scholarly journals Development of Gui for Ards Model Tool Using Matlab® Software

2020 ◽  
pp. 1-6
Author(s):  
Shaker Mahmoud Ebrahim ◽  
◽  
Bashayer M Abdullah ◽  
Keyword(s):  
Experimental Data ◽  
User Interface ◽  
Graphical User Interface ◽  
Carbon Residue ◽  
Research Center ◽  
Modeling Tool ◽  
Atmospheric Residue

An interface for the atmospheric residue desulfurization (ARD) modeling tool developed at petroleum research center (PRC). The modeling tool simulates and predicts the concentrations of sulfur, nickel, vanadium, asphaltene, and conradum carbon residue (CCR) versus time on stream. This tool for built in MATLAB®, requires experimental data for developed for its execution and produces a large number of figures as outputs. Using the developed interface data templates are populated in a friendly manner. In addition, the produced figures are presented in an organized format. The interface is built in the graphical user interface (GUI) of MATLAB.

scholarly journals Cyrface: An interface from Cytoscape to R that provides a user interface to R packages

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 192 ◽  
Author(s):  
Emanuel Gonçalves ◽  
Julio Saez-Rodriguez
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
User Interface ◽  
Graphical User Interface ◽  
Network Visualization ◽  
R Language ◽  
Software Packages ◽  
Graphical Interfaces ◽  
Basic Understanding ◽  
R Packages

There is an increasing number of software packages to analyse biological experimental data in the R environment. In particular, Bioconductor, a repository of curated R packages, is one of the most comprehensive resources for bioinformatics and biostatistics. The use of these packages is increasing, but it requires a basic understanding of the R language, as well as the syntax of the specific package used. The availability of user graphical interfaces for these packages would decrease the learning curve and broaden their application.   Here, we present a Cytoscape plug-in termed Cyrface that allows Cytoscape plug-ins to connect to any function and package developed in R. Cyrface can be used to run R packages from within the Cytoscape environment making use of a graphical user interface. Moreover, it links the R packages with the capabilities of Cytoscape and its plug-ins, in particular network visualization and analysis. Cyrface’s utility has been demonstrated for two Bioconductor packages (CellNOptR and DrugVsDisease), and here we further illustrate its usage by implementing a workflow of data analysis and visualization. Download links, installation instructions and user guides can be accessed from the Cyrface homepage (http://www.ebi.ac.uk/saezrodriguez/cyrface/).

scholarly journals Cyrface: An interface from Cytoscape to R that provides a user interface to R packages

F1000Research ◽  
2014 ◽  
Vol 2 ◽  
pp. 192
Author(s):  
Emanuel Gonçalves ◽  
Franz Mirlach ◽  
Julio Saez-Rodriguez
Keyword(s):  
Experimental Data ◽  
User Interface ◽  
Graphical User Interface ◽  
Network Visualization ◽  
R Language ◽  
Link Type ◽  
Software Packages ◽  
Graphical Interfaces ◽  
Basic Understanding ◽  
R Packages

There is an increasing number of software packages to analyse biological experimental data in the R environment. In particular, Bioconductor, a repository of curated R packages, is one of the most comprehensive resources for bioinformatics and biostatistics. The use of these packages is increasing, but it requires a basic understanding of the R language, as well as the syntax of the specific package used. The availability of user graphical interfaces for these packages would decrease the learning curve and broaden their application. Here, we present a Cytoscape app termed Cyrface that allows Cytoscape apps to connect to any function and package developed in R. Cyrface can be used to run R packages from within the Cytoscape environment making use of a graphical user interface. Moreover, it can link R packages with the capabilities of Cytoscape and its apps, in particular network visualization and analysis. Cyrface’s utility has been demonstrated for two Bioconductor packages (CellNOptR and DrugVsDisease), and here we further illustrate its usage by implementing a workflow of data analysis and visualization. Download links, installation instructions and user guides can be accessed from the Cyrface’s homepage (http://www.ebi.ac.uk/saezrodriguez/cyrface/) and from the Cytoscape app store (http://apps.cytoscape.org/apps/cyrface).

Granger Causality

2011 ◽  
pp. 511-532
Author(s):  
Tian Ge ◽  
Jianfeng Feng
Keyword(s):  
Experimental Data ◽  
User Interface ◽  
Reverse Engineering ◽  
Granger Causality ◽  
Graphical User Interface ◽  
Causal Model ◽  
Basis Pursuit ◽  
Sampled Data ◽  
Dynamic Causal Model ◽  
Real Experimental Data

As one of the most successful approaches to uncover complex network structures from experimental data, Granger causality has been widely applied to various reverse engineering problems. This chapter first reviews some current developments of Granger causality and then presents the graphical user interface (GUI) to facilitate the application. To make Granger causality more computationally feasible and satisfy biophysical constraints for dealing with increasingly large dynamical datasets, two attempts are introduced including the combination of Granger causality and Basis Pursuit when faced with non-uniformly sampled data and the unification of Granger causality and the Dynamic Causal Model as a novel Unified Causal Model (UCM) to bring in the notion of stimuli and modifying coupling. Several examples, both from toy models and real experimental data, are included to demonstrate the efficacy and power of the Granger causality approach.

Graphical User Interface With Reverse-Engineering of the Cyclic Voltammetry Method for Electrochemical Processes of Used Nuclear Fuel

2016 ◽  
Author(s):  
Samaneh Rakhshan Pouri ◽  
Supathorn Phongikaroon
Keyword(s):  
Experimental Data ◽  
Cyclic Voltammetry ◽  
User Interface ◽  
Reverse Engineering ◽  
Nuclear Fuel ◽  
Graphical User Interface ◽  
Time Interval ◽  
Data Sets ◽  
Time Duration ◽  
Used Nuclear Fuel

This work focuses on an interactive reverse-engineering program design for the cyclic voltammetry (CV) method to help elucidating, improving, and providing robustness in detection analysis in the absence of complete experimental data sets during an electrorefining process of used nuclear fuel reprocessing. The work has been implemented into a Graphical User Interface (GUI) of the commercial software MATLAB allowing an individual user to directly control and make adjustments to support material detection and accountability. Analyzing and reconstructing the CV plots for uranium (U) in a LiCl-KCl molten salt at 500°C under different scan rates and at 1, 2.5, 5, 7.5, and 10 wt% have been accomplished. These test values provide the current (amp) versus potential (V) and concentration of each species (mol/cm3) versus the operating time (s) graphs under different specified conditions. The computational code uses the electrochemical fundamentals coupling with various experimental values existing in the literature such as the diffusion coefficients, formal potentials, reversible/irreversible time duration for reverse engineering of the CV technique. The user needs to specify only the desired concentration of uranium and the scan rate. All other experimental data sets for each condition have been stored in the code and can be used to interpolate between the existence data. The developed routine can be used to detect the peaks at the reversible and irreversible parts despite deficiencies of experimental data in a very short run time (around one minute) with an adequate selected time interval of approximately 0.08 second. Results indicate that the model can trace the current versus potential graph with a low root-meant-square (RMS) error compared to the experimental reported in literature. The concentration of each species at the reversible and irreversible of anodic and cathodic sides can be calculated and are shown based on increasing time which provided a good view of the whole process.

HOW TO USE CROP GROWTH MODEL WOFOST FOR FORECASTING GROWTH AND YIELD OF A CROP

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
LAL SINGH ◽  
PARMEET SINGH ◽  
RAIHANA HABIB KANTH ◽  
PURUSHOTAM SINGH ◽  
SABIA AKHTER ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Growth And Yield ◽  
Weather Data ◽  
Data Sets ◽  
Crop Growth Model ◽  
Control Center ◽  
Field Crops ◽  
Crop Calendar

WOFOST version 7.1.3 is a computer model that simulates the growth and production of annual field crops. All the run options are operational through a graphical user interface named WOFOST Control Center version 1.8 (WCC). WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. The files with crop, soil and weather data are explained, as well as the run files and the output files. A general overview is given of the development and the applications of the model. Its underlying concepts are discussed briefly.

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