Relevant bibliographies by topics / Dynamic change models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dynamic change models'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Dynamic change models"

1

Kramer, Walter, Werner Ploberger, and Raimund Alt. "Testing for Structural Change in Dynamic Models." Econometrica 56, no. 6 (November 1988): 1355. http://dx.doi.org/10.2307/1913102.

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Kim, Yongku, and L. Mark Berliner. "Change of spatiotemporal scale in dynamic models." Computational Statistics & Data Analysis 101 (September 2016): 80–92. http://dx.doi.org/10.1016/j.csda.2016.02.013.

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Zeileis, Achim, Friedrich Leisch, Christian Kleiber, and Kurt Hornik. "Monitoring structural change in dynamic econometric models." Journal of Applied Econometrics 20, no. 1 (January 2005): 99–121. http://dx.doi.org/10.1002/jae.776.

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Medina, Esperanza G. Valdés y., and Leilani Medina Valdés. "Dynamic Models as Change Enablers in Educational Mathematics." Procedia - Social and Behavioral Sciences 176 (February 2015): 923–26. http://dx.doi.org/10.1016/j.sbspro.2015.01.559.

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Falkenström, Fredrik, Steven Finkel, Rolf Sandell, Julian A. Rubel, and Rolf Holmqvist. "Dynamic models of individual change in psychotherapy process research." Journal of Consulting and Clinical Psychology 85, no. 6 (2017): 537–49. http://dx.doi.org/10.1037/ccp0000203.

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Han, Xu, and Atsushi Inoue. "TESTS FOR PARAMETER INSTABILITY IN DYNAMIC FACTOR MODELS." Econometric Theory 31, no. 5 (September 15, 2014): 1117–52. http://dx.doi.org/10.1017/s0266466614000486.

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In this paper, we develop tests for structural breaks of factor loadings in dynamic factor models. We focus on the joint null hypothesis that all factor loadings are constant over time. Because the number of factor loading parameters goes to infinity as the sample size grows, conventional tests cannot be used. Based on the fact that the presence of a structural change in factor loadings yields a structural change in second moments of factors obtained from the full sample principal component estimation, we reduce the infinite-dimensional problem into a finite-dimensional one and our statistic c
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Keith, David A., H. Resit Akçakaya, Wilfried Thuiller, Guy F. Midgley, Richard G. Pearson, Steven J. Phillips, Helen M. Regan, Miguel B. Araújo, and Tony G. Rebelo. "Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models." Biology Letters 4, no. 5 (July 29, 2008): 560–63. http://dx.doi.org/10.1098/rsbl.2008.0049.

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Species responses to climate change may be influenced by changes in available habitat, as well as population processes, species interactions and interactions between demographic and landscape dynamics. Current methods for assessing these responses fail to provide an integrated view of these influences because they deal with habitat change or population dynamics, but rarely both. In this study, we linked a time series of habitat suitability models with spatially explicit stochastic population models to explore factors that influence the viability of plant species populations under stable and ch
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Reuning, Kevin, Michael R. Kenwick, and Christopher J. Fariss. "Exploring the Dynamics of Latent Variable Models." Political Analysis 27, no. 4 (April 11, 2019): 503–17. http://dx.doi.org/10.1017/pan.2019.1.

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Researchers face a tradeoff when applying latent variable models to time-series, cross-sectional data. Static models minimize bias but assume data are temporally independent, resulting in a loss of efficiency. Dynamic models explicitly model temporal data structures, but smooth estimates of the latent trait across time, resulting in bias when the latent trait changes rapidly. We address this tradeoff by investigating a new approach for modeling and evaluating latent variable estimates: a robust dynamic model. The robust model is capable of minimizing bias and accommodating volatile changes in
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de la Pena, Victor, and Ricardo Rivera. "Dynamic backtesting of value-at-risk models under regime change." Journal of Risk Model Validation 1, no. 4 (March 2008): 95–110. http://dx.doi.org/10.21314/jrmv.2008.013.

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Hilgert, Nadine, Ghislain Verdier, and Jean-Pierre Vila. "Change detection for uncertain autoregressive dynamic models through nonparametric estimation." Statistical Methodology 33 (December 2016): 96–113. http://dx.doi.org/10.1016/j.stamet.2016.08.003.

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Dissertations / Theses on the topic "Dynamic change models"

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Moreira, Evaldinólia Gilbertoni. "Dynamic coupling of multiscale land change models." Instituto Nacional de Pesquisas Espaciais, 2009. http://urlib.net/sid.inpe.br/mtc-m18@80/2008/11.10.16.23.

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Mudanças terrestres são resultados de uma complexa rede de interações entre fatores humanos e biofísicos, que atuam em diferentes escalas temporais e espaciais. Entender estes processos de mudanças terrestres de escalas locais a globais e seus impactos no sistema acoplado homem-natureza é um enorme desafio científico. Modelos em uma única escala podem não ser capazes capturar tais interações e processos de mudança. Esta tese apresenta uma metodologia para a construção de modelos de mudanças terrestre multiescala e multilocalidade, incluindo interações top-down e bottom-up. Numa primeira etapa,
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Zeileis, Achim, Friedrich Leisch, Christian Kleiber, and Kurt Hornik. "Monitoring structural change in dynamic econometric models." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2002. http://epub.wu.ac.at/1296/1/document.pdf.

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The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation - given a history period for which a regression relationship is known to be stable, we test whether incoming data are consistent with the previously established relationship. Procedures based on estimates of the regression coefficients are extended in three directions: we introduce (a) procedures based on OLS residuals, (b) rescaled statistics and (c) alternative asymptotic boundaries
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Barth, Volker. "Integrated assessment of climate change using structural dynamic models." Hamburg : Max-Planck-Inst. für Meteorologie, 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=968535933.

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Wu, Ning. "Artificial intelligence solutions for models of dynamic land use change." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610781.

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Wiener, Karl Kilian Konrad, and n/a. "DYNAMIC CHANGE PROCESS: HOW DO COGNITIVE READINESS DRIVERS INFORM CHANGE AGENTS ON EMPLOYEE BEHAVIOURAL CHANGE INTENTION." University of Canberra. n/a, 2008. http://erl.canberra.edu.au./public/adt-AUC20081217.120215.

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It is well accepted by now that most change initiatives are unsuccessful even though more organisations are experiencing change as they fight to retain and improve their competitiveness in the market place. It is against this background of change failure that researchers have looked for new strategies to improve change outcomes. Theoretical models conceptualising the dynamic change process advise on better change strategies, but little empirical evidence has demonstrated that these models are effective in improving change implementation outcomes. Theoretical models were also developed to count
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Zurell, Damaris. "Integrating dynamic and statistical modelling approaches in order to improve predictions for scenarios of environmental change." Phd thesis, Universität Potsdam, 2011. http://opus.kobv.de/ubp/volltexte/2011/5684/.

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Species respond to environmental change by dynamically adjusting their geographical ranges. Robust predictions of these changes are prerequisites to inform dynamic and sustainable conservation strategies. Correlative species distribution models (SDMs) relate species’ occurrence records to prevailing environmental factors to describe the environmental niche. They have been widely applied in global change context as they have comparably low data requirements and allow for rapid assessments of potential future species’ distributions. However, due to their static nature, transient responses to env
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Barth, Volker [Verfasser]. "Integrated assessment of climate change using structural dynamic models / Max-Planck-Institut für Meteorologie. Von Volker Barth." Hamburg : Max-Planck-Inst. für Meteorologie, 2003. http://d-nb.info/968535933/34.

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Davis, Michael C. "Dynamic models of price changes /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2001. http://wwwlib.umi.com/cr/ucsd/fullcit?p3026374.

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Kasun, Matthew R. (Matthew Roger) Carleton University Dissertation Engineering Systems and computer. "A meta-model for dynamic change management." Ottawa, 1993.

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Juntunen, M. (Marko). "Business model change as a dynamic capability." Doctoral thesis, Oulun yliopisto, 2017. http://urn.fi/urn:isbn:9789526216621.

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Abstract The purpose of this study is to explore the role of dynamic capability in business model change in Internet-based business start-ups and Internet-based business enterprises. This study reviews the literature on business models, business model change, and dynamic capability, and defines business model change as a dynamic capability that is expected to lead to increased speed of business change and competitive advantages over the long term. This empirical study of business model change is accomplished by the case study method. The study is based on field data of four case companies rega
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Books on the topic "Dynamic change models"

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Jeffers, J. N. R. Practitioners handbook on the modelling of dynamic change in ecosystems. Chichester: Published on behalf of the Scientific Committee on Problems of the Environment (SCOPE) of the International Council of Scientific Unions (ICSU) by Wiley, 1988.

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Gemmell, Norman. Dynamic sectoral linkages and structural change in a developing economy. Nottingham: University of Nottingham, Centre for Research in Economic Development and International Trade, 1998.

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Geert, Paul van. Dynamic systems of development: Change between complexity and chaos. New York: Harvester Wheatsheaf, 1994.

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Jeffers, J. N. R. Practitioner's handbook on the modelling of dynamic change in ecosystems. Chichester [West Sussex]: Published on behalf of the Scientific Committee on Problems of the Environment of the International Council of Scientific Unions by Wiley, 1988.

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Aulin-Ahmavaara, Pirkko. A dynamic input-output model with non-homogeneous labour for evaluation of technical change. Helsinki: Suomalainen Tiedeakatemia, 1987.

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Becker, Torbjörn. Common trends and structural change: A dynamic macro model for the pre- and postrevolution Islamic Republic of Iran. [Washington, D.C.]: International Monetary Fund, Research Department, 1999.

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Canada. Minerals and Metals Sector. Economic impact of carbon abatement policies and market structure: A dynamic general equilibrium analysis with imperfect competition. Ottawa: Industry Canada, 2001.

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Ulrike, Jessner, ed. A dynamic model of multilingualism: Perspectives of change in psycholinguistics. Clevedon, England: Multilingual Matters, 2002.

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Baily, Martin Neil. Labor productivity: Structural change and cyclical dynamics. Cambridge, MA: National Bureau of Economic Research, 1996.

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Hlinka, Anton. Každý sa môže zmeniť: Dynamické modely spravania. Bratislava: Don Bosco, 1994.

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Book chapters on the topic "Dynamic change models"

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Gómez M., Guillermo L. "Change and time in development and growth processes." In Dynamic Probabilistic Models and Social Structure, 117–69. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2524-6_3.

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Bahn, Olivier, Alain Haurie, and Roland Malhamé. "Limit Game Models for Climate Change Negotiations." In Advances in Dynamic and Mean Field Games, 27–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70619-1_2.

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Perz, Stephen, Joseph P. Messina, Eustaquio Reis, Robert Walker, and Stephen J. Walsh. "Scenarios of future Amazonian landscapes: Econometric and dynamic simulation models." In Amazonia and Global Change, 83–100. Washington, D. C.: American Geophysical Union, 2009. http://dx.doi.org/10.1029/2008gm000736.

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Bose, Prasanta, and Mark G. Matthews. "An Agent Mediated Approach to Dynamic Change in Coordination Policies." In Coordination Languages and Models, 164–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45263-x_11.

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Horincar, Roxana, Bernd Amann, and Thierry Artières. "Online Change Estimation Models for Dynamic Web Resources." In Lecture Notes in Computer Science, 395–410. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31753-8_33.

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Rangelova, E., W. van der Wal, M. G. Sideris, and P. Wu. "Numerical models of the rates of change of the geoid and orthometric heights over Canada." In Dynamic Planet, 563–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-49350-1_82.

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Dutta, Prajit K. "Dynamic Games with an Application to Climate Change Models." In Complex Social and Behavioral Systems, 61–82. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0368-0_137.

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Dutta, Prajit K. "Dynamic Games with an Application to Climate Change Models." In Encyclopedia of Complexity and Systems Science, 1–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-642-27737-5_137-2.

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Wu, Jianguo, G. Darrel Jenerette, and John L. David. "Linking Land-use Change with Ecosystem Processes: A Hierarchical Patch Dynamic Model." In Integrated Land Use and Environmental Models, 99–119. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05109-2_5.

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Andrey, Christopher, Olivier Bahn, and Alain Haurie. "Computing α-Robust Equilibria in Two Integrated Assessment Models for Climate Change." In Advances in Dynamic and Evolutionary Games, 283–300. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28014-1_14.

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Conference papers on the topic "Dynamic change models"

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Aitio, Antti, and David Howey. "Combining Non-Parametric and Parametric Models for Stable and Computationally Efficient Battery Health Estimation." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3180.

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Abstract Equivalent circuit models for batteries are commonly used in electric vehicle battery management systems to estimate state of charge and other important latent variables. They are computationally inexpensive, but suffer from a loss of accuracy over the full range of conditions that may be experienced in real-life. One reason for this is that the model parameters, such as internal resistance, change over the lifetime of the battery due to degradation. However, estimating long term changes is challenging, because parameters also change with state of charge and other variables. To addres
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Wang, Yu, Aniket Chakrabarti, David Sivakoff, and Srinivasan Parthasarathy. "Fast Change Point Detection on Dynamic Social Networks." 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.2017/417.

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A number of real world problems in many domains (e.g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model - where a network is defined at logical time-stamps. An important problem under this model is change point detection. In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot
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van der Aalst, W. M. P. "Generic workflow models: how to handle dynamic change and capture management information?" In Proceedings Fourth IFCIS International Conference on Cooperative Information Systems. CoopIS 99 (Cat. No.PR00384). IEEE, 1999. http://dx.doi.org/10.1109/coopis.1999.792163.

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Eger, Steffen, and Alexander Mehler. "On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-2009.

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Luo, Ping, Xue Cao, Hongga Li, and Manchun Li. "Dynamic simulation of land use change in shenzhen city based on Markov-logistic-CA models." In 2010 18th International Conference on Geoinformatics. IEEE, 2010. http://dx.doi.org/10.1109/geoinformatics.2010.5567756.

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Yi, Wei, Zhiqiang Gao, and Maosi Chen. "Dynamic modelling of future land-use change: a comparison between CLUE-S and Dinamica EGO models." In SPIE Optical Engineering + Applications, edited by Wei Gao, Thomas J. Jackson, Jinnian Wang, and Ni-Bin Chang. SPIE, 2012. http://dx.doi.org/10.1117/12.927781.

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Yepifanov, Sergiy V., Roman L. Zelenskyi, and Igor Loboda. "Modeling the GTE Under Its Dynamic Heating Conditions." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-26258.

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A modern gas turbine engine (GTE) is a complex non-linear dynamic system with the mutual effect of gas-dynamic and thermal processes in its components. The engine development requires the precise real-time simulation of all main operating modes. One of the most complex operating modes for modeling is “cold stabilization”, which is the rotors acceleration without completely heated up the turbine elements. The dynamic heating problem is a topical practical issue. Solving the problem requires coordinating a gas-path model with heat and stress models, which is also a significant scientific problem
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Cantero, Katty M., and Marco E. Sanjuan. "Dynamic Model Fuzzy Transition in DMC Type Controllers for Varying Dynamics in Bioreactors." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-67471.

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This research presents an approach to implement industrial type DMC (Dynamic Matrix Control) controllers in biological process where a fed-batch operation is required. In this type of operation a substantial change in the process dynamics occurs due to changes in volume, reaction rate, pressure, temperature, concentration, and other process variables. To compensate for changes in the dynamic, several matrix models are identified, and using a fuzzy supervisor a soft model transition is implemented, such that the controller and the process performance can adapt to the new operating conditions wi
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Kim, Eunkyeong, Takuya Yoshida, and Tatsurou Yashiki. "Dynamic Heat-Exchanger Model for Any Combination of Water and Steam States." In 2012 20th International Conference on Nuclear Engineering and the ASME 2012 Power Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/icone20-power2012-54203.

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The purpose of this study is to propose a dynamic heat transfer model for predicting transient heat recovery steam generator (HRSG) behaviors involving phase changes in heat exchanger tubes. The model deals with any combination of phase states by switching the equations for heat transfer coefficient, specific volume, and friction factor corresponding to their physical characteristics. The model also constrains the change of mass flow calculated by momentum balance to satisfy thermodynamic relationships which are neglected by conventional models. The simulation results show that the proposed mo
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Skartveit, Hanne-Lovise, Katherine J. Goodnow, and Magnhild Viste. "Visualized System Dynamics models as Information and Planning Tools." In 2003 Informing Science + IT Education Conference. Informing Science Institute, 2003. http://dx.doi.org/10.28945/2693.

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In this paper, we describe the use of visualization of system dynamics models as client information and management tools. System dynamics is a methodology for analyzing and understanding how complex systems change over time. System dynamic models have been developed for a broad range of information to client applications - from resource management problems to the mapping of stocks and flows on factory floors. The problem faced by many users of system dynamic models is their graphic complexity for users not trained in the field. This paper addresses new research into visualization of system dyn
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Reports on the topic "Dynamic change models"

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Li, Jing. Various New Statistical Models for Modeling and Change Detection in Multidimensional Dynamic Networks. Fort Belvoir, VA: Defense Technical Information Center, January 2014. http://dx.doi.org/10.21236/ada606729.

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Guldi, Jo. Parliament's Debates about Infrastructure: An Exercise in Using Dynamic Topic Models to Synthesize Historical Change (annotated version). Roy Rosenzweig Center for History and New Media, 2021. http://dx.doi.org/10.31835/ma.2021.08.

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Verburg, Peter H., Žiga Malek, Sean P. Goodwin, and Cecilia Zagaria. The Integrated Economic-Environmental Modeling (IEEM) Platform: IEEM Platform Technical Guides: User Guide for the IEEM-enhanced Land Use Land Cover Change Model Dyna-CLUE. Inter-American Development Bank, September 2021. http://dx.doi.org/10.18235/0003625.

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The Conversion of Land Use and its Effects modeling framework (CLUE) was developed to simulate land use change using empirically quantified relations between land use and its driving factors in combination with dynamic modeling of competition between land use types. Being one of the most widely used spatial land use models, CLUE has been applied all over the world on different scales. In this document, we demonstrate how the model can be used to develop a multi-regional application. This means, that instead of developing numerous individual models, the user only prepares one CLUE model applica
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Niyogi, Partha, and Robert C. Berwick. A Dynamical Systems Model for Language Change. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada307027.

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Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

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We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-worl
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Agarwal, Chetan, Glen M. Green, J. Morgan Grove, Tom P. Evans, and Charles M. Schweik. A review and assessment of land-use change models: dynamics of space, time, and human choice. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station, 2002. http://dx.doi.org/10.2737/ne-gtr-297.

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Robertson, A. W., M. Ghil, K. Kravtsov, and P. J. Smyth. Final Technical Report for "Collaborative Research: Regional climate-change projections through next-generation empirical and dynamical models". Office of Scientific and Technical Information (OSTI), April 2011. http://dx.doi.org/10.2172/1010911.

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Kravtsov, S., Andrew W. Robertson, Michael Ghil, and Padhraic J. Smyth. Final Technical Report for "Collaborative Research. Regional climate-change projections through next-generation empirical and dynamical models". Office of Scientific and Technical Information (OSTI), April 2011. http://dx.doi.org/10.2172/1010914.

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Ziegler, Nancy, Nicholas Webb, Adrian Chappell, and Sandra LeGrand. Scale invariance of albedo-based wind friction velocity. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40499.

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Obtaining reliable estimates of aerodynamic roughness is necessary to interpret and accurately predict aeolian sediment transport dynamics. However, inherent uncertainties in field measurements and models of surface aerodynamic properties continue to undermine aeolian research, monitoring, and dust modeling. A new relation between aerodynamic shelter and land surface shadow has been established at the wind tunnel scale, enabling the potential for estimates of wind erosion and dust emission to be obtained across scales from albedo data. Here, we compare estimates of wind friction velocity (u*)
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Smyth, Padhraic. Final Technical Report for Collaborative Research: Regional climate-change projections through next-generation empirical and dynamical models, DE-FG02-07ER64429. Office of Scientific and Technical Information (OSTI), July 2013. http://dx.doi.org/10.2172/1088032.

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