Motion In Space Research Articles

Infection spreading in cell culture as a reaction-diffusion wave

Infection spreading in cell culture occurs due to virus replication in infected cells and its random motion in the extracellular space. Multiplicity of infection experiments in cell cultures are conventionally used for the characterization of viral infection by the number of viral plaques and the rate of their growth. We describe this process with a delay reaction-diffusion system of equations for the concentrations of uninfected cells, infected cells, virus, and interferon. Time delay corresponds to the duration of viral replication inside infected cells. We show that infection propagates in cell culture as a reaction-diffusion wave, we determine the wave speed and prove its existence. Next, we carry out numerical simulations and identify three stages of infection progression: infection decay during time delay due to virus replication, explosive growth of viral load when infected cells begin to reproduce it, and finally, wave-like infection progression in cell culture characterized by a constant or slowly growing total viral load. The modelling results are in agreement with the experimental data for the coronavirus infection in a culture of epithelial cells and for some other experiments. The presence of interferon produced by infected cells decreases the viral load but does not change the speed of infection progression in cell culture. In the 2D modelling, the total viral load grows faster than in the 1D case due to the increase of plaque perimeter.

A spatiotemporal mechanism of visual attention: Superdiffusive motion and theta oscillations of neural population activity patterns

Recent evidence has demonstrated that during visual spatial attention sampling, neural activity and behavioral performance exhibit large fluctuations. To understand the origin of these fluctuations and their functional role, here, we introduce a mechanism based on the dynamical activity pattern (attention spotlight) emerging from neural circuit models in the transition regime between different dynamical states. This attention activity pattern with rich spatiotemporal dynamics flexibly samples from different stimulus locations, explaining many key aspects of temporal fluctuations such as variable theta oscillations of visual spatial attention. Moreover, the mechanism expands our understanding of how visual attention exploits spatially complex fluctuations characterized by superdiffusive motion in space and makes experimentally testable predictions. We further illustrate that attention sampling based on such spatiotemporal fluctuations provides profound functional advantages such as adaptive switching between exploitation and exploration activities and is particularly efficient at sampling natural scenes with multiple salient objects.

Shear lid motion in DEM shear calibration and the effect of particle rearrangement on the internal friction angle

Shear cell tests are now commonly applied in particulate calibration procedures for the discrete element method. Usually, only the shear stresses at the shear plane in the particle bed are examined in short-time instances. This work aimed to find the regularities between the internal friction angle, the vertical position of the shear lid, and the rearrangements of particles in the layers during the shear test with the time instance duration of tens to hundreds of seconds. The Schulze RST Ring Shear Tester was used to compare real experiments to numerical DEM models of non-cohesive materials. The shear tests were investigated for different contact models and DEM input parameters. We also simultaneously monitored the overall specimen stiffness, material dilatancy during calibration, and shear stress. Particle bed stiffness increases with increasing shear modulus and coefficient of static friction for two contact models. The internal friction angle, which is a bulk property of the material determined by a set of particle properties, determines the ability (or resistance) to make displacements of rigid non-deformable particles in the shear testing process. The angle of the resultant of the vector of particle motion in space from the horizontal plane correlates with the angle of internal friction (at each location of the shear cell).

An integrated rolling horizon and adaptive-refinement approach for disjoint trajectories optimization

Planning for multiple commodities simultaneously is a challenging task arising in divers applications, including robot motion or various forms of traffic management. Separation constraints between commodities frequently have to be considered to ensure safe trajectories, i.e., paths over time. Discrete decisions to ensure at least one of often multiple possible separation conditions renders planning of best possible continuous trajectories even more complex. Hence, the resulting disjoint trajectories optimization problems are mostly solved sequentially or with restricted planning space, potentially leading to losses in the usage of sparse resources and system capacities. To tackle these drawbacks, we develop a graph-based model for disjoint trajectories optimization with general separation requirements. We present a novel technique to derive a discretization for the full available space of motion. This can depict arbitrary, potentially non-convex, restricted areas. This necessitates solving an integer linear optimization program whose size scales with the number of discretization points. Thus, even for moderately sized instances a sufficiently detailed representation of space and time leads to models too large for state of the art hard- and software. To overcome this issue, we develop an adaptive-refinement algorithm: Starting from an optimal solution to the integer program in a coarse discretization, the algorithm re-optimizes trajectories in an adaptively-refined discretized neighborhood of the current solution. This is further integrated into a rolling horizon approach. We apply our approach to the integrated trajectory optimization and runway scheduling in the surrounding of airports. Computational experiments with realistic instances demonstrate the efficiency of the method.

Getting the nod: Pediatric head motion in a transdiagnostic sample during movie- and resting-state fMRI.

Head motion continues to be a major problem in fMRI research, particularly in developmental studies where an inverse relationship exists between head motion and age. Despite multifaceted and costly efforts to mitigate motion and motion-related signal artifact, few studies have characterized in-scanner head motion itself. This study leverages a large transdiagnostic public dataset (N = 1388, age 5-21y, The Healthy Brain Network Biobank) to characterize pediatric head motion in space, frequency, and time. We focus on practical aspects of head motion that could impact future study design, including comparing motion across groups (low, medium, and high movers), across conditions (movie-watching and rest), and between males and females. Analyses showed that in all conditions, high movers exhibited a different pattern of motion than low and medium movers that was dominated by x-rotation, and z- and y-translation. High motion spikes (>0.3mm) from all participants also showed this pitch-z-y pattern. Problematic head motion is thus composed of a single type of biomechanical motion, which we infer to be a nodding movement, providing a focused target for motion reduction strategies. A second type of motion was evident via spectral analysis of raw displacement data. This was observed in low and medium movers and was consistent with respiration rates. We consider this to be a baseline of motion best targeted in data preprocessing. Further, we found that males moved more than, but not differently from, females. Significant cross-condition differences in head motion were found. Movies had lower mean motion, and especially in high movers, movie-watching reduced within-run linear increases in head motion (i.e., temporal drift). Finally, we used intersubject correlations of framewise displacement (FD-ISCs) to assess for stimulus-correlated motion trends. Subject motion was more correlated in movie than rest, and 8 out of top 10 FD-ISC windows had FD below the mean. Possible reasons and future implications of these findings are discussed.

A feedback control strategy for improving the motion accuracy of underwater gliders in currents: Performance analysis and parameter optimization

As a promising autonomous observation platform for ocean phenomena, underwater glider can achieve space motion by adjusting its net buoyancy and attitude. In some high accuracy exploration missions, underwater glider needs to move on a specific trajectory, and the position that the glider resurfaces should be accurate enough. In fact, many uncertain factors will affect the glider motion and reduce the motion accuracy, especially the current. In this paper, we present a feedback control strategy for improving the glider motion accuracy in currents. The feedback control is achieved by intermittently adjusting the movable mass block rotation amount, which is energy-saving. Especially, the feedback control strategy considers the force condition change of the glider in different water depths and the different motion states of the glider in currents. Besides, we introduce the surrogate models to calculate the adjustment time length of movable mass block rotation amount, which can ensure that the feedback control has a higher reaction speed. To verify the effectiveness of the above strategy, the dynamic model of underwater glider is established, and the model is validated by using experimental data. The simulation results under specific working conditions illustrate that the feedback control strategy can effectively improve the glider motion accuracy in currents. To achieve the optimal control, we further propose a method to optimize the relevant parameters of feedback control strategy, considering the multiple working conditions and the energy consumption metric. The simulation results under specific working conditions verify the effectiveness of proposed optimization method. The research works can provide certain guidance for the achievement of the glider precision operation.

An iterative solution for improving the generalization ability of unsupervised skeleton motion retargeting

In this paper, a deep learning-based iterative solving method for unsupervised motion retargeting is proposed. Unlike existing methods, we do not utilize generative-adversarial-network-based (GAN-based) models as a solution for unsupervised motion retargeting, but utilize an iterative solution to yield retargeted motions based on a trained motion autoencoder. The proposed method includes a training phase and an execution phase. In the training phase, an autoencoder named the iterative motion retargeting network (ItMRnet) learns a continuous latent space of natural motion data. At the execution phase, the autoencoder-based hidden units are iteratively adjusted to yield retargeted motion sequences by minimizing the loss functions defined in motion space. Existing GAN-based methods need to train discriminators to distinguish whether the output of the generator is real or fake motion data. Obviously, the discriminator would have a poor discrimination ability for characters and motions that are not included in the training dataset. The proposed method does not use adversarial training which hurt generalization ability, but yields motion from a continuous latent space that represents a motion manifold, and can generate motions for those characters not included in the training dataset. That is, our method generalizes well to unseen motions and characters. The experimental results demonstrate that our method clearly outperforms state-of-the-art methods by reducing the motion retargeting error and motion deformation phenomenon, especially when the source or target character is not seen in the training dataset.

IMU-based full-body pose estimation for construction machines using kinematics modeling

Monitoring unsafe operations of construction machines is important for reducing accidents on sites. To automate safety monitoring, this paper studies pose estimation of construction machines, which describes their motions in 3D space. Inertial Measurement Unit (IMU) is considered accurate, cost-saving and user-friendly for estimating the pose of machines. Nevertheless, the complexity and instability of construction sites hinder the application of IMU to construction machines. Hence, this study first defines the information requirements of IMU-based full-body pose estimation for construction machines. Then, an efficient method is developed to generate the trajectories of a machine based on kinematics. Moreover, an optimal installation scheme for IMU sensors is investigated systematically to optimize the location and number of IMUs to install. Overall, this study contributes an IMU-based method to provide full-body poses of machines without environmental constraints on real construction sites, and a theoretical basis of optimal IMU installation strategy for reliable motion-related analyses.

Gaia, Fundamental Physics, and Dark Matter

The Gaia space astrometry mission is measuring accurate distances and space motions of more than two billion stars throughout our galaxy and beyond. This is a first look at how Gaia is contributing to fundamental physics, and in particular to our understanding of dark matter, for which a few examples are given from the current literature. One of our goals is to illustrate how deep and often surprising insight into very diverse areas of fundamental physics can be extracted from this new and enormous high-accuracy stellar data set. In this spirit, we finish by suggesting a search for a connection between stellar activity, dark matter streams, and planetary configuration in nearby exoplanetary systems, as has been tentatively proposed in the case of the solar system. Dark matter candidates that could be probed by such a test include anti-quark nuggets, which have also been postulated as a possible explanation of the anomalous heating of the solar corona, and of the matter–antimatter asymmetry in the universe.

Kinematic Motion Retargeting via Neural Latent Optimization for Learning Sign Language

Motion retargeting from a human demonstration to a robot is an effective way to reduce the professional requirements and workload of robot programming, but faces the challenges resulting from the differences between humans and robots. Traditional optimization-based methods are time-consuming and rely heavily on good initialization, while recent studies using feedforward neural networks suffer from poor generalization to unseen motions. Moreover, they neglect the topological information in human skeletons and robot structures. In this paper, we propose a novel neural latent optimization approach to address these problems. Latent optimization utilizes a decoder to establish a mapping between the latent space and the robot motion space. Afterward, the retargeting results that satisfy robot constraints can be obtained by searching for the optimal latent vector. Alongside with latent optimization, neural initialization exploits an encoder to provide a better initialization for faster and better convergence of optimization. Both the human skeleton and the robot structure are modeled as graphs to make better use of topological information. We perform experiments on retargeting Chinese sign language, which involves two arms and two hands, with additional requirements on the relative relationships among joints. Experiments include retargeting various human demonstrations to YuMi, NAO, and Pepper in the simulation environment and to YuMi in the real-world environment. Both efficiency and accuracy of the proposed method are verified.

The Cetus-Palca stream: A disrupted small dwarf galaxy

We present a new fully data-driven approach to derive spectro-photometric distances based on artificial neural networks. The method was developed and tested on Sloan Extension for Galactic Understanding and Exploration survey (SEGUE) data and will serve as a reference for the Contributed Data Product SPDISTof theWilliam HershelTelescope Enhanced Area Velocity Explorer (WEAVE) survey. With this method, the relative precision of the distances is of ∼13%. The catalogue of more than 300 000 SEGUE stars for which we have derived spectro-photometric distances is publicly available on the Vizier service of the Centre de Données de Strasbourg. With this 6D catalogue of stars with positions, distances, line-of-sight velocity, andGaiaproper motions, we were able to identify stars belonging to the Cetus stellar stream in the integrals of motion space. Guided by the properties we derived for the Cetus stream from this 6D sample, we searched for additional stars from the blue horizontal and red giant branches in a 5D sample. We find that the Cetus stream and the Palca overdensity are two parts of the same structure, which we therefore propose to rename the Cetus-Palca stream. We find that the Cetus-Palca stream has a stellar mass of ≃1.5 × 106 M⊙and presents a prominent distance gradient of 15 kpc over the ∼100° that it covers on the sky. Additionally, we also report the discovery of a second structure almost parallel to the Cetus stream and covering ∼50° of the sky, which could potentially be a stellar stream formed by the tidal disruption of a globular cluster that was orbiting around the Cetus stream progenitor.

APPLICATIONS OF QUATERNIONS IN GALILEAN SPACES

In this paper, Galilean orthogonal matrices in G^5 and G_1^5 are obtained with the help of unit quaternions. Moreover, Galilean orthogonal matrices in G^4 and G_1^4 are acquired. These matrices produce Galilelan motions in Galilean spaces. We investigate the invariance of the plane where shear motion is acting in Galilean and pseudo-Galilean spaces. Additionally, related examples of matrices that belong to both spaces are provided. With a similar method, dual Galilean orthogonal matrices are obtained by using unit dual quaternions. Finally, we strengthen our work with examples and draw their figures to explore visual representations.

Research on Postproduction of Film and Television Based on Computer Multimedia Technology

In the postproduction of film and television, the fast movement of objects or camera shake will lead to an excessive selection of key frames, resulting in information redundancy and affecting the stability of postproduction videos and video-editing effects. Aiming at this problem, this paper proposes a method of film and television postproduction based on digital media technology. Firstly, the film and video shots are segmented to find out the changing scenes; secondly, the bottom feature vector of each image is formed according to the color space entropy, motion vector, and motion region, and the video key frame is extracted based on digital media technology; finally, the pixel label information is established in the key frame, and the feature points located in the foreground are removed, while the time feature matching points and spatial feature matching points in the background are left, so as to achieve dynamic video mosaic. The experimental results show that the film and television postproduction method based on digital media technology has strong advantages compared with other methods, which can improve the stability score of video and reduce the average stitching error.

Efficient localization in warehouse logistics: a comparison of LMS approaches for 3D multilateration of passive UHF RFID tags

The Fourth Industrial Revolution, or Industry 4.0, aims at automating traditional manufacturing and industrial practices using modern smart technology. Autonomous mobile robots equipped with different sensors and employing different techniques have been proposed in the literature in the logistics and inventory warehouse management contexts. Efficient robot motion and planning depend on precise localization, mapping, and awareness of the environment. To properly localize items, recent attempts have been made employing radio frequency identification (RFID) in a 3D environment. This manuscript introduces four least mean squares methods to estimate the 3D positions of tags employing synthetic apertures and phase unwrapping. The proposed methods approach the localization problem solving a system of equations typical of multilateration methods to find the intersections of multiple hyperboloids. The novelty introduced here is the use of unwrapped phase distances to compute pseudo ranges for the multilateration problem. The use of such a technique is feasible thanks to precise mobile robot localization and custom navigation policies. All the analyzed methods are suitable for online localization due to reduced computation timings and have been tested on three different datasets. Two of them contain virtual data that has been generated by simulation, while the other one comes from an indoor experimental setup. Simulated tests show that combined antenna motions in 3D space (including Z-axis) improve the localization obtaining errors under the centimeter for 3D localization. Experimental tests obtained results as low as 13 cm of mean accuracy for 3D localization and 0.21 cm for 2D localization.

A Machine Learning Approach for Global Steering Control Moment Gyroscope Clusters

This paper addresses the problem of singularity avoidance for a 4-Control Moment Gyroscope (CMG) pyramid cluster, as used for the attitude control of a satellite using machine learning (ML) techniques. A data-set, generated using a heuristic algorithm, relates the initial gimbal configuration and the desired maneuver—inputs—to a number of null space motions the gimbals have to execute—output. Two ML techniques—Deep Neural Network (DNN) and Random Forest Classifier (RFC)—are utilized to predict the required null motion for trajectories that are not included in the training set. The principal advantage of this approach is the exploitation of global information gathered from the whole maneuver compared to conventional steering laws that consider only some local information, near the current gimbal configuration for optimization and are prone to local extrema. The data-set generation and the predictions of the ML systems can be made offline, so no further calculations are needed on board, providing the possibility to inspect the way the system responds to any commanded maneuver before its execution. The RFC technique demonstrates enhanced accuracy for the test data compared to the DNN, validating that it is possible to correctly predict the null motion even for maneuvers that are not included in the training data.

Physiological adaptations affecting drug pharmacokinetics in space: what do we really know? A critical review of the literature.

As human spaceflight progresses with extended mission durations, the demand for effective and safe drugs will necessarily increase. To date, the accepted medications used during missions (for space motion sickness, sleep disturbances, allergies, pain, and sinus congestion) are administered under the assumption that they act as safely and efficaciously as on Earth. However, physiological changes have been documented in human subjects in spaceflight involving fluid shifts, muscle and bone loss, immune system dysregulation, and adjustments in the gastrointestinal tract and metabolism. These alterations may change the pharmacokinetics (PK) and pharmacodynamics of commonly used medications. Frustratingly, the information gained from bed rest studies and from in‐flight observations is incomplete and also demonstrates a high variability in drug PK. Therefore, the objectives of this review are to report (i) the impact of the space environmental stressors on human physiology in relation to PK; (ii) the state‐of‐the‐art on experimental data in space and/or in ground‐based models; (iii) the validation of ground‐based models for PK studies; and (iv) the identification of research gaps.

On the Mechanism of Particle Motion in Space and Time

On the basis of a hypothesis about spiral motion of particles in space and time, It is derived from the theoretical expression for Planck constant and the De Broglie wave formulas and suggested a new physical constant.

Curve-guided 5-axis CNC flank milling of free-form surfaces using custom-shaped tools

A new method for 5-axis flank milling of free-form surfaces is proposed. Existing flank milling path-planning methods typically use on-market milling tools whose shape is cylindrical or conical, and is therefore not well-suited for meeting fine tolerances for manufacturing of benchmark free-form surfaces like turbine blades, gears, or blisks. In contrast, our optimization-based framework incorporates the shape of the tool into the optimization cycle and looks not only for the milling paths, but also for the shape of the tool itself. Given a free-form reference surface and a guiding path that roughly indicates the motion of the milling tool, tangential movability of quadruplets of spheres centered along a straight line is analyzed to indicate possible shapes and their motions. This results in G1 Hermite data in the space of rigid body motions that are interpolated and further optimized, both in terms of the motion and the shape of the milling tool itself. We demonstrate our algorithm on synthetic free-form surfaces and industrial benchmark datasets, showing that the use of custom-shaped tools is capable of meeting fine industrial tolerances and outperforms the use of classical, on-market tools.

Substructures, resonances, and debris streams

Context. The local stellar halo of the Milky Way contains the debris from several past accretion events. Aims. Here we study in detail the structure and properties of nearby debris associated with the Helmi streams, which was originally identified as an overdensity in integrals of motion space. Methods. We use 6D phase-space information from Gaia EDR3 combined with spectroscopic surveys, and we analyse the orbits and frequencies of the stars in the streams using various Galactic potentials. We also explore how the Helmi streams constrain the flattening, q, of the Galactic dark matter halo. Results. We find that the streams are split into substructures in integrals of motion space, most notably into two clumps in angular momentum space. The clumps have consistent metallicity distributions and stellar populations, supporting a common progeny. In all the realistic Galactic potentials explored, the Helmi streams’ stars depict a diffuse distribution close to Ωz/ΩR ∼ 0.7. At the same time, the reason for the substructure in angular momentum space appears to be a Ωz : Ωϕ resonance close to 1:1. This resonance is exactly 1:1 in the case where the (density) flattening of the dark halo is q = 1.2. For this halo shape, the substructure in angular momenta is also long-lasting. Conclusions. Our findings suggest that the structure of the Galactic potential leaves a clear imprint on the properties of phase-mixed debris streams.

Multiframe blind restoration with image quality prior

In dynamic environments, such as ground-based optic observations for space targets, anisotropic turbulence and random noise tend to produce different discriminate image degradations at every moment. These degradations severely reduce the quality of the target images and make restoration of these images very difficult. However, an inconsistent degradation implies that there is complementary information in such images. In this paper, a ranking network (Ranknet) is first proposed to ensure that input sequences have a consistent distribution upon degradation and squeeze the spatial distribution of the sample set. Then, an extraction-refinement neural network (ERnet) is proposed to extract complementary features and blindly reconstruct a clean image of the observation target. In ERnet, an extraction subnetwork (EN) uses 3D convolutions to extract discriminate features from multiframe input sequences, and a refinement subnetwork (RN) based on 2D convolution restores clean images by refining the effective features. In addition, a spatial–temporal attention module (STAM) is devoted to enhancing features through utilization of the high-quality features. Experimental results on the restorations of space target images and motion blur images confirm the superior performance of ERnet as compared with other state-of-the-art methods.