Flux-driven two-fluid simulations in a diverted tokamak exhibit a confinement transition when a critical heating threshold is exceeded, as in experimental observations of the L-H transition. The simulations show that electromagnetic drift-wave turbulence spontaneously generates a sheared E×B flow responsible for transport suppression in the edge. The toroidal-field asymmetry of the transition, here reproduced in simulations for the first time, arises from time-reversal symmetry breaking by finite collisionality, as demonstrated by a quasilinear calculation of the turbulent momentum flux. Scaling laws are analytically derived for the L-H power threshold in the high- and low-density branches, for the density minimum, and for the minimum power, all matching or surpassing the accuracy of existing empirical scalings.

Cancers identified in CT scans are usually accompanied by detailed radiology reports, but publicly available C T datasets often lack these essential reports. This absence limits their usefulness for developing accurate report generation AI. To address this gap, we present AbdomenAtlas 3.0, the first public, high-quality abdominal CT dataset with detailed, expert-reviewed radiology reports. All reports are paired with per-voxel masks and they describe liver, kidney and pancreatic tumors. AbdomenAtlas 3.0 has 9,262 triplets of CT, mask and report-3,955 with tumors. These CT scans come from 17 public datasets. Besides creating the reports for these datasets, we expanded their number of tumor masks by 4.2 ×, identifying 3,011 new tumor cases. Notably, the reports in AbdomenAtlas 3.0 are more standardized, and generated faster than traditional human-made reports. They provide details like tumor size, location, attenuation and surgical resectability. These reports were created by 12 board-certified radiologists using our proposed RadGPT, a novel framework that converted radiologist-revised tumor segmentation masks into structured and narrative reports. Besides being a dataset creation tool, RadGPT can also become a fully-automatic, segmentation-assisted report generation method. We benchmarked this method and 5 state-of-the-art report generation vision-language models. Our results show that segmentation strongly improves tumor detection in AI-made reports.

LoRa is widely used for low-power long-range communication, yet its legacy forward error correction (FEC) based on Hamming codes provides limited error correction capability. This study investigates the use of non-binary low-density paritycheck (NB-LDPC) codes as an alternative FEC scheme for LoRa, operating over Galois fields which align naturally with the highorder modulation used by LoRa chirp spread spectrum (CSS). Symbol-level log-likelihood ratios (LLRs) are derived for the NB-LDPC-based decoder and pseudo-soft LLRs are introduced for devices with unmodified commercial chipsets in which reliability metrics are unavailable. Under additive white Gaussian noise (AWGN), soft and hard decision-based NB-LDPC codes achieve approximately 2.5 dB and 0.7 dB coding gain, respectively, at a target 10% frame error rate (FER). Under correlated frequencyflat Rayleigh fading, gains of approximately 6.5 dB (soft) and 3.5 dB (hard) are observed, highlighting the increased robustness of NB-LDPC to fading. Comparison with a binary LDPC reference further highlights the advantage of symbol-level coding in LoRa as binary LDPC with soft-decision decoding underperforms the hard-decision NB-LDPC configuration with the gap becoming more pronounced in the presence of fading.

Pen-and-paper tracing is commonly used to think through algorithms before writing code, allowing learners to step through concrete executions and explore possible solutions. However, this process does not readily support abstraction. As traces grow, they lose structure. Steps accumulate into long sequences and are often skipped or compressed, leading to incomplete or inconsistent reasoning. As a result, learners struggle to extract reusable algorithmic patterns or generalize beyond individual instances. We present SketchMind, a proof-of-concept sketch-based programming system in which users construct executable traces of sorting algorithms through pen-based interactions. By performing operations directly on visual representations (e.g., arrays and variables) using pen strokes, users can construct and refine sorting algorithms without writing code. SketchMind supports four key capabilities: (1) executable tracing, where actions are recorded and replayed as runnable steps; (2) reusable pens, which encapsulate trace segments into user-defined procedures; (3) multi-level trace views across values, indices, and symbolic parameters; and (4) cross-instance validation, which applies learned procedures to new inputs and generates minimal counterexamples.

Predicting friction under varying acceleration is challenging for traditional analytical models. Although providing similar performance at constant acceleration, data-driven models leverage their flexibility to outperform analytical models over a wider range of accelerations. Long-Short Term Memory (LSTM) networks and Transformers are explored. To overcome the black-box characteristics of those models, this paper proposes analysing the Transformer’s attention matrices, which reveal the underlying dynamics of friction, successfully identifying historical dependencies and critical state transitions, such as the stick-slip phenomenon, thereby making a step towards explainability.

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.

This thesis investigates wind energy meteorology in complex cold-region environments, focusing on how boundary-layer processes and terrain-induced flow affect wind-resource assessment. Conventional approaches often assume horizontally homogeneous flow, logarithmic wind profiles, and extrapolation from turbine hub-height wind speeds. These assumptions are inadequate in mountainous and polar regions, where atmospheric stability, complex topography, and persistent directional flow produce strong spatial and vertical variability. Limited observations further complicate the validation and correction of modelled conditions.

The thesis examines two environments in which standard wind-resource assessment is challenged differently: the Swiss Alps and Antarctica. In the Swiss Alps, steep terrain, valleys, ridges, and mountain passes generate strong spatial variability over short distances. The Gotthard Pass serves as a key field site, where Doppler wind LiDAR measurements and turbine data are combined to investigate variability across an alpine wind park. A sequential LiDAR transect methodology is developed to infer spatial differences in wind profiles from repeated measurements with a single LiDAR deployed at multiple sites. The results reveal pronounced variations in wind shear, veering, and turbulent kinetic energy caused by directional channelling across the pass, affecting turbine performance over only a few hundred metres. Building on these observations, the thesis develops an analytical framework for estimating turbine power production from vertically resolved inflow profiles. Rather than reducing rotor inflow to a single hub-height wind speed, the method integrates varying conditions across the rotor-swept area. Accounting for wind shear, veer, and turbulence improves the representation of turbine power production in complex terrain and exposes limitations in standard assessment methods.

In Antarctica, the thesis addresses the complementary challenge of sparse observations and uncertain reanalysis data. At Princess Elisabeth Antarctica, Doppler wind LiDAR, sonic anemometer, turbine, and reanalysis data are used to assess local wind conditions and energy potential. Because reanalysis products may misrepresent local wind-speed distributions, a parametrised quantile-mapping method is developed to correct them using LiDAR and sonic-anemometer observations. This improves estimates of wind-energy production beyond the limited measurement period. Finally, the methodology is extended to the continental scale through the Antarctic Wind Atlas. ERA5-Land reanalysis, automatic weather station observations, Weibull statistics, topographic descriptors, and machine-learning-based parameter mapping are combined to provide high-resolution wind-speed distributions and wind-energy potential across Antarctica. Cross-validation against independent station data shows strong predictive performance for mean wind speed and wind-energy density. The correction reduces systematic ERA5-Land biases while preserving a Weibull-based description of the wind climate.

Overall, the thesis shows that reliable wind-energy assessment in complex alpine and polar environments requires spatially resolved observations, probabilistic statistical methods, and careful treatment of terrain-influenced flow. It provides practical tools for renewable-energy planning and advances understanding of boundary-layer variability in alpine and polar terrain.