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    <title>OAR@UM Collection:</title>
    <link>https://www.um.edu.mt/library/oar/handle/123456789/143620</link>
    <description />
    <items>
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        <rdf:li rdf:resource="https://www.um.edu.mt/library/oar/handle/123456789/145381" />
        <rdf:li rdf:resource="https://www.um.edu.mt/library/oar/handle/123456789/145380" />
        <rdf:li rdf:resource="https://www.um.edu.mt/library/oar/handle/123456789/145379" />
        <rdf:li rdf:resource="https://www.um.edu.mt/library/oar/handle/123456789/144070" />
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    </items>
    <dc:date>2026-04-15T21:59:23Z</dc:date>
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  <item rdf:about="https://www.um.edu.mt/library/oar/handle/123456789/145381">
    <title>Innovative methods for detecting sea turtle nests : a combination of UAV photogrammetry, GPR, and  artificial intelligence for non-invasive monitoring and conservation</title>
    <link>https://www.um.edu.mt/library/oar/handle/123456789/145381</link>
    <description>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, &#xD;
protecting nesting sites is essential for the long-term survival of their populations. Traditional nest &#xD;
detection methods are often invasive and may disturb nesting females. This study introduces a non&#xD;
invasive approach for detecting and monitoring sea turtle nests through the combined use of &#xD;
advanced technologies. Specifically, Ground Penetrating Radar (GPR) and Artificial Intelligence &#xD;
(AI) are employed to automatically identify turtle tracks and assist in locating potential nesting &#xD;
sites. &#xD;
As part of this study, fieldwork was conducted at Golden Bay, Malta, where a simulated nest of &#xD;
loggerhead turtle (Caretta caretta) was put together to evaluate how effectively and accurately &#xD;
GPR can find an underground chamber containing eggs. To confirm the radar data, a 3D LiDAR &#xD;
model was made of the internal structure of the simulated nest, thus providing a reference dataset &#xD;
for the interpretation of radargrams. Meanwhile, an AI algorithm was instructed to automatically &#xD;
recognize turtle tracks from beach photos, thus facilitating the identification of potential nesting &#xD;
areas. &#xD;
The integrative approach of these techniques demonstrates the potential of non-invasive &#xD;
technologies to enhance the efficiency of sea turtle nest detection and conservation. The findings &#xD;
contribute to the development of modern conservation strategies, particularly within small &#xD;
Mediterranean rookeries such as Malta, where nesting events are rare and spatially constrained.
Description: M.Sc.(Melit.)</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://www.um.edu.mt/library/oar/handle/123456789/145380">
    <title>High–resolution 3D reconstruction of sea caves in Malta through underwater photogrammetry techniques</title>
    <link>https://www.um.edu.mt/library/oar/handle/123456789/145380</link>
    <description>Title: High–resolution 3D reconstruction of sea caves in Malta through underwater photogrammetry techniques
Abstract: This thesis aims to develop a high-resolution, three-dimensional photogrammetric &#xD;
model of a selected sea cave in the Maltese Islands, this will allow for the monitoring of &#xD;
geomorphic change and the rate of coastal erosion. The resulting model will provide a &#xD;
spatially accurate and visually detailed baseline for scientific analysis of coastal &#xD;
geomorphology and long-term monitoring of erosional processes with data integrated &#xD;
from aerial, terrestrial, and underwater sources. This model will combine data sets from &#xD;
terrestrial, submerged and aerial views of the cave, something that at the time of writing &#xD;
has yet to be done. &#xD;
Data collection was accomplished via the use of two GoPro 7 Black editions for the &#xD;
photogrammetric model and an iPhone 15 for a LiDAR model of the terrestrial &#xD;
component of the cave, used by hand as a team member walked the accessible regions &#xD;
of the cave. A GoPro 13 black edition was carried by a second team member whilst &#xD;
snorkelling in grid patterns at the surface of the submerged portion. Finally, a DJI Mavic &#xD;
3 multispectral drone was used for the aerial components of the site, flown from a &#xD;
promontory above the cave site itself. &#xD;
The data collected was processed through Agisoft Metashape Professional v2.2.1 &#xD;
(Agisoft LLC, St Petersburg, Russia) with a model being created for each component of &#xD;
the cave. The four models once processed were integrated to form one model with &#xD;
scaling accuracy confirmed by the LiDAR model. The level of accuracy in the model &#xD;
allowed for specific measurements to be taken such as width or height, these &#xD;
measurements could allow for the calculation of the mass of rock likely to fall or give &#xD;
bathymetric data on the current submerged section. &#xD;
The combination of terrestrial, underwater, UAV, and LiDAR photogrammetry proved &#xD;
to be a robust approach for capturing both the external and internal morphology of the &#xD;
cave. Each method contributed complementary datasets: UAV photogrammetry &#xD;
effectively mapped the promontory and entrance geometry, while underwater and &#xD;
terrestrial images documented the cave’s internal surfaces in high detail. The integration &#xD;
of LiDAR scanning from the iPhone 15 enhanced the scaling accuracy of the final &#xD;
model, compensating for the potential geometric distortion associated with freehand &#xD;
image capture. This multi-platform approach aligns with recent studies that advocate for &#xD;
the combination of close-range photogrammetry and LiDAR to improve the geometric &#xD;
precision of complex natural structures (Colica et al., 2021; Furlani et al., 2023).
Description: M.Sc.(Melit.)</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://www.um.edu.mt/library/oar/handle/123456789/145379">
    <title>Coastal glow : gauging light in Maltese coastal areas whilst exploring the impact on turtle nesting preferences</title>
    <link>https://www.um.edu.mt/library/oar/handle/123456789/145379</link>
    <description>Title: Coastal glow : gauging light in Maltese coastal areas whilst exploring the impact on turtle nesting preferences
Abstract: Artificial Light at Night (ALAN) is an increasingly significant anthropogenic pressure &#xD;
affecting coastal and marine ecosystems worldwide. Species that rely on natural darkness &#xD;
for key biological processes, such as the Loggerhead turtle (Caretta caretta), are &#xD;
particularly vulnerable. Despite Malta being among the most light-polluted countries &#xD;
globally, and albeit recent increases in turtle nesting activity on its beaches, the influence &#xD;
of ALAN on local nesting-site selection has not yet been systematically studied. This &#xD;
dissertation examines the extent of ALAN along key Maltese nesting beaches and evaluates &#xD;
how light intensity interacts with physical beach characteristics to influence nesting &#xD;
suitability. &#xD;
Using data collected by Nature Trust Malta (NTM), combined with satellite-derived &#xD;
radiance measurements from the VIIRS Day-Night Band, this study assesses five primary &#xD;
nesting beaches: Gnejna, Golden Bay, Għajn Tuffieħa (Riviera), Għadira, and Ramla. &#xD;
Environmental variables examined include beach elevation profiles, sand-grain texture, &#xD;
vegetation proximity, lunar phase, cloud cover, and long-term changes in beach depth. &#xD;
Radiance data from 2012–2025 were analysed to determine spatial and temporal trends in &#xD;
coastal illumination. &#xD;
Results show clear variation in beach quality and ALAN levels. Beaches with lower &#xD;
radiance, gradual slopes, and suitable substrate—particularly Ramla—correspond with &#xD;
successful nesting attempts. Conversely, beaches exposed to high artificial illumination, &#xD;
especially parts of Għadira, show reduced suitability and fewer nesting events. Long-term &#xD;
radiance trends indicate increasing light pollution across several sites, consistent with &#xD;
other findings in other countries. These findings are not entirely conclusive, as they require &#xD;
further studies in order to establish whether nesting site selection is effected by the physical &#xD;
beach properties, ALAN, or whether it is the combination of the physical characteristics &#xD;
which directly alter ALAN levels which have the greatest impact. &#xD;
The study highlights the urgency of implementing improved lighting management, &#xD;
enforcing coastal protection guidelines, and adopting ALAN-reduction measures in &#xD;
sensitive habitats to help ensure the long-term viability of sea turtle populations in Maltese &#xD;
waters.
Description: M.Sc.(Melit.)</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://www.um.edu.mt/library/oar/handle/123456789/144070">
    <title>Intrinsic topologies on ordered structures : applications</title>
    <link>https://www.um.edu.mt/library/oar/handle/123456789/144070</link>
    <description>Title: Intrinsic topologies on ordered structures : applications
Abstract: This thesis is divided into two main parts. The first three chapters&#xD;
focus on the study of order and unbounded order convergence.                                                                             We examine various well-established definitions of order convergence, that&#xD;
have emerged over time, each associated with a corresponding                                                                         topology. These notions are compared in detail, with particular attention&#xD;
to the conditions under which they coincide or differ. Furthermore, in&#xD;
the context of a semi-finite measure space, we investigate the relationship                                                    between the topologies on L∞ arising from the duality (L∞, L1),&#xD;
and we compare these to the order topology. Notably, we establish&#xD;
a condition under which the Mackey topology is strictly weaker than&#xD;
the order topology.&#xD;
Compared to order convergence, unbounded order convergence is&#xD;
relatively new and is generally studied on Riesz spaces. In this thesis,                                                                   we explore it in the broader context of lattices. Our results show&#xD;
that, similar to the order topology, the unbounded order topology is&#xD;
independent of the definition of order convergence. In addition, we&#xD;
extend key properties known to hold in Riesz spaces to lattices. We&#xD;
prove that order continuity of unbounded order convergence is                                                                       equivalent to the lattice being infinitely distributive. Moreover, we show&#xD;
that the O-closure and uO-closure of a sublattice coincide and form a&#xD;
sublattice. Furthermore, we show that the uO-adherence of an ideal&#xD;
is an O-closed ideal. We also examine the MacNeille completion of a&#xD;
sublattice Y relative to that of a lattice L, identifying two conditions&#xD;
under which the completion of Y embeds regularly in that of L.&#xD;
The last chapter is dedicated to the study of lattice uniformities. It&#xD;
is known that for a locally solid Riesz space (X, τ ) there exists a locally&#xD;
solid linear topology uτ on X such that unbounded τ -convergence&#xD;
coincides with uτ -convergence. This topology is the weakest locally&#xD;
solid linear topology that agrees with τ on all order bounded subsets.&#xD;
Thus, for a uniform lattice (L, U), we introduce the weakest lattice&#xD;
uniformity U∗ on L that coincides with U on each order bounded&#xD;
subset of L. We see that if U is the uniformity induced by the topology&#xD;
of a locally solid Riesz space (X, τ ), then the U∗ -topology coincides&#xD;
with uτ . We also provide answers to several questions posed in [44, 37].
Description: Ph.D.(Melit.)</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
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