A Method for the Automatic Measurement of Wave Frequency

1976 ◽  
Vol 33 (10) ◽  
pp. 2318-2322 ◽  
Author(s):  
M. Donelan
Keyword(s):  
Root Mean Square ◽  
Vertical Velocity ◽  
Laboratory Data ◽  
Automatic Measurement ◽  
Field Measurements ◽  
Wave Frequency ◽  
Mean Square ◽  
Wave Age ◽  
Average Wind Speed ◽  
Surface Deviation

The idea is advanced that the dominant wave frequency in a locally wind-generated sea may be accurately estimated from the ratio of root-mean-square surface vertical velocity to root-mean-square surface deviation. Field and laboratory data are used to establish the relationship between this ratio and the frequency of the peak of the spectrum as a function of inverse wave age. Wave age and the nondimensional fetch parameter are shown to be uniquely related in the fetch-limited case. Finally a procedure is given for estimating the appropriate one-dimensional spectrum from automatic field measurements of average wind speed, root-mean-square surface deviation, and root-mean-square surface vertical velocity.

scholarly journals An Annual Cycle of Submesoscale Vertical Flow and Restratification in the Upper Ocean

2019 ◽  
Vol 49 (6) ◽  
pp. 1439-1461 ◽  
Author(s):  
Xiaolong Yu ◽  
Alberto C. Naveira Garabato ◽  
Adrian P. Martin ◽  
Christian E. Buckingham ◽  
Liam Brannigan ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Root Mean Square ◽  
Vertical Velocity ◽  
Annual Cycle ◽  
Observational Evidence ◽  
Upper Ocean ◽  
Vertical Flow ◽  
Mean Square ◽  
Ocean Stratification ◽  
Density Equation

AbstractNumerical simulations suggest that submesoscale turbulence may transform lateral buoyancy gradients into vertical stratification and thus restratify the upper ocean via vertical flow. However, the observational evidence for this restratifying process has been lacking due to the difficulty in measuring such ephemeral phenomena, particularly over periods of months to years. This study presents an annual cycle of the vertical velocity and associated restratification estimated from two nested clusters of meso- and submesoscale-resolving moorings, deployed in a typical midocean area of the northeast Atlantic. Vertical velocities inferred using the nondiffusive density equation are substantially stronger at submesoscales (horizontal scales of 1–10 km) than at mesoscales (horizontal scales of 10–100 km), with respective root-mean-square values of 38.0 ± 6.9 and 22.5 ± 3.3 m day−1. The largest submesoscale vertical velocities and rates of restratification occur in events of a few days’ duration in winter and spring, and extend down to at least 200 m below the mixed layer base. These events commonly coincide with the enhancement of submesoscale lateral buoyancy gradients, which is itself associated with persistent mesoscale frontogenesis. This suggests that mesoscale frontogenesis is a regular precursor of the submesoscale turbulence that restratifies the upper ocean. The upper-ocean restratification induced by submesoscale motions integrated over the annual cycle is comparable in magnitude to the net destratification driven by local atmospheric cooling, indicating that submesoscale flows play a significant role in determining the climatological upper-ocean stratification in the study area.

Sensitivity of batted-ball speed to swing speed models

2019 ◽  
Vol 233 (3) ◽  
pp. 416-423
Author(s):  
Derek Nevins ◽  
Lloyd Smith ◽  
Jeff Kensrud
Keyword(s):  
Root Mean Square Error ◽  
Regression Model ◽  
Gaussian Process ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Gaussian Process Regression ◽  
Field Measurements ◽  
Mean Square ◽  
Ball Speed ◽  
Swing Speed

In baseball and softball, batted-ball velocities depend strongly on bat speed prior to contact. The purpose of this study was to characterize the sensitivity of hit. ball speed to swing speed models. A pitching machine was used to project balls at varying speeds considered to be age appropriate for female fastpitch softball batters ranging from 9 to 24 years of age. Participants swung bats of varying moment of inertia and high-speed video was used to measure bat and ball kinematics for each hit. Several power law swing speed models were compared, including a generic model for all participants, age-specific models and participant-specific models. In addition, an exponential Gaussian process regression model was used. Estimates of batted-ball speeds were obtained by coupling the swing speed models to a model of the bat–ball collision. The root mean square error of model estimates ranged from 2.3 to 5.0 rad/s for swing speed models and 3.6 to 8.5 m/s for batted-ball speed estimates. The Gaussian process regression model performance was similar to the participant-specific models (swing speed root mean square error = 2.4 rad/s; batted-ball speed root mean square error = 3.7 m/s). The correlation of the Gaussian process regression model with field measurements was surprising given the limited number of inputs, which warrants further investigation. Agreement between all power law models and field measurements improved when the bat moment of inertia about its instantaneous center of rotation (rather than about the knob) was considered. This information will be of use to players and coaches to improve batting performance and bat selection.

The Relationship of Root Mean Square Value of Electromyography and Isometric Torque of Quadriceps in Normal Subjects

2016 ◽  
Vol 26 (1) ◽  
pp. 58
Author(s):  
Qiurong XIE ◽  
Zheng JIANG ◽  
Qinglu LUO ◽  
Jie LIANG ◽  
Xiaoling WANG ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Normal Subjects ◽  
Mean Square ◽  
Isometric Torque ◽  
Relationship Of ◽  
The Relationship

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

Intersession reliability of GPS-based and accelerometer-based physical variables in small-sided games with and without the offside rule

2021 ◽  
pp. 175433712098764
Author(s):  
Igor Junio de Oliveira Custódio ◽  
Gibson Moreira Praça ◽  
Leandro Vinhas de Paula ◽  
Sarah da Glória Teles Bredt ◽  
Fabio Yuzo Nakamura ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Global Positioning System ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Mean Square ◽  
Physical Demands ◽  
Intraclass Correlation Coefficients ◽  
Total Distance ◽  
Global Positioning ◽  
High Level

This study aimed to analyze the intersession reliability of global positioning system (GPS-based) distances and accelerometer-based (acceleration) variables in small-sided soccer games (SSG) with and without the offside rule, as well as compare variables between the tasks. Twenty-four high-level U-17 soccer athletes played 3 versus 3 (plus goalkeepers) SSG in two formats (with and without the offside rule). SSG were performed on eight consecutive weeks (4 weeks for each group), twice a week. The physical demands were recorded using a GPS with an embedded triaxial accelerometer. GPS-based variables (total distance, average speed, and distances covered at different speeds) and accelerometer-based variables (Player Load™, root mean square of the acceleration recorded in each movement axis, and the root mean square of resultant acceleration) were calculated. Results showed that the inclusion of the offside rule reduced the total distance covered (large effect) and the distances covered at moderate speed zones (7–12.9 km/h – moderate effect; 13–17.9 km/h – large effect). In both SSG formats, GPS-based variables presented good to excellent reliability (intraclass correlation coefficients – ICC > 0.62) and accelerometer-based variables presented excellent reliability (ICC values > 0.89). Based on the results of this study, the offside rule decreases the physical demand of 3 versus 3 SSG and the physical demands required in these SSG present high intersession reliability.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

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