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Unvortex lattice and topological defects in rigidly rotating multicomponent superfluids
No full textBy examining rotating ferromagnetic spinor condensates through the perspective of large spin, we identify a novel type of topological point defects in the magnetization texture. These defects are not predicted by conventional homotopy analysis but rather by the Riemann-Hurwitz formula. The magnetization texture in the system is described by an equal-area mapping from the plane to the sphere of magnetization, forming a lattice of uniformly charged Skyrmions. This lattice contains doubly-quantized (winding number = 2) point defects arranged on the sphere in a tetrahedral configuration. The fluid found to be rotating rigidly, except at the point defects, where the vorticity vanishes. This vorticity structure describes an unconventional "unvortex" lattice, which contrasts with the well-known vortex lattice in scalar rotating superfluids, where vorticity is concentrated exclusively within defect points. Numerical results are presented, confirming these predictions and demonstrating their persistence in smaller-spin condensates
Spatiotemporal patterns in Indo-Pacific reef cryptobenthic communities using image analysis and computer vision
No full textImperial Users onl
Bringing hearables to life: applications in sleep and glucose monitoring
No full textThis thesis explores an innovative use of hearables—in-ear sensing of neural function and vital signs—related to monitoring sleep and blood glucose levels. Through analysis and case studies, we demonstrate the utility and potential of in-ear electroencephalogram (EEG) and in-ear photoplethysmogram (PPG) devices for sleep staging and glucose monitoring. We first introduce a non-invasive method for automatic sleep staging in older adults using a single-channel ear-EEG approach. By extracting features from frequency, time, and structural complexity domains, substantial agreement with gold-standard human-scored hypnograms was achieved, with a kappa value of at least 0.61. This offers a portable and cost-effective alternative to traditional polysomnography.
The second case study leverages existing scalp EEG data to enhance in-ear EEG analysis through transfer learning. By fine-tuning scalp-EEG pre-trained models using ear-EEG data, classification accuracy improved by 4%, paving the way for a transition from standard wearables to hearables.
In physiological sensing, motion artifacts are often seen as nuisances. This work takes the approach that “no data is bad data” by using artifacts from hearables as behavioural cues. Activities such as sitting, speaking, chewing, and walking were classified from real-world data. Analysis using various machine learning techniques yielded high accuracy and introduced a novel approach to human activity recognition. Finally, we address a novel application in non-invasive glucose monitoring using an in-ear PPG device. This work establishes a relationship between raw PPG and blood glucose levels, with 82% of estimates falling within clinically acceptable Clarke error grid regions. Overall, this thesis demonstrates the promise of hearables for unobtrusive, continuous, and accessible health monitoring.Open Acces
GLP-1R associates with VAPB and SPHKAP at ERMCSs to regulate β-cell mitochondrial remodelling and function
No full textGlucagon-like peptide-1 receptor (GLP-1R) agonists (GLP-1RAs) ameliorate mitochondrial health by increasing mitochondrial turnover in metabolically relevant tissues. Mitochondrial adaptation to metabolic stress is crucial to maintain pancreatic β-cell function and prevent type 2 diabetes (T2D) progression. While the GLP-1R is well-known to stimulate cAMP production leading to Protein Kinase A (PKA) and Exchange Protein Activated by cyclic AMP 2 (Epac2) activation, there is a lack of understanding of the molecular mechanisms linking GLP-1R signalling with mitochondrial and β-cell functional adaptation. Here, we present a comprehensive study in β-cell lines and primary islets that demonstrates that, following GLP-1RA stimulation, GLP-1R-positive endosomes associate with the endoplasmic reticulum (ER) membrane contact site (MCS) tether VAPB at ER-mitochondria MCSs (ERMCSs), where active GLP-1R engages with SPHKAP, an A-kinase anchoring protein (AKAP) previously linked to T2D and adiposity risk in genome-wide association studies (GWAS). The inter-organelle complex formed by endosomal GLP-1R, ER VAPB and SPHKAP triggers a pool of ERMCS-localised cAMP/PKA signalling via the formation of a PKA-RIα biomolecular condensate which leads to changes in mitochondrial contact site and cristae organising system (MICOS) complex phosphorylation, mitochondrial remodelling, and β-cell functional adaptation, with important consequences for the regulation of β-cell insulin secretion and survival to stress
Rescue robots for casualty extraction: a comprehensive review
No full textThe development of robotic systems for search and rescue (SAR) operations holds great potential in reducing risks to rescue workers and enhancing the likelihood of successful rescue operations. Although many studies have been conducted on search and rescue robotics, more emphasis should be placed on developing rescue robots, especially those intended for physical rescue interventions, such as loading and transporting injured individuals (i.e., casualties) to a secure area—the process known as casualty extraction. The enabling technologies for such robots remain challenging due to the complexity of the tasks, significantly high safety considerations, and strict standards—since the robots are designed to interact with casualties physically. In addition to academic institutions, most research and technical implementations of state-of-the-art casualty extraction robots are carried out by military organisations. This paper presents a comprehensive review of the current state of the art in developing mobile rescue robots for casualty extraction. The existing casualty extraction robot proposals publicly available in the literature are discussed and evaluated in terms of their design and morphology. Moreover, this review details the proposed casualty extraction procedure that corresponds to the robot designs, the operation method, and the levels of autonomy of these robots. The existing state-of-the-art technologies are discussed and compared to evaluate the pros and cons of each system, providing a guideline for further research into areas where more effort could be applied. Based on the review and evaluation of the existing state-of-the-art, we identify critical research gaps that require further investigation to enhance the current state of the art and facilitate long-term development in rescue robotics research
Modelling Seabird Distributions in the Southern Ocean Using Citizen Science Data
No full textImperial Users onl
Implications of states' dependence on carbon dioxide removal for achieving the Paris temperature goal
No full textAchieving the Paris Agreement’s long-term temperature goal of limiting global warming well below 2°C while pursuing efforts to limit it to 1.5°C requires rapid and sustained reductions in greenhouse gas (GHG) emissions and CO2 to be withdrawn from the atmosphere and safely stored. However, pathways consistent with the Paris long-term temperature goal span a wide range of emission reductions in coming years: the IPCC indicates 34–60% cuts in GHG emissions between 2019 and 2030. This range is a major source of policy uncertainty. A key determinant of the rate at which emissions must be reduced this decade is the extent to which CO2 removal (CDR) is relied on later to withdraw emissions from the atmosphere. Here, we evaluate the dependence on CDR of 71 states, primarily in their near and long-term climate strategies submitted to the UNFCCC by May 2024, and the associated risks. Our analysis finds substantial ambiguities in how states plan to meet their climate targets. A feature of this ambiguity is that states expect to rely heavily on novel and conventional CDR options to meet their climate goals, and in some cases, rely on removals delivered in other states’ territories. Pathways that overshoot 1.5°C and use CDR to remove emissions produced in excess of the 1.5°C-aligned carbon budget will result in more severe climate change impacts and higher risks of crossing planetary tipping points. Moreover, states’ disclosed reliance on CDR is highly exposed to risks to its delivery, and non-delivery of planned CDR would raise global temperatures further, worsening impacts of climate change. Our findings provide a basis for enhanced scrutiny of states’ targets. The risks associated with heavy reliance on CDR to meet climate goals indicate that states should prioritize pathways that minimize overshoot and the reliance on CDR to reach net-zero CO2 emissions
Improved Global Estimation of Fluorescence Quantum Yield and its Climate Sensitivity
No full textImperial Users onl
Associations between Maternal Effects and the Maintenance of Same-Sex Sexual Behaviour in Rhesus Macaques
No full textImperial Users onl
Myographic data-driven insights for the integration of inter-muscular dynamics in gait prediction models
No full textHuman walking, also referred to as gait, is one of the most basic and intuitive movements for able-bodied adults. Losing the ability to walk, due to physiological or neurological injury, can therefore severely impact the quality of life of an individual. Assistive robotics have the potential to support patients during rehabilitation. For effective gait training, they need to maintain user engagement and enhance their sense of agency by accurately predicting motion intent, and providing natural gait kinematic outputs. To this effect, electromyography (EMG) , a sensing modality that tracks muscle activity, can be used as input to these gait prediction models. However, due to its sensitivity to electrode placement, lack of inter-session reproducibility, and noise levels, its deployment in clinical applications is limited.
The work presented in this thesis investigates multiple aspects of EMG-based gait prediction, to improve the integration of EMG in robotic aided rehabilitation. This includes: (1) sensor placement and spatial robustness for enhanced model performance, (2) a data-driven selection of muscle groups most suited as inputs to lower-limb kinematic predictions, and (3) the investigation of muscle inter-connectivity and functionality for more meaningful gait models. Results revealed that the inclusion of the trunk, as well as the targeted selection of muscles that drive movement, led to higher prediction accuracies across all investigated subjects. Furthermore, our analyses underline the importance of including inter-muscular and inter-joint dynamics for the selection of both inputs and outputs in gait prediction models.
This thesis bridges the gap between an engineering perspective and a more holistic understanding of human inter-muscular dynamics, combining myographic machine learning models with insights from clinical literature. With a targeted use of muscle activity inputs and physiology-inspired outputs, this thesis contributes towards a safer and more informed integration of EMG in gait models, for future applications in lower-limb assistive devices.Open Acces