Royal Institute of Technology

Predictions of the pedestal temperature profile calculated using a model for electron-temperature-gradient (ETG) turbulent electron heat transport Field et al. (2023 Philos. Trans. R. Soc. A, vol. 381, p. 20210228) are compared with the pedestal structure of H-mode plasmas in JET-Be/W (with Be wall and W divertor) over scans of the deuterium-tritium (D:T) isotope mix and hydrogenic gas fuelling rate Frassinetti et al. (2023 Nucl. Fusion, vol. 63, p. 112009). Predictions for the electron temperature at the location of the density pedestal top $T_e(\psi _N<^>{n_{e,top}})$ (where $\psi_N$ , is the normalised poloidal flux) are found to agree well with measured values over both scans across the full range of D:T ratio. However, the pedestal top temperature $T_{e,ped}$ , typically located somewhat inside the density pedestal top, is under-predicted by as much as a factor ${\sim} 2$ . This implies that the ETG heat flux scaling appropriate for the steep-density gradient region, on which the model is based, is not applicable where the density gradient is weak. This difference might be attributed to a difference between the physics of the ETG turbulence in regimes where the density gradient is either strong or weak, which are thought to be dominated by either the 'slab' or 'toroidal' branches of ETG turbulence. Other branches of turbulence might also play a role in the electron heat transport, particularly in regions of weak-density gradient. As in the experiment, the predicted $T_e$ across the pedestal decreases with the ratio of separatrix to pedestal density $n_{e,sep}/n_{e,ped}$ , which increases with the gas fuelling rate. Results from three models combining the ETG heat flux model with the EPED1 pedestal (EPED) model (Snyder et al., Phys. Plasmas, 2009, vol. 16, p. 056118) are also presented, including one which also incorporates the density pedestal prediction mode of Saarelma et al. (Nucl. Fusion, 2023, vol. 63, p. 052002), this model providing a complete prediction of the pedestal profiles.

In this article, we classify the set of asymptotic mass-like invari- ants for asymptotically hyperbolic metrics. It turns out that the mass is just one example (yet probably the most important one) among the family of in- variants we nd. These invariants can be described in terms of wave harmonic polynomials in Minkowski space. We associate them to geometric dierential operators the way that the mass is associated to the scalar curvature operator.

This paper examines how policy incentives and relative price signals are associated with firm-level solar photovoltaic (PV) innovation. Building on the induced innovation and directed technical change traditions, we analyze biadic patent applications—defined as inventions protected both in Europe and in at least one major non-European jurisdiction for 12 European countries over 1980–2015. We estimate Poisson models to assess how electricity prices, R D support, public direct investments, feed-in tariffs and financial support to consumers affect patent application in solar PV technology. Considering both intensive and extensive margin, our empirical results provide strong support for induced innovation and directed technical change mechanisms at the firm level.

Accurate 3D scene flow estimation is critical for autonomous systems to navigate dynamic environments safely, but creating the necessary large-scale, manually annotated datasets remains a significant bottleneck for developing robust perception models. Current self-supervised methods struggle to match the performance of fully supervised approaches, especially in challenging long-range and adverse weather scenarios, while supervised methods are not scalable due to their reliance on expensive human labeling. We introduce DoGFlow, a novel self-supervised framework that recovers full 3D object motions for LiDAR scene flow estimation without requiring any manual ground truth annotations. This paper presents our cross-modal label transfer approach, where DoGFlow computes motion labels directly from 4D radar Doppler measurements and transfers them to the LiDAR domain using dynamic-aware association and ambiguity-resolved propagation. On the challenging MAN TruckScenes dataset, DoGFlow substantially outperforms existing self-supervised methods and improves label efficiency by enabling LiDAR backbones to achieve over 90% of fully supervised performance with only 10% of the ground truth data.

X-rays are commonly used in imaging experiments due to their penetration power, which enables non-destructive resolution of internal structures in samples that are opaque to visible light. Time-resolved X-ray tomography is the state-of-the-art method for obtaining volumetric 4D (3D + time) information by rotating the sample and acquiring projections from different angular viewpoints over time. This method enables studies to address a plethora of research questions across various scientific disciplines. However, it has several limitations, such as incompatibility with single-shot experiments, challenges in rotating complex sample environments that restrict the achievable rotation speed or range, and the introduction of centrifugal forces that can affect the sample's dynamics. These limitations can hinder and even preclude the study of certain dynamics. Here, we present an implementation of an alternative approach, X-ray multi-projection imaging (XMPI), which eliminates the need for sample rotation. Instead, the direct incident X-ray beam is split into beamlets using beam splitting X-ray optics. These beamlets intersect at the sample position from different angular viewpoints, allowing multiple projections to be acquired simultaneously. We commissioned this setup at the ForMAX beamline at MAX IV, the first operational diffraction-limited storage ring. We present projections acquired from two different sample systems – fibers under mechanical load and particle suspension in multiphase flow – with distinct spatial and temporal resolution requirements. We demonstrate the capabilities of the ForMAX XMPI setup using the detector's full analog-to-digital converter range for the relevant sample-driven spatiotemporal resolutions: (i) at least 12.5 kHz frame rates with 4 µm pixel sizes (fibers) and (ii) 40 Hz acquisitions with 1.3 µm pixel sizes (multiphase flows). The presented setup and results form the basis for a permanent XMPI endstation at ForMAX, offering flexibility to adapt to the spatiotemporal requirements of the studied dynamics.