Knowledge management in intelligent tutoring systems

In the last years, Intelligent Tutoring Systems have been a very successful way for improving learning experience. Many issues must be addressed until this technology can be defined mature. One of the main problems within the Intelligent Tutoring Systems is the process of contents authoring: knowledge acquisition and manipulation processes are difficult tasks because they require a specialised skills on computer programming and knowledge engineering. In this thesis we discuss a general framework for knowledge management in an Intelligent Tutoring System and propose a mechanism based on first order data mining to partially automate the process of knowledge acquisition that have to be used in the ITS during the tutoring process. Such a mechanism can be applied in Constraint Based Tutor and in the Pseudo-Cognitive Tutor. We design and implement a part of the proposed architecture, mainly the module of knowledge acquisition from examples based on first order data mining. We then show that the algorithm can be applied at least two different domains: first order algebra equation and some topics of C programming language. Finally we discuss the limitation of current approach and the possible improvements of the whole framework.

Technology acquisition through licensing. Implications for firm strategy

Nowadays licensing practices have increased in importance and relevance driving the widespread diffusion of markets for technologies. Firms are shifting from a tactical to a strategic attitude towards licensing, addressing both business and corporate level objectives. The Open Innovation Paradigm has been embraced. Firms rely more and more on collaboration and external sourcing of knowledge. This new model of innovation requires firms to leverage on external technologies to unlock the potential of firms’ internal innovative efforts. In this context, firms’ competitive advantage depends both on their ability to recognize available opportunities inside and outside their boundaries and on their readiness to exploit them in order to fuel their innovation process dynamically. Licensing is one of the ways available to firm to ripe the advantages associated to an open attitude in technology strategy. From the licensee’s point view this implies challenging the so-called not-invented-here syndrome, affecting the more traditional firms that emphasize the myth of internal research and development supremacy. This also entails understanding the so-called cognitive constraints affecting the perfect functioning of markets for technologies that are associated to the costs for the assimilation, integration and exploitation of external knowledge by recipient firms. My thesis aimed at shedding light on new interesting issues associated to in-licensing activities that have been neglected by the literature on licensing and markets for technologies. The reason for this gap is associated to the “perspective bias” affecting the works within this stream of research. With very few notable exceptions, they have been generally concerned with the investigation of the so-called licensing dilemma of the licensor – whether to license out or to internally exploit the in-house developed technologies, while neglecting the licensee’s perspective. In my opinion, this has left rooms for improving the understanding of the determinants and conditions affecting licensing-in practices. From the licensee’s viewpoint, the licensing strategy deals with the search, integration, assimilation, exploitation of external technologies. As such it lies at the very hearth of firm’s technology strategy. Improving our understanding of this strategy is thus required to assess the full implications of in-licensing decisions as they shape firms’ innovation patterns and technological capabilities evolution. It also allow for understanding the so-called cognitive constraints associated to the not-invented-here syndrome. In recognition of that, the aim of my work is to contribute to the theoretical and empirical literature explaining the determinants of the licensee’s behavior, by providing a comprehensive theoretical framework as well as ad-hoc conceptual tools to understand and overcome frictions and to ease the achievement of satisfactory technology transfer agreements in the marketplace. Aiming at this, I investigate licensing-in in three different fashions developed in three research papers. In the first work, I investigate the links between licensing and the patterns of firms’ technological search diversification according to the framework of references of the Search literature, Resource-based Theory and the theory of general purpose technologies. In the second paper - that continues where the first one left off – I analyze the new concept of learning-bylicensing, in terms of development of new knowledge inside the licensee firms (e.g. new patents) some years after the acquisition of the license, according to the Dynamic Capabilities perspective. Finally, in the third study, Ideal with the determinants of the remuneration structure of patent licenses (form and amount), and in particular on the role of the upfront fee from the licensee’s perspective. Aiming at this, I combine the insights of two theoretical approaches: agency and real options theory.

Identification of surface protein complexes of Streptococcus pyogenes through protein microarray technology

A systematic characterization of the composition and structure of the bacterial cell-surface proteome and its complexes can provide an invaluable tool for its comprehensive understanding. The knowledge of protein complexes composition and structure could offer new, more effective targets for a more specific and consequently effective immune response against a complex instead of a single protein. Large-scale protein-protein interaction screens are the first step towards the identification of complexes and their attribution to specific pathways. Currently, several methods exist for identifying protein interactions and protein microarrays provide the most appealing alternative to existing techniques for a high throughput screening of protein-protein interactions in vitro under reasonably straightforward conditions. In this study approximately 100 proteins of Group A Streptococcus (GAS) predicted to be secreted or surface exposed by genomic and proteomic approaches were purified in a His-tagged form and used to generate protein microarrays on nitrocellulose-coated slides. To identify protein-protein interactions each purified protein was then labeled with biotin, hybridized to the microarray and interactions were detected with Cy3-labelled streptavidin. Only reciprocal interactions, i. e. binding of the same two interactors irrespective of which of the two partners is in solid-phase or in solution, were taken as bona fide protein-protein interactions. Using this approach, we have identified 20 interactors of one of the potent toxins secreted by GAS and known as superantigens. Several of these interactors belong to the molecular chaperone or protein folding catalyst families and presumably are involved in the secretion and folding of the superantigen. In addition, a very interesting interaction was found between the superantigen and the substrate binding subunit of a well characterized ABC transporter. This finding opens a new perspective on the current understanding of how superantigens are modified by the bacterial cell in order to become major players in causing disease.

Technological innovation in Emilia-Romagna: knowledge, practice, strategies

The present study aims at assessing the innovation strategies adopted within a regional economic system, the Italian region Emilia-Romagna, as it faced the challenges of a changing international scenario. As the strengthening of the regional innovative capabilities is regarded as a keystone to foster a new phase of economic growth, it is important also to understand how the local industrial, institutional, and academic actors have tackled the problem of innovation in the recent past. In this study we explore the approaches to innovation and the strategies adopted by the main regional actors through three different case studies. Chapter 1 provides a general survey of the innovative performance of the regional industries over the past two decades, as it emerges from statistical data and systematic comparisons at the national and European levels. The chapter also discusses the innovation policies that the regional government set up since 2001 in order to strengthen the collaboration among local economic actors, including universities and research centres. As mechanics is the most important regional industry, chapter 2 analyses the combination of knowledge and practices utilized in the period 1960s-1990s in the design of a particular kind of machinery produced by G.D S.p.A., a world-leader in the market of tobacco packaging machines. G.D is based in Bologna, the region’s capital, and is at the centre of the most important Italian packaging district. In chapter 3 the attention turns to the institutional level, focusing on how the local public administrations, and the local, publicly-owned utility companies have dealt with the creation of new telematic networks on the regional territory during the 1990s and 2000s. Finally, chapter 4 assesses the technology transfer carried out by the main university of the region – the University of Bologna – by focusing on the patenting activities involving its research personnel in the period 1960-2010.

Wide-scale population genomics of Atlantic bluefin tuna (Thunnus thynnus) inferred by novel high-throughput technology

My PhD project was focused on Atlantic bluefin tuna, Thunnus thynnus, a fishery resource overexploited in the last decades. For a better management of stocks, it was necessary to improve scientific knowledge of this species and to develop novel tools to avoid collapse of this important commercial resource. To do this, we used new high throughput sequencing technologies, as Next Generation Sequencing (NGS), and markers linked to expressed genes, as SNPs (Single Nucleotide Polymorphisms). In this work we applied a combined approach: transcriptomic resources were used to build cDNA libreries from mRNA isolated by muscle, and genomic resources allowed to create a reference backbone for this species lacking of reference genome. All cDNA reads, obtained from mRNA, were mapped against this genome and, employing several bioinformatics tools and different restricted parameters, we achieved a set of contigs to detect SNPs. Once a final panel of 384 SNPs was developed, following the selection criteria, it was genotyped in 960 individuals of Atlantic bluefin tuna, including all size/age classes, from larvae to adults, collected from the entire range of the species. The analysis of obtained data was aimed to evaluate the genetic diversity and the population structure of Thunnus thynnus. We detect a low but significant signal of genetic differentiation among spawning samples, that can suggest the presence of three genetically separate reproduction areas. The adult samples resulted instead genetically undifferentiated between them and from the spawning populations, indicating a presence of panmictic population of adult bluefin tuna in the Mediterranean Sea, without different meta populations.

The Effect of Support Measures on Involvement in Technology Transfer

The present PhD dissertation is dedicated to the general topic of knowledge transfer from academia to industry and the role of various measures at both institutional and university levels in support of commercialization of university research. The overall contribution of the present dissertation work refers to presenting an in-depth and comprehensive analysis of the main critical issues that currently exist with regard to commercial exploitation of academic research, while providing evidence on the role of previously underexplored areas (e.g. strategic use of academic patents; female academic patenting) in a general debate on the ways to successful knowledge transfer from academia to industry. The first paper, included in the present PhD dissertation, aims to address this gap by developing a taxonomy of literature, based on a comprehensive review of the existing body of research on government measures in support of knowledge transfer from academia to industry. The results of the review reveal that there is a considerable gap in the analysis of the impact and relative effectiveness of the public policy measures, especially in what regards the measures aimed at building knowledge and expertise among academic faculty and technology transfer agents. The second paper, presented as a part of the dissertation, focuses on the role of interorganizational collaborations and their effect on the likelihood of an academic patent to remain unused, and points to the strategic management of patents by universities. In the third paper I turn to the issue of female participation in patenting and commercialization; in particular, I find evidence on the positive role of university and its internal support structures in closing the gender gap in female academic patenting. The results of the research, carried out for the present dissertation, provide important implications for policy makers in crafting measures to increase the efficient use of university knowledge stock.

On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions.

Temporary teams learning and performance: the case of 3D printing technology in orthopaedic surgery

This dissertation contributes to the scholarly debate on temporary teams by exploring team interactions and boundaries.The fundamental challenge in temporary teams originates from temporary participation in the teams. First, as participants join the team for a short period of time, there is not enough time to build trust, share understanding, and have effective interactions. Consequently, team outputs and practices built on team interactions become vulnerable. Secondly, as team participants move on and off the teams, teams’ boundaries become blurred over time. It leads to uncertainty among team participants and leaders about who is/is not identified as a team member causing collective disagreement within the team. Focusing on the above mentioned challenges, we conducted this research in healthcare organisations since the use of temporary teams in healthcare and hospital setting is prevalent. In particular, we focused on orthopaedic teams that provide personalised treatments for patients using 3D printing technology. Qualitative and quantitative data were collected using interviews, observations, questionnaires and archival data at Rizzoli Orthopaedic Institute, Bologna, Italy. This study provides the following research outputs. The first is a conceptual study that explores temporary teams’ literature using bibliometric analysis and systematic literature review to highlight research gaps. The second paper qualitatively studies temporary relationships within the teams by collecting data using group interviews and observations. The results highlighted the role of short-term dyadic relationships as a ground to share and transfer knowledge at the team level. Moreover, hierarchical structure of the teams facilitates knowledge sharing by supporting dyadic relationships within and beyond the team meetings. The third paper investigates impact of blurred boundaries on temporary teams’ performance. Using quantitative data collected through questionnaires and archival data, we concluded that boundary blurring in terms of fluidity, overlap and dispersion differently impacts team performance at high and low levels of task complexity.