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Archaeozoology and the study of local breeds: insular contexts compared with central Italy
No full textIl progetto parte dall’indagine di siti archeologici inquadrabili dalla protostoria al medioevo situati in contesti insulari e nell’Italia centrale. La disciplina archeozoologica, oltre a descrivere aspetti ambientali, sociali ed economici dei contesti indagati, in questo progetto vuole identificare le caratteristiche fisiche degli animali domestici, per confrontarli con le razze autoctone dei medesimi territori. Oltre a chiarire aspetti storici e archeologici, il progetto vuole applicare criteri osteometrici per indagare la variabilità interna ai diversi gruppi animali, per selezionare campioni mirati per analisi isotopiche e del DNA mitocondriale. Lo scopo è di verificare le ipotesi formulate durante il progetto su temi come la mobilità animale e la variabilità interna delle mandrie e dei greggi. Lo stage svolto presso un allevamento di bovini Romagnoli è stato il luogo ideale per acquisire un’esperienza diretta del settore zootecnico. Inoltre, le testimonianze raccolte dalle interviste con gli allevatori delle isole hanno permesso di raggruppare informazioni etno-antropologiche, che possono comportare delle ricadute nell’interpretazione del dato archeologico. Le modalità d’allevamento tradizionali e storiche rappresentano quasi sempre la risposta equilibrata dell’uomo e dei suoi animali alle caratteristiche ambientali di un determinato territorio. Il progetto e l’etnoarcheozoologia vogliono fornire all’attuale settore zootecnico possibili esempi di micro-economie funzionali sul diversificato territorio italiano. Dal momento che solo la valorizzazione della storicità di alcune tipologie d’allevamento potrebbe essere il giusto stimolo alla ripresa di attività agro-pastorali più sostenibili e ormai perdute, a causa dell’orientamento odierno verso un allevamento di tipo intensivo, sempre più industrializzato e dall’alto impatto ambientale. Anche il recupero di alcuni prodotti tipici ormai scomparsi sul mercato, ma legati a queste antiche forme d’allevamento potrebbero mantenere in vita aree a rischio di spopolamento, garantendo così una sostenibilità ambientale, culturale ed economica.The project starts from the investigation of archaeological sites dating from protohistory to the Middle Ages located in insular contexts and in central Italy. The archaeozoological discipline, in addition to describing environmental, social and economic aspects of the contexts investigated, in this project aims to identify the physical characteristics of domestic animals, to compare them with the local breeds of the same territories. In addition to clarifying historical and archaeological aspects, the project aims to apply osteometric criteria to investigate the internal variability of different animal groups, to select targeted samples for isotopic and mitochondrial DNA analysis. The aim is to verify the hypotheses formulated during the project on topics such as animal mobility and internal variability of herds and flocks. The internship carried out at a Romagnola cattle farm was the ideal place to acquire direct experience of the livestock sector. Furthermore, the testimonies collected from interviews with island breeders have allowed us to gather ethno-anthropological information, which can have implications for the interpretation of archaeological data. Traditional and historical breeding methods almost always represent the balanced response of man and his animals to the environmental characteristics of a given territory. The project and ethnoarchaeozoology aim to provide the current livestock sector with possible examples of functional micro-economies on the diversified Italian territory. Since only the valorization of the historicity of some types of breeding could be the right stimulus to the recovery of more sustainable and now lost agro-pastoral activities, due to today's orientation towards intensive breeding, increasingly industrialized and with a high environmental impact. Even the recovery of some typical products now disappeared from the market, but linked to these ancient forms of breeding could keep alive areas at risk of depopulation, thus ensuring environmental, cultural and economic sustainability
The Great Rift: a multidisciplinary approach to the socio-ecological crisis
No full textLa crisi socio-ecologica rappresenta una delle sfide più complesse e interconnesse del nostro tempo. Fenomeni come il cambiamento climatico, la perdita di biodiversità e l’inquinamento si intrecciano con disuguaglianze economiche, sociali e di genere, configurando un’emergenza globale che richiede risposte integrate e sistemiche. Questa ricerca analizza le radici strutturali di tale crisi, evidenziando come il capitalismo, il patriarcato e il colonialismo abbiano plasmato un modello di sviluppo insostenibile, tanto ecologicamente quanto socialmente. Attraverso un approccio multidisciplinare e qualitativo, lo studio esamina le fratture ecologiche e sociali che caratterizzano questa crisi, integrando prospettive economiche, politiche, ecologiche e culturali. L’indagine si basa sull’analisi di testi accademici, rapporti istituzionali e casi studio per comprendere le dinamiche di potere che alimentano il collasso ecosistemico e perpetuano le disuguaglianze globali. Particolare attenzione è dedicata all’esplorazione di proposte trasformative e sistemiche, come la decrescita, l'ecofemminismo e l'economia ecologica, che offrono approcci complementari per ripensare le relazioni tra società, economia e ambiente. Questi modelli mirano a superare la logica della crescita illimitata e a promuovere un paradigma basato sulla giustizia ecosociale, sull’equilibrio ecologico e sulla sostenibilità a lungo termine. Il contributo principale della ricerca risiede nella capacità di connettere analisi critica e proposte operative, fornendo strumenti teorici e metodologici per affrontare la complessità della crisi socio-ecologica e immaginare percorsi di trasformazione sistemica. In questo modo, il lavoro ambisce ad arricchire il dibattito accademico e a contribuire alla costruzione di un futuro più equo e sostenibile.The socio-ecological crisis is one of the most serious challenges of our time. Climate change, biodiversity loss, and pollution are closely linked to economic, social, and gender inequalities. Together, these problems create a global emergency that calls for new and integrated responses. This research explores the structural causes of the crisis. It shows how capitalism, patriarchy, and colonialism have shaped a development model that harms the environment and produces injustice. The study adopts a qualitative and multidisciplinary approach. It draws on academic literature, institutional reports, and concrete case studies. The goal is to understand the power dynamics that drive ecological collapse and sustain global inequalities. A central focus is placed on possible solutions. In particular, the study examines the proposals of degrowth, ecofeminism, and ecological economics. These perspectives challenge the idea of infinite growth and offer alternative ways to live more justly and sustainably. The main contribution of this research is to connect critical analysis with concrete proposals. It provides tools to better understand the complexity of the crisis and to imagine deep systemic change. In doing so, it aims to enrich academic debate and support the construction of a more equitable and sustainable future
On the problem of obscure representations in Kant’s anthropology: a historical-critical survey
No full textThe present dissertation is divided into three main sections. In the first, the history of the doctrine of obscure representations is reconstructed, starting with the Leibnizian critique of the Cartesian classification of the degrees of cognition, passing through the major representatives of the vast debate that arose within the Wolffian school. We will discuss, in particular, the positions of Kant’s “masters”, Alexander Gottlieb Baumgarten and Georg Friedrich Meier, and then focus on the figure of Johann Georg Sulzer. One of the central claims of this research is that Kant‘s engagement with Sulzer’s psychological treatises exerted a substantial influence on his thought – both in articulating a broader conception of the unconscious and in his interest in understanding the effects of obscure representations on the will. The second section, after discussing the Kantian conception of empirical psychology and its development in anthropology, focuses on the theoretical assumptions of the Kantian transformation of the field of the unconscious, with particular regard to the aforementioned influence of Sulzer. The third and final section is devoted to the discussion of dunklen Vorstellungen in anthropology. The main objective here is to highlight the Kantian theory of the development of dunklen reflections from the comparison with the Meierian concept of evolution/Auswickelung
Graph neural network methods for representation and generation in drug discovery
Get PDFDrug discovery is a time-consuming and expensive process, often spanning over a decade and costing billions of dollars. This thesis advances graph-based machine learning approaches to accelerate this process, making three main contributions. First, we provide a comprehensive review of graph neural networks for conditional molecular generation, establishing a framework for understanding and comparing different methods. Building on these insights, we introduce AMCG (Atomic-Molecular Conditional Generator), a novel generative framework that achieves state-of-the-art performance while offering one-shot generation capability and effective property optimization via gradient ascent. Motivated by the heterophilic nature of molecular graphs — where connected atoms often have dissimilar features — we then develop MaxCutPool, a differentiable graph pooling technique based on the MAXCUT problem. By combining graph-theoretical principles with deep learning, MaxCutPool demonstrates superior performance on heterophilic graphs while remaining competitive on standard benchmarks and maintaining computational efficiency. Together, these contributions advance both the theoretical foundations of graph representation learning and provide practical tools for accelerating drug discovery
Identification of the genetic and the epigenetic profile of the Oral Leukoplakia for diagnostic and prognostic purposes
Get PDFThe Oral Squamous Cell Carcinoma (OSCC) stands as the most prevalent malignancy within the head and neck region. OSCC is acknowledged for its unfavourable prognosis, but mortality is closely linked to the stage at diagnosis. Early diagnosis of OSCC is achieved in only about half of all cases. Diagnostic delays necessitate highly invasive therapies, which, in turn, adversely impact the residual quality of life.
This PhD thesis is structured around two main projects that evaluate the clinical and economic aspects of OSCC diagnosis, treatment, and prognosis, with a particular focus on patients at high risk for OSCC development. The first project was to assess the financial burden of OSCC on healthcare systems and explore how education and training can improve the diagnostic skills of dental students and practitioners.
To achieve this, a retrospective observational study was conducted, analysing the costs associated with patients diagnosed with OSCC who underwent surgical treatment.
Additionally, a survey-based study was conducted. Dentistry students and practitioners with varying levels of clinical experience participated in the survey, which involved evaluating 40 clinical images of benign and malignant oral lesions.
The second project investigates the molecular and epigenetic mechanisms underlying OSCC with an emphasis on the predictive and diagnostic capabilities of DNA methylation analysis from oral brushing samples as a non-invasive method to identify patients at high risk of developing OSCC.
A case report was described, demonstrating how methylation analysis successfully predicted the malignant transformation of an apparently low-risk Oral Leukoplakia and identified the occurrence of a second carcinoma in the same patient.
Then, two prospective studies with larger populations were conducted applying the DNA methylation analysis from oral brushing samples to patients who had undergone surgical treatment for OSCC and those with OPMDs.
This PhD thesis is based on a combination of published paper and unpublished data
Uniting oceanography, fisheries stakeholders and citizen science to find solutions for environmental management in face of climate change
Get PDFThe issue of climate change is a widely accepted concern, with its impact evident in global temperature rise, extreme weather events, sea level rise, and biodiversity loss. The Mar Menor lagoon in southeastern Spain is one such ecosystem suffering from human-induced damage, including mining, agriculture, and urbanization, leading to eutrophication, species mortality, and invasion by alien species. Given that halting climate change is not feasible, the focus is on mitigating and adapting to its effects through nature-based solutions. These solutions promote collaboration between people and nature to address societal challenges while benefiting both human well-being and biodiversity.
Environmental management planning plays a critical role in adaptation, offering strategies to protect ecosystem services and address social aspects of socioecological systems. Such plans require scientific data and public participation, which enhances effectiveness, fosters collaboration, and bridges the gap between research and society. Stakeholders contribute with personal knowledge, improving monitoring efforts and boosting public trust in science. Citizen science, as a tool for gathering data, allows for greater community involvement, aiding scientific studies and promoting environmental education to shape public awareness and behavior.
This thesis evaluates the social, economic, and environmental aspects of the Mar Menor lagoon, focusing on stakeholders like tourists and fishermen. It explores the impact of extreme weather on the lagoon's oceanographic properties, stakeholder perceptions of climate change, and how citizen science and environmental education can support effective management. The findings show that extreme weather events can have lasting impacts on ecosystems, highlighting the need for mitigation strategies. Stakeholder involvement in management plans increases the chances of success by improving monitoring and boosting support. Citizen science can be an effective tool for data collection, while environmental education can improve awareness. Overall, the thesis underscores the importance of integrating scientific research with public engagement to balance human activities and natural ecosystems
Graphene-based bioelectronic interfaces and devices and interpretative models targeting glial cells
No full textBioelectronic interfaces have significantly advanced in recent years, offering potential treatments for vision impairments, spinal cord injuries, and neurodegenerative diseases. However, the classical neurocentric vision drives the technological development toward neurons. Emerging evidence highlights the critical role of glial cells in the nervous system. Among them, astrocytes significantly influence neuronal networks throughout life and are implicated in several neuropathological states. Although they are incapable to fire action potentials, astrocytes communicate through diverse calcium (Ca2+) signalling pathways, crucial for cognitive functions and brain blood flow regulation. Current bioelectronic devices are primarily designed to interface neurons and are unsuitable for studying astrocytes. Graphene, with its unique electrical, mechanical and biocompatibility properties, has emerged as a promising neural interface material. However, its use as electrode interface to modulate astrocyte functionality remains unexplored. The aim of this PhD work was to exploit Graphene-oxide (GO) and reduced GO (rGO)-coated electrodes to control Ca2+ signalling in astrocytes by electrical stimulation. We discovered that distinct Ca2+dynamics in astrocytes can be evoked, in vitro and in brain slices, depending on the conductive/insulating properties of rGO/GO electrodes. Stimulation by rGO electrodes induces intracellular Ca2+ response with sharp peaks of oscillations (“P-type”), exclusively due to Ca2+ release from intracellular stores. Conversely, astrocytes stimulated by GO electrodes show slower and sustained Ca2+ response (“S-type”), largely mediated by external Ca2+ influx through specific ion channels. Astrocytes respond faster than neurons and activate distinct G-Protein Coupled Receptor intracellular signalling pathways. We propose a resistive/insulating model, hypothesizing that the different conductivity of the substrate influences the electric field at the cell/electrolyte or cell/material interfaces, favouring, respectively, the Ca2+ release from intracellular stores or the extracellular Ca2+ influx. This research provides a simple tool to selectively control distinct Ca2+ signals in brain astrocytes in neuroscience and bioelectronic medicine
Data-driven mass estimation of heavy-duty vehicles
Get PDFThis study investigates mixed model- and learning-based approaches to the mass estimation problem in heavy-duty vehicles. Effective mass estimation allows for more precise adjustments to engine power, braking systems, and suspension settings, leading to improved vehicle handling, fuel efficiency, and overall safety. Although direct mass measurement using sensors is a viable option, the substantial costs and complexities associated with sensor maintenance, integration, and calibration drive the search for alternative solutions.
The objective of this thesis is to introduce a methodology to mass estimation problem in heavy-duty vehicles, which harnesses the advantages of model-based and learning-based estimation methods. The proposed methodology builds upon the integration of Long-Short Term Memory (LSTM), which is a type of recurrent neural network, that supervises a Recursive Least Squares (RLS) vehicle mass estimator. The RLS estimator relies on a longitudinal vehicle dynamical model. The supervisory LSTM network is offline trained to recognize when the vehicle is operated such that the RLS estimator leads to an estimate with the desired accuracy while online enables the mass estimate update by the RLS estimator based on signals that include vehicle speed, longitudinal acceleration, engine torque, and engine speed.
The LSTM network is trained and tested in this thesis work using datasets artificially generated by a widely used simulation environment called TruckMaker. The simulation results demonstrate that the proposed methodology effectively forecasts the reliability of the RLS mass estimator, showcasing the potential of LSTM networks in enhancing the accuracy and trustworthiness of mass estimation of heavy-duty vehicles.
This thesis also presents a benchmark learning-based approach to mass estimation in heavy vehicles, that uses an LSTM network. In this case, a two-layer LSTM network is designed that utilizes vehicle speed, longitudinal acceleration, engine speed and engine torque to estimate the vehicle mass
Reinforcement learning for dynamic resource allocation in distributed systems
No full textResource Allocation (RA) problems are ubiquitous across diverse domains, spanning from health services provisioning, supply chain management, personnel scheduling as well as task scheduling, cloud resources orchestration, and network capacity allocation. When the resource request changes over time, we refer to the field of Dynamic Resource Allocation (DRA) problems. DRA problems find applicability in domains such as cloud computing, network management, energy or water supply, and public transportation systems as they allow adjustment of the number of resources assigned for each component or user. However, conventional allocation strategies employed in DRA yield suboptimal policies, primarily due to their reactive nature. They tend to overlook the long-term implications of allocations over future time windows. This thesis proposes an innovative approach to tackle DRA problems by leveraging reinforcement learning (RL). Through trial and error, RL allows the learning of policies that proactively consider the effects of allocations over time and respond effectively to unexpected changes in demand. The effectiveness and efficiency of this proposed approach are supported by empirical evaluations on challenging DRA problems. Initially, it addresses task scheduling in heterogeneous worker-based distributed queues, integrating an adaptive RL method with the popular Celery task queuing system. Subsequently, the Thesis presents a deep reinforcement learning (DRL) based resource orchestrator tailored for managing virtual resources in Open Radio Access Network (O-RAN) infrastructures. Finally, it implements a DRL solution for efficient bike redistribution in Bike Sharing Systems (BSS), simultaneously optimizing operational costs for system operators. The results, derived from both synthetic and real-world data, underscore the superiority of RL approaches over greedy allocation strategies, demonstrating enhanced optimization of the specified objectives. Moreover, the thesis provides technical insights on how to efficiently design and implement these solutions in real-world and well-engineered prototypes
Essays in applied macroeconometrics
No full textThis thesis consists of three self-contained essays on applied macro-econometrics. The topics cover trend inflation, transmission of monetary policy, and persistence of unemployment.
The first chapter investigates the dynamics of trend inflation both at the individual level for the nineteen economic and monetary union countries and also for the euro area aggregate level. To this aim, a flexible unobserved components model and disaggregated data are used to estimate trend inflation. Following the estimation, I compare these measures to the other existing measures from the literature and empirically assess them relying on several metrics. The second chapter focuses on understanding ''multi-dimensional`` state-dependent transmission of monetary policy shocks to the asset prices in the US. To unveil various sources making the monetary policy transmission state-dependent, we construct a comprehensive data set and apply two different shrinkage methods to simultaneously assess the role of many potential sources of non-linearity. The third chapter investigates the hypothesis of unemployment hysteresis for GIPS countries (Greece, Ireland, Portugal, and Spain). While most of the existing empirical studies assume constant order of integration for unemployment over the sample period, we consider the possibility that, like many macroeconomic variables, unemployment might display changes in persistence, which might result in potential switches between the natural rate and hysteresis hypotheses