scholarly journals Within-Session Reliability and Validity of Overhand Ball Throw Test to Evaluate Power Ability in Junior Tennis Players

2021 ◽  
Vol 66 (3) ◽  
pp. 21-32
Author(s):  
KÁROLY DOBOS ◽  
PÉTER JÁNOS TÓTH
Keyword(s):  
Reliability And Validity ◽  
Field Tests ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Testing Session ◽  
Tennis Players ◽  
Relative Reliability ◽  
Junior Tennis ◽  
Positive Correlations ◽  
Dependent Sample

"ABSTRACT. Introduction: Coaches should be able to estimate successfully different physical attributes of junior tennis player’s performance such as power, in order to monitor players’ progress and to design the most appropriate training program. However, this process requires reliable and valid field tests. Objective: Aim of this study was to examine absolute and relative reliability of overhand ball throw (OBT) test within testing session and to investigate its validity. Methods: 257 Hungarian junior boy and girl tennis players (aged 11- 17) separated into four groups, performed OBT and serve speed (SS) tests of standardised protocol. Results: Dependent sample t-test revealed no significant (p= 0.31-57>0.05) difference between test and retest sample means within testing session and magnitude of effect size (dz=0.1-0.5) were trivial for all groups. Furthermore, all groups had low typical percentage error (CV= 3-4 %), and standard error of measurement values was consistently low (SEM= 0.12- 0.18). Within test-retest consistency illustrated strong relative reliability (ICC= 0.98-0.99). Moreover, significantly large to very large positive correlations were found between OBT and SS (r= 0.57-0.81; p˂0.01) tests. The coefficient of determination indicated that OBT explained 32-65% of the SS for groups. Conclusions: These findings suggest that absolute and relative reliability of OBT test is high within testing session and validity of OBT test is acceptable for measuring power ability of flat serve execution in junior tennis players."

scholarly journals RELATIONSHIP BETWEEN ISOKINETIC STRENGTH AND FIELD TESTS OF PHYSICAL FITNESS IN ELITE JUNIOR TENNIS PLAYERS 758

1996 ◽  
Vol 28 (Supplement) ◽  
pp. 127
Author(s):  
E. P. Roetert ◽  
T. J. McCormick ◽  
S. W. Brown ◽  
T. S. Ellenbecker
Keyword(s):  
Physical Fitness ◽  
Field Tests ◽  
Isokinetic Strength ◽  
Tennis Players ◽  
Junior Tennis

scholarly journals Neuromuscular Fitness Is Associated with Success in Sport for Elite Female, but Not Male Tennis Players

2021 ◽  
Vol 18 (12) ◽  
pp. 6512
Author(s):  
Karoly Dobos ◽  
Dario Novak ◽  
Petar Barbaros
Keyword(s):  
Field Tests ◽  
Competitive Performance ◽  
Tennis Players ◽  
Standing Long Jump ◽  
Different Types ◽  
Junior Tennis ◽  
Long Jump ◽  
Shuttle Run ◽  
Medicine Ball

Background: The purpose of the study was to examine whether neuromuscular fitness contributes significantly to the success of eAlite junior tennis players of differing ages and sexes. Methods: The 160 participants, who were elite Hungarian junior tennis players (aged 11–17), were separated into four groups within this study, and 10 different types of field tests were used. Results: A moderate significant correlation was found between the results of the 5 m run (r = −0.42; r = −0.45), standing long jump (r = 0.39; r = 0.56), overhand ball throw (r = 0.44; r = 0.53), serve (r = 0.39; r = 0.64), amount of push-ups in 30 seconds (r = 0.32; r = 0.48), 10 × 5 m run in a shuttle run (r = −0.34; r = −0.45), the spider run (r = −0.34; r = −0.52), and competitive tennis success among U14 and U18 girls. A significant correlation between the overhead medicine ball throw test value (r = 0.47) and the current competitive performance was found only among U18 elite female tennis players. In contrast, no correlation was found between the values of the U14 and U18 male tennis players and their current competitive performance. Conclusions: Additional studies are needed to identify interventions that can increase sport-specific neuromuscular fitness with the ultimate goal of achieving better performance.

scholarly journals Application of Artificial Neural Networks for Yield Modeling of Winter Rapeseed Based on Combined Quantitative and Qualitative Data

Agronomy ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 781 ◽  
Author(s):  
Gniewko Niedbała ◽  
Magdalena Piekutowska ◽  
Jerzy Weres ◽  
Robert Korzeniewicz ◽  
Kamil Witaszek ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Qualitative Data ◽  
Prediction Models ◽  
Saturated Fatty Acids ◽  
Field Tests ◽  
Industrial Applications ◽  
Raw Material ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Alpha Linolenic Acid

Rapeseed is considered as one of the most important oilseed crops in the world. Vegetable oil obtained from rapeseed is a valuable raw material for the food and energy industry as well as for industrial applications. Compared to other vegetable oils, it has a lower concentration of saturated fatty acids (5%–10%), a higher content of monounsaturated fatty acids (44%–75%), and a moderate content of alpha-linolenic acid (9%–13%). Overall, rapeseed is grown in all continents on an industrial scale, so there is a growing need to predict yield before harvest. A combination of quantitative and qualitative data were used in this work in order to build three independent prediction models, on the basis of which yield simulations were carried out. Empirical data collected during field tests carried out in 2008–2015 were used to build three models, QQWR15_4, QQWR31_5, and QQWR30_6. Each model was composed of a different number of independent variables, ranging from 21 to 27. The lowest MAPE (mean absolute percentage error) yield prediction error corresponded to QQWR31_5, it was 6.88%, and the coefficient of determination R2 was 0.69. As a result of the sensitivity analysis of the neural network, the most important independent variable influencing the final rapeseed yield was indicated, and for all the analyzed models it was “The kind of sowing date in the previous year” (KSD_PY).

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

scholarly journals How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

2021 ◽  
Vol 149 ◽  
Author(s):  
Junwen Tao ◽  
Yue Ma ◽  
Xuefei Zhuang ◽  
Qiang Lv ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Variable Selection ◽  
Mean Squared Error ◽  
Moving Average ◽  
Dynamic Bayesian Networks ◽  
Environmental Data ◽  
Control Measures ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Analysis Strategy ◽  
Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

scholarly journals Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4655
Author(s):  
Dariusz Czerwinski ◽  
Jakub Gęca ◽  
Krzysztof Kolano
Keyword(s):  
Machine Learning ◽  
Temperature Measurement ◽  
Stochastic Gradient Descent ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Bldc Motor ◽  
Temperature Estimation ◽  
Motor Operation

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

scholarly journals PETTLEP imagery and tennis service performance: an applied investigation

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Nafih Cherappurath ◽  
Masilamani Elayaraja ◽  
Dilshith A. Kabeer ◽  
Amila Anjum ◽  
Paris Vogazianos ◽  
...  
Keyword(s):  
Age Groups ◽  
Training Session ◽  
Performance Outcomes ◽  
Service Performance ◽  
Control Group ◽  
Specific Training ◽  
Tennis Players ◽  
Imagery Training ◽  
Before And After ◽  
Junior Tennis

AbstractTennis is one of the most popular and widely played sports enjoyed by players of different age groups and genders as a profession as well as a mode of recreation. A novel method, PETTLEP imagery combines both conventional and non-conventional style of training of an athlete and improves one’s performance. This study aimed to analyze the tennis service performance of junior tennis players based on PETTLEP imagery training. Forty-four junior male tennis players (Mage=13.22 years, SD=0.42) were selected for the study. The investigator handed over the MIQ-R questionnaire to all the participants in which they scored 16 and above points as per previous research. The participants were equally divided (n=11) into three experimental groups (E1, E2, and E3) and a control group. The service performance outcomes of all the players were compared before and after a training session. The three experimental groups were assigned with service-specific training, service-specific training combined with PETTLEP imagery training, and PETTLEP imagery training alone, respectively, for three days per week for 12 weeks. They were tested on their service accuracy based on the International tennis number (ITN) manual on-court assessment test. The data were assessed for normality and analyzed using non-parametric methods to reveal main effects (each training method alone) as well as to calculate the combined effect of PETTLEP and service-specific training. Certain significant improvements in tennis service were observed with service-specific training alone. Though it marginally outperformed the PETTLEP imagery method, the most improved services were observed with both PETTLEP and service-specific training utilized together. This implies an additive effect when both methods are used together.

scholarly journals Effect of Long-Term Unilateral Activity on Bone Mineral Density of Female Junior Tennis Players

1998 ◽  
Vol 13 (2) ◽  
pp. 310-319 ◽  
Author(s):  
Heidi Haapasalo ◽  
Pekka Kannus ◽  
Harri Sievänen ◽  
Matti Pasanen ◽  
Kirsti Uusi-Rasi ◽  
...  
Keyword(s):  
Bone Mineral Density ◽  
Bone Mineral ◽  
Mineral Density ◽  
Tennis Players ◽  
Junior Tennis

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

scholarly journals Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Boluwaji M. Olomiyesan ◽  
Onyedi D. Oyedum
Keyword(s):  
Solar Radiation ◽  
Comparative Study ◽  
Global Radiation ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Radiation Data ◽  
Proposed Model

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

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