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Experimental and numerical investigations on integrating hybrid solar photovoltaic/thermal panels with thermal energy storage
Full text linkThis abstract presents an overview of research focused on enhancing the solar fraction of
solar energy systems, primarily through the integration of photovoltaic/thermal (PV/T)
panels and phase change materials (PCM). Solar energy is abundant source of energy
with advantage of its availability and accessibility. However, there are several challenges
to overcome to maximise its potential. The study begins by highlighting the limitations
of PV cells, such as low conversion efficiency and increased operating temperatures due
to waste heat. To address these issues, the research focuses on integrating PCM with PV/T
panels to regulate temperature of panel and efficiently store excess heat. The study
assesses the performance of PV/T panels under various operational parameters at outdoor
testing and examines the influence of different energy storage methods, including sensible
and latent heat thermal energy storage. The use of PCMs within the panels is found to be
beneficial, but their slow heat release limitations necessitate the incorporation of a
separate LHTES (Latent Heat Thermal Energy Storage) unit for rapid energy delivery.
The research emphasizes the significance of maintaining controlled inlet temperatures for
the PV/T panel, particularly when phase transition materials are involved. A comparative
analysis is conducted to evaluate the thermal and electrical energy outputs of PV/T panels
with different energy storage configurations. Furthermore, the research highlights the
importance of choosing PCMs with appropriate melting temperatures to meet both
thermal and electrical energy demands. A simulation model is used to investigate the
impact of varying PCM configurations on system performance and identify optimal
operational conditions. In summary, this research contributes to the understanding of
PV/T panel performance, energy storage integration, and the crucial role of PCM in
maximising the solar fraction and practicality of solar energy systems
The role of business manager attitudes and perceptions in driving climate change risk action in the agricultural sector in Uganda
Full text linkMuch is already known about climate change risk mitigation and adaptation globally.
However, much needs to be done to make this knowledge cascaded down to a business
manager in the agricultural sector in Uganda. This study aimed to understand the role of
business manager perceptions and attitudes in influencing climate change risk action in
business organizations in the agricultural sector in Uganda with its particular climatic, social
and economic circumstances. An assessment was made of whether and how the climate
change risk perceptions of business managers from 16 companies engaged in downstream
agricultural processing differ from 15 managers engaged in commercial agricultural
production in Uganda.
The study utilized a phenomenological approach using comparative case study method. The
respondents were selected purposively from managed agriculture processor and producer
companies. It is believed that the study of perceptions and beliefs involves uncovering tacit
knowledge, knowledge in the minds of managers which cannot easily be articulated and
documented. The study therefore made use of George Kelly’s Personal Construct theory and
its repertory grid analysis technique for data collection, a very useful tool for making tacit
knowledge explicit. The study examined nine risks as elements for the repertory grid
exploring how business managers perceive there risks and how such perceptions influence
their climate change risk action in the agriculture sector in Uganda. The study also intended
to identify if there are variations in climate change risk perception between the agriculture
producers and processors in Uganda. The personal constructs generated from respondents
during the grid interviews are the units of analysis. The results were analyzed using Content
analysis, and Honey’s data analysis procedures.
The results indicate that as long as business managers perceive climate change risks to have
an effect on their business continuity or survival, their production capacities, their
profitability, their marketing decisions, affect their cost of production, influence their
investment decisions, there are available response options, and consider that they have the
capacity to manage those risks, they will take immediate action to put in place strategies to
respond to those climate change risks. There is no appreciable variation in climate change
risk perception between producers and processors. The study results provide policy makers
an opportunity to understand what concerns business owners along the agriculture value
chain for them to respond to climate change risks and also informs business owners the areas
of key concern that they have to reflect on as they consider climate change risk strategies
Knowledge-related processes critical to the enabling of systematic software asset reuse in a global IT company
Full text linkThis research presents a case study on the knowledge management processes critical for
achieving systematic software asset reuse in a global IT company. Reusing a software
asset and its related artefacts at selected other business clients drives innovation, increases
efficiency and can generate several million dollars in revenue from just one reuse. To
date, known software asset reuse success is limited. Despite practical relevance, research
has stagnated by mainly investigating technical reuse aspects.
This thesis addresses three gaps in the literature by looking from a business perspective
at the intellectual capital required for software asset reuse, presenting five real-life
software asset reuses and detailing the knowledge management processes towards
systematic software asset reuse. The answers to the research questions advance the
academic literature in the fields of intellectual capital, circular economy and knowledge.
Theoretical implications are: The intellectual capital required for systematic software
asset reuse is a particular software asset, the reusable software asset, further defined here.
The circular economy is enriched by adding a two-step distribution task to the reuse
process. The thesis refines knowledge management concerning intangible reuse.
A new finding is that software asset reuse requires a proactive decision to anticipate a
scarcity of knowledge in space or time, which has been identified as the software asset
reuse trigger. Reuse sets in before the life end of the software asset is reached. It creates
parallel software instances via abstraction, repurposing and adaptation. These boost the
asset lifetime as they are logically linked. They represent tailored solutions for a
heterogeneous client base and, therefore, target business-to-business niche markets.
This thesis makes an original contribution to knowledge by identifying that collaborative
sharing of the change required for one client with existing reusers leads to improved
software quality, surpassing that of other software constructs. Further, it claims that
reusable software assets target a parallel market to software products.
This research significantly contributes to practice: First, by demonstrating that reuse is
only feasible if the software asset can be adapted. Second, one reason for being of some
reusable software assets is to stop the flood of less funded individual software trying to
serve the same need. Third, a managerial guide provides advice on building reuse
capabilities in the IT organisation to support the change that drives software asset reuse
How will IT megatrends and Legal Tech technologies influence the future IT strategies of law firms in Germany? : an AI-enhanced real-time Delphi study
Full text linkThe findings of the study highlight the importance of Legal Tech for law firms in
Germany, emphasising its critical role in boosting profitability and competitiveness while
ensuring relevance in Germany’s rapidly evolving legal landscape. While Legal Tech is
particularly important for large law firms, it also offers significant advantages to small
and medium-sized law firms. The study’s participants agree that the integration of Legal
Tech will bring substantial benefits to all involved stakeholders, be they clients or
partners. Although these technologies need only to perform at 80% of human capability
in terms error rate to gain acceptance, they are expected to lead to an increase in law firm
spin-offs, empower smaller firms to handle larger cases, and facilitate the entry of third party providers into the market. Legal Tech is relevant to all fields of law and is set to
democratise access to legal services, shifting power towards consumers and significantly
enhancing ‘Access to Justice’.
The thesis investigates the impact of IT Megatrends and Legal Tech clusters on the IT
strategies of law firms in Germany, utilising the real-time Delphi method. Out of the
emerging technologies (IT Megatrends), ‘Generative AI’ (=7.47 on a scale of 0-10),
‘Quantum Computing’ (=5.75/10), and ‘Neuromorphic Computing’ (=5.2/10) will have
the highest impacts in 10+ years on the legal sector, according to the study’s results in
2024. The Legal Tech clusters ‘Legal and Compliance Analytics’ (=7.41/10), ‘Risk
Management’ (=7.21/10), ‘Document Automation’ (=7.5/10), ‘Software for Legal
Practices Management’ (=7.17/10), ‘Privacy Management Tools’ (=7.06/10), and ‘E-Discovery Solutions’ (=7.0/10) will have a high impact. The thesis also answers how
these Megatrends and Legal Tech Clusters (will) influence the IT strategy of law firms in
Germany, providing practical advice for CEOs and CIOs in the legal sector.
This thesis also offers a new approach to using the (real-time) Delphi method enhanced
by Artificial Intelligence. Integrating an AI’s “opinions” and “comments” into a Delphi
study to “simulate” an (engaged) human expert could prove a powerful enhancement for
traditional round-based and real-time Delphi studies and should be further researched.
Only a fraction of the participants recognised the AI's “participation” during the study.
This work also contributes to the method of Delphi studies by presenting statistical
findings that elucidate various aspects of participant behaviour in Delphi studies on the
experts’ opinion change behaviour. Enriching the academic debate, contractionary to Gnatzky et al. (2011, p.10), the so-called ‘Initial condition effect’ was substantiated
across all participant groups in this study. This finding is consistent with Battin et al.
(2023), who also observed a difference in how participants recruited early to the study
amended their ratings of the outcomes compared to those recruited later.
In line with the findings of Hussler et al. (2011), most participants did not significantly
alter their initial ratings, and changes were exceptionally rare across all groups.
Makkonen et al.’s (2016) observation that demographic factors such as age generation
and professional experience do not significantly influence opinion change during the
Delphi processes could be confirmed
Model learning of Markovian open quantum systems through stochastic techniques
Full text linkModel characterisation and model learning for quantum systems have long faced
challenges with resolving interpretable mathematical representations. State-of-the-art investigations look to learn with minimal measurements in noisy or experimentally inaccessible systems. We aim to address both interpretability and operating
with minimal measurements.
In this thesis, two machine learning algorithms are introduced to help in characterising and identifying open quantum systems. Both demonstrate the ability to
recover physically motivated models from experimental data in a more interpretable
manner than other methods in contemporary literature. The identification of appropriate models is performed in a larger space than any other similar algorithm in
the field.
One algorithm is a Monte Carlo exploration of model space, providing broad
insight into the space of appropriate models. The other is a genetic algorithm
that optimises for interpretability and accuracy to experimental data. Both are set
to construct models to explain the phenomenon of cooperative emission, informed
by both two time correlation measurements and lifetime measurements. We also
demonstrate their applicability to other measurements
Pore scale studies to understand the role of salt precipitation during underground CO2 storage
Full text linkUnderground CO2 sequestration in deep saline aquifers offers a promising approach for its
storage, leveraging their extensive distribution and significant capacity. However, CO2
injection can displace and evaporate water, leading to salt accumulation within pore spaces.
This phenomenon can alter the rock's petrophysical properties, potentially diminishing
permeability and causing pressure build-ups during injection. The need for this PhD thesis
arises from the limitations of existing research, which predominantly uses core flooding
experiments unable to adequately visualise salt precipitation patterns. Additionally, there has
been insufficient focus on the localised impacts and petrophysical changes under varied
experimental conditions such as flow rate. Moreover, the interplay between capillary and
viscous forces at the pore scale is not well understood. Consequently, this thesis aims to provide
a comprehensive pore-scale understanding of salt precipitation resulting from CO2 injection.
The initial objective focused on developing essential facilities and tools. To this end, an
advanced, high-pressure, high-temperature, in-situ µ-CT core flood experimental setup was
designed and commissioned, featuring an X-ray translucent core holder for enabling 3D in-situ
imaging of flow processes under conditions mimicking the subsurface environment. The
second objective entailed observing and analysing the impact of salt precipitation on pore
structure. Through in-situ time-lapse µ-CT imaging, CO2 drying experiments were conducted
to investigate potential salt deposition within a Doddington sandstone sample under conditions
typical of saline aquifers (90 bar and 50°C). These experiments aimed to distinguish between
capillary-influenced and viscous-influenced flow regimes. In Case 1, CO2 was injected at a rate
of 10 cc/hr (pore velocity of 1.4×10-4 m/s, bulk velocity of 7.4×10-4 m/s, microscopic capillary
number of 5.1×10-7
, macroscopic capillary number of 0.23, Reynolds number of 0.03, Bond
number of 7.3×10-5
) to emphasise capillary forces, whereas Case 2 employed a flow rate of 60
cc/hr (pore velocity of 5.6×10-4 m/s, bulk velocity of 3.0×10-3 m/s, microscopic capillary
number of 2.1×10-6
, macroscopic capillary number of 1.00, Reynolds number of 0.11, Bond
number of 7.3×10-5
) to highlight the role of viscous forces. In both cases, flow was vertical,
with CO2 injected from the top of the sample to the bottom.
The findings from both experimental cases indicated a general reduction in porosity by around
10%, leading to a significant decrease in CO2 permeability—by approximately 99% from the
onset of CO2 drying. This pronounced reduction was primarily due to extensive NaCl
accumulation and the formation of a partial salt crust at the sample inlets, significantly
impairing CO2 injectivity. The analysis revealed a non-uniform and heterogeneous salt distribution, increasing towards the top with notable accumulation near the CO2 inlet.
Throughout the study, salt relocation was observed in both cases, due to capillary-driven brine
redistribution during injection pauses. This phenomenon further intensified the existing
heterogeneous pattern (increasing towards the top with accumulation near the inlet) of salt
precipitation.
The interaction between capillary and viscous forces, influenced by the CO2 injection rate, was
evident in the observed spatial distribution of the precipitated salt. Capillary forces played a
significant role in shaping salt deposition patterns through brine redistribution, even in Case 2,
despite its higher flow rate. This observation was further confirmed in how different pore sizes
contributed to salt deposits, with viscous forces at higher flow rates modifying the typical
capillary-driven salt distribution within the pore system. These findings imply that in field
scenarios in general, regions with significantly smaller pores could experience more intense
salt deposition and associated damage.
Direct numerical simulations underscored the impacts of porosity and CO2 saturation on
absolute and effective CO2 permeability across the samples. A decrease in porosity typically
resulted in lower absolute and effective CO2 permeability. Nevertheless, some areas exhibited
an increase in CO2 permeability despite reductions in porosity and absolute permeability, which
was attributed to elevated CO2 saturation. This indicates that while the aggregate number of
flow paths decreased, the pathways specifically facilitating CO2 transport expanded due to the
displacement or evaporation of brine by dry CO2 flow. Overall, this phenomenon, particularly
near CO2 injector wellbores, suggests that heterogeneous salt precipitation and the presence of
localised salt accumulations at crucial points can substantially compromise CO2 injectivity,
even if the overall level of salt precipitation may seem moderate.
This thesis bridges significant knowledge gaps by demonstrating how advanced µ-CT imaging
of salt precipitation patterns and detailed analyses of CO2 effective permeability under varied
flow conditions enhance our understanding of the mechanisms of salt precipitation and the
interplay between capillary and viscous forces, providing insights for managing these
challenges during CO2 sequestration in saline aquifers
Android malware detection using machine learning techniques
Full text linkAndroid’s sustained and continued popularity makes it an attractive target for many
malware attacks. Due to the sensitive data on smartphones, detecting and prevent ing such attacks is crucial. Traditional signature-based detection techniques are
limited to spotting signatures in their signature databases, making them inadequate
in dealing with the significant increase in users and applications.
Machine learning does not depend on rigid signature databases and can effec tively detect new threats, known as zero-day attacks, by exploiting their similarities
to previously unknown vulnerabilities. Despite the efficacy of machine learning sys tems, they face challenges. One of the main challenges researchers face is the limited
availability of contemporary Android application datasets. This raises a big ques tion about the models built using outdated datasets and how they would fare with
modern malware. Furthermore, a notable issue observed in the literature is the lack
of consideration given to the practicality of deploying such systems.
This thesis starts with a critical review of previous work and highlights the lack
of up-to-date analysis tools and a reliance on outdated datasets. We present our
contemporary dataset and our automated feature extraction tool, Droid Dissector.
We then comprehensively analyze the Android feature space to identify the best
machine learning models and feature sets for Android malware detection. Notably,
our research findings indicated that using the costlier dynamic and individual hybrid feature models does not provide significant benefits compared to using static
features alone, and some historically significant feature sets perform poorly with
our contemporary dataset. Significantly, we found that accuracy could be improved
by ensembling models trained on static and dynamic features. Finally, we propose
a novel active learning-based online learning framework for Android malware detection. Our framework aims to bridge the gap between theoretical systems and
practical deployment and show how it compensates for the loss of accuracy caused
by real-world labeling delays. We introduce active learning for the first time in the
context of Android malware detection. Active learning enables the training of online
learning models without relying on the true label of each application instance
Risk measures, asymptotics and risk management
Full text linkThe management of tail risks is of utmost importance in financial markets to avoid
catastrophic disruptions, as seen in recent financial crises. This doctoral thesis examines
risk aggregation and management, particularly focussing on individual risks and their
aggregation. Multivariate generalised hyperbolic distributions are used to model marginal
distributions and dependencies, and the convex bound approximation method is used for
accurate risk aggregation. Explicit convex bound approximation formulas are derived for
risk measures such as Value at Risk, conditional tail expectation, and stop-loss premium,
and numerical examples of their precision are provided. Asymptotic equivalences are established between the sum of risks, its convex bounds, and individual risks, demonstrating
the robustness of the convex bound approximation method in the context of the generalised
hyperbolic distribution. Additionally, tail variance is introduced as a risk measure to
assess confidence in the use of capital adequacy based on conditional tail expectation, and
analytical formulas for conditional tail expectations and tail variances are presented for
various probability distributions. Distribution-free sharpened conditional tail inequalities
are also introduced, which notably reduce the confidence bounds compared to traditional
inequalities, including the Markov inequality. The asymptotic properties of tail variance
are also investigated. The findings not only unify the asymptotic formulas for conditional
tail expectation and tail variance with regard to quantiles that exist in the literature under
a very general distribution assumption, but also offer more accurate convergence rates,
addressing a previous gap in the literature.James Watt Scholarship
Evaluation of effective upscaling technique for CO₂ EOR compositional simulation using a commercial simulator
Full text linkCompositional simulation for CO2 EOR study is demanded for better representation of
the compositional effect and more accurate prediction compared to black oil simulation.
However, the involvement of complex physics and equations to solve for a fine scale
model require high computational cost in terms of facility and time, makes the upscaling
a must. Several upscaling methods have been proposed by researchers but the application
of the best method using commercial simulator needs further investigation.
In the initial part of the study, three compositional upscaling methods namely modified
K-value, transport coefficient and pseudo minimum miscibility pressure (PMMP) were
evaluated using a simple model with two components. The results have led to proceeding
with transport coefficients for further evaluation. Transport coefficients or alpha factors
act as modifiers to slow down or speed up the component flow rate in both oil and gas
phases. A detailed investigation was conducted with a more rigorous model with two/four
components, horizontal/vertical, 2D linear/2D 5-spot model, and immiscible/miscible
system.
The results showed good improvement were achieved by reduction of relative error with
the best cases for miscible CO2 with short correlation length and less heterogeneity over
immiscible cases with high heterogeneity. In terms of practical application using a
commercial simulator, the method was successfully applied up to 2D-2D 5-spot model
with a proposed workaround due to the limitation in the commercial simulator. Ideally,
directional alpha factors are required for a fully 3D model application which is not
available yet in the current version of the simulator. Nevertheless, a workflow and
guidelines were proposed to utilize the method for CO2 EOR compositional simulation
model for grid sensitivity
Functionalisation of polymeric materials with palladium nanostructures for applications in bioorthogonal prodrug activation
Full text linkThe use of bioorthogonal organometallic chemistry to facilitate the localised
conversion of a chemotherapy prodrug into an active drug has been explored in a
novel approach to targeted drug delivery. Chemotherapeutic prodrugs containing a
palladium (Pd)-labile propargyl protecting group have been established,
necessitating a suitable method to deliver the Pd trigger to its desired location. To
this end, implants that can be inserted into the tumour site and contain catalytic Pd
nanoparticles are being developed. This work demonstrates the functionalisation of
non-degradable polymeric materials, poly(ethylene glycol) (PEG) microbeads or
poly(2-hydroxyethyl methacrylate) (pHEMA)-based hydrogels, with Pd
nanostructures. New methods for the in situ preparation of Pd nanoparticles,
nanocubes and nanosheets inside these novel polymeric materials are presented.
The ability of these materials to facilitate the key depropargylation reaction using a
model compound, propargylated-Resorufin, is presented, with Pd-nanosheets
displaying superior catalytic activity, entrapment, and reusability. The distinctive
nanostructures synthesised within these PEG or pHEMA materials were examined
using electron microscopy techniques, also showing the complexity of the polymer
networks arising from inclusion of sheared gellan gum as a unique suspending
additive. The results presented herein contribute to the development of implantable
Pd-containing polymeric materials, capable of the required prodrug activating
depropargylation reaction, and preliminary in vitro cytotoxicity tests show promise
for the biocompatibility of some of these materials
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