Physical and chemical properties of edamame during bean development and application of spectroscopy-based machine learning methods to predict optimal harvest time

2021 ◽  
pp. 130799
Author(s):  
Dajun Yu ◽  
Nick Lord ◽  
Justin Polk ◽  
Kshitiz Dhakal ◽  
Song Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Properties ◽  
Physical And Chemical Properties ◽  
Harvest Time ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Optimal Harvest ◽  
Physical And Chemical ◽  
And Chemical Properties

PTML Multi-Label Algorithms: Models, Software, and Applications

2020 ◽  
Vol 20 (25) ◽  
pp. 2326-2337
Author(s):  
Bernabe Ortega-Tenezaca ◽  
Viviana Quevedo-Tumailli ◽  
Harbil Bediaga ◽  
Jon Collados ◽  
Sonia Arrasate ◽  
...  
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
Chemical Properties ◽  
Material Design ◽  
Biological Data ◽  
Physical And Chemical Properties ◽  
Computational Framework ◽  
Multiple Input ◽  
Physical And Chemical ◽  
And Chemical Properties

By combining Machine Learning (ML) methods with Perturbation Theory (PT), it is possible to develop predictive models for a variety of response targets. Such combination often known as Perturbation Theory Machine Learning (PTML) modeling comprises a set of techniques that can handle various physical, and chemical properties of different organisms, complex biological or material systems under multiple input conditions. In so doing, these techniques effectively integrate a manifold of diverse chemical and biological data into a single computational framework that can then be applied for screening lead chemicals as well as to find clues for improving the targeted response(s). PTML models have thus been extremely helpful in drug or material design efforts and found to be predictive and applicable across a broad space of systems. After a brief outline of the applied methodology, this work reviews the different uses of PTML in Medicinal Chemistry, as well as in other applications. Finally, we cover the development of software available nowadays for setting up PTML models from large datasets.

Statistical criteria of stellar population types

1966 ◽  
Vol 24 ◽  
pp. 101-110
Author(s):  
W. Iwanowska
Keyword(s):  
Stellar Population ◽  
Chemical Properties ◽  
Spectrophotometric Study ◽  
Physical And Chemical Properties ◽  
Population Type ◽  
The Galaxy ◽  
Distribution Parameters ◽  
Physical And Chemical ◽  
Statistical Criteria ◽  
And Chemical Properties

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.

Response of corn (Zea mays L.) to various organic-based fertilizers in marginal upland of Sta. Rita, Samar

2017 ◽  
pp. 31-43
Author(s):  
Berta Ratilla ◽  
Loreme Cagande ◽  
Othello Capuno
Keyword(s):  
Block Design ◽  
Management Strategies ◽  
Chemical Properties ◽  
Organic Fertilizers ◽  
Growth And Yield ◽  
Physical And Chemical Properties ◽  
Total N ◽  
Randomized Complete Block Design ◽  
Physical And Chemical ◽  
And Chemical Properties

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.

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