https://www.um.edu.mt/library/oar/handle/123456789/11478 2026-10-15T16:06:20Z 2026-10-15T16:06:20Z https://www.um.edu.mt/library/oar/handle/123456789/149078 2026-09-14T10:41:54Z 2022-01-01T00:00:00Z

Title: A novel chemical-based approach to produce flexible polyurethane foams with negative Poisson’s ratios Abstract: This work and its abstract are both under embargo until the restriction is lifted. Description: Ph.D.(Melit.)

2022-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/148524 2026-08-18T08:55:55Z 2026-01-01T00:00:00Z

Title: Time-dependent horizontal-to-vertical spectral ratio (HVSR) analysis of ambient seismic noise Abstract: Existing local research has employed the horizontal-to-vertical spectral ratio (HVSR) for static microzonation and long-term groundwater monitoring. Building on this work, this thesis provides the first high-resolution quantification of how sub-daily meteorological fluctuations, specifically wind-driven noise and atmospheric variables, directly modulate seismic site response parameters across the stations of the Malta Seismic Network in the Maltese archipelago. By establishing these correlations, this study enhances the methodological accuracy of HVSR in the Maltese context, providing a framework to isolate meteorological noise from geological signals and enabling more reliable site response assessments for local engineering and microzonation projects. This study analyses four years (2017-2020) of continuous seismic data recorded by the Malta Seismic Network to investigate temporal variability in HVSR parameters. The research evaluates how HVSR peak frequency and amplitude evolve and examines the potential influence of meteorological variables, including wind speed, precipitation, temperature, and relative humidity. Statistical analysis is used to identify stations that exhibit sensitivity to environmental forcing and to assess the extent to which such factors may influence the stability and interpretation of HVSR measurements in the Maltese Islands. Forward modelling was also used to confirm whether the observed meteorological effects can potentially create subsurface parameter changes, such as variations in soil moisture or density, which could affect the HVSR measurements. This thesis further confirms that HVSR parameters are sensitive to wind strength and reveals that two stations (MELT and CBH9) have a heightened sensitivity to wind speed compared to other stations within the Malta Seismic Network. Hence, caution should be exercised when analysing data from these stations during windy days. Association between HVSR and other meteorological parameters, including precipitation, revealed weak effect sizes, indicating that other short-term meteorological forcing is not registered by HVSR. Description: B.Sc. (Hons)(Melit.)

2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/145690 2026-04-16T13:16:30Z 2025-01-01T00:00:00Z

Title: Non-commutative probability : from classical to quantum probability Abstract: In classical probability theory, it is implicitly assumed that random variables commute. However, this assumption does not necessarily hold in all mathemat ical frameworks, such as those involving matrices. In this thesis, we will explore quantum probability, a non-commutative extension of classical probability. The main aim of this thesis shall be to examine how key concepts from classical probability can be generalized in the quantum setting. Description: M.Sc.(Melit.)

2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/145381 2026-04-07T13:20:32Z 2026-01-01T00:00:00Z

Title: Innovative methods for detecting sea turtle nests : a combination of UAV photogrammetry, GPR, and artificial intelligence for non-invasive monitoring and conservation Abstract: Sea turtle nesting represents one of the most vulnerable stages in their life cycle; therefore, protecting nesting sites is essential for the long-term survival of their populations. Traditional nest detection methods are often invasive and may disturb nesting females. This study introduces a non invasive approach for detecting and monitoring sea turtle nests through the combined use of advanced technologies. Specifically, Ground Penetrating Radar (GPR) and Artificial Intelligence (AI) are employed to automatically identify turtle tracks and assist in locating potential nesting sites. As part of this study, fieldwork was conducted at Golden Bay, Malta, where a simulated nest of loggerhead turtle (Caretta caretta) was put together to evaluate how effectively and accurately GPR can find an underground chamber containing eggs. To confirm the radar data, a 3D LiDAR model was made of the internal structure of the simulated nest, thus providing a reference dataset for the interpretation of radargrams. Meanwhile, an AI algorithm was instructed to automatically recognize turtle tracks from beach photos, thus facilitating the identification of potential nesting areas. The integrative approach of these techniques demonstrates the potential of non-invasive technologies to enhance the efficiency of sea turtle nest detection and conservation. The findings contribute to the development of modern conservation strategies, particularly within small Mediterranean rookeries such as Malta, where nesting events are rare and spatially constrained. Description: M.Sc.(Melit.)

2026-01-01T00:00:00Z