PCM-based battery thermal management systems, energy saving materials and LCA strategy for automotive

This PhD work arises from the necessity to give a contribution to the energy saving field, regarding automotive applications. The aim was to produce a multidisciplinary work to show how much important is to consider different aspects of an electric car realization: from innovative materials to cutting-edge battery thermal management systems (BTMSs), also dealing with the life cycle assessment (LCA) of the battery packs (BPs). Regarding the materials, it has been chosen to focus on carbon fiber composites as their use allows realizing light products with great mechanical properties. Processes and methods to produce carbon fiber goods have been analysed with a special attention on the university solar car Emilia 4. The work proceeds dealing with the common BTMSs on the market (air-cooled, cooling plates, heat pipes) and then it deepens some of the most innovative systems such as the PCM-based BTMSs after a previous experimental campaign to characterize the PCMs. After that, a complex experimental campaign regarding the PCM-based BTMSs has been carried on, considering both uninsulated and insulated systems. About the first category the tested systems have been pure PCM-based and copper-foam-loaded-PCM-based BTMSs; the insulated tested systems have been pure PCM-based and copper-foam-loaded-PCM-based BTMSs and both of these systems equipped with a liquid cooling circuit. The choice of lighter building materials and the optimization of the BTMS are strategies which helps in reducing the energy consumption, considering both the energy required by the car to move and the BP state of health (SOH). Focusing on this last factor, a clear explanation regarding the importance of taking care about the SOH is given by the analysis of a BP production energy consumption. This is why a final dissertation about the life cycle assessment (LCA) of a BP unit has been presented in this thesis.

Multidisciplinary approach to horse welfare in different management systems

From its domestication until nowadays, the horse has assumed multiple roles in human society. Over time, this condition and the lack of specific regulation have led to the development of different kinds of management systems for this species. This Ph.D. research project aims to investigate horses' welfare in different management practices and housing systems, considering a multidisciplinary approach, taking into account biological function, naturalness, and affective dimension. The results are presented in five articles that evidence risk factors that can mine horse welfare, and examine tools and parameters that can be employed for its assessment. Our research shows the importance of considering the evolutionary history and the species-specific and behavioural needs of horses in their management and housing. Sociality, free movement, diet composition and foraging routine, and the workload that these animals undergo are important factors that should be taken into account. Furthermore, this research has evidenced the importance of employing different parameters (e.g., behaviour, endocrinological parameters, and immune activity) in welfare assessment and proposes the use of horsehair DHEA (dehydroepiandrosterone) as a possible useful additional non-invasive measure for the investigation of long-term stress conditions. Finally, our results underline the importance of considering the affective dimension in welfare research. Recently, Judgement Bias Tests (JBT), which are based on the influence of affective states on the decision-making process, have been widely employed in animal welfare research. However, our studies show that the use of spatial JBT in horses can have some limitations. Still today several management systems do not fulfill species-specific needs of horses, thus the implementation of specific regulations could ameliorate horse welfare. A multidisciplinary approach to welfare assessment is fundamental, but it should be always remembered the individual and its own characteristics, which can influence not only physiological, immunological, and behavioural responses but also emotional and cognitive dimensions.

Low carbon systems for climate change mitigation and adaptation

Global warming and climate change have been among the most controversial topics after the industrial revolution. The main contributor to global warming is carbon dioxide (CO2), which increases the temperature by trapping heat in the atmosphere. Atmospheric CO2 concentration before the industrial era was around 280 ppm for a long period, while it has increased dramatically since the industrial revolution up to approximately 420 ppm. According to the Paris agreement it is needed to keep the temperature increase up to 2°C, preferably 1.5° C, to prevent reaching the tipping point of climate change. To keep the temperature increase below the range, it is required to find solutions to reduce CO2 emissions. The solutions can be low-carbon systems and transition from fossil fuels to renewable energy sources (RES). This thesis is allocated to the assessment of low-carbon systems and the reduction of CO2 by using RES instead of fossil fuels. One of the most important aspects to define the location and capacity of low-carbon systems is CO2 mass estimation. As mentioned, high-emission systems can be substituted by low-carbon systems. An example of high-emission systems is dredging. The global CO2 emission from dredging is relatively high which is associated with the growth of marine transport in addition to its high emission. Thus, ejectors system as alternative for dredging is investigated in chapter 2. For the transition from fossil fuels to RES, it is required to provide solutions for the RES storage problem. A solution could be zero-emission fuels such as hydrogen. However, the production of hydrogen requires electricity, and electricity production emits a large amount of CO2. Therefore, the last three chapters are allocated to hydrogen generation via electrolysis, at the current condition and scenarios of RES and variation of cell characteristics and stack materials, and its delivery.

Development of a Whole-Genome Sequencing (WGS) approach for the characterization of Legionella environmental isolates in water systems

Legionella is a Gram-negative bacterium that represent a public health issue, with heavy social and economic impact. Therefore, it is mandatory to provide a proper environmental surveillance and risk assessment plan to perform Legionella control in water distribution systems in hospital and community buildings. The thesis joins several methodologies in a unique workflow applied for the identification of non-pneumophila Legionella species (n-pL), starting from standard methods as culture and gene sequencing (mip and rpoB), and passing through innovative approaches as MALDI-TOF MS technique and whole genome sequencing (WGS). The results obtained, were compared to identify the Legionella isolates, and lead to four presumptive novel Legionella species identification. One of these four new isolates was characterized and recognized at taxonomy level with the name of Legionella bononiensis (the 64th Legionella species). The workflow applied in this thesis, help to increase the knowledge of Legionella environmental species, improving the description of the environment itself and the events that promote the growth of Legionella in their ecological niche. The correct identification and characterization of the isolates permit to prevent their spread in man-made environment and contain the occurrence of cases, clusters, or outbreaks. Therefore, the experimental work undertaken, could support the preventive measures during environmental and clinical surveillance, improving the study of species often underestimated or still unknown.

Methods and tools to foster territorial approaches for the assessment of food supply chains

The challenges of the current global food systems are often framed around feeding the world's growing population while meeting sustainable development for future generations. Globalization has brought to a fragmentation of food spaces, leading to a flexible and mutable supply chain. This poses a major challenge to food and nutrition security, affecting also rural-urban dynamics in territories. Furthermore, the recent crises have highlighted the vulnerability to shocks and disruptions of the food systems and the eco-system due to the intensive management of natural, human and economic capital. Hence, a sustainable and resilient transition of the food systems is required through a multi-faceted approach that tackles the causes of unsustainability and promotes sustainable practices at all levels of the food system. In this respect, a territorial approach becomes a relevant entry point of analysis for the food system’s multifunctionality and can support the evaluation of sustainability by quantifying impacts associated with quantitative methods and understanding the territorial responsibility of different actors with qualitative ones. Against this background the present research aims to i) investigate the environmental, costing and social indicators suitable for a scoring system able to measure the integrated sustainability performance of food initiatives within the City/Region territorial context; ii) develop a territorial assessment framework to measure sustainability impacts of agricultural systems; and iii) define an integrated methodology to match production and consumption at a territorial level to foster a long-term vision of short food supply chains. From a methodological perspective, the research proposes a mixed quantitative and qualitative research method. The outcomes provide an in-depth view into the environmental and socio-economic impacts of food systems at the territorial level, investigating possible indicators, frameworks, and business strategies to foster their future sustainable development.

Towards a sustainable approach for Cultural Heritage Conservation: developing micro-structured green materials and analytical protocols for their performance assessment

Cleaning is one of the most important and delicate procedures that are part of the restoration process. When developing new systems, it is fundamental to consider its selectivity towards the layer to-be-removed, non-invasiveness towards the one to-be-preserved, its sustainability and non-toxicity. Besides assessing its efficacy, it is important to understand its mechanism by analytical protocols that strike a balance between cost, practicality, and reliable interpretation of results. In this thesis, the development of cleaning systems based on the coupling of electrospun fabrics (ES) and greener organic solvents is proposed. Electrospinning is a versatile technique that allows the production of micro/nanostructured non-woven mats, which have already been used as absorbents in various scientific fields, but to date, not in the restoration field. The systems produced proved to be effective for the removal of dammar varnish from paintings, where the ES not only act as solvent-binding agents but also as adsorbents towards the partially solubilised varnish due to capillary rise, thus enabling a one-step procedure. They have also been successfully applied for the removal of spray varnish from marble substrates and wall paintings. Due to the materials' complexity, the procedure had to be adapted case-by-case and mechanical action was still necessary. According to the spinning solution, three types of ES mats have been produced: polyamide 6,6, pullulan and pullulan with melanin nanoparticles. The latter, under irradiation, allows for a localised temperature increase accelerating and facilitating the removal of less soluble layers (e.g. reticulated alkyd-based paints). All the systems produced, and the mock-ups used were extensively characterised using multi-analytical protocols. Finally, a monitoring protocol and image treatment based on photoluminescence macro-imaging is proposed. This set-up allowed the study of the removal mechanism of dammar varnish and semi-quantify its residues. These initial results form the basis for optimising the acquisition set-up and data processing.

Hardware-software design of embedded systems for intelligent sensing applications

This Thesis wants to highlight the importance of ad-hoc designed and developed embedded systems in the implementation of intelligent sensor networks. As evidence four areas of application are presented: Precision Agriculture, Bioengineering, Automotive and Structural Health Monitoring. For each field is reported one, or more, smart device design and developing, in addition to on-board elaborations, experimental validation and in field tests. In particular, it is presented the design and development of a fruit meter. In the bioengineering field, three different projects are reported, detailing the architectures implemented and the validation tests conducted. Two prototype realizations of an inner temperature measurement system in electric motors for an automotive application are then discussed. Lastly, the HW/SW design of a Smart Sensor Network is analyzed: the network features on-board data management and processing, integration in an IoT toolchain, Wireless Sensor Network developments and an AI framework for vibration-based structural assessment.

[In search of] Digital biomarkers for objective pain assessment

Pain is a highly complex phenomenon involving intricate neural systems, whose interactions with other physiological mechanisms are not fully understood. Standard pain assessment methods, relying on verbal communication, often fail to provide reliable and accurate information, which poses a critical challenge in the clinical context. In the era of ubiquitous and inexpensive physiological monitoring, coupled with the advancement of artificial intelligence, these new tools appear as the natural candidates to be tested to address such a challenge. This thesis aims to conduct experimental research to develop digital biomarkers for pain assessment. After providing an overview of the state-of-the-art regarding pain neurophysiology and assessment tools, methods for appropriately conditioning physiological signals and controlling confounding factors are presented. The thesis focuses on three different pain conditions: cancer pain, chronic low back pain, and pain experienced by patients undergoing neurorehabilitation. The approach presented in this thesis has shown promise, but further studies are needed to confirm and strengthen these results. Prior to developing any models, a preliminary signal quality check is essential, along with the inclusion of personal and health information in the models to limit their confounding effects. A multimodal approach is preferred for better performance, although unimodal analysis has revealed interesting aspects of the pain experience. This approach can enrich the routine clinical pain assessment procedure by enabling pain to be monitored when and where it is actually experienced, and without the involvement of explicit communication,. This would improve the characterization of the pain experience, aid in antalgic therapy personalization, and bring timely relief, with the ultimate goal of improving the quality of life of patients suffering from pain.

Governing algorithms in the big data era for balancing new digital rights - designing GDPR compliant and trustworthy XAI systems

This thesis investigates the legal, ethical, technical, and psychological issues of general data processing and artificial intelligence practices and the explainability of AI systems. It consists of two main parts. In the initial section, we provide a comprehensive overview of the big data processing ecosystem and the main challenges we face today. We then evaluate the GDPR’s data privacy framework in the European Union. The Trustworthy AI Framework proposed by the EU’s High-Level Expert Group on AI (AI HLEG) is examined in detail. The ethical principles for the foundation and realization of Trustworthy AI are analyzed along with the assessment list prepared by the AI HLEG. Then, we list the main big data challenges the European researchers and institutions identified and provide a literature review on the technical and organizational measures to address these challenges. A quantitative analysis is conducted on the identified big data challenges and the measures to address them, which leads to practical recommendations for better data processing and AI practices in the EU. In the subsequent part, we concentrate on the explainability of AI systems. We clarify the terminology and list the goals aimed at the explainability of AI systems. We identify the reasons for the explainability-accuracy trade-off and how we can address it. We conduct a comparative cognitive analysis between human reasoning and machine-generated explanations with the aim of understanding how explainable AI can contribute to human reasoning. We then focus on the technical and legal responses to remedy the explainability problem. In this part, GDPR’s right to explanation framework and safeguards are analyzed in-depth with their contribution to the realization of Trustworthy AI. Then, we analyze the explanation techniques applicable at different stages of machine learning and propose several recommendations in chronological order to develop GDPR-compliant and Trustworthy XAI systems.

Modelling, simulations and monitoring for sustainable plant and animal production systems through precision technique

Protected crop production is a modern and innovative approach to cultivating plants in a controlled environment to optimize growth, yield, and quality. This method involves using structures such as greenhouses or tunnels to create a sheltered environment. These productive solutions are characterized by a careful regulation of variables like temperature, humidity, light, and ventilation, which collectively contribute to creating an optimal microclimate for plant growth. Heating, cooling, and ventilation systems are used to maintain optimal conditions for plant growth, regardless of external weather fluctuations. Protected crop production plays a crucial role in addressing challenges posed by climate variability, population growth, and food security. Similarly, animal husbandry involves providing adequate nutrition, housing, medical care and environmental conditions to ensure animal welfare. Then, sustainability is a critical consideration in all forms of agriculture, including protected crop and animal production. Sustainability in animal production refers to the practice of producing animal products in a way that minimizes negative impacts on the environment, promotes animal welfare, and ensures the long-term viability of the industry. Then, the research activities performed during the PhD can be inserted exactly in the field of Precision Agriculture and Livestock farming. Here the focus is on the computational fluid dynamic (CFD) approach and environmental assessment applied to improve yield, resource efficiency, environmental sustainability, and cost savings. It represents a significant shift from traditional farming methods to a more technology-driven, data-driven, and environmentally conscious approach to crop and animal production. On one side, CFD is powerful and precise techniques of computer modeling and simulation of airflows and thermo-hygrometric parameters, that has been applied to optimize the growth environment of crops and the efficiency of ventilation in pig barns. On the other side, the sustainability aspect has been investigated and researched in terms of Life Cycle Assessment analyses.

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.

High-resolution stratigraphy and seismic site characterization of late Quaternary paleovalley systems (Adriatic coastal plain, Italy)

In highly urbanized coastal lowlands, effective site characterization is crucial for assessing seismic risk. It requires a comprehensive stratigraphic analysis of the shallow subsurface, coupled with the precise assessment of the geophysical properties of buried deposits. In this context, late Quaternary paleovalley systems, shallowly buried fluvial incisions formed during the Late Pleistocene sea-level fall and filled during the Holocene sea-level rise, are crucial for understanding seismic amplification due to their soft sediment infill and sharp lithologic contrasts. In this research, we conducted high-resolution stratigraphic analyses of two regions, the Pescara and Manfredonia areas along the Adriatic coastline of Italy, to delineate the geometries and facies architecture of two paleovalley systems. Furthermore, we carried out geophysical investigations to characterize the study areas and perform seismic response analyses. We tested the microtremor-based horizontal-to-vertical spectral ratio as a mapping tool to reconstruct the buried paleovalley geometries. We evaluated the relationship between geological and geophysical data and identified the stratigraphic surfaces responsible for the observed resonances. To perform seismic response analysis of the Pescara paleovalley system, we integrated the stratigraphic framework with microtremor and shear wave velocity measurements. The seismic response analysis highlights strong seismic amplifications in frequency ranges that can interact with a wide variety of building types. Additionally, we explored the applicability of artificial intelligence in performing facies analysis from borehole images. We used a robust dataset of high-resolution digital images from continuous sediment cores of Holocene age to outline a novel, deep-learning-based approach for performing automatic semantic segmentation directly on core images, leveraging the power of convolutional neural networks. We propose an automated model to rapidly characterize sediment cores, reproducing the sedimentologist's interpretation, and providing guidance for stratigraphic correlation and subsurface reconstructions.