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On the origin of the controversial electrostatic field effect in superconductors
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.
Journal Article
Dramatic pressure-sensitive ion conduction in conical nanopores
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.
Journal Article
StaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility Information
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.
Journal Article
An introduction to transfer entropy : information flow in complex systems
This book considers a relatively new metric in complex systems, transfer entropy, derived from a series of measurements, usually a time series.
eBook
Multi-lag latent variable models for industrial process monitoring in dynamic and static states
Process data collected in modern complex industries have both static and dynamic features, and current process monitoring algorithms only focus on analyzing the two features individually, ignoring the coupling between the two features. This paper proposes a multi-lag latent variable model (MLVM) capable of monitoring both static and dynamic features of industrial processes and addressing the shortcomings of multi-lag slow feature analysis in applications. Firstly, the slow features with multi-lag autocorrelation are extracted separately using slow feature analysis, and the autocorrelation coefficients of the slow features are calculated. Multi-lag dynamic and static features are obtained by setting thresholds, and the static characteristics are further analyzed using independent component analysis. Finally, multi-lag dynamic, static, and global statistics are obtained using Bayesian inference. In addition, the averaging process under online monitoring is proposed to reduce the noise impact on MLVM. The reconstruction-based contribution index for the MLVM is derived to diagnosis after a fault. Based on the Tennessee Eastman process, the superiority of MLVM over the known algorithm is verified, and the validity and interpretability of MLVM are proved.
Journal Article
Time-Dependent Behaviour of Brittle Rocks Based on Static Load Laboratory Tests
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.
Journal Article
A calibrated EMG-informed neuromusculoskeletal model can appropriately account for muscle co-contraction in the estimation of hip joint contact forces in people with hip osteoarthritis
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.
Journal Article
High-Resolution 3D FEM Stability Analysis of the Sabereebi Cave Monastery, Georgia
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
Journal Article
Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength
Foamed concrete (FC) shows advantageous applications in civil engineering, such as reduction in dead loads, contribution to energy conservation, or decrease the construction phase labor cost. Compressive Strength is considered the most important factor in terms of FC mechanical properties. In recent years, Artificial Neural Network (ANN) is one of popular and effective machine learning models, which can be used to accurately predict the FCCS. However, ANN’s structure and parameters are normally chosen by experience. In this study, therefore, the objective is to use particle swarm optimization (PSO) metaheuristic optimization (one of the effective soft computing techniques) to optimize the parameters and structure of a Levenberg–Marquardt-based Artificial Neural Network (LMA-ANN) for accurate and quick prediction of the FCCS. A total of 375 data of experiments on FC gathered from the available literature were used to generate the training and testing datasets. Various validation criteria such as mean absolute error, root mean square error, and correlation coefficient (R) were used for the validation of the models. The results showed that the PSO-LMA-ANN algorithm is a highly efficient predictor of the FCCS, achieving the highest value of R up to 0.959 with the optimized [5-7-6-1] structure. An interpretation of the mixture components and the FCCS using Partial Dependence Plots was also performed to understand the effect of each input on the FCCS. The dry density was the most important parameter for the prediction of FCCS, followed by the water/cement ratio, foam volume, sand/cement ratio, and the testing age. The results of the present work might help in accurate and quick prediction of the FCCS and the design optimization process of the FC.
Journal Article