midDRIFTS-based partial least square regression analysis allows predicting microbial biomass, enzyme activities and 16S rRNA gene abundance in soils of temperate grasslands

2013 ◽  
Vol 57 ◽  
pp. 504-512 ◽  
Author(s):  
Frank Rasche ◽  
Sven Marhan ◽  
Doreen Berner ◽  
Daniel Keil ◽  
Ellen Kandeler ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Microbial Biomass ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Rrna Gene ◽  
Temperate Grasslands ◽  
Least Square Regression

Social Media Use and Job Performance

2015 ◽  
Vol 5 (2) ◽  
pp. 59-74 ◽  
Author(s):  
Peerayuth Charoensukmongkol
Keyword(s):  
Social Media ◽  
Regression Analysis ◽  
Job Performance ◽  
Media Use ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Media Access ◽  
Social Media Use ◽  
Least Square Regression

The objective of this research is to examine the conditions that make social media use at work yield higher benefit to employee job performance. Survey data were collected from 211 employees in Thailand. Results from partial least square regression analysis show that although the intensity of social media use at work positively affects job performance, the benefit is significantly higher when (1) employees encounter high job demands, (2) social media access is allowed in the workplace, and (3) social media are accessed mostly from a personal computer instead of from mobile devices. These findings suggest some implications regarding the workplace policy on social media access during work.

Modeling CO2 exchange and meteorological factors of an apple orchard using partial least square regression

2020 ◽  
Vol 27 (35) ◽  
pp. 43439-43451 ◽  
Author(s):  
Jianfeng Yang ◽  
Yumin Duan ◽  
Xiaoni Yang ◽  
Mukesh Kumar Awasthi ◽  
Huike Li ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Co2 Exchange ◽  
Apple Orchard ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression

QSPR MODELLING OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES USING MULTIVARIATE REGRESSION METHODS AND ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 36 (06) ◽  
Author(s):  
NGUYEN MINH QUANG ◽  
TRAN NGUYEN MINH AN ◽  
NGUYEN HOANG MINH ◽  
TRAN XUAN MAU ◽  
PHAM VAN TAT
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stability Constants ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Structure Property ◽  
Least Square Regression ◽  
Thiosemicarbazone Complexes ◽  
Artificial Neural

In this study, the stability constants of metal-thiosemicarbazone complexes, logb11 were determined by using the quantitative structure property relationship (QSPR) models. The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The QSPR models were built by using the ordinary least square regression (QSPROLS), partial least square regression (QSPRPLS), primary component regression (QSPRPCR) and artificial neural network (QSPRANN). The best linear model QSPROLS (with k of 9) involves descriptors C5, xp9, electric energy, cosmo volume, N4, SsssN, cosmo area, xp10 and core-core repulsion. The QSPRPLS, QSPR PCR and QSPRANN models were developed basing on 9 varibles of the QSPROLS model. The quality of the QSPR models were validated by the statistical values; The QSPROLS: R2train = 0.944, Q2LOO = 0.903 and MSE = 1.035; The QSPRPLS: R2train = 0.929, R2CV = 0.938 and MSE = 1.115; The QSPRPCR: R2train = 0.934, R2CV = 0.9485 and MSE = 1.147. The neural network model QSPRANN with architecture I(9)-HL(12)-O(1) was presented also with the statistical values: R2train = 0.9723, and R2CV = 0.9731. The QSPR models also were evaluated externally and got good performance results with those from the experimental literature.

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