of technological development. Hence, the probability to adopt any technology is an increasing function of previously adopted, related technologies. The theory is empirically tested and supported in four inde-pendent inquiries, using two different exceptionally large datasets and different econometric methods. The exis-tence of a growing digital divide among companies is demonstrated for the period between 1994 and 2002. In addition, it is argued that the adoption of new e-business technologies by firms has strategic relevance be-cause this creates opportunities to conduct innovation, either to reduce production costs for a given output, to create a new product or service, or to deliver products to customers in a way that is new to the enterprise. Empiri-cal evidence is presented showing that e-business technologies are currently an important enabler of innovations. It is found that innovative firms are more likely to grow. Also, e-business related innovations are not found to be inferior to traditional kinds of innovations in terms of simultaneous occurrence with superior financial perform-ance of enterprises. Implications of these findings are discussed both for economists and management researchers.

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This dissertation consists of three independent studies - two empirical studies and one literature review - that examine different issues regarding the involvement of employees in innovation within the growing open innovation environment. In particular, I focus on the different facets and vital enablers that influence involving the general workforce in innovation, among which trust plays a critical role for their active involvement and their decision to contribute to innovation. In the first study, the focus is on a powerful set of enablers of high involvement innovation, namely; the new corporate web technologies, and their role in accelerating a wider base of collective innovation. The second study then examines the involvement of a very specialized category of the workforce in innovation which is the highly qualified external workforce. Those employees represent a rich yet underexplored resource of employee innovation. Finally, in the third study, I focus on exploring the different roles played by innovation intermediaries and argue that intermediaries could take a more active role in open innovation, through proposing the ‘trust incubator’ role. New insights coming from this thesis advance the current discussion of actively and effectively involving employees in innovation, as well as uncover important and current related issues and allow us to draw conclusions that are useful for both research and practice.:Introduction I Accelerating high involvement: The role of new technologies in enabling employee participation in innovation II Exploring the involvement of highly qualified external employees in innovation – an organizational perspective 1 INTRODUCTION 2 THEORETICAL BACKGROUND 2.1 The flexible external workforce 2.2 Employee involvement in innovation 2.3 The involvement of HQEE in innovation 3 RESEARCH METHODOLOGY 4 EMPIRICAL FINDINGS 5 CONCLUSION III Rethinking the role of trust in open innovation 1 INTRODUCTION 2 AN OVERVIEW OF TRUST 3 CONTEXTS OF TRUST IN OPEN INNOVATION 3.1 Supply chain development 3.2 Innovation clusters 3.3 Employee involvement in innovation 4 TRUST IN OPEN INNOVATION 4.1 Open innovation: The shift from knowledge creation to knowledge sharing 4.2 Open innovation opportunities & emerging trust challenges 5 TRUSTED INTERMEDIARIES IN HIGHLY INNOVATIVE CINTEXTS 5.1 Intermediaries – from brokers to trust incubators 5.2 Trusted intermediaries in the literature 6 CONCLUSION AND DIRECTION FOR FUTURE RESEARCH Further research in the innovation management field

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This study presents results of selective laser melting (SLM), powder metallurgy (PM), and casting technologies applied for producing Ti–TiB composites from Ti–TiB₂ powder. Diffraction patterns and microstructural investigations reveal that chemical reaction occurred between Ti and TiB₂ during all the three processes, leading to the formation of Ti–TiB composites. The ultimate compressive strength of SLM-processed and cast samples are 1421 and 1434 MPa, respectively, whereas the ultimate compressive strengths of PM-processed 25%, 29%, and 36% porous samples are 510, 414, and 310 MPa, respectively. The Young’s moduli of porous composite samples are 70, 45, and 23 GPa for 25%, 29%, and 36% porosity levels, respectively, and are lower than those of SLM-processed (145 GPa) and cast (142 GPa) samples. Fracture analysis of the SLM-processed and cast samples shows shear fracture and microcracks across the samples, whereas failure of porous samples occurs due to porosities and weak bonds among particles.