Relative Velocity Research Articles

Do galaxy mergers prefer under-dense environments?

Context. Galaxy mergers play a crucial role in galaxy evolution. However, the correlation between mergers and the local environment of galaxies is not fully understood. Aims. We aim to address the question of whether galaxy mergers prefer denser or less dense environments by quantifying the spatial clustering of mergers and non-mergers. We use two different indicators to classify mergers and non-mergers – classification based on a deep learning technique (f) and non-parametric measures of galaxy morphology, Gini-M20 (g). Methods. We used a set of galaxy samples in the redshift range 0.1 < z < 0.15 from the Galaxy and Mass Assembly (GAMA) survey with a stellar mass cut of log(M⋆/M⊙) > 9.5. We measured and compared the two-point correlation function (2pCF) of the mergers and non-mergers classified using the two merger indicators f and g. We measured the marked correlation function (MCF), in which the galaxies were weighted by f to probe the environmental dependence of galaxy mergers. Results. We do not observe a statistically significant difference between the clustering strengths of mergers and non-mergers obtained using 2pCF. However, using the MCF measurements with f as a mark, we observe an anti-correlation between the likelihood of a galaxy being a merger and its environment. Our results emphasise the advantage of MCF over 2pCF in probing the environmental correlations. Conclusions. Based on the MCF measurements, we conclude that the galaxy mergers prefer to occur in the under-dense environments on scales > 50 h−1 kpc of the large-scale structure (LSS). We attribute this observation to the high relative velocities of galaxies in the densest environments that prevent them from merging.

An analytical evaluation of tools for lipid isomer differentiation in imaging mass spectrometry

Imaging mass spectrometry has emerged as a powerful tool to map the spatial distributions of lipids and metabolites in biological tissues. However, these analyses are challenged by the multitude of isobaric (i.e., same nominal mass) and isomeric compounds present in most samples. Failure to adequately separate these compounds results in inaccurate or incomplete chemical identifications and produces composite images of spatial distribution arising from multiple compounds. A number of techniques have been developed to more completely resolve and identify this complex chemical milieu. These include methods that rely on condensed-phase chemical derivatization and gas-phase ion chemistry, or some combination thereof. This Young Scientist Feature focuses on summarizing the analytical figures of merit of these tools, highlighting their relative speeds, limits of detection, molecular specificities, and eases-of-use. It will also include current challenges and future perspectives for resolving structural isomers in imaging mass spectrometry experiments.

Quantitative correlation of ENPP1 pathogenic variants with disease phenotype

Ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) codes for a type 2 transmembrane glycoprotein which hydrolyzes extracellular phosphoanhydrides into bio-active molecules that regulate, inter alia, ectopic mineralization, bone formation, vascular endothelial proliferation, and the innate immune response. The clinical phenotypes produced by ENPP1 deficiency are disparate, ranging from life-threatening arterial calcifications to cutaneous hypopigmentation. To investigate associations between disease phenotype and enzyme activity we quantified the enzyme velocities of 29 unique ENPP1 pathogenic variants in 41 patients enrolled in an NIH study along with 33 other variants reported in literature. We correlated the relative enzyme velocities with the presenting clinical diagnoses, performing the catalytic velocity measurements simultaneously in triplicate using a high-throughput assay to reduce experimental variation. We found that ENPP1 variants associated with autosomal dominant phenotypes reduced enzyme velocities by 50 % or more, whereas variants associated with insulin resistance had non-significant effects on enzyme velocity. In Cole disease the catalytic velocities of ENPP1 variants associated with AD forms trended to lower values than those associated with autosomal recessive forms – 8-32 % vs. 33 % of WT, respectively. Additionally, ENPP1 variants leading to life-threatening vascular calcifications in GACI patients had widely variable enzyme activities, ranging from no significant differences compared to WT to the complete abolishment of enzyme velocity. Finally, disease severity in GACI did not correlate with the mean enzyme velocity of the variants present in affected compound heterozygotes but did correlate with the more severely damaging variant. In summary, correlation of ENPP1 enzyme velocity with disease phenotypes demonstrate that enzyme velocities below 50 % of WT levels are likely to occur in the context of autosomal dominant disease (due to a monoallelic variant), and that disease severity in GACI infants correlates with the more severely damaging ENPP1 variant in compound heterozygotes, not the mean velocity of the pathogenic variants present.

Effects of phase angle and sensor properties on on-orbit debris detection using commercial star trackers

The recent proliferation of resident space objects (RSOs) in low Earth orbit (LEO) threatens the sustainability of space as a resource and requires persistent monitoring to avoid collisions involving valuable space assets. State-of-the-art ground-based space surveillance techniques, due to their susceptibility to atmosphere, weather, and lighting conditions, tend to focus on RSOs with characteristic length greater than 10 cm or 1 dm. Consequently, millions of smaller LEO RSOs remain untracked by ground-based methods, which reduces overall space situational awareness. Onboard satellite sensors offer a space-based method for tracking RSOs. Prior research has investigated the feasibility of using commercial star trackers (CSTs) — optical sensors prevalent on most active spacecraft — to observe, detect, and estimate the position and velocity of RSOs larger than 10 cm. In a recent effort, we expanded on these feasibility studies by assessing the capabilities of CSTs to detect debris particles smaller than 10 cm in characteristic length. We modeled the particles as Lambertian spheres with zero phase angle and ten percent reflectivity and found that typical CSTs can detect properly illuminated debris of characteristic length between 1 cm and 10 cm even at distances of tens of kilometers. More sensitive CSTs can characterize decimeter-scale RSOs hundreds of kilometers away; alternatively, they can track centimeter-scale and smaller RSOs at closer distances. In this paper, we summarize these key results and extend our previous study by relaxing its zero-phase-angle assumption and characterizing the effect of Sun-RSO-CST phase angle on debris detection range. We identify a number of representative CSTs with publicly available optical characteristics and consider the effects of properties such as pixel pitch, focal length, aperture diameter, and field of view (FOV) on the capability of each CST to detect debris at a given distance and relative velocity (in the form of streak speed). We find that, for debris particles modeled as diffuse Lambertian spheres, Sun-RSO-CST phase angles as high as 57° result in no more than 20% reduction to the useful RSO-CST detection range. In addition, we find that pixel pitch and focal length, rather than aperture diameter, tend to determine the capability of a given CST to resolve two distinct RSOs. Furthermore, streak speed may serve as a stronger limiting factor for detection of smaller debris particles than for larger ones. Despite these limitations, the overall results indicate that CSTs have the potential to substantially enhance space-based debris detection capability.

A Multi-UAV cooperative mission planning method based on SA-WOA algorithm for three-dimensional space atmospheric environment detection

AbstractIn the application of rotorcraft atmospheric environment detection, to reflect the distribution of atmospheric pollutants more realistically and completely, the sampling points must be spread throughout the entire three-dimensional space, and the cooperation of multiple unmanned aerial vehicles (multi-UAVs) can ensure real-time performance and increase operational efficiency. In view of the problem of coordinated detection by multi-UAVs, the region division and global coverage path planning of the stereo space to be detected are studied. A whale optimization algorithm based on the simulated annealing-whale optimization algorithm (SA-WOA) is proposed, which introduces adaptive weights with the Levy flight mechanism, improves the metropolis criterion, and introduces an adaptive tempering mechanism in the SA stage. Path smoothing is subsequently performed with the help of nonuniform rational B-spline (NURBS) curves. The comparison of algorithms using the eil76 dataset shows that the path length planned by the SA-WOA algorithm in this paper is 10.15% shorter than that of the WOA algorithm, 13.25% shorter than the SA planning result, and only 0.95% difference from the optimal path length in the dataset. From the perspective of planning time, its speed is similar to WOA, with a relative speed increase of 27.15% compared to SA, proving that the algorithm proposed in this paper has good planning performance. A hardware system platform is designed and built, and environmental gas measurement experiments were conducted. The experimental results indicate that the multi-UAV collaborative environment detection task planning method proposed in this paper has certain practical value in the field of atmospheric environment detection.

Research of the Influence of Lateral Inflow Angles on the Cavitation Flow and Movement Characteristics of Underwater Moving Objects

This study examined the multi-phase flow field for a single object and two parallel/series objects under different incoming angles of lateral flow. The volume of fluid model, the Sauer–Schnerr cavitation model, and the six degrees of freedom (DOF) method were adopted to consider simulations of multi-phase flow, phase change, and object movement, respectively. The results show that, for a single object, the degree of asymmetry in the cavity profile depends on the component (the z-component) of the lateral inflow velocity in the direction perpendicular to the initial velocity of the object. As this component increases, the asymmetry of the cavity increases. The cavity length is related to the relative axial speed between the object and the water. For parallel objects, the cavity asymmetry is determined by the superimposed influence of the z-component of the lateral incoming speed and the high-pressure zone induced by the nearby object. The object located downstream relative to the lateral flow has a stronger cavity asymmetry than that of the upstream object, and the trajectory of the downstream object is more easily deviated than that of the upstream object. For tandem objects, with the increase in the lateral incoming angle, the supercavity length increases after the rear object enters into the front cavity. With the increase in the z-component of the lateral flow velocity, the deviation speed increases.

Realistic 3D human saccades generated by a 6-DOF biomimetic robotic eye under optimal control.

We recently developed a biomimetic robotic eye with six independent tendons, each controlled by their own rotatory motor, and with insertions on the eye ball that faithfully mimic the biomechanics of the human eye. We constructed an accurate physical computational model of this system, and learned to control its nonlinear dynamics by optimising a cost that penalised saccade inaccuracy, movement duration, and total energy expenditure of the motors. To speed up the calculations, the physical simulator was approximated by a recurrent neural network (NARX). We showed that the system can produce realistic eye movements that closely resemble human saccades in all directions: their nonlinear main-sequence dynamics (amplitude-peak eye velocity and duration relationships), cross-coupling of the horizontal and vertical movement components leading to approximately straight saccade trajectories, and the 3D kinematics that restrict 3D eye orientations to a plane (Listing's law). Interestingly, the control algorithm had organised the motors into appropriate agonist-antagonist muscle pairs, and the motor signals for the eye resembled the well-known pulse-step characteristics that have been reported for monkey motoneuronal activity. We here fully analyse the eye-movement properties produced by the computational model across the entire oculomotor range and the underlying control signals. We argue that our system may shed new light on the neural control signals and their couplings within the final neural pathways of the primate oculomotor system, and that an optimal control principle may account for a wide variety of oculomotor behaviours. The generated data are publicly available at https://data.ru.nl/collections/di/dcn/DSC_626870_0003_600.

Functional Forms for Lorentz Invariant Velocities

Lorentz invariance lies at the very heart of Einstein’s special relativity, and both the energy formula and the relative velocity formula are well-known to be invariant under a Lorentz transformation. Here, we investigate the spatial and temporal dependence of the velocity field itself u(x,t) and we pose the problem of the determination of the functional form of those velocity fields u(x,t) which are automatically invariant under a Lorentz transformation. For a single spatial dimension, we determine a first-order partial differential equation for the velocity u(x,t), which appears to be unknown in the literature, and we investigate its main consequences, including demonstrating that it is entirely consistent with many of the familiar outcomes of special relativity and deriving two new partial differential relations connecting energy and momentum that are fully compatible with the Lorentz invariant energy–momentum relations.

Varying primordial state fractions in exo- and endothermic SIDM simulations of Milky Way-mass haloes

Abstract Self-interacting dark matter (SIDM) is increasingly studied as a potential solution to small-scale discrepancies between simulations of cold dark matter (CDM) and observations. We examine a physically motivated two-state SIDM model with both elastic and inelastic scatterings. In particular, endothermic, exothermic, and elastic scattering have equal transfer cross sections at high relative velocities (vrel ≳ 400 km s−1). In a suite of cosmological zoom-in simulation of Milky Way-size haloes, we vary the primordial state fractions to understand the impact of inelastic dark matter self-interactions on halo structure and evolution. In particular, we test how the initial conditions impact the present-day properties of dark matter haloes. Depending on the primordial state fraction, scattering reactions will be dominated by either exothermic or endothermic effects for high and low initial excited state fractions respectively. We find that increasing the initial excited fraction reduces the mass of the main halo, as well as the number of subhaloes on all mass scales. The main haloes are cored, with lower inner densities and higher outer densities compared with CDM. Additionally, we find that the shape of the main halo becomes more spherical the higher the initial excited state fraction is. Finally, we show that the number of satellites steadily decreases with initial excited state fraction across all satellite masses.

Relative Seismic Velocity Variations at Axial Seamount Observed With Ambient Seismic Noise Capture Transition Point in Volcanic Inflation

AbstractTemporal changes in seismic velocity estimated from ambient seismic noise can be utilized to infer subsurface properties at volcanic systems. In this study, we process 7 years of continuous seismic noise at Axial Seamount and use cross‐correlation functions to calculate the relative seismic velocity changes (dv/v) beneath the caldera. We find a long‐term trend of decreasing velocity during rapid inflation, followed by slight increase in velocities as background seismicity increases and inflation rate decreases. Furthermore, we observe small short‐term increases in dv/v which coincide with short‐term deflation events. Our observations of changes in dv/v and their correlation with other geophysical data provide insights into how the top ∼1 km of the crust at Axial Seamount changes in response to subsurface magma movement and capture the transition from a period of rapid reinflation to a period where the caldera wall faults become critically stressed and must rupture to accommodate further inflation.

Power law dependence of unstable slip velocity on decreasing shear loading stiffness

Shear loading stiffness plays a critical role in conditioning the stability of slip on reactivated faults. However, a relationship linking peak slip velocities and shear loading stiffness is lacking. To explore this, we shear granite faults in double direct shear with shear loading stiffnesses spanning two orders to define the effects of shear loading stiffness in conditioning the transition from stable to unstable slip. Our results show that peak slip velocity and acceleration decrease as power law relationships with respect to the shear loading stiffness ratio, with identical exponent, and with a linear relationship between the peak slip velocity and acceleration. The maximum acceleration occurs during the velocity weakening process that limits the peak slip velocity. The power law exponent increases linearly with increasing normal stress. Experimental results also indicate that slip magnitude, stress drop and recurrence time of unstable slip events increase, and slip durations decrease with decreasing stiffness ratios. The spans of limit cycles of velocity versus shear stress decrease, and their shapes evolve from triangular to semicircular with increasing loading stiffness ratio. Stress drops mostly occur during deceleration. The deceleration phase dominates the unstable slip duration that decreases as peak slip velocity increases. Our results indicate that the average stress drop rates over a slip duration increase as a power law relationship with reducing shear loading stiffness, which also contributes to a lower shear loading stiffness producing increased slip velocities and accelerations. Our findings highlight that loading stiffness ratio is an underlying mechanism defining unstable slip behaviors with the normal stress merely conditioning the exponent. The present relationship of unstable slip velocity with shear loading stiffness suggests a way to evaluate hazard of an impending instability event based on the initial shear loading stiffness ratio that can be calculated through the linear-elastic behaviors at the early quasi-static phase.

Orbital Stability Study of the Taiji Space Gravitational Wave Detector

Space-based gravitational wave detection is extremely sensitive to disturbances. The Keplerian configuration cannot accurately reflect the variations in spacecraft configuration. Planetary gravitational disturbances are one of the main sources. Numerical simulation is an effective method to investigate the impact of perturbation on spacecraft orbits. This study shows that, in the context of the Taiji project, Earth’s gravity is an essential factor in the change in heliocentric formation configuration, contributing to the relative acceleration between spacecrafts in the order of O(10−6)m·s−2. Considering 00:00:00 on 27 October 2032 as the initial orbiting moment, under the influence of Earth’s gravitational perturbation, the maximum relative change in armlengths and variation rates of armlengths for Taiji is 1.6×105km, 32m·s−1, respectively, compared with the unperturbed Keplerian orbit. Additionally, by considering the gravitational perturbations of Venus and Jupiter, the armlength and relative velocity for Taiji are reduced by 16.01% and 17.45%, respectively, compared with when only considering that of Earth. The maximum amplitude of the formation motion indicator changes with the orbit entry time. Results show that the relative velocity increase between the spacecrafts is minimal when the initial orbital moment occurs in July. Moreover, the numerical simulation results are inconsistent when using different ephemerides. The differences between ephemerides DE440 and DE430 are smaller than those between DE440 and DE421.

Bandit approach for unmanned aerial vehicle-centric low earth orbit satellite selection

In this paper, the problem of optimal low earth orbit satellite (LEO-Sat) selection by unmanned aerial vehicle (UAV) in space air ground integrated networks (SAGINs) will be investigated. The primary objective is to maximize the UAV's achievable data rate while ensuring a prolonged remaining visible time for the selected LEO-Sat. This problem poses significant challenges due to the highly dynamic nature caused by the substantial relative velocity between UAV and LEO-Sats, making LEO-Sats' features, e.g., elevation angle, remaining visible time, and the available bandwidth strongly time-dependent. Moreover, some LEO-Sats will appear/disappear from UAV visibility within short time intervals. An online learning approach will be proposed to efficiently address this highly dynamic problem, where it will be modeled as contextual multi-armed bandit (MAB) with dynamic arms. Through this modeling, the time varying LEO-Sats' features as well as their dynamic appearance/disappearance will be taken into account while selecting the best LEO-Sat. Through extensive numerical analysis, the performance of the proposed approach nearly matches that of the highly complex optimal performance based on exhaustive searching with negligible time complexity, and it outperforms conventional schemes that individually maximize one of the LEO-Sats' features.

Assessing Spatiotemporal Behavior of Human Gait: A Comparative Study Between Low-Cost Smartphone-Based Mocap and OptiTrack Systems

AbstractThis study assessed the accuracy of a low-cost marker-based motion capture system with smartphone devices to estimate the spatiotemporal behavior of human gait in comparison with the performance of the commercial OptiTrack system. Initially, three test subjects were selected for the study, and after equipping them with passive retroreflective markers, they were recorded for gait velocities of 1.50, 1.90, and 2.30 $$m\bullet {s}^{-1}$$ m ∙ s - 1 while collecting kinematic data and videos. The results showed that the smartphone motion capture system exhibited significant spatiotemporal tracking and accuracy in the x-y trajectories and estimation of joint relative angles of the hip, knee, and ankle joints (θ1, θ2, and θ3, respectively) compared to the commercial OptiTrack system. In this comparison, an average goodness-of-FIT and normalized root mean square error of over 88.93% and 2.71% were obtained, respectively, for the joint relative angles of the hip and knee (θ1 and θ2) in all tests performed. However, the accuracy of the joint relative angle of the ankle (θ3, average FIT: 71.04% and nRMSE: 4.26%) was lower because of the low capture rate of the retroreflective markers in the smartphone system and the higher relative velocity in the lower extremities of the test subjects, which generated noise in the calculation of x-y trajectories. This decrease in accuracy has been reported in other studies. However, both motion capture systems experienced marker data loss at the hip, highlighting the need for improvement in the spatial distribution of the optical devices. The OptiTrack system demonstrated better optical redundancy but still required improvements. In contrast, the smartphone system, with its inherent limitations in terms of optical redundancy and spatial distribution, can be enhanced by incorporating multiple cameras for a three-dimensional view. Despite these limitations, the low-cost smartphone system showed optimal performance with minimal errors compared with the commercial system, making it a cost-effective option with potential for further development. The rapid advancement of smartphone technology and its accessibility make it an attractive choice for motion capture applications.

Gauging Centrifugal Instabilities in Compressible Free-Shear Layers via Nonlinear Boundary Region Equations

Curved free shear layers emerge in many engineering problems involving complex flow geometries, such as the flow over a backward-facing step, flows with wall injection in a boundary layer, the flow inside side-dump combustors, or wakes generated by vertical axis wind turbines, among others. Previous studies involving centrifugal instabilities have mainly focused on wall-flows where Taylor instabilities between two rotating concentric cylinders or Görtler vortices in boundary layers are generated. Curved free shear layer flows, however, have not received sufficient attention, especially in the nonlinear regime. The present work investigates the development of centrifugal instabilities in a curved free shear layer flow in the nonlinear compressible regime. The compressible Navier–Stokes equations are reduced to the nonlinear boundary region equations (BREs) in a high Reynolds number asymptotic framework, wherein the streamwise wavelength of the disturbances is assumed to be much larger than the spanwise and wall-normal counterparts. We study the effect of the freestream Mach number M∞, the shear layer thickness δ, the amplitude of the incoming disturbance A, and the relative velocity difference across the shear layer ΔV on the development of these centrifugal instabilities. Our parametric study shows that, among other things, the kinetic energy of the curved shear layer flow increases with increasing ΔV and A decreases with increasing delta. It was also found that increasing the disturbance amplitude of the incoming disturbance leads to significant growth in the mushroom-like structure’s amplitude and renders the secondary instability structures more prominent, indicating increased mixing for all Mach numbers under consideration.

Characterizing Parameter Scaling with Quantization for Deployment of CNNs on Real-Time Systems

Modern deep-learning models tend to include billions of parameters, reducing real-time performance. Embedded systems are compute-constrained while frequently used to deploy these models for real-time systems given size, weight, and power requirements. Tools like parameter-scaling methods help to shrink models to ease deployment. This research compares two scaling methods for convolutional neural networks, uniform scaling and NeuralScale, and analyzes their impact on inference latency, memory utilization, and power. Uniform scaling scales the number of filters evenly across a network. NeuralScale adaptively scales the model to theoretically achieve the highest accuracy for a target parameter count. In this study, VGG-11, MobileNetV2, and ResNet-50 models were scaled to four ratios: 0.25×, 0.50×, 0.75×, 1.00×. These models were benchmarked on an ARM Cortex-A72 CPU, an NVIDIA Jetson AGX Xavier GPU, and a Xilinx ZCU104 FPGA. Additionally, quantization was applied to meet real-time objectives. The CIFAR-10 and tinyImageNet datasets were studied. On CIFAR-10, NeuralScale creates more computationally intensive models than uniform scaling for the same parameter count, with relative speeds of 41% on the CPU, 72% on the GPU, and 96% on the FPGA. The additional computational complexity is a tradeoff for accuracy improvements in VGG-11 and MobileNetV2 NeuralScale models but reduced ResNet-50 NeuralScale accuracy. Furthermore, quantization alone achieves similar or better performance on the CPU and GPU devices when compared with models scaled to 0.50×, despite slight reductions in accuracy. On the GPU, quantization reduces latency by 2.7× and memory consumption by 4.3×. Uniform-scaling models are 1.8× faster and use 2.8× less memory. NeuralScale reduces latency by 1.3× and dropped memory by 1.1×. We find quantization to be a practical first tool for improved performance. Uniform scaling can easily be applied for additional improvements. NeuralScale may improve accuracy but tends to negatively impact performance, so more care must be taken with it.

Settling velocity of atmospheric particles in seawater: Based on hydrostatic sedimentation method using video imaging techniques

When atmospheric particles deposit to the ocean, their settling velocities and residence times associated are critical for their effects on oceanic ecosystems. We developed a hydrostatic sedimentation method using video imaging techniques to track particles of 5–20 μm in diameter falling into seawater and determine the particle settling velocities in relation to their diameter, shape, organic matter contained, and seawater salinity. The measured settling velocities varied from 0.025 to 0.41 mm/s. Irregular particle shape and organic matter contained in particles also, however, reduced the values. The settling velocities were decelerated by the dissolution process of particle in seawater. Combined with the experimental results, a formula for calculating the settling velocity formulae for atmospheric particles was estimated. Using this equation, the residence time of particles is estimated to be less than one month in continental shelf sea and more than 100 days in the oceans.

Design, assembly and testing of an upgraded aeroacoustics wind tunnel facility

The Aeroacoustics Laboratory in the Department of Aerospace Engineering at Penn State currently serves several areas of experimental research. Most notable in the laboratory is the Anechoic Chamber of which the Aeroacoustics Wind Tunnel is an integral part. In recent years the dominant areas of research have included model aircraft exhaust jets and model rotors for helicopter applications involving detailed noise experiments. In the jet noise area, the applications have included aircraft in both flyover and take-off configurations. In the flyover application the air flow surrounding the exhaust jet has a significant effect on the radiated noise and must be simulated to produce experimental results that will be most useful in preliminary design of noise suppression applications. The Aeroacoustics Wind Tunnel at Penn State serves the purpose for this simulation. During aircraft take-off, the aircraft airspeed is in a range exceeding 250 ft per second, or Mach number of over M = 0.22. In the years preceding 2022, the velocity of the free jet flow of the Penn State facility was limited to less than 200 ft/sec. This paper reports on a project to design upgrades to the existing wind tunnel to improve the test section flow velocity to values closer to the 250 ft/sec target. The approach began with a preliminary design of the return flow ducting to convert the open jet, open return wind tunnel to a closed return tunnel. The concept is to make use of the flow energy in the exhaust of the tunnel to boost the input energy to the inlet fan that drives the flow to the test section. An integral part of the preliminary design involved making use of an upgraded flow analysis computer code to predict the gain in test section velocity of the new facility. For the available horsepower of two fans in the facility, the configurations of the existing and upgraded wind tunnel were entered into this analysis code based on a quasi-one-dimensional formulation. The code included empirical data formulated to include the various shapes of the components of the tunnels. The relative flow velocities in each section used simple incompressible continuity to calculate all velocities in the component sections. The average dynamic pressure in each section is calculated from the flow (constant) density and the velocity squared. Each section had a non-dimensional pressure loss parameter calculated from empirical data (from the relevant literature). The basic code (using the Excel algorithms) was based on a predictor-corrector concept in which the calculation was initialized with the estimated velocity and pressure of the flow at the entrance to the test section. The loss in each following section is estimated to determine the loss in the total pressure and the cumulated loss compared to the gains made at the two fans in the circuit. The initial guess is adjusted based on the total pressure value at the end of the circuit. During the related experiments, measurements of the total and dynamic pressure were performed at several joints between sections and compared to the values predicted in the analysis code. Various settings of the horsepower of the drive fans were used and the results were compared to flow measurements for the tunnel configurations (predominantly for the open return and closed return tunnel setups available before and after the upgrade project.) Results of the analysis and experiments are presented and analyzed together with the remaining activities planned for the coming months.

Magnetic soft centirobot to mitigate biological threats

AbstractSoft robots have drawn a lot of interest in the field of human–robot interfaces because they can mimic the propulsion of soft bodies and archive complex tasks that cannot be made by rigid robots such as performing the complex motion, avoiding collisions by absorbing impacts, and shape adaptation by elastic deformation. Herein, drawing inspiration from creatures in the Cambrian period, such as Hallucigenia, we develop a centimeter‐sized soft robot with multiple magnetic legs (referred to as a soft centirobot). This robot is equipped with graphitic carbon nitride (g‐C3N4) nanosheets to kill biological threats by photogenerated reactive oxygen species under black light illumination. The motion of g‐C3N4 soft centirobot is controlled by magnetic actuation even in complex wastewater samples (with a relative speed of 0.12 body lengths per second). The magnetic multilegs work as a propeller to walk across and cover large regions, and water disinfection is more efficient than what could be achieved by nano/micrometer scale sheets of g‐C3N4. Finally, factors affecting the accelerated propulsion of g‐C3N4 soft centirobot such as design principle, structure geometry, body mass, driving mechanism, and magnetic sensitivity, have been investigated. We envision that such a photoactive 2D material‐based integrated centimeter‐sized robot shall find application in many areas where pathogen removal is required.

Evaluating the safety and efficiency impacts of forced lane change with negative gaps based on empirical vehicle trajectories

A lane-changing (LC) maneuver may cause the follower in the target lane (new follower) to decelerate and give up space, potentially affecting crash risk and traffic flow efficiency. In congested flow, a more aggressive LC maneuver occurs where the lane changer is partially next to the new follower and creates negative gaps, namely negative gap forced LC (NGFLC). Although NGFLC forms the foundation of sideswipe crashes, little has been done to address its impacts and the contributing factors. To tackle this issue, a total of 15,810 LC trajectory samples are extracted from three drone videos at different locations. These samples are categorized into NGFLC and normal LC groups for comparative analysis. Five commonly used conflict indicators are extended into two-dimensional to evaluate the crash risk of LC maneuver. The change of time gaps during LC maneuver are examined to quantify the impact of LC on traffic flow efficiency. We find that NGFLCs significantly increase crash risk, reflected by the number of hazardous LC events and potential crash areas compared to normal LC. Additionally, results reveal that both the lane changer and the new follower tend to maintain a larger time gap after NGFLCs. Factors including time headway, relative speed, and historical gaps in the target lane significantly affect NGFLC incidence. Once the movement of the leader in the original lane is taken into account, the prediction accuracy improves from 81% to 91%. The transferability tests indicate that the findings about the negative impact of NGFLC and the accuracy of its prediction model are consistent across different locations. These findings hold implications for driving assistance systems to better predict and mitigate NGFLCs.