Large Step Research Articles

Malleability of time through progress bars and throbbers

Compared to a stationary pattern, a moving pattern dilates the perception of time. However, when it comes to comparing only moving stimulus, the exact dilation effects are less clear. The time dilation may be attributed to either speed of motion, temporal and spatial frequency, stimulus complexity, or the number of changes in the stimulus pattern. In the present study, we used progress bars and throbbers for inducing impressions of fast and slow “apparent” motions while the speed of motion and distance covered was actually equivalent across all conditions. The results indicate that higher number of steps produced the impression of a faster progression leading to an underestimation of time, whereas a progression in large fewer steps, produced slower apparent progression, creating the illusion of dilated time. We suggest that the perception of time depends on the nature of the stimulus rather than the speed of motion or the distance covered by the stimulus.

Direct numerical simulation of interaction between a stationary crossflow instability and forward-facing steps

The interaction between forward-facing steps of several heights and a pre-existing critical stationary crossflow instability of a swept-wing boundary layer is analysed. Direct numerical simulations (DNS) are performed of the incompressible three-dimensional laminar base flow and the stationary distorted flow that arise from the interaction between an imposed primary stationary crossflow perturbation and the steps. These DNS are complemented with solutions of the linear and the nonlinear parabolised stability equations, used towards identifying the influence of linearity and non-parallelism near the step. A fully stationary solution of the Navier–Stokes equations is enforced numerically, in order to isolate the mechanisms pertaining to the interaction of the stationary disturbance with the step. Results provide insight into the salient modifications of the base laminar boundary layer due to the step, and the response of the incoming crossflow instability to these changes. The fundamental spanwise Fourier mode of the disturbance field gradually lifts up as it approaches the step and passes over it. The flow environment around the step is characterised by a sudden spanwise modulation of the base-flow streamlines. Additional stationary perturbation structures are induced at the step, which manifest in the form of spanwise-aligned velocity streaks near the wall. Shortly downstream of the step, the fundamental component of the crossflow perturbation maintains a rather constant amplification for the smallest steps studied. For the largest step, however, the fundamental crossflow perturbation is stabilised significantly shortly downstream of the largest step. This surprising result is ascribed to a modulation of the kinetic energy transfer between the base flow and the fundamental perturbation field, which is brought forward as a new step interaction mechanism. Possible non-modal growth effects at the step are discussed. Furthermore, the results from DNS indicate significant amplification of the high-order harmonic crossflow components downstream of the step.

Dynamic relationship network and international management of enterprise supply chain by particle swarm optimization algorithm under deep learning

AbstractThe traditional enterprise decision evaluation model based on neural network has the problems of mismatch with the optimal solution and slow convergence speed. In order to enable companies to make decisions that are in line with changes in the market, the particle swarm optimization (PSO) algorithm is used to optimize deep learning neural networks. Firstly, the model parameter setting is improved, and the inertia weight strategy of normal distribution attenuation is combined. On this basis, a normal distribution decay inertial weight particle swarm optimization (NDPSO) is proposed. The inertia weight of the optimized algorithm maintains a large value in the initial stage, which makes the PSO algorithm maintain a large step size in the optimization process and a small value in the later stage. Through experimental analysis, the trend parameter of the best normal distribution of the algorithm is obtained as 0.4433 and then using the detection function, the NDPSO algorithm is tested by two types of test functions. The NDPSO algorithm is compared with the optimization results of other algorithms which are optimized on the Sphere function. The minimum value of 554.29, the average value of 2032.11, and the standard deviation of 918.47, all of them are at the leading level. Taking into account other experimental results, it is proved that the normal distribution decay inertia weight can balance the global search and local development capabilities from the perspective of parameter improvement. It can speed up the convergence with ensuring the convergence accuracy. The improved PSO algorithm has certain optimization capabilities for neural network models. The use of optimized neural network models can enable companies to make decisions in line with changes in the market and optimize the dynamic relationship network of the company's supply chain, which is of great significance to the implementation of the company's international management.

Abstract 1880: High throughput screening of 30 PDX cell lines in a 3D ECM hydrogel platform, incorporating tumor, stroma and immune components to demonstrate simultaneous investigation of multiple anti-tumor modalities

Abstract High throughput screening offers tangible benefits towards rapidly testing various permutations of novel or existing therapeutic agents. In particular, tumor panels that cover a range of histotypes and molecular subtypes have been previously developed, such as the NCI-60, however they utilize cell lines and, in some cases, a 2D cell culture format, which limit their translatability to preclinical and clinical trials. Moreover, the biological complexity of the tumor microenvironment (TME) has revealed a need for more translatable 3D in vitro tumor models that reflect the in vivo physiological outcome to therapies, particularly with the explosion of immunotherapy programs in drug discovery which target the immune compartment of the TME. Here, we describe for the first time a 3D in vitro PDX panel comprising 30 distinct PDX models in coculture with fibroblasts and PBMCs in engineered extracellular matrix hydrogels that display distinct similarities to the three compartments of the TME - tumor, stroma, and immune cells. Histotypes in the panel include, amongst others, Non-small cell lung, colorectal, breast, pancreatic, gastric, melanoma, and renal cancers. The panel is constructed in a high throughput 96-well format and rapidly assays tumor growth delay and other endpoints such as apoptosis in a dose-dependent manner across various drug modalities such as small molecules, biologics and cell therapy. The panel has been tested against targeted therapy (Docetaxel, Cisplatin) and immunomodulatory agents (Pembrolizumab, Atezolizumab) and the results correlate to the corresponding PDX in vivo data. Interestingly, cisplatin treatment displayed an average size reduction of >30% for melanoma but a varied response in NSCLC tumors. And, several NSCLC responded more favorably to checkpoint inhibitors, however, the lung epidermoid PDX demonstrated no significant response. Moreover, subsequent cytokine analysis and immunofluorescence staining of several models revealed protein signatures of cancer-associated fibroblasts and CD3+ sequestration in the tumor stroma in some 3D models, suggesting the fibroblasts’ critical role in regulating the immune response. In short, the 30-PDX Panel described here represents a large step forward towards achieving translatable efficacy data at the earliest stages of drug discovery where little is known about the mechanism of action for a particular therapeutic agent or combination of agents. Citation Format: Kolin C. Hribar, Bin Xue, Christopher Harrod, Timothy Jensen, Julia Schüler. High throughput screening of 30 PDX cell lines in a 3D ECM hydrogel platform, incorporating tumor, stroma and immune components to demonstrate simultaneous investigation of multiple anti-tumor modalities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1880.

Transport in turbulent, recurrent flows: Time-extrapolation and statistical symmetrization

We investigate bubble dynamics in a submerged double-jet as an example of weakly coupled, Lagrangian transport. The setup resembles continuous casting of steel at Reynolds numbers Re=136000 to Re=272000. Using short time series of flow fields obtained from large-eddy simulations (LES) with a discrete bubble model, we calculate a database of transport patterns and time-extrapolate them to long durations in the framework of recurrence CFD (rCFD). A dedicated averaging procedure along bubble trajectories allows us to study their transport with large steps at little numerical costs and monitor their spatial distribution. Besides time-extrapolation for fixed Re with a single database, we demonstrate how several time series can be combined to (i) enforce symmetries and (ii) approximately model conditions in between. We compare bubble volume fraction fields and total hold-up obtained from LES and from rCFD and find very good to satisfying agreement with speed up factors of more than 500.

Reconfigurable Parfocal Zoom Metalens

AbstractZoom lenses with variable focal lengths and magnification ratios are essential for many optical imaging applications. Conventional zoom lenses are composed of multiple refractive optics, and optical zoom is attained via translational motion of one or more lens elements, which adds to module size, complexity, and cost. In this paper, a zoom lens design based on multi‐functional optical metasurfaces is presented, which achieves large step zoom ratios, minimal distortion, and diffraction‐limited optical quality without requiring mechanical moving parts. Two embodiments of the concept are experimentally demonstrated based on polarization‐multiplexing in the visible and phase change materials in the mid‐infrared, both yielding 10× parfocal zoom in accordance with the design.

On the Choice of Methods for Numerically Integrating the Equations of Transients in Electric Power Systems

Algorithms for solving the system of differential-algebraic equations describing electromechanical transients in electric power systems are considered along with matters concerned with ensuring the reliability and required accuracy of the solution results. Classical explicit methods, methods for implicit numerical integration and simultaneous solution of differential-algebraic systems of equations are used to model dynamic processes in power systems. On the basis of the methods used, difference models of electric power system components are obtained. The equations of transients in electric power systems are written in a homogeneous coordinate basis with the use of nodal voltage equations. A comparative analysis of algorithms based on the explicit Runge-Kutta method and implicit methods of Euler, trapezoids, Euler with fitting coefficients is carried out. The analysis results have shown that in terms of stability and accuracy, the trapezoid method has the greatest advantages, which is not inferior to the 4th order Runge-Kutta method and allows calculations with a large integration step to be carried out. The effectiveness of the combined method is shown, in which the Euler method with fitting is used to solve the differential equations describing electromagnetic processes, and the trapezoid method is used to solve the equations of electromechanical motion.

Large-stepsize integrators for charged-particle dynamics over multiple time scales

The Boris algorithm, a closely related variational integrator and a newly proposed filtered variational integrator are studied when they are used to numerically integrate the equations of motion of a charged particle in a mildly non-uniform strong magnetic field, taking step sizes that are much larger than the period of the Larmor rotations. For the Boris algorithm and the standard (unfiltered) variational integrator, satisfactory behaviour is only obtained when the component of the initial velocity orthogonal to the magnetic field is filtered out. The particle motion shows varying behaviour over multiple time scales: fast gyrorotation, guiding centre motion, slow perpendicular drift, near-conservation of the magnetic moment over very long times and conservation of energy for all times. Using modulated Fourier expansions of the exact and numerical solutions, it is analysed to which extent this behaviour is reproduced by the three numerical integrators used with large step sizes that do not resolve the fast gyrorotations.

Superresolved Motions of Single Molecular Catalysts during Polymerization Show Wide Distributions.

The motion of single molecular ruthenium catalysts during and after single turnover events of ring-opening metathesis polymerization is imaged through single-molecule superresolution tracking with a positional accuracy of ±32 nm. This tracking is achieved through the real-time incorporation of spectrally tagged monomer units into active polymer chain ends during living polymerization; thus, by design, only active-catalyst motion is detected and imaged, without convolution by inactive catalysts. The catalysts show diverse individualistic diffusive behaviors with respect to time that persist for up to 20 s. Catalysts occupy three mobility populations: quasi-stationary (23%), intermediate (53%, 65 nm), and large (24%, 145 nm) step sizes. Differences in catalyst mobility populations also exist between individual aggregates (p < 0.001). Such differential motion indicates widely different local catalyst microenvironments during the catalytic turnover. These mobility differences are uniquely observable through single-catalyst microscopy and are not measurable through traditional ensemble analytical techniques for characterizing the behavior of molecular catalysts, such as nuclear magnetic resonance spectroscopy. The measured distributions of active molecular catalyst motions would not be readily predictable through modeling or first-principles, and the range likely impacts individual catalyst turnover rate and selectivity. This range plausibly contributes to property distributions observable in bulk polymers, such as molecular weight polydispersity (e.g., 1.9 in this system), leading to a revised understanding of the mechanistic, microscale origins of macroscale polymer properties.

Cyclical Adversarial Attack Pierces Black-box Deep Neural Networks

Deep neural networks (DNNs) have shown vulnerability to adversarial attacks. By exploiting the transferability of adversarial examples, attackers can fool models under black-box settings without accessing the underlying information. However, they often exhibit weak performance when transferring to defenses, which may give a false sense of security. In this paper, we propose Cyclical Adversarial Attack (CA2), a general and straightforward method to boost the transferability to break defenders. We first revisit the momentum-based methods from the perspective of optimization and find that they usually suffer from the transferability saturation dilemma. To address this, CA2 performs cyclical optimization algorithm to produce adversarial examples. Unlike the standard momentum policy that accumulates the velocity to continuously update the solution, we divide the generation process into multiple phases and treat the velocity vectors from the previous phase as proper knowledge to guide a new adversarial attack with larger steps. Moreover, CA2 applies a novel and compatible augmentation algorithm at every optimization in a loop manner for enhancing the black-box transferability further, referred to as cyclical augmentation. Extensive experiments conducted on a variety of models not only validate the efficacy of each designed algorithm in CA2, but also illustrate the superiority of our method compared with the state-of-the-art transferable attacks. Our implemental code is publicly available at https://github.com/mesunhlf/CA2.

Effects of ambient temperature step changes on the heat storage and release in thermal protective clothing

Ambient temperature steps between typical non-work and hot work environments will lead to clothing heat storage and release, which is crucial for human health and thermal comfort. In this study, the influence of five types of ambient temperature steps (from 0ºC/5ºC/10ºC/15ºC/20ºC to 40ºC to 0ºC/5ºC/10ºC/15ºC/20ºC) on the heat storage and release in clothing was investigated using a thermal manikin. A moving and temperature-controlled refrigerated incubator was constructed to realize the temperature step conditions. Results showed that ambient temperature step magnitude was positively related to heat storage and release. Some 80% of the amount of heat storage would be completed in around 10 min after the temperature steps. Increasing the inner clothing layer weight and specific heat capacity for local clothing at the chest, thigh and calf improved their heat storage against the large temperature step conditions. As for the abdomen, its heat storage was the largest. However, the clothing layer configuration impacted its heat release, so it was necessary to guide the heat transfer to the skin. The heat storage in the outer layer was increased by 2% to 39% due to adding the inner layer of the garment. The stored heat discharged completely until the temperature step magnitude exceeded 32.5°C. A multiple linear regression formula was proposed to obtain the clothing average heat storage by considering ambient temperature step magnitude and clothing insulation. The results of this study could contribute to the optimization of thermal protective clothing and improvement of the research on human thermal comfort.

A Compressed Sensing Reconstruction Algorithm Based on Hyperbolic Function

The accuracy of the compressed sensing theory reconstruction algorithm is important for signal recovery. Sparsity Adaptive Matching Pursuit (SAMP) has a fixed step size in each iterative process of reconstruction, which has a significant impact on accuracy in actual use, often leading to overestimation and underestimation. In order to solve this problem, combined with the advantages of regular backtracking and variable step size, the article introduces the idea of retrospective in the atom selection stage. Then, the atoms are inspected using backtracking and re-screened. The algorithm uses regularization at the beginning to select atoms with higher energy and reduce the number of atoms in the candidate set at the threshold stage. During reconstruction, because the energy difference of the reconstructed signal decreases rapidly at the initial stage, and then the energy difference decreases slowly. This algorithm takes advantage of the large change rate of the hyperbolic function at the beginning and the slower change rate at the later stage. At first, a large step size is used. When the energy difference reaches a certain threshold, a small step size is used instead. After the actual simulation, the improved algorithm improves the reconstruction accuracy and the effect is better.

Analysis of the Morphology of the Growth Interface as a Function of the Gas Phase Composition during the PVT Growth of Silicon Carbide

The growth conditions of 75 mm SiC crystals in the PVT process is varied by different methods while the temperature field is kept constant. The addition of graphite into the source material leads to the formation of an ordered step flow with step heights of 0.014 µm, while the addition of graphite into the source together with N2 doping changes the step kinetics on the main facet, leading to very large, bunched steps of 0.17 µm. When elemental Si was added into the source material large macro steps are formed on the whole crystal surface. While the doping induced step bunching is related to the incorporation kinetics, the large steps induced in Si-rich conditions are attributed the reduction of surface energy. With the variation of the inert gas pressure the morphology of the surface is altered, similarly. Under low pressure conditions (0.2 mbar) a fine step structure evolves, while at a high pressure (40mbar) large surface steps are formed on the whole growth interface. Large surface steps are strongly impeded in their lateral motion at defects permeating the growth interface. At these sites the formation of foreign polytypes is facilitated.

Intrapopulation differences in polar bear movement and step selection patterns

BackgroundThe spatial ecology of individuals often varies within a population or species. Identifying how individuals in different classes interact with their environment can lead to a better understanding of population responses to human activities and environmental change and improve population estimates. Most inferences about polar bear (Ursus maritimus) spatial ecology are based on data from adult females due to morphological constraints on applying satellite radio collars to other classes of bears. Recent studies, however, have provided limited movement data for adult males and sub-adults of both sexes using ear-mounted and glue-on tags. We evaluated class-specific movements and step selection patterns for polar bears in the Chukchi Sea subpopulation during spring.MethodsWe developed hierarchical Bayesian models to evaluate polar bear movement (i.e., step length and directional persistence) and step selection at the scale of 4-day step lengths. We assessed differences in movement and step selection parameters among the three classes of polar bears (i.e., adult males, sub-adults, and adult females without cubs-of-the-year).ResultsAdult males had larger step lengths and less directed movements than adult females. Sub-adult movement parameters did not differ from the other classes but point estimates were most similar to adult females. We did not detect differences among polar bear classes in step selection parameters and parameter estimates were consistent with previous studies.ConclusionsOur findings support the use of estimated step selection patterns from adult females as a proxy for other classes of polar bears during spring. Conversely, movement analyses indicated that using data from adult females as a proxy for the movements of adult males is likely inappropriate. We recommend that researchers consider whether it is valid to extend inference derived from adult female movements to other classes, based on the questions being asked and the spatial and temporal scope of the data. Because our data were specific to spring, these findings highlight the need to evaluate differences in movement and step selection during other periods of the year, for which data from ear-mounted and glue-on tags are currently lacking.

Finite-sum smooth optimization with SARAH

We introduce NC-SARAH for non-convex optimization as a practical modified version of the original SARAH algorithm that was developed for convex optimization. NC-SARAH is the first to achieve two crucial performance properties at the same time—allowing flexible minibatch sizes and large step sizes to achieve fast convergence in practice as verified by experiments. NC-SARAH has a close to optimal asymptotic convergence rate equal to existing prior variants of SARAH called SPIDER and SpiderBoost that either use an order of magnitude smaller step size or a fixed minibatch size. For convex optimization, we propose SARAH++ with sublinear convergence for general convex and linear convergence for strongly convex problems; and we provide a practical version for which numerical experiments on various datasets show an improved performance.

Optimizing performance of a rover on partially known terrain

One of our earlier articles (Noble and Issac, 2019) proposed a method for finding optimal wheel torques for a six-wheeled rover for two types of objective functions, namely, (a) friction requirement and (b) torque requirement. In this technical note, an approach for seeking the optimal torque of a rover when the coefficient of friction under one wheel is proposed (Noble, 2021). The approach is demonstrated for a rover climbing a large step. The solutions are verified to be a local minima by checking Karush–Kuhn–Tucker conditions. The solutions indicate that when the coefficient of friction available in a patch is lower than the minimum required, the other two wheels require a higher coefficient of friction than the minimum required. In cases where the available friction is higher than the minimum required, there could be situations when two wheels need to apply only very low torques, or no torques at all, for the rover to climb.

Using nested grids to simulate sediment transport in the Caspian Sea with account taken of global and local scales

Introduction. The paper provides information on hydrodynamic and lithodynamic characteristics of processes in the Caspian Sea. A description of applied hydrodynamic models is presented. The numerical simulation of circulation, wave and lithodynamic conditions in the waters of the Northern Caspian Sea, middle and southern parts of the Caspian Sea was first conducted using composite computational grids. Simulated sediment transport results are presented. Results of numerical calculations of sediment accumulations in the second sweep of the Volga-Caspian canal are discussed.&#x0D; Materials and methods. Numerical modeling of coastal wave and hydrodynamic processes is performed using the Delft3d morphodynamic computer model. The Delft3D wave module includes a SWAN (Simulating Waves Nearshore) model for calculating the propagation and generation of waves. The hydrodynamic flow is simulated using the FLOW module, that solves non-stationary hydrodynamic equations and has a sediment transfer unit.&#x0D; Results. Computational grids are generated; they have a large step equal to 5,000 m. One grid is built for the middle and southern parts of the Caspian Sea and the other is made for the Northern Caspian; the grid step is equal to 1,000 m. The height and direction of significant waves in the Northern Caspian are provided. Sediment flows, averaged over 210 days in the SC, are provided. The authors also provide graphs of cumulative accumulation/erosion in the second sweep of the Volga-Caspian canal.&#x0D; Conclusions. The authors show that the average yearly shore-parallel sediment flow in the area of the marine edge of the Volga river delta has a southwesterly direction, and an estimate of its magnitude is provided. An assessment of sediment accumulation in the Volga-Caspian canal is made, and its results are compared with the data obtained in the course of dredging works.

Motion Planning of the Citrus-Picking Manipulator Based on the TO-RRT Algorithm

The working environment of a picking robot is complex, and the motion-planning algorithm of the picking manipulator will directly affect the obstacle avoidance effect and picking efficiency of the manipulator. In this study, a time-optimal rapidly-exploring random tree (TO-RRT) algorithm is proposed. First, this algorithm controls the target offset probability of the random tree through the potential field and introduces a node-first search strategy to make the random tree quickly escape from the repulsive potential field. Second, an attractive step size and a “step-size dichotomy” are proposed to improve the directional search ability of the random tree outside the repulsive potential field and solve the problem of an excessively large step size in extreme cases. Finally, a regression superposition algorithm is used to enhance the ability of the random tree to explore unknown space in the repulsive potential field. In this paper, independent experiments were carried out in MATLAB, MoveIt!, and real environments. The path-planning speed was increased by 99.73%, the path length was decreased by 17.88%, and the number of collision detections was reduced by 99.08%. The TO-RRT algorithm can be used to provide key technical support for the subsequent design of picking robots.

Research on indoor positioning of power grid equipment based on deep learning

In view of the increasing number of power grid equipment and a large number of indoor deployments, the existing positioning technology cannot meet the requirements. In this paper, a DOA estimation method based on deep learning is proposed to improve the indoor positioning accuracy. Firstly, by studying the propagation characteristics of wireless signals and the transmission characteristics of indoor wireless channels, a statistical channel model with multi subcarrier characteristics is established to generate channel impulse response information. Furthermore, the transformation matrix required for virtual antenna expansion is designed by using the idea of interpolation transformation. This matrix is combined with the channel impulse response to obtain the virtual antenna array data for DOA estimation. The DOA estimation model based on CNN is optimized to obtain high-precision DOA. Finally, the simulation results show that the positioning accuracy can be doubled by using the virtual antenna array technology. The performance of the two-stage DOA estimation algorithm proposed in this paper is slightly better than the accuracy of a large difference step. Even when there are fewer APS, the positioning accuracy is better than the traditional MUSIC algorithm, which is more suitable for indoor positioning of a large number of power grid equipment in the future.

Potential vorticity fronts and the late-time evolution of large-scale quasi-geostrophic flows

The late-time behaviour of freely evolving quasi-geostrophic flows with initial characteristic length scale $L_0$ larger than or equal to the deformation radius $L_D$ , $L_0/L_D \geq 1$ , is studied. At late time the flows are dominated by large multi-level vortices consisting of ascending terraces of well-mixed potential vorticity (PV), i.e. PV staircases. We examine how the number of mixed PV levels depends on the initial conditions, in particular $L_0/L_D$ . For sufficiently large values of $L_0/L_D \approx 5$ , a complete staircase with regular steps forms, but as $L_0/L_D$ decreases, the staircase becomes more irregular, with fewer mixed levels and the appearance of a large step centred on zero PV, corresponding to large regions of near-zero PV separating the multi-level vortices. This occurs because weak PV features in the initial field with scales smaller than $L_D$ undergo filamentation and are coarse-grained away or homogenised. For all values of $L_0/L_D$ considered, inverse cascades of potential energy commence at sufficiently late times. The onset of these cascades, even when the flow is initialised well within the ‘asymptotic model’ (AM) regime, suggests that the AM regime is not self-consistent: when potential vorticity fronts are well-resolved, frontal dynamics eventually drive ongoing flow evolution.