scholarly journals Discussion on Criterion of Determination of the Kinetic Parameters of the Linear Heating Reactions

Minerals ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 81
Author(s):  
Kui Li ◽  
Wei Zhang ◽  
Menglong Fu ◽  
Chengzhi Li ◽  
Zhengliang Xue
Keyword(s):  
Kinetic Parameters ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Linear Correlation ◽  
Euler Method ◽  
Kinetic Process ◽  
Mean Square ◽  
Heating Rates ◽  
Linear Heating ◽  
The Mean

Generally, the linear correlation coefficient is one of the most significant criteria to appraise the kinetic parameters computed from different reaction models. Actually, the optimal kinetic triplet should meet the following two requirements: first, it can be used to reproduce the original kinetic process; second, it can be applied to predict the other kinetic process. The aim of this paper is to attempt to prove that the common criteria are insufficient for meeting the above two purposes simultaneously. In this paper, the explicit Euler method and Taylor expansion are presented to numerically predict the kinetic process of linear heating reactions. The mean square error is introduced to assess the prediction results. The kinetic processes of hematite reduced to iron at different heating rates (8, 10 and 18 K/min) are utilized for validation and evaluation. The predicted results of the reduction of Fe2O3 → Fe3O4 indicated that the inferior linear correlation coefficient did provide better kinetic predicted curves. In conclusion, to satisfy the above two requirements of reproduction and prediction, the correlation coefficient is an insufficient criterion. In order to overcome this drawback, two kinds of numerical prediction methods are introduced, and the mean square error of the prediction is suggested as a superior criterion for evaluation.

QSAR Model Study of 2,3,4,5-tetrahydro-1H-pyrido[4,3-b]indole of Cystic-brosis-transmembrane Conductance-regulator Gene Potentiators

2021 ◽  
Vol 18 ◽  
Author(s):  
Yaru Si ◽  
Kang Ma ◽  
Yingfeng Hu ◽  
Hongzong Si ◽  
Honglin Zhai
Keyword(s):  
Gene Expression ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Gene Expression Programming ◽  
Heuristic Method ◽  
Transmembrane Conductance Regulator ◽  
Mean Square ◽  
Regulator Gene ◽  
Transmembrane Conductance ◽  
The Mean

Background: Cystic fibrosis (CF) is a genetic disease, which has no effective treatment. Objective: The aim of this study is to predict the EC50 value of 2,3,4,5-tetrahydro-1H-pyrido[4,3-b]indole core as a novel chemotype of potentiators to establish a highly predicting quantitative structure-activity relationship model. Methods: 41 products were optimized, and a linear model was built by a heuristic method in CODESSA program. In this study, 3 descriptors were selected and utilized to build a nonlinear model in gene expression programming. Results: The square of the correlation coefficient of the heuristic method is 0.57, and the s2 is 0.30. In gene expression programming, the square of correlation coefficient and the mean square error for the training set are 0.74 and 0.13, respectively. The square of correlation coefficient and the mean square error for the test set are 0.70 and 0.27, respectively. Conclusion: The GEP model has stronger predictive ability to help develop the novel structure of 2,3,4,5-tetrahydro-1H-pyrido[4,3-b]indole of cystic-brosis-transmembrane conductance-regulator gene potentiators.

Prediction of the Crystallinity of White Pine Using near Infrared Spectroscopy

2011 ◽  
Vol 183-185 ◽  
pp. 1215-1218
Author(s):  
Zhi Hua Qu ◽  
Li Hai Wang
Keyword(s):  
Infrared Spectroscopy ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Mean Square ◽  
White Pine ◽  
Pine Wood ◽  
The Mean

The crystallinity of wood is an important property of wood materials, it has an important effect on the physical, mechanical and chemical properties of cellulose fibers such as MOR, density, hardness increase, alpha-cellulose content, dimensional stability, moisture regain and dye sorption, chemical reactivity etc. The aims of this study were to investigate the ability of near infrared spectroscopy (NIR) to predict the crystallinity of white pine wood and the effect of spectra pretreatment on the prediction of crystallinity using NIR. Spectra were collected from wood powder a slowly rotating turntable and the crystallinity of wood was determined by X-ray diffractmeter (XRD) in this experiment. The results showed that NIR coupled with partial least square (PLS) method could be correlated with the crystallinity of white pine wood, and the ability of NIR prediction based on first derivative spectra was better than based on raw spectra or second derivative pretreated spectra. There was a significant correlation between NIR spectra and XRD determined crystallinity. The correlation coefficient for calibration (RC) was 0.932; the mean square error of calibration (RMSEC) was 0.022; the correlation coefficient for validation (RV) was 0.911; the mean square error of calibration (RMSEV) was 0.023. It was proved that NIR can rapidly and accurately predict white pine wood crystallinity.

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

Limit tolerances for sums of measurement errors

2018 ◽  
Vol 934 (4) ◽  
pp. 59-62
Author(s):  
V.I. Salnikov
Keyword(s):  
Normal Distribution ◽  
Mean Square Error ◽  
Gaussian Distribution ◽  
Measurement Errors ◽  
Theory And Practice ◽  
Mean Square ◽  
The Mean ◽  
Limiting Values ◽  
Limiting Value

The question of calculating the limiting values of residuals in geodesic constructions is considered in the case when the limiting value for measurement errors is assumed equal to 3m, ie ∆рred = 3m, where m is the mean square error of the measurement. Larger errors are rejected. At present, the limiting value for the residual is calculated by the formula 3m√n, where n is the number of measurements. The article draws attention to two contradictions between theory and practice arising from the use of this formula. First, the formula is derived from the classical law of the normal Gaussian distribution, and it is applied to the truncated law of the normal distribution. And, secondly, as shown in [1], when ∆рred = 2m, the sums of errors naturally take the value equal to ?pred, after which the number of errors in the sum starts anew. This article establishes its validity for ∆рred = 3m. A table of comparative values of the tolerances valid and recommended for more stringent ones is given. The article gives a graph of applied and recommended tolerances for ∆рred = 3m.

scholarly journals IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1631
Author(s):  
Bruno Guilherme Martini ◽  
Gilson Augusto Helfer ◽  
Jorge Luis Victória Barbosa ◽  
Regina Célia Espinosa Modolo ◽  
Marcio Rosa da Silva ◽  
...  
Keyword(s):  
Technology Acceptance ◽  
Mean Square Error ◽  
Ease Of Use ◽  
Coefficient Of Determination ◽  
Square Root ◽  
Mean Square ◽  
Use Context ◽  
The Mean ◽  
Acceptance Model ◽  
The Internet Of Things

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

Burst Erasures and the Mean-Square Error for Cyclic Parseval Frames

2011 ◽  
Vol 57 (7) ◽  
pp. 4622-4635 ◽  
Author(s):  
Bernhard G. Bodmann ◽  
Pankaj K. Singh
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Parseval Frames ◽  
The Mean

A MODIFIED RATIO TYPE ESTIMATOR OF FINITE POPULATION MEAN UNDER STRATIFIED RANDOM SAMPLING SCHEME

2021 ◽  
pp. 58-60
Author(s):  
Naziru Fadisanku Haruna ◽  
Ran Vijay Kumar Singh ◽  
Samsudeen Dahiru
Keyword(s):  
Mean Square Error ◽  
Random Sampling ◽  
Minimum Mean Square Error ◽  
Real Life ◽  
Population Data ◽  
Auxiliary Variable ◽  
Mean Square ◽  
Data Set ◽  
Population Mean ◽  
The Mean

In This paper a modied ratio-type estimator for nite population mean under stratied random sampling using single auxiliary variable has been proposed. The expression for mean square error and bias of the proposed estimator are derived up to the rst order of approximation. The expression for minimum mean square error of proposed estimator is also obtained. The mean square error the proposed estimator is compared with other existing estimators theoretically and condition are obtained under which proposed estimator performed better. A real life population data set has been considered to compare the efciency of the proposed estimator numerically.

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