Beyond pollinator reward: steps forward and knowledge gaps on the role of floral nectar in plant-animal interactions.

The work done within the framework of my PhD project has been carried out between November 2019 and January 2023 at the Department of Biological, Geological and Environmental Sciences of the University of Bologna, under the supervision of Prof. Marta Galloni and PhD Gherardo Bogo. A period of three months was spent at the Natural History Museum of Rijeka, under the supervision of Prof. Boštjan Surina. The main aim of the thesis was to investigate further the so-called pollinator manipulation hypothesis, which states that when a floral visitor gets in contact with a specific nectar chemistry, the latter affects its behavior of visit on flowers, with potential repercussions on the plant reproductive fitness. To the purpose, the topic was tackled by means of three main approaches: field studies, laboratory assessments, and bibliographic reviews. This research project contributes to two main aspects. First, when insects encounter nectar-like concentrations of a plethora of secondary metabolites in their food-environment, various aspects of their behavior relevant to flower visitation can be affected. In addition, the results I gained confirm that the combination of field studies and laboratory assessments allows to get more realistic pictures of a given phenomenon than the single approaches. Second, reviewing the existent literature in the field of nectar ecology has highlighted how crucial is to establish the origin of nectar biogenic amines to either confirm or reject the multiple speculations made on the role of nectar microbes in shaping plant-animal interactions.

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.

Business Clusters in Eastern Europe: Policy Analysis and Cluster Performance

Clusters have increasingly become an essential part of policy discourses at all levels, EU, national, regional, dealing with regional development, competitiveness, innovation, entrepreneurship, SMEs. These impressive efforts in promoting the concept of clusters on the policy-making arena have been accompanied by much less academic and scientific research work investigating the actual economic performance of firms in clusters, the design and execution of cluster policies and going beyond singular case studies to a more methodologically integrated and comparative approach to the study of clusters and their real-world impact. The theoretical background is far from being consolidated and there is a variety of methodologies and approaches for studying and interpreting this phenomenon while at the same time little comparability among studies on actual cluster performances. The conceptual framework of clustering suggests that they affect performance but theory makes little prediction as to the ultimate distribution of the value being created by clusters. This thesis takes the case of Eastern European countries for two reasons. One is that clusters, as coopetitive environments, are a new phenomenon as the previous centrally-based system did not allow for such types of firm organizations. The other is that, as new EU member states, they have been subject to the increased popularization of the cluster policy approach by the European Commission, especially in the framework of the National Reform Programmes related to the Lisbon objectives. The originality of the work lays in the fact that starting from an overview of theoretical contributions on clustering, it offers a comparative empirical study of clusters in transition countries. There have been very few examples in the literature that attempt to examine cluster performance in a comparative cross-country perspective. It adds to this an analysis of cluster policies and their implementation or lack of such as a way to analyse the way the cluster concept has been introduced to transition economies. Our findings show that the implementation of cluster policies does vary across countries with some countries which have embraced it more than others. The specific modes of implementation, however, are very similar, based mostly on soft measures such as funding for cluster initiatives, usually directed towards the creation of cluster management structures or cluster facilitators. They are essentially founded on a common assumption that the added values of clusters is in the creation of linkages among firms, human capital, skills and knowledge at the local level, most often perceived as the regional level. Often times geographical proximity is not a necessary element in the application process and cluster application are very similar to network membership. Cluster mapping is rarely a factor in the selection of cluster initiatives for funding and the relative question about critical mass and expected outcomes is not considered. In fact, monitoring and evaluation are not elements of the cluster policy cycle which have received a lot of attention. Bulgaria and the Czech Republic are the countries which have implemented cluster policies most decisively, Hungary and Poland have made significant efforts, while Slovakia and Romania have only sporadically and not systematically used cluster initiatives. When examining whether, in fact, firms located within regional clusters perform better and are more efficient than similar firms outside clusters, we do find positive results across countries and across sectors. The only country with negative impact from being located in a cluster is the Czech Republic.

Advances in understanding of osmotic dehydration and vacuum impregnation of fruits

Osmotic Dehydration and Vacuum Impregnation are interesting operations in the food industry with applications in minimal fruit processing and/or freezing, allowing to develop new products with specific innovative characteristics. Osmotic dehydration is widely used for the partial removal of water from cellular tissue by immersion in hypertonic (osmotic) solution. The driving force for the diffusion of water from the tissue is provided by the differences in water chemical potential between the external solution and the internal liquid phase of the cells. Vacuum Impregnation of porous products immersed in a liquid phase consist of reduction of pressure in a solid-liquid system (vacuum step) followed by the restoration of atmospheric pressure (atmospheric step). During the vacuum step the internal gas in the product pores is expanded and partially flows out while during the atmospheric step, there is a compression of residual gas and the external liquid flows into the pores (Fito, 1994). This process is also a very useful unit operation in food engineering as it allows to introduce specific solutes in the tissue which can play different functions (antioxidants, pH regulators, preservatives, cryoprotectants etc.). The present study attempts to enhance our understanding and knowledge of fruit as living organism, interacting dynamically with the environment, and to explore metabolic, structural, physico-chemical changes during fruit processing. The use of innovative approaches and/or technologies such as SAFES (Systematic Approach to Food Engineering System), LF-NMR (Low Frequency Nuclear Magnetic Resonance), GASMAS (Gas in Scattering Media Absorption Spectroscopy) are very promising to deeply study these phenomena. SAFES methodology was applied in order to study irreversibility of the structural changes of kiwifruit during short time of osmotic treatment. The results showed that the deformed tissue can recover its initial state 300 min after osmotic dehydration at 25 °C. The LF-NMR resulted very useful in water status and compartmentalization study, permitting to separate observation of three different water population presented in vacuole, cytoplasm plus extracellular space and cell wall. GASMAS techniques was able to study the pressure equilibration after Vacuum Impregnation showing that after restoration of atmospheric pressure in the solid-liquid system, there was a reminding internal low pressure in the apple tissue that slowly increases until reaching the atmospheric pressure, in a time scale that depends on the vacuum applied during the vacuum step. The physiological response of apple tissue on Vacuum Impregnation process was studied indicating the possibility of vesicular transport within the cells. Finally, the possibility to extend the freezing tolerance of strawberry fruits impregnated with cryoprotectants was proven.

Radar based improvements and diagnosis of the convective capabilities of the cosmo-2 model

In the last few years the resolution of numerical weather prediction (nwp) became higher and higher with the progresses of technology and knowledge. As a consequence, a great number of initial data became fundamental for a correct initialization of the models. The potential of radar observations has long been recognized for improving the initial conditions of high-resolution nwp models, while operational application becomes more frequent. The fact that many nwp centres have recently taken into operations convection-permitting forecast models, many of which assimilate radar data, emphasizes the need for an approach to providing quality information which is needed in order to avoid that radar errors degrade the model's initial conditions and, therefore, its forecasts. Environmental risks can can be related with various causes: meteorological, seismical, hydrological/hydraulic. Flash floods have horizontal dimension of 1-20 Km and can be inserted in mesoscale gamma subscale, this scale can be modeled only with nwp model with the highest resolution as the COSMO-2 model. One of the problems of modeling extreme convective events is related with the atmospheric initial conditions, in fact the scale dimension for the assimilation of atmospheric condition in an high resolution model is about 10 Km, a value too high for a correct representation of convection initial conditions. Assimilation of radar data with his resolution of about of Km every 5 or 10 minutes can be a solution for this problem. In this contribution a pragmatic and empirical approach to deriving a radar data quality description is proposed to be used in radar data assimilation and more specifically for the latent heat nudging (lhn) scheme. Later the the nvective capabilities of the cosmo-2 model are investigated through some case studies. Finally, this work shows some preliminary experiments of coupling of a high resolution meteorological model with an Hydrological one.

Ab initio quantum mechanical investigation of structural and chemical-physical properties of selected minerals for minero-petrological, structural ceramic and biomaterial applications

The purpose of this thesis is the atomic-scale simulation of the crystal-chemical and physical (phonon, energetic) properties of some strategically important minerals for structural ceramics, biomedical and petrological applications. These properties affect the thermodynamic stability and rule the mineral-environment interface phenomena, with important economical, (bio)technological, petrological and environmental implications. The minerals of interest belong to the family of phyllosilicates (talc, pyrophyllite and muscovite) and apatite (OHAp), chosen for their importance in industrial and biomedical applications (structural ceramics) and petrophysics. In this thesis work we have applicated quantum mechanics methods, formulas and knowledge to the resolution of mineralogical problems ("Quantum Mineralogy”). The chosen theoretical approach is the Density Functional Theory (DFT), along with periodic boundary conditions to limit the portion of the mineral in analysis to the crystallographic cell and the hybrid functional B3LYP. The crystalline orbitals were simulated by linear combination of Gaussian functions (GTO). The dispersive forces, which are important for the structural determination of phyllosilicates and not properly con-sidered in pure DFT method, have been included by means of a semi-empirical correction. The phonon and the mechanical properties were also calculated. The equation of state, both in athermal conditions and in a wide temperature range, has been obtained by means of variations in the volume of the cell and quasi-harmonic approximation. Some thermo-chemical properties of the minerals (isochoric and isobaric thermal capacity) were calculated, because of their considerable applicative importance. For the first time three-dimensional charts related to these properties at different pressures and temperatures were provided. The hydroxylapatite has been studied from the standpoint of structural and phonon properties for its biotechnological role. In fact, biological apatite represents the inorganic phase of vertebrate hard tissues. Numerous carbonated (hydroxyl)apatite structures were modelled by QM to cover the broadest spectrum of possible biological structural variations to fulfil bioceramics applications.

A Law and Economics Analysis of Trade Secrets: Optimal Scope of Law, Misappropriation and Alternative Damages Regimes

This thesis is primarily based on three core chapters, focused on the fundamental issues of trade secrets law. The goal of this thesis is to come up with policy recommendations to improve legal structure governing trade secrets. The focal points of this research are the following. What is the optimal scope of trade secrets law? How does it depend on the market characteristics such as degree of product differentiation between competing products? What factors need to be considered to balance the contradicting objectives of promoting innovation and knowledge diffusion? The second strand of this research focuses on the desirability of lost profits or unjust enrichment damage regimes in case of misappropriation of a trade secret. A comparison between these regimes is made and simple policy implications are extracted from the analysis. The last part of this research is an empirical analysis of a possible relationship between trade secrets sharing and misappropriation instances faced by firms.

Towards Unstructured Knowledge Integration in Natural Language Processing

In the last decades, Artificial Intelligence has witnessed multiple breakthroughs in deep learning. In particular, purely data-driven approaches have opened to a wide variety of successful applications due to the large availability of data. Nonetheless, the integration of prior knowledge is still required to compensate for specific issues like lack of generalization from limited data, fairness, robustness, and biases. In this thesis, we analyze the methodology of integrating knowledge into deep learning models in the field of Natural Language Processing (NLP). We start by remarking on the importance of knowledge integration. We highlight the possible shortcomings of these approaches and investigate the implications of integrating unstructured textual knowledge. We introduce Unstructured Knowledge Integration (UKI) as the process of integrating unstructured knowledge into machine learning models. We discuss UKI in the field of NLP, where knowledge is represented in a natural language format. We identify UKI as a complex process comprised of multiple sub-processes, different knowledge types, and knowledge integration properties to guarantee. We remark on the challenges of integrating unstructured textual knowledge and bridge connections with well-known research areas in NLP. We provide a unified vision of structured knowledge extraction (KE) and UKI by identifying KE as a sub-process of UKI. We investigate some challenging scenarios where structured knowledge is not a feasible prior assumption and formulate each task from the point of view of UKI. We adopt simple yet effective neural architectures and discuss the challenges of such an approach. Finally, we identify KE as a form of symbolic representation. From this perspective, we remark on the need of defining sophisticated UKI processes to verify the validity of knowledge integration. To this end, we foresee frameworks capable of combining symbolic and sub-symbolic representations for learning as a solution.