Journal articles: 'Dynamic change models'

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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 compares the pre- and postbreak subsample second moments of estimated factors. Our test is consistent under the alternative hypothesis in which a fraction of or all factor loadings have structural changes. The Monte Carlo results show that our test has good finite-sample size and power.

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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 changing climate scenarios in South African fynbos, a global biodiversity hot spot. Results indicate that complex interactions between life history, disturbance regime and distribution pattern mediate species extinction risks under climate change. Our novel mechanistic approach allows more complete and direct appraisal of future biotic responses than do static bioclimatic habitat modelling approaches, and will ultimately support development of more effective conservation strategies to mitigate biodiversity losses due to climate change.

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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 the latent trait. Simulations demonstrate that the robust model outperforms other models when the underlying latent trait is subject to rapid change, and is equivalent to the dynamic model in the absence of volatility. We reproduce latent estimates from studies of judicial ideology and democracy. For judicial ideology, the robust model uncovers shocks in judicial voting patterns that were not previously identified in the dynamic model. For democracy, the robust model provides more precise estimates of sudden institutional changes such as the imposition of martial law in the Philippines (1972–1981) and the short-lived Saur Revolution in Afghanistan (1978). Overall, the robust model is a useful alternative to the standard dynamic model for modeling latent traits that change rapidly over time.

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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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Dufour, Jean-Marie, and Jan F. Kiviet. "Exact tests for structural change in first-order dynamic models." Journal of Econometrics 70, no. 1 (January 1996): 39–68. http://dx.doi.org/10.1016/0304-4076(94)01683-6.

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Michaud, Jérôme. "Dynamic preferences and self-actuation of changes in language dynamics." Language Dynamics and Change 9, no. 1 (May 17, 2019): 61–103. http://dx.doi.org/10.1163/22105832-00901003.

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Abstract A puzzling fact about linguistic norms is that they are mainly stable, but the conventional variant sometimes changes. These transitions seem to be mostly S-shaped and, therefore, directed. Previous models have suggested possible mechanisms to explain these directed changes, mainly based on a bias favoring the innovative variant. What is still debated is the origin of such a bias. In this paper, we propose a refined taxonomy of mechanisms of language change and identify a family of mechanisms explaining self-actuated language changes. We exemplify this type of mechanism with the preference-based selection mechanism that relies on agents having dynamic preferences for different variants of the linguistic norm. The key point is that if these preferences align through social interactions, then new changes can be actuated even in the absence of external triggers. We present results of a multi-agent model and demonstrate that the model produces trajectories that are typical of language change.

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HENDERSON-SELLERS, A., and K. McGUFFIE. "Global climate models and 'dynamic' vegetation changes." Global Change Biology 1, no. 1 (February 1995): 63–75. http://dx.doi.org/10.1111/j.1365-2486.1995.tb00007.x.

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Schmitzer, Bernhard, and Benedikt Wirth. "Dynamic models of Wasserstein-1-type unbalanced transport." ESAIM: Control, Optimisation and Calculus of Variations 25 (2019): 23. http://dx.doi.org/10.1051/cocv/2018017.

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We consider a class of convex optimization problems modelling temporal mass transport and mass change between two given mass distributions (the so-called dynamic formulation of unbalanced transport), where we focus on those models for which transport costs are proportional to transport distance. For those models we derive an equivalent, computationally more efficient static formulation, we perform a detailed analysis of the model optimizers and the associated optimal mass change and transport, and we examine which static models are generated by a corresponding equivalent dynamic one. Alongside we discuss thoroughly how the employed model formulations relate to other formulations found in the literature.

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Nadelson, Louis S., Benjamin C. Heddy, Suzanne Jones, Gita Taasoobshirazi, and Marcus Johnson. "Conceptual Change in Science Teaching and Learning: Introducing the Dynamic Model of Conceptual Change." International Journal of Educational Psychology 7, no. 2 (June 24, 2018): 151. http://dx.doi.org/10.17583/ijep.2018.3349.

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Conceptual change can be a challenging process, particularly in science education where many of the concepts are complex, controversial, or counter-intuitive. Yet, conceptual change is fundamental to science learning, which suggests science educators and science education researchers need models to effectively address and investigate conceptual change. Consideration of the current research and extant models of conceptual change reflect a need for a holistic, comprehensive, and dynamic model of conceptual change. In response, we developed the Dynamic Model of Conceptual Change (DMCC), which uses multiple lines of research that explore the variables influencing conceptual change and the dynamic interactions that take place during the conceptual change process in science teaching and learning. Unique to the DMCC is the potential for iterations, regression, enter and exit points at various stages of the conceptual change process, and the influences of message recognition, message engagement and processing, and the nature of the resulting conceptual change. The DMCC contains elements from extant models along with previously un-emphasized influential conceptual change variables such as culture, society, attitude, practices, and personal epistemology. We constructed the DMCC to provide science educators and researchers a more holistic framework for exploring conceptual change in science instruction and learning.

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Dearden, Joel, Yi Gong, Mark Jones, and Alan Wilson. "Using the State Space of a BLV Retail Model to Analyse the Dynamics and Categorise Phase Transitions of Urban Development." Urban Science 3, no. 1 (March 11, 2019): 31. http://dx.doi.org/10.3390/urbansci3010031.

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Urban areas are now the dominant human habitat, with more influence than ever on economies, environment and our health. Dynamic urban models are increasingly applied to explore possible future scenarios of urban development to achieve sustainability. However, it is still challenging to use these models for prediction, taking into consideration the complex nature of urban systems, the nonlinear interactions between different parts of the system, and the large quantities of data output from simulations. The aim of this study is to analyse the dynamics of two hypothetical dynamic BLV (Boltzmann–Lotka–Volterra) retail models (two-zone and three-zone). Here, by visualising and analysing the qualitative nature of state space (the space of all possible initial conditions), we propose an alternative way of understanding urban dynamics more fully. This involves examining all possible configurations of an urban system in order to identify the potential development in future. Using this method we are able to identify a supply-demand balancing hyperplane and categorise two causes of phase transition of urban development: (A) change in variable values (e.g., building a new shopping centre) that cause the system to cross a basin boundary, (B) state space change (e.g., construction of a new motorway changes travel costs in the region) causes the containing basin to be modified. We also identify key characteristics of the dynamics such as velocity and how the phase space landscape changes over time. This analysis is then linked with equilibrium-size graphs, which allow insights from state space to be applicable to models with large numbers of zones. More generally this type of analysis can potentially offer insights into the nature of the dynamics in any dynamical-systems-type urban model. This is critical for increasing our understanding and helping stakeholders and policy-makers to plan for future urban changes.

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Das, S., and N. Vaswani. "Nonstationary Shape Activities: Dynamic Models for Landmark Shape Change and Applications." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 4 (April 2010): 579–92. http://dx.doi.org/10.1109/tpami.2009.94.

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Foley, Jonathan A., Samuel Levis, I. Colin Prentice, David Pollard, and Starley L. Thompson. "Coupling dynamic models of climate and vegetation." Global Change Biology 4, no. 5 (June 1998): 561–79. http://dx.doi.org/10.1046/j.1365-2486.1998.t01-1-00168.x.

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Dick, Taylor J. M., and James M. Wakeling. "Geometric models to explore mechanisms of dynamic shape change in skeletal muscle." Royal Society Open Science 5, no. 5 (May 2018): 172371. http://dx.doi.org/10.1098/rsos.172371.

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Skeletal muscle bulges when it contracts. These three-dimensional (3D) dynamic shape changes play an important role in muscle performance by altering the range of fascicle velocities over which a muscle operates. However traditional muscle models are one-dimensional (1D) and cannot fully explain in vivo shape changes. In this study we compared medial gastrocnemius behaviour during human cycling (fascicle length changes and rotations) predicted by a traditional 1D Hill-type model and by models that incorporate two-dimensional (2D) and 3D geometric constraints to in vivo measurements from B-mode ultrasound during a range of mechanical conditions ranging from 14 to 44 N m and 80 to 140 r.p.m. We found that a 1D model predicted fascicle lengths and pennation angles similar to a 2D model that allowed the aponeurosis to stretch, and to a 3D model that allowed for aponeurosis stretch and variable shape changes to occur. This suggests that if the intent of a model is to predict fascicle behaviour alone, then the traditional 1D Hill-type model may be sufficient. Yet, we also caution that 1D models are limited in their ability to infer the mechanisms by which shape changes influence muscle mechanics. To elucidate the mechanisms governing muscle shape change, future efforts should aim to develop imaging techniques able to characterize whole muscle 3D geometry in vivo during active contractions.

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Anderson, B. J., H. R. Akçakaya, M. B. Araújo, D. A. Fordham, E. Martinez-Meyer, W. Thuiller, and B. W. Brook. "Dynamics of range margins for metapopulations under climate change." Proceedings of the Royal Society B: Biological Sciences 276, no. 1661 (February 25, 2009): 1415–20. http://dx.doi.org/10.1098/rspb.2008.1681.

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We link spatially explicit climate change predictions to a dynamic metapopulation model. Predictions of species' responses to climate change, incorporating metapopulation dynamics and elements of dispersal, allow us to explore the range margin dynamics for two lagomorphs of conservation concern. Although the lagomorphs have very different distribution patterns, shifts at the edge of the range were more pronounced than shifts in the overall metapopulation. For Romerolagus diazi (volcano rabbit), the lower elevation range limit shifted upslope by approximately 700 m. This reduced the area occupied by the metapopulation, as the mountain peak currently lacks suitable vegetation. For Lepus timidus (European mountain hare), we modelled the British metapopulation. Increasing the dispersive estimate caused the metapopulation to shift faster on the northern range margin (leading edge). By contrast, it caused the metapopulation to respond to climate change slower , rather than faster, on the southern range margin (trailing edge). The differential responses of the leading and trailing range margins and the relative sensitivity of range limits to climate change compared with that of the metapopulation centroid have important implications for where conservation monitoring should be targeted. Our study demonstrates the importance and possibility of moving from simple bioclimatic envelope models to second-generation models that incorporate both dynamic climate change and metapopulation dynamics.

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Marzec, Patrycja. "Business Models of Start-Up Innovative Organizations." Kwartalnik Ekonomistów i Menedżerów 52, no. 2 (April 24, 2019): 161–73. http://dx.doi.org/10.5604/01.3001.0013.2381.

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The growing globalization challenges, as well as numerous civilization changes contribute to the change of management paradigms. Human capital is becoming an extremely valuable resource in any organization that enables dynamic adaptation to emerging market opportunities. Enterprises change their approach to their business, resulting in modern forms of organization. One of them is the start-up innovative organizations, which have received the most attention in this publication. The aim of the article is to identify the most commonly used business models in start-ups and to assess the changing trends in this area.

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Xue, Hua, and Yu Fei Wang. "Fuzzy Multiple Reference Models Adaptive Control of Induction Motors." Advanced Materials Research 354-355 (October 2011): 1285–88. http://dx.doi.org/10.4028/www.scientific.net/amr.354-355.1285.

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A new method of fuzzy multiple reference models adaptive control(FMRMAC) for dealing with significant and unpredictable system parameter variations is presented. In this method, different suitable reference model is chosen by fuzzy rules when changes occurred to the model parameters. A successful application to the speed servo system of dynamic model of induction motor (IM) shows this method works well with high dynamic performance under the condition of command speed change and load torque disturbance.

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KOSKINEN, JOHAN, ALBERTO CAIMO, and ALESSANDRO LOMI. "Simultaneous modeling of initial conditions and time heterogeneity in dynamic networks: An application to Foreign Direct Investments." Network Science 3, no. 1 (February 12, 2015): 58–77. http://dx.doi.org/10.1017/nws.2015.3.

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AbstractIn dynamic networks, the presence of ties are subject both to endogenous network dependencies and spatial dependencies. Current statistical models for change over time are typically defined relative to some initial condition, thus skirting the issue of where the first network came from. Additionally, while these longitudinal network models may explain the dynamics of change in the network over time, they do not explain the change in those dynamics. We propose an extension to the longitudinal exponential random graph model that allows for simultaneous inference of the changes over time and the initial conditions, as well as relaxing assumptions of time-homogeneity. Estimation draws on recent Bayesian approaches for cross-sectional exponential random graph models and Bayesian hierarchical models. This is developed in the context of foreign direct investment relations in the global electricity industry in 1995–2003. International investment relations are known to be affected by factors related to: (i) the initial conditions determined by the geographical locations; (ii) time-dependent fluctuations in the global intensity of investment flows; and (iii) endogenous network dependencies. We rely on the well-known gravity model used in research on international trade to represent how spatial embedding and endogenous network dependencies jointly shape the dynamics of investment relations.

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LEE, Michael S. Y., Matthew G. BARON, David B. NORMAN, and Paul M. BARRETT. "Dynamic biogeographic models and dinosaur origins." Earth and Environmental Science Transactions of the Royal Society of Edinburgh 109, no. 1-2 (March 2018): 325–32. http://dx.doi.org/10.1017/s1755691018000920.

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ABSTRACTA comprehensive analysis of early dinosaur relationships raised the possibility that the group may have originated in Laurasia (Northern Hemisphere), rather than Gondwana (Southern Hemisphere) as often thought. However, that study focused solely on morphology and phylogenetic relationships and did not quantitatively evaluate this issue. Here, we investigate dinosaur origins using a novel Bayesian framework uniting tip-dated phylogenetics with dynamic, time-sliced biogeographic methods, which explicitly account for the age and locality of fossils and the changing interconnections of areas through time due to tectonic and eustatic change. Our analysis finds strong support for a Gondwanan origin of Dinosauria, with 99 % probability for South America (83 % for southern South America). Parsimony analysis gives concordant results. Inclusion of time-sliced biogeographic information affects ancestral state reconstructions (e.g., high connectivity between two regions increases uncertainty over which is the ancestral area) and influences tree topology (disfavouring uniting fossil taxa from localities that were widely separated during the relevant time slice). Our approach directly integrates plate tectonics with phylogenetics and divergence dating, and in doing so reaffirms southern South America as the most likely area for the geographic origin of Dinosauria.

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Olsson, Simon, and Frank Noé. "Dynamic graphical models of molecular kinetics." Proceedings of the National Academy of Sciences 116, no. 30 (July 8, 2019): 15001–6. http://dx.doi.org/10.1073/pnas.1901692116.

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Most current molecular dynamics simulation and analysis methods rely on the idea that the molecular system can be represented by a single global state (e.g., a Markov state in a Markov state model [MSM]). In this approach, molecules can be extensively sampled and analyzed when they only possess a few metastable states, such as small- to medium-sized proteins. However, this approach breaks down in frustrated systems and in large protein assemblies, where the number of global metastable states may grow exponentially with the system size. To address this problem, we here introduce dynamic graphical models (DGMs) that describe molecules as assemblies of coupled subsystems, akin to how spins interact in the Ising model. The change of each subsystem state is only governed by the states of itself and its neighbors. DGMs require fewer parameters than MSMs or other global state models; in particular, we do not need to observe all global system configurations to characterize them. Therefore, DGMs can predict previously unobserved molecular configurations. As a proof of concept, we demonstrate that DGMs can faithfully describe molecular thermodynamics and kinetics and predict previously unobserved metastable states for Ising models and protein simulations.

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KUIJER, LOUWE B. "THE EXPRESSIVITY OF FACTUAL CHANGE IN DYNAMIC EPISTEMIC LOGIC." Review of Symbolic Logic 7, no. 2 (November 28, 2013): 208–21. http://dx.doi.org/10.1017/s1755020313000324.

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AbstractA commonly used dynamic epistemic logic is one obtained by adding commonknowledge and public announcements to a basic epistemic logic. It is known from Kooi (2007) that adding public substitutions to such a logic adds expressivity over the class K of models. Here I show that substitutions also add expressivity over the classes KD45, S4 and S5 of models. Since the combination of common knowledge, public announcements and substitutions, was shown in Kooi (2007) to be equally expressive to relativized common knowledge these results also show that relativized common knowledge is more expressive than common knowledge and public announcements over KD45, S4 and S5. These results therefore extend the result from van Benthem et al. (2006) that shows that relativized common knowledge is more expressive than common knowledge and public announcements over K.

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Howard, Wayne H., and C. Richard Shumway. "Nonrobustness of Dynamic Dual Models of the U.S. Dairy Industry." Northeastern Journal of Agricultural and Resource Economics 18, no. 1 (April 1989): 18–25. http://dx.doi.org/10.1017/s0899367x00000222.

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The robustness of dynamic dual model results across functional forms is examined for the U.S. dairy industry. Modified generalized Leontief (GL) and normalized quadratic (NQ) functional forms are compared by examining their consistency with properties of the competitive firm, estimated rates of adjustment for cows and labor, tests of technological change, and elasticities. Homogeneity and symmetry are maintained in both models. Convexity is not rejected by the GL and is not seriously violated by the NQ. Absence of technological change is rejected by both models, but quality indexes on labor and cows fully embody technological change occurring within labor and cows in the NQ but not in the GL. Policy-relevant elasticities differ greatly between the functional forms. Dynamic dual models are found to be non-robust in important ways to choice of functional form.

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Peng, Yue Ping, Jue Wang, and Chong Xun Zheng. "Study on Bioinformatics with Dynamic Characteristics Change of the Hippocampal Neuron Model Caused by the Alzheimer Disease." Applied Mechanics and Materials 475-476 (December 2013): 1592–98. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.1592.

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In the paper, based on the electrophysiological experimental dada, the neuron model under the pathology condition of Alzheimer Disease (AD) is built. The neuron models dynamic variation characteristics before and after the effect of AD are comparatively studied. Under the currents stimulation, compared with the normal neuron model, the AD neuron models dynamic characteristics have been obviously changed. Its changing range of the membranes interspike intervals (ISIs) obviously narrows, and the discharge patterns change rather single. In addition, its discharge threshold is much higher than the normal neuron models. So AD causes the neuron models excitability to depress markedly. The research reveals the neuron models dynamic characteristics changing under the effect of AD, which shows bioinformatics change of the neuron caused by AD. The research provides some theoretic basis for AD research by the neurodynamics theory.

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Xu, Ran, Richard P. DeShon, and Christopher R. Dishop. "Challenges and Opportunities in the Estimation of Dynamic Models." Organizational Research Methods 23, no. 4 (May 6, 2019): 595–619. http://dx.doi.org/10.1177/1094428119842638.

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Interest in modeling longitudinal processes is increasing rapidly in organizational science. Organizational scholars often employ multilevel or hierarchical linear models (HLMs) to study such processes given that longitudinal data in organizational science typically consist of observations over a relatively small number of time intervals ( T) nested within a relatively large number of units ( N; e.g., people, teams, organizations). In this paper, we first distinguish change and dynamics as common research foci when modeling longitudinal processes and then demonstrate that a unique set of inferential hazards exists when investigating change or dynamics using multilevel models. Specifically, multilevel models that include one or more time-lagged values of the dependent variable as predictors often result in substantially biased estimates of the model parameters, inflated Type I error rates, and ultimately inaccurate inference. Using Monte Carlo simulations, we investigate the bias and Type I error rates for the standard centered/uncentered hierarchical linear model (HLM) and compare them with two alternative estimation methods: the Bollen and Brand structural equation modeling (SEM) approach and the Arrelano and Bond generalized method of moments using instrumental variables (GMM-IV) approach. We find that the commonly applied hierarchical linear model performs poorly, whereas the SEM and GMM-IV approaches generally perform well, with the SEM approach yielding slightly better performance in small samples with large autoregressive effects. We recommend the Bollen and Brand SEM approach for general use when studying change or dynamics in organizational science.

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Moore, Rachel, and Brandon Pecoraro. "Dynamic Scoring: An Assessment of Fiscal Closing Assumptions." Public Finance Review 48, no. 3 (April 9, 2020): 340–53. http://dx.doi.org/10.1177/1091142120915759.

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Analysis of fiscal policy changes using general equilibrium models with forward-looking agents typically requires a counterfactual adjustment to some fiscal instrument in order to achieve the debt sustainability implied by the government’s intertemporal budget constraint. The choice of fiscal instrument can induce economic behavior unrelated to the policy change in models where Ricardian Equivalence does not hold. In this article, we use an overlapping generations framework to examine the effects of alternative fiscal closing assumptions on projected changes to economic aggregates following a change in tax policy, assessing the extent to which the bias associated with a particular fiscal instrument can be mitigated. While we find quantitative differences in projected macroeconomic activity across alternative fiscal instruments, these differences tend to shrink as the closing date is delayed. Ultimately, the choice of fiscal instrument becomes relatively unimportant if fiscal closing can be delayed sufficiently into the future.

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Moncrieff, G. R., S. Scheiter, J. A. Slingsby, and S. I. Higgins. "Understanding global change impacts on South African biomes using Dynamic Vegetation Models." South African Journal of Botany 101 (November 2015): 16–23. http://dx.doi.org/10.1016/j.sajb.2015.02.004.

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Stanton, Jessica C., Richard G. Pearson, Ned Horning, Peter Ersts, and H. Reşit Akçakaya. "Combining static and dynamic variables in species distribution models under climate change." Methods in Ecology and Evolution 3, no. 2 (September 28, 2011): 349–57. http://dx.doi.org/10.1111/j.2041-210x.2011.00157.x.

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Wårlind, D., B. Smith, T. Hickler, and A. Arneth. "Nitrogen feedbacks increase future terrestrial ecosystem carbon uptake in an individual-based dynamic vegetation model." Biogeosciences 11, no. 21 (November 13, 2014): 6131–46. http://dx.doi.org/10.5194/bg-11-6131-2014.

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Abstract. Recently a considerable amount of effort has been put into quantifying how interactions of the carbon and nitrogen cycle affect future terrestrial carbon sinks. Dynamic vegetation models, representing the nitrogen cycle with varying degree of complexity, have shown diverging constraints of nitrogen dynamics on future carbon sequestration. In this study, we use LPJ-GUESS, a dynamic vegetation model employing a detailed individual- and patch-based representation of vegetation dynamics, to evaluate how population dynamics and resource competition between plant functional types, combined with nitrogen dynamics, have influenced the terrestrial carbon storage in the past and to investigate how terrestrial carbon and nitrogen dynamics might change in the future (1850 to 2100; one representative "business-as-usual" climate scenario). Single-factor model experiments of CO2 fertilisation and climate change show generally similar directions of the responses of C–N interactions, compared to the C-only version of the model as documented in previous studies using other global models. Under an RCP 8.5 scenario, nitrogen limitation suppresses potential CO2 fertilisation, reducing the cumulative net ecosystem carbon uptake between 1850 and 2100 by 61%, and soil warming-induced increase in nitrogen mineralisation reduces terrestrial carbon loss by 31%. When environmental changes are considered conjointly, carbon sequestration is limited by nitrogen dynamics up to the present. However, during the 21st century, nitrogen dynamics induce a net increase in carbon sequestration, resulting in an overall larger carbon uptake of 17% over the full period. This contrasts with previous results with other global models that have shown an 8 to 37% decrease in carbon uptake relative to modern baseline conditions. Implications for the plausibility of earlier projections of future terrestrial C dynamics based on C-only models are discussed.

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Brown, Heidi E., Alex Young, Joceline Lega, Theodore G. Andreadis, Jessica Schurich, and Andrew Comrie. "Projection of Climate Change Influences on U.S. West Nile Virus Vectors." Earth Interactions 19, no. 18 (December 1, 2015): 1–18. http://dx.doi.org/10.1175/ei-d-15-0008.1.

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Abstract While estimates of the impact of climate change on health are necessary for health care planners and climate change policy makers, models to produce quantitative estimates remain scarce. This study describes a freely available dynamic simulation model parameterized for three West Nile virus vectors, which provides an effective tool for studying vectorborne disease risk due to climate change. The Dynamic Mosquito Simulation Model is parameterized with species-specific temperature-dependent development and mortality rates. Using downscaled daily weather data, this study estimates mosquito population dynamics under current and projected future climate scenarios for multiple locations across the country. Trends in mosquito abundance were variable by location; however, an extension of the vector activity periods, and by extension disease risk, was almost uniformly observed. Importantly, midsummer decreases in abundance may be offset by shorter extrinsic incubation periods, resulting in a greater proportion of infective mosquitoes. Quantitative descriptions of the effect of temperature on the virus and mosquito are critical to developing models of future disease risk.

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Li, Xiaoqiong, Mingfang Ting, Cuihua Li, and Naomi Henderson. "Mechanisms of Asian Summer Monsoon Changes in Response to Anthropogenic Forcing in CMIP5 Models*." Journal of Climate 28, no. 10 (May 12, 2015): 4107–25. http://dx.doi.org/10.1175/jcli-d-14-00559.1.

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Abstract Changes of the Asian summer monsoon in response to anthropogenic forcing are examined using observations and phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel, multirealization ensemble. In the twentieth century, CMIP5 models indicate a predominantly drying Asian monsoon, while in the twenty-first century under the representative concentration pathway 8.5 (RCP8.5) scenario, monsoon rainfall enhances across the entire Asian domain. The thermodynamic and dynamic mechanisms causing the changes are evaluated using specific humidity and winds, as well as the moisture budget. The drying trend in the CMIP5 historical simulations and the wetting trend in the RCP8.5 projections can be explained by the relative importance of dynamic and thermodynamic contributions to the total mean moisture convergence. While the thermodynamic mechanism dominates in the future, the historical rainfall changes are dominated by the changes in circulation. The relative contributions of aerosols and greenhouse gases (GHGs) on the historical monsoon change are further examined using CMIP5 single-forcing simulations. Rainfall reduces under aerosol forcing and increases under GHG forcing. Aerosol forcing dominates over the greenhouse effect during the historical period, leading to the general drying trend in the all-forcing simulations. While the thermodynamic change of mean moisture convergence in the all-forcing case is dominated by the GHG forcing, the dynamic change of mean moisture convergence in the all-forcing case is dominated by the aerosol forcing.

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Kallenberg, Bianca, Paul Tregoning, Janosch Fabian Hoffmann, Rhys Hawkins, Anthony Purcell, and Sébastien Allgeyer. "A new approach to estimate ice dynamic rates using satellite observations in East Antarctica." Cryosphere 11, no. 3 (May 17, 2017): 1235–45. http://dx.doi.org/10.5194/tc-11-1235-2017.

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Abstract. Mass balance changes of the Antarctic ice sheet are of significant interest due to its sensitivity to climatic changes and its contribution to changes in global sea level. While regional climate models successfully estimate mass input due to snowfall, it remains difficult to estimate the amount of mass loss due to ice dynamic processes. It has often been assumed that changes in ice dynamic rates only need to be considered when assessing long-term ice sheet mass balance; however, 2 decades of satellite altimetry observations reveal that the Antarctic ice sheet changes unexpectedly and much more dynamically than previously expected. Despite available estimates on ice dynamic rates obtained from radar altimetry, information about ice sheet changes due to changes in the ice dynamics are still limited, especially in East Antarctica. Without understanding ice dynamic rates, it is not possible to properly assess changes in ice sheet mass balance and surface elevation or to develop ice sheet models. In this study we investigate the possibility of estimating ice sheet changes due to ice dynamic rates by removing modelled rates of surface mass balance, firn compaction, and bedrock uplift from satellite altimetry and gravity observations. With similar rates of ice discharge acquired from two different satellite missions we show that it is possible to obtain an approximation of the rate of change due to ice dynamics by combining altimetry and gravity observations. Thus, surface elevation changes due to surface mass balance, firn compaction, and ice dynamic rates can be modelled and correlated with observed elevation changes from satellite altimetry.

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Kaplan, David. "Modeling Sustained Educational Change With Panel Data: The Case for Dynamic Multiplier Analysis." Journal of Educational and Behavioral Statistics 27, no. 2 (June 2002): 85–103. http://dx.doi.org/10.3102/10769986027002085.

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This article considers the problem of modeling sustained educational change via the use of dynamic multipliers applied to panel data. Dynamic multipliers arise from the incorporation of lagged endogenous variables in linear models. Three types of dynamic multipliers can be defined: (a) the impact multiplier, (b) interim multipliers, and (c) the long-run equilibrium multiplier. An impact multiplier gives the effect of a unit increase in an exogenous variable on an endogenous variable in the particular sample period. An interim multiplier gives the effect of a unit increase in an exogenous variable on an endogenous variable when that effect is sustained for a specified amount of time. A long-run equilibrium multiplier gives the effect of a unit increase in an exogenous variable on an endogenous variable when sustained into the indefinite future. This article seeks to develop and advocate dynamic multiplier analysis for education research. Extensions to multivariate dynamic linear models and multilevel linear models are provided. Three examples are presented to illustrate the methodology. The article closes with a discussion of the implications of dynamic multiplier analysis for education policy analysis.

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Yang, Zhiwei, and Weigang Wu. "The (T, L)-Path Model and Algorithms for Information Dissemination in Dynamic Networks." Information 9, no. 9 (August 24, 2018): 212. http://dx.doi.org/10.3390/info9090212.

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A dynamic network is the abstraction of distributed systems with frequent network topology changes. With such dynamic network models, fundamental distributed computing problems can be formally studied with rigorous correctness. Although quite a number of models have been proposed and studied for dynamic networks, the existing models are usually defined from the point of view of connectivity properties. In this paper, instead, we examine the dynamicity of network topology according to the procedure of changes, i.e., how the topology or links change. Following such an approach, we propose the notion of the “instant path” and define two dynamic network models based on the instant path. Based on these two models, we design distributed algorithms for the problem of information dissemination respectively, one of the fundamental distributing computing problems. The correctness of our algorithms is formally proved and their performance in time cost and communication cost is analyzed. Compared with existing connectivity based dynamic network models and algorithms, our procedure based ones are definitely easier to be instantiated in the practical design and deployment of dynamic networks.

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Perry, George L. W., and Neal J. Enright. "Spatial modelling of vegetation change in dynamic landscapes: a review of methods and applications." Progress in Physical Geography: Earth and Environment 30, no. 1 (January 2006): 47–72. http://dx.doi.org/10.1191/0309133306pp469ra.

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Because of the spatiotemporal scales involved and the logistical constraints in collecting landscape-level data, spatially explicit simulation models have become important tools in ecological and biogeographical studies conducted over broad extents. Here we review the methods used and some of the applications of landscape-level models of succession and disturbance dynamics. Mechanistic and stochastic models are compared and contrasted and the development, over the last 15 years, of spatial landscape models of ecological change is discussed. Coarse-grained spatial landscape models are compared with finer-grained individual-based approaches (eg, forest gap models). Management and monitoring applications of landscape models are considered alongside a discussion of the appropriate use of models in this context. A key area where spatial landscape models of the type described here need to develop is improved integration with the social sciences - both in terms of the parameters and the processes that the models incorporate. Finally issues related to scale and scaling are outlined and, in particular, the utility of methods for linking ecological models operating at disparate scales (eg, forest gap models versus landscape models) is examined.

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Wanamaker, Kayla A., and Shelley L. Brown. "The Relationship Between Patterns of Change in Dynamic Risk and Strength Scores and Reoffending for Men on Community Supervision." Criminal Justice and Behavior 48, no. 9 (February 19, 2021): 1208–28. http://dx.doi.org/10.1177/0093854821993512.

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Research is needed focusing on the predictive nature of dynamic risk and strength score changes. The current study includes 11,953 Canadian men under community supervision with Service Planning Instrument re-assessment data. Using a retrospective, multi-wave longitudinal design, hierarchical linear modeling (HLM) was conducted to assess patterns of change in total dynamic risk and strength scores across three to five timepoints over 30 months. Change parameters from the HLM were incorporated into regression models, linking change to three reoffending outcomes: technical violations, new charges, and new violent charges. Results indicated that total dynamic risk scores decreased over time and total dynamic strength scores increased over time, although the rate of change for both was gradual. Change in total dynamic risk scores was predictive of all outcomes, whereas change in total dynamic strength scores only predicted technical violations. Results demonstrated the utility of re-assessing dynamic risk and strength scores over time.

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Wegehenkel, M. "Modeling of vegetation dynamics in hydrological models for the assessment of the effects of climate change on evapotranspiration and groundwater recharge." Advances in Geosciences 21 (August 12, 2009): 109–15. http://dx.doi.org/10.5194/adgeo-21-109-2009.

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Abstract. Vegetation affects water balance of the land surface by e.g. storage of precipitation water in the canopy and soil water extraction by transpiration. Therefore, it is essential to consider the role of vegetation in affecting water balance by taking into account the temporal dynamics of e.g. leaf area index, rooting depth and stomatal conductance in hydrological models. However until now, most conceptual hydrological models do not treat vegetation as a dynamic component. This paper presents an analysis of the effects of the application of two different complex vegetation models combined with a hydrological model on the model outputs evapotranspiration and groundwater recharge. Both model combinations were used for the assessment of the effects of climate change on water balance in a mesoscale catchment loctated in the Northeastern German Lowlands. One vegetation model assumes a static vegetation development independent from environmental conditions. The other vegetation model calculates dynamic development of vegetation based on photosynthesis, respiration, allocation, and phenology. The analysis of the results obtained from both model combinations indicated the importance of taking into account vegetation dynamics in hydrological models especially if such models are used for the assessment of the impacts of climate change on water balance components.

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Lambin, Eric F. "Modelling and monitoring land-cover change processes in tropical regions." Progress in Physical Geography: Earth and Environment 21, no. 3 (September 1997): 375–93. http://dx.doi.org/10.1177/030913339702100303.

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Transformations in terrestrial ecosystems are increasingly regarded as an important element of global change. Quantitative data on where, when and why land-cover changes take place globally are still incomplete. This article reviews recent approaches to the monitoring and modelling of deforestation and dryland degradation in tropical regions. The review highlights the requirement to tailor the investigation method to the specific research question of interest. Different techniques to monitor land-cover changes at regional scales are analysed. The following modelling scenarios are discussed and illustrated by specific studies: projection of future land- cover changes with descriptive models, explanation of land-cover changes with empirical models, projection of future spatial patterns of changes with spatial statistical models, test of scenarios on future changes in land-cover with dynamic ecosystem models, and design of policy interventions with economic models. The article stresses the needs for a better integration of social science knowledge in land-cover change models and for a comprehensive theory of land-use changes.

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Arazmjoo, Hanieh, and Hossein Rahmanseresht. "A multi-dimensional meta-heuristic model for managing organizational change." Management Decision 58, no. 3 (September 13, 2019): 526–43. http://dx.doi.org/10.1108/md-07-2018-0795.

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Purpose The purpose of this paper is to offer a dynamic meta-heuristic model of effecting organizational change which informs smooth directing and routinizing change according to the specific situation relevant to every change attempt. Design/methodology/approach Because of the dynamic nature of variables and their interaction, developing a static model cannot be tenable. This study, therefore, attempts to generate a meta-heuristic method for constructing a dynamic organizational change model by combining qualitative methods (content analysis and Delphi Technique) and Artificial Neural Networks (Fuzzy Theory and Genetic Algorithm). Findings Each change program requires its unique method of implementation as change attempts are context specific. Hence, static models should give way to some dynamic ones. Whereas such static models abound, this paper stands out as offering a dynamic model for organizational change by using a rather unconventional method. Research limitations/implications This can be regarded as a road map informing higher echelons of the complexity and leadership of change, while at the same time helping change agents have access to acceptable amount of variables that can make their change attempts more promising. Originality/value This model contains more flexible variables which reflect the incumbent organizations’ situations. While almost all previous models of change attempt take into account a few/handful variables which are seen to impact on change solidly and independently. But such an analysis with the usual statistical and mathematical methods is not justified. This challenge is met here using metaheuristics and artificial intelligence methods. The model formulated, thus, is dynamic, non-linear and multi-dimensional. Entering the data related to any specific field turns it to a customized model suitable for use in a given field; and this is not only a contribution to the theory but also can allegedly increase the chance of the success of the change agent managing to utilize the optimal amount of variables suggested in this paper.

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Pacala, Stephen W., Charles D. Canham, and J. A. Silander Jr. "Forest models defined by field measurements: I. The design of a northeastern forest simulator." Canadian Journal of Forest Research 23, no. 10 (October 1, 1993): 1980–88. http://dx.doi.org/10.1139/x93-249.

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We introduce a new spatially explicit model of forest dynamics. The model is constructed from submodels that predict an individual tree's growth, survival, dispersal, and recruitment, and submodels that predict the local availability of resources. Competition is entirely mechanistic; plants interfere with one another only by depleting resources. We also describe maximum likelihood methods for estimating each of the submodels from data collected in the field. Over the past two years, we collected the necessary data for the dominant tree species in the Great Mountain Forest (Norfolk, Conn.). We report estimates of submodels for each species, and show that the calibrated population dynamic model predicts the structure and dynamics of natural forests. Finally, we contrast our model with the JABOWA–FORET family of forest models.

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Zhang, Wei-Bin. "Capital Accumulation, Technological Progress and Environmental Change in a Three-Sector Growth Model." International Journal of Information Systems and Social Change 3, no. 3 (July 2012): 1–18. http://dx.doi.org/10.4018/jissc.2012070101.

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This paper builds a dynamic growth model with wealth accumulation, technological change, and environmental change on the basis of the neoclassical growth theory with an alternative approach to household behavior. The model synthesizes the economic growth mechanism in the neoclassical growth theory, Arrow’s learning-by-doing, and the environmental change in some traditional dynamic models of environmental economics. It describes a dynamic interdependence among wealth accumulation, technological change, and environmental change under perfect competition with environmental taxes. The author simulated the model to demonstrate existence of equilibrium points and motion of the dynamic system. In particular, the author demonstrated effects of changes in the government policy and preference upon both short-run and long-run economic behavior of the system.

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Balocco, Raffaello, Angelo Cavallo, Antonio Ghezzi, and Jasmina Berbegal-Mirabent. "Lean business models change process in digital entrepreneurship." Business Process Management Journal 25, no. 7 (October 14, 2019): 1520–42. http://dx.doi.org/10.1108/bpmj-07-2018-0194.

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Purpose Business model change (BMC) is a process new ventures are frequently involved in, especially in dynamic environments like the digital industry: copying with it is a key issue for entrepreneurs attempting to shorten the transition between current and new business models (BMs) and avoid losses in terms of revenue, image and customer retention, while acquiring experience and validated learning in the process. The purpose of this paper is to propose a lean framework to support digital new ventures in the BMC process. Design/methodology/approach The study builds its contribution on two pillars: a review on BM and the lean thinking theories, and a multiple case study on three digital new ventures which underwent BMC. Findings The study shows how BMC in a digital context can beneficially follow lean principles, and how these principles can be integrated in an original lean framework to experiment on, validate and subsequently change a BM. Originality/value The authors provide the “single minute exchange of die” for BMC framework that extends and complements lean startup approaches to further relate lean thinking and BMC, thus operationalizing the process of BM experimenting and validation that enables change.

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Day, Steven M., Guang Yu, and David J. Wald. "Dynamic stress changes during earthquake rupture." Bulletin of the Seismological Society of America 88, no. 2 (April 1, 1998): 512–22. http://dx.doi.org/10.1785/bssa0880020512.

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Abstract We assess two competing dynamic interpretations that have been proposed for the short slip durations characteristic of kinematic earthquake models derived by inversion of earthquake waveform and geodetic data. The first interpretation would require a fault constitutive relationship in which rapid dynamic restrengthening of the fault surface occurs after passage of the rupture front, a hypothesized mechanical behavior that has been referred to as “self-healing.” The second interpretation would require sufficient spatial heterogeneity of stress drop to permit rapid equilibration of elastic stresses with the residual dynamic friction level, a condition we refer to as “geometrical constraint.” These interpretations imply contrasting predictions for the time dependence of the fault-plane shear stresses. We compare these predictions with dynamic shear stress changes for the 1992 Landers (M 7.3), 1994 Northridge (M 6.7), and 1995 Kobe (M 6.9) earthquakes. Stress changes are computed from kinematic slip models of these earthquakes, using a finite-difference method. For each event, static stress drop is highly variable spatially, with high stress-drop patches embedded in a background of low, and largely negative, stress drop. The time histories of stress change show predominantly monotonic stress change after passage of the rupture front, settling to a residual level, without significant evidence for dynamic restrengthening. The stress change at the rupture front is usually gradual rather than abrupt, probably reflecting the limited resolution inherent in the underlying kinematic inversions. On the basis of this analysis, as well as recent similar results obtained independently for the Kobe and Morgan Hill earthquakes, we conclude that, at the present time, the self-healing hypothesis is unnecessary to explain earthquake kinematics.

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Hasselblad, S., B. Björlenius, and B. Carlsson. "Use of dynamic models to study secondary clarifier performance." Water Science and Technology 37, no. 12 (June 1, 1998): 207–12. http://dx.doi.org/10.2166/wst.1998.0543.

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Efficient separation of solids and supernatant is crucial in an activated sludge system since performance of the system depends on the return of active biological solids. A simple discrete linear dynamic model is applied to secondary clarifier performance. Dynamic modelling could accurately predict movement of the sludge blanket height in secondary clarifiers and also indicate the level of the current limiting solids flux. Change in model parameters may indicate either altered sludge composition and settling characteristics, or disturbances in the sedimentation process.

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Niessen, Wolfgang Von, and Alexander Blumen. "Dynamic simulation of forest fires." Canadian Journal of Forest Research 18, no. 6 (June 1, 1988): 807–14. http://dx.doi.org/10.1139/x88-123.

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In this article we present the results of a simplified dynamic model for forest fires. We consider isotropic situations with different ignition mechanisms and different types of trees, models of crown and surface fires, and anisotropic cases that account for the influence of wind. We calculate the probabilities for a fire to become critical and characterize the temporal evolution and the spatial extent of the fire.

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Cardoso, Leonardo C. B., Maurício V. L. Bittencourt, and Alexandre A. Porsse. "Demand for light fuels in Brazil: an approach using spatial panel data models." Nova Economia 30, no. 1 (January 2020): 231–56. http://dx.doi.org/10.1590/0103-6351/4327.

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Abstract The need for changes in the current energy matrix is a reality due to the possibility of a shortage of fossil fuels and the environmental damage caused by emissions related to fossil fuel use. The correct prescription of public policies for energy markets depends on the knowledge of demand elasticities. Hence, the aim of this work was to estimate the main determinants of light fuel demands in Brazil. Dynamic and non-dynamic estimators were used, and the results indicated that both demands respond more to changes in gasoline prices than changes in ethanol prices. Therefore, public policies that aim to change consumption patterns should focus on gasoline prices.

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Journal articles on the topic 'Dynamic change models'

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