In August 2021, Askja volcano in Iceland returned to the spotlight after a sudden onset of rapid uplift followed decades of continuous subsidence. In this study the extended record of microgravity data from Askja between 1988 to 2017 is revisited, and new microgravity data from 2021 and 2022 are introduced, which were collected after the uplift had started. Askja caldera had been steadily subsiding since at least 1984 and was characterised by a net decrease in microgravity, potentially signalling the contraction of its magma chamber or eviction of magma either laterally or to deeper levels. The microgravity data indicate that despite ongoing subsidence between 2017 and early 2021, a significant gravity increase can be detected in the center of the caldera between 2017 and August 2021. This increase may be introduced during – or leading up to – the period of uplift. The new microgravity data also indicate that during the period of 40 cm uplift after August 2021 to fall 2022, gravity changes approach the free-air gradient, suggesting subsurface density decreases as a driving process. This process may relate to the vesiculation of magma previously emplaced in the volcano roots, a change in the hydrothermal system, or replacement of dense basaltic magma with less dense rhyolitic magma, or a combination of these processes. However, uncertainties for this period are elevated and may obscure a gravity signal expected from additional mass accumulation. The timing and high uncertainties of some campaigns make it challenging to be conclusive on the driving process behind the uplift, but future microgravity campaigns could help solve the ambiguity. The study also provides a description of potential pitfalls in microgravity campaigns and recommendations on how the reliability of microgravity data can be improved.

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Axisymmetric standing waves occur across a wide range of free surface flows. When these waves reach a critical height (steepness), wave breaking and jet formation occur. For travelling surface gravity waves, wave breaking is generally considered to limit wave height and reversible wave motion. In the ocean, the behaviour of directionally spread waves lies between the limits of purely travelling (two dimensions) and axisymmetric (three dimensions). Hence, understanding wave breaking and jet formation on axisymmetric surface gravity waves is an important step in understanding extreme and breaking waves in the ocean. We examine an example of axisymmetric wave breaking and jet formation colloquially known as the 'spike wave', created in the FloWave circular wave tank at the University of Edinburgh, UK. We generate this spike wave with maximum crest amplitudes of 0.15-6.0 m (0.024-0.98 when made non-dimensional by characteristic radius), with wave breaking occurring for crest amplitudes greater than 1.0 m (0.16 non-dimensionalised). Unlike two-dimensional travelling waves, wave breaking does not limit maximum crest amplitude, and our measurements approximately follow the jet height scaling proposed by Ghabache et al. (J. Fluid Mech., vol. 761, 2014, pp. 206-219) for cavity collapse. The spike wave is predominantly created by linear dispersive focusing. A trough forms, then collapses producing a jet, which is sensitive to the trough's shape. The evolution of the jets that form in our experiments is predicted well by the hyperbolic jet model proposed by Longuet-Higgins (J. Fluid Mech., vol. 127, 1983, pp. 103-121), previously applied to jets forming on bubbles.

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Preserving features of a surface as characteristic local shape properties captured e.g. by curvature, during non-rigid registration is always difficult where finding meaningful correspondences, assuring the robustness and the convergence of the algorithm while maintaining the quality of mesh are often challenges due to the high degrees of freedom and the sensitivity to features of the source surface. In this paper, we present a non-rigid registration method utilizing a newly defined semi-curvature, which is inspired by the definition of the Gaussian curvature. In the procedure of establishing the correspondences, for each point on the source surface, a corresponding point on the target surface is selected using a dynamic weighted criterion defined on the distance and the semi-curvature. We reformulate the cost function as a combination of the semi-curvature, the stiffness, and the distance terms, and ensure to penalize errors of both the distance and the semi-curvature terms in a guaranteed stable region. For a robust and efficient optimization process, we linearize the semi-curvature term, where the region of attraction is defined and the stability of the approach is proven. Experimental results show that features of the local areas on the original surface with higher curvature values are better preserved in comparison with the conventional methods. In comparison with the other methods, this leads to, on average, 75\%, 8\% and 82\% improvement in terms of quality of correspondences selection, quality of surface after registration, and time spent of the convergence process respectively, mainly due to that the semi-curvature term logically increases the constraints and dependency of each point on the neighboring vertices based on the point's degree of curvature.@en

The near-surface structural and chemical changes were investigated for pure copper against a tungsten carbide (WC) sphere during high tribological loading. Fundamental stages are identified in the Cu-WC tribo-system: (i) high tribological stress promotes grain refinement to the ultra-fine grains regime in the very beginning; (ii) nucleation of extremely fine (~3 nm) oxygen–enriched Cu nano particles in the near-surface layer and subsequent growth of the Cu ₂O oxide; (iii) formation of continuous nanostructured mixing layer with heterogeneous Cu and O distribution in the late stage. Near-surface mechanical mixing is presumably the main contribution to chemical modifications under high tribological loading. Our findings shed atomic-insights into intricate tribochemical modifications, one of the most intriguing phenomena in material-oriented tribology.

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The performance of deep learning (DL) algorithms for radar-based human motion recognition (HMR) is hindered by the diversity and volume of the available training data. In this article, to tackle the issue of insufficient training data for HMR, we propose an instance-based transfer learning (ITL) method with limited radar micro-Doppler (MD) signatures, alleviating the burden of collecting and annotating a large number of radar samples. ITL is a unique algorithm that consists of three interconnected parts, including DL model pretraining, correlated source data selection, and adaptive collaborative fine-tuning (FT). Any of the three components cannot be excluded; otherwise, the performance of the entire algorithm decreases. The experiments with a radar data set of six human motions show that ITL achieves state-of-the-art performance for HMR with limited training samples, outperforming several existing transfer learning approaches. Especially, when there are only 100 samples per person per class, ITL yields an F1 score of 96.7%. Last but not least, ITL is more generalized to human motion differences. Though adapted to recognize the persons' motions in a small-scale target data set, ITL can also classify the persons' motion data used for pretraining, achieving up to 11.0% F1 score enhancement over the conventional FT method.@en

Reliability of DC-DC converters is important in photovoltaic (PV) applications like building integrated PV systems, where the module-level converter may be stressed significantly. Understanding and predicting the most life-limiting components with accurate degradation models in such systems enable the design for reliability. In this paper, a mission profile-based reliability analysis framework for PV DC-DC converters is proposed where the inputs and models of the framework can be adjusted according to the converter topology, the components and the failure mechanisms under investigation. The framework is demonstrated by comparing the influence of two yearly mission profiles on the solder joint degradation of a MOSFET in an interleaved boost converter. This is done by using an electro-thermal circuit simulation in PLECS and a finite element MOSFET model in COMSOL. This framework allows for exploring more accurate models or even simplifying parts with low sensitivity in order to obtain a thorough understanding of their accuracy and to determine the overall converter reliability.

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One of the astounding consequences of quantum mechanics is that it allows the detection of a target using an incident probe, with only a low probability of interaction of the probe and the target. This 'quantum weirdness' could be applied in the field of electron microscopy to generate images of beam-sensitive specimens with substantially reduced damage to the specimen. A reduction of beam-induced damage to specimens is especially of great importance if it can enable imaging of biological specimens with atomic resolution. Following a recent suggestion that interaction-free measurements are possible with electrons, we now analyze the difficulties of actually building an atomic resolution interaction-free electron microscope, or "quantum electron microscope". A quantum electron microscope would require a number of unique components not found in conventional transmission electron microscopes. These components include a coherent electron beam-splitter or two-state-coupler, and a resonator structure to allow each electron to interrogate the specimen multiple times, thus supporting high success probabilities for interaction-free detection of the specimen. Different system designs are presented here, which are based on four different choices of two-state-couplers: a thin crystal, a grating mirror, a standing light wave and an electro-dynamical pseudopotential. Challenges for the detailed electron optical design are identified as future directions for development. While it is concluded that it should be possible to build an atomic resolution quantum electron microscope, we have also identified a number of hurdles to the development of such a microscope and further theoretical investigations that will be required to enable a complete interpretation of the images produced by such a microscope.

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The autonomous filling of creep-loading induced grain-boundary cavities by gold-rich precipitates at a temperature of 550 °C has been studied as a function of the applied load for Fe-Au alloys using synchrotron X-ray nano-tomography. The alloy serves as a model alloy for future self-healing creep resistant steels. The size, shape and spatial distribution of cavities and precipitates are analyzed quantitatively in 3D at a nanometer resolution scale. The filling ratios for individual cavities are determined and thus a map of the filling ratio evolution is obtained. It is found that the gold-rich precipitates only form at cavity surfaces and thereby repair the creep cavity. The shape of the cavities changes from equiaxed to planar crack like morphologies as the cavities grow. The time evolution of the filling ratio is explained by a simple model considering isolated cavities as well as linked cavities. The model predictions are in good agreement with the measurements.

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