scholarly journals Independent validation testing of the FLAME computer code, Version 1.0

1992 ◽  
Author(s):  
P. Martian ◽  
J.N. Chung
Author(s):  
Tadas Kaliatka ◽  
Eugenijus Uspuras ◽  
Chris M. Allison

The most important accident management measure to terminate a severe accident transient in a light water reactor is the injection of water to cool the uncovered degraded core. Several experimental and analytical investigations have been made on the subject of zirconium oxidation in the steam and air environment during the last 15 years. The significant group of experiments are the experiments, performed in QUENCH facility. QUENCH-10 experiment was the first experiment from the all QUENCH experiment series with air ingress. In this article, the RELAP/SCDAPSIM mod 3.5 code version and implemented different oxidation correlations were used to analyze the air ingress experiment QUENCH-10 with the purpose to perform the code and model assessment. The results of analysis will be used in the future computer code improvement and to increase the competence of code users.


2021 ◽  
Author(s):  
Doaa Hassan ◽  
Daniel Acevedo ◽  
Swapna Vidhur Daulatabad ◽  
Quoseena Mir ◽  
Sarath Chandra Janga

AbstractPseudouridine is one of the most abundant RNA modifications, occurring when uridines are catalyzed by Pseudouridine synthase proteins. It plays an important role in many biological processes and also has an importance in drug development. Recently, the single-molecule sequencing techniques such as the direct RNA sequencing platform offered by Oxford Nanopore technologies enable direct detection of RNA modifications on the molecule that is being sequenced, but to our knowledge this technology has not been used to identify RNA Pseudouridine sites. To this end, in this paper, we address this limitation by introducing a tool called Penguin that integrates several developed machine learning (ML) models (i.e., predictors) to identify RNA Pseudouridine sites in Nanopore direct RNA sequencing reads. Penguin extracts a set of features from the raw signal measured by the Oxford Nanopore and the corresponding basecalled k-mer. Those features are used to train the predictors included in Penguin, which in turn, is able to predict whether the signal is modified by the presence of Pseudouridine sites. We have included various predictors in Penguin including Support vector machine (SVM), Random Forest (RF), and Neural network (NN). The results on the two benchmark data sets show that Penguin is able to identify Pseudouridine sites with a high accuracy of 93.38% and 92.61% using SVM in random split testing and independent validation testing respectively. Thus, Penguin outperforms the existing Pseudouridine predictors in the literature that achieved an accuracy of 76.0 at most with an independent validation testing. A GitHub of the tool is accessible at https://github.com/Janga-Lab/Penguin.


Author(s):  
Harold L. Cole

We a real business cycle model with money and show how to compute the equilibrium outcomes using linearization methods. We illustrate the quantitative implications of our model by developing a Dynare computer code version of the model.


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