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Evaluating post 2024 election scenarios for the UK based on political party manifestos
No full textManifestos provide a vision for political parties to enact if elected to government. While manifestos may not be enacted in full, they provide some of the best information for the public in deciding how to vote. However, manifestos tend to contain both enactable policies (e.g., tax cuts) and outcomes or visions of what these policies may achieve for society (e.g., higher disposal income). These outcomes may be deliberately misleading, inaccurate or only a partial picture of how policies will materialise, as it is unlikely a single policy will influence a single outcome. This work creates a complex system model of the economic, societal and environmental landscape of the UK and assesses how it would be affected if political parties enacted their 2024 general election manifesto policies in full. Our model creates a more complete picture of how the UK may look under different parties, rather than examining the manifestos alone (using data solely from manifestos almost 30% of our model’s node values had no information, falling to just 5% after running the models).The model also has the capacity to provide a holistic reflection of the parties manifesto plans, illustrating the impacts each party’s policies could have, should they be enacted. Prior to integrated analysis the most right-wing of the parties studied; Reform UK, aligned strongly with the Conservative party, however, post analysis Reform became a clear outlier. We also demonstrate unintended, misreported or indirect effects of policies. Most notably, parties who had the strongest tax cutting policies resulted in lower average incomes and higher levels of inequality in society, despite the rhetoric provided for these policies in the party manifestos. The results demonstrate the ability to integrate multiple types of information across political, economic, environmental, and social landscapes to help visualise implications of policy and politics more widely
Three-dimensional interaction of twin tunnels numerical analysis of the Waterloo International Terminal case study
No full textBeing able to predict with precision and certainty how existing tunnels respond to new tunnelling works in urban areas is vital for the safety of the existing tunnels and for minimising the cost and environmental impact of the new tunnels. The three-dimensional interaction of tunnels in stiff, overconsolidated clays has mainly been restricted to field studies, with only a few generic numerical studies. Nonetheless, a large part of underground tunnel construction has happened and continues to occur in overconsolidated clays. The paper bridges this gap by using the case study of Waterloo International Terminal, where two new tunnels were excavated beneath two 70 year-old tunnels, to validate a numerical model. The validated numerical results provide new, valuable insights into the differences and similarities of the response of the existing tunnels depending on their typology (running or station tunnels) and on the time after the excavation of the new tunnels. Furthermore, they reveal the significance of the stiffness reduction factors that need to be applied to account for the segmental nature of the tunnel linings, highlighting the need for further research into the operational value of the tunnel stiffness
Linking experimental H2O vapor adsorption on biomass char with physicochemical char properties and MD simulation
No full textThe adsorption of H2O vapor on biomass char particles is gravimetrically measured in a temperature range of 298.15 K to 323.15 K. The results are correlated to results of a comprehensive structural and chemical char analysis using N2 and CO2 physisorption measurements with corresponding 2D-NLDFT models and temperature-programmed desorption measurements (TPD) for the detection of oxygen-containing functional groups (OFG). The adsorption isotherms of a highly porous and unfunctionalized model char show a distinct type V shape for hydrophobic, microporous materials, which is in accordance with its structural and chemical properties. In contrast, results for a highly functionalized model char show type II isotherm characteristics with high adsorption capacity at low H2O concentrations. The adsorption behavior of a beechwood char with a conversion history aligns more closely with that of the unfunctionalized model char, exhibiting differences that correlate with its less pronounced pore structure and higher proportion of OFG. Molecular dynamics (MD) simulations of ideal slit pores were conducted to confirm distinct effects and tendencies found in the H2O adsorption measurements with respect to outstanding char properties. The simulations confirm a strong binding tendency of H2O molecules to OFG, especially for comparably low H2O densities as well as a contribution of the confinement effect in small pores to the overall adsorption capacity
A Robust Decision-Making approach to evaluating state support for nuclear programmes under uncertainty: a UK case study
No full textSeveral western countries have announced ambitious nuclear energy programmes to boost energy security and support decarbonisation targets following the 2022 energy crisis. However, persistent delays, cost overruns, and volatile financial conditions hinder orthodox strategic planning and deter investment in a sector that has stagnated for two decades.
To accommodate for the deep uncertainty, this study employs a Robust-Decision Making (RDM) approach to develop financing policy recommendations to meet the United Kingdom (UK) government’s nuclear ambitions for 2050. Different revenue, financing, and government support schemes are tested via the RDM framework across wide-ranging plausible future conditions. Three deployment scenarios that meet the government’s target build upon current UK construction and planned initiatives, incorporating GenIII+ light-water technologies with varying amounts of large-scale reactors (LR) and Small Modular Reactors (SMR). Findings indicate that the financial and revenue structure of nuclear programmes, particularly the adoption of regulated models over market-aligned ones, helps manage financial risks by addressing uncertainties inherent in nuclear power deployment. This could in turn facilitate investment in the sector and restrict the risk costs falling on consumers. Direct government backing is discussed, and the fiscal cost of state majority investment is estimated and set against broader public energy funding
Tropical forest carbon accounting through deep learning-based species mapping and tree crown delineation
No full textTropical forests are essential ecosystems recognized for their carbon sequestration and biodiversity benefits. As the world undergoes a simultaneous data revolution and climate crisis, accurate data on the world’s forests are increasingly important. Completely novel in approach, this study proposes a methodology encompassing two bespoke deep learning models: (1) a single encoder, double decoder (SEDD) model to generate a species segmentation map, regularized by a distance map in training, and (2) an XGBoost model that estimates the diameter at breast height (DBH) based on tree species and crown measurements. These models operate sequentially: RGB images from the ReforesTree dataset undergo preprocessing before species identification, followed by tree crown detection using a fine-tuned DeepForest model. Post-processing applies the XGBoost model and custom allometric equations alongside standard carbon accounting formulas to generate final sequestration estimates. Unlike previous approaches that treat individual tree identification as an isolated task, this study directly integrates species-level identification into carbon accounting. Moreover, unlike traditional carbon estimation methods that rely on regional estimations via satellite imagery, this study leverages high-resolution, drone-captured RGB imagery, offering improved accuracy without sacrificing accessibility for resource-constrained regions. The model correctly identifies 67% of trees in the dataset, with accuracy rising to 84% for the two most common species. In terms of carbon accounting, this study achieves a relative error of just 2% compared to ground-truth carbon sequestration potential across the test set
Machine Learning for patient selection in corticosteroid decision making in knee osteoarthritis: a feasibility model
No full textBackground:
Relieving pain is central to the early management of knee osteoarthritis, with a plethora of pharmacological agents licensed for this purpose. Intra-articular corticosteroid injections are a widely used option, albeit with variable efficacy.
Aim:
To develop a machine learning model that predicts which patients will benefit from corticosteroid injections.
Methods:
Data from two prospective cohort studies (OAI and MOST) was combined. The primary outcome was patient-reported pain score following corticosteroid injection, assessed using the WOMAC pain scale, with significant change defined using Minimally Clinically Important Difference and Meaningful Within Person Change. A machine learning algorithm was developed, utilising Linear Discriminant Analysis, to predict symptomatic improvement, and examine the association between pain scores and patient factors by calculating the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and F2 score.
Results:
A total of 330 patients were included, with a mean age of 63.4 (SD: 8.3). The mean WOMAC pain score was 5.2 (SD: 4.1), with only 25.5% of patients achieving significant improvement in pain following corticosteroid injection. The machine learning model generated an accuracy of 67.8% (95 CI: 64.6% – 70.9%), F1 score of 30.8%, and an AUC score of 0.60.
Conclusion:
The model demonstrated feasibility to assist clinicians with decision-making in patient selection for corticosteroid injections. Further studies are required to improve the model prior to testing in clinical settings
Shallow recurrent decoder for reduced order modeling of E×B plasma dynamics
No full textReduced-order models (ROMs) are becoming increasingly important for rendering complex and multiscale spatiotemporal dynamics computationally tractable. Computationally efficient ROMs are especially essential for optimized design of technologies as well as for gaining physical understanding. Plasma simulations, in particular those applied to the study of E × B plasma discharges and technologies, such as Hall thrusters for spacecraft propulsion, require substantial computational resources in order to resolve the multidimensional dynamics that span across wide spatial and temporal scales. While high-fidelity computational tools are available, their applications are limited to simplified geometries and narrow conditions, making simulations of full-scale plasma systems or comprehensive parametric studies computationally prohibitive. In addition, experimental setups involve limitations such as the finite spatial resolution of diagnostics and constraints imposed by geometrical accessibility. Consequently, both scientific research and industrial development of plasma systems, including E × B technologies, can greatly benefit from advanced reduced-order modeling techniques that enable estimating the distributions of plasma properties across the entire system. We develop a model reduction scheme based upon a Shallow REcurrent Decoder (SHRED) architecture using as few measurements of the system as possible. This scheme employs a neural network to encode limited sensor measurements in time (of either local or global properties) and reconstruct full spatial state vector via a shallow decoder network. Leveraging the theory of separation of variables, the SHRED architecture demonstrates the ability to reconstruct complete spatial fields with as few as three-point sensors, including fields dynamically coupled to the measured variables but not directly observed. The effectiveness of the ROMs derived with SHRED is demonstrated across several plasma configurations representative of different geometries in typical E × B plasma discharges and Hall thrusters
Upper-gastrointestinal tract metabolite profile regulates glycaemic and satiety responses to meals with contrasting structure: a pilot study
No full textDietary interventions to combat non-communicable diseases focus on optimising food intake but overlook the influence of food structure. Here, we investigate how food structure influences digestion. In a randomised crossover study, ten healthy participants were fitted with nasoenteric tubes that allow simultaneous gastric and duodenal sampling, before consuming iso-nutrient chickpea meals with contrasting cellular structures. Primary outcome is gut hormone response. Secondary outcomes are intestinal content analysis, blood glucose and insulin response, subjective appetite changes and ad libitum energy intake. We show that the ‘broken’ and ‘intact’ cell structures of meals result in different digestive and metabolomic profiles, leading to distinct postprandial gut hormones, glycaemia and satiety responses. ‘Broken' meal structure elicits higher GIP, GLP-1, and blood glycaemia, driven by high starch digestibility and a sharp rise in gastric maltose within 30 minutes. ‘Intact’ meal structure produces a prolonged release of GLP-1 and PYY, elevated duodenal amino acids, and undigested starch at 120 minutes. This work highlights how food structure alters upper gastrointestinal-nutrient-sensing hormones, providing insights into the adverse effects of modern diets on obesity and type 2 diabetes. ISRCTN registration: ISRCTN18097249
Chemical Engineering Research. Reports of the 4th year research projects in the Department of Chemical Engineering at Imperial College London. Volume 7
No full textThis volume of Chemical Engineering Research collects the unedited research project reports written by 4th year undergraduates (Class of 2025) of the M.Eng. course on Chemical Engineering in the Department of Chemical Engineering at Imperial College London. The research project spans one term (Autumn) during the last year of the career. It emphasises independence, the ability to plan and pursue original project work for an extended period, produce a high-quality report, and present the work to an audience using appropriate visual aids. Students are also expected to produce a literature survey and to place their work in the context of prior art.
The papers presented showcase the diversity and depth of some of the research streams in the department but only touch on a small number of research groups and interests. For a complete description of the research at the department, the reader is referred to the departmental website¹.
The papers presented are in no particular order, and a manuscript number identifies them. Some papers refer to appendixes and/or supplementary information which are too lengthy to include. These files are available directly from the supervisors (see supervisor index at the end of the book). Some reports are missing and being embargoed, as they contain confidential information. A few reports correspond to industrial internships, called LINK projects, in collaboration with Shell.
The cover figure corresponds to a Voronoi neighbouring analysis of 0.01%AOT + 8%PS + DCM sample (taken from the work of Weng Kit Leong and Zewen Liu, manuscript 15)
Off-pump CABG is a safe option during pregnancy, where cardiopulmonary bypass (CPB) could pose additional significant surgical risk
No full textOff-pump coronary artery bypass grafting (OPCAB) is a surgical technique designed to treat coronary artery disease (CAD) by restoring blood flow to the myocardium through new arterial or venous grafts without the use of cardiopulmonary bypass (CPB). The gold standard
conduit, the left internal thoracic artery (LITA), is most commonly anastomosed to the distal left anterior descending (LAD) artery [1], with additional grafts based on anatomical requirements [1,2]. OPCAB offers distinct advantages over traditional on-pump CABG, notably reducing complications such as systemic inflammatory response, neurological deficits, and prolonged recovery [1,2]. It is particularly beneficial in patients at high risk for
stroke [3] or renal failure [4].
We report a rare case of successful OPCAB in a 27-year-old pregnant woman with homozygous familial hypercholesterolemia (FH) and multivessel CAD during her second trimester. This case underscores the feasibility, safety, and effectiveness of OPCAB in managing complex coronary disease during pregnancy, minimizing maternal and fetal risks associated with CPB