Rituali indigeni in Mesoamerica. La festa di Petición de Lluvias nella Montaña di Guerrero (Messico)

Questa tesi di carattere antropologico in ambito dottorale riguarda i rituali comunitari nelle comunità indigene messicane. Il principale oggetto della ricerca è il rituale della pioggia o di Petición de Lluvia, caratterizzato sia dal sacrificio animale che da una specifica relazione di causa-effetto con l’ambiente circostante. La ricerca etnografica è cominciata dall’ipotesi di voler verificare la persistenza nel tempo, e dunque nell’attualità, di procedure cerimoniali non appartenenti, almeno nella loro forma più lineare, alla religione cattolico-cristiana. Il luogo nel quale è avvenuta tale ricerca è la regione La Montaña di Guerrero, situata nel Messico sud-occidentale, e più precisamente la zona in cui vivono le comunità di etnia Nahua di San Pedro Petlacala, Acuilpa, e Xalpatláhuac che si trovano nelle vicinanze della cittadina di Tlapa de Comonfort. In un contesto ambientale profondamente rurale come quello della Montaña di Guerrero, la persistenza dei rituali evidenzia come le risorse naturali e gli agenti atmosferici - pioggia, vento, nubi - continuino a rappresentare gli elementi centrali che condizionano le variabili economiche di sussistenza e della riproduzione sociale. Il rituale di Petición de Lluvia rappresenta il momento di congiunzione tra la stagione secca e quella piovosa, tra la semina ed il raccolto del mais. Definito come una pratica religiosa nella quale il gruppo si identifica e partecipa con varie donazioni (ofrenda o deposito rituale), suddivisibili in alimenti/oggetti/preghiere ed azioni rituali, la cerimonia esprime l’auspicio di piogge abbondanti, con le quali irrigare i campi e continuare le attività umane. Il destinatario dell’offerta è la stessa divinità della pioggia, Tlaloc per le antiche civiltà mesoamericane, invocato sotto le mentite spoglie del santo patrono del 25 aprile, San Marcos. Il rituale è contraddistinto per tutta la sua durata dalla presenza del principale specialista religioso, sacerdote in lingua spagnola oppure «Tlahmáquetl» in lingua náhuatl.

Pyrolysis of peptides and proteins. Analytical study and environmental applications

Analytical pyrolysis was used to investigate the formation of diketopiperazines (DKPs) which are cyclic dipeptides formed from the thermal degradation of proteins. A quali/quantitative procedure was developed combining microscale flash pyrolysis at 500 °C with gas chromatography-mass spectrometry (GC-MS) of DKPs trapped onto an adsorbent phase. Polar DKPs were silylated prior to GC-MS. Particular attention was paid to the identification of proline (Pro) containing DKPs due to their greater facility of formation. The GC-MS characteristics of more than 80 original and silylated DKPs were collected from the pyrolysis of sixteen linear dipeptides and four model proteins (e.g. bovine serum albumin, BSA). The structure of a novel DKP, cyclo(pyroglutamic-Pro) was established by NMR and ESI-MS analysis, while the structures of other novel DKPs remained tentative. DKPs resulted rather specific markers of amino acid sequence in proteins, even though the thermal degradation of DKPs should be taken into account. Structural information of DKPs gathered from the pyrolysis of model compounds was employed to the identification of these compounds in the pyrolysate of proteinaceous samples, including intrinsecally unfolded protein (IUP). Analysis of the liquid fraction (bio-oil) obtained from the pyrolysis of microalgae Nannochloropsis gaditana, Scenedesmus spp with a bench scale reactor showed that DKPs constituted an important pool of nitrogen-containing compounds. Conversely, the level of DKPs was rather low in the bio-oil of Botryococcus braunii. The developed micropyrolysis procedure was applied in combination with thermogravimetry (TGA) and infrared spectroscopy (FT-IR) to investigate surface interaction between BSA and synthetic chrysotile. The results showed that the thermal behavior of BSA (e.g. DKPs formation) was affected by the different form of doped synthetic chrysotile. The typical DKPs evolved from collagen were quantified in the pyrolysates of archaeological bones from Vicenne Necropolis in order to evaluate their conservation status in combination with TGA, FTIR and XRD analysis.

Exact and heuristic algorithms for the integrated planning of multi-energy systems

The aim of this thesis is to present exact and heuristic algorithms for the integrated planning of multi-energy systems. The idea is to disaggregate the energy system, starting first with its core the Central Energy System, and then to proceed towards the Decentral part. Therefore, a mathematical model for the generation expansion operations to optimize the performance of a Central Energy System system is first proposed. To ensure that the proposed generation operations are compatible with the network, some extensions of the existing network are considered as well. All these decisions are evaluated both from an economic viewpoint and from an environmental perspective, as specific constraints related to greenhouse gases emissions are imposed in the formulation. Then, the thesis presents an optimization model for solar organic Rankine cycle in the context of transactive energy trading. In this study, the impact that this technology can have on the peer-to-peer trading application in renewable based community microgrids is inspected. Here the consumer becomes a prosumer and engages actively in virtual trading with other prosumers at the distribution system level. Moreover, there is an investigation of how different technological parameters of the solar Organic Rankine Cycle may affect the final solution. Finally, the thesis introduces a tactical optimization model for the maintenance operations’ scheduling phase of a Combined Heat and Power plant. Specifically, two types of cleaning operations are considered, i.e., online cleaning and offline cleaning. Furthermore, a piecewise linear representation of the electric efficiency variation curve is included. Given the challenge of solving the tactical management model, a heuristic algorithm is proposed. The heuristic works by solving the daily operational production scheduling problem, based on the final consumer’s demand and on the electricity prices. The aggregate information from the operational problem is used to derive maintenance decisions at a tactical level.

Timeliness, efficiency and public procurement in hip surgery in Italy

Considering different perspectives, the scope of this thesis is to investigate how to improve healthcare resources allocation and the provision efficiency for hip surgeries, a resource-intensive operation, among the most frequently performed on the elderly, with a trend in volume that is increasing in years due to population aging. Firstly, the effect of Time-To-Surgery (TTS) on mortality for hip fracture patients is investigated. The analysis attempts to account for TTS endogeneity due to the inability to fully control for variables affecting patient delay – e.g. patient severity. Exploiting an instrumental variable model, where being admitted on Friday or Saturday predicts longer TTS, findings show exogenous TTS does not have a significant effect on mortality. Thus suggesting surgeons prioritize patients effectively, neutralizing the adverse impact of longer TTS. Then, the volume-outcome relation for total hip replacement surgery is analyzed, seeking to account for selective referral, which may be present in elective surgery context, and induce reverse causality issue in the volume-outcome relation. The analysis employs a conditional choice model where patient travel distance from all regions' hospitals is used as a hospital choice predictor. Findings show the exogenous hospital volume significantly decreases adverse outcomes probability, especially in the short run. Finally, the change in public procurement design enforced in the Romagna LHA (Italy) is exploited to assess its impact on hip prostheses cost, surgeons' implant choice, and patient health outcomes. Hip prostheses are the major cost-driver of hip replacement surgeries, hence it is crucial to design the public tender such that implant prices are minimized, but cost-containment policies have to be weighted with patient well-being. Evidence shows that a cost reduction occurred without a significant surgeons’ choices impact. Positive or no effect of surgeons specialization is found on patients outcomes after the new procurement introduction.

Towards Unstructured Knowledge Integration in Natural Language Processing

In the last decades, Artificial Intelligence has witnessed multiple breakthroughs in deep learning. In particular, purely data-driven approaches have opened to a wide variety of successful applications due to the large availability of data. Nonetheless, the integration of prior knowledge is still required to compensate for specific issues like lack of generalization from limited data, fairness, robustness, and biases. In this thesis, we analyze the methodology of integrating knowledge into deep learning models in the field of Natural Language Processing (NLP). We start by remarking on the importance of knowledge integration. We highlight the possible shortcomings of these approaches and investigate the implications of integrating unstructured textual knowledge. We introduce Unstructured Knowledge Integration (UKI) as the process of integrating unstructured knowledge into machine learning models. We discuss UKI in the field of NLP, where knowledge is represented in a natural language format. We identify UKI as a complex process comprised of multiple sub-processes, different knowledge types, and knowledge integration properties to guarantee. We remark on the challenges of integrating unstructured textual knowledge and bridge connections with well-known research areas in NLP. We provide a unified vision of structured knowledge extraction (KE) and UKI by identifying KE as a sub-process of UKI. We investigate some challenging scenarios where structured knowledge is not a feasible prior assumption and formulate each task from the point of view of UKI. We adopt simple yet effective neural architectures and discuss the challenges of such an approach. Finally, we identify KE as a form of symbolic representation. From this perspective, we remark on the need of defining sophisticated UKI processes to verify the validity of knowledge integration. To this end, we foresee frameworks capable of combining symbolic and sub-symbolic representations for learning as a solution.

Computational methods applied to undeciphered scripts from the Aegean and Cyprus: Linear A and Cypro-Minoan

The study of ancient, undeciphered scripts presents unique challenges, that depend both on the nature of the problem and on the peculiarities of each writing system. In this thesis, I present two computational approaches that are tailored to two different tasks and writing systems. The first of these methods is aimed at the decipherment of the Linear A afraction signs, in order to discover their numerical values. This is achieved with a combination of constraint programming, ad-hoc metrics and paleographic considerations. The second main contribution of this thesis regards the creation of an unsupervised deep learning model which uses drawings of signs from ancient writing system to learn to distinguish different graphemes in the vector space. This system, which is based on techniques used in the field of computer vision, is adapted to the study of ancient writing systems by incorporating information about sequences in the model, mirroring what is often done in natural language processing. In order to develop this model, the Cypriot Greek Syllabary is used as a target, since this is a deciphered writing system. Finally, this unsupervised model is adapted to the undeciphered Cypro-Minoan and it is used to answer open questions about this script. In particular, by reconstructing multiple allographs that are not agreed upon by paleographers, it supports the idea that Cypro-Minoan is a single script and not a collection of three script like it was proposed in the literature. These results on two different tasks shows that computational methods can be applied to undeciphered scripts, despite the relatively low amount of available data, paving the way for further advancement in paleography using these methods.

Advancing digital healthcare research and machine learning-aided knowledge extraction amidst the COVID-19 pandemic

The rapid progression of biomedical research coupled with the explosion of scientific literature has generated an exigent need for efficient and reliable systems of knowledge extraction. This dissertation contends with this challenge through a concentrated investigation of digital health, Artificial Intelligence, and specifically Machine Learning and Natural Language Processing's (NLP) potential to expedite systematic literature reviews and refine the knowledge extraction process. The surge of COVID-19 complicated the efforts of scientists, policymakers, and medical professionals in identifying pertinent articles and assessing their scientific validity. This thesis presents a substantial solution in the form of the COKE Project, an initiative that interlaces machine reading with the rigorous protocols of Evidence-Based Medicine to streamline knowledge extraction. In the framework of the COKE (“COVID-19 Knowledge Extraction framework for next-generation discovery science”) Project, this thesis aims to underscore the capacity of machine reading to create knowledge graphs from scientific texts. The project is remarkable for its innovative use of NLP techniques such as a BERT + bi-LSTM language model. This combination is employed to detect and categorize elements within medical abstracts, thereby enhancing the systematic literature review process. The COKE project's outcomes show that NLP, when used in a judiciously structured manner, can significantly reduce the time and effort required to produce medical guidelines. These findings are particularly salient during times of medical emergency, like the COVID-19 pandemic, when quick and accurate research results are critical.