Velocity Data Research Articles

Higher systolic blood pressure in left not right upper limb is associated with all-cause mortality risk in a community-based population

Abstract Background and Purpose Previous studies have shown that elevated inter-arm systolic blood pressure difference (SBPD) is a valuable indicator for assessing the risk of all-cause mortality. However, no research currently reports that the different impact of higher SBP in which upper limb on all-cause mortality risk. This study investigates which side of the upper limbs has a higher SBP that is more associated with all-cause mortality risk in a community population. Methods The subjects were from the a Atherosclerosis Cohort of the Department of Cardiology of our University First Hospital from 2012 to 2021, and those with missing data of brachial-ankle pulse wave velocity and a history of myocardial infarction or stroke in the baseline survey were excluded. The endpoint was defined as all-cause mortality. SBPD was defined as the difference between the systolic blood pressure of the right arm minus the systolic blood pressure of the left arm by the noninvasive arteriosclerosis detection device BP-203RPEIII (Omron, Japan). SBPD was categorized into four groups: 0-10 mmHg (right arm higher <10mmHg), ≥ 10 mmHg (right arm higher ≥ 10 mmHg ), -10 to < 0 mmHg (left arm higher ≤10 mmHg), and < -10 mmHg (left higher > 10mmHg). The relationship between SBPD and all-cause mortality risk was determined by multivariable Cox proportional hazards regression after adjusting for covariates (age, body mass index, gender, current smoking, current drinking, estimated glomerular filtration rate, diabetes, hypoglycemic treatment, total cholesterol, triglycerides, lipid-lowering treatment, and antihypertensive treatment). Results Totally, 8628 participants were included, with a mean age of 56.58 ± 8.97 years old, and 64.51% (5566) were female. Among them, 380 (4.40%) had an SBPD ≥ 10 mmHg, and 409 (4.74%) had an SBPD < -10 mmHg. During a median follow-up of 9.87 years, 442 cases of all-cause mortality (5.19%) occurred. The Kaplan-Meier curve suggested that the greater the difference in SBPD, the higher the cumulative risk of all-cause mortality. The Log-rank test showed a significant statistical difference (P= 0.0115). Compared to right arm higher <10mmHg group, the risk of all-cause mortality was not significantly elevated in right arm higher ≥ 10 mmHg (adjusted HR=1.17, 95% CI: 0.74-1.84, P=0.507) or left arm higher ≤10 mmHg group (adjusted HR=0.90, 95%CI: 0.74-1.11, P=0.329). However, the risk of all-cause mortality significantly increased by 59% in left higher > 10mmHg Group (adjusted HR=1.59, 95% CI: 1.10-2.30, P=0.013). The results remained significant after adjusting for peripheral right arm SBP (adjusted HR=1.56, 95% CI: 1.08-2.24, P=0.018). Conclusion This study found that a higher SBP in the left arm is associated with a higher risk of all-cause mortality in a Beijing community-based population, and they should be monitored and intervened more intensively.Hazard Ratios for all-cause mortalitySurvival curves for SBPD groups

Flow Field Analysis and Development of a Prediction Model Based on Deep Learning

The velocity of ocean currents significantly affects the trajectory prediction of ocean drifters and the safe navigation of intelligent vessels. Currently, most ocean current predictions focus on time-based forecasts at specific fixed points. In this study, deep learning based on the flow field prediction model (CNNs–MHA–BiLSTMs) is proposed, which predicts the changes in ocean currents by learning from historical flow fields. Unlike conventional models that focus on single-point current velocity data, the CNNs–MHA–BiLSTMs model focuses on the ocean surface current information within a specific area. The CNNs–MHA–BiLSTMs model integrates multiple convolutional neural networks (CNNs) in parallel, multi-head attention (MHA), and bidirectional long short-term memory networks (BiLSTMs). The model demonstrated exceptional modelling capabilities in handling spatiotemporal features. The proposed model was validated by comparing its predictions with those predicted by the MIKE21 flow model of the ocean area within proximity to Dalian Port (which used a commercial numerical model), as well as those predicted by other deep learning algorithms. The results showed that the model offers significant advantages and efficiency in simulating and predicting ocean surface currents. Moreover, the accuracy of regional flow field prediction improved with an increase in the number of sampling points used for training. The proposed CNNs–MHA–BiLSTMs model can provide theoretical support for maritime search and rescue, the control or path planning of Unmanned Surface Vehicles (USVs), as well as protecting offshore structures in the future.

Nonstationary adaptive S-wave leakage suppression of ocean-bottom node data

Ocean-bottom nodes (OBNs) are widely used because of their wide azimuth, long-offset, and low-frequency advantages. However, in the vertical component of the OBN geophone, a significant amount of S-wave induced noise may be recorded. This significantly impacts the quality of the vertical component data and may affect the follow-up merging of dual-sensor data. Some commercially available methods apply normal-moveout correction, which requires velocity data. We avoid this with an adaptive matching method, which relies solely on seismic data. Therefore, we develop a novel adaptive subtraction method for S-wave leakage suppression using the horizontal component as the noise model. Our method effectively handles the nonstationarity of the input seismic data in all time and space directions, mitigating instability caused by manual selection of the smoothing radius. A method is introduced for estimating a global nonstationary smoothing radius using the noise model. Compared with commercial and stationary smoothing methods, our method can better suppress S-wave noise while balancing residual noise and signal leakage more effectively. Synthetic and field data examples demonstrate significant improvement with our method.

An Integrated Machine Learning Workflow to Estimate In Situ Stresses Based on Downhole Sonic Logs and Laboratory Triaxial Ultrasonic Velocity Data

AbstractThe optimum performance of various subsurface operations such as stimulation treatments, wellbore drilling, horizontal well placement, underground mining, and tunneling rely on accurate estimation of the in situ stresses. This study presents an integrated machine learning (ML) workflow for the reliable determination of the in situ stress in subsurface rock formations. The study workflow was completed in three phases. In the first phase, six supervised ML regression techniques were employed to develop the stress prediction models using laboratory true triaxial ultrasonic velocity tests (labTUV) data of subsurface rock samples retrieved from well 16A(78)‐32 located at the Utah FORGE geothermal site. In the second phase, subsurface geological formations were classified into rock facies using an unsupervised K‐means clustering algorithm. Finally, in the third phase, the optimized ML models were employed for predicting the in situ stresses in the corresponding rock facies in the well 16A(78)‐32 using field sonic logs. A comparison of evaluation metrics revealed the superior performance of ANN models for horizontal and vertical stress predictions with root mean squared error of 1.5, 0.6, and 1.7 MPa, and determination coefficient (R2) of 0.98, 0.98, and 0.96, for the testing/validation phases, respectively. The generalization capability of ML models was explored by uncovering the underlying physics. The mathematical expressions of constitutive relations were extracted from three ANN models. Further, a total of five rock facies were identified in the subsurface geological formations. The novel workflow would be capable of delivering reliable in situ stress profiles in subsurface geological formations without performing expensive field tests.

Development of an evaluation method for precipitation particle types by using disdrometer data

Abstract A method for evaluating precipitation particle types from the size-fall velocity data observed by disdrometers by combining an expectation-maximization (EM) algorithm and a self-organizing map (SOM) was developed. An EM algorithm was used to estimate the particle size-fall velocity relationships according to previous work, and the SOM map was used to classify the relationships into graupel or snow categories. The method was applied for snowfall data observed by the volume scanning video disdrometer at a site in Sapporo, Japan, and the daily graupel-to-solid precipitation ratio (G/S ratio) from 1 December, 2017 to 28 February, 2018 was obtained. The sensitivity of the results to different SOM configurations, SOM node groupings, and training datasets was also tested. The G/S ratio obtained from the simulation data of a meteorological model was compared as an example of the application of the product created by the new method.

Using open-path dual-comb spectroscopy to monitor methane emissions from simulated grazing cattle

Abstract. Accurate whole-farm or herd-level measurements of livestock methane emissions are necessary for anthropogenic greenhouse gas inventories and to evaluate mitigation strategies. A controlled methane (CH4) release experiment was performed to determine if dual-comb spectroscopy (DCS) can detect CH4 concentration enhancements produced by a typical herd of beef cattle in an extensive grazing system. Open-path DCS was used to measure downwind and upwind CH4 concentrations from 10 point sources of methane simulating cattle emissions. The CH4 mole fractions and wind velocity data were used to calculate CH4 flux using an inverse dispersion model, and the simulated fluxes were then compared to the actual CH4 release rate. For a source located 60 m from the downwind path, the DCS system detected 10 nmol mol−1 CH4 horizontal concentration gradient above the atmospheric background concentration with a precision of 6 nmol mol−1 in 15 min interval. A CH4 release of 3970 g d−1 was performed, resulting in an average concentration enhancement of 24 nmol mol−1 of CH4. The calculated CH4 flux was 4002 g d−1, showing good agreement with the actual CH4 release rate. Periodically altering the downwind path, which may be needed to track moving cattle, did not adversely affect the ability of the instruments to determine the CH4 flux. These results give us confidence that CH4 flux can be determined by grazing cattle with low disturbance and direct field-scale measurements.

Imaging of upper breakpoints of buried active faults through microtremor survey technology

Detecting buried active faults presents the challenge of precisely locating the upper breakpoint, the shallowest point in the Quaternary system where faults occur. Microtremor survey technology, unaffected by urban electromagnetic interference, offers an eco-friendly and efficient method for investigating buried faults and stratigraphic structures in urban areas. This research uses microtremor survey technology to identify the upper breakpoint of the buried Nankou-Sunhe Fault in Changping, Beijing. For data collection, 17 microtremor survey points were deployed across the northern section of the Nankou-Sunhe fault, employing a three-point nested circular array with a point spacing of approximately 200 m to form a profile spanning approximately 320 m. For data analysis, the spatial autocorrelation method was utilized. Each measurement point was divided into 9 sets of radii, ranging from a minimum of approximately 4 m to a maximum of 28 m. The correlation coefficients for each set were calculated, and the dispersion curve for each measurement point was generated by fitting the average coefficients with the Bessel function of the first kind of order zero. The apparent S-wave velocity was determined directly from the dispersion curve using empirical formulas and interpolated to generate the contour cross-section map. Integrating the section and inverted S-wave velocity data can significantly enhance interpretation accuracy, and based on these data, the spatial development characteristics and upper breakpoint locations of the Nankou-Sunhe fault zone were analyzed, and the strata shallower than 100 m were deduced. The results align well with known geological data, such as luminescence dating and 14C dating from boreholes at nearby locations.Graphical

High pressure and high temperature Brillouin scattering measurements of pyrope single crystals using flexible CO2 laser heating systems

Single-crystal Brillouin scattering measurements are important for interpreting seismic velocities within the Earth and other planetary interiors. These measurements are rare, however, at temperatures above 1000 K, due to the fact that the transparent samples cannot be heated by common laser heating systems operating at a wavelength on the order of 1 μm. Here we present Brillouin scattering data on pyrope collected at pressures up to 23.8 GPa and temperatures between 850 and 1900 K using a novel CO2 laser heating system confined in either a flexible hollow silica waveguide or an articulated arm with mirrors mounted in each junction to direct the laser to the exit point. Pyrope has been chosen because it has been extensively studied at high pressures and moderate temperatures and therefore it is an excellent sample for bench-marking the CO2 laser heating system. The new high-temperature velocity data collected in this study allow the room pressure thermal parameters of pyrope to be constrained more tightly, resulting in values that reproduce the temperature dependence of the unit-cell volume of pyrope measured in recent studies at ambient pressure. Aggregate wave velocities of pyrope calculated along an adiabat using the thermoelastic parameters determined in this study are larger than those obtained using published values, implying that velocities for many mantle components may be underestimated at mantle temperatures because high temperature experimental data are lacking.

Evaluation of particle tracking codes for dispersing particles in porous media

Particle tracking (PT) is a popular technique in microscopy, microfluidics and colloidal transport studies, where image analysis is used to reconstruct trajectories from bright spots in a video. The performance of many PT algorithms has been rigorously tested for directed and Brownian motion in open media. However, PT is frequently used to track particles in porous media where complex geometries and viscous flows generate particles with high velocity variability over time. Here, we present an evaluation of four PT algorithms for a simulated dispersion of particles in porous media across a range of particle speeds and densities. Of special note, we introduce a new velocity-based PT linking algorithm (V-TrackMat) that achieves high accuracy relative to the other PT algorithms. Our findings underscore that traditional statistics, which revolve around detection and linking proficiency, fall short in providing a holistic comparison of PT codes because they tend to underpenalize aggressive linking techniques. We further elucidate that all codes analyzed show a decrease in performance due to high speeds, particle densities, and trajectory noise. However, linking algorithms designed to harness velocity data show superior performance, especially in the case of high-speed advective motion. Lastly, we emphasize how PT error can influence transport analysis.

Electric Vehicle Energy Management via Traffic Light Detection and Segmental Velocity Forecasting

Abstract Predictive-based power control has been widely recognized as a promising approach to boost driving range and improve system-level energy efficiency for electric vehicles (EVs), in which vehicle velocity forecasting generally serves as a preliminary input to optimally schedule the operations of varying on-board electrical and thermal systems. A segment-based velocity forecasting approach for individual commuting vehicles developed in this study reveals that it is challenging to forecast the velocity at intersection segments only using the velocity data. To address this challenge, this study seeks to develop a YOLO-V2-based object detection deep network to recognize the traffic lights in advance, and leverage the detected signals to establish a forecasting model that integrates with the probability-based hybrid forecasting approach. The case study results show that the traffic light detection-based forecasting model can significantly improve the forecasting accuracy for intersection segments. Based on the forecasting velocity 5-15 seconds ahead, the effectiveness of model predictive control-based energy management strategy is further evaluated with a liquid-based battery thermal control system. The proposed Battery Thermal Management System (BTMS) model shows promising results in maintaining battery temperature within an appropriate range, thus improving the overall energy efficiency of the EV. Moreover, a traffic light-based real-time energy management framework is developed to directly control the power demand from the Air Conditioning (AC) system.

Simulation of a Tidal Current-Powered Freshwater and Energy Supply System for Sustainable Island Development

Sustainable development of islands cannot be achieved without the use of renewable energy to address energy and freshwater supply issues. Utilizing the widely distributed tidal current energy in island regions can enhance local energy and water supply security. To achieve economic and operational efficiency, it is crucial to fully account for the unique periodicity and intermittency of tidal current energy. In this study, a tidal current-powered freshwater and energy supply system is proposed. The marine current turbine adopts a direct-drive configuration and will be able to directly transfer the power of the turbine rotation to the seawater pump to improve the energy efficiency. Additionally, the system incorporates batteries for short-term energy storage, aimed at increasing the capacity factor of the electrolyzer. A simulation is conducted using measured inflow velocity data from a full 12 h tidal cycle. The results show that the turbine’s average power coefficient reaches 0.434, the electrolyzer’s average energy efficiency is 60.9%, the capacity factor is 70.1%, and the desalination system’s average specific energy consumption is 6.175 kWh/m3. The feasibility of the system design has been validated.

Small-scale Helmholtz resonators with grazing turbulent boundary layer flow

ABSTRACT Helmholtz resonators flush-mounted in a wall beneath turbulent boundary layer flow are studied by focusing on their flow-induced excitation and effect on the grazing turbulent flow. A particular focus lies on single resonators tuned to the most intense spatio-temporal fluctuations in the near-wall vertical velocity and wall-pressure, residing at a spatial scale of λ x + ≈ 250 (or temporal scale of T + ≈ 25 ). Resonators are examined in a boundary layer flow at R e τ ≈ 2280 . Two neck-orifice diameters of d + ≈ 68 and 102 are considered, and for each value of d + three different resonance frequencies are studied (corresponding to a period of T + ≈ 25 , as well as one lower, and one higher, period). The response of the TBL flow is analysed by employing velocity data from hot-wire anemometry and particle image velocimetry measurements. Passive resonance only affects streamwise velocity fluctuations in the region y + ≲ 25 , while vertical velocity fluctuations due to resonance reach up to y + ≈ 100 . A narrow-band increase in streamwise turbulence kinetic energy at the resonance scale co-exists with a more than 20% attenuation of lower-frequency energy. Current findings on single resonator cases will aid in the development of passive surfaces with distributed resonators for boundary-layer flow control.

Prediction of vibro-acoustic sound emissions based on mapped structural dynamics

In the product design of shell-like housing structures for vibrating bodies, acoustic emission plays a significant role in usability and customer comfort. Consequently, the prediction of the emitted sound, e.g., in the form of the radiated sound power, is desired in the earliest possible stage of the development cycle. In industry, performing extensive acoustic testing using microphone arrays or laser scanning vibrometry is often time-consuming or infeasible, as it requires an acoustically treated measurement chamber and specific measurement equipment. On the other hand, accelerometer data is more straightforward to obtain due to the lower cost of the sensors and relaxed constraints on the measurement chamber compared to acoustic measurements. Hence, this contribution utilizes available surface velocity data in an acoustic simulation of the free-field Helmholtz problem. In doing so, it compares a conventional element-based and a mesh-less data-driven mapping technique to investigate the reduction of the number of provided sensor positions for the radiation problem. The open-source boundary element framework NiHu is used to give accurate predictions of acoustic emissions over a wide frequency range. The approach is applied to an industrial problem and validated against microphone measurements.

Assessing physics-informed neural network performance with sparse noisy velocity data

The utilization of data in physics-informed neural network (PINN) may be considered as a necessity as it allows the simulation of more complex cases with a significantly lower computational cost. However, doing so would also make it prone to any issue with the data quality, including its noise. This study would primarily focus on developing a special loss function in the PINN to allow an effective utilization of noisy data. However, a study regarding the data location and amount was also conducted in order to allow a better data utilization in PINN. This study was conducted on a lid-driven cavity flow at Re = 200, 1000, and 5000 with a dataset of less than 100 velocity data and a maximum noise of 10% of the maximum velocity. The results show that by ensuring the data are distributed in a certain configuration, it has zero noise, and by using as much data as possible, the computational cost of PINN can be significantly reduced compared to without using any data at all. For Re = 200, it is 7.4 faster by using data, and this speedup is potentially higher for higher Re cases. For the noise in particular, it does not only make the PINN more inaccurate but also necessitate the usage of more data as this is the only way to make it more accurate. This issue though is capable to be solved with our new method, which only uses the data as an approximate solution, and the governing equation would figure out the details. This method was also shown to be capable to improve the PINN accuracy with the potential to almost completely eliminating the noise effect.

Skin-friction from temperature and velocity data around a wall-mounted cube

This paper reports an algorithm for measuring the time-averaged skin friction vector field τ¯(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\overline{\\pmb {\ au }}(\\pmb {X})$$\\end{document} starting from time-resolved temperature maps, acquired by a functional coating of temperature-sensitive paint. The algorithm is applied to a large area around a wall-mounted cube, immersed in the turbulent boundary layer over a flat plate. The method adopts a relaxed version of the Taylor Hypothesis operating on time-resolved maps of temperature fluctuations T′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T'$$\\end{document} measured on the slightly warmer bounding surface. The procedure extracts U¯T(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_T(\\pmb {X})$$\\end{document}, the celerity of displacement of T′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T'$$\\end{document}, as the best approximation of the forecasting provided by the frozen turbulence assumption near the wall, where its rigorous application is inappropriate. The τ¯(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\overline{\\pmb {\ au }}(\\pmb {X})$$\\end{document} estimation is based on the hypothesis of a linear relationship between U¯T(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_T(\\pmb {X})$$\\end{document} and U¯U(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_U(\\pmb {X})$$\\end{document}, chained to the one between U¯U(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_U(\\pmb {X})$$\\end{document} and U¯τ(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_\ au (\\pmb {X})$$\\end{document}. We assess the outcomes of the proposed algorithm against those derived by the 2D and 3D Lagrangian particle tracking (LPT) methodology ’Shake-The-Box’, whose advent has made available high-quality near-wall flow field information. Furthermore, data from high-density 2D time-resolved LPT allows exploring the suitability of the linear relationships chain between U¯T(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_T(\\pmb {X})$$\\end{document} and U¯τ(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_\ au (\\pmb {X})$$\\end{document} in the proposed context.

Different Origin Field‐Aligned Currents and Their Relationship With Auroral Intensity

AbstractThe ionospheric field‐aligned current (FAC) consists of three components related to ionospheric vortices and gradients of Pedersen and Hall conductances, with the first term being of magnetospheric origin and the latter two being of ionospheric origin (Eq. 1). We calculate each of them between 2010 and 2016 using line‐of‐sight velocity data from the Super Dual Auroral Radar Network radar and auroral electron precipitation data from the Defense Meteorological Satellite Program. Then, we investigate the contribution of each FAC component, as well as the relationship between each FAC component and auroral activity. Our result shows that ionospheric‐origin FACs contribute up to 54% (dawn) and 42% (dusk) within the auroral oval. Both magnetospheric‐origin FACs and ionospheric‐origin FACs initially increase and then stabilize as the SuperMAG electrojet (SME) index increases. After stabilization, these two currents have almost equally important roles, with an average contribution rate of 56% and 44%, respectively. Additionally, the ionospheric‐origin FACs are found to have a higher dependency on auroral intensity. We also find that magnetospheric‐origin FACs exhibit distribution patterns similar to the R1/R2 FAC systems because their directions are determined by vorticities. When the SME index exceeds 200 nT, the contribution from the Pedersen conductance gradient consistently exhibits downward currents near 70° magnetic latitude in the dusk and predawn sectors due to the combined effects of velocities and Pedersen conductance gradients. However, the distribution pattern from the Hall conductance gradient is not so clear due to the uncertainty in the angle between velocity and Hall conductance gradient.

Within-subject time series angular velocity differences between in-game high and low velocity fastballs in college baseball pitchers

PurposeThe purpose of the current study is to investigate the within-pitcher differences in time series angular velocities of the pelvis, trunk, shoulder, and elbow for high and low velocity fastballs in college baseball pitchers. MethodsIn-game data were retrospectively analyzed from 82 NCAA Division 1 pitchers ([1.89 ​± ​0.06] m, [92.8 ​± ​9.5] kg). Kinematic data were collected using an in-game markerless motion capture system. Time series data of pelvis, trunk, shoulder, and elbow angular velocities for each pitcher's fastest and slowest fastball were extracted for the pitch cycle (foot contact to ball release) and used for analysis. Within-subject time series comparisons were conducted using statistical parametric mapping (SPM) paired samples t-tests (α ​= ​0.012 5). ResultsEach of the tested segments were significantly faster in the fastest fastball trial compared to the slowest fastball trial. The duration of significance in reference to the pitch cycle, test statistic, and p-value, for each segment are as follows: Pelvis: 0%–4%, t ​= ​3.54, p ​= ​0.012; Trunk: 30%–67%, t ​= ​5.62, p ​< ​0.001; Shoulder External Rotation: 3%–50%, t ​= ​−6.03, p ​< ​0.001; Shoulder Internal Rotation: 96%–100%, t ​= ​4.11, p ​= ​0.008; Elbow: 75%–86%, t ​= ​4.13, p ​< ​0.001. DiscussionWithin-subjects differences exist in time series angular velocities when comparing the fastest and slowest fastball. These time series differences provide additional information to distinguish fastball velocity beyond what discrete metrics can provide. Pitchers should look to rotate each segment faster, and optimize the sequencing of these movements, to increase pitch velocity.

HX Velorum: Ellipsoidal/Rotational Binary With β Cep Type Component

ABSTRACTWe present the Transiting Exoplanet Survey Satellite (TESS) light curve analysis of HX Velorum, located in the Southern Hemisphere, where one of the components is found for the first time to be a Cep (BCEP) type pulsator. The TESS observations of HX Velorum were published in a total of 6 sectors. Fourier analysis of the observations reveals that the frequencies can be divided into two categories, one of which is related to the orbital period and the other related to the Cep‐type pulsation. Its non‐eclipsing light curve was analyzed with the well‐known Wilson–Devinney code, and we obtained the geometric and physical parameters of the components using published radial velocity and light curve data by making some assumptions. We obtained plausible masses and radii of the primary and secondary components as , and , , respectively. The obtained system's distance of pc is smaller than the Gaia distances found in the literature.

Acoustic emission and machine learning algorithms for particle size analysis in gas-solid fluidized bed reactors

In this work, a combination of an acoustic emission (AE) technique and a machine learning (ML) algorithm (Random Forest (RF) and Gradient Boosting Regressor (GBR)) is developed to characterize the particle size distribution in gas-solid fluidized bed reactors. A theoretical approach to explain the generation of acoustic emission signal in gas-solid flows is presented. An AE signal is generated in gas-solid fluidized beds due to the collision and friction between fluidized particles as well as between particles and the bed inner wall. The generated AE signal is in the form of an elastic wave with frequencies >100 KHz and it propagates through the gas-solid mixture. An inversion algorithm is used to extract the information about the particle size starting from the energy of the AE signal. The advantages of this AE technique are that it is a cheap, sensitive, non-intrusive, radiation-free, suitable for on-line measurements. Combining this AE technique with ML algorithms is beneficial for applications to industrial settings, reducing the cost of signal post-processing. Experiments were conducted in a pseudo-2D flat fluidized bed with four glass bead samples, with sizes ranging from 100 μm to 710 μm. AE signals were recorded with a sampling frequency of 5 MHz. The AE signal post-processing and data preparation for the ML process are explained. For the ML process, the AE frequency, AE energy and particle collision velocity data sets were divided into training (60%), cross-validation (20%) and test sets (20%). Two ensemble ML approaches, namely Random Forest and Gradient Boosting Regressor, are applied to predict particle sizes based on the AE signal features. The combination of these two models results in a coefficient of determination (R2) value greater than 0.9504.

Effects of multi-scale turbulent motions on aerodynamic performance of wind turbine under sand-laden conditions

Wind turbine installation in the desert and Gobi regions offers a promising approach for meeting long-term energy demands. However, the effect of multi-scale characteristics in sand-laden atmosphere flows on wind turbine aerodynamic performance has not been evaluated. In this study, wind velocity data collected from the Qingtu Lake Observation Array (QLOA) were employed to address this gap. Results show that up to 58% of the total turbulent kinetic energy (TKE) is accounted for by very large-scale motions (VLSMs), which make up a considerable portion of the TKE. The contributions of the large-scale motions (LSMs) and the small-scale motions (SSMs) to TKE are 36% and 6%, respectively. The contribution of multi-scale turbulent motions to the aerodynamic loads of wind turbine under sand-laden conditions has been quantified for the first time. The comparison demonstrates that while LSMs and SSMs exhibit a rapid drop in their contributions to wind turbine loads with height, VLSMs show a rapid increase. Wavelet analysis revealed a strong correlation between VLSMs and power, thrust, and blade root flapwise moment at periods ranging from 256 to 1024 s. This correlation weakens as the streamwise length scale of the turbulent coherent structure decreases. This study provides essential insights for optimizing wind turbine design and site selection in sand-laden environments.