Explore the vast range of titles available.

Superconducting quantum devices offer numerous applications, from electrical metrology and magnetic sensing to energy-efficient high-end computing and advanced quantum information processing. The key elements of quantum circuits are (single and double) Josephson junctions controllable either by electric current or magnetic field. The voltage control, commonly used in semiconductor-based devices via the electrostatic field effect, would be far more versatile and practical. Hence, the field effect recently reported in superconducting devices may revolutionise the whole field of superconductor electronics provided it is confirmed. Here we show that the suppression of the critical current attributed to the field effect, can be explained by quasiparticle excitations in the constriction of superconducting devices. Our results demonstrate that a miniscule leakage current between the gate and the constriction of devices perfectly follows the Fowler-Nordheim model of electron field emission from a metal electrode and injects quasiparticles with energies sufficient to weaken or even suppress superconductivity. A recent report on electrostatic field effect in superconducting devices provides a high potential for advanced quantum technology, but it remains controversial. Here, the authors report that the suppression of critical current, which was attributed to the field effect, can instead be explained by quasiparticle excitations in the constriction of superconducting devices.

The automatic detection of individual pigs can improve the overall management of pig farms. The accuracy of single-image object detection has significantly improved over the years with advancements in deep learning techniques. However, differences in pig sizes and complex structures within pig pen of a commercial pig farm, such as feeding facilities, present challenges to the detection accuracy for pig monitoring. To implement such detection in practice, the differences should be analyzed by video recorded from a static camera. To accurately detect individual pigs that may be different in size or occluded by complex structures, we present a deep-learning-based object detection method utilizing generated background and facility information from image sequences (i.e., video) recorded from a static camera, which contain relevant information. As all images are preprocessed to reduce differences in pig sizes. We then used the extracted background and facility information to create different combinations of gray images. Finally, these images are combined into different combinations of three-channel composite images, which are used as training datasets to improve detection accuracy. Using the proposed method as a component of image processing improved overall accuracy from 84% to 94%. From the study, an accurate facility and background image was able to be generated after updating for a long time that helped detection accuracy. For the further studies, improving detection accuracy on overlapping pigs can also be considered.

As a state-of-the-art computational method for simulating rock fracturing and fragmentation, the combined finite-discrete element method (FDEM) has become widely accepted since Munjiza (2004) published his comprehensive book of FDEM. This study developed a general-purpose graphic-processing-unit (GPGPU)-parallelized FDEM using the compute unified device architecture C/C ++ based on the authors’ former sequential two-dimensional (2D) and three-dimensional (3D) Y-HFDEM IDE (integrated development environment) code. The theory and algorithm of the GPGPU-parallelized 3D Y-HFDEM IDE code are first introduced by focusing on the implementation of the contact detection algorithm, which is different from that in the sequential code, contact damping and contact friction. 3D modelling of the failure process of limestone under quasi-static loading conditions in uniaxial compressive strength (UCS) tests and Brazilian tensile strength (BTS) tests are then conducted using the GPGPU-parallelized 3D Y-HFDEM IDE code. The 3D FDEM modelling results show that mixed-mode I–II failures are the dominant failure mechanisms along the shear and splitting failure planes in the UCS and BTS models, respectively, with unstructured meshes. Pure mode I splitting failure planes and pure mode II shear failure planes are only possible in the UCS and BTS models, respectively, with structured meshes. Subsequently, 3D modelling of the dynamic fracturing of marble in dynamic Brazilian tests with a split Hopkinson pressure bar (SHPB) apparatus is conducted using the GPGPU-parallelized 3D HFDEM IDE code considering the entire SHPB testing system. The modelled failure process, final fracture pattern and time histories of the dynamic compressive wave, reflective tensile wave and transmitted compressive wave are compared quantitatively and qualitatively with those from experiments, and good agreements are achieved between them. The computing performance analysis shows the GPGPU-parallelized 3D HFDEM IDE code is 284 times faster than its sequential version and can achieve the computational complexity of O(N). The results demonstrate that the GPGPU-parallelized 3D Y-HFDEM IDE code is a valuable and powerful numerical tool for investigating rock fracturing under quasi-static and dynamic loading conditions in rock engineering applications although very fine elements with maximum element size no bigger than the length of the fracture process zone must be used in the area where fracturing process is modelled.

Ion transporters in Nature exhibit a wealth of complex transport properties such as voltage gating, activation, and mechanosensitive behavior. When combined, such processes result in advanced ionic machines achieving active ion transport, high selectivity, or signal processing. On the artificial side, there has been much recent progress in the design and study of transport in ionic channels, but mimicking the advanced functionalities of ion transporters remains as yet out of reach. A prerequisite is the development of ionic responses sensitive to external stimuli. In the present work, we report a counterintuitive and highly nonlinear coupling between electric and pressure-driven transport in a conical nanopore, manifesting as a strong pressure dependence of the ionic conductance. This result is at odds with standard linear response theory and is akin to a mechanical transistor functionality. We fully rationalize this behavior on the basis of the coupled electrohydrodynamics in the conical pore by extending the Poisson–Nernst–Planck–Stokes framework. The model is shown to capture the subtle mechanical balance occurring within an extended spatially charged zone in the nanopore. The pronounced sensitivity to mechanical forcing offers leads in tuning ion transport by mechanical stimuli. The results presented here provide a promising avenue for the design of tailored membrane functionalities.

This book considers a relatively new metric in complex systems, transfer entropy, derived from a series of measurements, usually a time series.

Aftershocks are a response to changes in stress generated by large earthquakes and represent the most common observations of the triggering of earthquakes. The maximum magnitude of aftershocks and their temporal decay are well described by empirical laws (such as Bath’s law 1 and Omori’s law 2 ), but explaining and forecasting the spatial distribution of aftershocks is more difficult. Coulomb failure stress change 3 is perhaps the most widely used criterion to explain the spatial distributions of aftershocks 4 – 8 , but its applicability has been disputed 9 – 11 . Here we use a deep-learning approach to identify a static-stress-based criterion that forecasts aftershock locations without prior assumptions about fault orientation. We show that a neural network trained on more than 131,000 mainshock–aftershock pairs can predict the locations of aftershocks in an independent test dataset of more than 30,000 mainshock–aftershock pairs more accurately (area under curve of 0.849) than can classic Coulomb failure stress change (area under curve of 0.583). We find that the learned aftershock pattern is physically interpretable: the maximum change in shear stress, the von Mises yield criterion (a scaled version of the second invariant of the deviatoric stress-change tensor) and the sum of the absolute values of the independent components of the stress-change tensor each explain more than 98 per cent of the variance in the neural-network prediction. This machine-learning-driven insight provides improved forecasts of aftershock locations and identifies physical quantities that may control earthquake triggering during the most active part of the seismic cycle. Neural networks trained on data from about 130,000 aftershocks from around 100 large earthquakes improve predictions of the spatial distribution of aftershocks and suggest physical quantities that may control earthquake triggering.

Abnormal hip joint contact forces (HJCF) are considered a primary mechanical contributor to the progression of hip osteoarthritis (OA). Compared to healthy controls, people with hip OA often present with altered muscle activation patterns and greater muscle co-contraction, both of which can influence HJCF. Neuromusculoskeletal (NMS) modelling is non-invasive approach to estimating HJCF, whereby different neural control solutions can be used to estimate muscle forces. Static optimisation, available within the popular NMS modelling software OpenSim, is a commonly used neural control solution, but may not account for an individual’s unique muscle activation patterns and/or co-contraction that are often evident in pathological population. Alternatively, electromyography (EMG)-assisted neural control solutions, available within CEINMS software, have been shown to account for individual activation patterns in healthy people. Nonetheless, their application in people with hip OA, with conceivably greater levels of co-contraction, is yet to be explored. The aim of this study was to compare HJCF estimations using static optimisation (in OpenSim) and EMG-assisted (in CEINMS) neural control solutions during walking in people with hip OA. EMG-assisted neural control solution was more consistent with both EMG and joint moment data than static optimisation, and also predicted significantly higher HJCF peaks (p < 0.001). The EMG-assisted neural control solution also accounted for more muscle co-contraction than static optimisation (p = 0.03), which probably contributed to these higher HJCF peaks. Findings suggest that the EMG-assisted neural control solution may estimate more physiologically plausible HJCF than static optimisation in a population with high levels of co-contraction, such as hip OA.

Cumulative elastic and inelastic strain and associated internal stress changes as well as damage evolution over time in brittle rocks control the long-term evolution of the rockmass around underground openings or the land surface settlement. This long-term behaviour is associated with time-dependent deformation and is commonly investigated under static load (creep) conditions in laboratory scale. In this study, low Jurassic and Cobourg limestone samples were tested at different static load levels in unconfined conditions to examine the time to failure. Comparisons are made with longterm testing data in granites and limestones associated with the Canadian nuclear waste program and other data from the literature. Failure typically occurred within the time limits of the test program (4 months) with axial (differential) stress levels near or above the crack damage threshold (CD) estimated from baseline testing. The results also suggest that the time to failure of limestone is longer than that of granite at a given driving stress. Further insight into samples that did not reach failure was investigated and it was found that there was a clear division between failure and no failure samples based on the Maxwell viscosity of the samples tested (indicating that viscosity changes near the yield threshold of these rocks. Furthermore, samples showed a clear tendency towards failure within minutes to hours when loaded above CD and no failure was shown for samples loaded below CI (crack initiation threshold). Samples loaded between CD and CI show a region of uncertainty, with some failing and other not at similar driving stress-ratios. Although such testing is demanding in terms of setup, control of conditions, continuous utilization of test and data acquisition equipment and data processing, it yields important information about the long-term behaviour of brittle rocks, such as the expect time to failure and the visco-elastic behaviour. The information presented in this paper can be utilized for preliminary numerical studies to gain an understanding of potential impact of long-term deformations.

This study assesses the static stability of the artificial Sabereebi Cave Monastery southeast of Georgia's capital, Tbilisi. The cliff into which these Georgian-Orthodox caverns, chapels, and churches were carved consists of a five-layered sequence of weak sedimentary rock—all of which bear a considerable failure potential and, consequently, pose the challenge of preservation to geologists, engineers, and archaeologists. In the first part of this study, we present a strategy to process point cloud data from drone photogrammetry as well as from laser scanners acquired in- and outside the caves into high-resolution CAD objects that can be used for numerical modeling ranging from macro- to micro-scale. In the second part, we explore four distinct series of static elasto-plastic finite element stability models featuring different levels of detail, each of which focuses on specific geomechanical scenarios such as classic landsliding due to overburden, deformation of architectural features as a result of stress concentration, material response to weathering, and pillar failure due to vertical load. With this bipartite approach, the study serves as a comprehensive 3D stability assessment of the Sabereebi Cave Monastery on the one hand; on the other hand, the established procedure should serve as a pilot scheme, which could be adapted to different sites in the future combining non-invasive and relatively cost-efficient assessment methods, data processing and hazard estimation.HighlightsOne single high-resolution 3D FEM model allowing for failure zone identification on macro- to micro-scaleStrategy to process point cloud data from drone photogrammetry and laser scanners into composite FEM-suitable CAD objectsStrategy application to a real-life geoarchaeological case studyDemonstration of versatile FEM model usage for different geotechnical questionsFailure potential estimation across an underground compound consisting of seven caves and sub-caves