The Psychology of the Withdrawal Process: A Cross-Validation Test of Mobley's Intermediate Linkages Model of Turnover in Two Samples.

1984 ◽  
Vol 27 (1) ◽  
pp. 79-94 ◽  
Author(s):  
R. T. Mowday ◽  
C. S. Koberg ◽  
A. W. McArthur
Keyword(s):  
Cross Validation ◽  
Validation Test ◽  
Two Samples

The Psychology of the Withdrawal Process: A Cross-Validation Test of Mobley's Intermediate Linkages Model of Turnover in Two Samples

1984 ◽  
Vol 27 (1) ◽  
pp. 79-94
Author(s):  
Richard T. Mowday ◽  
Christine S. Koberg ◽  
Angeline W. McArthur
Keyword(s):  
Cross Validation ◽  
Validation Test ◽  
Two Samples

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

2020 ◽  
Vol 25 (40) ◽  
pp. 4296-4302 ◽  
Author(s):  
Yuan Zhang ◽  
Zhenyan Han ◽  
Qian Gao ◽  
Xiaoyi Bai ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inclusion Bodies ◽  
Cross Validation ◽  
Independent Set ◽  
K562 Cells ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Validation Test ◽  
Excess Number ◽  
Fold Cross Validation

Background: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen. Methods: In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors. Results: The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging. Conclusion: This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.

scholarly journals Leaf geometric morphometrics among populations of Dalbergia ecastaphyllum (L.) Taub.

2019 ◽  
Vol 35 (6) ◽  
Author(s):  
Daniel Vieira de Morais ◽  
Lorena Andrade Nunes ◽  
Vandira Pereira da Mata ◽  
Maria Angélica Pereira de Carvalho Costa ◽  
Geni da Silva Sodré ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Geometric Morphometrics ◽  
Cross Validation ◽  
Abiotic Factors ◽  
Principal Component ◽  
Canonical Variate ◽  
Validation Test ◽  
Significant Difference ◽  
Variate Analysis ◽  
Geographical Distances

Leaves are plant structures that express important traits of the environment where they live. Leaf description has allowed identification of plant species as well as investigation of abiotic factors effects on their development, such as gases, light, temperature, and herbivory. This study described populations of Dalbergia ecastaphyllum through leaf geometric morphometrics in Brazil. We evaluated 200 leaves from four populations. The principal component analysis (PCA) showed that the first four principal components were responsible for 97.81% of variation. The non-parametric multivariate analysis of variance (NPMANOVA) indicated significant difference between samples (p = 0.0001). The Mentel test showed no correlation between geographical distances and shape. The canonical variate analysis (CVA) indicated that the first two variables were responsible for 96.77 % of total variation, while the cross-validation test showed an average of 83.33%. D. ecastaphyllum leaves are elliptical and ovate.

scholarly journals Development of multistage 10-m shuttle run test for VO2max estimation in healthy adults

2021 ◽  
Keyword(s):  
Cross Validation ◽  
Age Groups ◽  
Healthy Adults ◽  
Coefficient Of Determination ◽  
Validation Test ◽  
Uniform Distributions ◽  
Gender And Age ◽  
Run Test ◽  
Shuttle Run ◽  
Spatial Limitation

Background and objective: The disadvantage of the traditional 20-m multistage shuttle run test (MST) is that it requires a long space for measurements and does not include various age groups to develop the test. Therefore, we developed a new MST to improve the spatial limitation by reducing the measurement to a 10-m distance and to resolve the bias via uniform distributions of gender and age. Material and methods: Study subjects included 120 healthy adults (60 males and 60 females) aged 20 to 50 years. All subjects performed a graded maximal exercise test (GXT) and a 10-m MST at five-day intervals. We developed a regression model using 70% of the subject's data and performed a cross-validation test using 30% of the data. Results: The male regression model's coefficient of determination (R2) was 58.8%, and the standard error of estimation (SEE) was 4.17 mL/kg/min. The female regression model's R2 was 69.2%, and the SEE was 3.39 mL/kg/min. The 10-m MST showed a high correlation with GXT on the VO2max (males: 0.816; females: 0.821). In the cross-validation test for the developed regression models, the male's SEE was 4.38 mL/kg/min, and the female's SEE was 4.56 mL/kg/min. Conclusion: Thus, the 10-m MST is an accurate and valid method for estimating the VO2max. Therefore, the 10-m MST developed by us can be used when the existing 20-m MST cannot be used due to spatial limitations and can be applied to both men and women in their 20s and 50s.

scholarly journals A New Approach to Forecasting Typhoon Frequency over the Western North Pacific

2009 ◽  
Vol 24 (4) ◽  
pp. 974-986 ◽  
Author(s):  
Ke Fan ◽  
Huijun Wang
Keyword(s):  
Prediction Model ◽  
North Pacific ◽  
Western North Pacific ◽  
Cross Validation ◽  
Forecast Model ◽  
Multilinear Regression ◽  
New Approach ◽  
Validation Test ◽  
Operational Forecasting ◽  
Close Fit

Abstract This paper presents a new approach for forecasting the typhoon frequency of the western North Pacific (WNP). The year-to-year increase or decrease in typhoon frequency is first forecasted to yield a net typhoon frequency prediction. Five key predictors for the year-to-year increment in the number of typhoons in the WNP have been identified, and a forecast model is established using a multilinear regression method based on data taken from 1965 to 2001. Using the forecast model, a hindcast of the typhoon frequency of the WNP during 2002–07 is made. The model exhibited a reasonably close fit for the period 1965–2007, including the larger anomalies in 1997 and 1998. It also accounted for the smaller variability of the typhoon frequency of the WNP during the validation period 2002–07 with an average root-mean-square error (RMSE) of 1.3 (2.85) during 2002–07 (1965–2001). The cross-validation test of the prediction model shows that the new approach and the prediction model demonstrate better prediction skill when compared to the models established based on typhoon frequency rather than the typhoon frequency increment. Thus, this new approach has the potential to improve the operational forecasting skill for typhoon frequency in the WNP.

scholarly journals CHLORIDE SENSOR FABRICATION BASED ON SPE Ag/AgCl THROUGH CYCLIC VOLTAMMETRIC TECHNIQUE: SCAN RATE EFFECT

SINERGI ◽  
2021 ◽  
Vol 25 (3) ◽  
pp. 351
Author(s):  
Mas'ud Asadullah ◽  
Sagir Alva ◽  
Ali Rinaldi ◽  
Rita Sundari
Keyword(s):  
Selectivity Coefficient ◽  
Rate Effect ◽  
Fabrication Process ◽  
Cyclic Voltammetric ◽  
Ion Sensor ◽  
Validation Test ◽  
Lifetime Test ◽  
Sensor Fabrication ◽  
Two Samples ◽  
Scan Rate

The Cyclic Voltammetric (CV) technique is one of the Ag/AgCl fabrication processes. In electrochemical processes using this CV technique, the microstructure of the surface of a substrate or electrode can affect the scan rate. Thus, this study aims to identify the scan rate effect of the Cl-ion sensor fabrication process using the CV technique on the performance of the Cl-ion sensor. First, the CV process was carried out in one cycle to grow the AgCl layer on the Ag surface. Then, this process was carried out at varied scan rates of 20, 40, 60, 80, and 100 mV/s. After completing the Ag/AgCl fabrication process, it was followed by the characterization process, selectivity coefficient test, lifetime test, and validation test to compare the test results of the Cl SPE Ag/AgCl ion sensor with Ag/AgCl commercial. The results showed that the optimum Cl-ion sensor response was obtained at the scan rate of 60 mV/s. Then, based on the validation test, the Cl-ion in the two samples did not show significant differences. Therefore, it indicates that the SPE Ag/AgCl ion sensor has the same performance as the Ag/AgCl commercial.

A Binary Classifier for the Prediction of EC Numbers of Enzymes

2019 ◽  
Vol 16 (5) ◽  
pp. 383-391 ◽  
Author(s):  
Hao Cui ◽  
Lei Chen
Keyword(s):  
Cross Validation ◽  
Enzyme Commission ◽  
Matthews Correlation Coefficient ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Binary Classifier ◽  
Two Samples ◽  
Functional Classes ◽  
Fold Cross Validation

Background: Identification of Enzyme Commission (EC) number of enzymes is quite important for understanding the metabolic processes that produce enough energy to sustain life. Previous studies mainly focused on predicting six main functional classes or sub-functional classes, i.e., the first two digits of the EC number. Objective: In this study, a binary classifier was proposed to identify the full EC number (four digits) of enzymes. Methods: Enzymes and their known EC numbers were paired as positive samples and negative samples were randomly produced that were as many as positive samples. The associations between any two samples were evaluated by integrating the linkages between enzymes and EC numbers. The classic machining learning algorithm, Support Vector Machine (SVM), was adopted as the prediction engine. Results: The five-fold cross-validation test on five datasets indicated that the overall accuracy, Matthews correlation coefficient and F1-measure were about 0.786, 0.576 and 0.771, respectively, suggesting the utility of the proposed classifier. In addition, the effectiveness of the classifier was elaborated by comparing it with other classifiers that were based on other classic machine learning algorithms. Conclusion: The proposed classifier was quite effective for prediction of EC number of enzymes and was specially designed for dealing with the problem addressed in this study by testing it on five datasets containing randomly produced samples.

Correlates of Rehabilitation Success Among African American and Caucasian SSDI Recipients with Mild Mental Retardation

2003 ◽  
Vol 34 (3) ◽  
pp. 25-32 ◽  
Author(s):  
Corey L. Moore ◽  
Reginald J. Alston ◽  
Chandra M. Donnell ◽  
Bridget Hollis
Keyword(s):  
Regression Analysis ◽  
African American ◽  
Mental Retardation ◽  
Cross Validation ◽  
Fiscal Year ◽  
Mild Mental Retardation ◽  
Research And Practice ◽  
Two Samples ◽  
Placement Services ◽  
Validation Research

The purpose of this study was to identify disparities in rehabilitation success (closure status 26) rates between Caucasian and African American SSDI recipients with mild mental retardation. A split-half cross-validation research design was utilized to evaluate two samples (i.e., screening and calibration) of case records obtained from the RSA-911 database for fiscal year 1998. Logistic regression analysis revealed that the odds of rehabilitation success for a Caucasian VR customer was almost two times the odds of rehabilitation success for an African American customer. Moreover, findings indicated that a significantly higher proportion of job placement services were provided to Caucasian SSDI recipients with mild mental retardation as opposed to African American SSDI recipients with mild mental retardation. Results are presented for closure status, and the implications of the findings for research and practice are discussed.

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