1,721,106 research outputs found
Seasonal climate prediction for the Australian sugar industry using data mining techniques
Get PDFThe ability to predict rainfall with adequate certainty and lead time is beneficial to both industry and public. Periods of high or low seasonal rainfall can have many follow on effects to agriculture, industry, public health and, water supply and management. In order to implement decisions, planning and management strategies to contend with these issues, the ability to predict seasonal rainfall quantities is of great importance (Klopper et al., 2006).
Climate conditions are known to influence the cultivation of Sugarcane influencing planting, harvesting and milling (Muchow and Wood, 1996; Everingham et al., 2002; Jones and
Everingham, 2005). Unforeseen climate events such as excessive rainfall, can adversely effect the agricultural practices related to Sugarcane cultivation. The Australian Sugarcane harvest period commences in May/June and aims to finish by November/December before the start of the rainy season (Everingham et al., 2002). The risk of excessive rainfall disrupting harvest operations is greatest towards the end of the sugarcane harvest period (Muchow and Wood, 1996; Everingham et al., 2002). Therefore, improved seasonal rainfall prediction during the October-December period is beneficial
A Novel Approach to Retrosynthetic Analysis Utilizing Knowledge Bases Derived from Reaction Databases
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Erratum to “In memoriam of Professor Shin-ichi Sasaki” [Chemometrics and Intelligent Laboratory Systems 51 (2000) 1]
No full textSoft Sensors: Chemoinformatic Model for Efficient Control and Operation in Chemical Plants
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Iterative Screening Methods for Identification of Chemical Compounds with Specific Values of Various Properties
No full textIdentification
of chemical compounds having desirable properties
is a central goal of screening campaigns. Iterative screening is a
means of surveying a set of compounds, during which their property
values are determined and used as feedback for regression models.
Quantitative models that assess the relationships between chemical
structures and property/activity are repeatedly updated through this
type of cycle, and the efficient sampling of compounds for the subsequent
test is a key factor in the early identification of target compounds.
Nevertheless, methodological approaches to comparisons and to establishing
the degree of extrapolation of sampled compounds, including the effects
of applicability domains, are still required. In the present study,
we conducted a series of virtual experiments to assess the characteristics
of different iterative screening methods. Genetic algorithm-based
partial least-squares regression, support vector regression, Bayesian
optimization with Gaussian Process (GP), and batch-based Bayesian
optimization with GP (GP_batch) were all compared, based on the analysis
of one million compounds extracted from the ZINC database. Our results
show that, irrespective of the diversity of the initial set of compounds,
it was possible to identify a compound having the desired property
value using the appropriate screening method. However, overall, the
GP_batch method was found to be preferable when evaluating properties
either which are difficult to predict or for which a key factor
is present in the set of molecular descriptors
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