Structurally distinct external solvent-exposed domains drive replication of major human prions
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99 GENERAL AND MISCELLANEOUS↗
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Drive-Leg Kinematics During the Windup and Pushoff Is Associated With Pitching Kinetics at Later Phases of the Pitch
Background: Inconsistent findings exist between drive-leg ground-reaction forces (GRFs) and pitching mechanics. Previous literature has largely reported drive-leg mechanics and GRFs at the start of the pushoff phase for their role in initiating force development. Little research has assessed drive-leg kinematics that includes a pitcher’s windup motion to determine its effects on subsequent phases in the pitching motion. Purpose/Hypothesis: The primary aim was to analyze the relationship between drive-leg knee valgus angle during the windup and subsequent pitching mechanics. We hypothesized that the drive-leg knee valgus angle during the early portion of the pitching motion would alter later phases’ pitching mechanics. A secondary aim was to assess GRFs to determine if the drive-leg knee valgus angle was associated with changes in force. We hypothesized that an increased drive-leg knee valgus angle would increase GRFs during the pitching motion. Study Design: Descriptive laboratory study. Methods: A total of 17 high school baseball pitchers (mean age, 16.1 ± 0.9 years; mean height, 180.0 ± 4.8 cm; mean weight, 75.5 ± 7.5 kg) volunteered for the study. Kinematic data and GRFs were collected using an electromagnetic tracking system and force plates. Pitchers threw maximal-effort fastballs from a mound at regulation distance. The drive-leg knee valgus angle was analyzed during the windup and pushoff phases of the pitch to determine its effects on other biomechanical variables throughout the pitching motion. Results: There was a significant relationship between drive-leg knee valgus angle during the windup ( Fchange 1,12) = 16.13; P = .002; R 2 = 0.695) and lateral GRF in the arm-cocking phase. Additionally, there was a significant relationship between drive-leg knee valgus angle during pushoff ( Fchange(2,11) = 10.21; P = .003; R 2 = 0.716) and lateral GRF in the arm-cocking phase and pitching-elbow valgus moment in the acceleration phase. Conclusion: Drive-leg knee valgus angle during the windup and pushoff had a significant relationship with drive-leg GRF and pitching-elbow valgus moment at later stages of the pitching cycle. Clinical Relevance: Assessments of drive-leg kinematics during the windup and pushoff may be useful in identifying inefficient movement patterns that can have an effect on the direction of a pitcher’s drive-leg force contribution, which can lead to increased forces on the throwing elbow.
Orthopedics↗
Corroborative Evaluation of the Real-World Energy Saving Potentials of InfoRich Eco-Autonomous Driving (iREAD) System
There has been an increasing interest in exploring the potential to reduce energy consumption of future connected and automated vehicles. People have extensively studied various eco-driving implementations that leverage preview information provided by on-board sensors and connectivity, as well as the control authority enabled by automation. Quantitative real-world evaluation of eco-driving benefits is a challenging task. The standard regulatory driving cycles used for measuring exhaust emissions and fuel economy are not truly representative of real-world driving, nor for capturing how connectivity and automation might influence driving trajectories. To adequately consider real-world driving behavior and potential “off-cycle” impacts, this paper presents four collaborative evaluation methods: large-scale simulation, in-depth simulation, vehicle-in-the-loop testing, and vehicle road testing. These four approaches, spanning simulation and testing aspects, evaluate real-world fuel economy benefits with different ranges and resolutions. The large-scale simulations leverage an extensive real-world driving database to assess overall eco-driving benefits across a range of road network and driving scenarios. The real-world driving data are further leveraged to generate representative driving routes for deeper evaluation. Based on the representative routes, in-depth simulation relying on high-fidelity models investigates how different traffic scenarios can impact the eco-driving performance. The vehicle-in-the-loop setup reinforces the in-depth simulations by conducting tests with an actual vehicle operated on a chassis dynamometer; the measured energy savings were indeed found to agree with the in-depth simulation savings estimates. Finally, limited but representative road testing with the fully integrated vehicle will be conducted to demonstrate the eco-driving capability and conclude the overall evaluation regimen.
ARPA-E↗
Real-World Driving Features for Identifying Intelligent Driver Model Parameters
Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver. It stands to reason that determining the parameter values from real-world driving data would serve the purpose of representing a real-world driver. Although the published literature is replete with techniques and consequences of using real-world driving data to determine parameter values for parametric driver models, none have explored them in the context of using shorter driving features where the parameter values may change over the course of a single trip for the same driver. In this study we consider the “intelligent driver model” (IDM) as our driver behavior model and use real-world driving data from the Transportation Secure Data Center (TSDC) maintained at the National Renewable Energy Laboratory (NREL). The TSDC includes real-world travel data from across the United States, from which NREL has created a wide range of driving routes consisting of road features such as speed limits, stop locations, and turn locations. The real-world driving data are categorized into different driving regimes and extracted into driving features. The driving features are then used to calibrate the parameter values for the IDM. The distribution of the parameters and the relationships among them are reported. The insights obtained from this study enable judicious usage of IDM or similar parametric driver models to represent baseline human driver behavior in simulations.
27 ARPA - Advanced Research Projects Agency-Energy↗
Real-World Driving Features for Identifying Intelligent Driver Model Parameters: Preprint
Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver. It stands to reason that determining the parameter values from real-world driving data would serve the purpose of representing a real-world driver. While the published literature is replete with techniques and consequences of using real-world driving data to determine parameter values for parametric driver models, none have explored them in the context of using shorter driving features where the parameter values may change over the course of a single trip for the same driver. In this study we consider the “Intelligent Driver Model” (IDM) as the driver behavior model to explore and real-world driving data from the Transportation Secure Data Center (TSDC) maintained at the National Renewable Energy Laboratory (NREL). The TSDC includes real-world travel data from across the United States, from which NREL has created a wide range of driving routes consisting of road features such as speed limits, stop locations and turn locations. The real-world driving data are categorized into different driving regimes and extracted into driving features. The driving features are then used to calibrate the parameter values for the IDM. The distribution of the parameters and the relationships among them are reported. The insights obtained from this study enable judicious usage of IDM or similar parametric driver models to represent baseline human driver behavior in simulations.
27 ARPA - Advanced Research Projects Agency-Energy↗
Quantifying automated vehicle benefits in reducing driving stress: a simulation experiment approach
Driving is a stressful activity because of the mental workload required to maneuver a vehicle in certain travel contexts, such as congested traffic, multi-modal networks requiring complex interaction with surrounding vehicles, and aggressive driving. Autonomous vehicles (AVs), on the other hand, can reduce the mental workload by performing most of the driving tasks and providing users with a comfortable ride. This study develops a pathway model to relate different health determinants, including travel reliability, safety, driving comfort, and value of time, to Autonomous vehicles driving and studies their impact on the value of driving stress. A case study example of Autonomous vehicles simulation is used to determine the impact of these health determinants. The value of driving stress in Autonomous vehicles is estimated as a function of the value of these individual health determinants. The results show that the perception of safe or unsafe driving in Autonomous vehicles is the most important factor in changing the perception of driving stress in Autonomous vehicles. Similarly, perceptions of comfortable driving in Autonomous vehicles and reduced workload with a higher value of time also reduce driving stress in Autonomous vehicles. These results allow Autonomous vehicles adoption models to explicitly consider driving stress reduction as a benefit and can improve understanding of Autonomous vehicles adoption, which may require quantitative analysis of underlying motivating benefits, including driving stress reduction.
Khattak, Zulqarnain H.↗
Control rod drive mechanism (CRDM) with remote disconnect mechanism
The drive assembly includes annular drive magnets extending around a top end of a drive shaft and annular drive coils extending around the drive magnets, separated by a pressure boundary. A latch assembly is coupled to the drive magnets and engages with the drive shaft in response to actuation of the drive assembly. The drive coils also rotate the drive magnets and the engaged latch assembly to axially displace the drive shaft. Deactivating the drive coils disengages the latch assembly from the drive shaft, dropping a connected control rod assembly via gravity into a nuclear fuel assembly.
Lobscheid, Christian↗
HCPerf: Driving Performance-Directed Hierarchical Coordination for Autonomous Vehicles
The rapid development of autonomous driving poses new research challenges to the on-vehicle computing system. In particular, the execution time of autonomous driving tasks highly depends on the specific driving environment. For instance, the execution time of configurable sensor fusion increases significantly as the scene becomes complex, which leads to end-to-end deadline misses from sensing to control and may cause accidents. Thus, a framework that can effectively utilize the system resources to guarantee the end-to-end deadlines of autonomous driving tasks as well as effectively prioritize the responsiveness and throughput of the control commands is crucial for autonomous driving. In this paper, we propose HCPerf, a performance-directed hierarchical coordination framework that intelligently coordinates the autonomous driving tasks with high execution time variation and complex dependencies according to the driving performance in real-time. Specifically, HCPerf mainly consists of two coordinators. The internal coordinator intelligently schedules the tasks according to the driving performance of the vehicle in order to help them meet the end-to-end deadlines while well prioritizing the responsiveness and throughput of the control commands. At the same time, the external coordinator dynamically tunes the rates of tasks according to the schedulability in order to efficiently utilize the system resource. We conduct extensive experiments on both simulation and hardware testbeds with the representative autonomous driving application. The results show that HCPerf can effectively improve the driving performance by 7.69%-45.94% in different driving scenarios.
Ma, Jialiang↗
Quantitative Evaluation of Autonomous Driving in CARLA
There has been a great deal of recent advancements in end-to-end imitation and reinforcement learning for self-driving vehicles. Despite this, there is a severe lack of standardized metrics for evaluating the performance of autonomous self-driving agents. Existing metrics are generally lacking in their ability to capture a wide range of driving behaviors and compare the severity of different failure cases. In this work, we introduce the Quantitative Evaluation for Driving metric, or QED, which assigns a quantitative score from 0-100 that captures the quality of driving for any driving agent. Our QED metric assesses different aspects of driving behavior including the ability to stay in the center of the lane, avoid weaving and erratic behavior, follow the speed limit, and avoid collisions, and it can be used under a wide range of driving scenarios. To show the effectiveness of our QED metric, we compare the scores generated by QED against scores assigned by human evaluators on a total of 30 different drivers and 6 different towns in the CARLA driving simulator. In ``easy'' evaluation scenarios, where it is relatively straightforward to distinguish better drivers from worse drivers, QED attains an average Pearson correlation of 0.96 and average Spearman correlation of 0.97 when compared against human evaluators. In ``hard'' evaluation scenarios, where it is far more ambiguous how to rank/score different types of bad driving behavior, QED attains an average Pearson correlation of 0.82 and average Spearman correlation of 0.75 when compared against human evaluators, which are both slighter higher than when we compare human evaluators against each other. While QED may not capture every characteristic that defines good driving, we consider it an important foundation for reproducibility and standardization in the community.
Gao, Shang↗
Leveraging Real-World Driving Data for Design and Impact Evaluation of Energy Efficient Control Strategies
Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies. In this study, two methods are presented of leveraging real-world data in both design optimization of energy efficient control strategies and in evaluating the real-world impact of those control strategies upon large-scale deployment. Through these methodologies, strategies with highest impact on energy savings were selected to be implemented as control algorithms. The developed algorithms were incorporated into a vehicle dynamics and powertrain control architecture implemented on a Cadillac CT6 demonstration vehicle. The control algorithms were then exercised on real-world driving scenarios to determine their impact on collective energy savings. The methodology utilizes the large-scale driving data sets maintained by the National Renewable Energy Laboratory to extract real-world driving scenarios and efficient simulation software tools. The insights obtained through this research help in guiding technology selection for energy efficient driving controls.
ARPA-E↗
Shapiro steps and stability of skyrmions interacting with alternating anisotropy under the influence of ac and dc drives
Here we use atomistic simulations to examine the sliding dynamics of a skyrmion in a two-dimensional system containing a periodic one-dimensional stripe pattern of variations between low and high values of the perpendicular magnetic anisotropy. The skyrmion changes in size as it crosses the interface between two anisotropy regions. On applying combined dc and ac driving in either parallel or perpendicular directions, we observe a wide variety of Shapiro steps, Shapiro spikes, and phase-locking phenomena. The phase-locked orbits have two-dimensional dynamics due to the gyrotropic or Magnus dynamics of the skyrmions and are distinct from the phase-locked orbits found for strictly overdamped systems. Along a given Shapiro step when the ac drive is perpendicular to the dc drive, the velocity parallel to the ac drive is locked while the velocity in the perpendicular direction increases with increasing drive to form Shapiro spikes. At the transition between adjacent Shapiro steps, the parallel velocity jumps up to the next step value, and the perpendicular velocity drops. The skyrmion Hall angle shows a series of spikes as a function of increasing dc drive, where the jumps correspond to the transition between different phase-locked steps. At high drives, the Shapiro steps and Shapiro spikes are lost. When both the ac and dc drives are parallel to the stripe periodicity direction, Shapiro steps appear, while if the dc drive is parallel to the stripe periodicity direction and the ac drive is perpendicular to the stripe periodicity, then there are only two locked phases, and the skyrmion motion consists of a combination of sliding along the interfaces between the two anisotropy values and jumping across the interfaces.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Driving mode analysis—How uncertain functional inputs propagate to an output
Abstract Driving mode analysis elucidates how correlated features of uncertain functional inputs jointly propagate to produce uncertainty in the output of a computation. Uncertain input functions are decomposed into three terms: the mean functions, a zero‐mean driving mode, and zero‐mean residual. The random driving mode varies along a single direction, having fixed functional shape and random scale. It is uncorrelated with the residual, and under linear error propagation, it produces an output variance equal to that of the full input uncertainty. Finally, the driving mode best represents how input uncertainties propagate to the output because it minimizes expected squared Mahalanobis distance amongst competitors. These characteristics recommend interpretation of the driving mode as the single‐degree‐of‐freedom component of input uncertainty that drives output uncertainty. We derive the functional driving mode, show its superiority to other seemingly sensible definitions, and demonstrate the utility of driving mode analysis in an application. The application is the simulation of neutron transport in criticality experiments. The uncertain input functions are nuclear data that describe how Pu reacts to bombardment by neutrons. Visualization of the driving mode helps scientists understand what aspects of correlated functional uncertainty have effects that either reinforce or cancel one another in propagating to the output of the simulation.
97 MATHEMATICS AND COMPUTING↗
Flashlamp drive system for a high-energy 10–100 kHz pulse-burst Nd:YAG laser
We present the design of the flashlamp drive system for the NG100 laser, a 1 J/pulse, 10–100 kHz, pulse-burst Nd:YAG laser system being developed for application in a Thomson scattering plasma diagnostic. This flashlamp drive system is under active development, with a prototype now being constructed. The flashlamp drive system is modular, with each module capable of driving a series pair of linear flashlamps. Each drive module contains and is controlled by a dedicated Analog Regulator Controller. Thus each module is independently operable and controllable. This modular approach imposes no intrinsic limit to the number of modules that may be applied to drive the flashlamp pairs in a laser system. Each flashlamp drive module has a switch-regulated topology. An 1800 V main capacitor bank provides 25 kJ of energy storage, while a lower voltage output capacitor bank provides filtering and the initial energy delivered to the flashlamps at the start of the drive pulse. The main bank is recharged after each flashlamp drive pulse. As energy is drawn from the output bank by the flashlamps, an IGBT switching regulator feeds current from the main bank through an inductor to replenish the output capacitor bank. The rate of replenishment is feedback-controlled to maintain a regulated supply of power to the flashlamp load, with a setpoint range of 0.07 to 1.65 MW. An Analog Regulator Controller produces two-state variable pulse width feedback switching of the regulator IGBT. The switching frequency is ≤ 20 kHz, dynamically adjusted to limit ripple of the flashlamp power to ±3% statistical standard deviation of mean. For development or troubleshooting, each module is operable independent of the laser digital control system (microcontroller and FPGA).
Plasma diagnostics - interferometry↗
The turbulence driving parameter of molecular clouds in disc galaxies
ABSTRACT Supersonic turbulence plays a pivotal role during the formation of molecular clouds and stars in galaxies. However, little is known about how the fraction of compressive and solenoidal modes in the velocity field evolves over time and how it depends on properties of the molecular cloud or the galactic environment. In this work, we carry out magnetohydrodynamical simulations of disc galaxies and study the time evolution of the turbulence driving parameter for an ensemble of clouds. We find that the time-averaged turbulence driving parameter is insensitive to the position of the cloud within the galaxy. The ensemble-averaged driving parameter is found to be rather compressive with b ∼ 0.5–0.7, indicating almost time-independent global star formation properties. However, each individual cloud shows a highly fluctuating driving parameter, which would strongly affect the cloud’s star formation rate. We find that the mode of turbulence driving can rapidly change within only a few Myr, both from solenoidal to compressive and vice versa. We attribute these changes to cloud collisions and to tidal interactions with clouds or overdensities in the environment. Last, we find no significant differences in the average driving parameter between hydrodynamic and initially strongly magnetized galaxies. However, the magnetic field tends to reduce the overall fluctuation of the driving parameter. The average driving and its uncertainty are seen to be in agreement with recent constraints on the turbulence driving mode for solar neighbourhood clouds.
Körtgen, Bastian↗
The driving mode of shock-driven turbulence
Turbulence in the interstellar medium (ISM) is crucial in the process of star formation. Shocks produced by supernova explosions, jets, radiation from massive stars, or galactic spiral-arm dynamics are amongst the most common drivers of turbulence in the ISM. However, it is not fully understood how shocks drive turbulence, in particular whether shock driving is a more solenoidal (rotational, divergence-free) or a more compressive (potential, curl-free) mode of driving turbulence. The mode of turbulence driving has profound consequences for star formation, with compressive driving producing three times larger density dispersion, and an order of magnitude higher star formation rate than solenoidal driving. Here, we use hydrodynamical simulations of a shock inducing turbulent motions in a structured, multiphase medium. This is done in the context of a laser-induced shock, propagating into a foam material, in preparation for an experiment to be performed at the National Ignition Facility (NIF). Specifically, we analyse the density and velocity distributions in the shocked turbulent medium, and measure the turbulence driving parameter $b=(\sigma _{\rho /\langle \rho \rangle }^{2\Gamma }-1)^{1/2}(1-\sigma _{\rho /\langle \rho \rangle }^{-2})^{-1/2}\mathcal {M}^{-1}\Gamma ^{-1/2}$, with the density dispersion σρ/<ρ>, the turbulent Mach number $\mathcal {M}$, and the polytropic exponent Γ. Purely solenoidal and purely compressive driving correspond to b ~ 1/3 and b ~ 1, respectively. As a result, using simulations in which a shock is driven into a multiphase medium with structures of different sizes and Γ < 1, we find b ~ 1 for all cases, showing that shock-driven turbulence is consistent with strongly compressive driving.
79 ASTRONOMY AND ASTROPHYSICS↗
Skyrmion dynamics and transverse mobility: skyrmion Hall angle reversal on 2D periodic substrates with dc and biharmonic ac drives
Here we numerically examine the dynamics of a skyrmion interacting with a two-dimensional periodic substrate under dc and biharmonic ac drives. We show that the Magnus force of the skyrmion produces circular orbits that can resonate with the ac drive and the periodicity of the substrate to create quantized motion both parallel and perpendicular to the dc drive. The skyrmion Hall angle exhibits a series of increasing and/or decreasing steps along with strongly fluctuating regimes. In the phase locked regimes, the skyrmion Hall angle is constant and the skyrmion motion consists of periodic orbits encircling an integer number of obstacles per every or every other ac drive cycle. We also observe phases in which the skyrmion moves at 90° with respect to the driving direction even in the presence of damping, a phenomenon called absolute transverse mobility that can exhibit reentrance as a function of dc drive. When the biharmonic ac drives have different amplitudes in the two directions, we find regimes in which the skyrmion Hall angle shows a sign reversal from positive to negative, as well as a reentrant pinning effect in which the skyrmion is mobile at low drives but becomes pinned at higher drives. These behaviors arise due to the combination of the Magnus force with the periodic motion of the skyrmions, which produce Shapiro steps, directional locking, and ratchet effects.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Estimating region-specific fuel economy in the United States from real-world driving cycles
Here we describe a method for estimating region-specific real-world light-duty vehicle fuel economy in the United States that is unique in both the size and representativeness of real-world driving that was considered, and for its ability to model regional variations in driving patterns. Over one million miles of national driving data were used to select real-world cycles representative of observed trip categories. The six cycles were compared to U.S. legislative cycles, revealing some key differences. Finally, a set of cycle weighting factors for 533 separate U.S. regions was derived from annual traffic statistics. Applying this method, it was found that regional fuel economy varies due to differences in driving patterns alone and that rural driving patterns lead to improved fuel economy (for conventional vehicles). The driving cycles and regional weighting factors described here are useful for testing and simulation studies, specifically those sensitive to regional variations in driving patterns.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Reflection on the Challenges, Accomplishments, and New Frontiers of Gene Drives
Ongoing pest and disease outbreaks pose a serious threat to human, crop, and animal lives, emphasizing the need for constant genetic discoveries that could serve as mitigation strategies. Gene drives are genetic engineering approaches discovered decades ago that may allow quick, super-Mendelian dissemination of genetic modifications in wild populations, offering hopes for medicine, agriculture, and ecology in combating diseases. Following its first discovery, several naturally occurring selfish genetic elements were identified and several gene drive mechanisms that could attain relatively high threshold population replacement have been proposed. This review provides a comprehensive overview of the recent advances in gene drive research with a particular emphasis on CRISPR-Cas gene drives, the technology that has revolutionized the process of genome engineering. Herein, we discuss the benefits and caveats of this technology and place it within the context of natural gene drives discovered to date and various synthetic drives engineered. Later, we elaborate on the strategies for designing synthetic drive systems to address resistance issues and prevent them from altering the entire wild populations. Lastly, we highlight the major applications of synthetic CRISPR-based gene drives in different living organisms, including plants, animals, and microorganisms.
59 BASIC BIOLOGICAL SCIENCES↗