Relevant bibliographies by topics / CSL-model

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  1. Journal articles

Academic literature on the topic 'CSL-model'

Author: Grafiati
Published: 1 April 2023

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Journal articles on the topic "CSL-model"

1

Remke, Anne, Boudewijn R. Haverkort, and Lucia Cloth. "CSL model checking algorithms for QBDs." Theoretical Computer Science 382, no. 1 (2007): 24–41. http://dx.doi.org/10.1016/j.tcs.2007.05.007.

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2

Spieler, David, Ernst Moritz Hahn, and Lijun Zhang. "Model Checking CSL for Markov Population Models." Electronic Proceedings in Theoretical Computer Science 154 (June 6, 2014): 93–107. http://dx.doi.org/10.4204/eptcs.154.7.

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3

Piscicchia, Kristian, Angelo Bassi, Catalina Curceanu, et al. "CSL Collapse Model Mapped with the Spontaneous Radiation." Entropy 19, no. 7 (2017): 319. http://dx.doi.org/10.3390/e19070319.

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4

Buckberry, Lorraine D., Ian S. Blagbrough, Barrie W. Bycroft, and P. Nicholas Shaw. "Bovine Pulmonary, Hepatic and Renal Tissues: Models for the Study of Mammalian C-S Lyase Enzymes." Alternatives to Laboratory Animals 21, no. 3 (1993): 360–70. http://dx.doi.org/10.1177/026119299302100306.

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C-S lyase (CSL) enzymes are responsible for the generation of toxicity via the cleavage of cysteine conjugates to generate reactive thiol species. In order to explore and characterise CSL activity in mammalian organs, cysteine conjugate CSL enzymes were isolated from bovine pulmonary, hepatic and renal tissues. Bovine tissue”, obtained from the abbatoir, affords a readily available source of viable CSL enzymes, without the necessity of sacrificing large numbers of laboratory animals simply to provide tissue. We have demonstrated that significant CSL activity exists in bovine tissues, and that the level of this activity is comparable with that found in human tissues. These enzymes provide an explanation for the previously reported episodes of bovine toxicity, and may provide a reasonable model for other mammalian CSL enzymes.
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5

Donatelli, S., S. Haddad, and J. Sproston. "Model Checking Timed and Stochastic Properties with CSL^{TA}." IEEE Transactions on Software Engineering 35, no. 2 (2009): 224–40. http://dx.doi.org/10.1109/tse.2008.108.

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6

Amparore, E. G., and S. Donatelli. "Efficient model checking of the stochastic logic CSL TA." Performance Evaluation 123-124 (July 2018): 1–34. http://dx.doi.org/10.1016/j.peva.2018.03.002.

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7

KITANO, Y., M. TAKATA, and Y. KOMURA. "EXTENDED CSL-MODEL FOR GRAIN BOUNDARY IN LAYER STRUCTURES." Le Journal de Physique Colloques 49, no. C5 (1988): C5–207—C5–212. http://dx.doi.org/10.1051/jphyscol:1988521.

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8

Blom, Stefan, Boudewijn R. Haverkort, Matthias Kuntz, and Jaco van de Pol. "Distributed Markovian Bisimulation Reduction aimed at CSL Model Checking." Electronic Notes in Theoretical Computer Science 220, no. 2 (2008): 35–50. http://dx.doi.org/10.1016/j.entcs.2008.11.012.

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9

Gao, Yang, Ming Xu, Naijun Zhan, and Lijun Zhang. "Model checking conditional CSL for continuous-time Markov chains." Information Processing Letters 113, no. 1-2 (2013): 44–50. http://dx.doi.org/10.1016/j.ipl.2012.09.009.

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10

Hoseini, S. Mahya, and Mohsen Soltanpour. "LONG-TERM PREDICTION OF CASPIAN SEA LEVEL UNDER CMIP6 SCENARIOS USING ARTIFICIAL NEURAL NETWORKS." Coastal Engineering Proceedings, no. 36v (December 31, 2020): 5. http://dx.doi.org/10.9753/icce.v36v.papers.5.

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Abstract:
Artificial Neural Network (ANN) is employed to predict the long-term Caspian Sea level (CSL). 114-year observed CSL data (1900-2014) and the precipitation and temperature of historical and future scenarios of Coupled Model Intercomparison Phase 6 (CMIP6) are used to predict the future fluctuations of CSL (2015-2050). The values of the statistical indices in training, validating and testing periods (1900-2014) indicate the efficiency of the ANN in reconstruction of the CSL. Considering the outputs of different climate change projections (CMIP6) and excluding the human interventions, the study predicts the CSL fluctuation range of -28 m to -26m until 2050.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/Kfj-gr65TR8
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