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Journal articles on the topic 'Variability analyzing'

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Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

Rajaram, Kumar, and Andreas Robotis. "Analyzing variability in continuous processes." European Journal of Operational Research 156, no. 2 (July 2004): 312–25. http://dx.doi.org/10.1016/s0377-2217(03)00044-4.

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2

Birdsong, David. "Analyzing variability in L2 ultimate attainment." Language, Interaction and Acquisition 12, no. 1 (July 27, 2021): 133–56. http://dx.doi.org/10.1075/lia.21001.bir.

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Abstract Ultimate attainment is typically more heterogeneous among second-language (L2) learners than among native speakers (e.g. Bley-Vroman, 1990). The present study offers a suite of simple analytical procedures aimed at exploring types and loci of variability in L2 attainment vis-à-vis those in the corresponding first language (L1), with special attention to certain learner-external factors that might condition such variabilities. To demonstrate the methods and their potential, we apply these procedures to learner and native acceptability judgment data published in Birdsong (1992). Under means analyses of item ratings and coefficients of variation (CV), a group of adult Anglophone learners of L2 French (ENS) are found to resemble native French controls (FNS). In contrast, under correlational analyses of ratings and CVs, ENS resemble FNS on grammatical items, but diverge on ungrammatical items. Correlational and means analyses of both CV and acceptability ratings reveal that ENS-FNS convergence is not predictable from the degree of FNS ratings variability, contra DeKeyser (2012). For both groups, we observe an interaction between FNS ratings variability and the grammatical status of items (ungrammatical vs. grammatical). Finally, for neither group do we find a relationship between the order of item presentation and ratings severity or CVs. We present our perspectives as a road map for future analyses of variabilities inherent in language learning outcomes.

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3

Jarrett, Jeffrey E., and Xia Pan. "Monitoring Variability and Analyzing Multivariate Autocorrelated Processes." Journal of Applied Statistics 34, no. 4 (May 2007): 459–69. http://dx.doi.org/10.1080/02664760701231849.

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4

De Palma, André, Cédric Fontan, and Asad J. Khattak. "Analyzing work departure time variability in Brussels." Reflets et perspectives de la vie économique XLIII, no. 4 (2004): 89. http://dx.doi.org/10.3917/rpve.434.0089.

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5

Brings, Jennifer, Marian Daun, Thorsten Weyer, and Klaus Pohl. "Analyzing goal variability in cyber-physical system networks." ACM SIGAPP Applied Computing Review 20, no. 2 (July 27, 2020): 19–35. http://dx.doi.org/10.1145/3412816.3412818.

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6

Choudhary, Tilendra, Mousumi Das, L. N. Sharma, and M. K. Bhuyan. "Analyzing seismocardiographic approach for heart rate variability measurement." Biomedical Signal Processing and Control 68 (July 2021): 102793. http://dx.doi.org/10.1016/j.bspc.2021.102793.

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7

Min, Chang-Gi. "Analyzing the Impact of Variability and Uncertainty on Power System Flexibility." Applied Sciences 9, no. 3 (February 8, 2019): 561. http://dx.doi.org/10.3390/app9030561.

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This study investigates the impact of variability and uncertainty on the flexibility of a power system. The variability and uncertainty make it harder to maintain the balance between load and generation. However, most existing studies on flexibility evaluation have not distinguished between the effects of variability and uncertainty. The countermeasures to address variability and uncertainty differ; thus, applying strategies individually tailored to variability and uncertainty is helpful for more efficient operation and planning of a power system. The first contribution of this study is in separating the variability and uncertainty, and determining which is more influential in terms of flexibility in specific system situations. A flexibility index, named the ramping capability shortage probability (RSP), is used to quantify the extent to which the variability and uncertainty affect the flexibility. The second contribution is to generate various scenarios for variability and uncertainty based on a modified IEEE-RTS-96, to evaluate the flexibility. The penetration level of renewable energy resources is kept the same in each scenario. The results of a sensitivity analysis show that variability is more effective than uncertainty for high and medium net loads.

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8

A. A. Rashid, Rabiatul, Puteri N. E. Nohuddin, and Zuraini Zainol. "Analyzing Climate Variability in Malaysia Using Association Rule Mining." International Journal of Engineering & Technology 7, no. 4.34 (December 13, 2018): 394. http://dx.doi.org/10.14419/ijet.v7i4.34.26881.

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Previous surveys proved that data mining is one of the methods that can be utilized for climate prediction, predominantly clustering and classification are the most applied methods in data mining to build a model to predict changes in the climate. Unlike the climate change, climate variability is a phenomenon where the occurrence of climate uncertainty is according to the changes year to year basis. This study is focusing to look at the effectiveness of the Association Rule Mining (ARM) techniques in predicting climate variability events in Malaysia. In this report, it explained how the patterns that exist within climate data is discovered using ARM and how the extracted pattern is used to predict climate variability. In this report also, a framework is developed to explain how ARM can generate rules and extract patterns from the data and how the extracted rules and patterns is used to develop a model for predicting climate variability event.

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9

Raimondi, Gianfranko, Aleksander Martynenko, S. Ostropolets, and N. Marchitto. "Artificial intelligence for heart rate variability analyzing with arrhythmias." Klinical Informatics and Telemedicine 14, no. 15 (December 20, 2019): 46–52. http://dx.doi.org/10.31071/kit2019.15.03.

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10

Vieira, Sidney Rose, Célia Regina Grego, and George Clarke Topp. "Analyzing spatial and temporal variability of soil water content." Bragantia 67, no. 2 (2008): 463–69. http://dx.doi.org/10.1590/s0006-87052008000200022.

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During the last two decades geoestatistical methods have been intensively used for in-depth descriptions of spatial variability. The objective of this study was to assess the spatial and temporal variability of soil water content. The measurements were taken with a TDR equipment to a 20 cm depth, in a nearly flat 1.2 ha field at the Central Experimental Farm of the Agriculture Canada, Ottawa. The soil classified as a Rideau soil series, is a clay loam soil. A square grid with 10 m spacing was laid out, resulting in 164 sampling points at which two TDR rods were installed to measure the water content down to 20 cm depth. Measurements were taken on 33 dates during the frost free months in 1987, 1988 and 1989. The spatial variability was analyzed examining the scaled semivariograms, the statistical parameters and the parameters of the models fit to individual semivariograms as a function of time. It was concluded that spatial dependence decreases as the soil gets drier and that results from one year connect almost continuously to other years. The topography and structure of topsoil horizon was the primary cause for the repeating spatial pattern of soil water content in successive samplings. The places where the mean value occurred in the field were more stable in time when there was spatial dependence. As the soil gets dryer the temporal stability of the spatial distribution tends to disappear due to the hydraulic conductivity controlling the water evaporation over the field

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11

Tsutakawa, Robert K. "Mixed Model for Analyzing Geographic Variability in Mortality Rates." Journal of the American Statistical Association 83, no. 401 (March 1988): 37–42. http://dx.doi.org/10.1080/01621459.1988.10478562.

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12

Son, Chang-Sik, Won-Seok Kang, Rock-Hyun Choi, Hyoung-Seob Park, Seongwook Han, and Yoon-Nyun Kim. "A probabilistic knowledge model for analyzing heart rate variability." Journal of the Korea Industrial Information System Society 20, no. 3 (June 30, 2015): 61–69. http://dx.doi.org/10.9723/jksiis.2015.20.3.061.

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13

SAERMARK, K., M. MOELLER, U. HINTZE, H. MOELGAARD, P. E. BLOCH THOMSEN, H. HUIKURI, T. MAKIKIALLIO, J. LEVITAN, and M. LEWKOWICZ. "COMPARISON OF RECENT METHODS OF ANALYZING HEART RATE VARIABILITY." Fractals 08, no. 04 (December 2000): 315–22. http://dx.doi.org/10.1142/s0218348x00000470.

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We compare three recently applied methods for analyzing heart rate variability: detrended fluctuation analysis (DFA), multiresolution wavelet analysis (WAV) and detrended time series analysis (DTS). In the comparison, both scale-dependent and scale-independent measures are considered. In agreement with recent results by Thurner et al.,9 we conclude that scale-dependent measures are well suited to separate healthy subjects from patients with heart disease. However, as regards the use in Kaplan-Meier cumulative survival curves, scale-independent measures (generally slope values) clearly outperform scale dependent measures (generally rms values). The comparison is mainly based on a database containing recordings from 428 patients with heart disease (myocardial infarct) and on a database containing 105 healthy subjects and 11 heart patients.

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14

Ali, Nazakat, Manzoor Hussain, and Jang-Eui Hong. "Analyzing Safety of Collaborative Cyber-Physical Systems Considering Variability." IEEE Access 8 (2020): 162701–13. http://dx.doi.org/10.1109/access.2020.3021460.

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15

König, M., and J. Timmer. "Analyzing X-ray variability by linear state space models." Astronomy and Astrophysics Supplement Series 124, no. 3 (September 1997): 589–96. http://dx.doi.org/10.1051/aas:1997104.

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16

Zhangyong, Li, Li Biao, and Xie Zhengxiang. "Extracting and analyzing sub-signals in heart rate variability." Colloids and Surfaces B: Biointerfaces 42, no. 2 (May 2005): 131–35. http://dx.doi.org/10.1016/j.colsurfb.2005.01.014.

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17

Karmakar, Chandan K., Jayavardhana Gubbi, Ahsan H. Khandoker, and Marimuthu Palaniswami. "Analyzing temporal variability of standard descriptors of Poincaré plots." Journal of Electrocardiology 43, no. 6 (November 2010): 719–24. http://dx.doi.org/10.1016/j.jelectrocard.2010.09.001.

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18

González-Cabán, Armando, and Charles W. McKetta. "Analyzing Fuel Treatment Costs." Western Journal of Applied Forestry 1, no. 4 (October 1, 1986): 116–21. http://dx.doi.org/10.1093/wjaf/1.4.116.

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Abstract Economically sound decisions on fuel treatment require knowledge of treatment costs. Fuel treatment costs derived using an economic cost concept on two National Forests were found to be higher than reported by accounting methods. Costs are sufficiently high and variable to question the economic feasibility of fuel treatments. Regression analysis did not show a strong relationship between fuel treatment costs and the physical characteristics of treatment sites. Management behavior and organization constraints may more successfully explain the magnitude and variability of fuel treatment costs. There are economies of scale in larger fuel treatment projects. Fuels managers emphasized hazard reduction over silvicultural objectives but believe that hazard targets could be achieved with lower fuel treatment levels. West. J. Appl. For 1:116-121, Oct. 1986.

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19

Drusch, Gautier, and J. M. Christian Bastien. "Analyzing Web pages visual scanpaths: between and within tasks variability." Work 41 (2012): 1559–66. http://dx.doi.org/10.3233/wor-2012-0353-1559.

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20

Samuels, Joanne G., and Robin L. Bliss. "Analyzing Variability in Pain Management Using Electronic Health Record Data." Journal of Nursing Care Quality 27, no. 4 (2012): 316–24. http://dx.doi.org/10.1097/ncq.0b013e318258a7d3.

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21

Trujano, R. Emmanuel, and Vladimir Orduña. "Reducing bias and analyzing variability in the time-left procedure." Behavioural Processes 113 (April 2015): 132–42. http://dx.doi.org/10.1016/j.beproc.2015.02.001.

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22

Sexton, Zachary F., Teresa J. Hughes, and Kiersten A. Wise. "Analyzing isolate variability of Macrophomina phaseolina from a regional perspective." Crop Protection 81 (March 2016): 9–13. http://dx.doi.org/10.1016/j.cropro.2015.11.012.

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23

Lenert, Matthew C., Randolph A. Miller, Yevgeniy Vorobeychik, and Colin G. Walsh. "A method for analyzing inpatient care variability through physicians’ orders." Journal of Biomedical Informatics 91 (March 2019): 103111. http://dx.doi.org/10.1016/j.jbi.2019.103111.

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24

Méndez, Fernando J., Melisa Menéndez, Alberto Luceño, and Inigo J. Losada. "Analyzing Monthly Extreme Sea Levels with a Time-Dependent GEV Model." Journal of Atmospheric and Oceanic Technology 24, no. 5 (May 1, 2007): 894–911. http://dx.doi.org/10.1175/jtech2009.1.

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Abstract A statistical model to analyze different time scales of the variability of extreme high sea levels is presented. This model uses a time-dependent generalized extreme value (GEV) distribution to fit monthly maxima series and is applied to a large historical tidal gauge record (San Francisco, California). The model allows the identification and estimation of the effects of several time scales—such as seasonality, interdecadal variability, and secular trends—in the location, scale, and shape parameters of the probability distribution of extreme sea levels. The inclusion of seasonal effects explains a large amount of data variability, thereby allowing a more efficient estimation of the processes involved. Significant correlation with the Southern Oscillation index and the nodal cycle, as well as an increase of about 20% for the secular variability of the scale parameter have been detected for the particular dataset analyzed. Results show that the model is adequate for a complete analysis of seasonal-to-interannual sea level extremes providing time-dependent quantiles and confidence intervals.

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25

Andronov, I. L., and V. I. Marsakova. "Variability of long-period pulsating stars. I. Methods for analyzing observations." Astrophysics 49, no. 3 (July 2006): 370–85. http://dx.doi.org/10.1007/s10511-006-0037-8.

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26

Dettmer, Jan, Charles W. Holland, and Stan E. Dosso. "Analyzing lateral seabed variability with Bayesian inference of seabed reflection data." Journal of the Acoustical Society of America 126, no. 1 (July 2009): 56–69. http://dx.doi.org/10.1121/1.3147489.

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27

Bezu, Abebe. "Analyzing Impacts of Climate Variability and Changes in Ethiopia: A Review." American Journal of Modern Energy 6, no. 3 (2020): 65. http://dx.doi.org/10.11648/j.ajme.20200603.11.

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28

Ueckerdt, Falko, Robert Brecha, and Gunnar Luderer. "Analyzing major challenges of wind and solar variability in power systems." Renewable Energy 81 (September 2015): 1–10. http://dx.doi.org/10.1016/j.renene.2015.03.002.

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29

Helmuth, B., B. R. Broitman, L. Yamane, S. E. Gilman, K. Mach, K. A. S. Mislan, and M. W. Denny. "Organismal climatology: analyzing environmental variability at scales relevant to physiological stress." Journal of Experimental Biology 213, no. 6 (February 26, 2010): 995–1003. http://dx.doi.org/10.1242/jeb.038463.

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30

Ghafouri-Azar, Mona, and Deg-Hyo Bae. "Analyzing the Variability in Low-Flow Projections under GCM CMIP5 Scenarios." Water Resources Management 33, no. 15 (December 2019): 5035–50. http://dx.doi.org/10.1007/s11269-019-02396-4.

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31

Fahrmeir, Ludwig, Frank Sagerer, and Gerald Sussmann. "Geoadditive regression for analyzing small-scale geographical variability in car insurance." Blätter der DGVFM 28, no. 1 (April 21, 2007): 47–65. http://dx.doi.org/10.1007/s11857-007-0014-2.

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32

Hayati, Fazel, Seed Maghsoodloo, Michael J. DeVivo, Robert E. Thomas, and Catherine Lemiere. "Quality control chart method for analyzing PEF variability in occupational asthma." American Journal of Industrial Medicine 51, no. 3 (2008): 223–28. http://dx.doi.org/10.1002/ajim.20559.

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33

Graña, Gilberto David, Cyrus P. Billimoria, and Kamal Sen. "Analyzing Variability in Neural Responses to Complex Natural Sounds in the Awake Songbird." Journal of Neurophysiology 101, no. 6 (June 2009): 3147–57. http://dx.doi.org/10.1152/jn.90917.2008.

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Studies of auditory processing in awake, behaving songbirds allow for the possibility of new classes of experiments, including those involving attention and plasticity. Detecting and determining the significance of plasticity, however, requires assessing the intrinsic variability in neural responses. Effects such as rapid plasticity have been investigated in the auditory system through the use of the spectrotemporal receptive field (STRF), a characterization of the properties of sounds to which a neuron best responds. Here we investigated neural response variability in awake recordings obtained from zebra finch field L, the analog of the primary auditory cortex. To quantify the level of variability in the neural recordings, we used three similarity measures: an STRF-based metric, a spike-train correlation-based metric, and a spike-train discrimination-based metric. We then extracted a number of parameters from these measures, quantifying how they fluctuated over time. Our results indicate that 1) awake responses are quite stable over time; 2) the different measures of response are complementary—specifically, the spike-train–based measures yield new information complementary to the STRF; and 3) different STRF parameters show distinct levels of variability. These results provide critical constraints for the design of robust decoding strategies and novel experiments on attention and plasticity in the awake songbird.

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34

Simpson, Sean L., Mohsen Bahrami, and Paul J. Laurienti. "A mixed-modeling framework for analyzing multitask whole-brain network data." Network Neuroscience 3, no. 2 (January 2019): 307–24. http://dx.doi.org/10.1162/netn_a_00065.

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The emerging area of brain network analysis considers the brain as a system, providing profound insight into links between system-level properties and health outcomes. Network science has facilitated these analyses and our understanding of how the brain is organized. While network science has catalyzed a paradigmatic shift in neuroscience, methods for statistically analyzing networks have lagged behind. To address this for cross-sectional network data, we developed a mixed-modeling framework that enables quantifying the relationship between phenotype and connectivity patterns, predicting connectivity structure based on phenotype, simulating networks to gain a better understanding of topological variability, and thresholding individual networks leveraging group information. Here we extend this comprehensive approach to enable studying system-level brain properties across multiple tasks. We focus on rest-to-task network changes, but this extension is equally applicable to the assessment of network changes for any repeated task paradigm. Our approach allows (a) assessing population network differences in changes between tasks, and how these changes relate to health outcomes; (b) assessing individual variability in network differences in changes between tasks, and how this variability relates to health outcomes; and (c) deriving more accurate and precise estimates of the relationships between phenotype and health outcomes within a given task.

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35

Seneviratne, Sonia I., and Randal D. Koster. "A Revised Framework for Analyzing Soil Moisture Memory in Climate Data: Derivation and Interpretation." Journal of Hydrometeorology 13, no. 1 (February 1, 2012): 404–12. http://dx.doi.org/10.1175/jhm-d-11-044.1.

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Abstract A revised framework for the analysis of soil moisture memory characteristics of climate models and observational data is derived from the approach proposed by Koster and Suarez. The resulting equation allows the expression of the month-to-month soil moisture autocorrelation as a function of 1) the initial soil moisture variability, 2) the (atmospheric) forcing variability over the considered time period, 3) the correlation between initial soil moisture and subsequent forcing, 4) the sensitivity of evaporation to soil moisture, and 5) the sensitivity of runoff to soil moisture. A specific new feature is the disentangling of the roles of initial soil moisture variability and forcing variability, which were both (for the latter indirectly) contributing to the seasonality term of the original formulation. In addition, a version of the framework entirely based on explicit equations for the underlying relationships (i.e., independent of soil moisture statistics at the following time step) is proposed. The validity of the derived equation is exemplified with atmospheric general circulation model (AGCM) simulations from the Global Land–Atmosphere Coupling Experiment (GLACE).

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36

Aoki, Mitsuhiro, Takezumi Nishibori, Yoichi Yokota, Kenichi Ando, Shigeo Sawai, Keisuke Mizuta, and Yatsuji Ito. "Efficiency of Analyzing Heart Rate Variability during Diagnosis and Treatment of Vertigo." Equilibrium Research 61, no. 1 (2002): 34–39. http://dx.doi.org/10.3757/jser.61.34.

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37

Bougara, Hanane, Kamila Baba Hamed, Christian Borgemeister, Bernhard Tischbein, and Navneet Kumar. "Analyzing Trend and Variability of Rainfall in The Tafna Basin (Northwestern Algeria)." Atmosphere 11, no. 4 (March 31, 2020): 347. http://dx.doi.org/10.3390/atmos11040347.

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Northwest Algeria has experienced fluctuations in rainfall between the two decades 1940s and 1990s from positive to negative anomalies, which reflected a significant decline in rainfall during the mid-1970s. Therefore, further analyzing rainfall in this region is required for improving the strategies on water resource management. In this study, we complement previous studies by dealing with sub basins that were not previously addressed in Tafna basin (our study area located in Northwest Algeria), and by including additional statistical methods (Kruskal–Wallis test, Jonckheere-Terpstra test, and the Friedman test) that were not earlier reported on the large scale (Northwest Algeria). In order to analyse the homogeneity, trends, and stationarity in rainfall time series for nine rainfall stations over the period 1979–2011, we have used several statistical tests. The results showed an increasing trend for annual rainfall after the break detected in 2007 for Djbel Chouachi, Ouled Mimoun, Sidi Benkhala stations using Hubert, Pettitt, and Buishand tests. The Lee and Heghinian test has detected a break at the same year in 2007 for all stations except Sebdou, Beni Bahdel, and Hennaya stations, which have a break date in 1980. We have confirmed this increasing trend for rainfall with other trend detection methods such as Mann Kendall and Sen’s method that highlighted an upward trend for all the stations in the autumn season, which is mainly due to an increase in rainfall in September and October. On a monthly scale, the date of rupture is different from one station to another because the time series are not homogeneous. In addition, we have applied three tests enabling further results: (i) the Jonckheere-Terpstra test has detected an upward trend for two stations (Khemis and Hennaya), (ii) Friedman test has indicated the difference between the mean rank again with Khemis and Hennaya stations and the Merbeh station, (iii) according to the Kruskal-Wallis test, there have been no variance detected between all the rainfall stations. The increasing trend in rainfall may lead to a rise in stream flow and enhance potential floods risks in low-lying regions of the study area.

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38

Zhao, Weilong, Zishen Xu, Wen Li, and Wei Wu. "Modeling and analyzing neural signals with phase variability using Fisher-Rao registration." Journal of Neuroscience Methods 346 (December 2020): 108954. http://dx.doi.org/10.1016/j.jneumeth.2020.108954.

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39

Kobayashi, Hiromitsu, Keita Ishibashi, and Hiroki Noguchi. "Heart Rate Variability; An Index for Monitoring and Analyzing Human Autonomic Activities." APPLIED HUMAN SCIENCE Journal of Physiological Anthropology 18, no. 2 (1999): 53–59. http://dx.doi.org/10.2114/jpa.18.53.

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40

Cameron, Kirk W., Ali Anwar, Yue Cheng, Li Xu, Bo Li, Uday Ananth, Jon Bernard, et al. "MOANA: Modeling and Analyzing I/O Variability in Parallel System Experimental Design." IEEE Transactions on Parallel and Distributed Systems 30, no. 8 (August 1, 2019): 1843–56. http://dx.doi.org/10.1109/tpds.2019.2892129.

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41

Daublain, Pierre, Kung-I. Feng, Michael D. Altman, Iain Martin, Suman Mukherjee, Rebecca Nofsinger, Alan B. Northrup, Richard Tschirret-Guth, Mark Cartwright, and Caroline McGregor. "Analyzing the Potential Root Causes of Variability of Pharmacokinetics in Preclinical Species." Molecular Pharmaceutics 14, no. 5 (April 3, 2017): 1634–45. http://dx.doi.org/10.1021/acs.molpharmaceut.6b01118.

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42

Hall, Damien, Ran Zhao, Masatomo So, Masayuki Adachi, Germán Rivas, John A. Carver, and Yuji Goto. "Recognizing and analyzing variability in amyloid formation kinetics: Simulation and statistical methods." Analytical Biochemistry 510 (October 2016): 56–71. http://dx.doi.org/10.1016/j.ab.2016.07.013.

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43

Grundeken, Maik J., Yuki Ishibashi, Philippe Généreux, Laura LaSalle, Javaid Iqbal, Joanna J. Wykrzykowska, Marie-Angèle Morel, et al. "Inter–Core Lab Variability in Analyzing Quantitative Coronary Angiography for Bifurcation Lesions." JACC: Cardiovascular Interventions 8, no. 2 (February 2015): 305–14. http://dx.doi.org/10.1016/j.jcin.2014.12.002.

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44

Ghaemi, Zahra, and Amanda D. Smith. "Analyzing variability and decomposing electricity-generation emission factors for three U.S. states." Sustainable Energy Technologies and Assessments 51 (June 2022): 101986. http://dx.doi.org/10.1016/j.seta.2022.101986.

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45

Misson, Laurent. "MAIDEN: a model for analyzing ecosystem processes in dendroecology." Canadian Journal of Forest Research 34, no. 4 (April 1, 2004): 874–87. http://dx.doi.org/10.1139/x03-252.

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Ecophysiological and dendroecological data from a temperate sessile oak (Quercus petraea (Matt.) Liebl.) stand in Belgium were used to develop and parameterize a dendroecological process-based model. The purpose of this model is to serve as a tool for exploring the relationship between climate variability and tree growth based on dendro ecological data. When parameterized, the model was able to correctly simulate measurements of bud-burst date, through fall (r2 = 0.95), soil water content (r2 = 0.81), transpiration (r2 = 0.80), and ring-width series from 1960 to 1999 (r2 = 0.46). Model sensitivity analysis showed that atmospheric vapor pressure deficit is the major controlling factor of transpiration in this type of ecosystem. The model shows that bole increment is principally controlled by temperature because it affects the phenological process of bud burst and thus the growing season length. Precipitation variability does not affect variation of transpiration rate and bole increment because calculated soil water stress is negligible during the simulation period. Discrepancies between observed and simulated bole increment may be a consequence of stand density variations and worm defoliation in the spring. The MAIDEN model is particularly suited for dendreocological analysis because it takes simple species, site condition, and climatic variables as input.

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46

Liu, Zihao, Tomoko Mizutani, Takuya Saraya, Masaharu Kobayashi, and Toshiro Hiramoto. "Cause analysis of width-dependence of on-current variability in thin gate-all-around silicon nanowire MOSFET." Japanese Journal of Applied Physics 61, SC (February 7, 2022): SC1002. http://dx.doi.org/10.35848/1347-4065/ac3a8c.

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Abstract In this study, the width dependence of on-current variability in extremely narrow gate-all-around silicon nanowire MOSFET down to 2 nm width is analyzed by variability decomposition into components as well as analyzing the Pelgrom plot. It is found that the current variability rapidly increases below 4 nm mainly due to quantum-effect-induced threshold voltage variability and silicon-thickness-fluctuation-induced mobility fluctuation. The current variability becomes even worse in 2 nm, which is fundamentally caused by line width roughness.

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47

Hart, L. P., and O. Schabenberger. "Variability of Vomitoxin in Truckloads of Wheat in a Wheat Scab Epidemic Year." Plant Disease 82, no. 6 (June 1998): 625–30. http://dx.doi.org/10.1094/pdis.1998.82.6.625.

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Wheat scab, caused by Gibberella zeae, has been a serious disease in parts of the Midwest. One factor contributing to the importance of wheat scab is the contamination of grain by the mycotoxin vomitoxin (deoxynivalenol, DON), a toxic secondary metabolite. The U.S. Food and Drug Administration advisory levels for vomitoxin in wheat and wheat products require an accurate and precise assessment of vomitoxin concentration. In this study, randomly collecting probes of wheat from individual trucks and analyzing the ground wheat from each probe produced significantly less variability than subsampling and analyzing 50 g of whole kernels from the probes. The variability introduced by subsampling the probes and analyzing 50 g of whole kernels affects the precision and confidence of vomitoxin estimates. Tables of confidence intervals were developed for different sampling and subsampling patterns. To be 95% certain that the true vomitoxin concentration does not exceed the sample estimate by 1 μg/g, analyzing either four individual probes or 5–12% subsamples of these probes would be sufficient. To increase the accuracy to about 0.5 μg/g, either an analysis of seven probes or a 5–12% subsample of 10 probes would be necessary, based on a one-sided confidence interval.

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Veiga, Sandro F., Emanuel Giarolla, Paulo Nobre, and Carlos A. Nobre. "Analyzing the Influence of the North Atlantic Ocean Variability on the Atlantic Meridional Mode on Decadal Time Scales." Atmosphere 11, no. 1 (December 18, 2019): 3. http://dx.doi.org/10.3390/atmos11010003.

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Important features of the Atlantic meridional mode (AMM) are not fully understood. We still do not know what determines its dominant decadal variability or the complex physical processes that sustain it. Using reanalysis datasets, we investigated the influence of the North Atlantic Ocean variability on the dominant decadal periodicity that characterizes the AMM. Statistical analyses demonstrated that the correlation between the sea surface temperature decadal variability in the Atlantic Ocean and the AMM time series characterizes the Atlantic multidecadal oscillation (AMO). This corroborates previous studies that demonstrated that the AMO precedes the AMM. A causal inference with a newly developed rigorous and quantitative causality analysis indicates that the AMO causes the AMM. To further understand the influence of the subsurface ocean on the AMM, the relationship between the ocean heat content (0–300 m) decadal variability and AMM was analyzed. The results show that although there is a significant zero-lag correlation between the ocean heat content in some regions of the North Atlantic (south of Greenland and in the eastern part of the North Atlantic) and the AMM, their cause-effect relationship on decadal time scales is unlikely. By correlating the AMO with the ocean heat content (0–300 m) decadal variability, the former precedes the latter; however, the causality analysis shows that the ocean heat content variability drives the AMO, corroborating several studies that point out the dominant role of the ocean heat transport convergence on AMO.

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Park, Dong-Su, Dong-Kyu Kim, and Ki-Won Chong. "A Method to Manage Requirements Analyzing the Commonality and Variability in Product Line." KIPS Transactions:PartD 13D, no. 7 (December 31, 2006): 909–22. http://dx.doi.org/10.3745/kipstd.2006.13d.7.909.

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Pletz, Everaldo, Felipe N. Arroyo, Diego H. Almeida, Raquel S. Cavalheiro, Julio C. Pigozzo, André L. Christoforo, and Francisco A. R. Lahr. "Analyzing the Variability of the Flexural Rigidity in Pre-stressed Laminated Wood Plates." International Journal of Materials Engineering 7, no. 5 (October 1, 2017): 88–92. http://dx.doi.org/10.5923/j.ijme.20170705.02.

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