Application of multivariate regression and artificial neural network modelling for prediction of physical and chemical properties of medicinal plants aqueous extracts

Author(s):  
Ana JURINJAK TUŠEK ◽  
Tamara JURINA ◽  
Maja BENKOVIĆ ◽  
Davor VALINGER ◽  
Ana BELŠČAK-CVITANOVIĆ ◽  
...  
2014 ◽  
Vol 488-489 ◽  
pp. 685-688
Author(s):  
Yu Xue Sun ◽  
Yuan Long Xiang ◽  
Ze Hua Wang ◽  
Li Ming Zheng

Influence factors of sidewall stability contain mechanical factors and chemical factors. Among them, the chemical factor is the internal cause of the sidewall instability. Based on the collection of the influence factors of physical and chemical properties of the shale, we decided the main factors that influence the stability through the simple correlation analysis and applied a kind of quantum neural network based on the multilayer excitation function. We established the quantum neural network forecast model of shale physical and chemical properties that can effectively improve the network convergence speed and the accuracy of prediction. The quantum neural network prediction model analysis show that human factors interference of this method is small, and the system parameters needed less, wide application, the result is reliable, can effectively reflect the shale physical and chemical properties and fabric characteristics, to provide a reliable basis for preventing sidewall instability.


1966 ◽  
Vol 24 ◽  
pp. 101-110
Author(s):  
W. Iwanowska

In connection with the spectrophotometric study of population-type characteristics of various kinds of stars, a statistical analysis of kinematical and distribution parameters of the same stars is performed at the Toruń Observatory. This has a twofold purpose: first, to provide a practical guide in selecting stars for observing programmes, second, to contribute to the understanding of relations existing between the physical and chemical properties of stars and their kinematics and distribution in the Galaxy.


2017 ◽  
pp. 31-43
Author(s):  
Berta Ratilla ◽  
Loreme Cagande ◽  
Othello Capuno

Organic farming is one of the management strategies that improve productivity of marginal uplands. The study aimed to: (1) evaluate effects of various organic-based fertilizers on the growth and yield of corn; (2) determine the appropriate combination for optimum yield; and (3) assess changes on the soil physical and chemical properties. Experiment was laid out in Randomized Complete Block Design, with 3 replications and 7 treatments, namely; T0=(0-0-0); T1=1t ha-1 Evans + 45-30-30kg N, P2O5, K2O ha-1; T2=t ha-1 Wellgrow + 45-30-30kg N, P2O5, K2O ha-1; T3=15t ha-1 chicken dung; T4=10t ha-1 chicken dung + 45-30-30kg N, P2O5, K2O ha-1; T5=15t ha-1 Vermicast; and T6=10t ha-1 Vermicast + 45-30-30kg N, P2O5, K2O ha-1. Application of organic-based fertilizers with or without inorganic fertilizers promoted growth of corn than the control. But due to high infestation of corn silk beetle(Monolepta bifasciata Horns), its grain yield was greatly affected. In the second cropping, except for Evans, any of these fertilizers applied alone or combined with 45-30-30kg N, P2O5, K2O ha-1 appeared appropriate in increasing corn earyield. Soil physical and chemical properties changed with addition of organic fertilizers. While bulk density decreased irrespective of treatments, pH, total N, available P and exchangeable K generally increased more with chicken dung application.


Sign in / Sign up

Export Citation Format

Share Document