Here are the data and the codes used in Hiramoto&Cline (2020). Imaris image data, Matlab code, and data in mat or fig (with metadata) files are provided. These Matlab codes require a statistic toolbox.
The data validates several results published in the study titled "A comparative study of the effect of random and preferred crystallographic orientations on dynamic recrystallization behavior using a cellular automata model" in Materials Today: Communications (https://doi.org/10.1016/j.mtcomm.2020.101200). The description of the files is:
1. Orientation files.zip has the orientation files to reproduce Fig. 15 onwards in the manuscript.
2. The excel file is the misorientation data for Fig. 11 in the manuscript.
3. The remaining two .txt files are for reproducing Fig. 4 in the manuscript.
The database is based on eight common tomato pests, including (1) Tetranychus urticae, (2) Bemisia argentifolii, (3) Zeugodacus cucurbitae, (4) Thrips palmi, (5) Myzus persicae, (6) Spodoptera litura, (7) Spodoptera exigua, and (8) Helicoverpa armigera.
The original images were collected from IPMImages database (https://www.ipmimages.org/index.cfm), National Bureau of Agricultural Insect Resources (NBAIR) (https://www.nbair.res.in /Databases/insectpests/index.php) and Google search. The image database contains 609 original images in 8 categories, and is amplified using image enhancement technology to have a total of 4263 images after enhancement. Image enhancement technologies include 90 degree rotation, 180 degree rotation, 270 degree rotation, horizontal flip, vertical flip and crop. Finally, the image size is unified in 299*299 and the image format is in .JPG file.
This database is divided into two datasets for tomato leaf images according to different image sources. The tomato leaf images of the first dataset are selected from the PlantVillage database with ten categories (nine disease categories and one health). Each image is composed of a single leaf and a single background, for a total of 14,531 images. After combining the original tomato leaf images and deleting unnecessary categories, we then adjusted the image size from 256 * 256 to 227 * 227. Afterwards, this database is divided into five subsets of 5-fold cross-validation. The detailed categories of the first dataset are:
(1) Bacterial spot,
(2) Early blight,
(4) Late blight,
(5) Leaf Mold,
(6) Septoria leaf spot,
(7) Target Spot,
(8) Tomato mosaic virus,
(9) Tomato yellow leaf curl virus,
(10) Two-spotted spider mite
The second dataset is images of Taiwan tomato leaves, with six categories (five disease categories and one health). It consists of a single leaf, multiple leaves, a single background and a complex background. We have 622 original images. The size of the picture is different, and we unified the image size to 227 * 227. Then we use data augmentation method to increase the number of pictures, including clockwise rotation with 90 degrees, 180 degrees, and 270 degrees; horizontal mirroring, vertical mirroring, reducing image brightness and increasing image brightness, etc. There are 4,976 images after data enhancement. The detailed categories of the second dataset are:
(1) Bacterial spot,
(2) Black leaf mold,
(3) Gray leaf spot,
(5) Late blight,
(6) Powdery mildew
The files in this record contain data for cloud-based multi-dimensional parallel dynamic programming algorithm for a hydropower station system
The files consist of:
cascade reservoir system data;
Source code and results of the parallel dynamic programming algorithm program on the physical machine;
Source code and results of the parallel dynamic programming algorithm program on the cloud virtual machine;