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.

Automatic Root Cause Analysis (RCA) systems aim to streamline the process of identifying the underlying cause of software failures in complex cloud-native environments. These systems employ graph-like structures to represent causal relationships between different components of a software application. These relationships are typically learned through performance and resource utilization metrics of the microservices in the system. To accomplish this objective, numerous RCA systems utilize statistical algorithms, specifically those falling under the category of causal discovery. These algorithms have demonstrated their utility not only in RCA systems but also in a wide range of other domains and applications. Nonetheless, there exists a research gap in the exploration of the feasibility and efficacy of multivariate time series causal discovery algorithms for deriving causal graphs within a microservice framework. By harnessing metric time series data from Prometheus and applying these algorithms, we aim to shed light on their performance in a cloudnative environment. Furthermore, we have introduced an adaptation in the form of an ensemble causal discovery algorithm. Our experimentation with this ensemble approach, conducted on datasets with known causal relationships, unequivocally demonstrates its potential in enhancing the precision of detected causal connections. Notably, our ultimate objective was to ascertain reliable causal relationships within Ericsson’s cloud-native system ’X,’ where the ground truth is unavailable. The ensemble causal discovery approach triumphs over the limitations of employing individual causal discovery algorithms, significantly augmenting confidence in the unveiled causal relationships. As a practical illustration of the utility of the ensemble causal discovery techniques, we have delved into the domain of anomaly detection. By leveraging causal graphs within our study, we have successfully applied this technique to anomaly detection within the Ericsson system. / System för automatisk rotorsaksanalys (RCA) syftar till att effektivisera process för att identifiera den underliggande orsaken till programvarufel i komplexa molnbaserade miljöer. Dessa system använder grafliknande strukturer att representera orsakssamband mellan olika komponenter i en mjukvaruapplikation. Dessa relationer lär man sig vanligtvis genom prestanda och resursutnyttjande mätvärden för mikrotjänsterna i systemet. För att uppnå detta mål använder många RCAsystem statistiska algoritmer, särskilt de som faller under kategorin orsaksupptäckt. Dessa algoritmer har visat att de inte är användbara endast i RCA-system men även inom en lång rad andra domäner och applikationer. Icke desto mindre finns det en forskningslucka i utforskningen av genomförbarhet och effektivitet av orsaksupptäckt av multivariat tidsserie algoritmer för att härleda kausala grafer inom ett mikrotjänstramverk. Genom att utnyttja metriska tidsseriedata från Prometheus och tillämpa Dessa algoritmer strävar vi efter att belysa deras prestanda i ett moln- inhemsk miljö. Dessutom har vi infört en anpassning i formen av en ensemble kausal upptäcktsalgoritm. Vårt experiment med denna ensemblemetod, utförd på datauppsättningar med kända orsakssamband relationer, visar otvetydigt sin potential för att förbättra precisionen hos upptäckta orsakssamband. Särskilt vår ultimata Målet var att fastställa tillförlitliga orsakssamband inom Ericssons molnbaserade systemet ’X’, där grundsanningen inte är tillgänglig. De ensemble kausal discovery approach segrar över begränsningarna av att använda individuella kausala upptäcktsalgoritmer, avsevärt öka förtroendet för de avslöjade orsakssambanden. Som en praktisk illustration av nyttan av ensemblens kausal upptäcktstekniker har vi fördjupat oss i anomalidomänen upptäckt. Genom att utnyttja kausala grafer inom vår studie har vi framgångsrikt tillämpat denna teknik för att detektera anomali inom Ericsson system

Hurricane Shoes and Other Stories is a collection of short stories that center on evolving and devolving relationships. The characters in these stories form tentative bonds with people in their lives while other relationships slip away. In "Hurricane Shoes," Katrina attempts to reinvent herself by ending an affair. Katrina's pregnancy and her mother's cancer pull the two women closer. "Da's Violets" is about Cheryl's changing relationship with her father. Just as he moves on from the wife who left him for her podiatrist, the wife returns with hopes to reconcile. These stories catch characters in moments when they must make difficult choices and endure the uncertainties and ambiguities of relationships. In "Lady Luck," Laurel is a cocaine addict and alcoholic on the verge of finalizing a divorce. She propositions a young man named River to exchange sex for money. River turns the table, and offers Laurel a deal where she'll have all the money and drugs she wants, as long as she helps him transport cocaine. "Bridges" is a coming of age story in which Linda and Kristin have a close call with a train, and Linda matures as she realizes love has limits. The catalyst for changing relationships is sometimes an exchange between characters. In "Special Son," Mark's father is dying of cancer. The father asks his son to take special care of his mother, and Mark needs his father to finally acknowledge Mark's sexuality. In "Swim or Sink," Doreen befriends her campground neighbor, Michael McBride. McBride has been living at the campground since he left his cheating wife, and he offers Doreen insight when she discovers her husband's infidelity. Together the stories function as a mosaic--each very different, but a complement to the others in forming a larger portrayal of relationships.

Yes / This report addresses the scientific and technological discoveries in the biological sciences that enabled the early interest in biological warfare to move from hurling infected corpses into enemy cities in ancient times, through use of small cultures of animal pathogens to sabotage enemy livestock in World War I, to the origins of organised military biological weapons (BW) programmes directed at humans, animals, and plants in the inter-war period. It builds on Dando¿s 1999 paper: The Impact of the Development of Modern Biology and Medicine on the Evolution of Offensive Biological Warfare Programs in the Twentieth Century.1 For the historical aspects of biological warfare programmes this report primarily draws from the Stockholm International Peace Research Institute volume: Biological and Toxin Weapons: Research, Development and Use from the Middle Ages to 1945.2

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