VictorJuiz/thesis huggingface dataset:
Solvodynamic printed silver nanowire composite films for pressure sensing applications
Keywords: pressure sensor
additive manufacturing
printed electronics
silver nanowires
flexible electronics
nanocomposite

This thesis examines some of the current issues with the use of non-contact printing techniques for fabricating conductive structures for applications in flexible pressure sensors.
Firstly, the concept of solvodynamic printing was introduced as a method to improve printing resolution using solvent-solvent interactions. By including an additional carrier solvent into the ink delivery system, both the footprint of the ejected ink and the degree of ink spreading on the substrate could be reduced. The proof-of-concept of this technique was demonstrated by printing lines of silver nanoparticles on polyethylene naphthalate substrates. Silver nanoparticle tracks with widths of 35.2 ± 7.0 m were achieved using a 300 μm nozzle. This is equivalent to 11.7 ± 2.3 % of the nozzle diameter. The result shows great potential in solvodynamic printing as not many modern printing techniques can achieve such nozzle to feature size ratios.
Next, the use of ligand exchange on silver nanowires to formulate a post-treatment free ink which can produce highly conductive printed silver nanowire structures was explored. The use of rigid bidentate linking ligands was studied. Among the ligands used, fumaric acid was shown to be the most effective in improving the film conductivity, reducing the sheet resistance by 70 % of a control sample from 26.5 Ω/□ to 7.66 Ω/□. The decrease in sheet resistance was attributed to the decrease in junction resistance of the nanowire film.
Lastly, a simple fabrication method was used to make piezoresistive composite films consisting of silver nanowires embedded in an ethylene-co-vinyl acetate matrix. The performances of the pressure sensor using compressive and tensile strains were compared. The properties of the pressure sensor were highly tunable based on the composition and design of the film. Overall, the films showed a low detection limit (1.47 ± 0.13 Pa), fast response time (11-22 ± 2.9 ms) and high sensitivity (67.3 ± 5.9 kPa-1). We further showed the successful application of the film as a pulse monitoring device.
Dicer-dependent transfer RNA-derived small RNAs and their role in gene regulation
Keywords: tRNA-derived small RNAs
small non-coding RNAs
gene regulation
RNA interference

Endogenous small non-coding RNAs (sncRNAs) are employed in mammalian cells to negatively regulate gene expression in a sequence-specific manner. This phenomenon is known as RNA interference (RNAi). Depending on the molecular mechanism, RNAi can be categorised into post-transcriptional gene silencing (PTGS) and transcriptional gene silencing (TGS). PTGS involves direct cleavage of mature messenger RNA (mRNA) or destabilisation of mRNA and/or translational repression in the cytoplasm. TGS is mediated by the establishment of heterochromatin at target loci in the nucleus. Transfer RNAs (tRNAs), which are essential for protein synthesis, have recently been identified as a source for a novel class of regulatory sncRNAs, termed tRNA-derived small RNAs (tsRNAs). The biogenesis of tsRNAs and their role in gene regulation remain largely elusive. By using a combination of unbiased bioinformatic prediction and classic molecular biology methodologies, I have studied the role of tsRNAs in the regulation of gene expression. Here I propose that Dicer is involved in tsRNA biogenesis and Dicer-dependent tsRNAs can promote gene silencing through a mechanism that is distinct from well-known PTGS and TGS. I show that Dicer-dependent tsRNAs may lead to gene silencing by targeting nascent transcripts, a mechanism termed nascent RNA silencing (NRS). Finally, I explored the therapeutic potential of tsRNAs and demonstrated the silencing of multiple genes by employing tsRNA mimics.
Developing Papua New Guinea teachers’ mathematical knowledge for teaching: prospective teachers and algebra concept
Keywords: MTED
MTED 2020

Mathematics teachers must have Mathematical Knowledge for Teaching (MKfT) as this is the knowledge required to teach in a school. It is seen that teachers who do not have an adequate knowledge of the MKfT are less well equipped to help students learn. A school curriculum that is meaningful and useful for Papua New Guineans is the driving force behind the recent educational reformations. However, school children still lack the application of the mathematical knowledge (SMK) in their everyday life which makes the learning of mathematics meaningless problematic in Papua New Guinea (PNG) and which could be attributed to mathematics teachers’ lack of MKfT. Hence, this study took an action reaction approach whereby cycle one was a base line survey that was conducted with beginning teachers and other stake holders in Goroka gauging their perceptions of the MKfT required by teachers to teach in secondary schools in PNG and the issues and challenges in developing this knowledge. Findings from cycle 1 showed that, some respondents perceived that trainee teachers’ SMK was good but needed PCK (an aspect of MKfT) development through a workshop. Therefore in cycle two the findings from the base line data were used to develop a workshop designed to enhance student teachers’ MKfT, and overall, the evaluation of the workshop found it successful in beginning this enhancement. It also found that more sustained professional development work with beginning teachers, mentors and teacher educators needed to be conducted in the PNG context.
No hands up: An investigation into the impact of cold-calling on primary school teacher expectations
Keywords: MLT
MLT2020

Teacher expectations have a significant impact on pupil outcomes. Expectations are the beliefs teachers hold about pupils’ potential academic achievements. Previous studies have investigated the impact of incorporating the strategies of high-expectation teacher on pupils’ attainment. Teacher expectations appear to be related to teachers’ self-efficacy beliefs and beliefs about intelligence. This study aimed to investigate the impact of cold-calling on teachers’ expectations. Cold-calling is a questioning strategy where teachers choose children from the whole class to answer their questions rather than rely on volunteers raising their hands. It is referred to frequently in professional documentation relating to high expectations. There is a lack of existing research into the decision-making process of teachers when choosing children to answer questions. The intervention was to incorporate cold-calling into the classroom practice of four participants in an English primary school setting. The Coronavirus school closures meant that the study had to be adapted due to no data being able to be collected. Had data collection been possible, a mixed-methods approach would have been used.
Quantitative data including potential changes to predictions of attainment and teacher self-efficacy using the established Teacher Self Efficacy Scale would have been collected. Focus groups would have provided qualitative data which would have been analysed using grounded theory. The potential findings have been considered based on existing findings in the literature and a critical analysis of the proposed research methods. Including cold-calling in a teacher’s practice may increase their expectations but there are additional factors which might affect this; however, it may also communicate increased expectations to pupils, which is strongly proven to raise outcomes due to initiating or encouraging a change to their behaviour to meet increased expectations.
How can I help?  A case study of parental engagement and its impact on student reading
Keywords: MLT
MLT2020

This practitioner research investigated the links between Year 7 students’ achievement in and engagement with reading and the amount of parental engagement they received at home with their reading. This work draws strongly on previous research that highlights the importance of parental engagement, whilst also providing a clear outline of what has been undertaken in the intervention.
Parents were invited to attend a Reading Information Evening which highlighted why reading was important to academic achievement and future careers. The evening also explained how the school was encouraging their child’s reading and what they could do at home to support their child’s reading engagement. Attendance at the intervention was optional which potentially meant that those who took part were already engaged, as opposed to the intervention creating engagement itself. This was a mixed-methods research project with qualitative data being collected from group interviews with Year 7 students and questionnaires completed by Year 7 students and quantitative data being collected through the Accelerated Reader Standardised Test for Assessment of Reading (STAR) and New Group Reading Tests (NGRT). All of these methods were used to determine whether students’ reading had been influenced by their parents, however, some data is missing due to the Covid-19 pandemic and subsequent school closure.
Despite the limitations of school closure, the findings indicate that increased parental engagement has positive repercussions on student reading achievement. Teachers also noted increased engagement from those students who indicated that they received parental support at home with their reading or that their parents displayed an interest in the Accelerated Reader (AR) programme. From looking at the relationship between students’ reading and the parental engagement they receive, the school can now consider how they harness the power of parents to maximise student outcomes.
Modular Calabi-Yau threefolds in string compactifications
Keywords: String Theory
Modularity Modular Form Zeta Function
Dyonic Black Hole Attractor Mechanism IIB
Calabi Yau

All elliptic curves defined over Q are modular. This is the statement of the modularity theorem that relates arithmetic properties of an elliptic curve to a modular form for a subgroup of SL(2,Z). This modularity has been (and still is) the subject of much research in number theory. At first, one might suspect that this kind of modularity is irrelevant for physics. Nevertheless, we will show in this thesis that modular Calabi-Yau threefolds are distinguished by certain physical processes. Most notably, by the attractor mechanism of IIB supergravity.
We will see that the zeta-function associated to an attractor variety of rank two will factor in a specified manner and that a search for such factorisations leads to an effective strategy for identifying examples of rank two attractor varieties with small h2,1 . We will also find and study a number of attractor varieties of rank two by taking the Hadamard product of two Picard-Fuchs equations of families of elliptic curves. As we will see, in many cases, the Hadamard product admits an involution with fixed points that are attractor points of rank two. We are able to identify the associated modular forms in both types of examples.
In the remainder of this thesis, we will explore the physical implication of mod- ularity. We will show that certain arithmetically interesting quantities such as critical values of the associated L-functions determine the area of the horizon of certain dyonic N = 2 black holes. Similarly, we find that topological string free energies, when evaluated at the mirror of a rank two attractor point, may also be expressed in terms of arithmetically interesting quantities.
Investigating the nexus between chromatin and metabolism
Keywords: Chromatin
Epigenetics
Microbiome
Metabolism
NGS sequencing

Metabolism and epigenetics are tightly connected within the cell, as many chromatin-modifying enzymes use metabolites as co-factors or substrates, and the energetic state of the cell can drive the activation of several transcriptional programmes. This thesis examines the nexus between chromatin and metabolism, both at the systemic and cellular level, in two different projects. First, the impact on a panel of weight-loss drugs on the gut microbiome of obese rats is investigated through shotgun metagenomic sequencing. Results highlight a concomitant change in weight and gut microbiome composition following treatment with Sibutramine, which acts through inhibition of appetite.
A second project focuses on alterations induced by a gain-of-function mutation in the metabolic enzyme Isocitrate Dehydrogenase I (IDH1), which spurs production of the oncometabolite 2-hydroxyglutarate (2HG), in the context of glioma. Using an isogenic human glioma cell line (U87), either wild-type or bearing the R132H IDH1 mutation, an IDH1 inhibitor (GSK864) and a combination of multi-omics techniques, we characterize the metabolic and epigenetic alterations induced. Results suggest an uncoupling between metabolism, transcriptome and the epigenome, as treatment with an IDH1 inhibitor, while successfully rescuing the metabolic defect and promoting, upon a longer treatment, changes in gene expression, did not correct the epigenetic alterations.
Luminosity functions using new radio surveys
Keywords: Radio Astronomy
Luminosity functions
active galactic nuclei
Star forming galaxies
AGN
interferometer
LOFAR
MeerKAT
VLA
Galaxy evolution

Galaxy evolution at radio wavelengths is often measured using luminosity functions which are commonly based on the luminosity (L) and redshift (z) of individual sources as well as the overall distribution for a sample. However, an important factor when K-correcting radio sources to their rest-frame luminosity is the spectral index (α). This is often assumed to be a constant value for the entire sample. In reality, there is a distributioninαwhich affects the rate at which sources drop below the flux limit of thesurvey. If not accounted for, the differing rates at which sources become unobservable can be mistaken as a genuine decline in source numbers and affect the evolution of the radio luminosity function (RLF).
To tackle this, I estimate RLFs incorporating theα-distribution for both active galactic nuclei (AGN) and star forming galaxies (SFGs). Data from the Low-Frequency Array (LOFAR) and the Very Large Array (VLA) are used to formulate 150 MHz RLFs for a sample of AGN. MeerKAT and VLA data are used to measure and model the 1.4 GHz RLFs for a sample of SFGs. Markov Chain Monte Carlo sampling is used to find parameters to fit models based onρ(L,z) andρ(L,z,α) for comparison.The main findings of this work are that the accurate modelling of theα-distribution has a significant effect on not only the shape of the RLF,but the value of the evolution parameter k, parameterised as (1 +z)k in pure evolution and pure density evolution. This is true for both AGN and SFGs.
The AGN RLF model has higher values of k when accounting for theα-distribution. Steep spectrum sources drop out of the flux limit of a survey earlier and therefore drive the RLF to evolve faster.
The SFG RLF model has lower values of k when α is accounted for, since high values of k are found to compensate for the absence ofαinformation.Since the aim of RLFs is to measure the evolution of galaxies, it is imperative that the α-distribution be included to obtain accurate estimates of the strength of any evolution.
Future work would aim to improve on this further through including an-other factor that affects the visibility of a source; the angular or projected linear size of the source. In this thesis, observations of the same field but using different antenna configurations have shown that high-resolution,long baseline imaging needs to be supplemented with short baseline observations to fully capture all scales of emission and all sources. Therefore, a radio telescope with an abundance of both short and long baselines is necessary. MeerKAT is such a telescope, as will be the SKA. Therefore, my work has set out the way forward for towards properly understanding the evolution of radio sources in the Universe.
Hierarchical modelling for financial data
Keywords: Limit order books
Deep learning
Portfolio optimisation
Microstructure data
Reinforcement learning

Algorithmic trading has gained great popularity and has been heavily used by institutional investors. Such a trading system involves mathematical and statistical modelling that makes decisions to buy or sell financial securities by following automated trading instructions. In this thesis, we research machine learning based data-driven approaches to construct such trading rules.
We utilise deep learning models to predict stock price movements and develop trading strategies based on the derived signals. Instead of using hand-crafted features, we propose neural network architectures to extract nonlinear features from limit order books data. We expand this work to study Bayesian Deep Learning models and Quantile Regression, obtaining uncertainty estimates on predictive outputs. These uncertainty estimates can be used for position sizing to improve trading performance.
However, a mapping from predictive signals to trade positions is non-trivial to construct. To solve this problem, we research Reinforcement Learning algorithms to optimise a measure of expected utility of final wealth, directly outputting trade positions thus bypassing the explicit forecasting step. Reinforcement Learning algorithms are used to trade individual assets and, subsequently, we extend this end-to-end training framework to portfolio optimisation and directly maximise the Sharpe ratio for portfolios, maximising return per risk.
Ecology and evolution of protective microbes
Keywords: Host-Pathogen Interactions
Experimental Evolution
Protective microbes
Mathematical Modelling
Defensive Symbiosis
Sexual Immune Dimorphism

Every host is colonized by a variety of microbes, some of which can protect their hosts from pathogen infection. Harbouring protective microbes (PMs) has evolutionary implications which often result in these microbes being costly for the host. Here I have investigated the evolutionary and ecological conditions under which the interaction between a host and a protective microbe are established. For this I have used populations of Caenorhabditis elegans worm hosts, bacteria possessing protective traits (Enterococcus faecalis) and pathogenic bacteria (Staphylococcus aureus). I have experimentally coevolved the host and protective microbe and infected the coevolving system at different intervals and host generations. Furthermore, I have investigated how host sex and mating status affects the interaction of the host with the surrounding bacteria. More generally, to assess how host dynamics are affected by protective microbes, I constructed SIR models in the absence and presence of the protective microbe. My results indicate that even the rare presence of the pathogen is enough to drive the evolution of microbe mediated protection, and that this is independent of the interval or initial pathogen presence. I find that both sexes use the benefits of the PM to increase their reproductive success, even though females invest more in egg production and males more in mate searching behaviour. The SIR models indicate that the protective microbe stabilises host dynamics under a range of different parameters once the cost-benefit ratio is greater than one. Overall these results suggest, that protective microbes have high potential to influence and stabilize host dynamics, even though the two host sexes might benefit differently from the provided protection.
Understanding P450 hydroxylation profiles
Keywords: MD
Computational model
P450
Docking
SoM pediction
P450BM3

The enzymatic approach to C-H functionalisation is a promising field within contemporary organic chemistry that complements parallel advances with chemical reagents. The cytochrome P450BM3, refined through aeons of evolution, offers a sustainable and elegant methodology for achieving synthetically valuable hydroxylations at ubiquitous C-H sites. This opens the potential for new synthetic strategies, relying on early-, mid- or late-stage C-H functionalisation. Gaining a working understanding of the regioselectivity profiles arising from such hydroxylation reactions is key to the wider adoption of this as a reliable strategy for molecular assembly.
This thesis describes a practical computational workflow that aims to rationalise selected experimental results obtained in the Wong and Robertson groups for the enzymatic hydroxylation of a bicyclic lactone and a spirocyclic amine derivative. The workflow was developed through three stages of increasing sophistication, starting with an investigation of the validity of an existing MD/docking protocol then building towards a stable and reproducible computational platform. The final iteration involved the preparation in silico of specific enzyme mutants based on the X-ray crystal structure of the Wild Type P450BM3, then performing molecular dynamics (MD) relaxation of these mutants to relieve the structural strain introduced by the process of mutagenesis and to account for enzyme solvation. The MD process was repeated for each mutant to produce four independent replicas that were clustered by a specified RMSD criterion for rigid ensemble docking with separate energy-minimised substrate conformers. The resulting docking poses were classified by the Fe=O•••H angle and O•••H distance, highlighting positions prone to hydrogen abstraction.
This approach predicted site of metabolism differences between two highly selective mutants, each of which hydroxylates one of two adjacent C-H positions of an N-protected spirocyclic amine. A new tactic for knowledge-guided mutagenesis, that eliminates the undesired binding pockets, suggested by this computational model, has enabled Wong’s group to achieve > 90% regioselectivity, > 90% stereoselectivity and > 90% conversion rates for the hydroxylation of selected N-protected cyclic amines. Potential avenues of future research are proposed and evaluated in the context of ongoing work within the groups on the further refinement and practical application of this computational methodology.