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Applications of model checking in the context of cyber security for digital twins
Get PDFCyber security attacks on Industrial Control Systems (ICSs) are increasingly sophisticated, targeting their ability to manage critical processes and posing risks to national infrastructure. Addressing this threat requires innovative methods to ensure the secure design and operation of ICS. Digital Twins (DTs) have emerged as a promising tool for enhancing the efficiency and cyber security of the systems they represent; however, their effectiveness depends on reliable intrusion detection methods and secure integration within existing industrial control environments. Securely deploying a DT to an ICS requires careful consideration of existing architecture and the potential security risks of incorporating the DT itself. Formal methods, in particular model checking, are an effective tool for analysing system design and detecting cyber security vulnerabilities.
We present two complementary applications of model checking techniques to support the deployment of DTs in ICS environments. We first develop a specification-based intrusion detection approach utilising the SPIN model checker and deploy it into a DT environment for a hydroelectric dam testbed. We explain the process we followed to develop Promela models from PLC code to detect inconsistencies between received data and specified system behaviours. Our evaluation shows that the models achieved performance on a par with machine learning approaches while maintaining explainability and delivering metrics of 99.99% precision, 99.05% recall, a 99.52% F1-score, and 99.05% accuracy.
We then address the expanded attack surface that can result from integrating DTs into ICSs. We explore this issue by developing a series of Alloy models that consider the dataflow between a DT and its underlying asset. The developed models incorporate novel modelling of an attacker’s action space to represent how threat actors can move through a network. Using our approach, we model our hydroelectric testbed DT to identify security vulnerabilities in our design and develop an improved network design to mitigate them. Our approach successfully identified security vulnerabilities within the DT-ICS integration and informed network design improvements to reduce the attack surface significantly. Our evaluation confirms that model checking techniques enhance both intrusion detection and security assessment, offering a structured and explainable alternative to machine learning methods. We discuss the merits and drawbacks of each of our approaches and discuss methods of expanding and improving them to support DT development
Cardiovascular disease in Type 2 Diabetes Mellitus: A precision medicine approach applying artificial intelligence for heart failure and mortality prediction
Get PDFCardiovascular diseases (CVDs) are the leading cause of morbidity and mortality worldwide, despite substantial advances in diagnosis and treatment. People who suffer from cardiovascular disease often have multiple risk factors and other chronic conditions. Additionally, medical events may be strongly influenced by socioeconomic status. Patient information can be obtained from electronic medical records (EMRs) that, unlike data from clinical trials and registries, provide a broad range of patient characteristics representative of the general population. EMRs covering a population of ~1.1 million people in Greater Glasgow & Clyde (GG&C) Health Board NHS over 50 years (the age at which the incidence and prevalence of disease affecting older people increase rapidly) were used. Information such as demographics, laboratory tests, primary-care prescriptions, hospitalisations and mortality was retrieved. Several steps were required to ensure that the extracted information was appropriate for analysis and transformed for investigations beyond traditional statistics. Accordingly, data on patients with type-2 diabetes mellitus (T2DM) were obtained to examine their health trajectories, including, incident heart failure and death. Novel risk prediction models were built to help understand the development of heart failure (HF) in patients with T2DM. The models were developed using random survival forest (RSF) methodology. This research highlights the limitations of traditional regression models and demonstrates the improvement of risk prediction with RSF methods, which outperformed traditional approaches in both discrimination and calibration. State-of-the-art machine learning interpretation was applied to discover key contributing factors to the development of heart failure and to all-cause mortality. External validation was applied by acquiring EMRs from Hong Kong, Special Administrative Region (SAR) China. The inclusion of two diverse populations found little evidence of ethnicity-related differences in risk factors. GG&C key risk factors for incident HF were loop diuretics, atrial fibrillation (AF), history of coronary artery disease (CAD), older age, lower levels of estimated glomerular filtration rate (eGFR), haemoglobin and serum albumin. Similarly, for Hong Kong, key risk factors were use of loop diuretics, insulin, lower serum albumin, haemoglobin, lymphocyte counts and eGFR. The model based on Hong Kong data showed slightly better performance compared to the Glasgow cohort for incident heart failure (C-index 0.88 and 0.87) and all-cause mortality (0.85 and 0.83). In both cohorts’ older women were more likely to be prescribed loop diuretics. Whether loop diuretics are just a marker of undiagnosed heart failure or whether they accelerate the progression of cardiovascular and renal disease is uncertain. Another key similarity was that patients had prevalent chronic kidney disease (CKD) events in the prescribed loop diuretics groups. Treatment with loop diuretics was strongly associated with all-cause mortality in GG&C and Hong Kong. (GG&C: adjusted hazard ratio: 2.93, (95% CI: 2.821 to 3.04); Hong Kong: adjusted hazard ratio: 1.75 (95% CI: 1.72 to 1.77). Only a minority of patients prescribed loop diuretics had a diagnosis of heart failure, end-stage renal disease or resistant hypertension. Finally, further investigation of social deprivation in GG&C underlined that 41% patients with T2DM were in the most deprived socioeconomic quintile and that they had a 36% higher rate for all-cause mortality compared to those who were least deprived (adjusted HR: 1.36, 95% CI 1.24–1.50, p < 0.005)
Advanced systems and processing solutions for enhancement of weak signal detection in radar systems
No full textAbstract not currently available
Modelling the failure process of concrete subjected to shock
No full textAbstract not currently available
Changepoint detection for net electricity demand modelling in Great Britain
Get PDFThis thesis investigates a changepoint detection methodology applied to net electricity demand modelling in Great Britain. With the increasing integration of renewable energy sources and the growing complexity of electricity demand patterns, accurately identifying abrupt changes, or “changepoints,” in demand data has become essential for reliable grid management. Initial analyses of national electricity demand data were conducted to explore the underlying features and factors influencing consumption patterns. By integrating Generalised Additive Models (GAMs) within a changepoint detection framework, this research introduces a flexible approach capable of capturing non-linear trends and seasonal variations without requiring extensive manual adjustments, allowing the model to adapt to diverse fluctuations in demand.
Traditional changepoint algorithms were evaluated, but the unique complexities of electricity demand data led to the development of a novel changepoint detection algorithm tailored to these demands. Simulation studies tested the proposed methodology under various mean shifts and noise levels, offering insights into how these parameters impact changepoint detection accuracy. The novel algorithm was subsequently applied to regional Grid Supply Point (GSP) electricity demand data, demonstrating how different demand patterns across geographic areas influence changepoint locations.
The findings underscore both the strengths and limitations of integrating model-based cost functions and GAMs within changepoint detection, particularly in managing daily and seasonal cycles, addressing computational constraints, and scaling across large datasets. This approach enhances the accuracy and flexibility of electricity demand modelling by effectively identifying abrupt changes, enabling a more robust response to demand variability
Elucidating the role of the TGF beta superfamily in metastatic spread of colorectal cancer
Get PDFColorectal cancer (CRC) is an aggressive disease and the leading cause of cancer death, characterised by high heterogeneity and various risk factors related to its etiology (1-3). During carcinogenesis, adenomatous polyps, which represent most premalignant lesions (85-90% of sporadic CRC), can develop into CRC (4-6). About 20-25% of CRC patients are diagnosed with metastatic disease, which is associated with poorer survival rates. CRC can spread to various tissues, including lymph nodes, liver, lungs, peritoneum, bones, and the central nervous system (7). The liver is the most common site for detecting metastatic CRC and is involved in 25% to 50% of cases. However, a rare form of CRC with bone metastasis occurs in 3% to 7% of patients; these individuals often have worse survival outcomes, with shorter survival times and limited treatment options (8-10). TGF-β and BMP signalling pathways are crucial mechanisms in tissue homeostasis, promoting cell proliferation and differentiation during crypt formation in the intestine (11-15). Dysregulation of these pathways in intestinal cells can impair their tumour-suppressing functions and facilitate tumour development (16-18). In particular, mutations in TGFBR2, BMPR1A, and SMAD4 have been identified as contributing to CRC carcinogenesis (2, 3, 19-23). Patients with consensus molecular subtype (CMS) 4, categorised based on gene expression signature, are associated with prominent activation of TGF-β, along with stromal infiltration, epithelial to mesenchymal
transition (EMT), and angiogenesis, leading to poorer survival outcomes (19).
In silico analysis of gene expression levels of TGF-β and BMP signalling components across CRC cell lines using the DepMap database, along with examination of key proteins and their phosphorylation statuses through Western blots, revealed that ligand activation occurs at various levels, with notable activation of upstream receptors such as TGFBR2, ACVR1B, and BMPR1A. Increased expression of signal transduction genes, including SMAD1, SMAD2, SMAD3, and SMAD5, was also observed. Interestingly, although phosphorylation of SMAD1/5/8 was detected in SW620 cell lines (a metastatic CRC cell line), SMAD4, a central element in signal transduction, was found to be downregulated. Apart from the differential expression of TGF-β and BMP signalling in CRC, the investigation of gene expression in these pathways within disease-free bone marrow (BM) cells using the Stemformatics database was also investigated to provide some insight into whether this morphogenic pathway is involved in metastatic spread to the bone. Here we demonstrated high levels of TGF-β and BMP signalling pathways in the bone marrow associated cells, indicating maintenance of homeostasis and serving as a baseline reference for further research.
Assessment of epithelial SMAD4 expression in colorectal polyps using immunohistochemistry (IHC) and digital weighted histoscoring with QuPath revealed that low SMAD4 levels in adenomatous polyps correlated with higher grades of dysplasia, different histological subtypes, the presence of metachronous polyps, and served as a prognostic marker. This marker indicated an increased risk of developing metachronous polyps, particularly in the tubulovillous polyp subtype. Additionally, transcriptomic analysis of tubulovillous polyps showed that upregulation of genes involved in protein deubiquitination occurs in polyps with
low SMAD4 levels, along with a likely enrichment in tyrosine metabolism, PPAR signalling, arginine and proline metabolism, leukocyte transendothelial migration, and basal cell carcinoma. In CRC, lower epithelial SMAD4 expression was strongly associated with higher tumour stages and increased tumour stroma. Moreover, lower SMAD4 expression in CRC tumours had prognostic significance, predicting decreased cancer-specific survival in CRC patients, especially in right-sided tumours. A combination of tumour SMAD4 levels and stroma percentages suggested that the worst survival outcome was in patients with low SMAD4
expression in the tumour and high stroma content. Transcriptomic analysis also identified downregulation of SOD3 and enrichment of aminopeptidase activity in this group.
Differential expression patterns at gene and protein levels, along with phosphorylation activity, were observed across the complex crosstalk of MAPK/ERK, WNT/β-catenin, TGF-β/BMP, and PI3K signalling pathways in CRC cell lines. However, the mechanisms regulating SMAD4 activity in CRC remain unclear.Testing the TGF-β and BMP signalling inhibitor (LDN-212854) in combination with the standard chemotherapy (Fluoropyrimidine; 5-FU) showed synergistic effects on CRC cell viability, cell cycle arrest, cell proliferation, and cell recovery in
an in vitro 2D study. Conditioned media from BM and hepatic cells influence changes in CRC behaviour. The 3D bioprinted SW620 spheroids in 2% alginate and 8% gelatin hydrogel supported physiological interactions, spheroid survival and growth, and were used for drug screening, demonstrating efficacy of the LDN-212854/5-FU combination in a more mechanophysical 3D system.
Further development of this in vitro 3D model to become multicell by incorporating metastatic CRC culture with BM and hepatic niches would enhancefuture CRC research and drug discovery
The digital fantastic: theorising the role of hesitation in experiences of fantasy in video games
No full textAbstract not currently available
Intestinal tissue mechanics regulate angiogenesis and stem cell proliferation through endothelial Piezo
Get PDFThe vasculature is an important component of stem cell niches, which is well characterised in many tissues. However, the angiocrine contribution to the intestinal stem cell niche remains largely understudied. Previously, our lab has uncovered an unrecognised crosstalk between the intestinal epithelium and the vasculature-like tracheal system in the fruit fly, Drosophila melanogaster. This is mediated by ROS and HIF-1 /FGF/FGFR signalling, which is necessary for tracheal remodelling and regenerative stem cell proliferation upon damage. However, we have also observed tracheal remodelling in contexts which are independant of ROS and therefore, this pathway alone is insufficient to justify the widely observed adaptation of the tracheal tissue to gut damage. As the vasculature is a very mechanosensitive tissue and the intestine undergoes significant morphological changes during regeneration, we hypothesised that mechanical changes induced in the intestine could contribute to tracheal remodelling and intestinal stem cell proliferation during regeneration and tumorigenesis. In my PhD work, I have found significant morphological changes in the intestinal epithelium which indicate altered cell mechanical state and upregulation of the mechanosensitive ion channel Piezo in the gut-associated tracheal system upon intestinal damage. Furthermore, in this thesis I show that Piezo is a regulator of previously described tracheal remodelling pathways through activation of Yki and find that the role of Piezo and the vascular intestinal stem cell niche is conserved between Drosophila and mammalian systems
To per-cyst or not: unravelling the secrets behind an attenuated Toxoplasma strain
No full textAbstract not currently available
Strengthening of concrete columns with pseudo-ductile hybrid FRP
No full textAbstract not currently available
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