Journal articles: 'Variability'

1

Miller, Patricia H. "Order in Variability, Variability in Order." Human Development 45, no. 3 (2002): 161–66. http://dx.doi.org/10.1159/000057071.

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McMurray, Bob, and Edward Wasserman. "Variability in languages, variability in learning?" Behavioral and Brain Sciences 32, no. 5 (October 2009): 459–60. http://dx.doi.org/10.1017/s0140525x09990926.

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AbstractIn documenting the dizzying diversity of human languages, Evans & Levinson (E&L) highlight the lack of universals. This suggests the need for complex learning. Yet, just as there is no universal structure, there may be no universal learning mechanism responsible for language. Language is a behavior assembled by many processes, an assembly guided by the language being learned.

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Yeo, Hee-Jung. "Investment and Firm Performance Variability." Journal of Korea Trade 27, no. 1 (February 28, 2023): 60–78. http://dx.doi.org/10.35611/jkt.2023.27.1.60.

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Purpose - The study analyzed 90 online firms worldwise and observed them for ten years to investigate their investments and firm performance variabilities. This study attemped to verify the existence of agency problems in online firms. Through this, the paper intends to expand the scope of research in the fields of investment and firm value both empirically and in theory. This study also attempted to supplement the insufficient logic of previous studies by analyzing the relationship between investment and profitability. Design/methodology - In this study, the investment is subdivided into over-, under-, and neutral investments, and an empirical analysis of the firm performance was conducted. As investment generally has long-term effects, the impact of a firm's investment on future firm performance and variabilities in firm performance was considered over the short-and medium-term period. Findings - It was found that there was a negative relationship between firms with an overinvestment and future firm performance. Underinvestment has no clear statistically significant results on firm performance. This implies that overinvestment causes more reduction in future firm performance than underinvestment. It was also found that underinvestment and overinvestment significantly increased the variability of firm performance. A positive significance was found between under- and over- investment with a variability of 3 years and overinvestment with a variability of 4 years in the future. A negative relationship was found between neutral investment propensity and future performance variabilities. Neutral investment has less effect on the future performance variability of a firm than a firm’s overinvestment and underinvestment. For online firms, underinvestment and overinvestment have a greater effect on the firm's future performance variability than neutral investment. Originality/value - The agency theory predicts that information asymmetry and adverse selection problems exacerbate conflicts of interest among stakeholders, thus firm performance. The study contributed to accumulating research on online firms that are currently underexplored by analyzing the investment behavior of major firms in the online industry.

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Čuřín, Michal. "Variabilita obrazu výjimečnosti / The Variability of the Image of Exceptionality." Stredoeurópske pohľady 2, no. 2 (December 22, 2020): 76–83. http://dx.doi.org/10.17846/cev.2020.02.2.76-83.

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Guerci, B., and J. P. Sauvanet. "Subcutaneous insulin: pharmacokinetic variability and glycemic variability." Diabetes & Metabolism 31, no. 4 (September 2005): 4S7–4S24. http://dx.doi.org/10.1016/s1262-3636(05)88263-1.

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Ayroles, Julien F., Sean M. Buchanan, Chelsea O’Leary, Kyobi Skutt-Kakaria, Jennifer K. Grenier, Andrew G. Clark, Daniel L. Hartl, and Benjamin L. de Bivort. "Behavioral idiosyncrasy reveals genetic control of phenotypic variability." Proceedings of the National Academy of Sciences 112, no. 21 (May 7, 2015): 6706–11. http://dx.doi.org/10.1073/pnas.1503830112.

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Quantitative genetics has primarily focused on describing genetic effects on trait means and largely ignored the effect of alternative alleles on trait variability, potentially missing an important axis of genetic variation contributing to phenotypic differences among individuals. To study the genetic effects on individual-to-individual phenotypic variability (or intragenotypic variability), we used Drosophila inbred lines and measured the spontaneous locomotor behavior of flies walking individually in Y-shaped mazes, focusing on variability in locomotor handedness, an assay optimized to measure variability. We discovered that some lines had consistently high levels of intragenotypic variability among individuals, whereas lines with low variability behaved as although they tossed a coin at each left/right turn decision. We demonstrate that the degree of variability is itself heritable. Using a genome-wide association study (GWAS) for the degree of intragenotypic variability as the phenotype across lines, we identified several genes expressed in the brain that affect variability in handedness without affecting the mean. One of these genes, Ten-a, implicates a neuropil in the central complex of the fly brain as influencing the magnitude of behavioral variability, a brain region involved in sensory integration and locomotor coordination. We validated these results using genetic deficiencies, null alleles, and inducible RNAi transgenes. Our study reveals the constellation of phenotypes that can arise from a single genotype and shows that different genetic backgrounds differ dramatically in their propensity for phenotypic variabililty. Because traditional mean-focused GWASs ignore the contribution of variability to overall phenotypic variation, current methods may miss important links between genotype and phenotype.

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Imaizumi, Satoshi, Hartono Abdoerrachman, Seiji Niimi, and Masanobu Kumada. "Vocal Variability." Koutou (THE LARYNX JAPAN) 8, no. 2 (1996): 116–22. http://dx.doi.org/10.5426/larynx1989.8.2_116.

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8

Suh, Sunghwan, and Jae Hyeon Kim. "Glucose Variability." Journal of Korean Diabetes 15, no. 4 (2014): 196. http://dx.doi.org/10.4093/jkd.2014.15.4.196.

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9

Normando, David. "Explaining variability." Dental Press Journal of Orthodontics 21, no. 6 (December 2016): 15–16. http://dx.doi.org/10.1590/2177-6709.21.6.015-016.edt.

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10

Damiani, Ferruccio, Reiner Hähnle, Eduard Kamburjan, Michael Lienhardt, and Luca Paolini. "Variability modules." Journal of Systems and Software 195 (January 2023): 111510. http://dx.doi.org/10.1016/j.jss.2022.111510.

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11

Service, F. J. "Glucose Variability." Diabetes 62, no. 5 (April 23, 2013): 1398–404. http://dx.doi.org/10.2337/db12-1396.

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12

Stokes, Patricia D. "Learned variability." Animal Learning & Behavior 23, no. 2 (June 1995): 164–76. http://dx.doi.org/10.3758/bf03199931.

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13

Siegler, Robert S. "Cognitive variability." Developmental Science 10, no. 1 (January 2007): 104–9. http://dx.doi.org/10.1111/j.1467-7687.2007.00571.x.

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14

Wensink, Maarten Jan, Linda Juel Ahrenfeldt, and Sören Möller. "Variability Matters." International Journal of Environmental Research and Public Health 18, no. 1 (December 28, 2020): 157. http://dx.doi.org/10.3390/ijerph18010157.

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Much of science, including public health research, focuses on means (averages). The purpose of the present paper is to reinforce the idea that variability matters just as well. At the hand of four examples, we highlight four classes of situations where the conclusion drawn on the basis of the mean alone is qualitatively altered when variability is also considered. We suggest that some of the more serendipitous results have their origin in variability.

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Le Floch, Jean-Pierre, and Laurence Kessler. "Glucose Variability." Journal of Diabetes Science and Technology 10, no. 4 (February 15, 2016): 885–91. http://dx.doi.org/10.1177/1932296816632003.

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16

Armstrong, Anna. "Variability matters." Nature Geoscience 4, no. 12 (November 30, 2011): 819. http://dx.doi.org/10.1038/ngeo1345.

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Stempel, D. A. "Asthma variability." European Respiratory Journal 21, no. 5 (May 2003): 909.2–910. http://dx.doi.org/10.1183/09031936.03.00114303b.

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18

Fairhall, Adrienne L. "Whither variability?" Nature Neuroscience 22, no. 3 (February 11, 2019): 329–30. http://dx.doi.org/10.1038/s41593-019-0344-0.

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19

Marshall, Lynne. "Audiometric variability." Journal of the Acoustical Society of America 89, no. 4B (April 1991): 1879. http://dx.doi.org/10.1121/1.2029362.

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20

Schluter, Dolph. "Wild variability." Nature 331, no. 6156 (February 1988): 496. http://dx.doi.org/10.1038/331496a0.

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21

Wrightson, Anne. "Laser Variability." Physiotherapy 77, no. 7 (July 1991): 460. http://dx.doi.org/10.1016/s0031-9406(10)61835-4.

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22

Davis, Kermit G., and Susan E. Kotowski. "Postural Variability." Human Factors: The Journal of the Human Factors and Ergonomics Society 56, no. 7 (April 4, 2014): 1249–61. http://dx.doi.org/10.1177/0018720814528003.

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23

Potter, Stephen B. "Polarimetric Variability." Proceedings of the International Astronomical Union 7, S285 (September 2011): 117–20. http://dx.doi.org/10.1017/s1743921312000403.

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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 model, since fluctuations on days are hard to produce by the latter model.Inter-band correlations and time lags also impose great constraints on models. Thus, constructing wavelength and time dependent models remains as future work.

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25

Simpkins, Graham. "Agulhas variability." Nature Climate Change 8, no. 3 (February 27, 2018): 188. http://dx.doi.org/10.1038/s41558-018-0111-3.

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26

Spens, Michael. "Stirling's variability." Architectural Research Quarterly 6, no. 2 (June 2002): 101–6. http://dx.doi.org/10.1017/s1359135502291551.

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Thomas Muirhead's letter (arq 6/1, pp5–6) raises interesting points concerning Stirling's ‘variability’ – a quality I for one do not dispute: in my article (arq 5/4, pp333–353) I had chosen to focus on schemes which for one reason or another have been less fully revisited than more celebrated icons.

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27

ADGER, DAVID. "Combinatorial Variability." Journal of Linguistics 42, no. 3 (October 13, 2006): 503–30. http://dx.doi.org/10.1017/s002222670600418x.

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The purpose of this paper is to provide a plausibility argument for a new way of thinking about intra-personal morphosyntactic variation. The idea is embedded within the framework of the Minimalist Program, and makes use of notions of feature interpretability and feature checking. Specifically, I argue that underspecification of uninterpretable features in a matching relation with interpretable features allows us to model categoricality and variability within a single system. Unlike many current approaches to intra-personal variation (which involve multiple grammars or building stochastic weightings into the grammar itself), the system attempts to predict (rather than capture) frequencies of variants. It does this by combining an evaluation metric for the acquisition of uninterpretable features with the standard properties of features and syntactic operations in the Minimalist framework. The argument is made through a case study of was/were variation in a Scottish dialect.

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28

Streets, David G., Barry M. Lesht, Jack D. Shannon, and Thomas D. Veselka. "Climatological variability." Environmental Science & Technology 19, no. 10 (October 1985): 887–93. http://dx.doi.org/10.1021/es00140a002.

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29

Lacey, C. G. "Quasar variability." Nature 371, no. 6494 (September 1994): 210. http://dx.doi.org/10.1038/371210a0.

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30

Cruz, Maria. "Superorbital Variability." Science 341, no. 6147 (August 15, 2013): 696.1–696. http://dx.doi.org/10.1126/science.341.6147.696-a.

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31

Labitzke, K., B. Naujokat, and J. J. Barnett. "Interannual variability." Advances in Space Research 10, no. 12 (January 1990): 163–84. http://dx.doi.org/10.1016/0273-1177(90)90395-g.

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32

Magnasco, Marcelo. "Measuring variability." HFSP Journal 1, no. 3 (September 2007): 147–51. http://dx.doi.org/10.2976/1.2784546.

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33

Collins, Marin. "Interviewer Variability." Market Research Society. Journal. 39, no. 1 (January 1997): 1–14. http://dx.doi.org/10.1177/147078539703900105.

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34

Sanger, Terence D. "Controlling Variability." Journal of Motor Behavior 42, no. 6 (October 29, 2010): 401–7. http://dx.doi.org/10.1080/00222895.2010.526496.

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35

Hand, Michael. "Informational variability." Synthese 99, no. 3 (June 1994): 417–56. http://dx.doi.org/10.1007/bf01063996.

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36

Toraldo, Alessio, and Claudio Luzzatti. "Which variability?" Brain and Language 96, no. 2 (February 2006): 154–56. http://dx.doi.org/10.1016/j.bandl.2005.08.013.

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37

Chhieng, David C., Lynya I. Talley, Janie Roberson, Rose Marie Gatscha, Nirag C. Jhala, and Paul A. Elgert. "Interobserver variability." Cancer 96, no. 2 (March 22, 2002): 67–73. http://dx.doi.org/10.1002/cncr.10477.

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38

Berger, Ronald D. "QT variability." Journal of Electrocardiology 36 (December 2003): 83–87. http://dx.doi.org/10.1016/j.jelectrocard.2003.09.019.

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39

Sardarova, Irada. "On variability." Filologiya məsələləri Journal of Philological Issues, no. 5 (2024): 100. http://dx.doi.org/10.62837/2024.5.100.

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40

Schleicher, Bernd, Axel Arbet-Engels, Dominik Baack, Matteo Balbo, Adrian Biland, Michael Blank, Thomas Bretz, et al. "Fractional Variability—A Tool to Study Blazar Variability." Galaxies 7, no. 2 (May 30, 2019): 62. http://dx.doi.org/10.3390/galaxies7020062.

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Active Galactic Nuclei emit radiation over the whole electromagnetic spectrum up to TeV energies. Blazars are one subtype with their jets pointing towards the observer. One of their typical features is extreme variability on timescales, from minutes to years. The fractional variability is an often used parameter for investigating the degree of variability of a light curve. Different detection methods and sensitivities of the instruments result in differently binned data and light curves with gaps. As they can influence the physics interpretation of the broadband variability, the effects of these differences on the fractional variability need to be studied. In this paper, we study the systematic effects of completeness in time coverage and the sampling rate. Using public data from instruments monitoring blazars in various energy ranges, we study the variability of the bright TeV blazars Mrk 421 and Mrk 501 over the electromagnetic spectrum, taking into account the systematic effects, and compare our findings with previous results. Especially in the TeV range, the fractional variability is higher than in previous studies, which can be explained by the much longer (seven years compared to few weeks) and more complete data sample.

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41

Musz, Elizabeth, and Sharon L. Thompson-Schill. "Semantic variability predicts neural variability of object concepts." Neuropsychologia 76 (September 2015): 41–51. http://dx.doi.org/10.1016/j.neuropsychologia.2014.11.029.

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42

Bahmani-Oskooee, Mohsen. "Effects of exchange rate variability on inflation variability." World Development 19, no. 6 (June 1991): 729–33. http://dx.doi.org/10.1016/0305-750x(91)90207-x.

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43

Jochum, Markus, Clara Deser, and Adam Phillips. "Tropical Atmospheric Variability Forced by Oceanic Internal Variability." Journal of Climate 20, no. 4 (February 15, 2007): 765–71. http://dx.doi.org/10.1175/jcli4044.1.

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Abstract Atmospheric general circulation model experiments are conducted to quantify the contribution of internal oceanic variability in the form of tropical instability waves (TIWs) to interannual wind and rainfall variability in the tropical Pacific. It is found that in the tropical Pacific, along the equator, and near 25°N and 25°S, TIWs force a significant increase in wind and rainfall variability from interseasonal to interannual time scales. Because of the stochastic nature of TIWs, this means that climate models that do not take them into account will underestimate the strength and number of extreme events and may overestimate forecast capability.

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44

Gilbertson, David T., Yi Peng, Brian Bradbury, James P. Ebben, and Allan J. Collins. "Hemoglobin Level Variability: Anemia Management among Variability Groups." American Journal of Nephrology 30, no. 6 (2009): 491–98. http://dx.doi.org/10.1159/000243565.

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45

Kuehni, Rolf. "Focal Color Variability and Unique Hue Stimulus Variability." Journal of Cognition and Culture 5, no. 3-4 (2005): 409–26. http://dx.doi.org/10.1163/156853705774648554.

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AbstractThe degree to which physiology and culture have affected the formation of primitive color categories continues to be a matter of discussion. In this paper the degree of agreement between the ranges of individual color term foci for the four hue-based color categories yellow, green, blue, and red and individual choices of Munsell samples representing for the observers Hering's four unique hues is investigated. The color term focus range data are extracted from the survey results of the 110 unwritten languages of the World Color Survey, also in terms of the Munsell color order system. Agreement of approximately 90% between the two has been found, indicating the likelihood of a strong color vision system related physiological component in the formation of these four primitive hue categories.

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Kalbag, Arjun, and Carl Wassgren. "Inter-tablet coating variability: Tablet residence time variability." Chemical Engineering Science 64, no. 11 (June 2009): 2705–17. http://dx.doi.org/10.1016/j.ces.2009.02.037.

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47

Likourezos, Vicki, Slava Kalyuga, and John Sweller. "The Variability Effect: When Instructional Variability Is Advantageous." Educational Psychology Review 31, no. 2 (January 12, 2019): 479–97. http://dx.doi.org/10.1007/s10648-019-09462-8.

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48

Park, Hanyong, and Jieun Lee. "“Perceived” talker variability in high-variability perceptual training." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A80. http://dx.doi.org/10.1121/10.0018236.

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It has been suggested that higher talker and acoustic variabilities in training materials are the driving factors impacting learning advantages, such as better generalization and longer retention of training effects. To investigate whether such advantages are diminished when trainees do not perceive talker variability in training stimuli, we gave three days of training on identifying Korean stop contrasts to three groups of English speakers: a group with three distinctive-voice stimuli (DV), a group with three similar-voice stimuli (SV), and a group with one-voice stimuli repeated three times (OV). We compared their performances twice: once right after the last training session and again one week later. Results showed the tendency that the DV group was better than the other two groups at generalizing their training effects to a novel talker, and this remained the same one week after their training. Although some participants in the SV group claimed they heard multiple voices during their training, their performance was similar to that of the OV group. Taken together, the results support the previously reported high-variability training benefits, and suggest that in training stimuli, the “perceived” talker variability, not just the talker and acoustic variability, is a driving factor for such benefits.

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49

Šnebergrová, J., H. Čížková, E. Neradová, B. Kapci, A. Rajchl, and M. Voldřich. "Variability of characteristic components of aronia." Czech Journal of Food Sciences 32, No. 1 (February 18, 2014): 25–30. http://dx.doi.org/10.17221/540/2012-cjfs.

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The variability of characteristic components of aronia (Aronia melanocarpa, black chokeberry) and to evaluate the chemical composition and quality of aronia products in order to complete the already available data and to facilitate authentication of fruit products were estimated. The mean values obtained for the set of aronia fruits were: soluble solids 19.9 °Brix, titratable acidity 9.8 g malic acid/kg, formol number 11.9 ml 0.1M NaOH/100 g, ash 6.7 g/kg, phosphorus 0.34 g/kg, potassium 2.90 g/kg, calcium  0.27 g/kg, magnesium 0.16 g/kg, sucrose 0.10 g/kg, glucose 47.1 g/kg, fructose 37.8 g/kg, sorbitol 66.1 g/kg, malic acid 9.6 g/kg, citric acid 1.0 g/kg, quinic acid 5.0 g/kg, isocitric acid 0.02 g/kg. These values can potentially be a useful tool to determine aronia content in foodstuffs or to determine adulteration of other fruit-based products with aronia. The most promising markers appeared to be the content of sorbitol, quinic acid and characteristic profile of anthocyanins measured by HPLC.  

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Holubec, V., and V. Dvořáček. "Protein structure variability of Aegilops species." Czech Journal of Genetics and Plant Breeding 41, Special Issue (July 31, 2012): 184–88. http://dx.doi.org/10.17221/6164-cjgpb.

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Journal articles on the topic 'Variability'

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