Institutional Processes and Discursive Strategies: Rhetoric and Vocabulary Analysis of CSR and Sustainability

The candidate tackled an important issue in contemporary management: the role of CSR and Sustainability. The research proposal focused on a longitudinal and inductive research, directed to specify the evolution of CSR and contribute to the new institutional theory, in particular institutional work framework, and to the relation between institutions and discourse analysis. The documental analysis covers all the evolution of CSR, focusing also on a number of important networks and associations. Some of the methodologies employed in the thesis have been employed as a consequence of data analysis, in a truly inductive research process. The thesis is composed by two section. The first section mainly describes the research process and the analyses results. The candidates employed several research methods: a longitudinal content analysis of documents, a vocabulary research with statistical metrics as cluster analysis and factor analysis, a rhetorical analysis of justifications. The second section puts in relation the analysis results with theoretical frameworks and contributions. The candidate confronted with several frameworks: Actor-Network-Theory, Institutional work and Boundary Work, Institutional Logic. Chapters are focused on different issues: a historical reconstruction of CSR; a reflection about symbolic adoption of recurrent labels; two case studies of Italian networks, in order to confront institutional and boundary works; a theoretical model of institutional change based on contradiction and institutional complexity; the application of the model to CSR and Sustainability, proposing Sustainability as a possible institutional logic.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.

Physiological and structural aspects of fruit and vegetable mild processing

Over the past years fruit and vegetable industry has become interested in the application of both osmotic dehydration and vacuum impregnation as mild technologies because of their low temperature and energy requirements. Osmotic dehydration is a partial dewatering process by immersion of cellular tissue in hypertonic solution. The diffusion of water from the vegetable tissue to the solution is usually accompanied by the simultaneous solutes counter-diffusion into the tissue. Vacuum impregnation is a unit operation in which porous products are immersed in a solution and subjected to a two-steps pressure change. The first step (vacuum increase) consists of the reduction of the pressure in a solid-liquid system and the gas in the product pores is expanded, partially flowing out. When the atmospheric pressure is restored (second step), the residual gas in the pores compresses and the external liquid flows into the pores. This unit operation allows introducing specific solutes in the tissue, e.g. antioxidants, pH regulators, preservatives, cryoprotectancts. Fruit and vegetable interact dynamically with the environment and the present study attempts to enhance our understanding on the structural, physico-chemical and metabolic changes of plant tissues upon the application of technological processes (osmotic dehydration and vacuum impregnation), by following a multianalytical approach. Macro (low-frequency nuclear magnetic resonance), micro (light microscopy) and ultrastructural (transmission electron microscopy) measurements combined with textural and differential scanning calorimetry analysis allowed evaluating the effects of individual osmotic dehydration or vacuum impregnation processes on (i) the interaction between air and liquid in real plant tissues, (ii) the plant tissue water state and (iii) the cell compartments. Isothermal calorimetry, respiration and photosynthesis determinations led to investigate the metabolic changes upon the application of osmotic dehydration or vacuum impregnation. The proposed multianalytical approach should enable both better designs of processing technologies and estimations of their effects on tissue.