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Time prediction of human evacuation from passenger ships based on machine learning methods
No full textDuring an emergency evacuation scenario, accurately and timely predicting human evacuation time beforehand is crucial for developing an efficient evacuation plan. This study aims to develop an innovative simulation-based framework in which series state-of-the-art Machine Learning (ML) models are applied to predict human evacuation time from passenger ships. It also develops a multi-dimensional decision-making approach to evaluate their performance from the perspectives of high prediction accuracy and timeliness to support rapid response during emergencies. Firstly, an agent-based modelling technique incorporating two objectives and seven influential factors specific to human evacuation scenarios onboard ships is used to simulate the evacuation process. Then, the evacuation model is validated using three indicators to ensure its accuracy and relevance. Secondly, nine state-of-the-art ML models are applied to predict and analyse human evacuation time. To further investigate the role of feature interactions and enhance predictive accuracy, an additional model called the Attention-enhanced Light Gradient Boosting Machine (Attention-LightGBM) is proposed. Additionally, four statistical indicators are utilised to monitor the performance of each model. Finally, a new weighted selection method based on analytic hierarchy process and entropy weight method is created to conduct a comprehensive assessment from the perspectives of accuracy and timeliness. The findings reveal that the Attention-LightGBM demonstrates significant advantages in prediction accuracy, while the LightGBM excels in prediction timeliness. This study not only provides theoretical and technical support for emergency management onboard ships but also suggests methodological advancements for future research on complex human evacuation scenarios from passenger ships. The source code is publicly available at: https://github.com/AdvMarTech/Eva_Predict_ML
Impact of pulmonary vein isolation on atrial fibrillation organisation: correlation of intracardiac and surface electrocardiogram measures
No full textIntroduction
Electrical activity in atrial fibrillation (AF) ranges from organized focal drivers to multiple wavelet re-entry. Understanding the effect of pulmonary vein isolation (PVI) on AF organization is clinically important, as it may optimize treatment strategies and outcomes. This study investigates the impact of PVI on AF organization and explores whether ventricular response regularity, measured from surface electrocardiograms (ECGs), reflects AF dynamics and electrophenotype.
Methods
Patients undergoing first-time PVI at Imperial College Healthcare NHS Trust between 2014 and 2022 were assessed pre- and post-PVI. AF organization was quantified using Shannon entropy (ShEn) and Sample entropy (SampEn) from coronary sinus (CS) electrograms. Ventricular response regularity was evaluated using surface ECG RR interval (RRI) variability and SampEn.
Results
PVI reduced ShEn and SampEn across all CS channels (e.g., SampEn CS 3-4: pre-ablation = 0.907 ± 0.512 vs. post-ablation = 0.790 ± 0.446, p 50 ms difference: r = 0.077, p = 0.008; normalized mean RRI difference: r = 0.144, p < 0.001; and normalized SampEn: r = 0.168, p < 0.001).
Conclusion
The reduction in atrial ShEn and SampEn post-PVI indicates increased AF organization. The significant correlation between atrial entropy and ventricular variability suggests that AF organization affects ventricular response, assessed via surface ECG metrics. These findings highlight the potential of ECG-based measures as proxies for intracardiac AF organization
A subspace method for 3D multiscale heat sink modelling and optimization
No full textThe increasing computational demands of modern microprocessors require efficient thermal management solutions. To address this design challenge, a novel 3D multiscale heat sink modelling and optimization framework is presented. The approach combines a multiscale momentum model with an iterative temperature-flux projection scheme that accurately resolves heat transport across the entire macroscale domain without relying on traditional homogenization methods. Unlike conventional fixed-grid topology optimization approaches, the framework maintains solution accuracy near solid/fluid interfaces while achieving superior computational efficiency, reducing memory requirements and computation time relative to equivalent explicit single scale simulations. Bayesian optimization is utilised to demonstrate the framework’s practical utility by designing multiscale heat sinks with hundreds of unit cells subject to homogeneous, and more accurate in-homogeneous surface heat flux, achieving significantly larger heat transfer in both cases, while maintaining pressure drop constraints. This framework enables the practical optimization of complex 3D heat sink designs at multiscale resolutions previously intractable with traditional explicit modelling approaches
Modelling punching shear failure under eccentric loading by means of nonlinear joint elements
No full textFlat slabs can fail in either flexure or punching shear. Punching failure is undesirable since it occurs suddenly without warning and can lead to progressive collapse. The paper describes a novel Joint shell Punching Model (JSPM), which models punching shear failure by combining nonlinear joint elements with reinforced concrete (RC) shell elements. The joint shear resistance is determined using the Critical Shear crack Theory as implemented in the next generation of Eurocode 2. The novelty of the approach is that the softening response of the joints is related to the joint shear displacement rather than the relative slab-column rotation as done in the classic JSPM. The method is validated for a series of test specimens from the literature. The proposed method is intended for modelling punching shear failure in whole building models subject to gravity and lateral loading
Advances in the biosynthesis of β-carotene and its derivatives in yeast
No full textβ-Carotene and its derivatives have been gaining huge interest due to its applications as food supplements, nutraceuticals, pharmaceuticals, pigments, etc. Owing to their high values, sustainable microbial production has been a heated research topic. Traditional production methods, such as plant extraction and chemical synthesis, face challenges in scalability, cost, and environmental impact. With advances in synthetic biology, yeast-based biosynthesis has emerged as a promising alternative. This review provides a comprehensive summary of recent progress in the metabolic engineering strategies and fermentation optimization approaches of yeast, particularly Saccharomyces cerevisiae and Yarrowia lipolytica, for the production of β-carotene and its derivatives. In contrast to previous reviews, this work emphasizes the shared biosynthetic logic underlying structurally related derivatives, classifying them into two major groups: xanthophylls (canthaxanthin, zeaxanthin, astaxanthin, and violaxanthin) and apocarotenoids (crocetin, retinol, β-ionone, β-cyclocitral, and strigolactones). Representative cases and transferable engineering/fermentation strategies are highlighted. Advantages and limitations of yeast species as production hosts are thoroughly compared, and potential strategies to improve the production are discussed. Future work may focus on broadening product diversity in different yeast hosts and enhancing biosynthetic efficiency for a more sustainable production
First detection of field-aligned currents using engineering magnetometers from the OneWeb mega-constellation
No full textRefining methods for attributing health impacts to climate change: a heat-mortality case study in Zürich
No full textHeat-related deaths occur throughout the summer months, peak during heatwaves, and are affected by temperature and exposed populations’ sensitivities to meteorological conditions. Previous studies found that climate change is increasing heat-related mortality worldwide. We build on existing epidemiological methods to shed light on the adverse effects of climate change on human health. We address limitations in existing methods and apply refined approaches to assess heat mortality attributable to human-induced climate change in Zürich, Switzerland, over 50 years (1969–2018) including a case study of summer 2018. Our methodological refinements affect how counterfactual climate scenarios are derived, and facilitate accounting for changing vulnerability, and assessing impacts during and outside heatwaves. We find nearly 1,700 heat-related deaths attributable to human-induced climate change between 1969 and 2018. Declining vulnerability to heat avoided at least 700 heat-related deaths. The approach described here could be applied elsewhere to quantify the effect of climate change on other health outcomes
Local nearby bifurcations lead to synergies in critical slowing down: the case of mushroom bifurcations
No full textThe behavior of nonlinear systems near critical transitions has significant implications for stability, transients,
and resilience in complex systems. Transient times, τ , become extremely long near phase transitions (or
bifurcations) in a phenomenon known as critical slowing down, and are observed in electronic circuits, circuit
quantum electrodynamics, ecosystems, and gene regulatory networks. Critical slowing down typically follows universal laws of the form τ ∼ |μ − μc|
β , with μ being the control parameter and μc its critical value. For instance, β = −1/2 close to saddle-node bifurcations. Despite intensive research on slowing down phenomena for single bifurcations, both local and global, the behavior of transients when several bifurcations are close to each other remains unknown. Here, we investigate transients near two saddle-node bifurcations merging into a transcritical one. Using a nonlinear gene-regulatory model and a normal form exhibiting a mushroom bifurcation diagram we show, both analytically and numerically, a synergistic, i.e., nonadditive, lengthening of transients due to coupled ghost effects and transcritical slowing down. We also show that intrinsic and extrinsic noise play opposite roles in the slowing down of the transition, allowing us to control the timing of the transition without compromising the precision of timing. This establishes molecular strategies to generate genetic timers
with transients much larger than the typical timescales of the reactions involved
