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Artículos de revistas sobre el tema "Models of long term"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 5 de febrero de 2022

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1

Welfe, Wladyslaw. "Long-term macroeconometric models." Economic Modelling 28, no. 1-2 (2011): 741–53. http://dx.doi.org/10.1016/j.econmod.2010.05.002.

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2

Bestuzhev-Lada, Igor. "Long term models at work." Futures 17, no. 1 (1985): 78–80. http://dx.doi.org/10.1016/0016-3287(85)90103-x.

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3

Phenice, Lillian A., and Robert J. Griffore. "Virtual Models of Long-Term Care." Educational Gerontology 38, no. 5 (2012): 338–49. http://dx.doi.org/10.1080/03601277.2010.544603.

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4

Lubben, James E. "Models for Delivering Long Term Care." Home Health Care Services Quarterly 8, no. 2 (1987): 5–22. http://dx.doi.org/10.1300/j027v08n02_02.

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5

Goliński, Adam, and Paolo Zaffaroni. "Long memory affine term structure models." Journal of Econometrics 191, no. 1 (2016): 33–56. http://dx.doi.org/10.1016/j.jeconom.2015.09.006.

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6

Paredes, S., T. Rocha, P. de Carvalho, J. Henriques, M. Harris, and J. Morais. "Long term cardiovascular risk models’ combination." Computer Methods and Programs in Biomedicine 101, no. 3 (2011): 231–42. http://dx.doi.org/10.1016/j.cmpb.2010.12.015.

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7

Birău, Ramona, and Jatin Trivedi. "Investigating Long-Term Volatility of Warsaw Stock Exchange Based on Garch Family Models." International Journal of Scientific Research 2, no. 9 (2012): 239–41. http://dx.doi.org/10.15373/22778179/sep2013/79.

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8

Webster, Barry, and Suja Ramakrishnan. "Using Evolutionary Models with Mutations to Predict Long-Term Trends in Simple Social Interactions." International Journal of Modeling and Optimization 5, no. 3 (2015): 234–40. http://dx.doi.org/10.7763/ijmo.2015.v5.468.

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9

Goldman, Beryl D. "NONTRADITIONAL STAFFING MODELS in Long-Term Care." Journal of Gerontological Nursing 24, no. 9 (1998): 29–34. http://dx.doi.org/10.3928/0098-9134-19980901-11.

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10

Schlegel, Michael, and Bernd R. Noack. "On long-term boundedness of Galerkin models." Journal of Fluid Mechanics 765 (January 21, 2015): 325–52. http://dx.doi.org/10.1017/jfm.2014.736.

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AbstractWe investigate linear–quadratic dynamical systems with energy-preserving quadratic terms. These systems arise for instance as Galerkin systems of incompressible flows. A criterion is presented to ensure long-term boundedness of the system dynamics. If the criterion is violated, a globally stable attractor cannot exist for an effective nonlinearity. Thus, the criterion can be considered a minimum requirement for control-oriented Galerkin models of viscous fluid flows. The criterion is exemplified, for example, for Galerkin systems of two-dimensional cylinder wake flow models in the tran

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11

Lorz, Alexander, and Benoît Perthame. "Long-term behaviour of phenotypically structured models." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 470, no. 2167 (2014): 20140089. http://dx.doi.org/10.1098/rspa.2014.0089.

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Phenotypically structured equations arise in population biology to describe the interaction of species with their environment that brings the nutrients. This interaction usually leads to the selection of the fittest individuals. Models used in this area are highly nonlinear, and the question of long-term behaviour is usually not solved. However, there is a particular class of models for which convergence to an evolutionary stable distribution is proved, namely when the quasi-static assumption is made. This means that the environment, and thus the nutrient supply, reacts immediately to the popu

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12

Angelelli, Joe. "Promising Models for Transforming Long-Term Care." Gerontologist 46, no. 4 (2006): 428–30. http://dx.doi.org/10.1093/geront/46.4.428.

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13

Burris, Terry E., Robert E. Nordquist, and J. James Rowsey. "Long-Term Evaluation of Epithelial Downgrowth Models." Cornea 5, no. 4 (1986): 211–22. http://dx.doi.org/10.1097/00003226-198605040-00004.

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14

Diaz Meneses, Gonzalo, and Ignacio Luri Rodríguez. "Comparing short- and long-term breastfeeding models." Journal of Social Marketing 5, no. 4 (2015): 338–56. http://dx.doi.org/10.1108/jsocm-11-2014-0084.

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Purpose – The purpose of this paper is, as the health benefits of breastfeeding for mother and child have already been fully ascertained, to highlight the differences on why short- and long-term breastfeeding mothers adopt their respective behaviours. Based on these findings, a model for long-term breastfeeding is put forward here. The aim is to identify the specific variables and processes that create confidence during the early days in breastfeeding mothers so that social marketing campaigns can be deployed more effectively to sustain this behaviour over time. Design/methodology/approach – T

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15

Kenley, C. Robert, and Donald C. Armstead. "Discounting models for long-term decision making." Systems Engineering 7, no. 1 (2003): 13–24. http://dx.doi.org/10.1002/sys.10051.

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16

Bikker, J. A., and P. J. A. Van Els. "Dynamic portfolio models with long-term restrictions." De Economist 141, no. 4 (1993): 515–42. http://dx.doi.org/10.1007/bf02375170.

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17

Schoenberg, F. "Short-term Exciting, Long-term Correcting Models for Earthquake Catalogs." Bulletin of the Seismological Society of America 90, no. 4 (2000): 849–58. http://dx.doi.org/10.1785/0119990090.

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18

Popov, V. "COVID-19 pandemic and long-term development trajectories of East Asian and Western economic models." Pathways to Peace and Security, no. 2 (2020): 41–62. http://dx.doi.org/10.20542/2307-1494-2020-2-41-62.

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The article examines the reasons for the superior performance of East Asia in containing the human and economic costs of the 2020 coronavirus pandemic. The East Asian model is based on solidarity and priority of collective interests over individual interests, whereas the Western model emphasizes competition and guarantees of individual rights. The quantifiable characteristics that allow to draw a distinction between the two models are income and wealth inequalities, property and control over corporations, institutional capacity of the state (measured as homicide rate and the size of shadow eco

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19

Keskomon, Nuttanit, and Jaturon Harnsomburana. "Thai Character-Word Long Short-Term Memory Network Language Models with Dropout and Batch Normalization." International Journal of Machine Learning and Computing 10, no. 6 (2020): 783–88. http://dx.doi.org/10.18178/ijmlc.2020.10.6.1006.

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20

Khusainova, Zh S. "Models of youth entrepreneurship encouraging in Kazakhstan: current state, systemic issues and long-term outlook." BULLETIN OF THE KARAGANDA UNIVERSITY. ECONOMY SERIES 99, no. 3 (2020): 49–63. http://dx.doi.org/10.31489/2020ec3/49-63.

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21

Ashina, Messoud, Jakob Møller Hansen, Bára Oladóttir á Dunga, and Jes Olesen. "Human models of migraine — short-term pain for long-term gain." Nature Reviews Neurology 13, no. 12 (2017): 713–24. http://dx.doi.org/10.1038/nrneurol.2017.137.

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22

Montanari, G. C., G. Pattini, and L. Simoni. "Long Term Behavior of XLPE Insulated Cable Models." IEEE Power Engineering Review PER-7, no. 7 (1987): 34. http://dx.doi.org/10.1109/mper.1987.5526956.

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23

Montanari, G. C., G. Pattini, and L. Simoni. "Long - Term Behavior of XLPE Insulated Cable Models." IEEE Transactions on Power Delivery 2, no. 3 (1987): 596–602. http://dx.doi.org/10.1109/tpwrd.1987.4308151.

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24

Rodrigues, Josemar, Vicente G. Cancho, Mário de Castro, and Francisco Louzada-Neto. "On the unification of long-term survival models." Statistics & Probability Letters 79, no. 6 (2009): 753–59. http://dx.doi.org/10.1016/j.spl.2008.10.029.

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25

Whitman, Clifford. "Successful Models of Community-Based Long Term Care:." Physical & Occupational Therapy In Geriatrics 8, no. 1-2 (1990): 3–8. http://dx.doi.org/10.1080/j148v08n01_02.

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26

Whitman, Clifford. "Successful Models of Community-Based Long Term Care:." Physical & Occupational Therapy In Geriatrics 8, no. 1 (1990): 3–8. http://dx.doi.org/10.1300/j148v08n01_02.

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27

Zhao, Xiaobing, and Xian Zhou. "Discrete-time survival models with long-term survivors." Statistics in Medicine 27, no. 8 (2008): 1261–81. http://dx.doi.org/10.1002/sim.3018.

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28

Mason, Diana J. "Long-term Care: Investing in Models That Work." JAMA 318, no. 16 (2017): 1529. http://dx.doi.org/10.1001/jama.2017.15230.

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29

Wang, Fei, Guixi Liu, Haoyang Zhang, and Zhaohui Hao. "Robust long-term correlation tracking with multiple models." IET Image Processing 13, no. 7 (2019): 1056–65. http://dx.doi.org/10.1049/iet-ipr.2018.6209.

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30

Williams, Laron K. "Long-Term Effects in Models with Temporal Dependence." Political Analysis 24, no. 2 (2016): 243–62. http://dx.doi.org/10.1093/pan/mpw009.

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A dominant trend in models with binary outcomes is to control for unmodeled duration dependence by including temporal dependence variables. A second, distinct trend is to interpret both the short- and long-term effects of explanatory variables in autoregressive models. While the first trend is nearly ubiquitous in models with binary outcomes, the second trend has yet to be applied consistently beyond models with continuous outcomes. While scholars use temporal splines and cubic polynomials to model the underlying hazard rate, they have neglected the fact that this causes the explanatory variab

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31

Qin, Likuan, and Vadim Linetsky. "Long-term factorization in Heath–Jarrow–Morton models." Finance and Stochastics 22, no. 3 (2018): 621–41. http://dx.doi.org/10.1007/s00780-018-0365-7.

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32

Clark, Terry L., Eva C. Voldner, Robin L. Dennis, Steven K. Seilkop, Mayer Alvo, and Marvin P. Olson. "The evaluation of long-term sulfur deposition models." Atmospheric Environment (1967) 23, no. 10 (1989): 2267–88. http://dx.doi.org/10.1016/0004-6981(89)90189-3.

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33

Khosla, Ravish, Sonia Fahmy, Y. Charlie Hu, and Jennifer Neville. "Prediction models for long-term Internet prefix availability." Computer Networks 55, no. 3 (2011): 873–89. http://dx.doi.org/10.1016/j.comnet.2010.10.005.

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34

Deelstra, G., and F. Delbaen. "Long-term returns in stochastic interest rate models." Insurance: Mathematics and Economics 17, no. 2 (1995): 163–69. http://dx.doi.org/10.1016/0167-6687(95)00018-n.

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35

Johnson, Bradley, and Jesse McNinch. "LONG-TERM MORPHOLOGICAL EVOLUTION MODEL." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 64. http://dx.doi.org/10.9753/icce.v36.sediment.64.

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Nearshore morphology models predicting storm-scale erosion have been in use for the past several decades. These empirical tools typically focus on a single time-scale, which limits the utilization. For example, models developed to predict cross-shore storm erosion are poorly suited for longer-term simulations that include the beach recovery between events and gradients in longshore transport. Herein, the one-dimensional model CSHORE is extended to include shoreline change associated with along- shore variation in transport. A comparison of model predictions with long-term shoreline data from S

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36

Zhang, Xueru, Mohammad Mahdi Khalili, and Mingyan Liu. "Long-Term Impacts of Fair Machine Learning." Ergonomics in Design: The Quarterly of Human Factors Applications 28, no. 3 (2019): 7–11. http://dx.doi.org/10.1177/1064804619884160.

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Machine learning models developed from real-world data can inherit potential, preexisting bias in the dataset. When these models are used to inform decisions involving human beings, fairness concerns inevitably arise. Imposing certain fairness constraints in the training of models can be effective only if appropriate criteria are applied. However, a fairness criterion can be defined/assessed only when the interaction between the decisions and the underlying population is well understood. We introduce two feedback models describing how people react when receiving machine-aided decisions and ill

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37

XUE, Zelong, and Yang XUE. "Multi Long-Short Term Memory Models for Short Term Traffic Flow Prediction." IEICE Transactions on Information and Systems E101.D, no. 12 (2018): 3272–75. http://dx.doi.org/10.1587/transinf.2018edl8087.

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38

Robert, Jacques. "Long-term and short-term models for studying anthracycline cardiotoxicity and protectors." Cardiovascular Toxicology 7, no. 2 (2007): 135–39. http://dx.doi.org/10.1007/s12012-007-0022-4.

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39

Moses, Julianne I. "SL9 impact chemistry: Long-term photochemical evolution." International Astronomical Union Colloquium 156 (May 1996): 243–68. http://dx.doi.org/10.1017/s0252921100115532.

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One-dimensional photochemical models are used to provide an assessment of the chemical composition of the Shoemaker-Levy 9 impact sites soon after the impacts, and over time, as the impact-derived molecular species evolve due to photochemical processes. Photochemical model predictions are compared with the observed temporal variation of the impact-derived molecules in order to place constraints on the initial composition at the impact sites and on the amount of aerosol debris deposited in the stratosphere. The time variation of NH3, HCN, OCS, and H2S in the photochemical models roughly paralle

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40

Wankowicz, Jacek, and Jerzy Bielecki. "Models of the long-term mechanical strength of long rod composite insulators." IEEE Transactions on Dielectrics and Electrical Insulation 17, no. 2 (2010): 360–67. http://dx.doi.org/10.1109/tdei.2010.5448089.

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41

Gournellos, Theodore. "A theoretical Markov chain model of the long term landform evolution." Zeitschrift für Geomorphologie 41, no. 4 (1997): 519–29. http://dx.doi.org/10.1127/zfg/41/1997/519.

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42

Ariza, M. P., K. G. Wang, and M. Ortiz. "Atomistic Models of Long-Term Hydrogen Diffusion in Metals." Advances in Science and Technology 93 (October 2014): 118–23. http://dx.doi.org/10.4028/www.scientific.net/ast.93.118.

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The effective and efficient storage of hydrogen is one of the key challenges in developing a hydrogen economy. Recently, intensive research has been focused on developing and optimizing metal-based nanomaterials for high-speed, high-capacity, reversible hydrogen storage applications. Notably, the absorption and desorption of hydrogen in nanomaterials is characterized by an atomic, deformation-diffusion coupled process with a time scale of the order of seconds to hours--far beyond the time windows of existing simulation technologies such as Molecular Dynamics (MD) and Monte Carlo (MC) methods.

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43

Sakaguchi, Hidetsugu, and Haruo Honjo. "Anomalous Fluctuations in Autoregressive Models with Long-Term Memory." Journal of the Physical Society of Japan 84, no. 10 (2015): 104002. http://dx.doi.org/10.7566/jpsj.84.104002.

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44

Tjiputra, Jerry F., Are Olsen, Laurent Bopp, et al. "Long-term surface pCO2 trends from observations and models." Tellus B: Chemical and Physical Meteorology 66, no. 1 (2014): 23083. http://dx.doi.org/10.3402/tellusb.v66.23083.

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45

Deelstra, Griselda. "Long-Term Returns in Stochastic Interest Rate Models: Applications." ASTIN Bulletin 30, no. 1 (2000): 123–40. http://dx.doi.org/10.2143/ast.30.1.504629.

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AbstractWe extend the Cox-Ingersoll-Ross (1985) model of the short interest rate by assuming a stochastic reversion level, which better reflects the time dependence caused by the cyclical nature of the economy or by expectations concerning the future impact of monetary policies. In this framework, we have studied the convergence of the long-term return by using the theory of generalised Bessel-square processes. We emphasize the applications of the convergence results. A limit theorem proves evidence of the use of a Brownian motion with drift instead of the integral . For practice, however, thi

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46

Pons, O., and M. Lemdani. "Estimation and test in long-term survival mixture models." Computational Statistics & Data Analysis 41, no. 3-4 (2003): 465–79. http://dx.doi.org/10.1016/s0167-9473(02)00178-0.

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47

Elliott, S., S. Golds, I. Sissons, and H. Wilson. "Long-term care: a review of global funding models." British Actuarial Journal 20, no. 1 (2015): 167–208. http://dx.doi.org/10.1017/s1357321714000300.

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AbstractThis paper looks at alternative long-term care (LTC) funding models and options. It analyses six global models and the potential applications to the United Kingdom. The paper provides a demographic overview for each of the six countries to illustrate the increasing global demand for LTC and the funding thereof that is becoming a global critical issue. It examines the approaches each country has taken to address the funding issue with varying degrees of public/private co-operation and partnerships. The authors hope that the lessons learned from these other markets will assist the United

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48

Van Cruchten, Steven J. "Preterm birth: Epidemiology, long-term morbidity and translational models." Reproductive Toxicology 72 (September 2017): 21. http://dx.doi.org/10.1016/j.reprotox.2017.06.082.

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49

MOISE, N. SYDNEY. "Long-Term Monitoring of Animal Models of Sudden Death." Journal of Cardiovascular Electrophysiology 11, no. 12 (2000): 1342–44. http://dx.doi.org/10.1046/j.1540-8167.2000.01342.x.

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50

Tang, Lisa, and Michael C. Ferris. "A Hierarchical Framework for Long-Term Power Planning Models." IEEE Transactions on Power Systems 30, no. 1 (2015): 46–56. http://dx.doi.org/10.1109/tpwrs.2014.2328293.

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