Skip to main content

Research Repository

Advanced Search

Outputs (7)

Utilising User Data from a Food-Sharing App to Evidence the "Heat-or-Eat" Dilemma (2024)
Presentation / Conference Contribution
Semple, T., Harvey, J., Rodrigues, L., Gillott, M., Figueredo, G., & Nica-Avram, G. (2024, May). Utilising User Data from a Food-Sharing App to Evidence the "Heat-or-Eat" Dilemma. Presented at 2nd Digital Footprints Conference: Linking Digital Data for Social Impact, Bristol, UK

Introduction & Background
Previous literature has found that financially vulnerable households often make involuntary spending trade-offs between necessities, particularly energy and food. This effect is especially pronounced during winter, when hom... Read More about Utilising User Data from a Food-Sharing App to Evidence the "Heat-or-Eat" Dilemma.

A SARS-CoV-2 minimum data standard to support national serology reporting (2024)
Journal Article
Urwin, E. N., Martin, J., Sebire, N., Harris, A., Johnson, J., Masood, E., …Jefferson, E. (2024). A SARS-CoV-2 minimum data standard to support national serology reporting. Annals of Clinical Biochemistry, https://doi.org/10.1177/00045632241261274

Background: Healthcare laboratory systems produce and capture a vast array of information, yet do not always report all of this to the national infrastructure within the United Kingdom. The global COVID-19 pandemic brought about a much greater need f... Read More about A SARS-CoV-2 minimum data standard to support national serology reporting.

A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings (2024)
Journal Article
Cartwright, S., Rothwell, B. C., Figueredo, G., Medina, H., Eastwick, C., Layton, J., & Ambrose, S. (2024). A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings. Tribology International, 196, Article 109670. https://doi.org/10.1016/j.triboint.2024.109670

Traditional methods of evaluating the performance of journal bearings, for example thermal-elastic-hydrodynamic- lubrication theory, are limited to simplified conditions that often fail to accurately model real-world components. Numerical models that... Read More about A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings.

An empirical critique of the low income low energy efficiency approach to measuring fuel poverty (2024)
Journal Article
Semple, T., Rodrigues, L., Harvey, J., Figueredo, G., Nica-Avram, G., Gillott, M., …Goulding, J. (2024). An empirical critique of the low income low energy efficiency approach to measuring fuel poverty. Energy Policy, 186, Article 114014. https://doi.org/10.1016/j.enpol.2024.114014

Fuel poverty is a complex socioenvironmental issue of increasing global significance. In England, fuel poverty is assessed via the Low Income Low Energy Efficiency (LILEE) indicator, yet concerns exist regarding the efficacy of this metric given its... Read More about An empirical critique of the low income low energy efficiency approach to measuring fuel poverty.

Active learning-driven uncertainty reduction for in-flight particle characteristics of atmospheric plasma spraying of silicon (2023)
Journal Article
Memon, H., Gjerde, E., Lynam, A., Chowdhury, A., De Maere, G., Figueredo, G., & Hussain, T. (2024). Active learning-driven uncertainty reduction for in-flight particle characteristics of atmospheric plasma spraying of silicon. Engineering Applications of Artificial Intelligence, 128, Article 107465. https://doi.org/10.1016/j.engappai.2023.107465

The first-of-its-kind use of the active learning (AL) framework in thermal spray is adapted to enhance the prediction accuracy of the in-flight particle characteristics. The successful AL framework implementation via Bayesian Optimisation is benefici... Read More about Active learning-driven uncertainty reduction for in-flight particle characteristics of atmospheric plasma spraying of silicon.

Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease (2022)
Journal Article
Gonem, S., Taylor, A., Figueredo, G., Forster, S., Quinlan, P., Garibaldi, J. M., McKeever, T. M., & Shaw, D. (2022). Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease. Respiratory Research, 23, Article 203. https://doi.org/10.1186/s12931-022-02130-6

Background: The National Early Warning Score-2 (NEWS-2) is used to detect patient deterioration in UK hospitals but fails to take account of the detailed granularity or temporal trends in clinical observations. We used data-driven methods to develop... Read More about Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease.

Pan-cancer analysis reveals TAp63-regulated oncogenic lncRNAs that promote cancer progression through AKT activation (2020)
Journal Article
Napoli, M., Li, X., Ackerman, H. D., Deshpande, A. A., Barannikov, I., Pisegna, M. A., …Flores, E. R. (2020). Pan-cancer analysis reveals TAp63-regulated oncogenic lncRNAs that promote cancer progression through AKT activation. Nature Communications, 11, Article 5156. https://doi.org/10.1038/s41467-020-18973-w

The most frequent genetic alterations across multiple human cancers are mutations in TP53 and the activation of the PI3K/AKT pathway, two events crucial for cancer progression. Mutations in TP53 lead to the inhibition of the tumour and metastasis sup... Read More about Pan-cancer analysis reveals TAp63-regulated oncogenic lncRNAs that promote cancer progression through AKT activation.