How do attitudes of habitual high-technology entrepreneurs to early-stage failure differ in Silicon Valley, Cambridge and Munich?

Entrepreneurs develop new technology ventures in uncertain conditions with unproven technologies and limited resources. The majority of such ventures fail, yet entrepreneurship is regarded as a national (and regional) engine for economic growth. This thesis aims to examine entrepreneurs’ attitudes to failure in order to reveal insight on how entrepreneurs learn and how they identify subsequent opportunities, and investigate possible regional differences in such attitudes and entrepreneurial responses. There is much literature on entrepreneurial failure but relatively little that is focused on attitudes to failure, the high-technology industry, or international comparisons. This thesis examines how entrepreneurs’ attitudes to failure in early-stage technology companies differ in the USA (Silicon Valley), UK (Cambridge) and Germany (Munich), and implications for entrepreneurial learning and opportunity identification in these regions. Interviews with habitual entrepreneurs explore their experiences of failed ventures, using a methodology from qualitative psychology - Interpretative Phenomenological Analysis (IPA) - for the gathering and analysis of data to reveal emergent trends. This analysis is then used to compare attitudes to failure within and between each region, and a preliminary conceptual framework is proposed for analyzing future experiences of entrepreneurial failure. Findings from this idiographic study suggest that although each entrepreneur’s experience of and attitudes to failure is unique, there are more commonalities than differences between regions. Furthermore, these findings reveal the importance of the use of language and narrative in the analysis of such accounts. In addition, the results allow reflection on the appropriateness and limitations of methodologies such as IPA for this subject. This thesis contributes to theory by examining ‘effectuation’ as a way to understand these experiences, and discussing the impact of findings in relation to attribution theory, prospect theory and real-options theory. This thesis contributes to practice by augmenting existing knowledge of entrepreneurial failure through the comparative (regional) approach and the industry-specific (high-technology) focus. It may also improve the preparedness of new practitioners and entrepreneurs, with positive implications for future entrepreneurial success

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words