Post-processing Data Research Articles

A Comparison of Force-Plate Based Center of Mass Estimation Algorithms.

Estimating horizontal center of mass (CoM) is an important process that is used in the control of self-paced treadmills, as well as in clinical and scientific biomechanical analysis. Many laboratories use motion-capture to estimate CoM, while others use force-plate based estimates, either because they cannot access motion-capture or they do not want to be taxed with post-processing optoelectronic data. Three force-plate derived center of mass estimation algorithms were compared against a benchmark motion-capture technique. Two of them have recently been reported in the literature, and both rely on numerical integration of 2nd-order differential equations. We propose a third technique that uses an algebraic equation to directly relate center of pressure to center of mass without numerical drift. Twenty-four healthy adults participated in a five-minute steady-state walking test to compare these algorithms. The sample-by-sample standard deviation of the three force-plate based algorithms from the motion-capture benchmark algorithm was evaluated. The algebraic technique provided less error than either of the two more common integration techniques (p<0.05). The results of this study support the viability of using only ground reaction forces for self-paced treadmills and also show that a simple algebraic model is preferred to integration approaches. The use of an algebraic estimation simplifies control implementation for self-paced treadmill applications and eliminates the need for event-based drift recalibration.

Development of an Internal Calibration Algorithm for Ultrahigh-Resolution Mass Spectra of Dissolved Organic Matter.

In order to obtain a spectrum with high mass accuracy, an internal calibration of Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) is inevitable. This in turn is critical for subsequent data processing and is generally performed using the commercial instrument software DataAnalysis in the benchmark calibration mode. However, no methodological study has systemically addressed the automated internal calibration of FTICR-MS spectra for dissolved organic matter (DOM) from different sources such as terrestrial and aquatic environments. In this study, a new piecewise algorithm, FTMSCalibrate, was developed to automatically calibrate FTICR-MS spectra in both positive and negative ion modes. FTMSCalibrate was found to reproduce 91.7% ± 4.4% (referred to as the true positive ratio) of the chemical formulas obtained by calibration using manual DataAnalysis. In addition to significantly reducing the mass error, FTMSCalibrate is more accurate in terms of the molecular formula assignment for low m/z peaks than Formularity and MFAssignR. FTMSCalibrate was compatible with deprotonated ions for FTICR-MS spectra in the negative ion mode as well as protonated and adduct ions, including Na- and K-adducts, for FTICR-MS spectra in the positive ion mode. These results suggest that FTMSCalibrate publicly available herein is a robust alternative for the internal calibration of FTICR-MS spectra during postdata processing and will facilitate DOM analysis by FTICR-MS.

Method for preparing catalyst layer, catalyst layer, and membrane-electrode assembly comprising same and fuel cell

Design and synthesis of new candidate drugs produces a large number of compounds that must be qualified and tested to evaluate their characteristics and potential applications. Therefore, many studies will be scheduled and, consequently, it will be necessary to arrange specific, reliable, fast and relatively cheap analytic methods to support this research. The manuscript proposes a new approach in the HPLC-MS/MS analysis by using a sole chromatographic set up, tuned to minimize the run time, without requiring high efficiency or resolution between the analytes. The chromatographic column was used only to avoid or limit the interference of sample matrix towards the analyte ionization process (matrix-effects). Then, the MS/MS properties were explored to solve the signal assignment, by performing a series of energy resolved experiments to optimize the parameters and applying an interesting post-processing data elaboration tool (LEDA). The reliability of the new approach was evaluated in a chemical stability study in PBS and human plasma samples of a series of isomeric compounds P-glycoprotein/Carbonic Anhydrase (P-gp/CA) hybrid inhibitors. The obtained results demonstrated the effectiveness (reliability 97%−100%) of the LEDA algorithm to recognize and to separate the possible isomers present in the samples. The obtained matrix-effects values (ME 96%−106%) established that the chromatographic set up (short column and fast elution gradient) was proper to avoid the matrix interferences, while recovery values (RE 88%−108%) indicate a suitable sample preparation, despite only a protein precipitation was carried out. The quantitative performances of proposed HPLC-MS/MS methods showed an accuracy ranging between 92% and 108% and a precision lower than 13% that allows to be confident on the determination of new P-gp/CA hybrid inhibitors in the degradation study. Therefore, the general procedure proposed was found adequate to study a series of isomeric compounds without their chromatographic separation but only by applying and developing the MS/MS features.

Application of LEDA algorithm for the recognition of P-glycoprotein and Carbonic Anhydrase hybrid inhibitors and evaluation of their plasma stability by HPLC-MS/MS analysis

Design and synthesis of new candidate drugs produces a large number of compounds that must be qualified and tested to evaluate their characteristics and potential applications. Therefore, many studies will be scheduled and, consequently, it will be necessary to arrange specific, reliable, fast and relatively cheap analytic methods to support this research. The manuscript proposes a new approach in the HPLC-MS/MS analysis by using a sole chromatographic set up, tuned to minimize the run time, without requiring high efficiency or resolution between the analytes. The chromatographic column was used only to avoid or limit the interference of sample matrix towards the analyte ionization process (matrix-effects). Then, the MS/MS properties were explored to solve the signal assignment, by performing a series of energy resolved experiments to optimize the parameters and applying an interesting post-processing data elaboration tool (LEDA). The reliability of the new approach was evaluated in a chemical stability study in PBS and human plasma samples of a series of isomeric compounds P-glycoprotein/Carbonic Anhydrase (P-gp/CA) hybrid inhibitors. The obtained results demonstrated the effectiveness (reliability 97%−100%) of the LEDA algorithm to recognize and to separate the possible isomers present in the samples. The obtained matrix-effects values (ME 96%−106%) established that the chromatographic set up (short column and fast elution gradient) was proper to avoid the matrix interferences, while recovery values (RE 88%−108%) indicate a suitable sample preparation, despite only a protein precipitation was carried out. The quantitative performances of proposed HPLC-MS/MS methods showed an accuracy ranging between 92% and 108% and a precision lower than 13% that allows to be confident on the determination of new P-gp/CA hybrid inhibitors in the degradation study. Therefore, the general procedure proposed was found adequate to study a series of isomeric compounds without their chromatographic separation but only by applying and developing the MS/MS features.

Study of Biodiesel Production Fluid Flow Patterns in LPD Type Static Mixing Reactors Using CFD Simulation for Sustainable Energy

Flow patterns can be analyzed accurately using a computational fluid dynamics model approach or CFD. The purpose of this study was to examine the pattern of fluid flow, pressure drop, fluid viscosity and FAME results in biodiesel production in a low pressure drop (LPD) static mixing reactor. The CFD method has three important stages, namely pre-processing (experimental data), solution search (boundary conditions and calculations) and post-processing (data presentation and validation). The simulation results show that the flow pattern occurred in turbulent flow with Reynolds number in each reactor above 3000 (Re > 3000). This research also produces a scenario that can reduce the pressure drop in the flow by performing a hole addition scenario, that successfully reduces the pressure drop by 5.13% compared to conventional LPD. Observations also show a decrease in viscosity in the hollow scenario with a decrease of 9.8% better than conventional LPD and an increase of 0.33% FAME in the scenario of adding holes. This study on biodiesel production can help increase the yield of better biodiesel with high quality and for the future can reduce or replace the use of non-renewable fossil fuels with renewable fuels so that the environment will remain sustainable.

The Diagnostic Value of Scanning in the Injury of Triceps Crus of Volleyball Players.

The study goal is to solve the problem of the diagnosis of triceps crus injury of volleyball players, meet the needs of volleyball players and team doctors for the correct diagnosis of triceps crus injury scanning, make up for the deficiency that triceps crus injury scanning diagnosis is easy to make mistakes, and improve the efficiency and ability of triceps crus injury diagnosis scanning. Because the experiment involves the technical action of volleyball jump serve, DELSYSR Trignomobile wireless portable surface electromyography tester (16 leads) made in the United States is selected to test the surface electromyography of the main muscle groups of college male volleyball players in the process of jump serve. The German made simi-3D motion image system is used to conduct three-dimensional synchronous test of athletes' jump serve action. The data analysis software adopts EMG work analysis, EMG analysis software, and simi-3D motion image analysis system for postprocessing data. The original signal is filtered (400 Hz for low pass and 20 Hz for high pass) and rectified. Finally, IEMG, EMG contribution rate, and RMS were calculated. This ensures the accuracy of the experiment.

Att-TasNet: Attending to Encodings in Time-Domain Audio Speech Separation of Noisy, Reverberant Speech Mixtures

Separation of speech mixtures in noisy and reverberant environments remains a challenging task for state-of-the-art speech separation systems. Time-domain audio speech separation networks (TasNets) are among the most commonly used network architectures for this task. TasNet models have demonstrated strong performance on typical speech separation baselines where speech is not contaminated with noise. When additive or convolutive noise is present, performance of speech separation degrades significantly. TasNets are typically constructed of an encoder network, a mask estimation network and a decoder network. The design of these networks puts the majority of the onus for enhancing the signal on the mask estimation network when used without any pre-processing of the input data or post processing of the separation network output data. Use of multihead attention (MHA) is proposed in this work as an additional layer in the encoder and decoder to help the separation network attend to encoded features that are relevant to the target speakers and conversely suppress noisy disturbances in the encoded features. As shown in this work, incorporating MHA mechanisms into the encoder network in particular leads to a consistent performance improvement across numerous quality and intelligibility metrics on a variety of acoustic conditions using the WHAMR corpus, a data-set of noisy reverberant speech mixtures. The use of MHA is also investigated in the decoder network where it is demonstrated that smaller performance improvements are consistently gained within specific model configurations. The best performing MHA models yield a mean 0.6 dB scale invariant signal-to-distortion (SISDR) improvement on noisy reverberant mixtures over a baseline 1D convolution encoder. A mean 1 dB SISDR improvement is observed on clean speech mixtures.

Machine learning classification of surface fracture in ultra-precision diamond turning using CSI intensity map images

With increasing focus on automation of machining processes in a smart factory, attention has turned to implementation of machine learning (ML) not only for tasks such as process parameter optimization, tool wear monitoring, and surface roughness prediction, but also for near-real-time determination of part quality using image data for inline inspection. As a first step, this work compares the performance of ML approaches using post-process image data, as opposed to numerical machining process data, for the discrete prediction of surface fracture in single-point diamond turned germanium for IR optics. Typical classifiers such as Random Forest and Support Vector Machine are employed for processing numerical machining parameters, while feedforward neural networks (FNNs), convolutional neural networks (CNNs), and deep CNNs (DCNNs) are utilized for processing intensity map images of the turned surfaces. The feasibility of using FNNs, CNNs, and DCNNs for identifying fracture in intensity maps is initially explored on raw images, and then by employing edge segmentation and edge detection techniques such as the Sobel, Prewitt, and Laplacian operators, as well as denoising methods as the Adaptive Gaussian Threshold filter. Finally, hybrid CNN and hybrid FNN models that combine numerical process data and image data are explored.Keywords

Lightweight and Amplitude-Free Ultrasonic Imaging Using Single-Bit Digitization and Instantaneous Phase Coherence.

In the field of ultrasonic nondestructive testing (NDT), the total focusing method (TFM) and its derivatives are now commercially available on portable devices and are getting more popular within the NDT community. However, its implementation requires the collection of a very large amount of data with the full matrix capture (FMC) as the worst case scenario. Analyzing all the data also requires significant processing power, and consequently, there is an interest in: 1) reducing the required storage capacity used by imaging algorithms, such as delay-and-sum (DAS) imaging and 2) allowing the transmission and postprocessing of inspection data remotely. In this study, a different implementation of the TFM algorithm is used based on the vector coherence factor (VCF) that is used as an image itself. This method, also generally known as phase coherence imaging, presents certain advantages, such as a better sensitivity to diffracting geometries, consistency of defect restitution among different views, and an amplitude-free behavior as only the instantaneous phase of the signal is considered. Some drawbacks of this method must also be mentioned, including the fact that it poorly reproduces planar reflectors and presents a lower signal-to-noise ratio (SNR) than amplitude-based methods. However, previous studies showed that it can be used as a reliable tool for crack-like defect sizing. Thus, a lightweight acquisition process is proposed through single-bit digitization of the signal, followed by a phase retrieval method based on the rising and falling edge locations, allowing to feed the phase coherence imaging algorithm. Simulated and experimental tests were first performed in this study on several side-drilled holes (SDHs) in a stainless steel block and then extended to an experimental study on angled notches in a 19.05-mm ( 3/4" )-thick steel sample plate through multiview imaging. Results obtained using the array performance indicator (API) and the contrast-to-noise ratio (CNR) as quantitative evaluation parameters showed that the proposed lightweight acquisition process, which relies on binary signals, allows a reduction of the data throughput of up to 47 times. This throughput reduction is achieved while still presenting very similar results to phase coherence imaging based on the instantaneous phase derived from the Hilbert transform of the full waveform. In an era of increasing wireless network speed and cloud computing, these results allow considering interesting perspectives for the reduction of inspection hardware costs and remote postprocessing.

Electromagnetic Signatures from Supermassive Binary Black Holes Approaching Merger

Abstract We present fully relativistic predictions for the electromagnetic emission produced by accretion disks surrounding spinning and nonspinning supermassive binary black holes on the verge of merging. We use the code Bothros to post-process data from 3D general relativistic magnetohydrodynamic simulations via ray-tracing calculations. These simulations model the dynamics of a circumbinary disk and the mini-disks that form around two equal-mass black holes orbiting each other at an initial separation of 20 gravitational radii, and evolve the system for more than 10 orbits in the inspiral regime. We model the emission as the sum of thermal blackbody radiation emitted by an optically thick accretion disk and a power-law spectrum extending to hard X-rays emitted by a hot optically thin corona. We generate time-dependent spectra, images, and light curves at various frequencies to investigate intrinsic periodic signals in the emission, as well as the effects of the black hole spin. We find that prograde black hole spin makes mini-disks brighter since the smaller innermost stable circular orbit angular momentum demands more dissipation before matter plunges to the horizon. However, compared to mini-disks in larger separation binaries with spinning black holes, our mini-disks are less luminous: unlike those systems, their mass accretion rate is lower than in the circumbinary disk, and they radiate with lower efficiency because their inflow times are shorter. Compared to a single black hole system matched in mass and accretion rate, these binaries have spectra noticeably weaker and softer in the UV. Finally, we discuss the implications of our findings for the potential observability of these systems.

A detailed experimental and modeling comparison of molecular radiative heat loss in a spark-ignition engine

Radiative heat transfer has been chiefly considered negligible in internal combustion engines, except for Diesel engines where soot radiation was recognized as a significant radiative transfer source. Only more recently, detailed simulations have shown that molecular radiation can be substantial as well. In extension to this, molecular radiative heat transfer can reach detectable levels of about 5–10% of the total heat transfer in spark-ignited engines. The broadband radiative nature of the significant emitting molecules, carbon dioxide and water, makes it necessary to address whether radiative trapping plays a substantial role in either total heat loss or energy redistribution within the cylinder. An experimental setup that allows measurements of the infrared emissions at a 2-crank-angle-degree resolution was developed to determine the importance of radiative trapping by carbon dioxide and water. Measurements were conducted in the well-characterized and documented TCC-III engine at the University of Michigan. The engine operated on a stoichiometric propane/air mixture at a speed of 1,300 RPM. Large Eddy Simulations with added line-by-line photon Monte-Carlo simulations of the molecular radiation were an integral part of this study to plan and devise the measurements. Post-processing of the simulation data included an accurate representation of the experimental volume from where infrared signals are collected. Additionally, the experimental data were used for validation of the photon Monte-Carlo simulations. Joint analysis of experimental and simulated spectra allowed quantifying the significance of radiative trapping. Results show the significant role of radiative trapping when predicting radiative heat transfer in the TCC-III engine.

Technological mediation and 3D visualizations in construction engineering practice

The generation and use of 3D images and visualizations through remote sensing, Building Information Modeling, and Augmented Reality technologies, have come to play a significant role in construction engineering practice. Although these technologies are promising, their potential can be misjudged when potential end-users are unaware of key assumptions that were made by developers. Realistic expectations require insights into the ways in which these technologies transform input collected into 3D visualizations and how these visualizations are possibly used on construction sites. This study’s objective is hence to explore the form of technological mediation that the generation and use of 3D images and visualizations provide between a human and objects, or aspects of these objects, that would otherwise be largely imperceptible to professionals in construction practice. We show that algorithms pre- and post-process data through their technological selectivities, which function as mediators. Double mediations of augmented and engaged relationships play a dominant role in the use of 3D images and visualizations and enhance the situational awareness of professionals in construction practice. This is the first study that applies this perspective to increase the understanding of the mediating role of 3D images and visualizations in construction practice.

Accurate relativistic observables from postprocessing light cone catalogs

We introduce and study a new scheme to construct relativistic observables from post-processing light cone data. This construction is based on a novel approach, LC-Metric, which takes general light cone or snapshot output generated by arbitrary N-body simulations or emulations and solves the linearized Einstein equations to determine the spacetime metric on the light cone. We find that this scheme is able to determine the metric to high precision, and subsequently generate accurate mock cosmological observations sensitive to effects such as post-Born lensing and nonlinear ISW contributions. By comparing to conventional methods in quantifying those general relativistic effects, we show that this scheme is able to accurately construct the lensing convergence signal. We also find the accuracy of this method in quantifying the ISW effects in the highly nonlinear regime outperforms conventional methods by an order of magnitude. This scheme opens a new path for exploring and modeling higher-order and nonlinear general relativistic contributions to cosmological observables, including mock observations of gravitational lensing and the moving lens and Rees-Sciama effects.

Textile structures for limiting the effects of maritime and fluvial disasters

Water is essential for the lives of humans, animals and plants, while representing an indispensable resource for the economy, and its management goes beyond national borders. Water pollution with oil residues affects both surface water and groundwater; its consequences on the organoleptic properties of water, aquatic fauna and flora are particularly damaging for all ecosystems and their biodiversity. Mechanical recovery of the pollutant provides, according to the international norms, the limitation of the polluted surface and the concentration of the pollutant in order to recover and store the water-hydrocarbon mixture, with limits related to the agitation of the marine/fluvial environment and distance from the intervention base to the polluted area. The choice of blocking/storage systems must take into account several factors, such as type and amount of pollutant recovered, the flow of recovery units, area of use, hydro-weather conditions in the field, mode of transport and mode of location in terrain. This study presents the constructive solutions for the dams made of textile materials, used in case of disasters in the maritime and fluvial area. Post-processing of data, performed with the help of IBM multiplatform software suite, highlighted the variation intervals of the structural parameters for 7 composite materials differentiated by: location, mass, raw material, thickness and thermal resistance. These composites were the basis for computer-aided designs of 14 experimental models (EM), differentiated by the dimensions used, the materials of the floats, the skirt and the area of use (maritime from 4bf to 10 bf or fluvial).

Toward automating post processing of aquatic sensor data

Sensors measuring environmental phenomena at high frequency commonly report anomalies related to fouling, sensor drift and calibration, and datalogging and transmission issues. Suitability of data for analyses and decision making often depends on manual review and adjustment of data. Machine learning techniques have potential to automate identification and correction of anomalies, streamlining the quality control process. We explored approaches for automating anomaly detection and correction of aquatic sensor data for implementation in a Python package (pyhydroqc). We applied both classical and deep learning time series regression models that estimate values, identify anomalies based on dynamic thresholds, and offer correction estimates. Techniques were developed and performance assessed using data reviewed, corrected, and labeled by technicians in an aquatic monitoring use case. Auto-Regressive Integrated Moving Average (ARIMA) consistently performed best, and aggregating results from multiple models improved detection. pyhydroqc includes custom functions and a workflow for anomaly detection and correction.

Experimental Determination of Heat Transfer Coefficient on Impingement Cooled Gear Flanks: Validation of the Evaluation Method

AbstractUnderstanding the heat transfer characteristics of impingement cooling of high-speed high-power gears is essential to design a reliable gearbox for a new generation of jet engines. However, experimental data on the impingement cooling of gears is limited in the literature. The experimental setup at the Institute of Thermal Turbomachinery aims at closing this gap. It includes a rotating gear instrumented with thermocouples. The measured temperatures are used to determine a spatially resolved heat transfer coefficient distribution on the gear tooth. The iterative evaluation approach applied in the postprocessing of the experimental data is validated with two reference cases. First, it is shown that the interpolation of temperature data between thermocouple locations leads to inaccurate results and would not be valid for the evaluation of the experiments, even if the number of thermocouples were increased. The iterative evaluation approach can reproduce the reference heat transfer coefficient distributions very accurately even with a low spatial resolution of temperature data. A new iterative method based on the Levenberg–Marquardt algorithm is implemented within this study. The new method generally converges faster than the existing method. The difference in required computational time is negligible in the easy to evaluate high heat transfer case, whereas a speed-up of up to three times is observed in the relatively cumbersome evaluation of the low heat transfer case.

Wavelet Model of Geomagnetic Field Variations and Its Application to Detect Short-Period Geomagnetic Anomalies

Geomagnetic data analysis is an important basis for the investigation of the processes in the near-Earth space, Earth magnetosphere, and ionosphere. The negative impact of geomagnetic anomalies on modern technical objects and human health determine the applied significance of the investigation and requires the creation of effective methods for timely detection of the anomalies. Priory complicated structure of geomagnetic data makes their formalization and analysis difficult. This paper proposes a wavelet model for geomagnetic field variations. It describes characteristic changes and anomalies of different amplitude and duration. Numerical realization of the model provides the possibility to apply it in online analysis. We describe the process of model identification and show its efficiency in the detection of sudden, short-period geomagnetic anomalies occurring before and during magnetic storms. Raw second data of the Paratunka and Magadan observatories and post-processed minute data were used in the paper. The question of noise effect on the proposed model results was under consideration.

Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation

Although the recent development of solar power forecasting through machine learning approaches, such as the machine learning models based on numerical weather prediction (NWP) data, has been remarkable, their extreme error requires an increase in the amount of reserve capacity procurement used for the power system safety. Hence, a reduction of the serious overestimation is necessary for efficient grid operation. However, despite the importance of the above issue, few studies have focused on the model design, suppressing serious errors, to the best of the authors’ knowledge. This study investigates a prediction model that can reduce the huge overestimation of the solar irradiance, which poses a risk to the power system. The specific approaches used are as follows: the employment of Support Vector Quantile Regression (SVQR), the utilization of Meso-scale Ensemble Prediction System (MEPS, Meso-scale EPS for the regions of Japan) data, which is based on the forecasts from Meso-scale Model (MSM) as explanatory variables, and the hyperparameter adjustment. The performance of the models is verified in the one day-ahead forecasting for surface solar irradiance at five sites in the Kanto region as the numerical simulation, where their forecasting errors are measured by the root mean square error (RMSE) and the 3σ error, which corresponds to the 99.87% quantile error of the order statistics. The test results indicate the following findings: the SVRs’ RMSE and 3σ error tend to be trade-offs in the case of varying the penalty of the regularization term; by using SVR as a post-processing tool for MSM or MEPS data, both of the score of their metrics can be improved from original data; the MEPS-based SVQR (MEPS-SVQR) could provide superior performance in both metrics in comparison with the MSM-based SVQR (MSM-SVQR) if the parameters are properly adjusted. Although the time period and the type of MEPS data used for the validation are limited, our report is expected to help the design of NWP-based machine learning models to enable short-term solar power forecasts with a low risk of overestimation.

Raw Data to Results: A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy.

Single-molecule localization microscopy (SMLM) is an advanced microscopy method that uses the blinking of fluorescent molecules to determine the position of these molecules with a resolution below the diffraction limit (∼5–40 nm). While SMLM imaging itself is becoming more popular, the computational analysis surrounding the technique is still a specialized area and often remains a “black box” for experimental researchers. Here, we provide an introduction to the required computational analysis of SMLM imaging, post-processing and typical data analysis. Importantly, user-friendly, ready-to-use and well-documented code in Python and MATLAB with exemplary data is provided as an interactive experience for the reader, as well as a starting point for further analysis. Our code is supplemented by descriptions of the computational problems and their implementation. We discuss the state of the art in computational methods and software suites used in SMLM imaging and data analysis. Finally, we give an outlook into further computational challenges in the field.

Ready for Real Time: Performance of Global Navigation Satellite System in 2019 Mw 7.1 Ridgecrest, California, Rapid Response Products

Abstract Global Navigation Satellite Systems (GNSSs) have undergone notable advancement in the last few decades, leading to the availability of a dataset with capabilities well beyond its original intended purpose. The proliferation of high-rate (1 Hz or greater) GNSS receivers in areas of seismological interest now allows for routine consideration of dynamic earthquake ground motions, with centimeter-level displacement accuracy via precise point positioning methods. Real-time (RT) GNSS observations, from stations that are both telemetered and processed to displacement with minimal latency, have lower accuracy compared to post-processed (PP) GNSS displacements due to imprecise knowledge of atmospheric conditions, satellite clocks, and satellite orbits in RT. Whether the quality of RT high-rate GNSS is sufficient for use in rapid response products remains to be thoroughly examined. Here, we highlight RT GNSS displacement time series processed during the 2019 Mw 7.1 Ridgecrest, California, earthquake in the context of common rapid-response products, magnitude estimation, and kinematic fault-slip models. We discuss how these data can be used to supplement RT seismic data for rapid characterization of significant earthquakes. We find that kinematic fault-slip models using RT GNSS data retain the general spatiotemporal characteristics of those with PP data, with subtle differences in size and amplitude of modeled slip asperities. We demonstrate the effect of these rapid seismic source models using RT GNSS data on the U.S. Geological Survey product ShakeMap—a downstream ground-motion prediction algorithm informed by the rupture dimensions estimated in the slip model. Discrepancies in the ShakeMap estimate are minor, within ±12% change, with the most severe variation at the fault edges. Our analysis suggests that, when used in conjunction with available seismic data sources, RT GNSS is sufficient and valuable for rapid earthquake characterization.