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Relevant bibliographies by topics / CSL-model

Academic literature on the topic 'CSL-model'

Author: Grafiati

Published: 1 April 2023

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Journal articles on the topic "CSL-model"

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

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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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Piscicchia, Kristian, Angelo Bassi, Catalina Curceanu, Raffaele Grande, Sandro Donadi, Beatrix Hiesmayr, and Andreas Pichler. "CSL Collapse Model Mapped with the Spontaneous Radiation." Entropy 19, no. 7 (June 29, 2017): 319. http://dx.doi.org/10.3390/e19070319.

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

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

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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 (December 2008): 35–50. http://dx.doi.org/10.1016/j.entcs.2008.11.012.

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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 (January 2013): 44–50. http://dx.doi.org/10.1016/j.ipl.2012.09.009.

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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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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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Book chapters on the topic "CSL-model"

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Zhang, Lijun, David N. Jansen, Flemming Nielson, and Holger Hermanns. "Automata-Based CSL Model Checking." In Automata, Languages and Programming, 271–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22012-8_21.

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D’Aprile, Davide, Susanna Donatelli, and Jeremy Sproston. "CSL Model Checking for the GreatSPN Tool." In Lecture Notes in Computer Science, 543–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30182-0_55.

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Kwiatkowska, Marta, Gethin Norman, and António Pacheco. "Model Checking CSL until Formulae with Random Time Bounds." In Lecture Notes in Computer Science, 152–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45605-8_10.

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Schwarick, Martin, and Monika Heiner. "CSL Model Checking of Biochemical Networks with Interval Decision Diagrams." In Computational Methods in Systems Biology, 296–312. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03845-7_20.

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Zhao, Yang, and Gianfranco Ciardo. "Tackling Truncation Errors in CSL Model Checking through Bounding Semantics." In Computer Performance Engineering, 58–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40725-3_6.

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Gao, Yang, Ernst Moritz Hahn, Naijun Zhan, and Lijun Zhang. "CCMC: A Conditional CSL Model Checker for Continuous-Time Markov Chains." In Automated Technology for Verification and Analysis, 464–68. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02444-8_36.

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Johnson, Annette, Cassandra McKay-Jackson, and Giesela Grumbach. "Logic Model Development to Aid Project Planning." In Critical Service Learning Toolkit. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190858728.003.0011.

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The logic model, a tool that has been around since the 1970s, was defined by Bickman (1987) in the late 1980s and became popularized in the 1990s. Bickman saw the logic model as a presentation of how the program will work to solve identified problems under certain conditions. Basically, a logic model shows a graphic depiction of a program, its goals, and underlying assumptions and a plan of action and outcomes. This model is helpful for the school- based practitioner to clearly articulate critical service learning (CSL) goals to school administrators. According to the Kellogg Foundation, a logic model provides a visual way to present a program in a systematic fashion (Kellogg, 2004). In this instance, it is a visual map of the CSL project depicting the project’s goals. This includes articulating the understanding of CSL and why it is believed it will work; providing a concise format to share with others; and conveying the relationships among the resources available to operate the program, the activities planned, and the desired changes or results. In sum, the logic model represents a graphic depiction of CSL and its benefits. One benefit of using the logic model is that it helps the school- based practitioner think through the CSL project- planning process in its entirety as youth develop and plan activities, identify needed resources, and anticipate what is needed to evaluate it. Practitioners may use the logic model in a couple of ways: (a) The logic model can demonstrate the benefits of the CSL project to administration to gain buy- in; and (b) once the program is launched, the logic model may be used to incorporate what the youth envision. The school- based practitioner must remain true to the CSL tenets of allowing youth to plan and design the project. Perhaps one of the nicest benefits of a logic model is that it creatively illustrates the CSL program’s components to stakeholders in a succinct way. A completed logic model allows stakeholders to quickly review the program’s goals, activities, and projected outcomes; furthermore, it presents a summary of complex theory as understandable units.

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Johnson, Annette, Cassandra McKay-Jackson, and Giesela Grumbach. "The Nuts and Bolts of Critical Service Learning Theoretical Foundations." In Critical Service Learning Toolkit. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190858728.003.0006.

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Critical service learning (CSL), social and emotional learning (SEL), and positive youth development (PYD) represent key elements for promot¬ing healthy attitudes and behaviors among youth. This chapter explains each component and provides a theoretical overview. As mentioned in Chapter 1, CSL represents a therapeutic strategy that encompasses a philosophy of youth empowerment. CSL emphasizes youth becoming empowered to view themselves in relation to others, as partners, to bring about change in their environment. Mitchell (2008) defined CSL as an approach that challenges youth to become self-aware of how their own situations influence their relationships within their community. When these relationships are based on the concerns of the community, they can facilitate CSL through the examination of issues of power, privilege, and oppression— and disparaging assumptions of class, gender, and race— and then take action to address unjust and inequitable social and economic systems (Cipolle, 2010). Youth engage in critical thinking about the problems they face within their own communities and are encouraged to take action. The critical approach to service learning promotes social justice and challenges the status quo. The approach to CSL involves three key elements: “working to redistribute power amongst all participants in the service learning relationship, developing authentic relationships in the classroom and in the community and working from a social change perspective” (Mitchell, 2008, p. 50). The goal of CSL is to examine power relations, challenge oppressive institutions, and cultivate in youth the power to take action. In our model, the CSL approach presents “student voice” as a necessary component to create a sense of empowerment and authentic engagement. The community web-mapping tool discussed in Chapter 3 serves as the vehicle for students to address social justice issues as they compare and contrast their vision of perfect and imperfect communities (Figure 2.1). Social and emotional learning is a framework that provides opportunities for young people to acquire the skills necessary for maintaining personal well-being and positive relationships across their life span (Elbertson, Brackett, & Weissberg, 2009). The five competency clusters for students are the following: 1. Self- awareness: the ability to accurately recognize one’s emotions and thoughts and how they influence behavior.

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Cheng, Hsiu-Jen. "A TL-TPACK Model on CSL Pre-Service Teachers' Competencies of Online Instruction." In Preparing Foreign Language Teachers for Next-Generation Education, 198–225. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0483-2.ch011.

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This chapter aims to introduce the integration of TPACK into a Chinese pre-service teacher training program and discuss its outcomes and challenges. First, the concept of TPACK was introduced and relevant TPACK research and its constraints in the previous studies were discussed. Through the partnership between a Chinese pre-service teacher training program in Taiwan and a Chinese learning program in the States, the author developed a Teaching and Learning Model, entitled TL-TPACK model, integrating practicum, course design, advisors, peer cooperation, and reflections—five training strategies to ensure the training and learning outcome. At the end of the chapter, an empirical Chinese pre-service teacher training study applying the TL-TPACK model was conducted to investigate pre- service teachers' seven TPACK competences and Chinese learners' learning performance. Finally, research implications and suggestions for future studies were discussed.

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Johnson, Annette, Cassandra McKay-Jackson, and Giesela Grumbach. "Essential Elements for Promoting Student Voice: An Expanded Framework for Critical Service Learning." In Critical Service Learning Toolkit. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190858728.003.0007.

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This critical service learning (CSL) model has youth voice as the cornerstone and places it at the center of the approach. The term youth voice describes a strategy in which young people are authentically engaged in working toward changing the systems that directly affect their lives. Instead of their input remaining at the discussion level, youth conceptualize, research, and develop action plans to make recommendations to policymakers. This places youth in a unique position to advocate for their communities, which provides a real- world opportunity for them to learn components of the change process. Practitioners must create a safe and supportive environment to encourage youth voice. The practice environment, including the culture, structure, and group climate, must be intentionally created and sustained to make youth feel safe and supported enough to raise their voices. The youth’s voices, strengths, talents, actions, and achievements are continuously integrated into the CSL approach and are infused throughout all components of every activity, including brainstorming ideas (community web mapping), research, planning, evaluation, decision- making, advocacy, and reflections. In this model, the community web mapping (Lantieri, 1999) is central to the develop¬ment of the students’ voice. The practitioner facilitates the brainstorming for community web mapping. This activity may span three to five sessions and encourages youth to brainstorm about what they would like to change within their communities (or within their schools). The brainstorming session is youth centered and involves little to no adult redirection. This provides a chance for youth to verbalize their ideas without adult censorship. It allows youth to engage in active construction and integration of problem-solving, decision-making, and conflict resolution and may enable them to explore and confront social injustices on behalf of themselves or others. The literature suggests that when youth have authentic CSL experiences, academic achievement and civic competence increase (Howard, 2006; Kielsmeier, Scales, Roehlkepartain, & Neal, 2004). Youth who have these experiences develop a stronger sense of community, improve their prosocial attitudes and behaviors, and learn powerful lessons through social action.

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Conference papers on the topic "CSL-model"

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Sproston, J., and S. Donatelli. "Backward stochastic bisimulation in CSL model checking." In First International Conference on the Quantitative Evaluation of Systems, 2004. QEST 2004. Proceedings. IEEE, 2004. http://dx.doi.org/10.1109/qest.2004.1348036.

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"CSL Model Checking for Generalized Stochastic Petri Nets." In Third International Conference on the Quantitative Evaluation of Systems - (QEST'06). IEEE, 2006. http://dx.doi.org/10.1109/qest.2006.13.

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Elvio Gilberto Amparore and Susanna Donatelli. "Model checking CSL^TA with Deterministic and Stochastic Petri Nets." In Networks (DSN). IEEE, 2010. http://dx.doi.org/10.1109/dsn.2010.5544425.

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Alfonso Hoyos, Nyky Joel, Juan Pablo Navarro Cabiativa, and Andres Eduardo Gaona Barrera. "Sign identification model of the Colombian Sign Language (CSL) alphabet based on Computational Intelligence." In 2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI). IEEE, 2022. http://dx.doi.org/10.1109/colcaci56938.2022.9905253.

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"MathMC: A Mathematica-Based Tool for CSL Model Checking of Deterministic and Stochastic Petri Nets." In Third International Conference on the Quantitative Evaluation of Systems - (QEST'06). IEEE, 2006. http://dx.doi.org/10.1109/qest.2006.29.

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Sheshasaayee, Ananthi, and Swetha Margaret T A. "A theoretical model to improve capability in CSL for multi-tier applications in SaaS environment." In 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). IEEE, 2015. http://dx.doi.org/10.1109/icgciot.2015.7380495.

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Li, Hsuan. "The Designing of CSL Teacher Empowering Training Model of Robot-Assisted Language Learning based on the TPACK Framework." In 2021 International Conference on Advanced Learning Technologies (ICALT). IEEE, 2021. http://dx.doi.org/10.1109/icalt52272.2021.00089.

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Hamza, Mohamed, Tarek M. Hatem, Dierk Raabe, and Jaafar A. El-Awady. "Hydrogen Diffusion and Segregation in Alpha Iron ∑ 3 (111) Grain Boundaries." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-53118.

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Polycrystalline material generally exhibits degradation in its mechanical properties and shows more tendency for intergranular fracture due to segregation and diffusion of hydrogen on the grain boundaries (GBs). Understanding the parameters affecting the diffusion and binding of hydrogen within GBs will allow enhancing the mechanical properties of the commercial engineering materials and developing interface dominant materials. In practice during forming processes, the coincidence site lattice (CSL) GBs are experiencing deviations from their ideal configurations. Consequently, this will change the atomic structural integrity by superposition of sub-boundary dislocation networks on the ideal CSL interfaces. For this study, the ideal ∑ 3 111 [110] GB structure and its angular deviations in BCC iron within the range of Brandon criterion will be studied comprehensively using molecular statics (MS) simulations. The clean GB energy will be quantified, followed by the GB and free surface segregation energies calculations for hydrogen atoms. Rice-Wang model will be used to assess the embrittlement impact variation over the deviation angles. The results showed that the ideal GB structure is having the greatest resistance to embrittlement prior GB hydrogen saturation, while the 3° deviated GB is showing the highest susceptibility to embrittlement. Upon saturation, the 5° deviated GB appears to have the highest resistance instead due to the lowest stability of hydrogen atoms observed in the free surfaces of its simulation cell. Molecular dynamics (MD) simulations are then applied to calculate hydrogen diffusivity within the ideal and deviated GB structure. It is shown that hydrogen diffusivity decreases significantly in the deviated GB models. In addition, the 5° deviated GB is representing the local minimum for diffusivity results suggesting the existence of the highest atomic disorder and excessive secondary dislocation accommodation within this interface.

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Du, Yu, Yongkang Wong, Wenguang Jin, Wentao Wei, Yu Hu, Mohan Kankanhalli, and Weidong Geng. "Semi-Supervised Learning for Surface EMG-based Gesture Recognition." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/225.

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Conventionally, gesture recognition based on non-intrusive muscle-computer interfaces required a strongly-supervised learning algorithm and a large amount of labeled training signals of surface electromyography (sEMG). In this work, we show that temporal relationship of sEMG signals and data glove provides implicit supervisory signal for learning the gesture recognition model. To demonstrate this, we present a semi-supervised learning framework with a novel Siamese architecture for sEMG-based gesture recognition. Specifically, we employ auxiliary tasks to learn visual representation; predicting the temporal order of two consecutive sEMG frames; and, optionally, predicting the statistics of 3D hand pose with a sEMG frame. Experiments on the NinaPro, CapgMyo and csl-hdemg datasets validate the efficacy of our proposed approach, especially when the labeled samples are very scarce.

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Asadi, Sadegh, and Abbas Khaksar. "Analytical and Numerical Sand Production Prediction Calibrated with Field Data, Example from High-Rate Gas Wells." In SPE Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210776-ms.

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Abstract Sand production prediction is essential from the early stages of field development planning for well completion design and later for production management. Unconsolidated and weakly consolidated sandstones are prone to fail at low flowing bottom hole pressures during hydrocarbon production. To predict the sand-free drawdown, a robust sand prediction model that integrates near-wellbore and in-situ stresses, rock mechanical properties, well trajectory, reservoir pressure, production and depletion trends is required. Sanding prediction models should be calibrated with field data such as production and well tests observation. In the absence of field data, numerical techniques can provide a reliable estimate on potential onset and severity of sanding at various reservoir pressures. In this study, analytical and finite-element numerical models are independently used to predict the onset of sanding and volume of produced sand from high rate has wells with weakly consolidated sandstone reservoirs in onshore, Western Australia. The analytical method uses a poro-elastic model and core-calibrated log-derived rock strength profiles with an empirical effective rock strength factor (ESF). In the study, the ESF was calibrated against documented field sanding observation from a well test extended flow period at the initial reservoir pressure under a low drawdown pressure. The numerical method uses a poro-elasto-plastic model defined from triaxial core tests. The rock failure criterion in the numerical method is based on a critical strain limit (CSL) corresponding to the failure of the inner wall of thick-walled cylinder core tests that can also satisfy the existing wells sanding observations. To verify the onset and severity of sanding predicted by the analytical model, numerical simulations for an identical sandstone interval are developed to investigate the corresponding CSL. This combined analytical and numerical modelling calibrated with field data provided high confidence in the sanding evaluation and their application for future well completion and sand management decisions. The analytical model was finally used for sanding assessment over field life pressure condition because of its processing simplicity, speed and flexibility in assessing various pressure and rock strength scenarios with sensitivity analysis over the whole production interval in compared with the numerical method which is more suitable for single-depth, single pressure condition and well and perforation trajectory modelling.

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