Digital 3D reconstruction as a research environment in art and architecture history: uncertainty classification and visualisation

The dissertation addresses the still not solved challenges concerned with the source-based digital 3D reconstruction, visualisation and documentation in the domain of archaeology, art and architecture history. The emerging BIM methodology and the exchange data format IFC are changing the way of collaboration, visualisation and documentation in the planning, construction and facility management process. The introduction and development of the Semantic Web (Web 3.0), spreading the idea of structured, formalised and linked data, offers semantically enriched human- and machine-readable data. In contrast to civil engineering and cultural heritage, academic object-oriented disciplines, like archaeology, art and architecture history, are acting as outside spectators. Since the 1990s, it has been argued that a 3D model is not likely to be considered a scientific reconstruction unless it is grounded on accurate documentation and visualisation. However, these standards are still missing and the validation of the outcomes is not fulfilled. Meanwhile, the digital research data remain ephemeral and continue to fill the growing digital cemeteries. This study focuses, therefore, on the evaluation of the source-based digital 3D reconstructions and, especially, on uncertainty assessment in the case of hypothetical reconstructions of destroyed or never built artefacts according to scientific principles, making the models shareable and reusable by a potentially wide audience. The work initially focuses on terminology and on the definition of a workflow especially related to the classification and visualisation of uncertainty. The workflow is then applied to specific cases of 3D models uploaded to the DFG repository of the AI Mainz. In this way, the available methods of documenting, visualising and communicating uncertainty are analysed. In the end, this process will lead to a validation or a correction of the workflow and the initial assumptions, but also (dealing with different hypotheses) to a better definition of the levels of uncertainty.

Efficient and low-computational predictive models for spectral sensors

Spectral sensors are a wide class of devices that are extremely useful for detecting essential information of the environment and materials with high degree of selectivity. Recently, they have achieved high degrees of integration and low implementation cost to be suited for fast, small, and non-invasive monitoring systems. However, the useful information is hidden in spectra and it is difficult to decode. So, mathematical algorithms are needed to infer the value of the variables of interest from the acquired data. Between the different families of predictive modeling, Principal Component Analysis and the techniques stemmed from it can provide very good performances, as well as small computational and memory requirements. For these reasons, they allow the implementation of the prediction even in embedded and autonomous devices. In this thesis, I will present 4 practical applications of these algorithms to the prediction of different variables: moisture of soil, moisture of concrete, freshness of anchovies/sardines, and concentration of gasses. In all of these cases, the workflow will be the same. Initially, an acquisition campaign was performed to acquire both spectra and the variables of interest from samples. Then these data are used as input for the creation of the prediction models, to solve both classification and regression problems. From these models, an array of calibration coefficients is derived and used for the implementation of the prediction in an embedded system. The presented results will show that this workflow was successfully applied to very different scientific fields, obtaining autonomous and non-invasive devices able to predict the value of physical parameters of choice from new spectral acquisitions.

Different dietary fibres included in the gestation and lactation diet shape sow's microbiota, modify colostrum and milk composition and affect piglet growth and health

This thesis reports three experimental studies that may contribute to understand how the sources or types of dietary fibres (DFs) included in sow diet with similar level of total DFs influence the composition of colostrum and milk and their related effects on offspring performance and gut microbiota. The first study showed that decreasing the level of hemicelluloses (HCs) in sow’s lactation diet increased the proportion of butyrate and the concentration of volatile fatty acids (VFAs), copper and threonine in milk. Simultaneously, the post-weaning growth of low birthweight piglets was improved, and the diarrhoea occurrence was reduced during the second week post-weaning. The second study showed that the level of HCs in the diet of lactating sows affected their faecal microbiota, modified the VFA profile in sow’s faeces during lactation and barely impacted the faecal microbiota of slow and fast growing piglets. The third study showed that replacing a source soluble DFs by one of insoluble DFs in sow’s diet during late gestation and lactation reduced farrowing duration, increased total VFAs and lactoferrin concentrations in colostrum, improved growth performance from birth to 1 day of lactation, during the post-weaning period and throughout the study, and reduced diarrhoea occurrence during the first week post-weaning. Finally, a fourth study proposed a workflow to analyse low biomass samples from the umbilical cord blood aiming at investigating the existence of a pre-birth microbiota with no substantial findings to confirm this hypothesis. Overall, the results of these studies confirmed that, besides the level of DFs, the sources, and the types of DFs included in the sow's diet shape the sow's microbiota, influence the composition of colostrum and milk, and improve offspring performance, but with limited impacts on the microbiota of piglets.

Identification of potential miRNAs related to classification and treatment response of actinic keratosis

The Workflow activity was the following: Preliminary phase: Identification of 18 Formalin-fixed paraffin embedded (FFPE) samples (9 patients) («matched» 9 AK lesions and 9 SCC lesions). Working on biopsies samples we perform an extraction and RNA analysis with droplet Digital PCR (ddPCR) and we perform the data analysis. Second and final step phase: Evaluation of additional 39 subjects (36 men and 3 women). Results: We perform an evaluation and comparison of the following miRNA: miR-320 (a miRNA involved in apoptosis and cell proliferation control; miR-204, a miRNA involved in cell proliferation in and miRNA-16-5p, a miRNA involved in apoptosis).Conclusion: Our data suggest that there is no significant variation in the expression of the three tested microRNAs between adjacent AK lesions and squamous-cell carcinoma. However, a relevant trend has been observed Furthermore, by evaluating the miRNA expression trend between keratosis and carcinoma of the same patient, it is observed that there is no "uniform trend": for some samples the expression rises for the transition from AK to SCC and viceversa.

Modelling the infrastructure of the “Śivadharma Database”

A Digital Scholarly Edition is a conceptually and structurally sophisticated entity. Throughout the centuries, diverse methodologies have been employed to reconstruct a text transmitted through one or multiple sources, resulting in various edition types. With the advent of digital technology in philology, these practices have undergone a significant transformation, compelling scholars to reconsider their approach in light of the web. In the digital age, philologists are expected to possess (too) advanced technical skills to prepare interactive and enriched editions, even though, in most cases, only mechanical or documentary editions are published online. The Śivadharma Database is a web Content Management System (CMS) designed to facilitate the preparation, publication, and updating of Digital Scholarly Editions. By providing scholars with a user-friendly CRUD web application to reconstruct and annotate a text, they can prepare their textus with additional components such as apparatus, notes, translations, citations, and parallels. It is possible by leveraging an annotation system based on HTML and graph data structure. This choice is made because the text entity is multidimensional and multifaceted, even if its sequential presentation constrains it. In particular, editions of South Asian texts of the Śivadharma corpus, the case study of this research, contain a series of phenomena that are difficult to manage formally, such as overlapping hierarchies. Hence, it becomes necessary to establish the data structure best suited to represent this complexity. In Śivadharma Database, the textus is an HTML file readily displayable. Textual fragments, annotated via an interface without requiring philologists to write code and saved in the backend, form the atomic unit of multiple relationships organised in a graph database. This approach enables the formal representation of complex and overlapping textual phenomena, allowing for good annotation expressiveness with minimal effort to learn the relevant technologies during the editing workflow.