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Teaching natural sciences with the help of children's literature. An interdisciplinary and poetic approach to sustainability education.
Get PDFLa necessità di essere immersi nelle storie, mediante una continua produzione e fruizione di narrazioni, è una caratteristica distintiva del genere umano. Anche la biologia, per quanto facente parte delle Scienze Naturali, è una disciplina che “racconta una storia”, perché ha a che fare col tempo, con le relazioni tra organismi, con le profondità e le trasformazioni della vita. La letteratura per l’infanzia ha una valenza interdisciplinare che permette di aprire scenari narrativi capaci di raggiungere i più diversi campi del sapere, compreso quello scientifico. A partire da tali assunti e dalle teorie di una pedagogia della lettura che promuove la fruizione dei libri da parte dei bambini senza il proposito di istruire e conformare, la presente ricerca di dottorato si concentra sull’analisi di romanzi e racconti per l’infanzia al fine di individuare e selezionare brani, paragrafi e pagine capaci di veicolare contenuti corretti afferenti alle scienze biologiche e ai suoi metodi, ma espressi in una forma in senso lato poetica, emozionale, evocativa. Nel presente contributo si intende far emergere uno specifico potenziale didattico sparpagliato nei romanzi per ragazzi, con lo scopo di offrire spunti e sollecitazioni per un approccio interdisciplinare e narrativo ai percorsi di apprendimento/insegnamento delle scienze del bios, nella speranza di incoraggiare, specialmente nei bambini della scuola primaria, un interesse profondo e una partecipazione empatica al mondo dei viventi.The intrinsic human need to be immersed in narratives, facilitated by the continual generation and reception of stories, constitutes a defining trait of the human species. Even biology, despite being classified within the Natural Sciences, is a discipline that "tells a story"—as it deals with time, relationships between organisms, and the depths and transformations of life. Children's literature possesses an inherent interdisciplinary dimension, facilitating the construction of narrative frameworks that extend across diverse domains of knowledge, including the scientific field. Starting from these assumptions and the theories of a pedagogy of reading that promotes children's enjoyment of books without the intent to instruct or conform, this doctoral research focuses on the analysis of novels and stories for children, aiming to identify and select passages, paragraphs, and pages that convey correct content related to biological sciences and its methods, yet expressed in a broadly poetic, emotional, and evocative form. This contribution seeks to highlight a specific pedagogical potential scattered throughout children's novels, with the aim of offering insights and stimuli for an interdisciplinary and narrative approach to the educational pathways of life sciences, hoping to encourage, especially in primary school children, a deep interest and empathetic engagement with the living world
Data analytics and predictive models for sustainable multimodal mobility in future smart cities
Get PDFThis thesis investigates the integration of data analytics and predictive modeling to enhance multimodal urban mobility systems within the framework of smart cities. By leveraging diverse datasets—including Wi-Fi signals, mobile phone data, and vehicular sensor inputs—predictive models are developed to simulate and analyze pedestrian and vehicular mobility patterns. Two case studies are presented: the dynamics of pedestrian mobility in cultural heritage cities (Ferrara, ˇSibenik, and Dubrovnik) and vehicular traffic analysis in the Emilia-Romagna region. Advanced mobility algorithms and simulation tools, such as agent-based models and optimal velocity frameworks, are employed to optimize traffic flow and urban planning strategies. These methods provide actionable insights into infrastructure planning, pedestrian movement behaviors, and regional traffic management. The proposed models offer a robust framework for real-time decision-making and support sustainable urban mobility practices. The research underscores the transformative potential of data-driven models in urban mobility modeling and analysis, enabling the design of more efficient transportation systems and fostering urban sustainability. The methodologies developed in this work provide a scalable foundation for addressing complex challenges in mobility systems and advancing smart city infrastructures
From green to speech: a discursive, terminological, and multimodal analysis of book ecology at Écosociété and in Francophone publishing
Get PDFNotre thèse propose une analyse critique et multimodale (MCDA) de l’écologie du livre, envisagée comme un champ d’action de nouvelles dynamiques en matière de pratiques discursives, communicationnelles, terminologiques et éditoriales sous l’angle de l’engagement écopolitique. Elle se propose d’encadrer la terminologie spécialisée et les représentations discursives multimodales dans les discours éditoriaux francophones abordant des questions écologiques, afin de saisir en même temps le débat sur le rôle actuel du livre dans la lutte contre la crise climatique. Le but de la recherche est de rédiger un état de l’art sur les publications écologiques dans l’écosystème éditorial francophone, à travers les feuilles de route et rapports pour une édition responsable, tout en encadrant les pratiques innovantes de la maison indépendante Écosociété. Le projet vise à retracer l’horizon discursif et terminologique de l’écologie du livre, en s’appuyant sur le contexte québécois et montréalais en tant qu’exemplum à transmigrer dans le panorama international, à partir de pratiques multimodales et militantes. Les résultats de la recherche, qui comprennent une théorisation du discours écologique éditorial et l’identification de stratégies discursives multimodales et de terminologies émergentes, constitueront le premier chapitre de la thèse. Les chapitres suivants seront dédiés à cette maison d’édition militante, cas d’étude central, engagée dès sa fondation et rayonnant à l’international, notamment par son adhésion à l’Alliance des éditeurs indépendants et l’affaire Noir Canada (2008). Par l’analyse de son discours éditorial, de son catalogue et de ses bandes dessinées « écolos », nous proposons une cartographie de l’édition militante. Définir l’écologie du livre s’impose comme un enjeu crucial pour saisir les nouvelles tendances terminologiques et explorer la pervasivité du défi climatique dans une perspective interdisciplinaire. Enfin, l’analyse multimodale met en lumière les cadrages métaphoriques structurant les positionnements éditoriaux, en façonnant de nouveaux scénarios dans un horizon écotopique.This thesis offers a critical and multimodal analysis (MCDA) of book ecology, conceptualized as a field of action for new dynamics in discursive, terminological, and editorial practices within ecopolitical engagement. Specifically, it seeks to frame specialized terminology and multimodal discursive representations within Francophone editorial discourses addressing ecological issues, with the dual aim of understanding both the current debate on the book’s role in the climate crisis and the evolving terminological landscape. The research analyzes ecological and climate-related discourses in the publishing sector, applying the engaged paradigm of climate crisis response to this cultural and symbolic field. It aims not only to provide an overview of ecological publications in Francophone contexts—through roadmaps and reports for sustainable publishing—but also to outline emerging practices that rethink the role of publishing within a broader ecopolitical perspective. The project maps the discursive and terminological horizon of book ecology, using Francophone contexts as exempla to be transposed onto the international landscape, starting from Écosociété’s multimodal and activist strategies. Defining the ecology of the book emerges as a key issue, both to identify new trends in specialized terminology and to explore how climate and environmental challenges permeate editorial discourse and shared practices through interdisciplinary reflection. This approach, encompassing editorial and climate-related discourses, the book’s ecosystem, and ecocritical and ecolinguistic studies, interrogates the interrelation of terms—such as bibliodiversity and decarbonization—while addressing the environmental constraints facing the sector. In this perspective, terminology becomes a mirror of resistance for a field that is material, social, and symbolic. Multimodal analysis identifies metaphorical framings and discursive strategies that shape editorial positions, contributing to the theorization of ecological publishing and thus opening paths toward new ecotopian imaginaries, shaping new scenarios in an ecotopian perspective
Job-level online predictive modelling for sustainable HPC systems
Get PDFHigh-Performance Computing (HPC) systems are pivotal in addressing complex computational challenges across various scientific and industrial domains. However, their significant energy consumption and environmental impact pose critical sustainability challenges. One possible solution to tackle these challenges is the development of job level predictive models, with the aim of optimizing system throughput while minimizing environmental impact. While promising, these practices are currently not employed in HPC systems due to several important limitations which make them impractical in production environments. The research makes several contributions in addressing such limitations, and making job-level predictive modelling a practical solution towards more sustainable and efficient HPC environments. We first create two comprehensive datasets, namely PM100 and F-DATA, to overcome the scarcity of publicly available, fine-grained job-level data. These datasets facilitate in-depth analysis of job execution characteristics, such as power consumption and resource allocation, and serve as fundamental tools for job-level predictive modelling. Then, online ML-based predictive algorithms are developed to predict key job execution characteristics, including failure, power consumption, and memory/compute-bound nature. These models operate online, leveraging only submission-time features to infer the prediction into job scheduling and resource allocation decision-making. We employ our predictive models into frameworks, e.g. MCBound and UoPC, suitable for deployment in production environment. Such frameworks enable both system-level optimizations and end-user awareness, fostering improved end-user experience, performance and sustainability.
This work emphasizes the importance of job-level predictive modelling for sustainable HPC workload management. By addressing the limitations of such practices, our research contributes to the broader mission of sustainable computing, setting the stage for more environmentally conscious HPC systems
The association between donor-derived cell-free DNA and acute rejection in heart transplant patients
Get PDFCardiac transplantation is currently the only effective therapy for patients with end-stage heart failure. Despite its life-saving potential, heart transplantation is associated with several significant challenges, the most prominent of which are post- transplant complications. Among these, the risk of infections is heightened due to the immunosuppressive medications required to prevent organ rejection, while the possibility of graft rejection remains one of the primary concerns. The gold standard for diagnosing transplant rejection remains endomyocardial biopsy (EMB). However, this invasive procedure has several drawbacks, making it a less-than-ideal solution for long-term monitoring of heart transplant recipients. In light of these limitations, the study presented in this thesis explores the potential of a non- invasive approach for detecting transplant rejection: liquid biopsy. This technique involves analyzing blood samples to detect and quantify specific biomarkers of rejection, particularly donor-derived cell-free DNA (dd-cfDNA). dd-cfDNA is DNA that is released into the bloodstream when cells die, and its presence can indicate immune-mediated damage to the transplanted heart. Since dd-cfDNA is of donor origin, its levels in the recipient’s blood can be a direct reflection of transplant rejection, providing a more accessible and less invasive diagnostic method. The methodology employed in this study uses NGS to analyze single nucleotide polymorphisms (SNPs), which are genetic variations that can distinguish between the donor's and the recipient's DNA. By sequencing these SNPs, the study can quantify the fraction of dd-cfDNA present in the patient’s bloodstream. Specialized software is then used to calculate the precise amount of dd-cfDNA, which serves as an indicator of ongoing rejection. The study aims to establish a reliable and early biomarker for transplant rejection, which could potentially replace or complement the traditional biopsy method, offering a less invasive, more cost-effective alternative for monitoring transplant recipients over time
Investigation of Boltzmann-Gibbs learning engines: high dimensional inference in mean-field theory and optimization of deep networks with finite resources
No full textThis thesis investigates the inference properties of Boltzmann-Gibbs learning engines, focusing on their ability to learn from data and applying this understanding to optimization problems. The study focuses on high-dimensional inference in Restricted Boltzmann Machines (RBMs). These models rely on inferring structures from data as a prerequisite to leverage their generative capabilities. Central to this work is the teacher-student paradigm, where a teacher network generates datasets analyzed by a student network. Learning performance is evaluated in Bayes-optimal and mismatched regimes. In low-temperature settings, the student network effectively learns through memorization. Conversely, high-temperature datasets induce a modern signal retrieval (sR) phase, where the student aggregates partial information from noisy inputs. These findings are validated for different RBMs architectures and for the Hopfield model case. Inference capabilities are further enhanced by introducing ferromagnetic coupling among replicated Hopfield networks. This coupling significantly expands the sR region beyond what can be achieved by modifying unit priors. However, as the number of coupled student networks grows, the sensitivity of the learning region size diminishes, confirming that aligned replicas enhance learning efficiency without allowing for improbable weight regularizations. Finally, we explore energy-efficient training by optimizing the classification performance of deep neural networks (DNNs) under finite resource constraints, addressing a critical challenge in sustainable machine learning and satellite technology. By optimizing the distribution of neurons within hidden layers, the robustness of DNNs against radiation noise is improved. Insights drawn from Boltzmann Machines inform the identification of optimal architectures based on thermodynamic parameters, such as form factors and inverse temperatures. Experimental validation across multiple datasets demonstrates measurable improvements in network robustness. Together, these results provide a unified perspective on the learning performance of RBMs and related models. They also contribute to advancing sustainable technology applications by enabling robust neural network designs that perform efficiently under resource constraints
Work in transition: standard and sustainability in the Italian organic food value chain
No full textThis thesis is the outcome of three years of PhD research and several months of ethnographic and qualitative fieldwork in rural areas of Ragusa and in the wholesale markets of Bologna (CAAB) and Padua (MAAP). It explores the transformations in the agri-food supply chain, focusing on the industrial organic sector. The research investigates the evolution of relationships between different actors and segments of the supply chain, analyzing labour conditions within the broader context of the green transition. Using a supply chain approach and adopting the labour regime framework (Baglioni et al., 2023), it examines changes at both meso and micro levels, situating them within the Corporate Environmental Food Regime (Friedmann, 2005). It highlights how sustainability narratives often align with the "conventionalization" of organic agriculture (Buck et al., 1994), driven by large agribusinesses that use private certifications to consolidate power and marginalize smaller producers. The research identifies key dynamics of restructuring: increasing control by large distributors, the decline of traditional wholesale markets in favour of centralized logistics hubs, and the growing asymmetries within the supply chain. The ethnographic approach uncovers how these shifts affect labour conditions on the ground, especially for migrant workers. It shows how social differentiations—particularly along lines of gender and race—shape labour dynamics and the reproduction of the workforce. Furthermore, the thesis examines the pivotal role of intermediaries and the contradictions of green transition policies, which, while promoting sustainability, often neglect the economic vulnerabilities of labourers. Finally, it documents various forms of resistance enacted by workers, emphasizing their strategies to access rights and services in a context of systemic marginalization.
These findings call for a more nuanced understanding of the intersection between labour, sustainability, and power, and contribute to broader debates on justice and resistance in contemporary agri-food systems.Questa tesi, frutto di tre anni di ricerca dottorale e di diversi mesi di lavoro etnografico nelle aree rurali della provincia di Ragusa (Sicilia) e nei mercati ortofrutticoli all’ingrosso di Bologna (CAAB) e Padova (MAAP), analizza le trasformazioni della filiera agroalimentare, con un focus sul settore industriale del biologico. L’obiettivo è indagare l’evoluzione delle relazioni tra attori e segmenti della filiera, esaminando al contempo le condizioni lavorative nel contesto della ristrutturazione verde in atto in Italia. Attraverso un approccio di filiera e adottando il quadro teorico dei labour regimes (Baglioni et al., 2023), lo studio osserva i cambiamenti a livello meso e micro, inserendoli nel contesto del Corporate Environmental Food Regime (Friedmann, 2005). Viene messo in luce come le narrazioni sulla sostenibilità si intreccino con la “convenzionalizzazione” del biologico (Buck et al., 1994), guidata da grandi imprese agroindustriali che, attraverso certificazioni private, rafforzano squilibri di potere già esistenti e marginalizzano i piccoli produttori. La ricerca individua dinamiche chiave della ristrutturazione in corso: il rafforzamento del controllo da parte della grande distribuzione, il declino dei mercati all’ingrosso tradizionali e l’ascesa di hub logistici centralizzati. L’approccio etnografico rivela l’impatto di tali cambiamenti sulle condizioni di lavoro, in particolare per i lavoratori migranti. Differenziazioni sociali, legate a genere e razza, emergono come elementi determinanti nelle dinamiche lavorative e nei processi di riproduzione della forza lavoro. La tesi analizza anche il ruolo centrale degli intermediari e le contraddizioni delle politiche di transizione ecologica, che spesso trascurano le disuguaglianze vissute dai lavoratori. Infine, documenta le strategie di resistenza adottate da questi ultimi per accedere a diritti e servizi, contribuendo a un dialogo più ampio su giustizia sociale e sostenibilità nei sistemi agroalimentari contemporanei
Influence of eco-destructive ideologies on environmental communication in workplaces in Italy and Pakistan
No full textThis study critically examines the influence of eco-destructive ideologies embedded in flood narratives during the floods that affected Pakistan in 2022 and Italy in 2023. Using a framework that combines principles of ecolinguistics and Critical Discourse Analysis (CDA), this research explores how eco-destructive ideologies embedded in media narratives can influence environmental communication in workplaces. Given that workplaces are central to societal functioning, the study investigates how politically framed hegemonic narratives of utilitarianism and economic growth shape workplace responses to climate-related disasters. It argues that eco-destructive stories obscure the urgency for climate action and may contribute to inaction in the workplace, sustainability policies and practices. By analyzing flood narratives in Italy and Pakistan, this research highlights how ideologies manifested in media discourse, often employed as political tools, impact organizational responses during extreme weather events. The study is based on a corpus of news articles from Dawn, BBC, ANSA, The Independent and DW, focusing on linguistic patterns such as metaphors and passivization. These patterns reveal how divisive political agendas, from both far-right and leftist perspectives, externalize blame, frame floods as “natural”, “bad weather” or inevitable, and politicize the discourse to downplay climate change’s role in floods. The study finds the key “eco-destructive ideologies” identified through analysis of the dataset: the attribution of climate change to “bad weather”, conspiracy theories, the exclusion and/or stigmatization of Indigenous and marginalized peoples, sensationalism, economic growth imperatives, and the conceptualization of floods through religious and apocalyptic schemas. The findings indicate that ideological frames in flood narratives in both contexts favour short-term economic priorities over sustainable and ecologically suitable solutions. Following the ecosophy of this study and suggestions by Prof. Daanish Mustafa, it is concluded that harmful narratives shaping our understanding of floods can be challenged, resisted, and transformed to promote ecological justice and resilience
Legimatics and AI tools for the monitoring of EU Legislation in agrifood and SDGs
No full textThis work presents a comprehensive mechanism with algorithms for annotating legal norms, classifying EU legislation, and linking them to SDGs objectives. The dataset comprised 15082 EU legislative documents in AKN file format from 1962 to 2021. Complete work is divided into three tasks: Detection and annotation of legal Definitions, Model design for classification of EU legislative documents to Goals and Targets of SDGs, and Linking EU legislative documents to Goals and Targets of SDGs. The first task is performed using Symbolic AI supported by LegalXML annotation. The annotation of only Delimiting Definitions is the target of this task. For the purpose two independent Artificial Intelligence-based algorithms are designed for two different scenarios. These algorithms are implemented in Python using the ElementTree library and rule-based mining to annotate targeted text. The annotation is validated through indentation checks in the AKN format. The first algorithm annotates 899, while the second algorithm annotates 1,272 documents. A total of 11,705 Definitions are successfully annotated. For the second task, a new ML-based multilabel class model is designed to link EU legislative texts to the Goals and Targets of SDGs. Based upon the literature review, two algorithms, SVM and KNN, were tried. SVM outperforms KNN with an accuracy of 53.34%, a weighted F-score of 70.04% and a macro F-score of 57.94% on the SDGs classification at the Goals level. At the Target level, SVM achieved 46.56% accuracy, 56.60% weighted F-score and 30.61% macro F-score. In the third task, legislative text and annotated Delimiting Definitions are successfully linked with the Goals and Targets of SDGs using model designed in second task. By integrating annotation, classification, and linking of EU legislation with SDGs, this research provides a robust mechanism for policymakers and researchers to monitor legislative alignment with SDGs objectives, enabling informed decision-making and effective policy formulation
Environmental sustainability in the italian audiovisual industries
No full textThis study investigates the evolving field of environmental sustainability in audiovisual production, exploring newly introduced practices commonly referred to as “sustainable production,” “green shooting,” and “low-impact filmmaking,” among other associated terms, all of which entail the promotion and implementation of “eco-friendly audiovisual and cinematographic production, in terms of sustainability, the use of resources and the preservation of natural spaces”. A central focus of the study is the emerging role of sustainability professionals in the audiovisual sector—variously known as Sustainability Managers or Consultants, Green Managers or Stewards—whose responsibilities span from pre-production through post-production. Operating at the level of department heads, Sustainability Managers integrate sustainability protocols across all aspects of production and beyond, including script development and strategic communication through "planet placement" content, while influencing areas such as festival management, content development, and, to an extent, distribution. By extension, the study also looks at how this line of work intersects with the work of committed public institutions related to environmental policy in the development of ecological minimum standards and cultural subsidies, such as regional Film Commissions and cultural funds related to the audiovisual sector, and observes the introduction of new stakeholders in audiovisual production, such as audit, certification, ecolabelling and verification bodies alongside initiatives invested both in formal, quality-assured sustainable production training and the exchange of knowledge, information and best practices, occurring across sustainability initiatives and networks. Finally, the study situates these developments within the Italian context, highlighting how national reforms, EU regulations, and post-COVID recovery funding have accelerated sustainability measures in the country's audiovisual sector, through financial incentives for green productions, environmental guidelines for exhibitors, and investments in energy-efficient cultural infrastructure—all unfolding across a dynamic media landscape marked by demand value for original audiovisual content, the growth of streaming platforms and the SVOD market and government backing via tax credits