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Funder: Deutsche Forschungsgemeinschaft; doi: http://dx.doi.org/10.13039/501100001659, For the growth of low-defect crack-free GaN heterostructures on large-area silicon substrates, compositional grading of AlGaN is a widely adapted buffer technique to restrict the propagation of lattice-mismatch induced defects and balance the thermal expansion mismatch-induced tensile stress. So far, a consolidation of the design strategy of such step-graded buffers has been impaired by the incomplete understanding of the effect of individual buffer design parameters on the mechanical and microstructural properties of the epilayers. Herein, we have analyzed a series of metal-organic chemical vapor deposition grown GaN/graded-AlGaN/AlN/Si heterostructures through in situ curvature measurements and post-growth x-ray diffraction (XRD). Our results reveal that in such epi structures, the GaN layer itself induces more compressive stress than the AlGaN buffer, but the underlying AlGaN layers dictate the magnitude of this stress. Furthermore, for a fixed AlGaN buffer thickness, the mean-stress accumulated during the GaN growth is found to be correlated with its structural properties. Specifically, one µm thick GaN layers that acquire 1.50 GPa or higher compressive mean-stress are seen to possess 20 2 ˉ 1 XRD ω-FWHM values less than 650 arc-sec. Also, the evolution of instantaneous stresses during the growth of the AlGaN layers is found to be a valuable indicator for buffer optimization, and composition difference between successive layers is established as a crucial criterion. The results also show that increasing the total buffer thickness (for a fixed number of steps) or increasing the number of steps (for a fixed total buffer thickness) may not always be beneficial. Irrespective of the buffer thickness, optimized high electron mobility transistor structures show similarly low sheet-resistance (∼350 Ω □)−1 and high mobility (∼2000 cm2 V−1 s −1) at room temperature.

In an earlier work, we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of a quantum GAN. We applied the model on a realistic High-Energy Physics (HEP) use case: the exact theoretical simulation of a calorimeter response with a reduced problem size. This paper explores the dual- PQC GAN for a more practical usage by testing its performance in the presence of different types of quantum noise, which are the major obstacles to overcome for successful deployment using near-term quantum devices. The results propose the possibility of running the model on current real hardware, but improvements are still required in some areas., Comment: 6 pages, 5 figures, Proceedings of the 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021)

Funder: EPSRC Centre for Doctoral Training, Funder: Munro–Greaves Bursary (Queens’ College Cambridge), This note considers a finite dimensional statistical model for the Calderón problem with piecewise constant conductivities. In this setting it is shown that injectivity of the forward map and its linearisation suffice to prove the invertibility of the information operator, resulting in a Bernstein–von-Mises theorem and optimality guarantees for estimation by Bayesian posterior means.

Both natural ecosystems and biochemical reaction networks involve populations of heterogeneous agents whose cooperative and competitive interactions lead to a rich dynamics of species' abundances, albeit at vastly different scales. The maintenance of diversity in large ecosystems is a longstanding puzzle, towards which recent progress has been made by the derivation of dynamical mean-field theories of random models. In particular, it has recently been shown that these random models have a chaotic phase in which abundances display wild fluctuations. When modest spatial structure is included, these fluctuations are stabilized and diversity is maintained. If and how these phenomena have parallels in biochemical reaction networks is currently unknown. Making this connection is of interest since life requires cooperation among a large number of molecular species, and the origin of life is hotly debated. In this work, we find a reaction network whose large-scale behavior recovers the random Lotka-Volterra model recently considered in theoretical ecology. We clarify the assumptions necessary to derive its large-scale description, and reveal the underlying assumptions made on the noise to recover previous dynamical mean-field theories. Then, we show how local detailed balance and the positivity of reaction rates, which are key physical requirements of chemical reaction networks, provide obstructions towards the construction of an associated dynamical mean-field theory of biochemical reaction networks. We outline prospects and challenges for the future, and argue for a synthetic approach to a physical theory of the origin of life.

Morphological computation proposes the idea that in a physical system, certain computational processes can be off-loaded to the body. However, the concept has still eluded serious theoretical quantification attempts, unlike traditional computational theory. This perspective examines the notion of morphological computation from the well established theories of traditional computation and computational complexity, drawing parallels between the two, to understand the differences and similarities. Further, we look at the quantification efforts of morphological computation and attempt to link it to the unexplored field of control complexity. We argue that the development of complexity theory for control problems is necessary to study and utilize the concept of morphological computation, if it is possible.

Alkaline magmatism develops in the rear arc area of Java, Sunda Arc, at different range of slab depth; ~270-580 km in central section and ~150 km in the east. We collate published geochemical data of volcanic rocks from four alkaline volcanoes (Muria, Lasem, Bawean, and Ringgit-Beser) and perform statistical analysis to evaluate geochemical characteristics of each suite. A set of major and trace elements is scaled and transformed using principal component analysis (PCA) and then followed by implementation of k-means algorithm to cluster the data points based on Euclidian distances. K-means clustering of the dataset suggests that Central Java alkalines are most elevated in K2O and total alkali. The algorithm further suggests that Muria samples can be clustered into two, owing to these components. These two clusters, however, are not well reflected on trace element-based clustering. Lasem volcanics show distinct cluster high in Na2O/K2O and SiO2, while Bawean samples are mixed into both Muria clusters. Ringgit-Beser alkalines show two distinct clusters tied to MgO and enrichment in Ba, Rb, and Sr. Our findings suggest that the potassium and LILE enrichment in these alkaline rocks is independent of slab depth and is most likely regulated by tectonic-related arc segmentation in Java subduction zone.

Funder: John Simon Guggenheim Memorial Foundation; doi: https://doi.org/10.13039/100005851, Funder: Einstein Visiting Fellow Program, The Rayleigh–Bénard system with stress-free boundary conditions is shown to have a global attractor in each affine space where velocity has fixed spatial average. The physical problem is shown to be equivalent to one with periodic boundary conditions and certain symmetries. This enables a Gronwall estimate on enstrophy. That estimate is then used to bound the L 2 norm of the temperature gradient on the global attractor, which, in turn, is used to find a bounding region for the attractor in the enstrophy–palinstrophy plane. All final bounds are algebraic in the viscosity and thermal diffusivity, a significant improvement over previously established estimates. The sharpness of the bounds are tested with numerical simulations.

Objective. Neuroadaptive paradigms that systematically assess event-related potential (ERP) features across many different experimental parameters have the potential to improve the generalizability of ERP findings and may help to accelerate ERP-based biomarker discovery by identifying the exact experimental conditions for which ERPs differ most for a certain clinical population. Obtaining robust and reliable ERPs online is a prerequisite for ERP-based neuroadaptive research. One of the key steps involved is to correctly isolate electroencephalography artifacts in real time because they contribute a large amount of variance that, if not removed, will greatly distort the ERP obtained. Another key factor of concern is the computational cost of the online artifact handling method. This work aims to develop and validate a cost-efficient system to support ERP-based neuroadaptive research. Approach. We developed a simple online artifact handling method, single trial PCA-based artifact removal (SPA), based on variance distribution dichotomies to distinguish between artifacts and neural activity. We then applied this method in an ERP-based neuroadaptive paradigm in which Bayesian optimization was used to search individually optimal inter-stimulus-interval (ISI) that generates ERP with the highest signal-to-noise ratio. Main results. SPA was compared to other offline and online algorithms. The results showed that SPA exhibited good performance in both computational efficiency and preservation of ERP pattern. Based on SPA, the Bayesian optimization procedure was able to quickly find individually optimal ISI. Significance. The current work presents a simple yet highly cost-efficient method that has been validated in its ability to extract ERP, preserve ERP effects, and better support ERP-based neuroadaptive paradigm.

Funder: Engineering and Physical Sciences Research Council; doi: https://doi.org/10.13039/501100000266, Recently published density functional theory results using the PBE functional (Whaley-Baldwin and Needs 2020 New J. Phys. 22 023020) suggest that elemental sulfur does not adopt the simple-cubic (SC) P m 3 ̄ m phase at high pressures, in disagreement with previous works (Rudin and Liu 1999 Phys. Rev. Lett. 83 3049--52; Gavryushkin et al 2017 Phys. Status Solidi B 254 1600857). We carry out an extensive set of calculations using a variety of different exchange–correlation functionals (both local and non-local), and show that even though under LDA and PW91 a high-pressure SC phase does indeed become favourable at the static lattice level, when zero-point energies (ZPEs) are included, the transition to the SC phase is suppressed in every case, owing to the larger ZPE of the SC phase; thus confirming the transition sequence as R 3 ̄ m → BCC, with no intervening SC phase. We reproduce these findings with pseudopotentials that explicitly include core electronic states, and show that even at these high pressures, only the n = 3 valence shell contributes to bonding in sulfur. We then compare our findings against the all-electron code ELK, which is in excellent agreement with our pseudopotential results, and examine the roles of the exchange and correlation contributions to the total energy. We further calculate anharmonic vibrational corrections to the ZPEs of the two phases, and find that such corrections are several orders of magnitude smaller than the ZPEs and are thus negligible. The effect of finite temperatures is also considered, and we show that the P m 3 ̄ m phase becomes even more unfavourable with an increase in temperature. Finally, the experimental consequences of our results on the equation of state of sulfur and its superconducting critical temperature are explicitly calculated.

We demonstrate the use of a convolutional neural network to perform neutron-gamma pulse shape discrimination, where the only inputs to the network are the raw digitised SiPM signals from a dual scintillator detector element made of 6LiF:ZnS(Ag) scintillator and PVT plastic. A realistic labelled dataset was created to train the network by exposing the detector to an AmBe source, and a data-driven method utilsing a separate PMT was used to assign labels to the recorded signals. This approach is compared to the charge integration and continuous wavelet transform methods and is found to provide superior levels of discrimination, achieving an AUC of 0.995 +/- 0.003. We find that the neural network is capable of extracting interpretable features directly from the raw data. In addition, by visualising the high-dimensional representations of the network with the t-SNE algorithm, we discover that not only is this method robust to minor mislabeling of the training dataset but that it is possible to identify an underlying substructure within the signals that goes beyond the original labelling. This technique could be utilised to explore and cluster complex, raw detector data in a novel way that may reveal more insights than standard analysis methods., 13 pages, 8 figures, submitted to Nucl. Instr. Meth. A

Active control of chirality in the terahertz frequency range is of great importance in many scientific areas, which include research into fundamental optical phenomena, investigation of novel materials, spectroscopy, imaging, wireless communications and chemistry. The lack of efficient, integrated and fast-reconfigurable polarization modulators has hindered, so far, the full exploitation of applications in all the aforementioned fields. Metamaterials are artificial resonant elements possessing unique remarkable properties such as high efficiency and miniaturization capability. The interplay of metallic metamaterial arrays with electrostatically tunable monolayer graphene has been demonstrated to be a valid approach for the realization of a novel class of THz devices. In this work, the realization of active chiral graphene/metamaterial modulator is presented. The versatility of this experimental approach allowed the device integration with broadband sources such as terahertz time domain spectrometers as well as with quantum cascade lasers. A continuous rotation of the polarization plane > 30° has been reported with a reconfiguration speed > 5 MHz. These results pave the way to the integration of fast terahertz polarization modulators in all the applications where these devices are in great demand.

We have performed numerical simulations of the electromagnetic hoop stress, ���� , in a REBaCuO disk bulk reinforced by a metal ring during pulsed-field magnetization (PFM) using a solenoid coil, in which the superconducting characteristics of the bulk material were assumed to have realistic J c-B-T ones. The compressive and tensile ���� stresses were applied in the bulk during the ascending and descending stages of PFM, respectively. The time and position dependences of the mechanical stresses were estimated. The possibility of mechanical fracture due to these hoop stresses and the effect of the metal ring reinforcement were discussed., Japan Society for the Promotion of Science (JSPS) KAKENHI Grant No. 15K04646

The microstructure of green-emitting InGaN/GaN quantum well (QW) samples grown at different temperatures was studied using cross-section scanning transmission electron microscopy (STEM) and plan-view cathodoluminescence (CL). The sample with the lowest InGaN growth temperature exhibits microscale variations in the CL intensity across the sample surface. Using STEM analysis of such areas, the observed darker patches do not correspond to any observable extended defect. Instead, they are related to changes in the extent of gross-well width fluctuations in the QWs, with more brightly emitting regions exhibiting a high density of such fluctuations, whilst dimmer regions were seen to have InGaN QWs with a more uniform thickness.

In this work, the capability of restricted Boltzmann machines (RBMs) to find solutions for the Kitaev honeycomb model with periodic boundary conditions is investigated. The measured groundstate (GS) energy of the system is compared and, for small lattice sizes (e.g. $3 \times 3$ with $18$ spinors), shown to agree with the analytically derived value of the energy up to a deviation of $0.09\%$. Moreover, the wave-functions we find have $99.89\%$ overlap with the exact ground state wave-functions. Furthermore, the possibility of realizing anyons in the RBM is discussed and an algorithm is given to build these anyonic excitations and braid them for possible future applications in quantum computation. Using the correspondence between topological field theories in (2+1)d and 2d CFTs, we propose an identification between our RBM states with the Moore-Read state and conformal blocks of the $2$d Ising model., Comment: (15+5) pages, 11 figures, 5 tables: Minor journal revisions and additions. + Journal ref

We study biochemical reaction networks capable of product discrimination inspired by biological proofreading mechanisms. At equilibrium, product discrimination, the selective formation of a ‘correct’ product with respect to an ‘incorrect product’, is fundamentally limited by the free energy difference between the two products. However, biological systems often far exceed this limit, by using discriminatory networks that expend free energy to maintain non-equilibrium steady states. Non-equilibrium systems are notoriously difficult to analyze and no systematic methods exist for determining parameter regimes which maximize discrimination. Here we introduce a measure that can be computed directly from the biochemical rate constants which provides a condition for proofreading in a broad class of models, making it a useful objective function for optimizing discrimination schemes. Our results suggest that this measure is related to whether a network is processive or distributive. Processive networks are those that have a single dominant pathway for reaction progression, such as a protein complex that must be assembled sequentially. While distributive networks are those that have many effective pathways from the reactant to the product state; e.g. a protein complex in which the subunits can associate in any order. Non-equilibrium systems can discriminate using either binding energy (energetic) differences or activation energy (kinetic) differences. In both cases, proofreading is optimal when dissipation is maximized. In this work, we show that for a general class of proofreading networks, energetic discrimination requires processivity and kinetic discrimination requiring distributivity. Optimal discrimination thus requires both maximizing dissipation and being in the correct processive/distributive limit. Sometimes, adjusting a single rate may put these requirements in opposition and in these cases, the error may be a non-monotonic function of that rate. This provides an explanation for the observation that the error is a non-monotonic function of the irreversible drive in the original proofreading scheme of Hopfield and Ninio. Finally, we introduce mixed networks, in which one product is favored energetically and the other kinetically. In such networks, sensitive product switching can be achieved simply by spending free energy to drive the network toward either the processive limit or the distributive limit. Biologically, this corresponds to the ability to select between products by driving a single reaction without network fine tuning. This may be used to explore alternate product spaces in challenging environments.

Active systems are characterized by a continuous production of entropy at steady state. We study the statistics of entropy production within a lattice-based model of interacting active particles that is capable of motility-induced phase separation. Exploiting a recent formulation of the exact fluctuating hydrodynamics for this model, we provide analytical results for its entropy production statistics in both typical and atypical (biased) regimes. This complements previous studies of the large deviation statistics of entropy production in off-lattice active particle models that could only be addressed numerically. Our analysis uncovers an unexpectedly intricate phase diagram, with five different phases arising (under bias) within the parameter regime where the unbiased system is in its homogeneous state. Notably, we find the concurrence of first order and second order nonequilibrium phase transition curves at a bias-induced tricritical point, a feature not yet reported in previous studies of active systems.

In this work, we investigate the effect of anti-site disorder on the half-metallic properties of a Mn2FeAl Heusler alloy thin film. The film was grown on TiN-buffered MgO 001 substrates via magnetron sputtering. A detailed structural characterization using x-ray diffraction (XRD) and anomalous XRD showed that the film crystallizes in the partially disordered L21 B structure with 33% disorder between the Mn(B) and Al(D) sites. We measure a positive anisotropic magnetoresistance in the film, which is an indication of non-half metallic behaviour. Our x-ray magnetic circular dichroism sum rules analysis shows that Mn carries the magnetic moment in the film, with a positive Fe moment. Experimentally determined moments correspond most closely with those found by density functional calculated for the L21 B structure with Mn(B) and Al(D) site disorder, matching the experimental structural analysis. We thus attribute the deviation from half-metallic behaviour to the formation of the L21 B structure. To realize a half-metallic Mn2FeAl film it is important that the inverse Heusler XA structure is stabilized with minimal anti-site atomic disorder.

Objective. Laser-accelerated protons offer an alternative delivery mechanism for proton therapy. This technique delivers dose-rates of ≥109 Gy s−1, many orders of magnitude greater than used clinically. Such ultra-high dose-rates reduce delivery time to nanoseconds, equivalent to the lifetime of reactive chemical species within a biological medium. This leads to the possibility of inter-track interactions between successive protons within a pulse, potentially altering the yields of damaging radicals if they are in sufficient spatial proximity. This work investigates the temporal evolution of chemical species for a range of proton energies and doses to quantify the circumstances required for inter-track interactions, and determine any relevance within ultra-high dose-rate proton therapy. Approach. The TOPAS-nBio Monte Carlo toolkit was used to investigate possible inter-track interactions. Firstly, protons between 0.5 and 100 MeV were simulated to record the radial track dimensions throughout the chemical stage from 1 ps to 1 μs. Using the track areas, the geometric probability of track overlap was calculated for various exposures and timescales. A sample of irradiations were then simulated in detail to compare any change in chemical yields for independently and instantaneously delivered tracks, and validate the analytic model. Main results. Track overlap for a clinical 2 Gy dose was negligible for biologically relevant timepoints for all energies. Overlap probability increased with time after irradiation, proton energy and dose, with a minimum 23 Gy dose required before significant track overlap occurred. Simulating chemical interactions confirmed these results with no change in radical yields seen up to 8 Gy for independently and instantaneously delivered tracks. Significance. These observations suggest that the spatial separation between incident protons is too large for physico-chemical inter-track interactions, regardless of the delivery time, indicating such interactions would not play a role in any potential changes in biological response between laser-accelerated and conventional proton therapy.

The use of Ring Imaging Cherenkov detectors (RICH) offers a powerful technique for identifying the particle species in particle physics. These detectors produce 2D images formed by rings of individual photons superimposed on a background of photon rings from other particles. The RICH particle identification (PID) is essential to the LHCb experiment at CERN. While the current PID algorithm has performed well during LHC data-taking periods between 2010 to 2018, its complexity poses a challenge for LHCb computing infrastructure upgrades towards multi-core architectures. The high particle multiplicity environment of future LHC runs strongly motivates shifting towards high-throughput computing for the online event reconstruction. In this contribution, we introduce a convolutional neural network (CNN) approach to particle identification in LHCb RICH. The CNN takes binary input images from the two RICH detectors to classify particle species. The input images are polar-transformed sub-sections of the RICH photon-detection planes. The model is hyperparameter-optimised and trained on classification accuracy with simulated collision data for the upcoming LHC operation starting in 2022. The PID performance of the CNN is comparable to the conventional algorithm, and its simplicity renders it suitable for fast online reconstruction through parallel processing. We show that under conditions of reduced combinatorial background, as expected from the introduction of timing resolution to the RICH detectors in future upgrades, the network achieves a particle identification performance close to 100 %, with simultaneous misclassification of the most prevalent particle species approaching 0 %.

Helical coil accelerators are a recent development in laser-driven ion production, acting on the intrinsically wide divergence and broadband energy spectrum of laser-accelerated protons to deliver ultra-low divergence and quasi-monoenergetic beams. The modularity of helical coil accelerators also provides the attractive prospective of multi-staging. Here we show, on a proof-of-principle basis, a two-stage configuration which allows optical tuning of the energy of the selected proton beamlet. Experimental data, corroborated by particle tracing simulations, highlights the importance of controlling precisely the beam injection. Efficient post-acceleration of the protons with an energy gain up to ∼16 MeV (∼8 MeV per stage, at an average rate of ∼1 GeV m−1) was achieved at an optimal time delay, which allows synchronisation of the selected protons with the accelerating longitudinal electric fields to be maintained through both stages.

Trapped-flux magnets comprising stacked superconducting tape constitute a promising development to increase the power density of electrical machines, whilst at the same time keeping the complexity required in their construction in manageable levels that allow their use in applications such as aircraft propulsion. However, the conditions in which superconducting stacks operate inside an electrical motor differ quite significantly from the materials characterization experiments commonly developed to model their behaviour. This work presents the results of studying the applicability of these devices as magnetic flux source in the rotor of synchronous machines considering the influence of whole magnetic circuit. Several aspects are assessed, such as flux harmonics, magnetization, losses and demagnetization. Analytical expressions, which provide limited accuracy but allow fast calculations, are used for this purpose. The results illustrate the different trade-offs that arise during the design of a synchronous electric motor using trapped-flux magnets.

Funder: Royal Society; doi: http://dx.doi.org/10.13039/501100000288, Funder: Trinity College, University of Cambridge; doi: http://dx.doi.org/10.13039/501100000727, Funder: John Fell Fund, University of Oxford; doi: http://dx.doi.org/10.13039/501100004789, We use a deep neural network (NN) to detect and place region-of-interest (ROI) boxes around ultracold atom clouds in absorption and fluorescence images—with the ability to identify and bound multiple clouds within a single image. The NN also outputs segmentation masks that identify the size, shape and orientation of each cloud from which we extract the clouds’ Gaussian parameters. This allows 2D Gaussian fits to be reliably seeded thereby enabling fully automatic image processing. The method developed performs significantly better than a more conventional method based on a standardized image analysis library (Scikit-image) both for identifying ROI and extracting Gaussian parameters.

This study explores the effect of different technology choices on the life cycle Greenhouse Gases (GHG) emissions of wind turbines. It aims at identifying possible improvement pathways and quantifying potential benefits. First, an automated Life Cycle Assessment (LCA) model is developed and validated against other studies. The breakdown of the resulting Impact Of Energy (IOE) by life stages, components and materials is presented for a selected reference turbine. Sensitivity analyses are conducted on the different hypotheses to model the turbine life cycle. The model is then applied to analyze the potential effect of turbine size, foundation type, tower type, drivetrain technology, carbon-fiber blades and thermoplastic polymer resin for the blades. The tower type (especially wooden tower) and the thermoplastics (allowing to recover materials at the end-of-life) are identified to have the biggest potential to reduce the life cycle environmental impact of wind energy.

The SIDDHARTA-2 experiment at the DAΦNE collider aims to perform the first kaonic deuterium x-ray transitions to the fundamental level measurement, with a systematic error at the level of a few eV. To achieve this challenging goal the experimental apparatus is equipped with 384 Silicon Drift Detectors (SDDs) distributed around its cryogenic gaseous target. The SDDs developed by the SIDDHARTA-2 collaboration are suitable for high precision kaonic atoms spectroscopy, thanks to their high energy and time resolutions combined with their radiation hardness. The energy response of each detector must be calibrated and monitored to keep the systematic error, due to processes such as gain fluctuations, at the level of 2–3 eV. This paper presents the SIDDHARTA-2 calibration method which was optimized during the preliminary phase of the experiment in the real background conditions of the DAΦNE collider, which is a fundamental tool to guarantee the high precision spectroscopic performances of the system over long periods of data taking, as that required for the kaonic deuterium measurement.

This paper presents an analysis of the power loss of the upstream turbine for yaw misalignment depending on the inflow condition. High-quality experimental field data, obtained from a lidar wind profiler, met mast, GPS based yaw sensor, turbine data, and load data is used. To reduce the spreading of the power loss during yaw misalignment, the rotor effective wind speed is estimated using load data. Furthermore, the so-called power loss coefficient currently used in most engineering models, for yaw misalignment, is assessed at various inflow conditions. As main finding, the power loss coefficient increases for stronger vertical wind shear. In addition, there is an asymmetry with respect to the direction of the yaw misalignment that is enhanced by the wind shear exponent and the wind veer. Neglecting these two effects could result in in-conservative wake-steering decisions at stable stratification

In this paper we propose an approach for performing fault detection and identification in clock ensembles based on the generalized likelihood ratio test. We show that by applying a set of purposefully-designed statistical tests, one can successfully detect faults occurring in a clock of the ensemble, and identify which measurement in the ensemble is most likely to have triggered the detection. We first develop the theoretical framework for the characterization of the detectors and their performance, and validate the derivations via Monte Carlo simulations. Then, we apply the statistical tests to an ensemble of cesium clocks, aiming at detecting and identifying three types of non-nominal behaviors. The faulty conditions are obtained by injecting a pattern of phase steps, a phase and frequency drift, and an oscillatory phase component.

In the course of the geotechnical investigations for a new runway at Munich Airport the hydro-mechanical behaviour of organogenic clays was studied. With the aim of an efficient use of materials the organogenic clay shall be used for noise and view barriers. As the organogenic clays have increased arsenic contents the percolation of such structures should be minimized. For the detailed investigation of the hydro-mechanical behaviour and for the determination of seepage quantities, a large scale, 5 m high, 30 m long and 25 m wide test fill was constructed. Due to the low permeability of the organogenic clays in compacted condition a cover design with a drainage mat and a top layer without an additional sealing layer was chosen. Measurements of the water balance were collected since the installation in 2008. The results show very low leachate quantities, although humid climatic conditions with average annual precipitation of approx. 745 mm were present. The low leachate rates can be mainly attributed to the capillary-breaking effect of the drainage geocomposite and the high water retention capacity of the topsoil. Based on the results the presented simple cover system can be considered an efficient and effective solution for minimizing infiltration water into core materials of embankments and noise barriers in road-way design.

Objective. Dual energy computed tomography (DECT) has been shown to provide additional image information compared to conventional CT and has been used in clinical routine for several years. The objective of this work is to present a DECT implementation for a Small Animal Radiation Research Platform (SARRP) and to verify it with a quantitative analysis of a material phantom and a qualitative analysis with an ex-vivo mouse measurement. Approach. For dual energy imaging, two different spectra are required, but commercial small animal irradiators are usually not optimized for DECT. We present a method that enables dual energy imaging on a SARRP with sequential scanning and an Empirical Dual Energy Calibration (EDEC). EDEC does not require the exact knowledge of spectra and attenuation coefficients; instead, it is based on a calibration. Due to the SARRP geometry and reconstruction algorithm, the calibration is done using an artificial CT image based on measured values. The calibration yields coefficients to convert the measured images into material decomposed images. Main results. To analyze the method quantitatively, the electron density and the effective atomic number of a material phantom were calculated and compared with theoretical values. The electron density showed a maximum deviation from the theoretical values of less than 5% and the atomic number of slightly more than 6%. For use in mice, DECT is particularly useful in distinguishing iodine contrast agent from bone. A material decomposition of an ex-vivo mouse with iodine contrast agent was material decomposed to show that bone and iodine can be distinguished and iodine-corrected images can be calculated. Significance. DECT is capable of calculating electron density images and effective atomic number images, which are appropriate parameters for quantitative analysis. Furthermore, virtual monochromatic images can be obtained for a better differentiation of materials, especially bone and iodine contrast agent.

The development of rare-earth barium copper oxide (REBCO) coated conductors with an extremely high critical current density under ultra-high fields opens up a high-field path towards large-scale fusion. The latest technology has inspired cable-in-conduit conductors such as conductor on round core wires, twisted stacked tape conductor cables and Rutherford cables with outstanding current-carrying capacities. In order to realise an inductance balance and decrease magnetic diffusion, these cables have been twisted or folded to a certain extent, thus breaking the mechanical behaviour of the ceramic superconductor and limiting their potential for ultra-high-field applications. One possible solution is to employ a non-twisted cable, which offers maximum protection of its mechanical properties and enables a parallel orientation of the toroidal field vector to the surface of REBCO coated conductors, and at the same time decreases the influence of perpendicular fields on the critical current of REBCO cables. However, the applied physics community’s attitude towards using non-twisted, parallel REBCO stacked-tape cables is one of scepticism, the main argument being that the nonlinear E–J behaviour associated with screening current in the parallel stack might lead to a field distortion and reduce the performance of superconductivity. Recent analyses have demonstrated that the effect of screening current decreases significantly owing to a wavelike magnetic field distribution along the cable. The authors obtained similar results using H-formulation and T–A formulation based finite element methods and demonstrated that the non-twisted cable may be feasible for DC current transmission toroidal field coils in magnetic-confinement devices. Furthermore, the electromechanical behaviour of toroidal field coils has been evaluated via the Maxwell stress, solved by using an A–V formulation. It was discovered that the stress generated by the toroidal field coils is within the stress tolerance of the REBCO coated conductor, something which is of great significance in promoting the application of REBCO coated conductors for ultra-high-field magnetic-confinement plasma devices.

Sterile neutrinos are a minimal extension of the standard model of particle physics. A laboratory-based approach to search for this particle is via tritium β-decay, where a sterile neutrino would cause a kink-like spectral distortion. The Karlsruhe Tritium Neutrino (KATRIN) experiment extended by a multi-pixel Silicon Drift Detector system has the potential to reach an unprecedented sensitivity to the keV-scale sterile neutrino in a lab-based experiment. The new detector system combines good spectroscopic performance with a high rate capability. In this work, we report about the characterization of charge-sharing between pixels and the commissioning of a 47-pixel prototype detector in a MAC-E filter.

Injection of cryogenic propellants (e.g. liquid oxygen) into low-pressure environment (e.g. upper-stage rocket engine) may trigger flashing phenomenon, which severely affects the propellants’ mixing and combustion. In order to unveil the characteristics of flashing sprays, numerical models of flashing sprays were developed and validated. First, a developed model based on Adachi-correlation was employed for the flashing spray simulation. The results show good agreements with the experiments, both for the flashing spray morphology and temperature distribution. In the near-injector region, the flashing evaporation dominates the spray vaporization with the evaporation mass flow rate of about 2 orders of magnitude higher than that by the other heat transfers, whereas downstream the injector, the external heat transfer (i.e. heat conduction and convection) does. Furthermore, a new flashing spray model based on the nucleate boiling theory was proposed, which shows an improved agreement of the droplet temperature between the simulation and test data.

Even though charge transport in semiconducting polymers is of relevance for a number of potential applications in (opto-)electronic devices, the fundamental mechanism of how charges are transported through organic polymers that are typically characterized by a complex nanostructure is still open. One of the challenges which we address here, is how to gain controllable experimental access to charge transport at the sub-100 nm lengthscale. To this end charge transport in single poly(diketopyrrolopyrrole-terthiophene) fiber transistors, employing two different solid gate dielectrics, a hybrid Al2O3/self-assembled monolayer and hexagonal boron nitride, is investigated in the sub-50 nm regime using electron-beam contact patterning. The electrical characteristics exhibit near ideal behavior at room temperature which demonstrates the general feasibility of the nanoscale contacting approach, even though the channels are only a few nanometers in width. At low temperatures, we observe nonlinear behavior in the current–voltage characteristics in the form of Coulomb diamonds which can be explained by the formation of an array of multiple quantum dots at cryogenic temperatures.

Future wind energy control applications require surrogate flow models that can capture processes on short time scales. Dynamic extensions of wake models might be a useful low cost solution. So far, studies have tested the proposals mostly in LES or LIDAR campaigns. At the same time, readily obtainable turbine SCADA measurements are increasingly being used directly for model input or validation. Newly available high resolution recordings allow for the study of processes on shorter time scales. The present study examines the modelling capabilities of a dynamic wake model by utilizing high frequency SCADA data. Therefore, the data is first analyzed for cross-correlation and time delays. Secondly, the dynamic wake model, initialized with SCADA measurements, is used to predict a two turbine wake interaction. An offline tuning algorithm is used to adapt the model for the shorter time scales.

We present an x-ray Computed Tomography setup that integrates a seven degrees of freedom robotic arm as a sample holder within an existing laboratory x-ray computed tomography setup. We aim to provide a flexible sample holder that is able to execute non-standard and task-specific trajectories for complex samples. The robotic arm is integrated with a unified software package that allows for path planning, collision detection, geometric calibration and reconstruction of the sample. The calibration is necessary to identify the accurate pose of the sample which deviates from the expected pose due to inaccurate placement of the robotic arm. With our software the user is able to command the robotic arm to execute arbitrary trajectories for a given sample in a safe manner and output its reconstruction to the user. We present experimental results with a circular trajectory where the robotic sample holder achieves identical visual quality compared to a conventional sample holder.

In this work, a novel rotor rebalancing algorithm is tested in a simulation environment to evaluate its performance when facing both aerodynamic and inertial imbalances. The algorithm, starting from a generic measurement collected on the wind turbine fixed frame, is capable of remotely minimizing once per revolution vibrations, avoiding the need for on-site inspections, and without requiring detailed information about the machine. Indeed, once access to the pitch system is granted, this algorithm simply iteratively computes the pitch angle that needs to be applied to each individual blade in order to rebalance the rotor. Several turbulent time histories, with changing mean inflows, were simulated with the goal of testing the proposed method in realistic field conditions. Overall, the algorithm proved capable of significantly reducing the desired once per revolution vibrations in 3 to 4 iterations, irrespective of the imbalance root cause, its severity and its location. The method also appeared quite robust, showing that the found rebalanced configurations guarantee reduced vibrations no matter the machine operating point.

The toolbox for time-resolved direct measurements of electron dynamics covers a variety of methods. Since the experimental effort is increasing rapidly with achievable time resolution, there is an urge for simple and robust measurement techniques. Within this paper prove of concept experiments and numerical simulations are utilized to investigate the applicability of a new setup for the generation of ultrashort electron pulses in the energy range of 300 eV up to 1.6 keV. The experimental approach combines an in-vacuum liquid microjet and a few-cycle femtosecond laser system, while the threshold for electron impact ionization serves as a gate for the effective electron pulse duration. The experiments prove that electrons in the keV regime are accessible and that the electron spectrum can be easily tuned by laser intensity and focal position alignment with respect to the water jet. Numerical simulations show that a sub-picosecond temporal resolution is achievable., 13 Pages, 6 Fugures

Objective. In clinical positron emission tomography (PET) imaging, quantification of radiotracer uptake in tumours is often performed using semi-quantitative measurements such as the standardised uptake value (SUV). For small objects, the accuracy of SUV estimates is limited by the noise properties of PET images and the partial volume effect. There is need for methods that provide more accurate and reproducible quantification of radiotracer uptake. Approach. In this work, we present a deep learning approach with the aim of improving quantification of lung tumour radiotracer uptake and tumour shape definition. A set of simulated tumours, assigned with ‘ground truth’ radiotracer distributions, are used to generate realistic PET raw data which are then reconstructed into PET images. In this work, the ground truth images are generated by placing simulated tumours characterised by different sizes and activity distributions in the left lung of an anthropomorphic phantom. These images are then used as input to an analytical simulator to simulate realistic raw PET data. The PET images reconstructed from the simulated raw data and the corresponding ground truth images are used to train a 3D convolutional neural network. Results. When tested on an unseen set of reconstructed PET phantom images, the network yields improved estimates of the corresponding ground truth. The same network is then applied to reconstructed PET data generated with different point spread functions. Overall the network is able to recover better defined tumour shapes and improved estimates of tumour maximum and median activities. Significance. Our results suggest that the proposed approach, trained on data simulated with one scanner geometry, has the potential to restore PET data acquired with different scanners.

In the recent years machine learning techniques have attracted the attention of wind energy community to make use of the large amount of available data produced from the running wind turbines. These modern wind turbines are typically equipped with measurement systems and sensors that can provide a wealth of information about the operating conditions of the machine. Nevertheless, not all the acquired raw data can be used effectively to enhance the operation of a turbine. This work addresses the question of estimating the damage equivalent loads (DEL) of different components of a drivetrain. The estimation is based on low frequency sampled typically available SCADA measurements. Typical SCADA measurements that are used as input for the estimation model are generator rotational speed, low speed shaft torque and generator torque as well as, wind speed and direction. Several machine learning methods as random forests (RF), support vector machines (SVM), linear regression (LR), decision trees and neural networks (NN) were developed, exhibiting different behavior for each approach. The qualitative and quantitative performance of each algorithm are evaluated and compared against each other. Furthermore, analysis of importance of the input features is presented.

Second harmonic generation efficiency (SHGE) strongly depends on the length of the interaction material along the beam propagation axis. Since a nanoscale interaction length is considered too short even in the optical wavelength scale, the attained SHGE through nanomaterials is usually too low to be of practical use. In this study, it will be shown that by properly adjusting the conduction-band electron density in a semiconductor nanomaterial under a certain optical pumping rate (active tuning), the SHGE can be effectively tuned from being super-low to being ultra-high. Such sharp tunability is only valid for small-scale materials as their density of conduction-band electrons can be rapidly switched between high and low under moderate optical pumping. Using an experimentally verified computational model, we have observed that at a given frequency, for a certain range of conduction-band electron densities, the SHGE can reach up to 1080% for Ga-As and 230% for silicon nanomaterials under active tuning, with respect to the intensity of the first harmonic of the input signal. Such SHGEs are unprecedented, which is very promising for generating higher harmonics via cascaded second harmonic generation performed via adaptive tuning of the conduction band electron density at each stage.

Investigation of the sheet metal formability is an important issue in the design of components. During metal forming different deformation histories occur which may cause various damage and failure mechanisms on the micro-level affecting the quality of products. Therefore, description of the material characteristics by means of a linear limit change curve is not sufficient because the remaining formability and the fracture processes also depend on deformation history. For this purpose the aluminium alloy AA6016 is systematically investigated in order to obtain their forming capability as well as the damage and fracture behaviour after complex deformation paths. Different uniaxial tests with various initially unloaded and preloaded specimens have been performed to identify the effect of the forming history, the loading direction and the stress state on the formability and the mechanical parameters of the investigated material. The results can be used to propose a critical ratio between the pre-strain and the change in loading direction from which on shear failure occurs instead of necking behaviour leading to the loss of formability.

We analyse biased ensembles of trajectories for the random-field Ising model on a fully-connected lattice, which is described exactly by mean-field theory. By coupling the activity of the system to a dynamical biasing field, we find a range of dynamical phase transitions, including spontaneous symmetry breaking into ordered states. For weak bias, the phase behaviour is controlled by extrema of the free energy, which may be local minima or saddle points. For large bias, the system tends to states of extremal activity, which may differ strongly from free energy minima. We discuss connections of these results to random first-order transition theory of glasses, which motivates an extension of the analysis to random-field Ising models where the dynamical activity is not symmetric under magnetisation reversal., 26 pages, 10 figures

We investigated the use of dielectric layers produced by atomic layer deposition (ALD) as an approach to strain mitigation in composite silicon/superconductor devices operating at cryogenic temperatures. We show that the addition of an ALD layer acts to reduce the strain of spins closest to silicon/superconductor interface where strain is highest. We show that appropriately biasing our devices at the hyperfine clock transition of bismuth donors in silicon, we can remove strain broadening and that the addition of ALD layers left T 2 (or temporal inhomogeneities) unchanged in these natural silicon devices.

In building life cycle assessment, construction site processes are currently hardly accounted for in detail. If considered, the emission values are determined by globally capturing effort values and by allocating them to individual work steps, equipment, or materials. To identify improvement potential to decarbonize construction activities, detailed process data must be captured for specific processes to determine the carbon dioxide emissions of single processes. The results can then be aggregated to larger process units e.g. to components or trades. To fully implement this approach, a recording of process-specific data at construction site is necessary. Therefore, the basic framework to record specific processes at construction site is developed in this article and LCA methodology to systematically link them to emissions is explained. Further, the method of computer vision for automated recording of construction site activities is being presented.

The role of the divertor configuration and divertor plasma physics on the L–H transition is poorly understood, leading to large uncertainties in predicting the L–H power threshold in future devices. This contribution reports on edge perpendicular plasma flow measurements by Doppler backscattering in JET L–H transition experiments with the outer divertor strike-point at different positions: horizontal target (HT), vertical target (VT), and in the corner configuration (between the HT and VTs). The edge perpendicular flow was found to be significantly affected by changes in the divertor configuration in the region inside the separatrix. Our results do not show evidence for the existence of a critical edge flow shear needed to achieve H-mode for different divertor configurations, with a larger shear observed for the VT configuration. No significant change in the shear flow and in the density fluctuation level is measured preceding the L–H transition in the region just inside the separatrix. The dynamics of the L–H transitions are also influenced by the divertor configuration with divertor oscillations (DOs) observed only in the HT configuration. Interestingly, DOs are associated with marked changes in the edge perpendicular flow around the separatrix.

International audience; Mini-EUSO is a high sensitivity imaging telescope that observes the Earth from the ISS in the ultraviolet band (2904÷430 nm), through the UV-transparent window in the Russian Zvezda module. The instrument, launched in 2019 as part of the ESA mission Beyond, has a field of view of 44°, a spatial resolution on the Earth surface of 6.3 km and a temporal resolution of 2.5 microseconds. The telescope detects UV emissions of cosmic, atmospheric and terrestrial origin on different time scales, from a few microseconds upwards. Mini-EUSO main detector optics is composed of two Fresnel lenses focusing light onto an array of 36 Hamamatsu multi-anode photomultiplier tubes, for a total of 2304 pixels. The telescope also contains: two ancillary cameras to complement measurements in the near infrared and visible ranges, an array of Silicon-PhotoMultipliers and UV sensors to manage night-day transitions. In this work we will describe the in-flight operations and performances of the various instruments in the first months after launch.

The remanent domain structures of composite element magnetic barcodes have been imaged using photo-emission electron microscopy with contrast from x-ray magnetic circular dichroism (XMCD-PEEM) and analysed with reference to the results of micromagnetic simulations. The magnetisation configuration at the end of wide strips is found to be perpendicular to the majority magnetisation direction. This transitions to an incomplete rotation for nominal strip widths below 300 nm and is found to affect the mechanics of magnetisation reversal for nominal strip widths below 200 nm, owing to a difference in magnetisation orientation when an external magnetic field is applied that is just smaller than the magnetic coercivity of the structures and a corresponding change in reversal dynamics. This change in domain structure as strip width decreases is consistent with both the influence of shape anisotropy and with measurements of magnetic hysteresis. The magnetisation reversal characteristics of composite element structures are found to be dependent on the relative magnetisation configurations of neighbouring strips, which in turn are found to vary stochastically upon the application and removal of a magnetic field along the easy axis of the structure. It is found that the application of a canted field is necessary to ensure sharp, consistent magnetisation reversal of bits when writing a binary code. These results confirm that either improved lithography of narrower strips or non-rectangular elements would be necessary to further increase the number of individually programmable bits in a barcode.

A series of single InGaN/GaN quantum wells (QWs) with a Si-doped InGaN underlayer were studied to investigate the impact of the underlayer on photoluminescence efficiency and recombination dynamics. The thickness of the GaN capping layer was varied between samples, which changed the electric field across the QW due to band bending near the surface. When directly exciting the wells, thermionic emission of carriers results in a rapid drop in the photoluminesence efficiency with increasing temperature such that no emission is observed above 100 K. However, exciting above the energy of the barriers caused the intensity of the QW emission to drop more slowly, with up to 12% of the 10 K emission intensity remaining at 300 K. This difference is attributed to hole transfer from the underlayer into the QW, which increases in efficiency at higher temperatures, and is enhanced by stronger electric fields present in the GaN barriers of samples with thinner GaN capping layers. Further, the sample with the narrowest cap layer of 2 nm has a different shape and characteristic time for its photoluminescence decay transient and a different emission energy temperature dependence than the other samples. This behaviour was ascribed to a change in carrier localisation for this sample due to a reversal of the net field across the well compared to the other samples.

Funder: Cambridge Commonwealth, European and International Trust; doi: https://doi.org/10.13039/501100003343, Funder: Natural Sciences and Engineering Research Council of Canada (NSERC), We develop a resource theory of symmetric distinguishability, the fundamental objects of which are elementary quantum information sources, i.e., sources that emit one of two possible quantum states with given prior probabilities. Such a source can be represented by a classical-quantum state of a composite system $XA$, corresponding to an ensemble of two quantum states, with $X$ being classical and $A$ being quantum. We study the resource theory for two different classes of free operations: $(i)$ ${\rm{CPTP}}_A$, which consists of quantum channels acting only on $A$, and $(ii)$ conditional doubly stochastic (CDS) maps acting on $XA$. We introduce the notion of symmetric distinguishability of an elementary source and prove that it is a monotone under both these classes of free operations. We study the tasks of distillation and dilution of symmetric distinguishability, both in the one-shot and asymptotic regimes. We prove that in the asymptotic regime, the optimal rate of converting one elementary source to another is equal to the ratio of their quantum Chernoff divergences, under both these classes of free operations. This imparts a new operational interpretation to the quantum Chernoff divergence. We also obtain interesting operational interpretations of the Thompson metric, in the context of the dilution of symmetric distinguishability.

Quantifying parenchymal tissue changes in the lungs is imperative in furthering the study of radiation induced lung damage (RILD). Registering lung images from different time-points is a key step of this process. Traditional intensity-based registration approaches fail this task due to the considerable anatomical changes that occur between timepoints. This work proposes a novel method to successfully register longitudinal pre- and post-radiotherapy (RT) lung computed tomography (CT) scans that exhibit large changes due to RILD, by extracting consistent anatomical features from CT (lung boundaries, main airways, vessels) and using these features to optimise the registrations. Pre-RT and 12 month post-RT CT pairs from fifteen lung cancer patients were used for this study, all with varying degrees of RILD, ranging from mild parenchymal change to extensive consolidation and collapse. For each CT, signed distance transforms from segmentations of the lungs and main airways were generated, and the Frangi vesselness map was calculated. These were concatenated into multi-channel images and diffeomorphic multichannel registration was performed for each image pair using NiftyReg. Traditional intensity-based registrations were also performed for comparison purposes. For the evaluation, the pre- and post-registration landmark distance was calculated for all patients, using an average of 44 manually identified landmark pairs per patient. The mean (standard deviation) distance for all datasets decreased from 15.95 (8.09) mm pre-registration to 4.56 (5.70) mm post-registration, compared to 7.90 (8.97) mm for the intensity-based registrations. Qualitative improvements in image alignment were observed for all patient datasets. For four representative subjects, registrations were performed for three additional follow-up timepoints up to 48 months post-RT and similar accuracy was achieved. We have demonstrated that our novel multichannel registration method can successfully align longitudinal scans from RILD patients in the presence of large anatomical changes such as consolidation and atelectasis, outperforming the traditional registration approach both quantitatively and through thorough visual inspection.

Funder: Trinity College, University of Cambridge; doi: http://dx.doi.org/10.13039/501100000727, Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it improves the computational performance and often the transferability of a model. Here we focus on two popular subselection schemes applied to this end: CUR decomposition, derived from a low-rank approximation of the feature matrix, and farthest point sampling (FPS), which relies on the iterative identification of the most diverse samples and discriminating features. We modify these unsupervised approaches, incorporating a supervised component following the same spirit as the principal covariates (PCov) regression method. We show how this results in selections that perform better in supervised tasks, demonstrating with models of increasing complexity, from ridge regression to kernel ridge regression and finally feed-forward neural networks. We also present adjustments to minimise the impact of any subselection when performing unsupervised tasks. We demonstrate the significant improvements associated with PCov-CUR and PCov-FPS selections for applications to chemistry and materials science, typically reducing by a factor of two the number of features and samples required to achieve a given level of regression accuracy.