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Evaluation of the antibacterial, anti-biofilm, and anti-quorum sensing activities of plant extracts and essential oils against bovine mastitis causing pathogens
No full textBovine mastitis is the most common disease that leads to economic loss and is a major concern for the dairy cattle community worldwide. The most common mastitis-causing organisms are bacteria, that can be classified into contagious and environmental. Some common contagious pathogens are Staphylococcus aureus, Streptococcus dysgalactiae and Streptococcus agalactiae, while the environmental pathogens include mainly Streptococcus spp. and coliforms species.
The development of antibiotic-resistant strains of pathogens has become a critical challenge in antibiotic treatment, thus, it is necessary to look for alternatives to antibiotic therapy, particularly those based on natural products such as plants.
The present research aimed at evaluating the opportunity to use plant extracts and essential oils against bovine mastitis causing pathogens as a potential alternative or supplement to conventional antibiotics. The study included two in vitro projects.
The first project aimed at assessing of the antibacterial and anti-quorum sensing activities of different extracts from six South African plants, as well as their cytotoxicity. Plant extracts were evaluated against ATCC strains of Staphylococcus aureus and Staphylococcus epidermidis, and two clinical isolates of S. aureus. The antibacterial investigations suggest the potential usefulness of the extracts of Vahcellia karroo and Terminalia sericea as antibacterial agents with good activity against staphylococci causing bovine mastitis and with anti-quorum sensing properties.
The second project investigated the bactericidal activity of 11 essential oils and 2 blends against 5 strains responsible of bovine mastitis: S. agalactiae, S. dysgalactiae, S. uberis, S. aureus, and S. epidermidis. The assessment of both the reduction of biofilm formation and the inhibition of pre-formed biofilm, was carried out against biofilm-formative strains of S. aureus and S. epidermitis. Origanum vulgare and Cinnamomum zeylanicum resulted to be the most active essential oils against the above mentioned bacteria, inhibiting both the bacterial growth and the formation of biofilm
Dramaturgy as thinking and practice. The work of the dramaturg in contemporary theatre
No full textLa dramaturgie come pensiero e pratica presenta un approccio innovativo alla dramaturgie contemporanea intesa come conoscenza pratica volta a indagare i processi creativi, il rapporto con il pubblico e il posizionamento del dramaturg. Il cambio di paradigma di intendere le prove come il processo di creazione di uno spettacolo e non come l'esecuzione di un quadro precedentemente definito ha aperto nuove possibilità e ha richiesto la vicinanza del dramaturg in sala prove. Il lavoro esplora come la figura tradizionale del dramaturg abbia rimodellato le connotazioni portando a una crisi delle nozioni di intellettuale e teorico, richiedendo un diverso quadro di approccio. Attraverso un approccio interdisciplinare e transdisciplinare che coinvolge il pensiero ecologico, relazionale e processuale, la ricerca affronta la dramaturgie come pratica condivisa, politicizzata e catalitica, e la figura del dramaturg come facilitatore e mobilizzatore del processo di pensiero (e quindi di creazione) degli altri. Da un lato, offre diversi concetti critici attraverso i quali ricostruisce i modi di pensare della dramaturgie, come la responsabilità, la conversazione e la focalizzazione. Dall'altro lato, individua la macro e la microdramaturgie come strumento di analisi per le pratiche contemporanee, che serve a indagare le dinamiche drammaturgiche che si formano intorno alle strutture della realtà immaginaria di un'opera con l'obiettivo di dispiegarne le potenzialità intrinseche in termini concreti, mentre la macrodramaturgie elabora le strutture della realtà concreta di un determinato contesto, rendendo la dramaturgie un veicolo di cambiamento reale. I due poli servono a delineare il pensiero drammaturgico, la pratica e la funzione del dramaturg in casi di studio provenienti dal contesto italiano e internazionale.Dramaturgy as Thinking and Practice is an innovative approach on contemporary dramaturgy as a practical knowledge aimed to investigate creative processes, relationship with the audience and self-positioning of dramaturg. The paradigm shift of understanding rehearsals as the process of creating a performance and not the execution of a previously defined framework has open up for a new possibilities and has demanded the proximity of the dramaturg in the rehearsal room. The work explores how the traditional figure of the dramaturg has reshaped the connotations leading to a crisis in the notions of the intellectual and theorist, demanding a different framework of approach. Through an interdisciplinary and transdisciplinary approach involving ecological, relational and process thinking, the research addresses dramaturgy as a shared, politicized, and catalytic practice, and the figure of dramaturg as facilitator and mobilizer of the process of thinking (and thus creating) of others. It offers, on the one hand, several critical concepts through which it reconstructs dramaturgical ways of thinking, such as response-ability, conversation, and focalization. On the other hand, it identifies macro and micro dramaturgy as a tool of analysis for contemporary practices, which serves to investigate the dramaturgical dynamics formed around the structures of the imaginary reality of a work with the aim to unfold its inherent potential in concrete terms, meanwhile macro-dramaturgy elaborates the structures of the concrete reality of a given context, making the dramaturgy a vehicle for real change. The two poles serve to delineate dramaturgical thinking, practice and function of dramaturg in case studies from the Italian and international context
Promoting the development of active job search competence. An empirical study in the employment services of Emilia-Romagna.
No full textLe crisi economiche e la discontinuità lavorativa hanno determinato l’emergere di programmi di riforma del mercato del lavoro a partire dalla fine degli anni’90 del XX secolo con la programmazione di politiche attive e la nascita dei Servizi pubblici e privati per il Lavoro (SPIL). I SPIL sono stati strutturati per accompagnare la persona nella ricerca attiva del lavoro per la realizzazione del progetto personale e professionale e un piano di azioni per realizzarlo in funzione della piena occupazione e il contenimento dell’esclusione sociale.
Il presente dottorato industriale presso ENFAP Emilia-Romagna ha l’obiettivo di avviare il processo di validazione di uno strumento di analisi in ingresso dei comportamenti di ricerca attiva del lavoro degli utenti dei SPIL. L’obiettivo è fornire agli operatori uno strumento formativo utile a inquadrare la situazione di partenza degli utenti per progettare un intervento per promuovere la competenza di ricerca attiva del lavoro. Alla base una nuova lettura pedagogica dei SPIL considerati nella loro funzione “formativa” e non di supporto all’inserimento lavorativo.
Lo studio ha riguardato nello specifico l’individuazione, la traduzione e l’adattamento della Job search behavior scale (Stevenor & Zickar, 2022) somministrata a un campione ragionato di 461 utenti presso ENFAP Emilia-Romagna, i Centri per l’Impiego di Modena e Sassuolo sia, per un sottogruppo, dei servizi per il lavoro erogati mediante il social media Instagram.
L’analisi in componenti principali dei dati ha confermato la presenza delle due dimensioni della competenza di ricerca attiva del lavoro già individuate nella scala originale – denominate preparazione e ricerca attiva – e ha fatto emergere una terza dimensione che abbiamo denominato dimensione sociale. Tale dimensione farebbe riferimento ai comportamenti caratterizzati dal coinvolgimento della propria rete sociale per la ricerca occupazionale in coerenza con diversi contributi empirici presenti in letteratura che mostrerebbero l’efficacia della rete sociale nella ricerca del lavoro.Economic crises and job discontinuity have led to the emergence of labor market reform programs since the late 1990s, with the implementation of active policies and the creation of Public and Private Employment Services (PES). PES were structured to assist individuals in their active job search, aiming to achieve personal and professional projects and a plan of actions for achieving full employment and reducing social exclusion.This industrial PhD program at ENFAP Emilia-Romagna aims to initiate the process of validating a tool for analyzing the active job search behaviors of PES users. The objective is to provide operators with a training tool to assess the starting situation of users in order to design a training intervention that promotes active job search competence. This is based on a new pedagogical perspective of PES, viewed in their “training” function rather than merely as support for job placement. The study specifically involved identifying, translating, and adapting the Job Search Behavior Scale (Stevenor & Zickar, 2022) and administering it to a selected sample of 461 users at ENFAP Emilia-Romagna, the Employment Centers of Modena and Sassuolo, and, for a subgroup, through job services provided via the social media platform Instagram.Principal component analysis of the data confirmed the presence of the two dimensions of active job search competence identified in the original scale – termed preparation and active search – and revealed a third dimension we have named the social dimension. This dimension refers to behaviors characterized by engaging one's social network in the job search, consistent with various empirical contributions in the literature showing the effectiveness of social networks in job searching
Essays in political economy
No full textThis dissertation explores the interplay between norms, preferences, and information across three distinct chapters. It investigates how information derived from commemoration, news, or social media interacts with social norms, such as trust and pro-social behavior, and political preferences, including regime support in autocracy and demand for regulation. Chapter 1 explores the transmission of social norms through collective memory in transient communities. Utilizing novel data on online donation ads for small personal items as a proxy for pro-social behavior, the study reveals that individuals are less likely to engage in such behavior when reminded about past repression through commemoration. This emphasizes the crucial role of collective memory in shaping historical legacies, even in transient communities. Examining the demand for state regulation during the COVID-19 pandemic, Chapter 2 challenges existing theories by incorporating fear alongside trust. Analyzing survey data from 61 Russian regions, the study finds that fear of the virus increases demand for regulation. The findings highlight a critical scope condition: the impact of trust on regulation is conditional on fear, with high levels of fear decreasing the effect of trust. This offers insights into how fear of social threats shapes support for state intervention, especially in crises. Concluding the dissertation, Chapter 3 establishes an empirical link between exposure to information on casualties, contrasting war propaganda, and war and regime support in Russia after the full-scale invasion of Ukraine in February 2022. Analyzing the changes in social media engagement in response to verified information on Russian war fatalities in Ukraine, the study reveals that accurate information on the human cost of war disrupts the spread of war propaganda and has the potential to erode support for the autocrat at war. This underscores the broader implications of countering false narratives in the context of independent media and misinformation
Novel prototypes of electro-activated molecular machines
No full textThe work presented in this thesis deals with the investigation of new prototypes of molecular machines, based on rotaxane and pseudorotaxane architectures, by means of voltammetric and spectroscopic techniques. The discussion is divided in two parts.
Part I concerns the investigation of electro-switchable molecular muscles, based on mechanically interlocked molecules. This study is performed on systems of increasing complexity, starting from [2]rotaxanes and arriving to polymers.
In Chapters 3 and 4, [2]- and [3]rotaxanes, characterized by the presence of three stations for the macrocycle(s), are investigated. In both systems, the macrocycle(s) movement can be controlled through a combination of stimuli, resulting in a processive and directional motion.
In Chapter 5, daisy chain rotaxanes, dimers of the [2]rotaxanes discussed in Chapter 3, are investigated. These systems can be switched between an extended and a contracted conformation, and they represent the monomeric units for the realization of polymeric molecular muscles.
In Chapter 6, the properties of electro-switchable polymeric molecular muscles, composed by the daisy chains investigated in Chapter 5, are discussed. The repeating units of these poly-daisy chains contract and extend upon electrical stimulation, and this motion is expected to be transmitted to the polymer itself, resulting in an amplification of the effect.
Part II concerns the investigation of rotaxanes and pseduorotaxanes based on heteroditopic calix[6]arenes and cationic guests.
In Chapters 8 and 9, novel calix[6]arene macrocycles, functionalized with thiourea or dansyl units, and their related pseudorotaxanes are investigated. In both cases, the calix[6]arene functionalization adds new features to the pseudorotaxane.
In Chapters 10 and 11, the influence of orientational isomerism on the properties of [2]- and [3]rotaxanes is investigated. The [3]rotaxanes discussed in Chapter 10 display similar properties, while the [2]rotaxanes described in Chapter 11, characterized by a calix[6]arene and a stilbazolium unit, exhibit distinct photophysical and photochemical properties
Design with(in) urban ecosystems. Designing new interconnections between data, environment and citizens
No full textLa tesi indaga l’applicazione di design methods e digital methods per favorire l’interazione tra attori su scala urbana, con l’obiettivo di costruire azioni mirate a
contrastare problemi globali. Essa propone un modello e una serie di formati basati sulla data literacy e sulla visualizzazione e comunicazione dei dati, rivolti a differenti gruppi di attori, per instaurare forme di collaborazione utili ad affrontare problemi di interesse globale, con un focus particolare sulla crisi climatica. Partendo dal presupposto che i problemi globali, per la loro complessità, richiedano azioni collettive, transdisciplinari e multidisciplinari per essere compresi, l’ipotesi di ricerca discute la necessità di individuare pratiche di interazione e collaborazione basate sull’aumento delle competenze, utili a favorire una comprensione delle problematiche estesa anche a fasce di popolazione normalmente escluse. Un’ulteriore riflessione alla base della tesi riguarda la necessità di orientare le pratiche progettuali verso forme di collaborazione oltre-che-umane, che includano, cioè, attori non umani, come i sistemi digitali e gli assemblaggi naturali, considerandoli parte integrante dei processi di interazione. In particolare, viene proposta l’adozione delle “pratiche di design mediate dai dati” (data mediated design practices). Nell’ambito della ricerca è stata attivata una collaborazione con il progetto H2020 ReSET - Restarting Economy in Support of the Environment through Technology, che ha permesso l’attuazione di iniziative sperimentali volte a testare gli elementi progettuali proposti, con un focus sul rischio climatico legato alle ondate di calore. Il progetto ReSET ha inoltre reso possibile l’installazione di un’infrastruttura di monitoraggio delle ondate di calore nel territorio di Bologna, basata su centraline climatiche open hardware e open source, finalizzata alla raccolta e diffusione, in formato aperto, di dati ambientali relativi ad aree verdi precedentemente non disponibili.The thesis investigates the application of design and digital methods to foster interaction between actors on an urban scale, aimed at constructing actions to counter global
problems. It proposes a model and a series of formats based on data literacy, as well as data visualization and communication, oriented towards different groups of actors to establish forms of collaboration useful to address global issues, with a particular focus on the climate crisis. Assuming that global problems, due to their complexity, require collective, transdisciplinary, and multidisciplinary actions to be understood, the research hypothesis discusses the necessity of identifying interaction and collaboration practices based on the enhancement of skills to foster a broader understanding of the issues, including segments of the population that are normally excluded. Another key reflection underlying the thesis concerns the need to orient design practices toward more-than-human forms of collaboration, considering non-human actors, such as digital systems and natural assemblages, as integral parts of interactive processes. In particular, the adoption of “data mediated design practices” is proposed.
As part of the research, a collaboration with the H2020 ReSET project - Restarting Economy in Support of the Environment through Technology - was activated. This
collaboration enabled the implementation of experimental initiatives aimed at testing the proposed design elements, with a focus on climate risk related to heatwaves. The
ReSET project also facilitated the installation of a heatwave monitoring infrastructure in the Bologna area, based on open hardware and open-source climate stations, aimed at collecting and disseminating environmental data in open formats for selected green areas that were previously unavailable
Green components for high-power energy storage devices
No full textIn response to the need for sustainable energy storage solutions aligned with the European Green Deal, this thesis explores the development of next-generation supercapacitors using scalable and eco-friendly methods. The research integrates waste-derived activated carbon, water-processable binders, and sustainable electrolytes to create an environmentally responsible supercapacitor production process. Chapter 1 introduces the global energy landscape, emphasizing the role of supercapacitors in reducing fossil fuel dependence. The study is structured into three key areas. Chapter 2 focuses on the valorization of biodigester waste into high-performance activated carbon, achieving a surface area of 1867 m²/g and capacitance of 200 F/g in aqueous KOH. Prototypes demonstrated double the capacitance of commercial devices, validating industrial scalability. Chapter 3 investigates biodegradable binders and water-soluble separators as sustainable alternatives to conventional toxic materials. Pullulan-glycerol binders supported large-scale electrode production, achieving a high active material content and competitive performance with green electrolytes like γ-valerolactone and propylene carbonate. Pouch cells assembled with these materials exhibited an energy density of 17 Wh/kg and a power density of 7 kW/kg, with low degradation over cycling. Chapter 4 introduces alternative electrolytes, including water-in-salt electrolytes (WiSE) and deep eutectic solvents (DES), to enhance the electrochemical stability window and increase cell voltage. Symmetric EDLCs with WiSE using potassium acetate achieved superior stability (1.7 V vs. 1.2 V) and capacitance retention over 6000 cycles. Additionally, a patent-pending hybrid redox capacitor using DES-based electrolytes with redox-active species reached an energy density of 108 Wh/kg and a specific power of 30 kW/kg. This research demonstrates the feasibility of sustainable supercapacitors by integrating waste-derived materials, biodegradable binders, and novel electrolytes. The findings contribute to advancing green energy storage technologies, supporting renewable energy systems and electric mobility, while addressing industrial scalability and market applicability
Radio access techniques in industrial internet of things networks
No full textThe Industrial Internet of Things (IIoT) is a key enabler of the Industry 4.0 paradigm, facilitating the development of efficient, high-performance applications involving industrial assets equipped with wireless connectivity. However, the potential of IIoT is limited by traditional wired communication systems, which have scalability and flexibility issues. In this context, 5th Generation (5G) technology is a substantial improvement over traditional wireless networks and effectively meets many IIoT requirements. Nevertheless, for applications requiring ultra-low latency, high data rates and higher device density, it is evident the need of the next generation of mobile radio networks, the 6th Generation (6G). By leveraging Terahertz (THz) communications, 6G aims to meet the advanced requirements of IIoT. Given these challenges, this thesis provides an in-depth analysis of key IIoT use cases and their demands. It proposes innovative radio access techniques that extend the current state-of-the-art solutions, focusing on enhancing communication performance in industrial environments, where multiple applications with varying demands coexist. In particular, several 5G uplink scheduling algorithms are analyzed, proposing novel solutions to manage traffic correlations and variations in traffic patterns to achieve Ultra-Reliable Low-Latency Communication (URLLC) requirements. The potential of THz communications is then explored through the design of contention-based Medium Access Control (MAC) protocols in both single-hop and multi-hop networks, highlighting the challenges of operating at such high frequencies while also showcasing the benefits that can be achieved. Additionally, this study explores hybrid communication strategies, analyzing the coexistence of pull and push-based communications to optimize performance across multiple devices sharing the same channel. Results are obtained from both mathematical analysis and simulation approaches. Through these contributions, this thesis not only advances the theoretical understanding of wireless communications in IIoT contexts but also demonstrates the feasibility and effectiveness of the proposed solutions in addressing the complex communication needs of Industry 4.0
The quest to fairness in algorithmic decisions: ethical, legal and technical solutions
No full textThe rise of artificial intelligence (AI) and automation has intensified ethical and legal concerns, with fairness at the core of this discourse. This doctoral dissertation examines how fairness is conceptualised, guaranteed, and operationalised in algorithmic systems impacting human lives. As AI increasingly supports or replaces human judgment, concerns about algorithmic discrimination have become central to research and policy. Rather than rigidly defining fairness, this study explores its practical implementation in decision-making contexts with significant societal impact. Employing a multi-disciplinary legal informatics approach, the dissertation synthesises legal, philosophical, and ethical literature to provide a comprehensive overview of fairness theories and debates. It introduces a novel, multi-dimensional framework integrating legal and philosophical definitions while critically analysing EU anti-discrimination laws. By evaluating their strengths and limitations, the study refines a robust framework to address algorithmic discrimination effectively. The research also examines technical bias mitigation techniques, such as fairness metrics and synthetic data, assessing their potential in reducing algorithmic discrimination. Additionally, it offers interpretative guidance on the EU Artificial Intelligence Act (AI Act), ensuring stakeholders can navigate its obligations in practice. A key outcome of this work is the Fair, Transparent, Accountable, and Legal (Fair-y-TALe) checklist, a harm-based tool designed to prevent, evaluate, and mitigate bias throughout the AI lifecycle. Aligned with the AI Act’s provisions, this checklist operationalises fairness in algorithmic decisions, aiding in identifying and addressing discriminatory harms. Through this interdisciplinary approach, the dissertation contributes to advancing fair and accountable AI systems
Machine learning for software engineering
No full textThe explosive growth of open-source repositories creates opportunities and challenges for Machine Learning for Software Engineering (ML4SE). Current methods struggle with: (i) code frequently reformatted or minified, obscuring stylistic signals; (ii) the lack of standardised benchmarks for repository recommendation; and (iii) the need to scale to billions of files in archives such as Software Heritage. Objectives. This thesis aims to (1) develop an authorship-attribution technique resilient to common code transformations, (2) conduct the first large-scale systematic mapping of repository-recommendation research, and (3) design a multi-label classifier that operates at archive scale. Methods. A language-agnostic stylometric representation based on Concrete Syntax Tree (CST) path-contexts is introduced, contrasted with Abstract Syntax Trees (ASTs). A systematic mapping study screens over 1 700 papers and distils 43 primary studies, revealing gaps in benchmark standardisation and scalability. To address these, DRAGON is proposed, a sentence-pair BERT model with focal loss and adaptive thresholding, trained on 825 k repositories and 239 GitRanking topics. Results. On untransformed code, CST-based stylometry lifts top-1 author-recognition accuracy from 51 % to 68 %, a 17 % absolute gain over AST baselines. After formatting or minification, recognition falls for both, yet CST still leads, showing the limited privacy such transformations afford. DRAGON raises F1@5 by 11 % over prior work and maintains this performance even when 34 % of projects lack a README. All datasets, model checkpoints, and evaluation scripts are released under permissive licences. Contributions. (i) A transformation-resilient stylometry pipeline; (ii) the largest systematic map of repository-recommendation research; (iii) the first repository classifier evaluated on Software-Heritage-scale data; and (iv) practical guidelines for representation, scale-aware engineering, and responsible deployment. Impact. The findings enable accurate topic tagging, strengthen forensic analysis, and guide ML4SE systems that keep pace with open-source ecosystem growth