Elastic model-based segmentation of 3-D neuroradiological data sets

1999 ◽  
Vol 18 (10) ◽  
pp. 828-839 ◽  
Author(s):  
A. Kelemen ◽  
G. Szekely ◽  
G. Gerig
Keyword(s):  
Elastic Model ◽  
Data Sets ◽  
Model Based

scholarly journals Predicting Damage to Hop Cones by Tetranychus urticae (Acari: Tetranychidae)

2021 ◽  
Author(s):  
Joanna L Woods ◽  
Anne E Iskra ◽  
David H Gent
Keyword(s):  
Risk Factors ◽  
Tetranychus Urticae ◽  
Crop Damage ◽  
Equation Modeling ◽  
Data Sets ◽  
Current Management ◽  
Late Season ◽  
Model Based ◽  
Predatory Arthropods ◽  
Cone Damage

Abstract Twospotted spider mite (Tetranychus urticae Koch) is a cosmopolitan pest of numerous plants, including hop (Humulus lupulus L.). The most costly damage from the pest on hop results from infestation of cones, which are the harvested product, which can render crops unsalable if cones become discolored. We analyzed 14 yr of historical data from 312 individual experimental plots in western Oregon to identify risk factors associated with visual damage to hop cones from T. urticae. Logistic regression models were fit to estimate the probability of cone damage. The most predictive model was based on T. urticae-days during mid-July to harvest, which correctly predicted occurrence and nonoccurrence of cone damage in 91 and 93% of data sets, respectively, based on Youden’s index. A second model based on the ratio of T. urticae to predatory arthropods late in the season correctly predicted cone damage in 92% of data sets and nonoccurrence of damage in 77% of data sets. The model based on T. urticae abundance performed similarly when validated in 23 commercial hop yards, whereas the model based on the predator:prey ratio was relatively conservative and yielded false-positive predictions in 11 of the 23 yards. Antecedents of these risk factors were explored and quantified by structural equation modeling. A simple path diagram was constructed that conceptualizes T. urticae invasion of hop cones as dependent on prior density of the pest on leaves in early spring and summer, which in turn influences the development of predatory arthropods that mediate late-season density of the pest. In summary, the biological insights and models developed here provide guidance to pest managers on the likelihood of visual cone damage from T. urticae that can inform late-season management based on both abundance of the pest and its important predators. This is critically important because a formal economic threshold for T. urticae on hop does not exist and current management efforts may be mistimed to influence the pest when crop damage is most probable. More broadly, this research suggests that current management practices that target T. urticae early in the season may in fact predispose yards to later outbreaks of the pest.

QSAR model based on weighted MCS trees approach for the representation of molecule data sets

2013 ◽  
Vol 27 (2) ◽  
pp. 185-201 ◽  
Author(s):  
Bernardo Palacios-Bejarano ◽  
Gonzalo Cerruela García ◽  
Irene Luque Ruiz ◽  
Miguel Ángel Gómez-Nieto
Keyword(s):  
Qsar Model ◽  
Data Sets ◽  
Model Based

Using Conditional Restricted Boltzmann Machines to Model Complex Consumer Shopping Patterns

2019 ◽  
Vol 38 (4) ◽  
pp. 711-727 ◽  
Author(s):  
Feihong Xia ◽  
Rabikar Chatterjee ◽  
Jerrold H. May
Keyword(s):  
Data Sets ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Model Based ◽  
Model Complex ◽  
Purchase Patterns

We develop and apply a model based on conditional restricted Boltzmann machines to analyze intertemporal crossproduct purchase patterns in enormous consumer purchase data sets.

A risky asset model with strong dependence through fractal activity time

1999 ◽  
Vol 36 (04) ◽  
pp. 1234-1239 ◽  
Author(s):  
C. C. Heyde
Keyword(s):  
Brownian Motion ◽  
Strong Dependence ◽  
Geometric Brownian Motion ◽  
Risky Asset ◽  
Data Sets ◽  
Activity Time ◽  
Scaling Properties ◽  
Model Based ◽  
Black Scholes Model ◽  
Black Scholes

The geometric Brownian motion (Black–Scholes) model for the price of a risky asset stipulates that the log returns are i.i.d. Gaussian. However, typical log returns data shows a leptokurtic distribution (much higher peak and heavier tails than the Gaussian) as well as evidence of strong dependence. In this paper a subordinator model based on fractal activity time is proposed which simply explains these observed features in the data, and whose scaling properties check out well on various data sets.

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