We have published several articles explaining dark energy (DE) through repulsive gravitation.This explanation led to many very interesting results.In addition to solving the mystery of DE, this solution notably explained cosmic inflation and the absence of antimatter in our universe.Unfortunately, this repulsive gravitation relied on the hypothesis of a negative gravitational mass for antimatter, a hypothesis that has proven false following experiments at CERN.This new article proposes another way to explain the existence of repulsive gravitation, this time based on the second component of general relativity (which is responsible for the Lense-Thirring effect and frame-dragging).This component of general relativity, that we call "gravitic" field, is the equivalent of the magnetic field in electromagnetics.Just as magnets can attract or repel each other, we propose that this gravitic field can also generate attractive or repulsive gravitational forces.When this field is repulsive, it can result in a negative fictitious apparent gravitational mass.Replacing the gravitational mass hypothesis with these potentially repulsive gravitic fields allows us to recover most of the results from our previous studies on DE, which erroneously relied on the assumption of a negative gravitational mass.

Each year, the European Migration Network (EMN) country factsheets deliver an overview of key migration and international protection developments within all EMN Member States and observer countries. The factsheets include both a textual component and a visual component. In this paper, we introduce a curated dataset of the textual component of these reports over 35 countries and 13 years (2012-2024.) The dataset was created to facilitate European-level research on migration policies and developments, and promote the use of reliable sources about migration in data science and media research, particularly at a time when the spread of online misinformation about migration constitutes a serious issue. The dataset transforms the original document texts into a tabular format, with columns corresponding to country, year, section, subsection, content, and harmonized title section. We illustrate the value of the dataset with concrete analyses and propose envisioned applications and uses of the dataset. The dataset is accessible through a DOI link.

Masked Diffusion Language Models (MDLMs) have recently emerged as a promising alternative to autoregressive models. Unlike standard diffusion-based approaches, MDLMs are not explicitly conditioned on a timestep, raising a natural question: do these models internally represent denoising progress, and how is such information used downstream? In this work, we show that MDLMs do in fact encode a latent representation related to the diffusion timestep within their residual streams. We find that this signal can be reliably extracted using probes across layers, indicating that denoising progress is decodable from internal activations. We further demonstrate that steering the model along a low-dimensional subspace associated with the inferred timestep allows us to systematically modulate its notion of denoising progress, leading to controlled and predictable changes in model confidence and entropy. Finally, we analyse the geometry of the identified representation, showing that it exhibits structured and interpretable properties in activation space, and shedding light on how such a signal is manipulated by these models.

Recent advances in Quantum Computing (QC) and Generative AI (GenAI) have demonstrated strong potential for modeling complex, high-dimensional data, motivating growing interest in quantum-enhanced generative intelligence. Among Deep Generative Models (DGMs) Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) have been widely applied across urban-environmental systems over the past few years. However, the extension of these paradigms into the Quantum Computing (QC) domain introduces fundamentally new architectural, computational, and practical properties that remain insufficiently understood and largely underdeveloped in the field of urban ecological sustainability. In particular, there is a clear research gap regarding how QGANs and QVAEs are designed, evaluated, and made practically feasible for urban-environmental intelligence in smarter eco-cities. Therefore, this study examines the emerging role of QGANs and QVAEs in advancing urban and environmental systems within the evolving landscape of smarter eco-cities. It focuses on their hybrid quantum–classical architectures, training strategies, domain-specific applications, and empirical performance, with an emphasis on technical feasibility, design choices, and applied relevance in data-intensive, dynamic environments. Key findings reveal that, with respect to the architectural paradigms of QGANs, hybrid quantum–classical and fully QGAN architectures demonstrate enhanced expressivity, noise resilience, and scalability on NISQ devices, with advanced designs exploiting entanglement, variational circuits, and transfer learning to optimize latent-space quality and training efficiency. Regarding the empirical performance of QGANs, the study indicates that fully QGANs achieve superior image generation fidelity, resolution, and overall quality compared to classical GANs, while hybrid quantum-classical architectures enable efficient unsupervised learning, robust convergence, and practical deployment on real-world datasets. As to the architectural paradigms and empirical performance of QVAEs, the study shows that QVAEs and hybrid quantum–classical VAEs enhance latent-space representation, reconstruction quality, and sampling efficiency across image-based, structured, and domain-specific datasets. These models support effective anomaly detection, feature fusion, and scalable similarity search, with hybrid architectures providing more practical scalability and robustness on current hardware. These findings highlight the transformative role of hybrid quantum–generative models for high-dimensional, real-time smarter eco-city intelligence. Building on these findings, the study introduces a structured conceptual framework for integrating quantum generative adversarial learning and variational inference to advance urban-environmental systems in smarter eco-cities. The framework connects foundational QC principles, operational components, algorithmic routines, classical generative modeling paradigms, and quantum-enhanced intelligence operationalizations in smarter eco-cities, establishing a clear pathway from theoretical fundaments to prospective real-world deployment. The insights derived from this study provides guidance for researchers, practitioners, and policymakers to advance urban ecological sustainability goals through quantum-enhanced generative intelligence.

C-reactive protein (CRP) is an important biomarker of inflammation and infection, but many biosensors still struggle to combine high sensitivity with operation in physiologically relevant media. Here, a scalable graphene-based electrochemical impedance spectroscopy (EIS) platform for label-free, real-time CRP detection is reported. Anti-CRP antibodies were immobilized through non-covalent functionalization using 1-pyrenebutyric acid N-hydroxysuccinimide ester (PBASE), preserving the electrical properties of graphene. The device operated robustly in 1× PBS, addressing Debye screening under realistic ionic conditions. A concentration-dependent impedance response was obtained, with a practical limit of detection of 384 pM, a dynamic range up to 240 nM to address premature saturation avoidance for sub-clinical purposes, and a sensitivity of 22.6%/decade. Non-specific antibody controls showed no measurable response, confirming the selectivity and potential of the proposed platform for clinical diagnostics.

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.

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.

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.

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.

Barrel cortex has a characteristic somatotopic map, where each whisker maps to a single barrel. Each cortical column therefore has a known principal whisker input, so a functional response can be studied against structure identified in advance. Individual barrels are visible in the CCFv3 average template, but the parcellation stops at area and layer. Anatomically detailed modelling of the full region had been done in rat, and no model of mouse barrel cortex reached this level of detail. In this thesis, I identified the features that distinguish barrel cortex, and supplied them to a model built in atlas space with the barrel columns annotated.

Firstly, I segmented 33 barrels in CCFv3, and used an area-preserving flatmap to annotate the barrel columns representing the posteromedial barrel subfield. The resulting columns follow the curvature of the cortex, span full depth and never overlap. The annotations are released with the flatmap as a tool for data integration and analysis.

Then, I used this enhanced atlas space and a pipeline validated in rat non-barrel somatosensory cortex to build a detailed model. The model contains 33 barrel columns, approximately 230,000 neurons and around 357 million synapses, drawn from 60 morphological and 15 electrical types. Every inhibitory neuron additionally carries a molecular class, so the model can be compared against mouse measurements directly. The connectome is built from anatomy alone, with no rule imposing how likely one cell type is to connect to another. I extended the pipeline with morphological synthesis, generalised electrical models and a new set of thalamocortical projections, with VPM confined to individual barrels and POm spread across the field.

In barrel cortex, layer 4 dendritic trees grow preferentially towards, and remain within, the barrel that carries their principal whisker input. Dendritic polarity has been described in individual cell types, but not measured across them. To address this, I curated dendritic reconstructions from mouse barrel cortex and applied the same descriptors to every cell, so classes can be ranked against one another. Polarity is class-dependent and concentrated in layer 4, in spiny stellate cells, non-Martinotti somatostatin neurons and parvalbumin neurons. I used the result to build two models differing in a single property of basal dendritic synthesis: one polarised, one symmetric. Finally, I ran in silico experiments on a 3x3 grid of barrel columns and the associated septum, with C2 as the principal column throughout. I simulated three experimental contexts: the quiet and whisking brain states, and active touch of the C2 whisker. Membrane potential and spikes were recorded from every neuron, with the afferent connectivity of each known. The model reproduces patterns reported in mouse: the laminar sequence of the touch response, the recruitment order of the molecular classes, and their dependence on brain state.

We decomposed the spiking response using cyclicity analysis and retained two components. The two modes have distinct spatial profiles: the first is focused on the principal column, while the second crosses the septum and the neighbouring barrels. The same decomposition applied across the two circuits divides the signal into a common and a contrast plane. The contrast plane isolates the unique response contributions of each circuit. Layer 4 dendritic polarisation sharpens the touch response in space and advances it in time.