From coating curved parts to creeping lava, thin-film gravitational drainage over complex surfaces can produce abrupt accumulation, usually associated with sharp edges or defects. Here, experiments, simulations, and asymptotics show that a smooth saddle alone generates a logarithmically singular ridge, regularized within a dynamic region where drainage balances hydrostatic pressure and capillarity. In a Newtonian lubrication picture, this mechanism can inform redistribution in manufacturing and geophysics, motivating extensions to complex rheology and interacting geometric features.

Quantum subspace expansion (QSE) offers promising avenues to perform spectral calculations on quantum processors but comes with a large measurement overhead. Informationally complete measurements, such as classical shadows, were recently proposed to overcome this bottleneck. Here, we report the first large-scale implementation of QSE with informationally complete measurements. In particular, we probe the quantum phase transition of a spin model with three-body interactions, for which we observe accurate ground state energy recovery and mitigation of local order parameters across system sizes of up to 80 qubits. We achieve this by reformulating QSE as a constrained optimization problem, obtaining rigorous statistical error estimates and avoiding numerical ill conditioning. With over 3 ×104 measurement basis randomizations per circuit and the evaluation of 𝒪⁡(1014) Pauli traces, this represents one of the most significant realizations of classical shadows to date.

A search is performed for lepton-number-violating B − → D ( * ) + μ − μ − decays, using data collected by the LHCb experiment in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 5.4 fb − 1 . No significant signal is observed, and upper limits are set on the branching fractions, B ( B − → D + μ − μ − ) < 4.6 × 10 − 8 and B ( B − → D * + μ − μ − ) < 5.9 × 10 − 8 , at the 95% confidence level.

Energy-from-Waste (EfW) facilities play a crucial role in addressing the global waste problem and serve as a valuable energy provider. To further enhance the overall system performance, the integration of EfW plants with storage systems for excess heat recovery, and the combination with relevant technologies for multi-generation processes are promising pathways. With hydrogen being a leading alternative to fossil fuels, and with water electrolysis considered the only technology capable of meeting the green hydrogen production demand, this study investigates the integration potential of electrolyzer technologies in Energy-from-Waste systems. It offers a comparative techno-economic analysis of coupling alkaline (AEL), anion exchange membrane (AEM), proton exchange membrane (PEM), and solid oxide (SOEC) electrolyzers with an EfW facility. The research sheds light on potential profitability, studying uncertainties and conducting sensitivity analysis. Additionally, a dynamic energy and mass balance model of the AEL technology is developed, accounting for thermal and electrochemical processes, accurately simulating real-plant behavior and allowing diverse operating conditions. Parameter fitting and validation show promising results with R$^2$ scores reaching up to 0.98. By enabling dimensioning and control, its applications include cost and energy efficiency optimization of industrial scale alkaline electrolyzers. Furthermore, the study explores performance of the model under intermittent solar power sources and investigates degradation at a high level, providing insights for further integration with EfW systems.

We study credit risk premia of large international banks. Credit risk premium is defined as the credit spread net of an estimate of default risk. Our findings are threefold: (1) we show that credit risk premium accounts for two-thirds of the total price of default risk since the financial crisis, (2) this premium had been negative until just before the financial crisis but then rose dramatically and remained rather high until just recently, and (3) we document that risk-bearing capacity of the bank drives the risk premium in the credit market. That is, investors in the credit market require a premium for bearing risk due to the leverage growth of the bank. These findings are consistent with the theories that relate the risk premia to the health of the financial sector. Furthermore, potential risk factors related to the bank, default swap market, macroeconomic condition, and regulatory environment cannot eliminate the premium earned by the leverage.

Understanding turbulence in magnetically confined fusion plasmas is a major element in the reliable design and operation of fusion power plants. Turbulence produces interacting fluctuations ranging from the Larmor radius to the size of the machine, which are notoriously difficult to model. In particular, despite recent advances, the interaction of microturbulence, i.e. turbulence on the scale of the Larmor radius, with macroturbulence, on the scale of the machine size, is not well understood. In order to describe the microturbulence, usually a gyrokinetic ordering is imposed, where the amplitude of the turbulent fluctuations is assumed to be small and separated from the background ($\delta$-f approach). On the other hand, in order to describe the macroturbulence, a drift-kinetic ordering is usually imposed, where the turbulent fluctuations are assumed to have a large spatial extent and large amplitude, with no separation between equilibrium and fluctuating quantities (full-f approach). In this thesis, we combine both orderings and self-consistently derive a comprehensive full-f drift-kinetic and $\delta$-f gyrokinetic model. The standard two-fluid drift-reduced Braginskii model and the $\delta$-f gyrokinetic model are retrieved in the appropriate limits. Then, we use an orthogonal Hermite-Laguerre basis expansion to efficiently include collisions and obtain a numerically efficient model.

Simulations are carried out, first, for a higher-order drift-kinetic model in a linear plasma device showing clear limitations of this model. Simulations using the comprehensive full-f drift-kinetic and $\delta$-f gyrokinetic model are then carried out in the same geometry. A hybrid spectral and finite difference numerical scheme is implemented and the simulations show that the limitations seen in the extended drift-kinetic model are removed. Microturbulence is not present at the large ion-ion collision frequency, however, when collisions are suppressed, the gyrokinetic fields are seen to affect the turbulence.

Stepping up in complexity, we carry out simulations of the comprehensive full-f drift-kinetic and $\delta$-f gyrokinetic model in a tokamak geometry. We use two different levels of resistivity corresponding to increasing the input power. At low power, the simulations share several similarities with the simulations in the linear plasma device. At high power, the formation of a transport barrier at the plasma edge through an increase in the radial electric field is observed while the gyrokinetic fields are dominating the turbulence in the core of the device through universal modes.

Finally, we focus on the boundary conditions of the model. In the vicinity of solid walls, a Debye sheath forms where the quasi-neutrality assumption is violated. Therefore, any global simulation must impose boundary conditions at the sheath entrance. We consistently derive sheath boundary conditions in the case of perpendicular incidence of the magnetic field with the wall. Simulations with these boundary conditions are carried out in a linear plasma device geometry. The sheath boundary conditions are shown to have an impact on the particle balance and equilibrium state of the plasma.

Low-density lipoproteins (LDL) are naturally occurring, monodisperse nanoparticles composed of proteins and lipids with an average diameter in the range of 20 nm. Their biological functions such as cholesterol trafficking and their roles in various diseases have been extensively studied. However, their properties as nanoparticles are seldom discussed. In this thesis, we investigate the formation of a protein corona on LDL, establish methods for its characterization, and explore the use of LDL-protein complex in cancer detection, biomarker discovery and disease monitoring. First, we show that LDL isolated from human serum or plasma carry a protein corona. Polyethylene glycol (PEG)-based precipitation followed by size-exclusion chromatography (SEC) preserves exchangeable proteins associated with LDL, while density-gradient ultracentrifugation largely removes the soft corona and yields LDL whose protein composition is dominated by ApoB-100. The two isolation strategies produce particles with distinct hydrodynamic size, homogeneity, and protein composition. Furthermore, incubation studies with purified proteins show negligible interaction of LDL with human serum albumin and apotransferrin, while immunoglobulin G exhibits only weak association, indicating that corona formation is selective rather than a consequence of indiscriminate adsorption of abundant plasma proteins. Building on this discovery, we focus on the study of LDL together with their protein corona, that we call protein-rich lipoproteins (ProLips), specifically on their potential in cancer detection. The tool we use is a proteomic comparison between samples from cancer patients and sample from healthy donors. Compared with whole-serum proteomics, ProLips proteomics consistently reveals substantially more differentially expressed proteins and provides clearer separation between healthy individuals and patients with melanoma, colon carcinoma, and liver cancer. The reduced contribution of highly abundant plasma proteins improves the detection of disease-associated proteins. Finally, six proteins as a melanoma-associated biomarker set are identified and validated. At last, we evaluate whether ProLips proteomics can monitor disease progression and treatment response. In a melanoma rat model, ProLips protein profiles distinguish different stages of tumor development. We show that in colorectal cancer patients sampled before, during, and after surgery, temporal changes in ProLips composition reflect surgery-associated remodeling, while persistent deviations after surgery are associated with clinical relapse. In liver cancer patients receiving immunotherapy, ProLips proteomics captures individualized treatment-associated changes, and both TMT- and DIA-based analyses reveal consistent biological trends despite differences in individual proteins identified.

The emerging technology of wearable robots is gradually transforming our daily life. Up to 2026, many commericallized exoskeleton products have flourished, including those from companies ReWalk, HAL, ULS Robotics, Kenqing Technologies, ready on market to improve wearer's mobility and endurance. Nearly all the market-available exoskeletons are motor-driven, based on bulky external components, rigid transmission elements, or complex control architectures, which decreases wearing willingness. Developing soft wearable robots can well address some of the challenges of hard exoskeletons, improving wearing comfort, unobtrusiveness, and social ac-ceptance. However, to realize soft wearable robots critically depends on soft, thin, compact, and lightweight actuators to deliver sufficient torque (10 ~ 100 Nm) and displacement (5 ~ 30 cm) while maintaining energy-efficiency and reliability. This thesis investigates soft actuators in textile format (textile actuators) to enable unobtrusive actuation on body. This thesis explores the design, modeling, fabrication, experimental characterization, and application scenarios of fabric actuators based on shape memory alloys (SMAs) and electrostatic clutches (ESClutches). This thesis has three contributions to soft wearable robotics. Firstly, by encapsulating ESClutches and commercially available SMA springs into woven fabrics, we developed an energy-efficient fabric actuator (weighing 55 grams) that can lift 40 N mass and consumes milliwatts power for statically holding the mass. Secondly, using mechanics-informed principles, we developed X-Crossing architecture to enable SMA fabrics of 4.5 grams lifting 1 kg mass with 50% contraction, outperforming published SMA fabrics, including knitted and knotted SMA fabric actuators. Thirdly, we developed variable-stiffness mechanics model for X-Crossing SMA fabric actuator. This model predicts the actuator performances and add understandings to the actuator characteristics, including the peak in its performance curves and fundamental limits on actuator contraction. In this thesis, we also demonstrate the applications of developed fabric actuators. The clutchable fabric actuator mounted on the upper limb of a mannequin lifted 2.6 kilograms of mass, reducing 70 % energy consumption by using ESClutches compared to that without using ESClutches. Using this actuator, we demonstrate a waistband with controllable tightness as exoskeleton anchoring for force transmission to body, and using the ESClutches reduces 80% of energy consumption in defined tasks. We also demonstrate the X-Crossing architectured SMA fabric actuator can be well controlled to bend a mannequin elbow to target angles, and this fabric actuator can be fabricated as wrist sleeve to generate compression up to 13 kPa, and then maintain a residual compression pressure of 5 kPa with zero energy cost.

This thesis explores the role of carbon in climate-neutral energy systems. Climate mitigation is often framed as the decarbonization of the energy system, but in reality, in a climate-neutral system, carbon would still be present and circulate through remaining fossil fuels, biomass, CO2, synthetic fuels, chemicals, and negative emission technologies. The interactions between these components lead to complex mechanisms, calling for a systemic vision of carbon flows in the energy system.

To address this challenge, this work develops the Energy-Carbon Cycle Framework, which formalises the coupling between carbon sources and sinks under a neutrality constraint. It shows that residual fossil use is a possible system choice only with sufficient availability of carbon sinks. This concept is then translated into practice within the EnergyScope TD energy system model through a method that tracks the carbon flows between the different energy system technologies.

The proposed method is applied to the French energy system under climate neutrality constraints. This future climate-neutral energy system is compared with the national French strategy and other reference prospective documents. The results propose a clear merit order of lignocellulosic biomass use, in which sacrificing biomass to store carbon to reach climate neutrality is prioritised.

Building on this foundation, the thesis explores the specific case of the aviation sector. It is shown that e-bio-fuels are the most optimal pathway to produce sustainable aviation fuels (SAF), although this optimality is subject to uncertainty, which can favour the e-fuel pathway. The production cost of SAF, the total system cost, and the energy required for their production all depend strongly on the selectivity of the Fischer-Tropsch process towards jet fuel.

This work highlights that domestic production of SAF can reduce the need for flexible gas-based electricity production. These results are evaluated for different uncertainties surrounding the energy system, but the impact of interconnection and the recent downward trend in renewable energy CAPEX significantly reduce the initial values, bringing them to almost no effect.

Energy and carbon exchange between countries forces the expansion of the scope of this work to a European energy system, via the EnergyScope Multi-Cells model. To reduce the computational load of such an expansion, spatial and temporal aggregation methods are proposed and validated, leading to a reduction of the model's solve time by approximately 98 % without major change in energy system behaviour.

This new version of the model allows for uncertainty analysis of the European energy system. The robust decision-making method is used to identify carbon management strategies that are robust to uncertainties across multiple metrics, including total cost and life cycle impact assessment. This method particularly underlines that achieving European climate neutrality requires 339â 1018 MtCO2/yr of geological sequestration.

Overall, this work demonstrates that carbon remains a central component of climate-neutral energy systems. Rather than being eliminated, carbon resources must be understood from a systemic perspective, balancing two main roles: fulfilling the demand for carbonaceous sustainable fuels and enabling the system to reach climate neutrality by offsetting carbon sources with carbon sinks.

Vertebrate locomotion is produced by spinal circuits, sensory feedback and body mechanics acting as one closed loop. Which of these components generates the rhythm, and in what proportion, has been debated for a century, and the question is hard to settle experimentally: removing a sensory pathway shows that it is necessary for a behavior, but not that any particular circuit is sufficient to produce it, because every other sensorimotor loop is still in place. This thesis addresses the problem computationally, using spiking neural networks coupled to simulated bodies, fluids and physical robots. The spiking level was chosen because its manipulations — silencing a defined interneuron class, removing a mechanotransduction channel, cutting synaptic connections — have direct experimental counterparts, so that models constrained by data can return falsifiable predictions.

Four studies are presented. The first introduces a spiking neuromechanical model of the salamander that reproduces fictive locomotion at the level of the full cord, hemicord, isolated segments and hemisegments, and produces both swimming and walking from a single architecture under modular descending drive. Intermediate levels of axial stretch feedback increase speed, lower the cost of transport, reduce intersegmental phase lag and enlarge the range of conditions under which the network functions, whereas excessive feedback makes it uncontrollable by descending commands. The second study takes the same model into a completely transected spinal cord with incomplete axonal regrowth. Open-loop networks recover rhythmicity but never the healthy locomotor phenotype; intermediate feedback below the lesion makes recovery faster and complete, on a time scale compatible with the experimental recovery of swimming, while excessive feedback restores movement immediately but in a pathological regime: restoring movement is therefore not the same as restoring healthy locomotion. The third study identifies a low-order spinal circuit for collective behavior in zebrafish, in which intraspinal proprioceptors deliver curvature-based glycinergic inhibition onto rhythm-generating V2a interneurons. A model constrained entirely by experimental data and a bio-inspired robot running the same controller in a real turbulent wake spontaneously adjust their tail beat phase according to their distance to neighboring fish, saving energy in the process. The computational results establish sufficiency alongside the necessity demonstrated with \emph{in vivo} experiments. The fourth study shows that adult zebrafish swim in two mechanically optimal modes, and that the longitudinal reach of two commissural inhibitory populations, rather than their targets, is the variable that selects between them.

Together these results support a specific view of locomotor control: central pattern generation, sensory feedback and body mechanics are partially redundant, the best-performing and most robust configuration is an intermediate one, and the spinal cord shapes the geometry of movement as well as its timing.