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Articoli di riviste sul tema "Variability Models"

Autore: Grafiati

Pubblicato: 14 dicembre 2024

Ultima modifica: 25 luglio 2025

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1

Temple, Paul, Mathieu Acher, Jean-Marc Jezequel, and Olivier Barais. "Learning Contextual-Variability Models." IEEE Software 34, no. 6 (2017): 64–70. http://dx.doi.org/10.1109/ms.2017.4121211.

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2

Lamprecht, Anna-Lena, Stefan Naujokat, and Ina Schaefer. "Variability Management beyond Feature Models." Computer 46, no. 11 (2013): 48–54. http://dx.doi.org/10.1109/mc.2013.299.

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3

Beuche, Danilo, Holger Papajewski, and Wolfgang Schröder-Preikschat. "Variability management with feature models." Science of Computer Programming 53, no. 3 (2004): 333–52. http://dx.doi.org/10.1016/j.scico.2003.04.005.

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4

Rees, Martin J. "Models for Variability in AGNs." Symposium - International Astronomical Union 159 (1994): 239–48. http://dx.doi.org/10.1017/s0074180900175096.

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In this talk I shall address three different processes relevant to continuum variability in AGNs. The first two refer to the physical conditions in the regions responsible for the non-thermal emission, and the implications of high brightness temperatures. The third is the distinctive type of flare that results when a star is tidally disrupted by a massive black hole; this process, which merits much further study, it likely to be specially important as a diagnostic of physical conditions in low-luminosity nearby nuclei.

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5

Schipper, M., and C. Wilkinson. "INCORPORATING PRODUCT VARIABILITY INTO QUALITY MODELS." Acta Horticulturae, no. 476 (November 1998): 49–58. http://dx.doi.org/10.17660/actahortic.1998.476.5.

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6

Mastichiadis, Apostolos, and John G. Kirk. "Models of Variability in Blazar Jets." Publications of the Astronomical Society of Australia 19, no. 1 (2002): 138–42. http://dx.doi.org/10.1071/as01108.

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AbstractDuring the last decade multiwavelength observations of blazars have revealed many interesting patterns in their emission across the EM spectrum. In the present article we will review the time-dependent one-zone models and the models which advocate an acceleration and a radiation zone, and we will make some comparisons between them, especially in light of recent observations of the so-called TeV blazars.

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7

Merck, Derek, Gregg Tracton, Rohit Saboo, et al. "Training models of anatomic shape variability." Medical Physics 35, no. 8 (2008): 3584–96. http://dx.doi.org/10.1118/1.2940188.

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8

Hayden, Brian. "Resource Models of Inter-Assemblage Variability." Lithic Technology 15, no. 3 (1986): 82–89. http://dx.doi.org/10.1080/01977261.1986.11754486.

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9

Aslin, Richard N. "MODELS OF OCULOMOTOR VARIABILITY IN INFANCY." Monographs of the Society for Research in Child Development 62, no. 2 (1997): 146–49. http://dx.doi.org/10.1111/j.1540-5834.1997.tb00521.x.

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10

van Groenendaal, Willem J. H. "Estimating NPV variability for deterministic models." European Journal of Operational Research 107, no. 1 (1998): 202–13. http://dx.doi.org/10.1016/s0377-2217(97)00138-0.

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11

Christensen, O. B., M. A. Gaertner, J. A. Prego, and J. Polcher. "Internal variability of regional climate models." Climate Dynamics 17, no. 11 (2001): 875–87. http://dx.doi.org/10.1007/s003820100154.

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12

Srivastava, A. "Stochastic models for capturing image variability." IEEE Signal Processing Magazine 19, no. 5 (2002): 63–76. http://dx.doi.org/10.1109/msp.2002.1028353.

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13

Xie, Hongmei, Keshe Ni, and Wenyu Liu. "Variability ordering of multiplicative frailty models." Metrika 79, no. 6 (2015): 659–70. http://dx.doi.org/10.1007/s00184-015-0571-7.

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14

Karataş, Ahmet Serkan, and Halit Oğuztüzün. "Attribute-based variability in feature models." Requirements Engineering 21, no. 2 (2014): 185–208. http://dx.doi.org/10.1007/s00766-014-0216-9.

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15

Donnelly, Peter, and Ward Whitt. "On reinforcement-depletion compartmental urn models." Journal of Applied Probability 26, no. 3 (1989): 477–89. http://dx.doi.org/10.2307/3214406.

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Abstract (sommario):

We verify and extend a conjecture of Purdue (1981) concerning the stochastic monotonicity of absorption times in a class of compartmental urn models. We also describe the effect of increased variability in the reinforcement sizes. Finally, we investigate variability in the content process for large populations. In many applications, compartmental models substantially under-represent the variability observed in the data, so that there has been considerable interest in modifying the model to increase the variability. We show that the squared coefficient of variation of the content is not asympto

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16

Donnelly, Peter, and Ward Whitt. "On reinforcement-depletion compartmental urn models." Journal of Applied Probability 26, no. 03 (1989): 477–89. http://dx.doi.org/10.1017/s0021900200038080.

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Abstract (sommario):

We verify and extend a conjecture of Purdue (1981) concerning the stochastic monotonicity of absorption times in a class of compartmental urn models. We also describe the effect of increased variability in the reinforcement sizes. Finally, we investigate variability in the content process for large populations. In many applications, compartmental models substantially under-represent the variability observed in the data, so that there has been considerable interest in modifying the model to increase the variability. We show that the squared coefficient of variation of the content is not asympto

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17

Zhang, L. L., L. M. Zhang, and W. H. Tang. "Similarity of soil variability in centrifuge models." Canadian Geotechnical Journal 45, no. 8 (2008): 1118–29. http://dx.doi.org/10.1139/t08-066.

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The soil specimen in a centrifuge model is subject to spatial variability depending on the method of sample preparation and the stress field induced by the centrifugal acceleration, even though it is intended to be uniformly prepared. In contrast to extensive measurements for studying the variability of in situ soil properties, soil variability in centrifuge models, especially that which is based on data at very close sampling distances, is less understood. In this paper, the variability of soil density in two centrifuge models is presented. Random field theory is adopted to characterize the s

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18

Sullivan, Roger J., and Henry F. Lyle, III. "Economic models are not evolutionary models." Behavioral and Brain Sciences 28, no. 6 (2005): 836. http://dx.doi.org/10.1017/s0140525x05430149.

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Henrich et al. reject the “selfishness axiom” within a narrowly-defined economic model, and are premature in claiming that they have demonstrated cross-cultural variability in “selfishness” as defined in broader evolutionary theory. We also question whether a key experimental condition, anonymity, can be maintained in the small, cohesive, social groupings employed in the study.

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19

Kawaguchi, Toshihiro, and Shin Mineshige. "AGN Variability." Symposium - International Astronomical Union 194 (1999): 356–63. http://dx.doi.org/10.1017/s0074180900162291.

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A number of monitoring observations of continuum emission from Active Galactic Nuclei (AGNs) have been made in optical-X-ray bands. The results obtained so far show (i) random up and down on timescales longer than decades, (ii) no typical timescales of variability on shorter timescales and (iii) decreasing amplitudes as timescales become shorter. The second feature indicates that any successful model must produce a wide variety of shot-amplitudes and -durations over a few orders in their light curves. In this sense, we conclude that the disk instability model is favored over the starburst mode

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20

Moon, Mi-Kyeong. "Variability Dependency Analysis for Generating Business Process Models based on Variability Decisions." KIPS Transactions:PartD 16D, no. 5 (2009): 791–800. http://dx.doi.org/10.3745/kipstd.2009.16d.5.791.

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21

Olaniyi, G.A., A.O. Adewale, and E.O. Oyeyemi. "Variability of hmF2 over San Vito: Comparison with International Reference Ionosphere (IRI) Model Predictions." Greener Journal of Physical Sciences 3, no. 2 (2013): 58–65. https://doi.org/10.5281/zenodo.3441438.

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A mid-latitude station was selected for this study to investigate the variations in the diurnal and seasonal median values of the height of the peak electron density of the F2-layer of the ionospheric region. Namely, San Vito (SAN)(40.60N,17.80E). Data were arranged into seasons as follow: December Solstices (November, December and January), March Equinox (February, March and April), June Solstices (May, June and July) and September Equinox (August, September and October). The diurnal and seasonal mean values were calculated, recorded and used in the analysis and comparison, this is the basis

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22

Alakoz, Gennady M., Alexander Vladimirovich Kotov, Morgan Madec, Sergey I. Plyaskota, Svetlana Mikhailovna Tolpygo, and Elizaveta Bydanova. "Sensory variability: rank models and neurophysiological prototypes." International Journal of System of Systems Engineering 12, no. 2 (2022): 150. http://dx.doi.org/10.1504/ijsse.2022.124978.

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23

Tolpygo, Svetlana Mikhailovna, Sergey I. Plyaskota, Morgan Madec, Alexander Vladimirovich Kotov, Gennady M. Alakoz, and Elizaveta Bydanova. "Sensory variability: rank models and neurophysiological prototypes." International Journal of System of Systems Engineering 12, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijsse.2022.10048860.

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24

STÖCKER, SABINE. "STABILITY DUE TO VARIABILITY IN DAISYWORLD MODELS." Journal of Biological Systems 03, no. 02 (1995): 331–39. http://dx.doi.org/10.1142/s0218339095000319.

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The Daisyworld is a mathematical model to describe a coupling between biosphere and climate. It was developed by J.E. Lovelock in context with the so-called Gaia-Hypothesis, which postulates that conditions on earth are suitable for life because of the existence of life itself. Some variations of the classical model will be discussed and so it can be shown, that the occurrence of mutations of the existing species will not harm the capability of regulating the system. Furthermore models provoking structures either in time or in space by self-organization will be presented. Such structures in ti

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25

Park, Bernadette, and Charles M. Judd. "Measures and models of perceived group variability." Journal of Personality and Social Psychology 59, no. 2 (1990): 173–91. http://dx.doi.org/10.1037/0022-3514.59.2.173.

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26

Radenen, Mathieu, and Thierry Artières. "Handling signal variability with contextual markovian models." Pattern Recognition Letters 35 (January 2014): 236–45. http://dx.doi.org/10.1016/j.patrec.2013.08.015.

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27

Ashburner, John, and Stefan Klöppel. "Multivariate models of inter-subject anatomical variability." NeuroImage 56, no. 2 (2011): 422–39. http://dx.doi.org/10.1016/j.neuroimage.2010.03.059.

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28

Burr, Robert L., and Marie J. Cowan. "Autoregressive spectral models of heart rate variability." Journal of Electrocardiology 25 (January 1992): 224–33. http://dx.doi.org/10.1016/0022-0736(92)90108-c.

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29

Behseta, Sam, Robert E. Kass, and Garrick L. Wallstrom. "Hierarchical models for assessing variability among functions." Biometrika 92, no. 2 (2005): 419–34. http://dx.doi.org/10.1093/biomet/92.2.419.

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30

Hawkins, M. R. S. "Quasar Variability: New Surveys and New Models." International Astronomical Union Colloquium 184 (2002): 351–56. http://dx.doi.org/10.1017/s0252921100031006.

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AbstractIn this paper results from a monitoring programme of a large sample of quasars comprising regular yearly observations over a period of 23 years are presented. Structure functions of the light curves are calculated and compared with predictions for models of quasar variability of current interest. These include recently published models of varibility from accretion disk instability, variability from starbursts or supernovae, and variations caused by the microlensing effect of compact bodies along the line of sight. The analysis favours the accretion disk model for low luminosity AGN, bu

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31

Zerva, Aspasia. "Seismic loads predicted by spatial variability models." Structural Safety 11, no. 3-4 (1992): 227–43. http://dx.doi.org/10.1016/0167-4730(92)90016-g.

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32

Elston, D. A., and C. A. Glasbey. "Variability within system models: A case study." Agricultural Systems 37, no. 3 (1991): 309–18. http://dx.doi.org/10.1016/0308-521x(91)90039-d.

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33

Ford, Jessica H., Mark V. Bravington, and Jooke Robbins. "Incorporating individual variability into mark-recapture models." Methods in Ecology and Evolution 3, no. 6 (2012): 1047–54. http://dx.doi.org/10.1111/j.2041-210x.2012.00243.x.

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34

Auer, Martin T., and Steven W. Effler. "Variability in Photosynthesis: Impact on DO Models." Journal of Environmental Engineering 115, no. 5 (1989): 944–63. http://dx.doi.org/10.1061/(asce)0733-9372(1989)115:5(944).

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35

Huppenkothen, Daniela, Brendon J. Brewer, David W. Hogg, et al. "DISSECTING MAGNETAR VARIABILITY WITH BAYESIAN HIERARCHICAL MODELS." Astrophysical Journal 810, no. 1 (2015): 66. http://dx.doi.org/10.1088/0004-637x/810/1/66.

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36

Phillips, Adam S., Clara Deser, and John Fasullo. "Evaluating Modes of Variability in Climate Models." Eos, Transactions American Geophysical Union 95, no. 49 (2014): 453–55. http://dx.doi.org/10.1002/2014eo490002.

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37

Kolokoltseva, Irina, and Yaroslav Ivanyo. "Models of Variability of Locust Pest Number." System Analysis & Mathematical Modeling 6, no. 4 (2024): 465–77. https://doi.org/10.17150/2713-1734.2024.6(4).465-477.

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The article presents the results of constructing factor models characterizing the relationship between the number of locust pests and meteorological factors (daily air temperature and daily precipitation amounts), taking into account the natural and climatic characteristics of the territory on the example of municipal districts of the Irkutsk region. Two solutions to the problem are proposed: the construction of a regression expression of the dependence of the effective feature on average air temperatures and precipitation amounts for June; definition of a regression expression with a calculat

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38

Shimbara, Daisuke, Motoshi Saeki, Shinpei Hayashi, and Øystein Haugen. "Handling Quantity in Variability Models for System-of-Systems." International Journal of Software Engineering and Knowledge Engineering 31, no. 05 (2021): 693–724. http://dx.doi.org/10.1142/s0218194021500200.

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Problem: Modern systems contain parts that are themselves systems. Such complex systems thus have sets of subsystems that have their own variability. These subsystems contribute to the functionality of a whole system-of-systems (SoS). Such systems have a very high degree of variability. Therefore, a modeling technique for the variability of an entire SoS is required to express two different levels of variability: variability of the SoS as a whole and variability of subsystems. If these levels are described together, the model becomes hard to understand. When the variability model of the SoS is

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39

Bányai, Mihály, Zsombor Koman, and Gergő Orbán. "Population activity statistics dissect subthreshold and spiking variability in V1." Journal of Neurophysiology 118, no. 1 (2017): 29–46. http://dx.doi.org/10.1152/jn.00931.2016.

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Response variability, as measured by fluctuating responses upon repeated performance of trials, is a major component of neural responses, and its characterization is key to interpret high dimensional population recordings. Response variability and covariability display predictable changes upon changes in stimulus and cognitive or behavioral state, providing an opportunity to test the predictive power of models of neural variability. Still, there is little agreement on which model to use as a building block for population-level analyses, and models of variability are often treated as a subject

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40

Ndiaye, Cassien Diabe, Elsa Mohino, Juliette Mignot, and Dr MOHAMADOU SAIDOU SALL. "On the Detection of Externally Forced Decadal Modulations of the Sahel Rainfall over the Whole Twentieth Century in the CMIP6 Ensemble." Journal of Climate 35, no. 21 (2022): 6939–54. https://doi.org/10.1175/JCLI-D-21-0585.1.

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Abstract The Sahel semiarid region was marked during the twentieth century by significant modulations of its rainfall regime at the decadal time scale. Part of these modulations have been associated with the internal variability of the climate system, linked to changes in oceanic sea surface temperature. More recently, several studies have highlighted the influence of external forcings during the dry period in the 1980s and the recovery around the 2000s. In this work we evaluate the internally and externally driven decadal modulations of Sahel rainfall during the entire twentieth century using

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41

Solanki, Sami K. "Solar irradiance variability." Proceedings of the International Astronomical Union 2, no. 14 (2006): 279. http://dx.doi.org/10.1017/s1743921307010587.

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42

Yeh, Sang‐Wook, Seung‐Hwon Hyun, In‐Hong Park, and Xiao‐Tong Zheng. "Surface temperature variability in climate models with large and small internal climate variability." Quarterly Journal of the Royal Meteorological Society 147, no. 738 (2021): 3004–16. http://dx.doi.org/10.1002/qj.4112.

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43

Zavala-Garay, J., C. Zhang, A. M. Moore, et al. "Sensitivity of Hybrid ENSO Models to Unresolved Atmospheric Variability." Journal of Climate 21, no. 15 (2008): 3704–21. http://dx.doi.org/10.1175/2007jcli1188.1.

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Abstract A common practice in the design of forecast models for ENSO is to couple ocean general circulation models to simple atmospheric models. Therefore, by construction these models (known as hybrid ENSO models) do not resolve various kinds of atmospheric variability [e.g., the Madden–Julian oscillation (MJO) and westerly wind bursts] that are often regarded as “unwanted noise.” In this work the sensitivity of three hybrid ENSO models to this unresolved atmospheric variability is studied. The hybrid coupled models were tuned to be asymptotically stable and the magnitude, and spatial and tem

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44

Thiersen, Hannes, Michael Zacharias, and Markus Böttcher. "Simulations of Stochastic Long-term Variability in Leptonic Models for External-Compton and Synchrotron Self-Compton Dominated Blazars." Astrophysical Journal 925, no. 2 (2022): 177. http://dx.doi.org/10.3847/1538-4357/ac4013.

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Abstract In this work we investigate the nature of multiwavelength variability of blazars from a purely numerical approach. We use a time-dependent one-zone leptonic blazar emission model to simulate multiwavelength variability by introducing stochastic parameter variations in the emission region. These stochastic parameter variations are generated by Monte Carlo methods and have a characteristic power-law index of α = −2 in their power spectral densities. We include representative blazar test cases for a flat spectrum radio quasar and a high-synchrotron peaked BL Lacertae object for which the

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45

SPANIER, FELIX, and MATTHIAS WEIDINGER. "HADRONIC MODELING OF AGN VARIABILITY." International Journal of Modern Physics: Conference Series 08 (January 2012): 293–98. http://dx.doi.org/10.1142/s2010194512004734.

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The ongoing search for extragalactic gamma-ray sources reveals more and more blazars not classified as high-peaked BL Lac objects. These sources may not be understood in the context of purely leptonic emission models. In the present paper we study lepto-hadronic emission models, especially their time variation patterns. Since a number of non-linear processes are involved, timing relations are far more complex than in purely leptonic models and might eventually be used to discriminate different emission scenarios. First complete time dependent applications to 3C279 and 3C454.3 are presented.

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46

Harari, Ohad, Chaim I. Garfinkel, Shlomi Ziskin Ziv, et al. "Influence of Arctic stratospheric ozone on surface climate in CCMI models." Atmospheric Chemistry and Physics 19, no. 14 (2019): 9253–68. http://dx.doi.org/10.5194/acp-19-9253-2019.

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Abstract. The Northern Hemisphere and tropical circulation response to interannual variability in Arctic stratospheric ozone is analyzed in a set of the latest model simulations archived for the Chemistry-Climate Model Initiative (CCMI) project. All models simulate a connection between ozone variability and temperature/geopotential height in the lower stratosphere similar to that observed. A connection between Arctic ozone variability and polar cap surface air pressure is also found, but additional statistical analysis suggests that it is mediated by the dynamical variability that typically dr

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47

Perry, J. N. "Some Models for Spatial Variability of Animal Species." Oikos 51, no. 2 (1988): 124. http://dx.doi.org/10.2307/3565634.

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48

Ball, Frank, and Peter Donnelly. "A Unified Approach to Variability in Compartmental Models." Biometrics 44, no. 3 (1988): 685. http://dx.doi.org/10.2307/2531583.

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49

Moon, Mi-Kyeong, and Keun-Hyuk Yeom. "Variability Analysis Approach for Business Process Family Models." KIPS Transactions:PartD 15D, no. 5 (2008): 621–28. http://dx.doi.org/10.3745/kipstd.2008.15-d.5.621.

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50

Nestler, Steffen, Katharina Geukes, and Mitja D. Back. "Modeling Intraindividual Variability in Three-Level Multilevel Models." Methodology 14, no. 3 (2018): 95–108. http://dx.doi.org/10.1027/1614-2241/a000150.

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Abstract. The mixed-effects location scale model is an extension of a multilevel model for longitudinal data. It allows covariates to affect both the within-subject variance and the between-subject variance (i.e., the intercept variance) beyond their influence on the means. Typically, the model is applied to two-level data (e.g., the repeated measurements of persons), although researchers are often faced with three-level data (e.g., the repeated measurements of persons within specific situations). Here, we describe an extension of the two-level mixed-effects location scale model to such three-

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