Features of Designing Control Systems of Tested Aviation Moving Platforms

This paper describes the features of designing control systems for moving platforms with equipment of different types including optical sensors, antennas, video cameras, and observation equipment. The features of linearization of the mathematical description are described. Such features include the influence of the hysteresis in the gyroscopic device and the backlash in the controlling drive of the platform. The recommendations for linearization of the non-linearities as mentioned earlier are given. The features of introducing disturbances in the mathematical description of moving platforms are represented. The technique of creating forming filters for the simulation of disturbances caused by irregularities of relief of road and terrain is described. Such an approach is relevant for moving platforms operated on land vehicles. The procedure for creating a robust controller resistant to internal and external disturbances is given. The synthesis of the control system for the moving platform has been realized. The simulation results are represented. The obtained results can be useful for the control systems of the different moving vehicle

Click Chemistry Derived Copper Artificial Metallo-Nucleases with Discrete Nucleic Acid Targeting Properties

The design of copper compounds with artificial metallo-nuclease (AMN) activity is an important goal as these agents are mechanistically unique compared to established metallodrugs. This thesis reports the development of a new dinuclear copper AMN, called Cu2 -BPL-C6, which was prepared using click chemistry methodology. This compound demonstrates site-specific DNA recognition with low micromolar damaging activity. The complex was rationally designed to induce enhanced DNA damage by coordinating two redox-active copper centres coordinated to distal phenanthroline groups that are linked by an aliphatic spacer. DNA binding experiments—including circular dichroism spectroscopy, agarose gel electrophoresis, and fluorescence quenching experiments—revealed preferential binding for AT-rich DNA. The oxidative cleavage mechanism of Cu2 BPL-C6 was elucidated using in vitro molecular and biophysical assays with spin trapping antioxidants and free radical scavengers. Next, the quantification of genomic DNA damage, along with the types of oxidative lesions produced, were monitored using single molecule analysis of peripheral blood mononuclear cells exposed to Cu2 -BPL-C6. Broad spectrum anticancer screening in collaboration with the National Cancer Institute (NCI-60) revealed selectivity against several melanoma, breast, colon, and non-small cell lung cell lines. A small library of Cu2 BPL-C6 congeners were then synthesised to observe the effects of modifying the linker length and composition of this class of agent. To extend the application of targeted AMNs further, a library of bis-acridine ligand scaffolds were screened with Holliday Junction (HJ) DNA for their recognition properties. The lead compound in the screen, a C6-linked acridine derivative, was then covalently modified using click chemistry. Here, an azide-phenanthroline group was tethered to a mono-acridine derived scaffold and coordinated with copper(II). Efforts to generate a functionalised bis-acridine scaffold are established. Multiplex PAGE and microscale thermophoresis assays were established for high-throughput screening of HJ recognising compounds. Using these platforms, the mono-acridine hybrid was screened for its selective HJ DNA cleavage properties

Physics Informed Neural Networks:Deployment and Evaluation in Sparse Data Application

Neural networks have demonstrated remarkable success in various domains but they often struggle with generalization beyond their training data. To address these limitations and enhance the robustness of machine learning models, this thesis explores the integration of domain knowledge into neural networks through two approaches, network analysis and ordinary differential equations (ODEs). We begin by investigating neural network performance in diverse tasks, such as hyperglycemia/hypoglycemia diagnosis using exosome profiles and oxygen uptake estimation from sensor measurements. The study then progresses to more structured data with complex networks. Subsequently, we incorporate network structure into machine learning using graph neural networks, applying this method to an air quality forecasting task where locations and their correlations form a network. An alternative approach is then investigated by integrating ODE systems describing dynamical systems into a data-driven machine learning framework. This comprises the development of advanced techniques to enable neural networks to learn underlying physics, including ODE Normalization, Gradient Balancing, Causal Training, and Domain Decomposition. These methods address challenges in training with stiff systems across large domains. The frameworks in this research are then validated using simulated data for the Lorenz system and a system of ODEs modelling mosquito populations. This work is further developed to accommodate real-life observations, by making adjustments to model inputs, neural network architecture, and activation functions. This extended framework is then evaluated against real-world mosquito counts in an inverse problem setting, learning relationships between meteorological conditions and mosquito development. Our results demonstrate that incorporating domain knowledge into neural networks enhances model generalizability, improving both accuracy and extrapolation capabilities. Moreover, this approach maintains the explainability of the added knowledge while leveraging the flexibility of machine learning models

Targeted Therapeutic Strategies in Pancreatic and Gastroesophageal Cancers: Precision Medicine Approaches to Overcoming Drug Resistance and Advancing Organoid Development

Pancreatic ductal adenocarcinoma (PDAC) and gastroesophageal cancer (GEC) are among the most lethal malignancies. The absence of early symptoms means that most patients are diagnosed at late stages with advanced disease. Despite progress in treatment, the overall prognosis remains poor mainly due to their inherent and/or acquired resistance to conventional therapies. The efficacy of new treatment strategies has been hampered by the lack of efficient preclinical models recapitulating the heterogeneity and complexity of these cancers resulting in the ineffective clinical translation of novel targeted therapeutic options. Therefore, we explored the impact of traditional two-dimensional (2D) and threedimensional (3D) culture conditions on cancer stem cell (CSC) markers, epithelialmesenchymal transition, hypoxia profiles, and treatment responses in PDAC systems. We found that 3D cultures more closely mimic the tumour microenvironment and exhibit distinct cellular behaviours, suggesting that 3D organoids provide a more accurate model for studying treatment responses and therapeutic resistance. Acquired drug resistance to 5-FU (the backbone chemotherapeutic of treatment regimens for PDAC and GEC) in two patient-derived organoids (PDOs) were developed to investigate the mechanisms of chemotherapeutic resistance and identify novel targeted therapeutic strategies. Transcriptomic and proteomic analyses identified key dysregulated therapeutic vulnerabilities. Phototoxic peptide conjugates targeting the modulators of drug resistance, and pharmacological inhibitors circumvented the development of resistance. Finally, we established and characterised PDOs from treatment-naïve GEC patients and demonstrated the feasibility of using PDOs to evaluate sensitivity to chemotherapy. Here we identified actionable targets from the genomic and transcriptomic landscape of the organoids matched to their original tumour to highlight the translatability of precision medicine. Our research demonstrated that advanced 3D organoid systems represent valuable tools for modelling drug resistance and offer opportunities to discover novel therapeutic approaches to prevent the emergence of drug resistance, to improve patient outcomes through more effective and personalised treatments

AI That Makes You Think: Designing Systems for Guided Reasoning and Reflection

Current AI-augmented reasoning systems often optimize decision-making by providing rapid, automated insights. However, this can lead to cognitive overload and over-reliance, undermining human critical thinking. This paper explores how AI can take on the role of a structured reasoning guide rather than a passive assistant by actively shaping cognitive engagement. We propose that AI should strategically introduce guided reflection pauses, scaffold reasoning skills, and track cognitive progress, ensuring users actively engage with reasoning tasks rather than passively consuming AI-generated insights. Our framework adapts principles from intelligent tutoring systems (ITS), ensuring that AI fosters structured problem-solving and metacognitive growth rather than replacing human thought

Mapping Gender Bullying Through the Lens of Intra‑actions in a Private Day and Boarding School

This paper maps a socio-ecological approach to gender bullying as part of a participatory action research project which took place in a private day and boarding school in Ireland. It applies the new defnition of bullying proposed (and recently published in 2024) by UNESCO and the World Anti-Bullying Forum (2022) and explores the networks of relationships in the school and the underpinning social norms and power imbalances therein. The core research question asks: Does gender bullying happen at this school? Through active engagement with students as co-researchers, school staff as steering group members, and two university researchers, this question was explored through a qualitative research design using focus group interviews and a Digital Dropbox. The application of Barad’s (1998; 2007; 2011) concept of agential realism is proffered to help unravel the entanglement and complexity of gender bullying. We propose that the mapping of the intra-actions of human beings with different discourses, objects, materials, spaces, and time, assists in making sense of the normativity of gender expectations and its complex array of inclusions and exclusions

Learning-based Methods for Optimising Shared Mobility Systems with Multimodal Data

This thesis explores the use of learning-based methods in Shared Mobility Systems (SMS), utilising multimodal data to address three key operational challenges: improper parking behaviour, energy consumption prediction, and pollution-aware routing. The overarching goal is to improve the efficiency, sustainability, and user experience of SMS through data-driven, task-specific solutions. The first challenge is addressed by developing U-Park, a user-centric parking recommendation system. It predicts trip destinations and parking availability in real time using multimodal inputs, including partial trip data, GPS trajectories, and environmental features. Combining an attention-based RNN and a contextualised parking model, U-Park improves the chances of finding available parking by up to 29.66%. The second contribution focuses on privacy-aware energy consumption modelling for shared battery electric vehicles. A Federated Learning (FL) framework enables model training across distributed data sources without sharing raw data. FL algorithms and local models are evaluated on multimodal features such as speed, altitude, and derived variables. The proposed FedAvg-LSTM model reduces mean absolute error by up to 67.84% and supports deployment in edge-cloud environments. For the third challenge, a pollution-aware route planning system is introduced. Multimodal data from fixed and mobile air quality sensors is used to construct a high-resolution PM2.5 map, combining temporal imputation with spatial interpolation. Models including IDW, RF, LSTM, and Conv-LSTM are evaluated for short-term forecasting. The resulting pollutant maps inform route selection, reducing average exposure by 25.88% with minimal extra travel distance. These contributions highlight the value of integrating multimodal data and adopting tailored learning approaches. The thesis also discusses challenges such as data sparsity, integration uncertainty, and model explainability, and outlines future directions including ensemble learning, uncertainty-aware modelling, and multi-objective optimisation

Shaping player experience: Understanding the impact of team sport coaching pedagogy and practices

Recognised as a complex pedagogical process, sport coaching as an applied domain has continued to receive significant research attention based on the impact that team sport coaches can have on player development. The aim of this thesis was to explore the role of a range of coaching practices and strategies including pedagogy, intentions for impact, fidelity of practice and streaming on player experience in team sport. Chapter 3 investigated games-based approaches in Gaelic games contexts and their impact on player development and coaching attitudes. One of the key findings of this study revealed a lack of coherence in the understanding of the approach. Given this lack of understanding an investigation into the use of game form activities was deemed to be necessary. Thus, Chapter 4 explored how coaches use game form practices and the sources of their pedagogy. The findings suggest that coaches utilise a range of approaches, in and very pragmatic manner and often lacking coherence from a theoretical stance. Furthermore, their coaching was very individualised and highly contextual and could be understood through the lens of intentions for impact. Thus, Chapter 5 built upon these findings with an investigation into how these coaches formed their intentions for impact. These intentions could be viewed through the lens of fidelity of practice, a desire for realistic practices which replicate the game. Interestingly, this cohort were overwhelmingly focused on physiological fidelity. Chapter 6 was an exploration into the participant experience of youth Gaelic games. The findings of this broad study suggest increased seriousness seemed to meet some participant needs, but there is an apparent need for better support for coaches to manage the complexity they face in their practice. Following this series of snapshots of select coaching domains, the final two studies were focused on tools which coaches can use to better inform practice. Following the investigation into youth Gaelic games, Chapter 7 investigated one such tool which can offer support to coaches in the form streaming, or ability grouping. While participants viewed streaming in a favourable light, reflections also highlighted a perception that the implementation of streaming was hindered by a series of challenges deemed to accelerate the different rates of development and thus impact playing experience. Lastly, Chapter 8 proposed a model of fidelity of practice for team sport coaches in the form of affective, physiological, action and conceptual fidelity, the manipulation of these forms allowing the coach to shape the player experience. The overarching finding of this thesis suggests that coaches need support to deliver an appropriate enacted curriculum through different pedagogical interventions to facilitate a more equitable player experience with chapter-specific findings discussed throughout the thesis

Unlocking the Stability of Multi-Component Pharmaceutical Forms

The field of computational chemistry is constantly developing new predictive methods to guide experiments. Co-crystals are a promising development for improving the properties of active molecules, which is an exciting prospect specifically for the pharmaceutical field in formulating active pharmaceutical ingredients (API). To minimise computational cost, quantum mechanical models based on density functional theory (DFT) can be supported where appropriate with faster semi-empirical density functional tight binding (DFTB). These types of models can be used to predict the enthalpy of formation of the co-crystal structures. My results show that the full DFT methodologies predict the enthalpy of formation well for a broad range of co-crystals, with the DFTB methods giving high-throughput predictions for simple co-crystals but failing for larger, more complex APIs. Another area of intensive research in multi-component pharmaceutical forms is the development of anti-cancer artificial metallonucleases (AMNs) that can be used to recognise and damage nucleic acids. This is aided by the metal centre which promotes oxidative processes chiefly responsible for cleavage activity. Thus, ensuring coordination of the metals in their parent AMN scaffolds is imperative for them to function correctly. Click chemistry is a recently discovered modular process to produce various AMNs with differing terminal groups. The goal here is to produce various polynuclear AMN structures, as these have previously been found to have a greater activity than mononuclear congeners, resulting in greater DNA damaging effects. This thesis aims to predict metal ion binding properties based on a wide range of scaffolds. The pendant groups can influence the strength of the metal to scaffold binding. DFT calculations are used to predict the effect of a broad range of molecular groups in place of the hetero-aromatic donors and explore how this can improve the metal binding in molecular scaffolds

Measuring features of asexual identity development: the development and validation of a psychometric scale

This study describes the initial steps in the development of the 32-item Assessment of Asexual Identity Development Scale (AAID) to measure variables unique to asexuality. Items were developed through a thematic analysis of findings from previous literature and a pilot measure was administered to a sample of expert reviewers for content analysis (n=15). Exploratory factor analysis (n=825) and confirmatory factor analysis (CFA) (n=826) confirmed the dimensionality, reliability and validity of the AAID scale and good model fit was obtained (comparative fit index = .96, root mean square error of approximation = .038, CMIN/DF = 2.20, χ2=1318.84). Six factors emerged: Discovering Asexuality, Being Asexual, Asexual Community, Disclosure, Navigating Relationships and Navigating Relationships: Desires. Standardised factor loadings of all items were high or moderate, and all subscales indicated good to excellent internal reliability (ω = .72–.93). This study supports the internal consistency of the AAID and its subscales, and construct and discriminant validity. Finally, this research demonstrates that AAID scores were stable over five weeks. This measure is a reliable and useful tool to evaluate the development of an asexual identity and will contribute to the growing body of literature on asexualit