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.

Studio del ciclo geochimico del Mercurio all'interfaccia aria-neve nel plateau Antartico

Lo studio si basa sull’analisi di campioni di neve superficiale (0-3 cm) e sub-superficiale (3-6 cm) raccolti nei pressi della “clean area” di Concordia Station, nel plateau Antartico, e di campioni di deposizione raccolti a seguito di eventi di precipitazione nello stesso sito. Il sito di campionamento “clean area” è designato allo studio dei valori di fondo nel plateau antartico, poiché ritenuto privo di contaminazione antropica. L’obiettivo è la quantificazione delle concentrazioni in ultra-tracce di mercurio per lo studio di eventuali patterns relativi al ciclo geochimico dell’elemento nei periodi interessati da radiazione solare. Inoltre, per studiare i processi che controllano il ciclo del mercurio nel plateau Antartico sono stati considerati sia i parametri meteorologici che le variazioni di elementi chimici usati per tracciare le possibili sorgenti emissive ed i meccanismi di scambio che avvengono per il mercurio all’interfaccia aria-neve. Le analisi sono state svolte con ICP-SFMS, una tecnica di spettrometria in grado di rilevare concentrazioni nell’ordine del ppt. A causa dei limiti di rilevazione molto bassi, tutte le provette usate per le analisi e la conservazione dei campioni sono state trattate al fine di minimizzare la contaminazione. I campioni destinati alle analisi di mercurio sono stati trattati con acido, seguendo la procedura per la stabilizzazione di metalli volatili. Il lavoro svolto non ha identificato particolari processi di trasporto e di scambio mentre invece riporta episodi di contaminazione della “clean area” dovuti in parte alla non usuale provenienza del vento ed in parte al crescente impatto antropico dovuto alle attività delle stazioni di ricerca. Lo studio ha messo in luce le problematiche legate al possibile impatto della base di ricerca Concordia Station sui processi naturali evidenziando la necessità di ulteriori studi e approfondimenti sul tema della contaminazione della designata “area pulita” per il campionamento di neve.

Instrument transformers with Digital outputting high voltage power networks

In recent years, energy modernization has focused on smart engineering advancements. This entails designing complicated software and hardware for variable-voltage digital substations. A digital substation consists of electrical and auxiliary devices, control and monitoring devices, computers, and control software. Intelligent measurement systems use digital instrument transformers and IEC 61850-compliant information exchange protocols in digital substations. Digital instrument transformers used for real-time high-voltage measurements should combine advanced digital, measuring, information, and communication technologies. Digital instrument transformers should be cheap, small, light, and fire- and explosion-safe. These smaller and lighter transformers allow long-distance transmission of an optical signal that gauges direct or alternating current. Cost-prohibitive optical converters are a problem. To improve the tool's accuracy, amorphous alloys are used in the magnetic circuits and compensating feedback. Large-scale voltage converters can be made cheaper by using resistive, capacitive, or hybrid voltage dividers. In known electronic voltage transformers, the voltage divider output is generally on the low-voltage side, facilitating power supply organization. Combining current and voltage transformers reduces equipment size, installation, and maintenance costs. These two gadgets cost less together than individually. To increase commercial power metering accuracy, current and voltage converters should be included into digital instrument transformers so that simultaneous analogue-to-digital samples are obtained. Multichannel ADC microcircuits with synchronous conversion start allow natural parallel sample drawing. Digital instrument transformers are created adaptable to substation operating circumstances and environmental variables, especially ambient temperature. An embedded microprocessor auto-diagnoses and auto-calibrates the proposed digital instrument transformer.