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Cold atom-based quantum technology for probing fundamental physics
No full textAdvancing our knowledge of the universe increasingly relies on technologies capable of extremely precise measurement. Quantum sensors have emerged as promising tools for searching for new physics by pushing the boundaries of precision in measuring time, acceleration, and gravity. Two promising platforms are long-baseline atom interferometers and atomic clocks, both of which rely on cold atoms. This thesis lies at the intersection of cold-atom physics, quantum technology, and particle phenomenology.
We report experimental progress from the AION project, a next-generation atom interferometer for the detection of ultra-light dark matter and mid-frequency gravitational waves. Within the collaboration, we present the first results from a red magneto-optical trap (MOT) for strontium-88 atoms. A seed–amplifier injection-locked laser system was developed to address the 1S0–3P1 transition at 689 nm, delivering 13.8 mW of light to the science chamber. Using this setup, we produced an atomic cloud at a temperature of (812 ± 4) nK in the narrowband red MOT, marking a critical milestone in the project.
Secondly, the potential of next-generation atomic and molecular clocks in constraining theories that violate the weak equivalence principle (EP) is investigated. A search for variations in the electron–proton mass ratio is performed using publicly available data from UTC. A statistical framework is then developed to model clock noise and forecast sensitivity to signals arising in theories of dark matter, dark energy, unification, and other EP-violating scenarios, providing new and improved constraints. The framework is packaged into a tool that translates clock noise characteristics into constraints on fundamental physics theories. A preliminary study investigating the effect of data gaps in signal recovery shows that annual signals with amplitudes above 10⁻¹⁶ can be recovered even with up to 83% missing data.Open Acces
Rough kernel hedging
No full textPreprint versionBuilding on the functional-analytic framework of operator-valued kernels and un-truncated signature kernels [38], we propose a scalable, provably convergent signature-based algorithm for a broad class of high-dimensional, path-dependent hedging problems. We make minimal assumptions on market dynamics by modelling them as general geometric rough paths, yielding a fully model-free approach. Moreover, by means of a representer theorem, we provide theoretical guarantees on the existence and uniqueness of a global minimum of the resulting optimization problem, and derive an analytic solution under highly general loss functions. Similar to the popular deep hedging [5]-but in a more rigorous fashion-our method can also incorporate additional features by means of the underlying operator-valued kernel, such as trading signals, news analytics, and past hedging decisions, aligning closely with true machinelearning practice. Keywords Rough paths • Signature kernels • Hedging Mathematics Subject Classification (2020) 46C07 • 60L10 • 60L20 1 Introduction In idealized, complete, and frictionless markets, it is theoretically possible to perfectly hedge financial derivatives, thereby eliminating risk through appropriate hedging strategies. However, real markets are incomplete due to transaction costs, market impact, liquidity constraints, and other frictions, makin
Development of a biomicrofluidic model of the gastrointestinal-lymphatic interface
No full textThe lymphatics have typically been viewed as the secondary circulatory system of the body with their primary function being to drain fluid, proteins and cellular debris, collectively termed lymph, from the interstitial space. However, it has been recognised that the lymphatics fulfil a wider range of homeostatic processes, and that dysregulation of these processes can result in a variety of diseases. The lymphatics associated with the gut are particularly important as they undertake a number of specialised roles including the absorption of lipids via the chylomicron pathway - an innate lipid transport mechanism where dietary lipids are packaged and secreted by the enterocytes of the gut, taken up by the initial lymphatics and delivered to the peripheral tissues. Interestingly, the intestinal lymphatics have emerged as an interesting site of oral drug delivery as therapeutics can be targeted to the lymphatics by integrating with chylomicron handling processes to increase bioavailability and better treat lymph-mediated disease. Current models of the gut-lymphatic interface are based on animal models and conventional cell culture platforms that do not fully ecapitulate in vivo morphology and function, and therefore, lack predictive power for basic research and the preclinical evaluation of drug candidates. This thesis aims to produce a novel biomicrofluidic model of the gut-lymphatic interface to study these tissues in both health and disease states and
provide a platform to test the lymphotropic capacity of molecules. This work optimises
and characterises the model in the context of in vivo morphology and function,
demonstrates its utility in probing lipid metabolism and shows it can be a valuable tool
to study disease.Open Acces
Strong field effects in high harmonics generation from dielectric crystals
No full textHigh harmonics generation (HHG) is an ultrafast, strong-field frequency upconversion process, which is a source of sub-fs pulses spanning into the extreme ultraviolet (EUV) regime. In a solid target, the harmonic signal is a result of sub-cycle electron motion within the electronic band structure of the crystal and therefore is known to be an excellent probe for intrinsic physical properties of crystalline solids.
This dissertation delivers experimental results on HHG in wide-bandgap dielectric crystals in the transmission geometry. The results link the electron trajectory picture to the experimental observables. In the scope of this work, two experimental projects are discussed.
To study the strong-field nonlinear propagation of an ultrafast beam through a wide bandgap dielectric, a z-scan technique: measurement of the non-linear refraction and non-linear absorption was performed on bulk 200 μm Magnesium Oxide (MgO) crystal. The non-linear refractive index of MgO was determined, for the first time under fs laser beam, to be n2(780 nm)=1.1±0.2 x10 -20 m2/W. An in situ spectral measurement also allowed for benchmarking of solid-sample nonlinear propagation calculations. The calculations enabled modelling of the propagation of the driving field up to the last nanometres of the sample which generate the observed EUV harmonics. Using a transverse spatial coherence model of harmonic generation, the intricate spatiospectral structures present in the far-field projection were justified with a single electron trajectory picture as a direct result of the complex phase and spatial structure of the driving electric field...Open Acces
Artificial intelligence-driven surrogate modelling and optimisation for hot-stamped safety-critical automotive components
No full textThe aim of this study is to establish a comprehensive framework for developing and evaluating advanced Artificial intelligence-driven surrogate models (AISM) with structured data representations, tailored for industrial applications in metal stamping, particularly hot stamping. This research addresses critical challenges in achieving accurate and efficient simulations for complex geometries by exploring the capabilities of image-based and graph-based AISMs. Case studies and evaluation criteria were defined for performance assessment in terms of accuracy, scalability, and computational efficiency.
The research focuses on: (1) validating image-based AISM for cold stamping simulations with simplified geometries, comparing its performance against traditional scalar-based models; (2) evaluating the potential of image-based AISM for hot stamping with limited data to determine whether large datasets are essential for effective training; (3) extending image-based AISM to real-world geometries and integrating it with a differentiable shape generator for real-time, non-parametric shape optimisation; and (4) proposing an efficient, scalable graph-based AISM architecture based on graph convolutional operations but also integrates the advantages of images, aiming to address challenges faced by existing graph-based surrogate models.
Overall, the findings underscored the advantages of structured data representations, because both image-based and graph-based AISMs have demonstrated superior accuracy and scalability compared to scalar-based approaches. The findings related to aspect (2) revealed that even with small data, AISM with structured data representations can achieve high accuracy, challenging the conventional need for extensive datasets in industrial applications. The findings related to aspect (3) demonstrated the effectiveness of the proposed non-parametric shape optimisation framework, positioning it as a viable tool for improving design efficiency and precision in metal stamping applications. The findings related to aspect (4) provided a methodology of surrogate modelling with integrated advantages of images and graphs. These research findings provided insights for future development of surrogate modelling and optimisation, aiming for better efficiency, accuracy and generalisability.Open Acces
Brain inspired isfet array: edge-computing for point-of-care diagnostics
No full textRecent years have witnessed growth in the development of point-of-care for real-time medical diagnostics. The COVID-19 pandemic highlighted the need for technologies that can provide rapid and accurate diagnosis of infectious diseases without requiring specialised labs. While lateral flow tests supported mass testing during the pandemic, they suffer from low accuracy and do not allow multiplexing of several diseases, which becomes critical as any pandemic progresses. While most techniques rely on optical methods, electrochemical sensing enables miniaturisation, scalability and robustness. We qualify the integration of electrochemical sensing with novel AI algorithms as ‘sensor learning’, leveraging on-chip methodologies to automatically calibrate the sensors and extract accurate diagnostic information. This work presents a spatial correlation between the non-ideal effects to facilitate inter-pixel processing using neuromorphic ISFETs. We begin with neuromorphic ISFET arrays using spike domain encoding and spatial device compensation. This is followed by a completely autonomous cluster topology of neuron-based pixels based on a multiple-channel Integrate and Fire architecture for temporal integration and spatial averaging. The designs have been implemented in TSMC 180nm and TSMC 65nm CMOS technology. We have also created a novel winner-take-all (WTA) architecture for background inhibition in ISFET neurons that can form Clustered WTA and Distributed WTA architecture while at the same time performing drift compensation using temporal and spatial averaging. We have also implemented an in-pixel detection mechanism that uses the calcium conductance channel to allow the circuit to adapt the spike frequency and help us reduce the activity from the neuron before and after the amplification event. Further, we have established a state-of-the-art with our first models for Lab-on-chip platforms that have been trained to identify infectious diseases and cancer biomarkers using tinyML. In addition, this thesis also presents a framework that can accelerate our testing response to future pandemics using AI at the edge.Open Acces
From waste to wealth: can fluidized-bed gasification transform industrial practices?
No full textGasification is emerging as a key technology for producing energy and useful products from waste, while reducing landfill impact. Among the various gasification technologies, fluidized bed systems have emerged as particularly suitable for sustainable waste processing, owing to their superior thermal uniformity and ability to operate at industrial scales. However, the widespread deployment of fluidized bed gasification still faces major technical, economic, and environmental challenges. Continuous operation is hindered by tar formation and equipment fouling, a persistent challenge that has remained the primary technological barrier for decades, driving the need for integrated and advanced mitigation strategies, and bed material agglomeration induced by ash melting. Additionally, syngas quality can significantly deteriorate when heterogeneous feedstocks are used without advanced control strategies. The need for complex downstream tar removal systems also undermines the cost-competitiveness of this technology compared to alternatives for biofuel production. This vision paper does not aim to provide a comprehensive review of the relevant literature, but rather to synthesize the current state of the art and project it into the future, with a strong forward-looking perspective intended to guide technological innovation, industrial development, and policymaking with reference to the European and Chinese perspectives. Therefore, promising approaches to overcome current limitations are explored, including the development of energy efficient and economically competitive in situ and ex situ tar mitigation solutions, the implementation of advanced multivariable control strategies, and the application of machine learning for real-time process optimization. Furthermore, novel reactor designs and schemes are assessed with the goal of intensifying hydrogen concentration in the syngas and intrinsically minimizing pollutant and tar formation, such as in chemical looping gasification, sorption-enhanced gasification, and supercritical water gasification. Finally, by integrating recent experimental results and techno-economic assessments, this work offers a holistic perspective on how fluidized bed gasification of waste can be scaled up to industrial applications and reach syngas-based synthetic natural gas costs of below 50 €/MWh
Making creative thinking visible: learner and teacher experiences of boundary objects as epistemic tools in adolescent classrooms
No full textCreative thinking has become more important in education globally due to industry demand and a fast-paced world. In this study, boundary objects that can be tangible and digital objects are investigated to understand their role in facilitating creative thinking across five subject areas for teenagers aged 13–18 and their teachers, in their natural learning environment. A multiple case study method is used to investigate learners’ and their teachers’ experience in using boundary objects, to enable communication and understanding between individuals or groups in learning. Participants from an inner London secondary school comprised case groups: 8 Teachers and 16 Learners (8 from the lower school, aged 13–15 years, and 8 from the upper school, aged 16–18 years). Participants were invited through email and a short presentation. Consented participants were organised into male and female across teachers and students and were approached in lessons where boundary objects were being used. Data was collected through interviews and comprised photos of tool use, analysed through Reflexive Thematic Analysis for data analysis. The resulting five themes for teacher and student themes showed that boundary objects were perceived to facilitate creative thinking across all case groups within the studied context, with important insights such as iterative design, which develops real-world skills; metacognition, which is critical in learning and enables students to actively question their own thinking; memory, which is very important in enabling students to remember what they learned and how; and individual liberty, suggesting that learning need not be linear nor prescribed but that there must be freedom to learn in ways that are enjoyable and challenging too, amongst others. This study’s interpretive results indicate that when participants experience the use of boundary objects in a natural classroom or learning setting, the learning process is perceived to bring benefits that allow the process of creative thinking to occur
From strong ties to no ties: configurations for first-customer acquisition in tech startups
No full textAttracting customers is one of the most important milestones for technology ventures. Ties are generally considered as beneficial when attracting start-up resources, such as first customers, because they can mitigate information asymmetry to overcome liabilities of newness and smallness. Despite this, when and how entrepreneurs use different network approaches to attract their ventures' first paying customers remains understudied. We rely on the concept of tie strength to distinguish between ventures acquiring customers via pre-existing strong ties, weak ties, or no ties (i.e., through market-based mechanisms). Using fuzzy-set Qualitative Comparative Analysis on 72 entrepreneurs from 72 Flemish technology ventures, complemented by extensive qualitative data, we identify distinct, equifinal configurations of founder, firm, and environmental attributes that are associated with acquiring customers through strong, weak, or no ties. Our post-hoc performance analyses further reveal performance differences: while attracting customers through no ties is associated with higher revenues, only using strong ties to attract first paying customers is associated with higher survival at scale. Our findings have important practical implications for entrepreneurs and technology commercialization policies. Overall, our study contributes a network-based perspective to customer acquisition to the literatures on entrepreneurial resource acquisition, entrepreneurial marketing and technology entrepreneurship
Astrocytes and neurons exhibit partially shared but distinct composite receptive fields for natural stimuli
No full textAstrocytes are increasingly recognized as active participants in sensory processing, but whether they show selective responsesto stimulus features, analogous to neuronal receptive fields, is not yet established. To address this, we used two-photon calciumimaging in the auditory cortex of anesthetized mice during presentation of natural ultrasonic vocalizations. Our aim was to com-pare astrocytic responses with those of neighboring neurons and to determine whether astrocytes exhibit feature-selectivereceptive fields. Event detection showed that astrocytic calcium activity is highly heterogeneous, but only a minority of eventswere consistently stimulus-linked. To examine this stimulus-driven subset, we estimated receptive field features using maximumnoise entropy modeling and compared them with those of concurrently recorded neurons. Despite qualitative similarities inreceptive-field features, analysis of modulation spectra and principal angles showed that astrocytic and neuronal receptive fieldsoverlap but occupy distinct regions of feature space. This indicates that astrocytes and neurons are tuned to partially shared,but not identical, dimensions of the sensory stimulus. Our findings indicate that astrocytes respond to diverse sensory features,playing a complementary role to neuronal encoding. This suggests that astrocytic calcium activity is not simply a reflection ofneuronal firing, but instead represents a distinct component of cortical sensory processing.
NEW & NOTEWORTHY We used two-photon imaging to record calcium activity in astrocytes and neighboring neurons duringpresentation of natural ultrasonic vocalizations. We show that astrocyte activity is highly heterogeneous across spatial and tem-poral scales. Further analyses indicate that a subset of astrocyte calcium activity is stimulus-linked and tuned to dimensions ofthe stimulus that partially overlap with, but are not identical to, those encoded by neurons