scholarly journals Observational Learning with Externally Imposed and Self-Controlled Frequency of Model Demonstration

2020 ◽  
Vol 27 (3) ◽  
pp. 3-7
Author(s):  
Weronika Łuba-Arnista ◽  
Michał Biegajło
Keyword(s):  
Learning Process ◽  
Observational Learning ◽  
Learning Effect ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Skill Learning ◽  
The Self ◽  
Control Group ◽  
Motor Skill Learning ◽  
The Mean

Abstract Introduction. The aim of this study was to investigate the effect of learning a complex gymnastic routine with different frequencies of externally imposed and self-controlled model demonstration. Material and Methods. Thirty undergraduate physical education (PE) students were randomly assigned to 3 groups: G100 (100% frequency), GS (self-controlled frequency) and GC (control group). Each participant from groups G100 and GS performed 150 trials of a complex gymnastic routine during 10 practice sessions. The learning effect was evaluated on the basis of the mean absolute error value and measured during pre-acquisition (baseline), acquisition (practice sessions) and post-acquisition (retention and transfer tests) phases. Results. It was revealed that observational learning with self-controlled and high externally imposed frequency of model demonstration proved to be equally effective. The differences were found during the acquisition phase only. The self-controlled group achieved higher outcomes than the externally imposed group. Conclusions. This study indicates that performance during practice sessions does not always reflect the permanency and adaptability of the motor skill learning process.

scholarly journals Football Juggling Learning Alters the Working Memory and White Matter Integrity in Early Adulthood: A Randomized Controlled Study

2021 ◽  
Vol 11 (9) ◽  
pp. 3843
Author(s):  
Yifan Shi ◽  
Kelong Cai ◽  
Hao Zhu ◽  
Xiaoxiao Dong ◽  
Xuan Xiong ◽  
...  
Keyword(s):  
Working Memory ◽  
White Matter ◽  
Motor Skill ◽  
Early Adulthood ◽  
Skill Learning ◽  
Neural Mechanisms ◽  
White Matter Integrity ◽  
Control Group ◽  
Motor Skill Learning ◽  
Experimental Group

Cross-sectional studies suggest that motor skill learning is associated with working memory (WM) and white matter integrity (WMI). However, it has not been established whether motor skill learning improves WM performance, and information on its neural mechanisms have not been clearly elucidated. Therefore, this study compared WM and WMI across time points prior to and following football juggling learning, in early adulthood (18–20 years old), relative to a control group. Study participants in the experimental group were subjected to football juggling for 10 weeks while participants in the control category went on with their routine life activities for the same period of time and were not involved in the learning-related activities. Data on cognitive measurements and that from diffusion tensor imaging (DTI) were collected before and after learning. There was a significant improvement in WM performance of the experimental group after motor learning, although no improvement was observed in the control group. Additionally, after learning, DTI data revealed a significant increase in functional anisotropy (FA) in the genu of corpus callosum (GOCC) and the right anterior corona radiata (R.ACR) in the experimental group. Moreover, the better WM associated with football juggling learning was correlated to a higher FA. Mediation analysis suggested that FA in the GOCC acts as a mediation variable between football juggling learning and WM. These findings show that motor skill learning improves the WM and remodels WMI in early adulthood. With a particular emphasis on the importance of WMI in motor skill learning and WM, this study also revealed the possible neural mechanisms mediated by WMI.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

Visualizing the Variance of a Random Variable

2011 ◽  
Vol 18 (01) ◽  
pp. 71-85
Author(s):  
Fabrizio Cacciafesta
Keyword(s):  
Stochastic Dominance ◽  
Mean Absolute Error ◽  
Random Variable ◽  
Absolute Error ◽  
Second Order ◽  
Taylor Formula ◽  
Order Stochastic Dominance ◽  
The Mean ◽  
Second Order Stochastic Dominance ◽  
Options Theory

We provide a simple way to visualize the variance and the mean absolute error of a random variable with finite mean. Some application to options theory and to second order stochastic dominance is given: we show, among other, that the "call-put parity" may be seen as a Taylor formula.

scholarly journals Direct Calculation of Thermodynamic Wet-Bulb Temperature as a Function of Pressure and Elevation

2013 ◽  
Vol 30 (8) ◽  
pp. 1757-1765 ◽  
Author(s):  
Sayed-Hossein Sadeghi ◽  
Troy R. Peters ◽  
Douglas R. Cobos ◽  
Henry W. Loescher ◽  
Colin S. Campbell
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Direct Calculation ◽  
Ambient Air ◽  
Absolute Error ◽  
Mean Square ◽  
Air Temperatures ◽  
Order Polynomial ◽  
The Mean ◽  
Very High

Abstract A simple analytical method was developed for directly calculating the thermodynamic wet-bulb temperature from air temperature and the vapor pressure (or relative humidity) at elevations up to 4500 m above MSL was developed. This methodology was based on the fact that the wet-bulb temperature can be closely approximated by a second-order polynomial in both the positive and negative ranges in ambient air temperature. The method in this study builds upon this understanding and provides results for the negative range of air temperatures (−17° to 0°C), so that the maximum observed error in this area is equal to or smaller than −0.17°C. For temperatures ≥0°C, wet-bulb temperature accuracy was ±0.65°C, and larger errors corresponded to very high temperatures (Ta ≥ 39°C) and/or very high or low relative humidities (5% < RH < 10% or RH > 98%). The mean absolute error and the root-mean-square error were 0.15° and 0.2°C, respectively.

scholarly journals A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ludi Wang ◽  
Wei Zhou ◽  
Ying Xing ◽  
Xiaoguang Zhou
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Mean Absolute Error ◽  
Wearable Sensors ◽  
Absolute Error ◽  
Noninvasive Estimation ◽  
Treatment Of Hypertension ◽  
The Mean ◽  
Artificial Neural Network Ann ◽  
Blood Pressure Estimation

The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) using the PPG method has received considerable interest. In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed. The multitaper method (MTM) is used for feature extraction, and an artificial neural network (ANN) is used for estimation. Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP.

Validating the usefulness of sectorwise regression of visual field in the central 10°

2021 ◽  
pp. bjophthalmol-2020-317391
Author(s):  
Takashi Omoto ◽  
Hiroshi Murata ◽  
Yuri Fujino ◽  
Masato Matsuura ◽  
Takehiro Yamashita ◽  
...  
Keyword(s):  
Visual Field ◽  
Mean Absolute Error ◽  
Open Angle Glaucoma ◽  
Absolute Error ◽  
Open Angle ◽  
Clustering Method ◽  
Significant Difference ◽  
The Mean ◽  
Test Points

AimTo evaluate the usefulness of the application of the clustering method to the trend analysis (sectorwise regression) in comparison with the pointwise linear regression (PLR).MethodsThis study included 153 eyes of 101 patients with open-angle glaucoma. With PLR, the total deviation (TD) values of the 10th visual field (VF) were predicted using the shorter VF sequences (from first 3 to 9) by extrapolating TD values against time in a pointwise manner. Then, 68 test points were stratified into 29 sectors. In each sector, the mean of TD values was calculated and allocated to all test points belonging to the sector. Subsequently, the TD values of the 10th VF were predicted by extrapolating the allocated TD value against time in a pointwise manner. Similar analyses were conducted to predict the 11th–16th VFs using the first 10 VFs.ResultsWhen predicting the 10th VF using the shorter sequences, the mean absolute error (MAE) values were significantly smaller in the sectorwise regression than in PLR. When predicting from the 11th and 16th VFs using the first 10 VFs, the MAE values were significantly larger in the sectorwise regression than in PLR when predicting the 11th VF; however, no significant difference was observed with other VF predictions.ConclusionAccurate prediction was achieved using the sectorwise regression, in particular when a small number of VFs were used in the prediction. The accuracy of the sectorwise regression was not hampered in longer follow-up compared with PLR.

Accuracy Assessment of TanDEM-X 90 and CartoDEM Using ICESat-2 Datasets for Plain Regions of Ratlam City and Surroundings

2021 ◽  
Vol 10 (1) ◽  
pp. 59
Author(s):  
Unnati Yadav ◽  
Ashutosh Bhardwaj
Keyword(s):  
Mean Absolute Error ◽  
Accuracy Assessment ◽  
Absolute Error ◽  
Terrain Analysis ◽  
Mean Square ◽  
High Quality ◽  
Ice Cloud ◽  
The Mean ◽  
Cut And Fill ◽  
Statistical Results

The spaceborne LiDAR dataset from the Ice, Cloud, and Land Elevation Satellite (ICESat-2) provides highly accurate measurements of heights for the Earth’s surface, which helps in terrain analysis, visualization, and decision making for many applications. TanDEM-X 90 (90 m) and CartoDEM V3R1 (30 m) elevation are among the high-quality openly accessible DEM datasets for the plain regions in India. These two DEMs are validated against the ICESat-2 elevation datasets for the relatively plain areas of Ratlam City and its surroundings. The mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) of TanDEM-X 90 DEM are 1.35 m, 1.48 m, and 2.19 m, respectively. The computed ME, MAE, and RMSE for CartoDEM V3R1 are 3.05 m, 3.18 m, and 3.82 m, respectively. The statistical results reveal that TanDEM-X 90 performs better in plain areas than CartoDEMV3R1. The study further indicates that these DEMs and spaceborne LiDAR datasets can be useful for planning various works requiring height as an important parameter, such as the layout of pipelines or cut and fill calculations for various construction activities. The TanDEM-X 90 can assist planners in quick assessments of the terrain for infrastructural developments, which otherwise need time-consuming traditional surveys using theodolite or a total station.

Intraocular lenses optic power calculation with seven formulas in short eyes

2021 ◽  
pp. 57-61
Author(s):  
K.B. Pershin ◽  
◽  
N.F. Pashinova ◽  
I.A. Likh ◽  
А.Y. Tsygankov ◽  
...  
Keyword(s):  
Axial Length ◽  
Mean Absolute Error ◽  
Optical Power ◽  
Absolute Error ◽  
Intraocular Lenses ◽  
Power Calculation ◽  
Retrospective Comparison ◽  
Monofocal Iol ◽  
The Mean ◽  
Axial Eye Length

Purpose. The choice of the optimal formula for calculating the IOL optical power in patients with an axial eye length of less than 20 mm. Material and methods. A total of 78 patients (118 eyes) were included in the prospective study. 1st group included 30 patients (52 eyes) with short eyes (average axial eye length of 19.60±0.42 (18.54–20.0) mm), 2nd group consisted of 48 patients (66 eyes) with a axial length 22.75±0.46 (22.0–23.77) mm. Various monofocal IOL models were used. The average follow-up period was 13 months. IOL optical power was calculated using the SRK/T formula, retrospective comparison – according to the formulas Hoffer-Q, Holladay II, Olsen, Haigis, Barrett Universal II and Kane. Results. In 1st group, the mean absolute error was determined for the formulas Haigis, Olsen, Barrett Universal II, Kane, SRK/T, Holladay II and Hoffer-Q (0.85, 0.78, 0.21, 0.17, 0.79, 0.73, 0.19 respectively). When comparing the formulas, significant differences were found for the formulas Hoffer-Q, Barrett Universal II and Kane in comparison with the formulas Haigis, Olsen, SRK/T and Holladay II (p<0.05) in all cases, respectively. In 2nd group, the mean absolute error was determined for the formulas Haigis, Olsen, Barrett Universal II, Kane, SRK/T, Holladay II and Hoffer-Q (0.15, 0.16, 0.23, 0.10, 0.19, 0.23, 0,29 respectively). In 2nd group, there were no significant differences between the studied formulas (p>0.05). Conclusion. This paper presents an analysis of data on the effectiveness of seven formulas for calculating the IOL optical power in short (less than 20 mm) eyes in comparison with the normal axial length. The advantage of the Hoffer-Q, Barrett Universal II and Kane formulas over Haigis, Holladay II, Olsen, and SRK/T is shown. Key words: cataract, hypermetropia, short eyes, calculation of the IOL optical power.

scholarly journals Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Mauricio Villarroel ◽  
Sitthichok Chaichulee ◽  
João Jorge ◽  
Sara Davis ◽  
Gabrielle Green ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Heart Rate ◽  
Respiratory Rate ◽  
Preterm Infants ◽  
Neonatal Intensive Care ◽  
Mean Absolute Error ◽  
Vital Signs ◽  
Absolute Error ◽  
Video Camera ◽  
The Mean

AbstractThe implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identification of suitable time periods and regions of interest from which vital signs can be estimated. We carried out a clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care. A total of 426.6 h of video and reference vital signs were recorded for 90 sessions from 30 preterm infants in the Neonatal Intensive Care Unit (NICU) of the John Radcliffe Hospital in Oxford. Each preterm infant was recorded under regular ambient light during daytime for up to four consecutive days. We developed multi-task deep learning algorithms to automatically segment skin areas and to estimate vital signs only when the infant was present in the field of view of the video camera and no clinical interventions were undertaken. We propose signal quality assessment algorithms for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. The mean absolute error between the reference and camera-derived heart rates was 2.3 beats/min for over 76% of the time for which the reference and camera data were valid. The mean absolute error between the reference and camera-derived respiratory rate was 3.5 breaths/min for over 82% of the time. Accurate estimates of heart rate and respiratory rate could be derived for at least 90% of the time, if gaps of up to 30 seconds with no estimates were allowed.

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