This thesis describes the development of a small molecule sensor for the detection of specific protein-protein interactions. The responsive sensor has been designed to produce a measurable signal upon a bivalent interaction with a dimeric protein target, and has the potential to probe the biological significance of specific binding events.

Smart Specialisation Strategy (S3) has recently attracted the attention of many scholars, pundits and policy makers involved in regional studies, as a new industrial policy able to fill the gap between the weak capacity of Europe to innovate in comparison to its strong academic base and research institutions. S3 is described as a policy aimed to encourage structural changes, through the generation of new domains of opportunities, according to the strengths and potentialities of each region and therefore with a “place-based” outlook. Its primary element of novelty, in comparison to the previous policy approaches, is constituted by the Entrepreneurial Discovery Process (EDP), which represents the modality among institutions, firms, R&D centres, universities, through which the direction(s) of the structural change is organised. To study S3, this Ph.D. thesis focuses on two pillars considered central to understand its rationales: relatedness and entrepreneurship. On one hand, the idea of relatedness is useful to understand the economic structure of a territory and its evolution through its network of connections, outlining possible areas of future development. On the other hand, entrepreneurship, somehow a missing dimension of S3, can be considered as part of the process of opportunity scanning to “challenge” inefficiencies of the society through new models of production and consumption, proactiveness of institutions, business development strategies of firms or cultural mindset of people. The aim of the thesis is to explore this relatedness-entrepreneurship relationship within S3, using a multi-level framework of analysis able to integrate the different aspects of the two concepts, providing theoretical and empirical advancements. The thesis is structured as follows: a general introduction on S3, three papers, which analyse Italy, focusing on the case of Tuscany and some final conclusions that sum up the findings of the papers and provide some further policy insights. The content of the three papers is reported hereinafter. In the first paper the analysis is conducted in the Italian provinces defining entrepreneurship as the creation of a new business and relatedness as one of the principal mechanisms that could explain the origin of innovation in connection with a given territorial knowledge base. The distinctiveness of this first paper seeds in the study of this relationship across individual industries, computing separate measures of external and internal relatedness across 27 sectors (among manufacturing and KIBS). The results suggest a broader and positive impact of external relatedness on the concentration of new firms at the territorial level in comparison to the impact of internal relatedness. The implications suggest that Knowledge Spillover Theory of Entrepreneurship can be included in the cognitive framework of S3 (newborns as expression of knowledge exchanged at the local level) and that innovation policies aimed to promote path creation should consider existent strengths of the territories. The second paper studies the EDP, integrating the concept of relatedness, useful in the initial phases of design and scoping, with the one of institutional entrepreneurship as an expression of the impact of agency in the micro-dynamics that rule the final outcome of innovation policies. This framework is applied to the case of Tuscany, using a mixed methodology. As a first picture of proximity connections between sectors of Tuscany, an original computation of the “Industry Space” of Tuscany is realised (using the methodology of Hidalgo et al., 2007). Then the Technological Districts’ managers and/or coordinators are interviewed, as a sort of fact checking with the Industry Space results, to understand how they define their planning strategies and through which mechanisms they integrate knowledge and combine firms and R&D specialities. Results confirm the necessity to integrate the two concepts to obtain a more realistic “policy orientation map”, and the broader horizon released by relatedness if deeply analysed with case studies at a micro-level and if directly discussed with some central agents embedded in the regional network of proximities. The third paper studies the entrepreneurial styles (as real business men) and their ways of integrating and combining knowledge, adopting a micro interpretation on the concept of relatedness. The paper aims to identify what role can play these entrepreneurial figures as fundamental “micro pieces” in the scanning process of future opportunities of regional transformation promoted by S3. The methodology adopts a qualitative approach, using semi-structured interviews administered to a selected set of 24 entrepreneurs in Tuscany. The sample of the entrepreneurs, selected with a purposeful criterion, has been built thanks to the help of key informants. The gathered data are codified with the help of Gioia methodology, in order to derive some characteristics of the entrepreneur and the firms to describe some “emerging properties”. Then, a ladder of entrepreneurial typologies, able to group the specific characteristics derived from the interviews, is proposed. Results suggest a “distributed technology transfer model” as a complementary bottom up strategy to converge towards a new cyber-manufacturing regime of production.

abstract: 中医药是中华文明的瑰宝，中药材是中医药文化和产业的核心。随着近年来国家相关政策出台，中药材产业的发展备受瞩目。由于中药材产业链条长，层级多，各层级间信息不对称，因而中药材市场普遍具有“假”、“乱”、“杂”的问题。 A公司的中药材全产业链服务商模式，通过对上游各主要专营商的整合，形成一定的平台综合集采能力，并开始得到下游医药厂家、药店认可，在市场逐步形成品牌号召力。本文实证研究A公司商业模式的转型对中药材市场价格的影响，进而分析中药材全产业链服务商模式在中药材行业健康发展中所发挥的积极作用。研究结果表明，上下游产销结合的中药材全产业链服务商模式，只有在形成一定收购规模，对市场价格产生一定影响的时候，才能充分释放药材质量的信号，润滑药材交易市场，提高收购价格，增加市场波动率，发挥价格发现作用。由于中药材市场的信息不对称程度较高，如果产销结合模式仍处于初级开创阶段，产销结合模式释放的药材质量信号则不足以全面改善信息不对称的状况。 / Dissertation/Thesis / Doctoral Dissertation Business Administration 2019

The International market of crude oil and natural gas is well established and very competitive. Knowledge about costs is important in helping to understand the current position of producers within the industry. In the eyes of the producers, the lower the costs the more profitable they will become given the price of crude. This thesis focuses on an economic analysis of crude oil exploration in Saskatchewan and Alberta. In a competitive market, the producers require estimates of finding costs in both regions. The public policies that are designed to encourage crude exploration also rely heavily on reliable estimates of these costs. The results show that Saskatchewans per-unit finding cost is significantly lower than Albertas in spite of the geological differences between the two provinces. The finding costs are estimated by using a methodology (Uhler 1979) that has been widely accepted within economic literature of non-renewable resources. The results support the hypothesis that finding costs in both regions are increasing and the argument that these costs will converge in the long-run, except for the last six years of the analysis.

The International market of crude oil and natural gas is well established and very competitive. Knowledge about costs is important in helping to understand the current position of producers within the industry. In the eyes of the producers, the lower the costs the more profitable they will become given the price of crude. This thesis focuses on an economic analysis of crude oil exploration in Saskatchewan and Alberta. In a competitive market, the producers require estimates of finding costs in both regions. The public policies that are designed to encourage crude exploration also rely heavily on reliable estimates of these costs. The results show that Saskatchewans per-unit finding cost is significantly lower than Albertas in spite of the geological differences between the two provinces. The finding costs are estimated by using a methodology (Uhler 1979) that has been widely accepted within economic literature of non-renewable resources. The results support the hypothesis that finding costs in both regions are increasing and the argument that these costs will converge in the long-run, except for the last six years of the analysis.

Abstract In today's world, enormous amounts of data are being collected everyday. Thus, the problems of storing, handling, and utilizing the data are faced constantly. As the human mind itself can no longer interpret the vast datasets, methods for extracting useful and novel information from the data are needed and developed. These methods are collectively called knowledge discovery methods. In this thesis, a novel combination of feature selection and data modeling methods is presented in order to help with this task. This combination includes the methods of basic statistical analysis, linear correlation, self-organizing map, parallel coordinates, and k-means clustering. The presented method can be used, first, to select the most relevant features from even hundreds of them and, then, to model the complex inter-correlations within the selected ones. The capability to handle hundreds of features opens up the possibility to study more extensive processes instead of just looking at smaller parts of them. The results of k-nearest-neighbors study show that the presented feature selection procedure is valid and appropriate. A second advantage of the presented method is the possibility to use thousands of samples. Whereas the current rules of selecting appropriate limits for utilizing the methods are theoretically proved only for small sample sizes, especially in the case of linear correlation, this thesis gives the guidelines for feature selection with thousands of samples. A third positive aspect is the nature of the results: given that the outcome of the method is a set of conditional probabilities, the derived model is highly unrestrictive and rather easy to interpret. In order to test the presented method in practice, it was applied to study two different cases of steel manufacturing with hot strip rolling. In the first case, the conditional probabilities for different types of retentions were derived and, in the second case, the rolling conditions for the occurrence of wedge were revealed. The results of both of these studies show that steel manufacturing processes are indeed very complex and highly dependent on the various stages of the manufacturing. This was further confirmed by the fact that with studies of k-nearest-neighbors and C4.5, it was impossible to derive useful models concerning the datasets as a whole. It is believed that the reason for this lies in the nature of these two methods, meaning that they are unable to grasp such manifold inter-correlations in the data. On the contrary, the presented method of conditional probabilities allowed new knowledge to be gained of the studied processes, which will help to better understand these processes and to enhance them.

This Master's describes knownledge discovery from process logs by using process mining algorithms. Chosen algorithms are described in detail. These aim to create process model based on event log analysis. The goal is to design such components, which would be able to import the process and run the simulations. Results from components can be used for short term planning.

This thesis investigates the price discovery process between the stock and the credit default swap market (CDS). We link the financial theory of efficient markets and the underlying models and conditions involved in CDSs, the stock market and financial crashes. This study uses publicly listed firms and the European market CDS series to construct a matched stock portfolio and uses financial data collected between the years 2019 to 2021. The purpose is to better understand the price discovery process during a potential new type of crisis in modern financial history. It could potentially allow portfolio managers, traders, arbitrageurs and stakeholders who monitor systematic indices to gauge the level of risk in the overall economy. It can also better inform regulators about how the CDS and the stock market reacted to each other during the COVID-19 pandemic. This deductive and quantitative research is based on secondary data gathered from the Eikon financial database. It uses a vector autoregressive model to test a hypothesis regarding the price discovery process between the stock and CDS portfolios.  Our results show that when using only the variables for the CDS and stock market, both variables cause each other, which is to say a feedback effect is present between the CDS Europe index and the matched portfolio of stocks. When adding the three control variables, the stock variable no longer causes the CDS variable, while the CDS variable still causes the stock variable. We conclude that the European credit default swap index leads the matched portfolio of stocks in the price discovery process with our chosen variables.

The managing of complex business processes, which are changed due to globalization, calls for the development of powerful information systems that offer generic process modeling and process execution capabilities. Even though contemporary information systems are more and more utilized in enterprises, their actual impact in automatizing complex business process is still limited by the difficulties encountered in design phase. Actually this design phase is time consuming, often subjective and incomplete. In the scope of this study, a reverse approach is followed. Instead of starting with process design, the method of discovering interesting patterns from the navigation traces is taken as basis and a new data analysis methodology named &ldquo / From-to Chart Based Process Discovery&rdquo / is proposed. In this hybrid methodology &ldquo / from-to chart&rdquo / , which is fundamentally dedicated to material handling issues on production floor, is used as the front-end to monitor the transitions among activities of a realistic event log and convert these raw relations into optimum activity sequence. Then a revised version of process mining, which is the back-end of this methodology, upgrades optimum activity sequence into process model.

The dissertation describes qualitative research undertaken to study the discovery process in a class at the individual, group and whole class levels. This process is demonstrated on the case of discovering the parity of number zero (odd or even) in two class discussions. These discussions took place in a class of fourth year pupils at primary school, taught by the author in a constructivist way, within a one month period of each other. The research database contains also audio and video recordings, written work of the observed pupils, the author's teaching diary and it is analyzed and compared with literature. The methodology focuses on classifying the broad spectrum of pupils' responses and thinking processes not only in relation to the selected problem but also to related problems. Some of the described events are commented on with recommendations how to handle the didactic situations that occurred or compared with hypothetical situations typical for transmissive teaching. The main results of this work fall in the cognitive, metacognitive and social area are summarized into seventeen points that highlight the importance of discussion for the complexity of pupils' knowledge, for the enriching and refining their ideas, increasing their autonomy and ability to cooperate.