VictorJuiz/thesis huggingface dataset:
Impact of large, periodic deformations on flow and transport in soft porous media
Keywords: fluid mechanics
soft porous media
biological tissues

Soft porous media, such as soils, hydrogels, or soft biological tissues, are characterised by a strong coupling between deformation and fluid flow due to complex rearrangements of the pore space. In these materials, the impact of large, periodic loads on the deformation and fluid flow - and the resulting coupling with solute transport - are of particular interest in the context of, for example, physiological flows and the motion of nutrients or contaminants. However, the basic features of this coupling of flow and transport with poromechanical deformation have not previously been systematically investigated. In this thesis, we study this problem using a 1D continuum model based on large-deformation poroelasticity, focusing on a periodically loaded soft porous medium subject to confined compression. We begin by identifying the relevant parameter space and then by exploring the impact of the key dimensionless control parameters on the poromechanical coupling, focusing on those regulating the applied periodic load. We find that the amplitude and period of deformation influence the mechanical response of the material, which can belong to either a slow-loading or a fast-loading regime. Specifically, the deformation is more localised at the piston as the loading period becomes faster relative to the poroelastic timescale, while nonlinear effects are increased as the loading amplitude increases. We then introduce solute transport, including advection, molecular diffusion, and hydrodynamic dispersion. We show that each transport mechanism is affected by the periodic deformation in specific ways during a single loading cycle, and that the poromechanical regimes have a direct impact on the resultant movement of solute over several loading cycles. In analogy with soft biological tissues, we then consider soft porous media divided into one or more layers characterised by different material properties and study the impact of this complex structure on the poromechanical response to large, periodic loads and on the infiltration of solute. We conclude with an experimental campaign where we fabricate and characterise soft porous media to empirically study their poromechanical response to periodic loads and the resultant transport of solute. We compare these results with our model, finding good qualitative agreement. Despite the complexity of the problem, the models and experiments reported in this work are relatively simple, flexible, and generic. Our results therefore provide unique insights on flow and transport in periodically loaded soft porous media, with relevance to a variety of applications and contexts.
Health equity beyond borders: The demands of justice in global health
Keywords: Genuine Opportunity for Health
Global Justice
Health Justice
Distributive Justice
Health Equity

Health is widely regarded as being among our most valuable goods and highest ideals. At the same time, the contemporary state of global health makes for grim reading with immense suffering and deprivation across mortality and morbidity, especially within developing countries. This thesis is a work of political philosophy which takes seriously the complex reality of global injustice in our world today. It provides a comprehensive political philosophical account of what justice demands in the sphere of global health. My arguments in the thesis unfold over broadly two parts. In the first part of the thesis, I analyse some foundational debates in health and global health including how to conceptualise the nature and moral significance of health and healthcare, and how it ought to be balanced against other health and non-health pursuits both domestically and globally. These foundational discussions, together, provide the grounding for the forthcoming discussions while highlighting the importance of taking a global perspective in discussions of health justice. In the second part of the thesis, I present a novel theory of global health justice I call Genuine Opportunity for Health (GOH). GOH argues that the currency of global health justice is pluralistic (incorporating health outcomes alongside considerations of health security and the costs of pursuing and maintaining health), and that everyone, regardless of nationality or place of birth, should have genuine opportunity to attain a decent and secure standard of health. GOH has important normative and practical implications across a range of dimensions, including in our conceptualisation of national responsibility, in the requirements of our rescue and global health duties, and in the permissibility of interventions to address brain drain.
Individual and collective behaviour of bacteria under oxidative stress
Keywords: bacterial stress response
machine learning
phenotypic heterogeneity
oxidative stress
cell-cell interactions
single-cell imaging and analysis
bacterial gene regulation
chaos

Bacteria, the most abundant organisms supporting life on Earth, inhabit various niches, from soil and aquatic life, to residing in hosts. Bacteria frequently experience oxidative stress during aerobic growth, host immune attack, microbiome interactions, and under antibiotic treatment. Here, we study bacteria under hydrogen peroxide (H2O2) and observe that individual cells exhibit variability in their oxidative stress response. Such diversification of cellular phenotype is often observed in response to changing environments, but the underlying causes of cell-cell variability and its implications for cellular function in the context of stress adaptation remain unclear. Cellular response variability is typically attributed to stochasticity in biochemical reactions inside a cell. Our machine learning algorithm reveals that the variability in cellular response is not stochastic but determined by the precise response of cells to dynamic levels of H2O2, created by short-ranged cell-cell interactions. We complement single-cell time-lapse imaging with mathematical modeling to probe the consequences of single-cell variability on population adaptation. We find that the H2O2 scavenging activity of cells creates strong H2O2 gradients, thereby protecting a large proportion of the population. We show that the gene expression fluctuations of individual cells during constant H2O2 treatment are in fact driven by chaos. Although it has been suggested that chaos may play a role in many biological phenomena, it has remained challenging to disentangle chaos from noise as a source of variability in biological data. The close correspondence between our experiments and model allowed us to show that chaos emerges from deterministic feedbacks between cells and their environment. These feedbacks amplify small differences in initial conditions, resulting in diverging stress response dynamics that lead to seemingly random phenotypic variability. Next, we investigate how individual cells manage to regulate the levels of many diverse genes that encode oxidative stress tolerance factors. We find that the dynamics of over two dozen genes create a diversity of spatiotemporal expression patterns that benefit the stress adaptation of a cell population. Overall, we show that bacterial stress responses can generate variability under stress via deterministic factors without noise. Our work provides a general approach for uncovering the hidden variables that drive variability in cellular responses to environmental changes and for probing the regulatory mechanisms from molecular, single-cell, and population perspectives.
Generative models: theory and applications
Keywords: variational autoencoders
generative modeling
diffusion models

Given some samples from a data distribution, the central aim of generative modeling is to generate more samples from approximately the same distribution. This framework has recently seen an explosion in popularity, with impressive applications in image generation, language modeling and protein synthesis. The striking success of these methods motivates two key questions: under what conditions do generative models provide accurate approximations to the underlying data distribution, and can we extend the range of scenarios in which they may be applied? This thesis considers these questions in the context of two classes of generative models: diffusion models and importance weighted autoencoders.
Diffusion models work by iteratively applying noise to the data distribution and then learning to remove this noise. They were originally introduced for real-valued data. However, for many potential applications our data is most naturally defined on another state space - perhaps a manifold, or a discrete space. We describe extensions of diffusion models to arbitrary state spaces, using generic Markov processes for noising, and show how such models can be effectively learned. We also provide a detailed study of a specific extension to discrete state spaces. Next, we investigate the approximation accuracy of diffusion models. We derive error bounds for flow matching - a generalization of diffusion models - and improve upon state-of-the-art bounds for diffusion models, using techniques inspired by stochastic localization.
Importance weighted autoencoders (IWAEs) work by learning a latent variable representation of the data, using importance sampling in the evidence lower bound to get a tighter variational objective. IWAEs suffer from several limitations, including posterior variance underestimation, poor signal-to-noise ratio during training, and weight collapse in the importance sampling ratios. We propose an extension of the IWAE - the VR-IWAE - that addresses the first two of these three issues. We then provide a detailed theoretical study of the third, showing that it persists even for the VR-IWAE. We provide empirical demonstrations of these phenomena on a range of simulated and real-world data.
The Caribbean Cold War: between exile and revolution, 1945-1960
Keywords: democracy
exile
Latin America
Caribbean
revolution
Cold War

In the late 1940s, the governments of Cuba, Venezuela, Guatemala or Costa Rica saw themselves as engaged in a regional conflict between democracy and dictatorship. These countries were led by political parties born from anti-dictatorial struggles, opposing military dictatorships through exiled activism, armed struggle, and diplomacy. While they aspired to democratise their countries, and legislated to that effect, they identified a group of dictatorships as their enemy: in the Dominican Republic, Nicaragua, and Honduras. Between 1945 and 1960, two regional blocs emerged and traded blows: governments plotted coups in neighbouring countries, armed proxy armies of exiles, and denounced their rivals at international gatherings.
In this thesis I seek to explore this conflict. My argument is that it can be understood as a ‘Caribbean Cold War’: an ideological rivalry between two transnational blocs, which fought indirectly via proxies. At the centre of this conflict was the question of democracy: how should it be defined and implemented? While the self-proclaimed democratic bloc saw their rivals as military autocrats, the latter engaged in their own language and definitions of democracy. At stake was the meaning of the concept itself in the region, and its future.
Viewing this conflict as a regional cold war changes the way we understand the broader, global Cold War between the United States and the Soviet Union. It highlights the need to acknowledge the existence of alternative ideological cleavages at the regional level, where some governments contested the supremacy of the socialist-capitalist binary with an alternative axis that better reflected their grievances. This thesis thus uncovers a Caribbean position on the global Cold War which defied, and did not align neatly, with the superpowers of the time.
To examine this conflict, this thesis is divided into six chapters. The first chapter analyses the creation of the anti-dictatorial political parties through the 1920s and 1930s, and their rise to power in the mid-1940s. The second chapter explores the emergence of the regional conflict in the late 1940s, and how democratic governments coordinated their actions internationally. The third chapter focuses on Cuba, analysing the insurrection against Fulgencio Batista (1952-1958) as a flashpoint in this regional conflict with far-reaching consequences. Chapter four explores how military leaders in authoritarian regimes justified their rule and proposed their own understanding of democracy. The fifth chapter contextualises this regional conflict within attitudes by the superpowers of the Cold War towards democracy in Latin America. Finally, the sixth chapter examines how the Cuban Revolution of 1959 divided the unity of the anti-dictatorial bloc by introducing a new understanding of democracy, which in turn unravelled the ideological axis which had underpinned conflict in the region.
The role of biodiversity in Southeast Asian tropical forest restoration
Keywords: enrichment planting
dipterocarpaceae
remote sensing
Malaysia
Sabah
restoration

Tropical forests in Sabah, Malaysian Borneo, are threatened by land use change and continued overexploitation. Active restoration of dipterocarp-dominated forests is used to increase the restoration value of these landscapes, and knowledge of the ecological mechanisms that promote tropical forest functioning is critical to realising this goal..
In this thesis, I use a long-term, field-scale experiment to investigate the effectiveness of active restoration techniques in a once-logged lowland dipterocarp forest. I show that enrichment planting of dipterocarp seedlings increases satellite-derived metrics of aboveground biomass, canopy cover, and leaf area index. I then show a clear relationship between ecosystem functioning and the biodiversity of enrichment planted seedlings. This relationship is likely driven by species-specific niche differences which generate stronger complementary effects through a more functionally diverse seedling cohort and is unlikely to be generated via stochastic processes as hypothesised under neutral theory. .
At the local scale, I demonstrate that the survival of enrichment planted seedlings in a selectively logged forest may benefit from increasing densities of the surrounding tree matrix as a result of potential positive density-dependence. I also add to the evidence base that light is a primary driver of growth and survival within selectively logged dipterocarp forests. More data concerning the seedling environment, including microclimate variation, will help to provide a more comprehensive assessment of logged forest dynamics..
Finally, I show that survival and growth of enrichment planted seedlings are similar for most investigated species across an old-growth and secondary forest, demonstrating the capacity for old-growth forests to provide restoration target information from which to compare active restoration success. I also identify a growth-survival trade-off in both primary and secondary forests and demonstrate that my studied selectively logged dipterocarp forest is in an early successional stage and has not experienced a collapse in seedling growth and recruitment. However, I call for more work to understand the variables underlying species-specific responses to habitat perturbation..
This thesis helps to inform future restoration design, implementation, and monitoring across Sabah. To maximise the benefits of forest restoration to climate security, economic growth, and social wellbeing, a data-driven approach to forest restoration projects that utilises current ecological theory will become increasingly important.
Learning cell representations in temporal and 3D contexts
Keywords: representation learning
histology
machine learning
cell cycle

Cell morphology and its changes under different circumstances is one of the primary ways by which we can understand biology. Computational tools for characterization and analysis, therefore, play a critical role in advancing studies involving cell morphology.
In this thesis, I explored the use of representation learning and self-supervised methods to analyze nuclear texture in fluorescence imaging across different contexts and scales. To analyze the cell cycle using 2D temporal imaging data, as well as DNA damage in 3D imaging data, I employed a simple model based on the VAE-GAN architecture. Through the VAE-GAN model, I constructed manifolds in which the latent representations of the data can be grouped and clustered based on textural similarities without the need for exhaustive training annotations. I used these representations, as well as manually engineered features, to perform various analyses both at the single cell and tissue levels.
The application on the cell cycle data revealed that common tasks such as cell cycle staging and cell cycle time estimation can be done even with minimal fluorescence information and user annotation. On the other hand, the texture classes derived to characterize DNA damage in 3D histology images unveiled differences between control and treated tissue regions. Lastly, by aggregating cell-level information to characterize local cell neighborhoods, interactions between DNA-damaged cells and immune cells can be quantified and some tissue microstructures can be identified.
The results presented in this thesis demonstrated the utility of the representations learned through my approach in supporting biological inquiries involving temporal and 3D spatial data. The quantitative measurements computed using the presented methods have the potential to aid not only similar experiments on the cell cycle and DNA damage but also in exploratory studies in 3D histology.
Multidimensional mass photometry
Keywords: single particle
Mass photometry

Mass Photometry (MP), an emergent technology based on interferometric scattering principles, has revolutionised the field of biophysics by enabling label-free, solution-phase mass measurements at the single-molecule level. Traditionally, MP analysis and experiments focus on discrete events, characterising distributions of measurements from single molecules interacting with a glass surface. Our research challenges this convention, demonstrating that these events are not isolated but interconnected, encoding deeper insights of the underlying particle.
In this context, we introduce Tandem-MP, a novel approach that assigns binding and unbinding events to individual particles, thereby facilitating the measurement of dissociation at the single-molecule level. This method reveals intricate patterns that illuminate the structural organisation of protein complexes in unprecedented detail.
Recognising that conventional analysis methods utilise only a small fraction of the information encoded within the data, we developed an innovative particle-centric analysis pipeline. This framework automatically tailors the analysis to each molecule, enabling continuous tracking of both mass and position of particles without the need for experimental adjustments. This approach not only addresses the limitations inherent in standard MP methodologies, but also unveils a wealth of quantitative, orthogonal information.
To exemplify the utility of our approach, we investigated two distinct systems: citrate synthase and adeno-associated viruses. For citrate synthase, our method uncovered complex dissociation pathways at the level of individual molecules. In the study of adeno-associated viruses, we identified unique signatures of their surface interactions, a means to directly report capsid stability, and a way to differentiate between equal-mass serotypes.
This research demonstrates the true capabilities of MP by providing a proof-of-concept framework that leverages and exploits single-molecule measurement, paving the way for new discoveries in molecular biophysics.
Corporate criminal liability under European Union law
Keywords: strict liability
rule of law
corporate omission
corporate  law
EU criminal law
specific corporate offence
due diligence defence
corporate mens rea
corporate criminal liability
criminal law

This thesis advances a solution to corporate crime in the European Union (EU). It is specially designed to fit in the current framework of EU criminal law, and to be accepted by the Member States, which have sometimes appeared reluctant towards new developments in this field of law.
Following an assessment of various national and international doctrines of corporate criminal responsibility, and a critical analysis of the current situation of corporate liability in EU criminal law, the thesis proposes a general framework for specific corporate offences to be adopted at EU level. However, the main objective here is not to advance a defined, specific corporate offence to be enacted by EU law – a quite unrealistic goal – but rather to develop the common elements and concepts to be used every time a specific corporate offence is intended to be introduced in different fields of criminal law, at EU level.
This common conceptual framework for specific corporate offences contains the general features of such offences. The actus reus is a failure to prevent subordinate individuals from committing an offence. With regard to the mens rea, the model is one of strict liability, supplemented by a due diligence defence.
This model is very flexible and can accommodate even stigmatic offences. It respects due process requirements and the principles of criminal law but, at the same time, it is an efficient instrument in the fight against corporate crime, focusing not only on deterrence and retribution, but also on compliance and prevention.
Therefore, this solution pursues, as an ultimate goal, one of the objectives of the EU, namely the promotion of social justice and protection through the rule of law. However, the model also has an intermediate objective: to be an efficient tool for the EU in its efforts to address corporate crime.
Analytical approaches to the modelling of multi-row turbine arrays

Keywords: wind energy
tidal energy

The increasing recognition of wind turbines as an effective source of renewable power generation that can deliver a net zero carbon future, has led to the construction of larger turbine arrays. Predicting the performance of large turbine arrays requires consideration of many physical factors, such as array geometry, turbine operation, inflow conditions and turbulent wake mixing. Due to the large parameter space that an array may be optimised over, low-order models with low computational cost are often employed. This thesis is concerned with the extension of one of those low-order models; the hybrid inviscid-viscous model of multi-row turbine arrays.
The first extension presented is to the inviscid portion of the model by generalising the linear momentum actuator disk theory, such that the system with non-uniform inflow may be analytically solved utilising a single cubic equation. The impact of shear on a single turbine is then explored, followed by an analysis of a multi-row turbine, which indicates possible performance gains due to varying turbine resistance at each row.
The second extension is in the construction of a novel wake mixing model based on the momentum deficit and self-similarity of Reynolds stresses. This self-similar mixing model is then compared with the results of a "uniform" mixing model. It is shown that the predictions made by each model are similar when the mixing rate parameterised in the uniform model is accurate for a given turbine resistance and array configuration.
The final extension presented in the thesis concerns itself with “anisotropic blockage”, in which a turbine is not equidistant from all nearby boundaries or lines of symmetry with other turbines. A range of Reynolds-averaged Navier-Stokes (RANS) simulations are conducted, followed by a new theoretical method of estimating the turbine efficiency based on scale-separation arguments. This method is benchmarked against an existing "isotropic blockage" model and is found to agree better with the RANS simulations.