As part of preparedness efforts in dealing with this, it is important for Indonesia to prepare guidelines for preparedness in dealing with COVID-19.

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This guideline is intended for health workers as a reference in preparing for COVID-19. This guideline is provisional because it has been prepared by adopting WHO interim guidelines so that it will be updated in accordance with disease developments and the current situation.

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The dataset is mainly used for leak detection and localization.

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As developers create or analyze an application,they often want to visualize the code through some graphical notation that aids their understanding of the code’s structure or behavior. In order to do this, we develop a integrated debugger.The debugger first record the walkthrough of application as assembly instructions by dynamic way.Then compression mapping block transforms previous outcome into three-dimensional-linked list structure,which then transformed into tree structure by the improved suffix tree algorithm.

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The development of electronic nose (e-nose) for a rapid, simple, and low-cost meat assessment system becomes the concern of researchers in recent years. Hence, we provide time-series datasets that were recorded from e-nose for beef quality monitoring experiment. This dataset is originated from 12 type of beef cuts including round (shank), top sirloin, tenderloin, flap meat (flank), striploin (shortloin), brisket, clod/chuck, skirt meat (plate), inside/outside, rib eye, shin, and fat.

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The presented database contains thermal images (thermograms) of the plantar region. The database was obtained from 122 subjects with a diabetes diagnosis (DM group) and 45 non-diabetic subjects (control group). The relevance of this database consists in to study how the temperature is distributed in the plantar region of both groups and how their differences can be measured. Previous reports in the literature have established that an increase in the plantar temperature is associated with a higher ulceration risk.

Instructions: 

The files are organized in folders with unique nomenclature: two letters to indicate the group (CG for the control group and DM for the diabetic group), three digits to number the folder and the last letter indicating the subject gender (male (M) or female (F)). In each folder, thermograms of the left and right foot (*.png) are provided separately following the same name of the folder plus a letter indicating L (left) or R (right) foot, e. g. CG001_F_L.png. RGB thermograms are only illustrative since these do not contain direct temperature information but they provide a thermal map by using a false-color palette. For obtaining the temperature value associated with each pixel refer to the corresponding *.csv file (e. g. CG001_F_L.csv).  You can graph the *.csv file as an image and use any other color palette for visualizing the temperature map. The plantar analysis made in the associated work (same name of the database in IEEE Access Journal) and in other associated works (refer to the corresponding author link) have used the angiosome division of the plantar region. Then in each folder, there is a subfolder containing four images (*.png and *.csv) that correspond to the four plantar angiosomes of each foot. The same nomenclature is used with the inclusion of three letters at the end, indicating the angiosome LCA, LPA, MCA and MPA (e. g. CG001_F_L_MPA.csv) [1-2].  For each subject, the database contains 20 files, (10 *.png  images and 10 *.csv files), for a total of 1670 RGB images and 1670 temperature files. The database is expected to provide a valuable source to increase research about the potential of infrared thermography for the early diagnosis of diabetic foot problems [3-4], allowing the development of more powerful techniques. The script generate_thermogram.m (MATLAB) is provided for generating a 3D visualization of the data. Some related works are listed below:

 

[1] Peregrina-Barreto, H., Morales-Hernandez, L. A., Rangel-Magdaleno, J. J., Avina-Cervantes, J. G., Ramirez-Cortes, J. M., & Morales-Caporal, R. (2014). Quantitative estimation of temperature variations in plantar angiosomes: a study case for diabetic foot. Computational and mathematical methods in medicine2014.

[2] Hernandez-Contreras, D., Peregrina-Barreto, H., Rangel-Magdaleno, J., Gonzalez-Bernal, J. A., & Altamirano-Robles, L. (2017). A quantitative index for classification of plantar thermal changes in the diabetic foot. Infrared Physics & Technology81, 242-249.

[3] Hernandez-Contreras, D., Peregrina-Barreto, H., Rangel-Magdaleno, J., & Gonzalez-Bernal, J. (2016). Narrative review: Diabetic foot and infrared thermography. Infrared Physics & Technology78, 105-117.

[4] Hernandez-Contreras, D. A., Peregrina-Barreto, H., Rangel-Magdaleno, J. D. J., & Orihuela-Espina, F. (2019). Statistical Approximation of Plantar Temperature Distribution on Diabetic Subjects Based on Beta Mixture Model. IEEE Access7, 28383-28391.

 

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