A Semantic-based Framework for Digital Survey of Architectural Heritage

The research aims at developing a framework for semantic-based digital survey of architectural heritage. Rooted in knowledge-based modeling which extracts mathematical constraints of geometry from architectural treatises, as-built information of architecture obtained from image-based modeling is integrated with the ideal model in BIM platform. The knowledge-based modeling transforms the geometry and parametric relation of architectural components from 2D printings to 3D digital models, and create large amount variations based on shape grammar in real time thanks to parametric modeling. It also provides prior knowledge for semantically segmenting unorganized survey data. The emergence of SfM (Structure from Motion) provides access to reconstruct large complex architectural scenes with high flexibility, low cost and full automation, but low reliability of metric accuracy. We solve this problem by combing photogrammetric approaches which consists of camera configuration, image enhancement, and bundle adjustment, etc. Experiments show the accuracy of image-based modeling following our workflow is comparable to that from range-based modeling. We also demonstrate positive results of our optimized approach in digital reconstruction of portico where low-texture-vault and dramatical transition of illumination bring huge difficulties in the workflow without optimization. Once the as-built model is obtained, it is integrated with the ideal model in BIM platform which allows multiple data enrichment. In spite of its promising prospect in AEC industry, BIM is developed with limited consideration of reverse-engineering from survey data. Besides representing the architectural heritage in parallel ways (ideal model and as-built model) and comparing their difference, we concern how to create as-built model in BIM software which is still an open area to be addressed. The research is supposed to be fundamental for research of architectural history, documentation and conservation of architectural heritage, and renovation of existing buildings.

Adaptable security in the cloud-to-thing continuum

Recent technological advancements have played a key role in seamlessly integrating cloud, edge, and Internet of Things (IoT) technologies, giving rise to the Cloud-to-Thing Continuum paradigm. This cloud model connects many heterogeneous resources that generate a large amount of data and collaborate to deliver next-generation services. While it has the potential to reshape several application domains, the number of connected entities remarkably broadens the security attack surface. One of the main problems is the lack of security measures to adapt to the dynamic and evolving conditions of the Cloud-To-Thing Continuum. To address this challenge, this dissertation proposes novel adaptable security mechanisms. Adaptable security is the capability of security controls, systems, and protocols to dynamically adjust to changing conditions and scenarios. However, since the design and development of novel security mechanisms can be explored from different perspectives and levels, we place our attention on threat modeling and access control. The contributions of the thesis can be summarized as follows. First, we introduce a model-based methodology that secures the design of edge and cyber-physical systems. This solution identifies threats, security controls, and moving target defense techniques based on system features. Then, we focus on access control management. Since access control policies are subject to modifications, we evaluate how they can be efficiently shared among distributed areas, highlighting the effectiveness of distributed ledger technologies. Furthermore, we propose a risk-based authorization middleware, adjusting permissions based on real-time data, and a federated learning framework that enhances trustworthiness by weighting each client's contributions according to the quality of their partial models. Finally, since authorization revocation is another critical concern, we present an efficient revocation scheme for verifiable credentials in IoT networks, featuring decentralization, demanding minimum storage and computing capabilities. All the mechanisms have been evaluated in different conditions, proving their adaptability to the Cloud-to-Thing Continuum landscape.

Ricerca e applicazione di metodologie ecotossicologiche nel monitoraggio di ambienti marino-costieri: Sviluppo di nuovi bioassay e biomarker

Obiettivo del lavoro è stato lo sviluppo e la validazione di nuovi bioassay e biomarker quali strumenti da utilizzare in un approccio ecotossicologico integrato per il biomonitoraggio di ambienti marino-costieri interessati da impatto antropico negli organismi che vivono in tali ambienti. L’ambiente reale impiegato per l’applicazione in campo è la Rada di Augusta (Siracusa, Italia). Una batteria di bioassay in vivo e in vitro è stata indagata quale strumento di screening per la misura della tossicità dei sedimenti. La batteria selezionata ha dimostrato di possedere i requisiti necessari ad un applicazione di routine nel monitoraggio di ambienti marino costieri. L’approccio multimarker basato sull’impiego dell’organismo bioindicatore Mytilus galloprovincialis in esperimenti di traslocazione ha consentito di valutare il potenziale applicativo di nuovi biomarker citologici e molecolari di stress chimico parallelamente a biomarker standardizzati di danno genotossico ed esposizione a metalli pesanti. I mitili sono stati traslocati per 45 giorni nei siti di Brucoli (SR) e Rada di Augusta, rispettivamente sito di controllo e sito impattato. I risultati ottenuti supportano l’applicabilità delle alterazioni morfometriche dei granulociti quale biomarker di effetto, direttamente correlato allo stato di salute degli organismi che vivono in un dato ambiente. Il significativo incremento dell’area dei lisosomi osservato contestualmente potrebbe riflettere un incremento dei processi degradativi e dei processi autofagici. I dati sulla sensibilità in campo suggeriscono una valida applicazione della misura dell’attività di anidrasi carbonica in ghiandola digestiva come biomarker di stress in ambiente marino costiero. L’utilizzo delle due metodologie d’indagine (bioassay e biomarker) in un approccio ecotossicologico integrato al biomonitoraggio di ambienti marino-costieri offre uno strumento sensibile e specifico per la valutazione dell’esposizione ad inquinanti e del danno potenziale esercitato dagli inquinanti sugli organismi che vivono in un dato ambiente, permettendo interventi a breve termine e la messa a punto di adeguati programmi di gestione sostenibile dell’ambiente.

Definition of Food Safety Criteria for Bacteria Food-Borne Pathogens in Ready to Eat products

The aims of this research study is to explore the opportunity to set up Performance Objectives (POs) parameters for specific risks in RTE products to propose for food industries and food authorities. In fact, even if microbiological criteria for Salmonella and Listeria monocytogenes Ready-to-Eat (RTE) products are included in the European Regulation, these parameters are not risk based and no microbiological criteria for Bacillus cereus in RTE products is present. For these reasons the behaviour of Salmonella enterica in RTE mixed salad, the microbiological characteristics in RTE spelt salad, and the definition of POs for Bacillus cereus and Listeria monocytogenes in RTE spelt salad has been assessed. Based on the data produced can be drawn the following conclusions: 1. A rapid growth of Salmonella enterica may occurr in mixed ingredient salads, and strict temperature control during the production chain of the product is critical. 2. Spelt salad is characterized by the presence of high number of Lactic Acid Bacteria. Listeria spp. and Enterobacteriaceae, on the contrary, did not grow during the shlef life, probably due to the relevant metabolic activity of LAB. 3. The use of spelt and cheese compliant with the suggested POs might significantly reduce the incidence of foodborne intoxications due to Bacillus cereus and Listeria monocytogenes and the proportions of recalls, causing huge economic losses for food companies commercializing RTE products. 4. The approach to calculate the POs values and reported in my work can be easily adapted to different food/risk combination as well as to any changes in the formulation of the same food products. 5. The optimized sampling plans in term of number of samples to collect can be derive in order to verify the compliance to POs values selected.

Metal additive manufacturing of soft magnetic material for electric machines

This research work concerns the application of additive manufacturing (AM) technologies in new electric mobility sectors. The unmatched freedom that AM offers can potentially change the way electric motors are designed and manufactured. The thesis investigates the possibility of creating optimized electric machines that exploit AM technologies, with potential in various industrial sectors, including automotive and aerospace. In particular, we will evaluate how the design of electric motors can be improved by producing the rotor core using Laser Powder Bed Fusion (LPBF) and how the resulting design choices affect component performance. First, the metallurgical and soft magnetic properties of the pure iron and silicon iron alloy parts (Fe-3% wt.Si) produced by LPBF will be defined and discussed, considering the process parameters and the type of heat treatment. This research shows that using LPBF, both pure iron and iron silicon, the parts have mechanical and magnetic properties different from the laminated ones. Hence, FEM-based modeling will be employed to design the rotor core of an SYN RM machine to minimize torque ripple while maintaining structural integrity. Finally, we suggest that further research should extend the field of applicability to other electrical devices.

Mapping new non-genetic dependencies in malignant pleural mesothelioma

Malignant Pleural Mesothelioma (MPM) is a very aggressive cancer whose incidence is growing worldwide. MPM escapes the classical models of carcinogenesis and lacks a distinctive genetic fingerprint, keeping obscure the molecular events that lead to tumorigenesis. This severely impacts on the limited therapeutic options and on the lack of specific biomarkers, concurring to make MPM one of the deadliest cancers. Here we combined a functional genome-wide loss of function CRISPR/Cas9 screening with patients’ transcriptomic and clinical data, to identify genes essential for MPM progression. Besides, we explored the role of non-coding RNAs to MPM progression by analysing gene expression profiles and clinical data from the MESO-TCGA dataset. We identified TRIM28 and the lncRNA LINC00941 as new vulnerabilities of MPM, associated with disease aggressiveness and bad outcome of patients. TRIM28 is a multi-domain protein involved in many processes, including transcription regulation. We showed that TRIM28 silencing impairs MPM cells’ growth and clonogenicity by blocking cells in mitosis. RNA-seq profiling showed that TRIM28 loss abolished the expression of major mitotic players. Our data suggest that TRIM28 is part of the B-MYB/FOXM1-MuvB complex that specifically drives the activation of mitotic genes, keeping the time of mitosis. In parallel, we found LINC00941 as strongly associated with reduced survival probability in MPM patients. LINC00941 KD profoundly reduced MPM cells’ growth, migration and invasion. This is accompanied by changes in morphology, cytoskeleton organization and cell-cell adhesion properties. RNA-seq profiling showed that LINC00941 KD impacts crucial functions of MPM, including HIF1α signalling. Collectively these data provided new insights into MPM biology and demonstrated that the integration of functional screening with patients’ clinical data is a powerful tool to highlight new non-genetic cancer dependencies that associate to a bad outcome in vivo, paving the way to new MPM-oriented targeted strategies and prognostic tools to improve patients risk-based stratification.

Distributed ledger technologies between anonymity and transparency: AML/CFT regulation of cryptocurrency ecosystems in the EU

The advent of Bitcoin suggested a disintermediated economy in which Internet users can take part directly. The conceptual disruption brought about by this Internet of Money (IoM) mirrors the cross-industry impacts of blockchain and distributed ledger technologies (DLTs). While related instances of non-centralisation thwart regulatory efforts to establish accountability, in the financial domain further challenges arise from the presence in the IoM of two seemingly opposing traits: anonymity and transparency. Indeed, DLTs are often described as architecturally transparent, but the perceived level of anonymity of cryptocurrency transfers fuels fears of illicit exploitation. This is a primary concern for the framework to prevent money laundering and the financing of terrorism and proliferation (AML/CFT/CPF), and a top priority both globally and at the EU level. Nevertheless, the anonymous and transparent features of the IoM are far from clear-cut, and the same is true for its levels of disintermediation and non-centralisation. Almost fifteen years after the first Bitcoin transaction, the IoM today comprises a diverse set of socio-technical ecosystems. Building on an analysis of their phenomenology, this dissertation shows how there is more to their traits of anonymity and transparency than it may seem, and how these features range across a spectrum of combinations and degrees. In this context, trade-offs can be evaluated by referring to techno-legal benchmarks, established through socio-technical assessments grounded on teleological interpretation. Against this backdrop, this work provides framework-level recommendations for the EU to respond to the twofold nature of the IoM legitimately and effectively. The methodology cherishes the mutual interaction between regulation and technology when drafting regulation whose compliance can be eased by design. This approach mitigates the risk of overfitting in a fast-changing environment, while acknowledging specificities in compliance with the risk-based approach that sits at the core of the AML/CFT/CPF regime.

Big data and AI applications for health and research. Co-regulation mechanisms as a prosed solution for data protection law issues

Big data and AI are paving the way to promising scenarios in clinical practice and research. However, the use of such technologies might clash with GDPR requirements. Today, two forces are driving the EU policies in this domain. The first is the necessity to protect individuals’ safety and fundamental rights. The second is to incentivize the deployment of innovative technologies. The first objective is pursued by legislative acts such as the GDPR or the AIA, the second is supported by the new data strategy recently launched by the European Commission. Against this background, the thesis analyses the issue of GDPR compliance when big data and AI systems are implemented in the health domain. The thesis focuses on the use of co-regulatory tools for compliance with the GDPR. This work argues that there are two level of co-regulation in the EU legal system. The first, more general, is the approach pursued by the EU legislator when shaping legislative measures that deal with fast-evolving technologies. The GDPR can be deemed a co-regulatory solution since it mainly introduces general requirements, which implementation shall then be interpretated by the addressee of the law following a risk-based approach. This approach, although useful is costly and sometimes burdensome for organisations. The second co-regulatory level is represented by specific co-regulatory tools, such as code of conduct and certification mechanisms. These tools are meant to guide and support the interpretation effort of the addressee of the law. The thesis argues that the lack of co-regulatory tools which are supposed to implement data protection law in specific situations could be an obstacle to the deployment of innovative solutions in complex scenario such as the health ecosystem. The thesis advances hypothesis on theoretical level about the reasons of such a lack of co-regulatory solutions.

Automatic risk evaluation in elderly patients based on Autonomic Nervous System assessment

Dysfunction of Autonomic Nervous System (ANS) is a typical feature of chronic heart failure and other cardiovascular disease. As a simple non-invasive technology, heart rate variability (HRV) analysis provides reliable information on autonomic modulation of heart rate. The aim of this thesis was to research and develop automatic methods based on ANS assessment for evaluation of risk in cardiac patients. Several features selection and machine learning algorithms have been combined to achieve the goals. Automatic assessment of disease severity in Congestive Heart Failure (CHF) patients: a completely automatic method, based on long-term HRV was proposed in order to automatically assess the severity of CHF, achieving a sensitivity rate of 93% and a specificity rate of 64% in discriminating severe versus mild patients. Automatic identification of hypertensive patients at high risk of vascular events: a completely automatic system was proposed in order to identify hypertensive patients at higher risk to develop vascular events in the 12 months following the electrocardiographic recordings, achieving a sensitivity rate of 71% and a specificity rate of 86% in identifying high-risk subjects among hypertensive patients. Automatic identification of hypertensive patients with history of fall: it was explored whether an automatic identification of fallers among hypertensive patients based on HRV was feasible. The results obtained in this thesis could have implications both in clinical practice and in clinical research. The system has been designed and developed in order to be clinically feasible. Moreover, since 5-minute ECG recording is inexpensive, easy to assess, and non-invasive, future research will focus on the clinical applicability of the system as a screening tool in non-specialized ambulatories, in order to identify high-risk patients to be shortlisted for more complex investigations.

Modeling Alzheimer disease with iPSCs and mouse model for the identification of risk factors, new targets, and potential therapeutical strategies

Alzheimer's disease (AD) is the most common neurodegenerative disease in elderly. Donepezil is the first-line drug used for AD. In section one, the experimental activity was oriented to evaluate and characterize molecular and cellular mechanisms that contribute to neurodegeneration induced by the Aβ1-42 oligomers (Aβ1-42O) and potential neuroprotective effects of the hybrids feruloyl-donepezil compound called PQM130. The effects of PQM130 were compared to donepezil in a murine AD model, obtained by intracerebroventricular (i.c.v.) injection of Aβ1-42O. The intraperitoneal administration of PQM130 (0.5-1 mg/kg) after i.c.v. Aβ1-42O injection improved learning and memory, protecting mice against spatial cognition decline. Moreover, it reduced oxidative stress, neuroinflammation and neuronal apoptosis, induced cell survival and protein synthesis in mice hippocampus. PQM130 modulated different pathways than donepezil, and it is more effective in counteracting Aβ1-42O damage. The section two of the experimental activity was focused on studying a loss of function variants of ABCA7. GWA studies identified mutations in the ABCA7 gene as a risk factor for AD. The mechanism through which ABCA7 contributes to AD is not clear. ABCA7 regulates lipid metabolism and critically controls phagocytic function. To investigate ABCA7 functions, CRISPR/Cas9 technology was used to engineer human iPSCs and to carry the genetic variant Y622*, which results in a premature stop codon, causing ABCA7 loss-of-function. From iPSCs, astrocytes were generated. This study revealed the effects of ABCA7 loss in astrocytes. ABCA7 Y622* mutation induced dysfunctional endocytic trafficking, impairing Aβ clearance, lipid dysregulation and cell homeostasis disruption, alterations that could contribute to AD. Though further studies are needed to confirm the PQM130 neuroprotective role and ABCA7 function in AD, the provided results showed a better understanding of AD pathophysiology, a new therapeutic approach to treat AD, and illustrated an innovative methodology for studying the disease.

Modeling of Electric Hybrid Vehicles based on Innovative Energy Storage Technologies for Super Sports Cars Applications

Today, the contribution of the transportation sector on greenhouse gases is evident. The fast consumption of fossil fuels and its impact on the environment has given a strong impetus to the development of vehicles with better fuel economy. Hybrid electric vehicles fit into this context with different targets, starting from the reduction of emissions and fuel consumption, but also for performance and comfort enhancement. Vehicles exist with various missions; super sport cars usually aim to reach peak performance and to guarantee a great driving experience to the driver, but great attention must also be paid to fuel consumption. According to the vehicle mission, hybrid vehicles can differ in the powertrain configuration and the choice of the energy storage system. Lamborghini has recently invested in the development of hybrid super sport cars, due to performance and comfort reasons, with the possibility to reduce fuel consumption. This research activity has been conducted as a joint collaboration between the University of Bologna and the sportscar manufacturer, to analyze the impact of innovative energy storage solutions on the hybrid vehicle performance. Capacitors have been studied and modeled to analyze the pros and cons of such solution with respect to batteries. To this aim, a full simulation environment has been developed and validated to provide a concept design tool capable of precise results and able to foresee the longitudinal performance on regulated emission cycles and real driving conditions, with a focus on fuel consumption. In addition, the target of the research activity is to deepen the study of hybrid electric super sports cars in the concept development phase, focusing on defining the control strategies and the energy storage system’s technology that best suits the needs of the vehicles. This dissertation covers the key steps that have been carried out in the research project.

Multiscale modeling of light-harvesting systems based on rhodamine B aggregates

Rhodamine B (RB) has been successfully exploited in the synthesis of light harvesting systems, but since RB is prone to form dimers acting as quenchers for the fluorescence, high energy transfer efficiencies can be reached only when using bulky and hydrophobic counterions acting as spacers between RBs. In this PhD thesis, a multiscale theoretical study aimed at providing insights into the structural, photophysical and optical properties of RB and its aggregates is presented. At the macroscopic level (no atomistic details) a phenomenological model describing the fluorescence decay of RB networks in presence of both quenching from dimers and exciton-exciton annihiliation is presented and analysed, showing that the quenching from dimers affects the decay only at long times, a feature that can be exploited in global fitting analysis to determine relevant chemical and photophysical information. At the mesoscopic level (atomistic details but no electronic structure) the RB aggregation in water in presence of different counterions is studied with molecular dynamics (MD) simulations. A new force field has been parametrized for describing the RB flexibility and the RB-RB interaction driving the dimerization. Simulations correctly predict the RB/counterion aggregation only in presence of bulky and hydrophobic counterion and its ability to prevent the dimerization. Finally, at the microscopic level, DFT calculations are performed to demonstrate the spacing action of bulky counterions, but standard TDDFT calculations are showed to fail in correctly describing the excited states of RB and its dimers. Moreover, also standard procedures proposed in literature for obtaining ad hoc functionals are showed to not work properly. A detailed analysis on the effect of the exact exchange shows that its short-range contribution is the crucial quantity for ameliorating results, and a new functional containing a proper amount of such an exchange is proposed and successfully tested.

Developing a radiomic based machine learning model to identify patients with resected non-small-cell lung cancer at high risk of relapse

Background There is a wide variation of recurrence risk of Non-small-cell lung cancer (NSCLC) within the same Tumor Node Metastasis (TNM) stage, suggesting that other parameters are involved in determining this probability. Radiomics allows extraction of quantitative information from images that can be used for clinical purposes. The primary objective of this study is to develop a radiomic prognostic model that predicts a 3 year disease free-survival (DFS) of resected Early Stage (ES) NSCLC patients. Material and Methods 56 pre-surgery non contrast Computed Tomography (CT) scans were retrieved from the PACS of our institution and anonymized. Then they were automatically segmented with an open access deep learning pipeline and reviewed by an experienced radiologist to obtain 3D masks of the NSCLC. Images and masks underwent to resampling normalization and discretization. From the masks hundreds Radiomic Features (RF) were extracted using Py-Radiomics. Hence, RF were reduced to select the most representative features. The remaining RF were used in combination with Clinical parameters to build a DFS prediction model using Leave-one-out cross-validation (LOOCV) with Random Forest. Results and Conclusion A poor agreement between the radiologist and the automatic segmentation algorithm (DICE score of 0.37) was found. Therefore, another experienced radiologist manually segmented the lesions and only stable and reproducible RF were kept. 50 RF demonstrated a high correlation with the DFS but only one was confirmed when clinicopathological covariates were added: Busyness a Neighbouring Gray Tone Difference Matrix (HR 9.610). 16 clinical variables (which comprised TNM) were used to build the LOOCV model demonstrating a higher Area Under the Curve (AUC) when RF were included in the analysis (0.67 vs 0.60) but the difference was not statistically significant (p=0,5147).