scholarly journals Optimal prediction in isotropic spatial process under spherical type variogram model with application to corn plant data

2021 ◽  
Vol 1940 (1) ◽  
pp. 012003
Author(s):  
W Somayasa ◽  
D K Sutiari ◽  
W Sutisna
2019 ◽  
Vol 1341 ◽  
pp. 062029
Author(s):  
W Somayasa ◽  
G A Wibawa ◽  
Ruslan ◽  
D K Sutiari

1952 ◽  
Vol 44 (3) ◽  
pp. 449-449
Author(s):  
C DeWitt ◽  
M Livingood ◽  
K Miller
Keyword(s):  

Author(s):  
Agung Riyadi

The One of many way to connect to the database through the android application is using volleyball and RESTAPI. By using RestAPI, the android application does not directly connect to the database but there is an intermediary in the form of an API. In android development, Android-volley has the disadvantage of making requests from large and large data, so an evaluation is needed to test the capabilities of the Android volley. This research was conducted to test android-volley to retrieve data through RESTAPI presented in the form of an application to retrieve medicinal plant data. From the test results can be used by volley an error occurs when the back button is pressed, in this case another process is carried out if the previous volley has not been loaded. This error occurred on several android versions such as lollipops and marshmallows also on some brands of devices. So that in using android-volley developer need to check the request queue process that is carried out by the user, if the data retrieval process by volley has not been completed, it is necessary to stop the process to download data using volley so that there is no Android Not Responding (ANR) error.Keywords: Android, Volley, WP REST API, ANR Error


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel E. Runcie ◽  
Jiayi Qu ◽  
Hao Cheng ◽  
Lorin Crawford

AbstractLarge-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present , a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.


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