http://theses.ncl.ac.uk/jspui/handle/10443/60 2026-04-30T00:22:54Z 2026-04-30T00:22:54Z Jolley, Martin James http://theses.ncl.ac.uk/jspui/handle/10443/6747 2026-04-29T11:19:40Z 2025-01-01T00:00:00Z

Title: On the development and application of seeding metrics to improve river flow measurements using image velocimetry Authors: Jolley, Martin James Abstract: This thesis presents a novel approach to improving river flow measurement accuracy by optimising seeding metrics used in Particle Tracking Velocimetry (PTV), a non-contact method that estimates surface flow by tracking the movement of individual tracers—such as foam or debris—between frames of video footage. The study focuses on refining how seeding density, dispersion, and a combined metric known as the Seeding Density Index (SDI) are measured and applied under vary ing spatial and temporal conditions. SDI is developed as a post-processing tool that quantifies the quality of tracer conditions in each video frame by combining observed seeding density and dispersion values, and is shown to correlate with the accuracy of velocity measurements. Unlike traditional methods that rely on filtering input data, this research shifts the focus to post-analysis optimisation and error correction, improving the reliability of velocity estimates derived from video footage. High-resolution video was captured at several UK river sites, alongside synthetic datasets with controlled tracer conditions. All data was processed using an adapted version of Kanade-Lucas Tomasi Image Velocimetry (KLT-IV) software, developed to support new post-processing tech niques. The synthetic simulations confirmed the effectiveness of the approach, while real-world footage validated the findings and demonstrated the practical benefits of applying SDI in routine f low monitoring. The first analysis uses synthetic videos to assess how different combinations of seeding density and dispersion affect flow measurement accuracy, and to define SDI values that can be used to estimate error in each video. The second analysis applies these findings to real-world case studies, recalculating discharge based on filtered results to test how well the metrics hold in natural conditions. The third analysis shifts focus to spatial effects, identifying where within a river cross-section measurements are most reliable based on seeding characteristics. These insights are then applied to site data to evaluate their practical value. Together, these findings demonstrate how SDI can be used to improve the consistency and ac curacy of river discharge estimation from video data, offering a practical tool for enhancing hydro logical monitoring in a range of environmental settings. This research recommends the adoption of post-processed SDI as a standard component of image velocimetry workflows, particularly in oper ational monitoring contexts, where rapid, automated, and accurate flow estimation is increasingly essential. Description: Ph. D. Thesis.

2025-01-01T00:00:00Z Jiang, Jianan http://theses.ncl.ac.uk/jspui/handle/10443/6746 2026-04-24T11:19:43Z 2025-01-01T00:00:00Z

Title: A novel transition metal dichalcogenide – reduced graphene oxide electrocatalyst family for hydrogen evolution reaction Authors: Jiang, Jianan Abstract: The global energy crisis underscores the critical need for sustainable hydrogen production via efficient electrocatalysts. This study presents a novel family of transition metal dichalcogenide-reduced graphene oxide (MoS2-rGO) hybrids and doped variants (V, W, Co) for the hydrogen evolution reaction (HER). Using orthogonal experimental design (L9(33)), we optimized synthesis parameters (rGO content, heating temperature, duration) to develop a MoS2-rGO composite with 0.8 wt% rGO, synthesized at 200°C for 24 h. This optimized catalyst achieved superior HER performance in acidic media (0.5 M H2SO4), exhibiting a low overpotential (η10 = -0.34 Vs RHE) and Tafel slope (98.2 mV/dec), significantly outperforming undoped MoS2 (η10 > 0.40 Vs RHE, Tafel slope 113.0 mV/dec). While Pt/C remains the benchmark catalyst with ultra-low overpotential (~0.05 V vs. RHE) and a Tafel slope of ~30 mV/dec, its high cost and scarcity hinder widespread application. In contrast, our MoS2-rGO catalysts offer a noble-metal-free alternative with enhanced conductivity and stability, facilitated by rGO integration. XRD and Raman spectroscopy confirmed structural improvements, while vanadium doping increased active site exposure, tungsten doping introduced sulfur vacancies to optimize hydrogen adsorption energy, and cobalt doping altered electronic structures. Systematic characterization via XRD, XPS, BET, UV-vis, Raman spectroscopy, SEM and electrochemical techniques elucidated the structural and electronic contributions of each component. The synergistic effects of rGO and dopants were highlighted, with the Co0.05W0.05S2/rGO heterostructure showing promising HER activity. This work establishes a rational framework for designing noble-metal-free HER catalysts, emphasizing the interplay between defect engineering, interfacial interactions, and scalable synthesis strategies. The integration of orthogonal optimization with multi-technique characterization provides a robust pathway for advancing green hydrogen technologies. Description: Ph. D. Thesis.

2025-01-01T00:00:00Z Jantasin, Pattaramas http://theses.ncl.ac.uk/jspui/handle/10443/6745 2026-04-24T10:56:44Z 2025-01-01T00:00:00Z

Title: A Social Semiotic Analysis of EFL Students’ Multimodal Composing: The Use of Modes, Remaking of Signs, and Semiotic Awareness Authors: Jantasin, Pattaramas Abstract: In a word full of multimodal communication, Contemporary communication rarely relies solely on spoken or written form of language, but is increasingly made with multiple modes, e.g. writing, audio, and image. This prompts a call for educators to rethink literacy in language classrooms. Despite a growing interest in multimodality in L2 contexts, the investigation of students’ multimodal composing and semiotic awareness remains underexplored, particularly in the Thai EFL context. This study examines multimodal composing activities of Thai university students in one EFL classroom, drawing on multimodal social semiotics (Bezemer and Kress, 2016) and multiliteracies (Cope and Kalantzis, 2009), to understand three aspects: the multimodal design of the students’ produced texts, the remaking of signs, and their semiotic awareness while composing multimodally. The students’ multimodal texts produced as part of an L2 multimodal composing project—short summaries, digital posters, and oral presentations of their innovative product—were analysed using social semiotics to gain insights into the first two aspects. Background questionnaires, recordings of planning and composing processes, and interview responses were collected to understand the context and reasons for their semiotic choices, enriching the social semiotic analysis. The recordings and interviews were also deductively and inductively analysed to explore emerging semiotic awareness. The analysis of two focal cases reveals that Thai EFL students were able to produce expressive multimodal texts, and multimodal composing fostered their semiotic awareness even without any training on multimodal meaning-making. However, material, contextual, and social factors were found to crucially shape the students’ semiotic choices, the meaning represented in the texts, and the extent of their semiotic awareness. The findings underscore the need to recognise the critical roles of diverse representational modes, influencing factors, and semiotic awareness for more powerful and accurate communication. The study presents a conceptualisation of the complexity of L2 multimodal composing and offers pedagogical implications to enhance language learners’ semiotic repertoire and awareness to meet the evolving communicative demands of the world. Description: Ph. D. Thesis. (Integrated)

2025-01-01T00:00:00Z Binks, Rachel Louise http://theses.ncl.ac.uk/jspui/handle/10443/6744 2026-04-23T10:39:05Z 2025-01-01T00:00:00Z

Title: Bayesian inference on the order of stationary vector autoregressions with application to multivariate modelling of electroencephalography data Authors: Binks, Rachel Louise Abstract: Vector autoregressions (VARs) are widely used for modelling multivariate time series. VARs have an associated order p; given observations at the preceding p time points, the variable at time t is conditionally independent of all earlier history. The model order is therefore intrinsic to the characterisation of the process. It is common to assume a VAR is stationary, which requires the means, variances and covariances of the process to be constant over time. This can be enforced by imposing the stationarity condition which restricts the parameter space of the autoregressive coefficients to the stationary region. However, implementing this constraint is difficult as the stationary region has a complex geometry. Fortunately, pioneering recent work has provided a solution for enforcing stationarity in autoregressions of fixed order p based on a reparameterisation in terms of a set of interpretable and unconstrained transformed partial autocorrelation matrices. In this research, focus is placed on the difficult problem of allowing p to be unknown, developing priors and computational inference that take full account of order uncertainty. To this end, a comparison of existing approaches for determining the order of station ary univariate autoregressions is provided. An approach employing shrinkage priors for partial autocorrelations is then generalised for the multivariate case, using the cumula tive shrinkage and multiplicative gamma process priors to increasingly shrink the partial autocorrelation matrices with increasing lag. Identifying the lag beyond which these ma trices become equal to zero then determines p. Methods for identifying whether a partial autocorrelation matrix is effectively zero are developed. The work is illustrated through application to neural activity data. In particular, a detailed discussion of methods to decompose a VAR into latent processes is provided, which is then used to investigate ultradian rhythms in the brain. Relationships between different regions of the brain are investigated through Granger causality plots. Description: PhD Thesis

2025-01-01T00:00:00Z