http://theses.ncl.ac.uk/jspui/handle/10443/5223 2026-06-10T12:46:35Z 2026-06-10T12:46:35Z Harper, Scott http://theses.ncl.ac.uk/jspui/handle/10443/6802 2026-06-09T10:26:31Z 2025-01-01T00:00:00Z

Title: On the Security and Privacy of Animal Technologies Authors: Harper, Scott Abstract: As the Internet of Things (IoT), smart devices, and their corresponding mobile apps are becoming increasingly widespread, they are expanding into various different industries. One of these rapidly expanding sectors is animal technologies, which includes systems and devices designed to assist with animal care. In pet tech only, it is projected to reach a market value of $3.7 billion by 2026 [104]. However, these systems bring new security, privacy, and safety risks to users, their animals, and their homes. Despite these concerns, the risks of these systems, as well as the users’ apprehensions about these issues, remain under-researched. This lack of research and data protection regulations in this space leaves users vulnerable to attacks and hampers their ability to protect themselves effectively. This PhD work investigates various aspects of the security and privacy of these technologies to inform the current state of risk and user perceptions. Security and Privacy of Animal Apps: In the first part of this thesis, our work involves a range of tools used to perform static, dynamic, network traffic, and privacy policy analysis on a set of 40 animal Android apps (both farm animals and pets). We identify poor security and privacy practices that do not effectively gain the consent of the user and communicate their details in ways that may leave them vulnerable. We additionally find that some of the apps are communicating the user’s login details in plaintext in non-secure http traffic, leaving them vulnerable to very obvious, yet dangerous attacks, by anyone who is able to view this network traffic. These issues were communicated to the companies responsible, with those who responded having the issue fixed upon later retesting. Sensor-based IoT Identification: The second part of the thesis looks at a possible identification method to be used by resource-constrained IoT devices, with limited interaction methods, such as those used on and around animals e.g., at a large scale on a farm. In our proof-of-concept implementation, by utilising the accelerometers already present in such devices (i.e., the Nordic Thingy 52 and 53), we capture the data pattern created from physically tapping two IoT devices together. Our results showcase the feasibility of implementing such a system that is able to correctly identify matching tapping events from IoT devices which want to pair for secure communication. We test a range of signal processing methods, such as the correlation coefficient and energy of the signals, combining those found to be effective for our final similarity calculation. The proposed system is able to achieve an EER of 3.5% when comparing 100 samples of data against each other, with possible adjustments to the threshold to get a lower FAR if needed. User Studies of Animal Technologies Security and Privacy: In this final part of this PhD work, we turn our focus to the users of these systems (more specifically, pet owners). We design a user study in the form of an online survey to understand their views, concerns, and actions regarding these systems. Using Academic Prolific, we distribute this to 593 participants from the UK, US, and Germany (roughly 200 from each) targeting specifically pet owners. This study gives insight into the apps and devices used by pet owners, the perceived advantages and disadvantages, concerns, incidents that have occurred, as well as the different perceptions around the data that may be collected by them and how they might protect themselves from these risks. Despite only a few reported incidents with these technologies, we find 521 of the participants expressed concern about an incident, such as a data leak, and the well-being and safety of their pet. Despite these concerns, these participants took far fewer precautions toward protecting the security and privacy of these systems compared to what they employ for their general online security and privacy. The findings of this PhD research give perspective to the overall security and privacy of animal technologies. Our work contributes to the body of knowledge in a holistic and comprehensive way, i.e., regulations review, system studies, secure system design, and user studies. We provide discussions and recommendations for multiple stakeholders such as academic and industrial researchers and designers, farm owners and managers, policymakers, and the end users of animal technologies. Description: PhD Thesis

2025-01-01T00:00:00Z Kucukgoz, Burak http://theses.ncl.ac.uk/jspui/handle/10443/6774 2026-05-13T11:18:31Z 2025-01-01T00:00:00Z

Title: Enhanced predictive models for macular hole surgery outcomes Authors: Kucukgoz, Burak Abstract: This thesis presents research work conducted in the field of retinal image analysis. More specifi cally, the work is directed at the employment of deep learning (DL) based image informatics for the analysis of diverse real world phenomena where features of interest are very difficult to distinguish. The evaluation of idiopathic full-thickness macular holes (MHs) holds critical clinical importance as MHs represent one of the strongest predictors of surgical success, influ encing both anatomical closure and functional visual recovery– a key motivation for developing robust deep learning frameworks to quantify their characteristics and predict postoperative out comes. In this context, three distinct parts to retinal image analysis are proposed. The first part addresses critical research questions on the quantitative assessment of MH, the role of DL in postoperative visual acuity (VA) prediction, the integration of automated optical coherence tomography (OCT) analysis for clinical decision-making, and the potential of DL models to improve diagnostic accuracy and support clinical practices. Hence, this part presents a compre hensive image informatics framework to create a high-quality spectral-domain OCT (SD-OCT) image dataset, providing a robust DL-based predictive model of VA in patients following surgery with MH and presenting an automated solution for non-standardised SD-OCT datasets. The imaging data undergoes preprocessing, quality assurance, and anomaly detection procedures. Seven state-of-the-art DL predictive models are then designed, implemented, trained, and tested with multiple two-dimensional (2D) input channels on the SD-OCT dataset. The models are quantitatively compared using four evaluation metrics. The method concludes the impact of the following surgery by predicting VA. Overall, the obtained results confirm that the fully automated approach with input from seven central SD-OCT images from each patient may robustly predict VA measurements using a high-quality SD-OCT image dataset. Following this, three-dimensional (3D) convolutional neural networks are integrated to train the model. 3D networks generally outperformed the 2D networks in some evaluation metrics; however, it came with the sacrifice of significantly more computational complexity. The second part identifies key research questions related to common sources of uncertainty in OCT images and proposes an effective method for representing and quantifying this uncertainty in DL-based predictive models. Furthermore, the study compares the proposed UQ method with existing approaches. In this context, the study highlights the significance of uncertainty, especially in dealing with the SD-OCT images. Predicting postoperative VA through DL models is crucial for decision-making and patient advisement, though their black-box behaviour is opaque to users and uncertainty associated with their predictions is not typically stated, leading to a lack of trust among clinicians and patients. To meet this need, an uncertainty-aware regression model is introduced for predicting postoperative VA using 3D SD-OCT images. The model not only x predicts VA post-surgery but also quantifies the associated uncertainty, enhancing reliability and trustworthiness. Qualitative evaluation shows that the proposed model outperforms commonly used methods in terms of prediction accuracy and reliability, demonstrating robust performance on out-of-sample data, including low-quality images and previously unseen instances. This makes the model a promising tool for clinical settings, improving the reliability of DL models in predicting VA. The third is the segmentation of the retinal external limiting membrane layer, where any disruptions in this layer are associated with worse visual outcomes in patients with idiopathic full-thickness MHs. Precise image-wise binary annotations are used to segment the retinal external limiting membrane (ELM) layer. Finally, qualitative and quantitative results are systematically compared with seven state-of-the-art DL-based segmentation methods to identify the ELM layer with an automated system. Additionally, it examines the feasibility of integrating automated ELM layer segmentation into clinical workflows while incorporating the latest advancements in DL-based ELM detection. The results confirm the efficacy of DL in retinal image analysis, providing a foundation for future enhancements in clinical applications. Future work will explore enhancing the models’ performance and efficiency, and extending the approach to other retinal conditions. Keywords: Image Analysis, Machine Learning, Deep Learning, Visual Acuity Measurement, Optical Coherence Tomography Description: Ph. D. Thesis.

2025-01-01T00:00:00Z Sahar, Ayesha http://theses.ncl.ac.uk/jspui/handle/10443/6762 2026-05-08T11:27:47Z 2025-01-01T00:00:00Z

Title: Advancing scientific knowledge representation : standardisation and integration in tolerogenic therapies Authors: Sahar, Ayesha Abstract: In this thesis, we use data integration and analysis methods and examine the impact of data standardisation to enhance our understanding of tolerogenic dendritic cell (tolDC) therapies. Standardisation and structuring of the data are extremely valuable for it to be useful and accessible. Emerging biological fields face unique difficulties, including limited data availability, a lack of standardisation and challenges in knowledge management from different studies due to varied methodologies. These issues demand the development and application of specialised techniques and strategies tailored to their specific data handling and management needs. This thesis focuses on one such emerging field, “tolerogenic Dendritic Cell Therapy”, which has demonstrated significant potential. Like all biomedical experiments, developing these therapies involves several crucial steps that must be well-documented for comparison and replication purposes. Reporting frameworks, like Minimum Information Models can aid in standardising these descriptions; Minimum Information about Tolerogenic AntigenPresenting cells (MITAP) was created in 2016 in this field for this purpose. We evaluate MITAP’s impact on the field of tolDC therapies by analysing a selection of literature. We found that MITAP is utilised in a minority of relevant papers (14%), but where it is applied, there is slightly more metadata available. This suggests that while MITAP has had some success, further efforts are needed for standardised reporting to become widespread in the discipline. In order to further aid the comparison, re-purposing and re-use of data about tolDC therapies, we built a method to identify and integrate the most significant information related to tolerogenic dendritic cell therapies into a knowledge graph structure. A key aspect of the knowledge graph is ensuring that the merged data is relevant to the field. We employ knowledge extraction techniques to identify and collect relevant information from research articles, integrating this with publicly available datasets to enrich the knowledge base. We successfully embedded this data into a comprehensive knowledge graph comprising 120k entities extracted from full-text articles and additional integration of 92k relationships from other relevant databases. The use of knowledge extraction techniques from research articles ensured the relevance of the integrated data to the field. It also allowed us to gain more insights from publications with unpublished experimental data, as shown in the example queries. This knowledge graph can act as a base for the generation of further hypotheses as well as a database for the storage and retrieval of relevant information about tolDC therapies. Having built the knowledge graph our focus shifts to considering queries about the tolDC therapies that give us a better understanding of the degree of standardisation, about the underlying biology and the social environment in the field. We formulated diverse queries encompassing heterogeneity concerns. The results demonstrated the effectiveness of tolKG in promptly addressing these queries, a task that would either necessitate specialised expertise or significant manual scrutiny if pursued conventionally. Through the utilisation of tolKG, we streamline tasks such as comparison and analysis and even facilitate the generation of novel hypotheses. In summary, we found that a knowledge graph is an effective way to integrate data. Moreover, the addition of data from the literature makes it more meaningful, especially for emerging fields where there is a lack of experimental data sharing. Text mining from literature enables the extraction of more relationships that are specific to a field. As a result, it can help to perform an effective analysis and comparison of the tolDC therapy field. Together, this work helps establish the groundwork for applying data science methods in tolDC therapies making several kinds of comparisons possible which are not possible without it. The methodologies employed are specifically tailored to the data sources of tolDC therapies. Nonetheless, these strategies are not restricted to this particular domain; they primarily depend on the input data sources, which makes them usable in other areas of biology as well. Description: PhD Thesis

2025-01-01T00:00:00Z Vidal Peña, Gonzalo Andrés http://theses.ncl.ac.uk/jspui/handle/10443/6731 2026-04-10T11:17:05Z 2025-01-01T00:00:00Z

Title: Automated design, build, test, learn workflows to engineer synthetic genetic networks Authors: Vidal Peña, Gonzalo Andrés Abstract: Synthetic biology is an interdisciplinary field that pursues the engineering of biological systems. The design, build, test, learn (DBTL) cycle is at the core of engineering disciplines and is iterated until a desired goal is achieved. Synthetic biology is still defining abstractions, standards and developing a software ecosystem to iterate the DBTL cycle. The aim is to work in a similar way as other engineering disciplines, making designs with a computational aided design (CAD) tool that can simulate the expected behaviour of the designed biological system, and that can communicate to build tools to create a physical implementation of the biological system. After the biological system is built, it is tested by taking measurements of its behaviour. The test has to be automated, calibrated and standardised to get high quantity and quality data that can inform the learn stage properly. Given the diversity of synthetic biology and its applications the DBTL cycle could have different needs when the researcher needs to engineer a genetic network, a metabolic pathways, a strain or a protein, among others. The focus of this work is in creating DBTL cycle workflows for engineering synthetic genetic network dynamics, because it allows to control the logic of a system and how that logic state is reached and maintained over time with direct applications in biochemical production, drug dosage, and the study of pattern formation and developmental biology. Existing tools for engineering genetic network dynamics do not cover the whole DBTL cycle and lack connections, leaving several gaps. Most tools do not use standardised inputs and outputs hindering the connectivity between tools and slowing the research process. To iterate faster through the DBTL cycle it has to be closed and automated by leveraging software tools and liquid handling robots. Software tools have to be compatible with standards to make them useful and accessible for the community, promoting the use of best practices. The workflow has to be flexible to accommodate different needs and resources, to be used for researchers without a wetlab, with non-automated wetlab and with lab automation. Here I have created a set of software tools tackling different DBTL cycle stages that are modular and leverage standards to connect and automate the DBTL cycle for genetic network engineering. The workflows developed in this work provides novel teaching and research tools available for different needs. Description: Ph. D. Thesis.

2025-01-01T00:00:00Z