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The evolving individual protein intake and environmental footprint in China
No full textChina’s shift from plant-based to animal-based protein has raised concerns over its health and environmental consequences. Drawing dietary data from nine provinces (1997–2011), this study quantifies the environmental impacts of changing protein consumption and highlights trade-offs between improved diet quality and environmental sustainability. Notable disparities emerge across regions and socioeconomic groups: high-income, male, and well-educated individuals tend to overconsume protein, while older adults, low-income groups, and people with obesity often face protein deficits. Scenario analysis suggests that curbing overconsumption could offset the environmental costs of addressing deficiencies. While adopting the Chinese Dietary Guidelines may increase environmental pressures, these can be mitigated by replacing red meat with poultry or plant-based protein. Our findings call for integrated dietary policies that align health and environmental objectives. Future guidelines should account for regional dietary cultures and the nutritional needs of vulnerable groups to promote more sustainable and equitable food system
Marangoni-driven redistribution and activity of Piezo1 molecules in epithelial and cancer cells
No full textThe activity and distribution of Piezo1 molecules, along with the maturity and strength of focal adhesions (FAs), serve as critical factors influencing cell mechanosensing. Notably, migrating epithelial cells and mesenchymal-like cancer cells exhibit significantly different behaviors regarding these elements. In cancer cells, Piezo1 molecules are distributed uniformly, while in epithelial cells, their distribution is heterogeneous. In epithelial cells, Piezo1 molecules tend to group around FAs, a phenomenon that is enhanced by actomyosin contractility. However, a reduction in contractility results in a more uniform distribution of Piezo1 molecules. The expression and activity levels of Piezo1 molecules are markedly higher in cancer cells compared to epithelial cells. The activity of Piezo1 molecules correlates with the intracellular calcium concentration. Despite the extensive experimental studies on the properties of migrating epithelial and mesenchymal-like cancer cells, the physical explanations remain lacking. The primary objective of this theoretical study is to explore: (i) the inhomogeneous distribution of Piezo1 molecules in epithelial cells in relation to the Marangoni effect, (ii) the heightened activity of Piezo1 molecules in cancer cells by specifying the driving force, and (iii) the influence of membrane-mediated interactions among Piezo1 molecules grouped near FAs in epithelial cells on their activity
Probabilistic Inversion Modelling of Atmospheric Gaseous Emissions
No full textGreenhouse gas (GHG) emissions are a primary driver of contemporary climate change, contributing to rising global temperatures, increasing frequency of extreme weather events, and widespread ecological disruption. Effective mitigation depends not only on reducing emissions but also on accurately detecting, locating and quantifying them. Reliable source characterisation underpins national climate strategies, industrial compliance, and international agreements aimed at stabilising the global climate system. This thesis develops probabilistic inversion frameworks that integrate atmospheric trans port models, Bayesian inference, and machine learning to improve the estimation of gas emission source characteristics from ground-based measurements. First, we address limitations of the widely used Gaussian plume model, where dispersion parameters are often fixed via atmospheric stability classes, introducing bias when meteorological classifications are inaccurate. We propose a gradient-based Markov chain Monte Carlo inversion scheme that jointly infers dispersion parameters alongside source location, emission rate, background concentration, and sensor error. Application to both controlled-release data and simulations demonstrates improved accuracy and uncertainty quantification compared to traditional methods. Second, we tackle the challenge of real-time inversion in obstructed, unsteady-state flow fields, where computational fluid dynamics (CFD) solvers are too expensive for sequential inference. We design deep-learning surrogate models trained on high-fidelity CFD outputs and embed them within particle filters, enabling near-instantaneous Bayesian estimation of time-varying source parameters. Validation on the Chilbolton methane release dataset and complex synthetic environments shows comparable accuracy to full CFD inversion at orders-of-magnitude lower computational cost. Finally, we synthesise these contributions, explore integration with emerging satellite based inversion systems, and outline pathways for scaling these methods to regional and global monitoring networks. Together, these contributions provide physically grounded, computationally efficient tools for GHG monitoring. By advancing both parameter estimation accuracy and operational feasibility, this work supports scalable, uncertainty-aware frameworks for emissions quantification, informing policy, compliance, and mitigation strategies
A Machine Learning-Embedded theoretical model for precise adhesive layer stress prediction in composite bonded joints
Get PDFAdhesive-bonded joints are widely used in engineering applications ranging from aerospace, automobile to advanced superconducting materials due to their ability to effectively join dissimilar materials. While extensive research has investigated the factors influencing their failure modes, particularly stresses within the adhesive layer, accurate stress prediction remains challenging for joints with arbitrary geometries. This difficulty arises primarily from the complex effects of eccentric loading across varying material combinations and joint configurations. This study proposed an integrated approach combining experimental testing, numerical simulation, and machine learning to predict normal and shear stresses in single-lap joints. First, tensile tests are performed on multi-material joints (Al-Al, CFRP-CFRP, and Al-CFRP) to validate the developed finite element models. Then, theoretical models are derived for asymmetric joint configurations. For including the effects of eccentric loading, a dataset of 300 simulation results is generated to train a deep neural network (DNN) model for predicting the bending moment factors (K factors) across diverse joint geometries and material pairings. The DNN-derived K factors demonstrate exceptional accuracy when integrated into theoretical stress predictions, significantly outperforming conventional methods while maintaining robust adaptability. This work addresses a key joint mechanics challenge and offers a versatile framework for optimizing adhesive joint design in engineering
Periodic review inventory control for an omnichannel retailer with partial lost-sales
No full textWe investigate the management of stock for a business with integrated online and offline store-fronts selling products facing uncertainty in demand. The integration of channels includes an opportunity for customers to have items sent directly to their home in case of a store stockout. We model a two-echelon divergent, periodic-review inventory model, with partial lost-sales at the store level and an online demand channel. The problem is developed as a Stochastic Dynamic Program minimising inventory costs. For the zero lead-time case, we prove desirable properties and develop ordering decisions based on optimality of a base-stock policy. For positive lead-time, we highlight the effectiveness of adding order caps to reduce system costs. In an extensive numerical study, we improve standard heuristic methods in the literature on costs by up to 19%. Further, we apply methods to real life data for a large mobile phone retailer, Tesco Mobile, with our methods outperforming the internal benchmark method. We show how the company’s target service level can be reached, with a reduction of inventory between 75% and 99% at the store level. By focusing on effective yet interpretable policies, we suggest methods that can be used to aid a decision maker in a practical context
ZTF SN Ia DR2 follow-up : Exploring the origin of the Type Ia supernova host galaxy step through Si II velocities
No full textThe relation between Type Ia supernovae (SNe Ia) and the stellar masses of their host galaxy is well documented. In particular, Hubble residuals display a distinct luminosity shift based on host mass. This is known as the mass step. This effect is widely used as an additional correction factor in the standardisation of SN Ia luminosities. We investigate the Hubble residuals and the mass step of normal SNe Ia in the context of Si II λ 6355 velocities based on 277 normal SNe Ia that are near their peak in the second data release (DR2) of the Zwicky Transient Facility (ZTF). We divided the sample into high-velocity (HV) and normal-velocity (NV) SNe Ia, separated at 12,000 km s −1 . This produced a sample of 70 HV and 207 NV objects. We then explored potential environment- and/or progenitor-related effects by investigating the Si II λ 6355 velocities with parameters such as the light-curve stretch x 1 , the colour c , and the host galaxy properties. Although we only find a marginal difference between the Hubble residuals of HV and NV SNe Ia, the NV mass step is 0.149 ± 0.024 mag (6.3 σ ). The HV mass step is smaller, 0.046 ± 0.041 mag (1.1 σ ), and is consistent with zero. The difference between the NV and HV mass steps is modest, at ∼2.2 σ . Moreover, the clearest subtype difference appears for SNe in central regions ( d DLR 1), whereas NV SNe display stronger environmental trends. Our results indicate that NV SNe Ia appear to be more environmentally sensitive, particularly in central likely metal-rich and older regions, while HV SNe Ia show weaker and subset-dependent trends. This suggests that applying a universal mass-step correction might introduce biases, and that incorporating refined classifications and/or environment-dependent factors, such as the location within the host, might improve future cosmological analyses beyond the standard x 1 and c cuts
Studies of CP violation in the Bs →J/ψϕ channel, and prospects for future measurements at the ATLAS detector
Get PDFThis thesis presents an analysis of the B0s →J/ψϕ decay, using the Full Run-2 proton-proton collision dataset at 13 TeV collected by the ATLAS detector at the LHC, corresponding to 139 fb−1 of integrated luminosity. The following quantities of physics interest are measured: the average decay width of the heavy and light B0s meson states, Γ, the width difference, ∆Γs, between the B0s meson eigenstates, and the mass difference between light and heavy states ∆ms. ∆Γs = 0.0620 ±0.0034 (stat.) ±0.0016 (syst.)ps−1 Γ = 0.6695 ±0.0011 (stat.) ±0.0011 (syst.)ps−1 ϕs =−0.069 ±0.030 (stat.) ±0.020 (syst.)rad ∆ms = 17.889 ±0.060 (stat.) ±0.061 (syst.)ps−1 At the time of writing, these results are not finalised and the exact values may change before official publication, and are ATLAS internal results. Work presented in this thesis looks to develop methods with the goal of improving these results and builds on an ongoing analysis by the “BsJPsiPhi” team. There are three investigations presented in this thesis; one into the choice of the mass sidebands, a second into the use of sPlot statistical weighting as a background rejection model, and the third, a study of mass-time correlations in B0s meson background. A separate study into the material budget at ATLAS, including consideration of the material map variation systematics is also presented
Investigating 15N@C60 using pulsed EPR and the use of new techniques in resonator production
Get PDFImprovements in timekeeping offer a wide range of benefits, and are of particular interest for global navigation satellite systems (GNSS). The accuracy and reliability of these can be dramatically improved with an improved receiver clock. A chip scale atomic clock (CSAC) could offer greatly improved performance when compared to current quartz oscillators. A CSAC is under investigation at Lancaster University, using 15N@C60, this is a Nitrogen-15 atom trapped inside a fullerene cage, and showsgreat potential for use in a CSAC; it offers potential improvements in size, weight and power in comparison to other CSAC technologies, which are very important in making a CSAC feasible to use, especially in consumer electronics. The Lancaster group’s prototype clock has room for improvement. Firstly, an investigation into potential improvements in the resonator was conducted. Litz wire and 3D printed resonators were developed and tested, showing worse Q factors than currently used solenoid resonators. A 3D printed form for making new solenoid resonators was created, and improved the creation process of new solenoids. Clock stability is parametrised by Allan deviation, which measures the rate of drift of a clock; this has not yet been measured at the clock transition. Progress has been made towards this in the form of a measurement of T2 relaxation time in 15N@C60 at 60MHz. This was found to be 3.9 ± 0.5µs and 5 ± 1µs for 2.1mT and 2.0mT transitions respectively, giving frequency linewidths of δf = 80 ± 10kHz (2.1mT) and δf = 60 ± 10kHz (2.0mT). Taking these to be the clock transition linewidths, Allan deviation estimates of: σy(τ ) = 2.2 ± 0.3 × 10−4τ1/2 for 2.1mT, and σy(τ ) = 1.6±0.3×10−4τ1/2 for 2.0mT, were calculated. Finally, an investigation into the signal-to-noise (S/N) ratio was carried out: the noise was found to be higher than theoretical levels, and did not reduce with averaging as much as expected. While the Allan deviation estimates are low, the issues with S/N ratio show clear potential for improvement in the setup, which may allow for a measurement of the line width to be made at the clock transition
Scaffolding and Engagement are Coupled During Shared Book Reading’s Word-Learning Moments
Get PDFThis study tested the assumption that caregiver scaffolding and child engagement are tightly coupled during shared book reading’s word-learning moments. It also examined whether this coupling is consistent or variable across print and digital reading media. Word-learning episodes were coded from a corpus of videorecorded shared-reading interactions between caregiver-child dyads (N = 78, children’s age range = 4;0 – 5;11). Results support the prediction that scaffolding and engagement are coupled during word-learning moments. This coupling was robust across reading media. Further, child age was a significant predictor of engagement. These findings confirm that engagement is a critical social interaction mechanism involved in the scaffolding process that supports word learning