scholarly journals A Look-Ahead Simulation Algorithm for DBN Models of Biochemical Pathways

Author(s):  
Sucheendra K. Palaniappan ◽  
Matthieu Pichené ◽  
Grégory Batt ◽  
Eric Fabre ◽  
Blaise Genest
2016 ◽  
Vol 15 (1) ◽  
pp. 1-3
Author(s):  
Bernd Marcus
Keyword(s):  

2019 ◽  
Vol 6 (1) ◽  
pp. 44-49
Author(s):  
Tania Muñoz Jiménez ◽  
Aurora Torres Soto ◽  
María Dolores Torres Soto

En este documento se describe el desarrollo e implementación de un modelo para simular computacionalmente la dinámica del crecimiento y migración del cáncer cervicouterino, considerando sus principales características: proliferación, migración y necrosis, así como sus etapas de desarrollo. El modelo se desarrolló mediante un autómata celular con enfoques paralelo y secuencial. El autómata celular se basó en el modelo de Gompertz para simular las etapas de desarrollo de este cáncer, el cual se dividió en tres etapas cada una con diferentes comportamientos durante la simulación. Se realizó un diseño experimental con parámetros de entrada que se seleccionaron a partir de la investigación literaria y su discusión con médicos expertos. Al final del proceso de investigación, se logró obtener un algoritmo computacional de simulación muy bueno comparado con el modelo médico de Gompertz y se encontraron los mejores parámetros para su ejecución mediante un diseño factorial soportado estadísticamente. This paper describes the development and implementation of a model to computationally simulate the growth and migration dynamics of cervical cancer, considering its main characteristics: proliferation, migration and necrosis, as well as its stages of development. The model was developed by means of a cellular automaton with parallel and sequential approaches. The cellular automaton was based on the model of Gompertz to simulate the stages of development of this cancer, which was divided into three stages, each with different behaviors during the simulation. An experimental design was carried out with input parameters that were selected from literary research and its discussion with expert physicians. At the end of the research process, a very good simulation algorithm was obtained compared to the Gompertz medical model and the best parameters for its execution were found by means of a statistically supported factorial design.


1943 ◽  
Vol 12 (23) ◽  
pp. 230-231
Author(s):  
Guenther Stein
Keyword(s):  

2016 ◽  
Vol 136 (10) ◽  
pp. 692-697
Author(s):  
Shuto Higa ◽  
Chikatoshi Yamada ◽  
Kei Miyagi ◽  
Shuichi Ichikawa

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 170-OR
Author(s):  
JINGYI QIAN ◽  
MICHAEL P. WALKUP ◽  
SHYH-HUEI CHEN ◽  
PETER H. BRUBAKER ◽  
DALE BOND ◽  
...  

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2007-P
Author(s):  
RENA R. WING ◽  
JEANNE CLARK ◽  
MARK ESPELAND ◽  
JAMES O. HILL ◽  
ROBERT W. JEFFERY ◽  
...  

Author(s):  
Beny Septian Panjaitan And Rahmad Husein

This study aimed at analyzing the cognitive dimension based on Revised BloomTaxonomy in reading questions in Look Ahead an English Course for Senior HighSchool Level 1, 2, & 3. This study used quantitative research design. The sampleswere 141 reading questions which taken by using random sampling technique byusing Statistical Program for Social Science (SPSS) version 20.0. in Look Aheadan English Course for Senior High School Level 1, 2, & 3. The data were analyzedby using Table analysis of cognitive dimension of Revised Bloom Taxonomy. Theanalysis showed that the most dominant cognitive dimension of Revised BloomTaxonomy in remembering dimension (57.45%). The second dominant cognitivedimension is understanding dimension (26.24%). The third dominant cognitivedimension is evaluating dimension (10.64%). The fourth dominant cognitivedimension is creating dimension (3.55%). The fifth dominant cognitive dimension isanalyzing dimension (2.13%). There was no cognitive dimension of applyingdimension that applied in reading question of the textbooks.


2019 ◽  
Vol 118 (7) ◽  
pp. 73-76
Author(s):  
Sharanabasappa ◽  
P Ravibabu

Nowadays, during the process of Image acquisition and transmission, image information data can be corrupted by impulse noise. That noise is classified as salt and pepper noise and random impulse noise depending on the noise values. A median filter is widely used digital nonlinear filter  in edge preservation, removing of impulse noise and smoothing of signals. Median filter is the widely used to remove salt and pepper noise than rank order filter, morphological filter, and unsharp masking filter. The median filter replaces a sample with the middle value among all the samples present inside the sample window. A median filter will be of two types depending on the number of samples processed at the same cycle i.e, bit level architecture and word level architecture.. In this paper, Carry Look-ahead Adder median filter method will be introduced to improve the hardware resources used in median filter architecture for 5 window and 9 window for 8 bit and 16 bit median filter architecture.


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