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An introduction to latent variable growth curve modeling concepts, issues, and application

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Duncan, Terry E; Strycker, Lisa A

2013-01-01

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader's familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book's CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples.Updated throughout, the second edition features three new chapters-growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group is...
Modeling water scarcity over south Asia: Incorporating crop growth and irrigation models into the Variable Infiltration Capacity (VIC) model

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Troy, Tara J.; Ines, Amor V. M.; Lall, Upmanu; Robertson, Andrew W.

2013-04-01

Large-scale hydrologic models, such as the Variable Infiltration Capacity (VIC) model, are used for a variety of studies, from drought monitoring to projecting the potential impact of climate change on the hydrologic cycle decades in advance. The majority of these models simulates the natural hydrological cycle and neglects the effects of human activities such as irrigation, which can result in streamflow withdrawals and increased evapotranspiration. In some parts of the world, these activities do not significantly affect the hydrologic cycle, but this is not the case in south Asia where irrigated agriculture has a large water footprint. To address this gap, we incorporate a crop growth model and irrigation model into the VIC model in order to simulate the impacts of irrigated and rainfed agriculture on the hydrologic cycle over south Asia (Indus, Ganges, and Brahmaputra basin and peninsular India). The crop growth model responds to climate signals, including temperature and water stress, to simulate the growth of maize, wheat, rice, and millet. For the primarily rainfed maize crop, the crop growth model shows good correlation with observed All-India yields (0.7) with lower correlations for the irrigated wheat and rice crops (0.4). The difference in correlation is because irrigation provides a buffer against climate conditions, so that rainfed crop growth is more tied to climate than irrigated crop growth. The irrigation water demands induce hydrologic water stress in significant parts of the region, particularly in the Indus, with the streamflow unable to meet the irrigation demands. Although rainfall can vary significantly in south Asia, we find that water scarcity is largely chronic due to the irrigation demands rather than being intermittent due to climate variability.
Modelling spatio-temporal variability of Mytilus edulis (L.) growth by forcing a dynamic energy budget model with satellite-derived environmental data

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Thomas, Yoann; Mazurié, Joseph; Alunno-Bruscia, Marianne; Bacher, Cédric; Bouget, Jean-François; Gohin, Francis; Pouvreau, Stéphane; Struski, Caroline

2011-11-01

In order to assess the potential of various marine ecosystems for shellfish aquaculture and to evaluate their carrying capacities, there is a need to clarify the response of exploited species to environmental variations using robust ecophysiological models and available environmental data. For a large range of applications and comparison purposes, a non-specific approach based on 'generic' individual growth models offers many advantages. In this context, we simulated the response of blue mussel ( Mytilus edulis L.) to the spatio-temporal fluctuations of the environment in Mont Saint-Michel Bay (North Brittany) by forcing a generic growth model based on Dynamic Energy Budgets with satellite-derived environmental data (i.e. temperature and food). After a calibration step based on data from mussel growth surveys, the model was applied over nine years on a large area covering the entire bay. These simulations provide an evaluation of the spatio-temporal variability in mussel growth and also show the ability of the DEB model to integrate satellite-derived data and to predict spatial and temporal growth variability of mussels. Observed seasonal, inter-annual and spatial growth variations are well simulated. The large-scale application highlights the strong link between food and mussel growth. The methodology described in this study may be considered as a suitable approach to account for environmental effects (food and temperature variations) on physiological responses (growth and reproduction) of filter feeders in varying environments. Such physiological responses may then be useful for evaluating the suitability of coastal ecosystems for shellfish aquaculture.
Stochastic process corrosion growth models for pipeline reliability

International Nuclear Information System (INIS)

Bazán, Felipe Alexander Vargas; Beck, André Teófilo

2013-01-01

Highlights: •Novel non-linear stochastic process corrosion growth model is proposed. •Corrosion rate modeled as random Poisson pulses. •Time to corrosion initiation and inherent time-variability properly represented. •Continuous corrosion growth histories obtained. •Model is shown to precisely fit actual corrosion data at two time points. -- Abstract: Linear random variable corrosion models are extensively employed in reliability analysis of pipelines. However, linear models grossly neglect well-known characteristics of the corrosion process. Herein, a non-linear model is proposed, where corrosion rate is represented as a Poisson square wave process. The resulting model represents inherent time-variability of corrosion growth, produces continuous growth and leads to mean growth at less-than-one power of time. Different corrosion models are adjusted to the same set of actual corrosion data for two inspections. The proposed non-linear random process corrosion growth model leads to the best fit to the data, while better representing problem physics
Using structural equation modeling to investigate relationships among ecological variables

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Malaeb, Z.A.; Kevin, Summers J.; Pugesek, B.H.

2000-01-01

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude -0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0
Accounting for inherent variability of growth in microbial risk assessment.

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Marks, H M; Coleman, M E

2005-04-15

Risk assessments of pathogens need to account for the growth of small number of cells under varying conditions. In order to determine the possible risks that occur when there are small numbers of cells, stochastic models of growth are needed that would capture the distribution of the number of cells over replicate trials of the same scenario or environmental conditions. This paper provides a simple stochastic growth model, accounting only for inherent cell-growth variability, assuming constant growth kinetic parameters, for an initial, small, numbers of cells assumed to be transforming from a stationary to an exponential phase. Two, basic, microbial sets of assumptions are considered: serial, where it is assume that cells transform through a lag phase before entering the exponential phase of growth; and parallel, where it is assumed that lag and exponential phases develop in parallel. The model is based on, first determining the distribution of the time when growth commences, and then modelling the conditional distribution of the number of cells. For the latter distribution, it is found that a Weibull distribution provides a simple approximation to the conditional distribution of the relative growth, so that the model developed in this paper can be easily implemented in risk assessments using commercial software packages.
Nitrogen deposition outweighs climatic variability in driving annual growth rate of canopy beech trees: Evidence from long-term growth reconstruction across a geographic gradient.

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Gentilesca, Tiziana; Rita, Angelo; Brunetti, Michele; Giammarchi, Francesco; Leonardi, Stefano; Magnani, Federico; van Noije, Twan; Tonon, Giustino; Borghetti, Marco

2018-07-01

In this study, we investigated the role of climatic variability and atmospheric nitrogen deposition in driving long-term tree growth in canopy beech trees along a geographic gradient in the montane belt of the Italian peninsula, from the Alps to the southern Apennines. We sampled dominant trees at different developmental stages (from young to mature tree cohorts, with tree ages spanning from 35 to 160 years) and used stem analysis to infer historic reconstruction of tree volume and dominant height. Annual growth volume (G V ) and height (G H ) variability were related to annual variability in model simulated atmospheric nitrogen deposition and site-specific climatic variables, (i.e. mean annual temperature, total annual precipitation, mean growing period temperature, total growing period precipitation, and standard precipitation evapotranspiration index) and atmospheric CO 2 concentration, including tree cambial age among growth predictors. Generalized additive models (GAM), linear mixed-effects models (LMM), and Bayesian regression models (BRM) were independently employed to assess explanatory variables. The main results from our study were as follows: (i) tree age was the main explanatory variable for long-term growth variability; (ii) GAM, LMM, and BRM results consistently indicated climatic variables and CO 2 effects on G V and G H were weak, therefore evidence of recent climatic variability influence on beech annual growth rates was limited in the montane belt of the Italian peninsula; (iii) instead, significant positive nitrogen deposition (N dep ) effects were repeatedly observed in G V and G H ; the positive effects of N dep on canopy height growth rates, which tended to level off at N dep values greater than approximately 1.0 g m -2 y -1 , were interpreted as positive impacts on forest stand above-ground net productivity at the selected study sites. © 2018 John Wiley & Sons Ltd.
A new model for simulating growth in fish

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Johannes Hamre

2014-01-01

Full Text Available A real dynamic population model calculates change in population sizes independent of time. The Beverton & Holt (B&H model commonly used in fish assessment includes the von Bertalanffy growth function which has age or accumulated time as an independent variable. As a result the B&H model has to assume constant fish growth. However, growth in fish is highly variable depending on food availability and environmental conditions. We propose a new growth model where the length increment of fish living under constant conditions and unlimited food supply, decreases linearly with increasing fish length until it reaches zero at a maximal fish length. The model is independent of time and includes a term which accounts for the environmental variation. In the present study, the model was validated in zebrafish held at constant conditions. There was a good fit of the model to data on observed growth in Norwegian spring spawning herring, capelin from the Barents Sea, North Sea herring and in farmed coastal cod. Growth data from Walleye Pollock from the Eastern Bering Sea and blue whiting from the Norwegian Sea also fitted reasonably well to the model, whereas data from cod from the North Sea showed a good fit to the model only above a length of 70 cm. Cod from the Barents Sea did not grow according to the model. The last results can be explained by environmental factors and variable food availability in the time under study. The model implicates that the efficiency of energy conversion from food decreases as the individual animal approaches its maximal length and is postulated to represent a natural law of fish growth.
Nonconvex Model of Material Growth: Mathematical Theory

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Ganghoffer, J. F.; Plotnikov, P. I.; Sokolowski, J.

2018-06-01

The model of volumetric material growth is introduced in the framework of finite elasticity. The new results obtained for the model are presented with complete proofs. The state variables include the deformations, temperature and the growth factor matrix function. The existence of global in time solutions for the quasistatic deformations boundary value problem coupled with the energy balance and the evolution of the growth factor is shown. The mathematical results can be applied to a wide class of growth models in mechanics and biology.
Winter Arctic sea ice growth: current variability and projections for the coming decades

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Petty, A.; Boisvert, L.; Webster, M.; Holland, M. M.; Bailey, D. A.; Kurtz, N. T.; Markus, T.

2017-12-01

Arctic sea ice increases in both extent and thickness during the cold winter months ( October to May). Winter sea ice growth is an important factor controlling ocean ventilation and winter water/deep water formation, as well as determining the state and vulnerability of the sea ice pack before the melt season begins. Key questions for the Arctic community thus include: (i) what is the current magnitude and variability of winter Arctic sea ice growth and (ii) how might this change in a warming Arctic climate? To address (i), our current best guess of pan-Arctic sea ice thickness, and thus volume, comes from satellite altimetry observations, e.g. from ESA's CryoSat-2 satellite. A significant source of uncertainty in these data come from poor knowledge of the overlying snow depth. Here we present new estimates of winter sea ice thickness from CryoSat-2 using snow depths from a simple snow model forced by reanalyses and satellite-derived ice drift estimates, combined with snow depth estimates from NASA's Operation IceBridge. To address (ii), we use data from the Community Earth System Model's Large Ensemble Project, to explore sea ice volume and growth variability, and how this variability might change over the coming decades. We compare and contrast the model simulations to observations and the PIOMAS ice-ocean model (over recent years/decades). The combination of model and observational analysis provide novel insight into Arctic sea ice volume variability.
Stochastic models for tumoral growth

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Escudero, Carlos

2006-02-01

Strong experimental evidence has indicated that tumor growth belongs to the molecular beam epitaxy universality class. This type of growth is characterized by the constraint of cell proliferation to the tumor border and the surface diffusion of cells at the growing edge. Tumor growth is thus conceived as a competition for space between the tumor and the host, and cell diffusion at the tumor border is an optimal strategy adopted for minimizing the pressure and helping tumor development. Two stochastic partial differential equations are reported in this paper in order to correctly model the physical properties of tumoral growth in (1+1) and (2+1) dimensions. The advantage of these models is that they reproduce the correct geometry of the tumor and are defined in terms of polar variables. An analysis of these models allows us to quantitatively estimate the response of the tumor to an unfavorable perturbation during growth.
On Latent Growth Models for Composites and Their Constituents.

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Hancock, Gregory R; Mao, Xiulin; Kher, Hemant

2013-09-01

Over the last decade and a half, latent growth modeling has become an extremely popular and versatile technique for evaluating longitudinal change and its determinants. Most common among the models applied are those for a single measured variable over time. This model has been extended in a variety of ways, most relevant for the current work being the multidomain and the second-order latent growth models. Whereas the former allows for growth function characteristics to be modeled for multiple outcomes simultaneously, with the degree of growth characteristics' relations assessed within the model (e.g., cross-domain slope factor correlations), the latter models growth in latent outcomes, each of which has effect indicators repeated over time. But what if one has an outcome that is believed to be formative relative to its indicator variables rather than latent? In this case, where the outcome is a composite of multiple constituents, modeling change over time is less straightforward. This article provides analytical and applied details for simultaneously modeling growth in composites and their constituent elements, including a real data example using a general computer self-efficacy questionnaire.
Evaluating spatial and temporal variability in growth and mortality for recreational fisheries with limited catch data

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Li, Yan; Wagner, Tyler; Jiao, Yan; Lorantas, Robert M.; Murphy, Cheryl

2018-01-01

Understanding the spatial and temporal variability in life-history traits among populations is essential for the management of recreational fisheries. However, valuable freshwater recreational fish species often suffer from a lack of catch information. In this study, we demonstrated the use of an approach to estimate the spatial and temporal variability in growth and mortality in the absence of catch data and apply the method to riverine smallmouth bass (Micropterus dolomieu) populations in Pennsylvania, USA. Our approach included a growth analysis and a length-based analysis that estimates mortality. Using a hierarchical Bayesian approach, we examined spatial variability in growth and mortality by assuming parameters vary spatially but remain constant over time and temporal variability by assuming parameters vary spatially and temporally. The estimated growth and mortality of smallmouth bass showed substantial variability over time and across rivers. We explored the relationships of the estimated growth and mortality with spring water temperature and spring flow. Growth rate was likely to be positively correlated with these two factors, while young mortality was likely to be positively correlated with spring flow. The spatially and temporally varying growth and mortality suggest that smallmouth bass populations across rivers may respond differently to management plans and disturbance such as environmental contamination and land-use change. The analytical approach can be extended to other freshwater recreational species that also lack of catch data. The approach could also be useful in developing population assessments with erroneous catch data or be used as a model sensitivity scenario to verify traditional models even when catch data are available.
Quantifying Variability in Growth and Thermal Inactivation Kinetics of Lactobacillus plantarum.

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Aryani, D C; den Besten, H M W; Zwietering, M H

2016-08-15

The presence and growth of spoilage organisms in food might affect the shelf life. In this study, the effects of experimental, reproduction, and strain variabilities were quantified with respect to growth and thermal inactivation using 20 Lactobacillus plantarum strains. Also, the effect of growth history on thermal resistance was quantified. The strain variability in μmax was similar (P > 0.05) to reproduction variability as a function of pH, aw, and temperature, while being around half of the reproduction variability (P plantarum strains, and the pHmin was between 3.2 and 3.5, the aw,min was between 0.936 and 0.953, the [HLamax], at pH 4.5, was between 29 and 38 mM, and the Tmin was between 3.4 and 8.3°C. The average D values ranged from 0.80 min to 19 min at 55°C, 0.22 to 3.9 min at 58°C, 3.1 to 45 s at 60°C, and 1.8 to 19 s at 63°C. In contrast to growth, the strain variability in thermal resistance was on average six times higher than the reproduction variability and more than ten times higher than the experimental variability. The strain variability was also 1.8 times higher (P 10-log10 differences after thermal treatment. Accurate control and realistic prediction of shelf life is complicated by the natural diversity among microbial strains, and limited information on microbiological variability is available for spoilage microorganisms. Therefore, the objectives of the present study were to quantify strain variability, reproduction (biological) variability, and experimental variability with respect to the growth and thermal inactivation kinetics of Lactobacillus plantarum and to quantify the variability in thermal resistance attributed to growth history. The quantitative knowledge obtained on experimental, reproduction, and strain variabilities can be used to improve experimental designs and to adequately select strains for challenge growth and inactivation tests. Moreover, the integration of strain variability in prediction of microbial growth and
Influence of weather and climate variables on the basal area growth of individual shortleaf pine trees

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Pradip Saud; Thomas B. Lynch; Duncan S. Wilson; John Stewart; James M. Guldin; Bob Heinemann; Randy Holeman; Dennis Wilson; Keith Anderson

2015-01-01

An individual-tree basal area growth model previously developed for even-aged naturally occurring shortleaf pine trees (Pinus echinata Mill.) in western Arkansas and southeastern Oklahoma did not include weather variables. Individual-tree growth and yield modeling of shortleaf pine has been carried out using the remeasurements of over 200 plots...
A Probit Model for the State of the Greek GDP Growth

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Stavros Degiannakis

2015-08-01

Full Text Available The paper provides probability estimates of the state of the GDP growth. A regime-switching model defines the probability of the Greek GDP being in boom or recession. Then probit models extract the predictive information of a set of explanatory (economic and financial variables regarding the state of the GDP growth. A contemporaneous, as well as a lagged, relationship between the explanatory variables and the state of the GDP growth is conducted. The mean absolute distance (MAD between the probability of not being in recession and the probability estimated by the probit model is the function that evaluates the performance of the models. The probit model with the industrial production index and the realized volatility as the explanatory variables has the lowest MAD value of 6.43% (7.94% in the contemporaneous (lagged relationship.
Self-similar measures in multi-sector endogenous growth models

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La Torre, Davide; Marsiglio, Simone; Mendivil, Franklin; Privileggi, Fabio

2015-01-01

We analyze two types of stochastic discrete time multi-sector endogenous growth models, namely a basic Uzawa–Lucas (1965, 1988) model and an extended three-sector version as in La Torre and Marsiglio (2010). As in the case of sustained growth the optimal dynamics of the state variables are not stationary, we focus on the dynamics of the capital ratio variables, and we show that, through appropriate log-transformations, they can be converted into affine iterated function systems converging to an invariant distribution supported on some (possibly fractal) compact set. This proves that also the steady state of endogenous growth models—i.e., the stochastic balanced growth path equilibrium—might have a fractal nature. We also provide some sufficient conditions under which the associated self-similar measures turn out to be either singular or absolutely continuous (for the three-sector model we only consider the singularity).
An application of the variable-r method to subpopulation growth rates in a 19th century agricultural population

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Corey Sparks

2009-07-01

Full Text Available This paper presents an analysis of the differential growth rates of the farming and non-farming segments of a rural Scottish community during the 19th and early 20th centuries using the variable-r method allowing for net migration. Using this method, I find that the farming population of Orkney, Scotland, showed less variability in their reproduction and growth rates than the non-farming population during a period of net population decline. I conclude by suggesting that the variable-r method can be used in general cases where the relative growth of subpopulations or subpopulation reproduction is of interest.
Modeling tumor growth in the presence of a therapy with an effect on rate growth and variability by means of a modified Gompertz diffusion process.

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Román-Román, Patricia; Román-Román, Sergio; Serrano-Pérez, Juan José; Torres-Ruiz, Francisco

2016-10-21

In experimental studies on tumor growth, whenever the time evolution of the relative volume of a tumor in an untreated (control) group can be fitted by a Gompertz diffusion process there is a possibility that an antiproliferative therapy, which modifies the growth rate of the process that fits the treated group, may also affect its variability. The present paper proposes several procedures for the estimation of the time function included in the infinitesimal variance of the new process, as well as the time function affecting the growth rate (which is included in the infinitesimal mean). Also, a hypothesis testing is designed to confirm or refute the need for including such a time-dependent function in the infinitesimal variance. In order to validate and compare the proposed procedures a simulation study has been carried out. In addition, a proposal is made for a strategy aimed at finding the optimal combination of procedures for each case. A real data application concerning the effects of cisplatin on a patient-derived xenograft (PDX) tumor model showcases the advantages of this model over others that have been used in the past. Copyright © 2016 Elsevier Ltd. All rights reserved.
Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots

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Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.

2013-01-01

Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…

Theoretical growth rates, periods, and pulsation constants for long-period variables

International Nuclear Information System (INIS)

Fox, M.W.; Wood, P.R.

1982-01-01

Theoretical values of the growth rate, period, and pulsation constant for the first three radial pulsation modes in red giants (Population II and galactic disk) and supergiants have been derived in the linear, nonadiabatic approximation. The effects of altering the surface boundary conditions, the effective temperature (or mixing length), and the opacity in the outer layers have been explored. In the standard models, the Q-value for the first overtone can be much larger (Q 1 1 roughly-equal0.04); in addition, the Q-value for the fundamental mode is reduced from previous values, as is the period ratio P 0 /P 1 . The growth rate for the fundamental mode is found to increase with luminosity on the giant branch while the growth rate for the first overtone decreases. Dynamical instabilities found in previous adiabatic models of extreme red giants do not occur when nonadiabatic effects are included in the models. In some massive, luminous models, period ratios P 0 /P 1 approx.7 occur when P 0 approx.2000--5000 days; it is suggested that the massive galactic supergiants and carbon stars which have secondary periods Papprox.2000--7000 days and primary periods Papprox.300--700 days are first-overtone pulsators in which the long secondary periods are due to excitation of the fundamental mode. Some other consequences of the present results are briefly discussed, with particular emphasis on the mode of pulsation of the Mira variables. Subject headings: stars: long-period variables: stars: pulsation: stars: supergiants
Investigating the effect of growth and financial strength variables on the financial leverage: Evidence from the Tehran Stock Exchange

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Iman Dadashi

2013-04-01

Full Text Available The primary objective of this study is to investigate the effect of growth and financial strength variables on the financial leverage for some listed companies in the Tehran Stock Exchange. For this purpose, a sample of 700 firm-years among listed companies in the Tehran Stock Exchange over the period 2006-2010 was examined. In the present study, the growth variables, including asset growth, profit growth and sales growth; and financial strength calculated by the Altman Z-bankruptcy model have been considered as independent variables. In addition, the ratios of long-term debt to total assets, long-term debt to fixed assets, total long-term debt and short-term receivable facilities to equity capital and total long-term debt and short-term receivable facilities to total assets are used as measures of financial leverage and dependent variables. The results indicate that there is a negative and significant relationship between assets growth and some indexes of financial leverage. There is also a positive and significant relationship between the variables of profit growth, sales growth and financial strength with financial leverage measures.
Variable amplitude fatigue crack growth behavior - a short overview

International Nuclear Information System (INIS)

Singh, Konjengbam Darunkumar; Parry, Matthew Roger; Sinclair, Ian

2011-01-01

A short overview concerning variable amplitude (VA) fatigue crack growth behavior is presented in this paper. The topics covered in this review encompass important issues pertaining to both single and repeated overload transients. Reviews on transient post overload effects such as plasticity induced crack closure, crack tip blunting, residual stresses, crack deflection and branching, activation of near threshold mechanisms, strain hardening are highlighted. A brief summary on experimental trends and finite element modelling of overload induced crack closure is also presented
Variable amplitude fatigue crack growth behavior - a short overview

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Singh, Konjengbam Darunkumar [Indian Institute of Technology, Guwahati (India); Parry, Matthew Roger [Airbus Operations Ltd, Bristol (United Kingdom); Sinclair, Ian [University of Southampton, Southampton (United Kingdom)

2011-03-15

A short overview concerning variable amplitude (VA) fatigue crack growth behavior is presented in this paper. The topics covered in this review encompass important issues pertaining to both single and repeated overload transients. Reviews on transient post overload effects such as plasticity induced crack closure, crack tip blunting, residual stresses, crack deflection and branching, activation of near threshold mechanisms, strain hardening are highlighted. A brief summary on experimental trends and finite element modelling of overload induced crack closure is also presented.
Multimodel Ensembles of Wheat Growth: Many Models are Better than One

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Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W.; Rotter, Reimund P.; Boote, Kenneth J.; Ruane, Alexander C.; Thorburn, Peter J.; Cammarano, Davide;

2015-01-01

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop model scan give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 2438 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Multimodel Ensembles of Wheat Growth: More Models are Better than One

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Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W.; Rotter, Reimund P.; Boote, Kenneth J.; Ruane, Alex C.; Thorburn, Peter J.; Cammarano, Davide;

2015-01-01

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Model for analyzing growth kinetics of a slowly growing Mycobacterium sp

International Nuclear Information System (INIS)

Lambrecht, R.S.; Carriere, J.F.; Collins, M.T.

1988-01-01

This report describes a simple method for quantifying viable mycobacteria and for determining generation time. We used statistical models and computer analysis of growth curves generated for the slowly growing mycobacterium Mycobacterium paratuberculosis under controlled conditions to derive a mathematical formula relating the dependent variable, growth, to the independent variables, log10 number of organisms in the inoculum (inoculum size) and incubation time. Growth was measured by a radiometric method which detects 14 CO 2 release during metabolism of a 14 C-labeled substrate. The radiometric method allowed for early detection of growth and detected as few as three viable bacteria. The coefficient of variation between culture vials inoculated with the same number of M. paratuberculosis was 0.083. Radiometric measurements were highly correlated to spectrophotometric and plate count methods for measuring growth (r = 0.962 and 0.992, respectively). The proportion of the total variability explained by the model in a goodness of fit test was 0.9994. Application of the model to broth cultures provided accurate estimates of the number of M. paratuberculosis (standard error = 0.21, log10 scale) and the growth rate (coefficient of variation, 0.03). Generation time was observed to be dependent upon the number of organisms in the inoculum. The model accurately described all phases of growth of M. paratuberculosis and can likely be applied to other slowly growing microorganisms
Individual-Tree Diameter Growth Models for Mixed Nothofagus Second Growth Forests in Southern Chile

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Paulo C. Moreno

2017-12-01

Full Text Available Second growth forests of Nothofagus obliqua (roble, N. alpina (raulí, and N. dombeyi (coihue, known locally as RORACO, are among the most important native mixed forests in Chile. To improve the sustainable management of these forests, managers need adequate information and models regarding not only existing forest conditions, but their future states with varying alternative silvicultural activities. In this study, an individual-tree diameter growth model was developed for the full geographical distribution of the RORACO forest type. This was achieved by fitting a complete model by comparing two variable selection procedures: cross-validation (CV, and least absolute shrinkage and selection operator (LASSO regression. A small set of predictors successfully explained a large portion of the annual increment in diameter at breast height (DBH growth, particularly variables associated with competition at both the tree- and stand-level. Goodness-of-fit statistics for this final model showed an empirical coefficient of correlation (R2emp of 0.56, relative root mean square error of 44.49% and relative bias of −1.96% for annual DBH growth predictions, and R2emp of 0.98 and 0.97 for DBH projection at 6 and 12 years, respectively. This model constitutes a simple and useful tool to support management plans for these forest ecosystems.
Growth models and analysis of development

Energy Technology Data Exchange (ETDEWEB)

Mathur, G

1979-10-01

This paper deals with remnants of neoclassical elements in Keynesian and post-Keynesian thought, and attempts to demonstrate that the elimination of these elements from our modes of thinking would not impoverish economic analysis as a means of solving real problems. In the Keynesian analysis the causation from investment to savings is exhibited in terms of income determination. When put in terms of a capital-theory model, the vector of savings is represented in two ways: real savings and counterpart real savings. The former coincides with the investment vector and the latter with the vector of consumption goods foregone for diverting resources towards equipment making. Thus the Keynesian causation in capital theory terms makes the concept of national savings as an independent variable redudant. The Robinsonian causation in a golden age with full employment and its reversal of direction in a steady state with non-employment are then considered. But in each of these, variables like rate of savings and output/capital ratio are found to be dormant variables. They are termed as null variables which, being of no account in both full-employment and unemployment situations, could, without loss, be deleted from the repertory of analytical tools. The Harrod formula of warranted rate of growth, when put in causal form, thus becomes a redundant portion of economics of growth. The real determinants of the growth rate and real wage rate on which the analysis of growth or of development should be based, are also depicted.
Growth/no growth models for Zygosaccharomyces rouxii associated with acidic, sweet intermediate moisture food products

DEFF Research Database (Denmark)

Nielsen, Cecilie Lykke Marvig; Kristiansen, Rikke M.; Nielsen, Dennis Sandris

2015-01-01

sugar and fat and a traditionally long self-life of sweet IMFs, the presence of Z. rouxii in the raw materials for IMFs has made assessment of the microbiological stability a significant hurdle in product development. Therefore, knowledge on growth/no growth boundaries of Z. rouxii in sweet IMFs...... is important to ensure microbiological stability and aid product development. Several models have been developed for fat based, sweet IMFs. However, fruit/sugar based IMFs, such as fruit based chocolate fillings and jams, have lower pH and aw than what is accounted for in previously developed models....... In the present study growth/no growth models for acidified sweet IMFs were developed with the variables aw (0.65-0.80), pH (2.5-4.0), ethanol (0-14.5% (w/w) in water phase) and time (0-90 days). Two different strains of Z. rouxii previously found to show pronounced resistance to the investigated variables were...
The Relationship Between Posttraumatic Growth and Psychosocial Variables in Survivors of State Terrorism and Their Relatives.

Science.gov (United States)

Cárdenas-Castro, Manuel; Faúndez-Abarca, Ximena; Arancibia-Martini, Héctor; Ceruti-Mahn, Cristián

2017-08-01

The present study explores reports of growth in survivors and family members of victims of state terrorism ( N = 254) in Chile from 1973 to 1990. The results indicate the presence of reports of posttraumatic growth ( M = 4.69) and a positive and statistically significant correlation with variables related to the life impact of the stressful events ( r = .46), social sharing of emotions ( r = .32), deliberate rumination ( r = .37), positive reappraisal ( r = .35), reconciliation ( r = .39), spiritual practices ( r = .33), and meaning in life ( r = .51). The relationship between growth and forgiveness is not statistically significant. The variables that best predict posttraumatic growth are positive reappraisal (β = .28), life impact (β = .24), meaning in life β = .23), and reconciliation (β = .20). The forward-method hierarchical model indicates that these variables are significant predictors of growth levels, R 2 = .53, F(8, 210) = 30.08, p state terrorism manage to grow after these experiences, and the redefinition of meaning in life and the positive reappraisal of the traumatic experiences are the elements that make it possible to create a new narrative about the past.
A mean-field game economic growth model

KAUST Repository

Gomes, Diogo A.; Lafleche, Laurent; Nurbekyan, Levon

2016-01-01

Here, we examine a mean-field game (MFG) that models the economic growth of a population of non-cooperative, rational agents. In this MFG, agents are described by two state variables - the capital and consumer goods they own. Each agent seeks
Human Capital Variables and Economic Growth in Nigeria: An Interactive Effect

Directory of Open Access Journals (Sweden)

Adenike Mosunmola Osoba

2017-05-01

Full Text Available Various studies have focused on the relationship between human capital and economic growth all over the world. However, there is still a missing gap on the joint influence of human capital investment components on economic growth particularly in Nigeria. This study therefore examines the interactive effects of the relationship between human capital investment components and economic growth in Nigeria for the period of 1986 – 2014. The study employed secondary annual data on education expenditure, health expenditure, real gross domestic product and gross capital formation obtained from the Central Bank Statistical bulletin, 2014. The data were analyzed using Fully Modified Ordinary Least Squares (FMOLS technique. The results of the study showed that there was positive and significant relationship between the interactive effects of human capital components and growth in Nigeria. The study concluded that the interactive effect of the human capital variables was also in conformity with the theoretical proposition that increase in human capital will enhance growth as stipulated in the modified Solow growth model by Mankiw, Romer & Weil (1992.
Spatial variability and macro‐scale drivers of growth for native and introduced Flathead Catfish populations

Science.gov (United States)

Massie, Danielle L.; Smith, Geoffrey; Bonvechio, Timothy F.; Bunch, Aaron J.; Lucchesi, David O.; Wagner, Tyler

2018-01-01

Quantifying spatial variability in fish growth and identifying large‐scale drivers of growth are fundamental to many conservation and management decisions. Although fish growth studies often focus on a single population, it is becoming increasingly clear that large‐scale studies are likely needed for addressing transboundary management needs. This is particularly true for species with high recreational value and for those with negative ecological consequences when introduced outside of their native range, such as the Flathead Catfish Pylodictis olivaris. This study quantified growth variability of the Flathead Catfish across a large portion of its contemporary range to determine whether growth differences existed between habitat types (i.e., reservoirs and rivers) and between native and introduced populations. Additionally, we investigated whether growth parameters varied as a function of latitude and time since introduction (for introduced populations). Length‐at‐age data from 26 populations across 11 states in the USA were modeled using a Bayesian hierarchical von Bertalanffy growth model. Population‐specific growth trajectories revealed large variation in Flathead Catfish growth and relatively high uncertainty in growth parameters for some populations. Relatively high uncertainty was also evident when comparing populations and when quantifying large‐scale patterns. Growth parameters (Brody growth coefficient [K] and theoretical maximum average length [L∞]) were not different (based on overlapping 90% credible intervals) between habitat types or between native and introduced populations. For populations within the introduced range of Flathead Catfish, latitude was negatively correlated with K. For native populations, we estimated an 85% probability that L∞ estimates were negatively correlated with latitude. Contrary to predictions, time since introduction was not correlated with growth parameters in introduced populations of Flathead Catfish
Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models.

Science.gov (United States)

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential
Investigation of Mediational Processes Using Parallel Process Latent Growth Curve Modeling

Science.gov (United States)

Cheong, JeeWon; MacKinnon, David P.; Khoo, Siek Toon

2010-01-01

This study investigated a method to evaluate mediational processes using latent growth curve modeling. The mediator and the outcome measured across multiple time points were viewed as 2 separate parallel processes. The mediational process was defined as the independent variable influencing the growth of the mediator, which, in turn, affected the growth of the outcome. To illustrate modeling procedures, empirical data from a longitudinal drug prevention program, Adolescents Training and Learning to Avoid Steroids, were used. The program effects on the growth of the mediator and the growth of the outcome were examined first in a 2-group structural equation model. The mediational process was then modeled and tested in a parallel process latent growth curve model by relating the prevention program condition, the growth rate factor of the mediator, and the growth rate factor of the outcome. PMID:20157639
Interannual variability of growth and reproduction in Bursera simaruba: the role of allometry and resource variability.

Science.gov (United States)

Hulshof, Catherine M; Stegen, James C; Swenson, Nathan G; Enquist, Carolyn A F; Enquist, Brian J

2012-01-01

Plants are expected to differentially allocate resources to reproduction, growth, and survival in order to maximize overall fitness. Life history theory predicts that the allocation of resources to reproduction should occur at the expense of vegetative growth. Although it is known that both organism size and resource availability can influence life history traits, few studies have addressed how size dependencies of growth and reproduction and variation in resource supply jointly affect the coupling between growth and reproduction. In order to understand the relationship between growth and reproduction in the context of resource variability, we utilize a long-term observational data set consisting of 670 individual trees over a 10-year period within a local population of Bursera simaruba (L.) Sarg. We (1) quantify the functional form and variability in the growth-reproduction relationship at the population and individual-tree level and (2) develop a theoretical framework to understand the allometric dependence of growth and reproduction. Our findings suggest that the differential responses of allometric growth and reproduction to resource availability, both between years and between microsites, underlie the apparent relationship between growth and reproduction. Finally, we offer an alternative approach for quantifying the relationship between growth and reproduction that accounts for variation in allometries.
On the growth estimates of entire functions of double complex variables

Directory of Open Access Journals (Sweden)

Sanjib Datta

2017-08-01

Full Text Available Recently Datta et al. (2016 introduced the idea of relative type and relative weak type of entire functions of two complex variables with respect to another entire function of two complex variables and prove some related growth properties of it. In this paper, further we study some growth properties of entire functions of two complex variables on the basis of their relative types and relative weak types as introduced by Datta et al (2016.
Growth Curve Models and Applications : Indian Statistical Institute

CERN Document Server

2017-01-01

Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas. There is no one-size-fits-all approach to growth measurement. The selected papers in this volume build on presentations from the GCM workshop held at the Indian Statistical Institute, Giridih, on March 28-29, 2016. They represent recent trends in GCM research on different subject areas, both theoretical and applied. This book includes tools and possibilities for further work through new techniques and modification of existing ones. The volume includes original studies, theoretical findings and case studies from a wide range of app lied work, and these contributions have been externally r...
Bayesian modeling of Clostridium perfringens growth in beef-in-sauce products.

Science.gov (United States)

Jaloustre, S; Cornu, M; Morelli, E; Noël, V; Delignette-Muller, M L

2011-04-01

Models on Clostridium perfringens growth which have been published to date have all been deterministic. A probabilistic model describing growth under non-isothermal conditions was thus proposed for predicting C. perfringens growth in beef-in-sauce products cooked and distributed in a French hospital. Model parameters were estimated from different types of data from various studies. A Bayesian approach was proposed to model the overall uncertainty regarding parameters and potential variability on the 'work to be done' (h(0)) during the germination, outgrowth and lag phase. Three models which differed according to their description of this parameter h(0) were tested. The model with inter-curve variability on h(0) was found to be the best one, on the basis of goodness-of-fit assessment and validation with literature data on results obtained under non-isothermal conditions. This model was used in two-dimensional Monte Carlo simulations to predict C. perfringens growth throughout the preparation of beef-in-sauce products, using temperature profiles recorded in a hospital kitchen. The median predicted growth was 7.8×10(-2) log(10) cfu·g(-1) (95% credibility interval [2.4×10(-2), 0.8]) despite the fact that for more than 50% of the registered temperature profiles cooling steps were longer than those required by French regulations. Copyright © 2010 Elsevier Ltd. All rights reserved.

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models.

Directory of Open Access Journals (Sweden)

Frieda Beauregard

Full Text Available Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839 covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study
Development of a Physically-Based Methodology for Predicting Material Variability in Fatigue Crack Initiation and Growth Response

National Research Council Canada - National Science Library

Chan, Kwai

2004-01-01

... of aerospace structural alloys. In this three-year program, physics-based fatigue crack initiation and growth models were developed and integrated into a probabilistic micromechanical code for treating fatigue life variability...
Analisis Hubungan antara Berbagai Model Gabungan Proksi Investment Opportunity Set dan Real Growth dengan Menggunakan Pendekatan Confirmatory Factor Analysis”

Directory of Open Access Journals (Sweden)

Muhammad Yusuf

2016-02-01

Full Text Available This study develops and makes composite observed variables from individual Investment Opportunity Set (IOS proxies into one latent variable using structural equation models with a confirmatory factor analysis approach. Six composite investment opportunity set proxies are then created based on some individual proxies, namely price related IOS and investment related IOS. These composite IOS proxies are correlated with the real growth to prove that the model has consistency and ability to predict the real growth. A confirmatory factor analysis results in all observed variables that make latent variables for each model show different result in every model. At model 1, the CFA result show that every price related IOS proxies at model 1 have significant measurement model fit. At model 2, the Confirmatory Factor Analysis (CFA result show that every price related IOS proxies at model 2 have significant measurement model fit, except for one proxies named Rasio Capital Expenditure to Total Book Asset (RACTE. At model 3, the CFA result show that every price related IOS proxies at model 2 have significant measurement model fit, except for one proxies named Book Value of Property, Plant and Equipment to Book Value of Asset(BVPPEBVA. At model 4, the CFA result show that every price related IOS proxies at model 1 have significant measurement model fit. At model 5, the CFA result show that every price related IOS proxies at model 1 have significant measurement model fit. At model 6, the CFA result show that there is no significant measurement model fit for every investment related IOS proxies. Correlation test for all models show almost different result in every models. At model 1, the correlation test show that there is a weak, not significant-positive correlation between price related IOS proxies as latent variable, and real growth proxies. At model 2, the correlation test shows that there is a weak, significant negative correlation between price
Major Changes in Growth Rate and Growth Variability of Beech (Fagus sylvatica L. Related to Soil Alteration and Climate Change in Belgium

Directory of Open Access Journals (Sweden)

Nicolas Latte

2016-08-01

Full Text Available Global change—particularly climate change, forest management, and atmospheric deposition—has significantly altered forest growing conditions in Europe. The influences of these changes on beech growth (Fagus sylvatica L. were investigated for the past 80 years in Belgium, using non-linear mixed effects models on ring-width chronologies of 149 mature and dominant beech trees (87–186 years old. The effects of the developmental stage (i.e., increasing tree size were filtered out in order to focus on time-dependent growth changes. Beech radial growth was divided into a low-frequency signal (=growth rate, mainly influenced by forest management and atmospheric deposition, and into a high-frequency variability (≈mean sensitivity, mainly influenced by climate change. Between 1930 and 2008, major long-term and time-dependent changes were highlighted. The beech growth rate has decreased by about 38% since the 1950–1960s, and growth variability has increased by about 45% since the 1970–1980s. Our results indicate that (1 before the 1980s, beech growth rate was not predominantly impacted by climate change but rather by soil alteration (i.e., soil compaction and/or nitrogen deposition; and (2 since the 1980s, climate change induced more frequent and intense yearly growth reductions that amplified the growth rate decrease. The highlighted changes were similar in the two ecoregions of Belgium, although more pronounced in the lowlands than in the uplands.
The Biasing Effects of Unmodeled ARMA Time Series Processes on Latent Growth Curve Model Estimates

Science.gov (United States)

Sivo, Stephen; Fan, Xitao; Witta, Lea

2005-01-01

The purpose of this study was to evaluate the robustness of estimated growth curve models when there is stationary autocorrelation among manifest variable errors. The results suggest that when, in practice, growth curve models are fitted to longitudinal data, alternative rival hypotheses to consider would include growth models that also specify…
Improving plot- and regional-scale crop models for simulating impacts of climate variability and extremes

Science.gov (United States)

Tao, F.; Rötter, R.

2013-12-01

Many studies on global climate report that climate variability is increasing with more frequent and intense extreme events1. There are quite large uncertainties from both the plot- and regional-scale models in simulating impacts of climate variability and extremes on crop development, growth and productivity2,3. One key to reducing the uncertainties is better exploitation of experimental data to eliminate crop model deficiencies and develop better algorithms that more adequately capture the impacts of extreme events, such as high temperature and drought, on crop performance4,5. In the present study, in a first step, the inter-annual variability in wheat yield and climate from 1971 to 2012 in Finland was investigated. Using statistical approaches the impacts of climate variability and extremes on wheat growth and productivity were quantified. In a second step, a plot-scale model, WOFOST6, and a regional-scale crop model, MCWLA7, were calibrated and validated, and applied to simulate wheat growth and yield variability from 1971-2012. Next, the estimated impacts of high temperature stress, cold damage, and drought stress on crop growth and productivity based on the statistical approaches, and on crop simulation models WOFOST and MCWLA were compared. Then, the impact mechanisms of climate extremes on crop growth and productivity in the WOFOST model and MCWLA model were identified, and subsequently, the various algorithm and impact functions were fitted against the long-term crop trial data. Finally, the impact mechanisms, algorithms and functions in WOFOST model and MCWLA model were improved to better simulate the impacts of climate variability and extremes, particularly high temperature stress, cold damage and drought stress for location-specific and large area climate impact assessments. Our studies provide a good example of how to improve, in parallel, the plot- and regional-scale models for simulating impacts of climate variability and extremes, as needed for
Stochastic Individual-Based Modeling of Bacterial Growth and Division Using Flow Cytometry

Directory of Open Access Journals (Sweden)

Míriam R. García

2018-01-01

Full Text Available A realistic description of the variability in bacterial growth and division is critical to produce reliable predictions of safety risks along the food chain. Individual-based modeling of bacteria provides the theoretical framework to deal with this variability, but it requires information about the individual behavior of bacteria inside populations. In this work, we overcome this problem by estimating the individual behavior of bacteria from population statistics obtained with flow cytometry. For this objective, a stochastic individual-based modeling framework is defined based on standard assumptions during division and exponential growth. The unknown single-cell parameters required for running the individual-based modeling simulations, such as cell size growth rate, are estimated from the flow cytometry data. Instead of using directly the individual-based model, we make use of a modified Fokker-Plank equation. This only equation simulates the population statistics in function of the unknown single-cell parameters. We test the validity of the approach by modeling the growth and division of Pediococcus acidilactici within the exponential phase. Estimations reveal the statistics of cell growth and division using only data from flow cytometry at a given time. From the relationship between the mother and daughter volumes, we also predict that P. acidilactici divide into two successive parallel planes.
A model for AGN variability on multiple time-scales

Science.gov (United States)

Sartori, Lia F.; Schawinski, Kevin; Trakhtenbrot, Benny; Caplar, Neven; Treister, Ezequiel; Koss, Michael J.; Urry, C. Megan; Zhang, C. E.

2018-05-01

We present a framework to link and describe active galactic nuclei (AGN) variability on a wide range of time-scales, from days to billions of years. In particular, we concentrate on the AGN variability features related to changes in black hole fuelling and accretion rate. In our framework, the variability features observed in different AGN at different time-scales may be explained as realisations of the same underlying statistical properties. In this context, we propose a model to simulate the evolution of AGN light curves with time based on the probability density function (PDF) and power spectral density (PSD) of the Eddington ratio (L/LEdd) distribution. Motivated by general galaxy population properties, we propose that the PDF may be inspired by the L/LEdd distribution function (ERDF), and that a single (or limited number of) ERDF+PSD set may explain all observed variability features. After outlining the framework and the model, we compile a set of variability measurements in terms of structure function (SF) and magnitude difference. We then combine the variability measurements on a SF plot ranging from days to Gyr. The proposed framework enables constraints on the underlying PSD and the ability to link AGN variability on different time-scales, therefore providing new insights into AGN variability and black hole growth phenomena.
Predicting long-term streamflow variability in moist eucalypt forests using forest growth models and a sapwood area index

Science.gov (United States)

Jaskierniak, D.; Kuczera, G.; Benyon, R.

2016-04-01

A major challenge in surface hydrology involves predicting streamflow in ungauged catchments with heterogeneous vegetation and spatiotemporally varying evapotranspiration (ET) rates. We present a top-down approach for quantifying the influence of broad-scale changes in forest structure on ET and hence streamflow. Across three catchments between 18 and 100 km2 in size and with regenerating Eucalyptus regnans and E. delegatensis forest, we demonstrate how variation in ET can be mapped in space and over time using LiDAR data and commonly available forest inventory data. The model scales plot-level sapwood area (SA) to the catchment-level using basal area (BA) and tree stocking density (N) estimates in forest growth models. The SA estimates over a 69 year regeneration period are used in a relationship between SA and vegetation induced streamflow loss (L) to predict annual streamflow (Q) with annual rainfall (P) estimates. Without calibrating P, BA, N, SA, and L to Q data, we predict annual Q with R2 between 0.68 and 0.75 and Nash Sutcliffe efficiency (NSE) between 0.44 and 0.48. To remove bias, the model was extended to allow for runoff carry-over into the following year as well as minor correction to rainfall bias, which produced R2 values between 0.72 and 0.79, and NSE between 0.70 and 0.79. The model under-predicts streamflow during drought periods as it lacks representation of ecohydrological processes that reduce L with either reduced growth rates or rainfall interception during drought. Refining the relationship between sapwood thickness and forest inventory variables is likely to further improve results.
In silico modeling for tumor growth visualization.

Science.gov (United States)

Jeanquartier, Fleur; Jean-Quartier, Claire; Cemernek, David; Holzinger, Andreas

2016-08-08

Cancer is a complex disease. Fundamental cellular based studies as well as modeling provides insight into cancer biology and strategies to treatment of the disease. In silico models complement in vivo models. Research on tumor growth involves a plethora of models each emphasizing isolated aspects of benign and malignant neoplasms. Biologists and clinical scientists are often overwhelmed by the mathematical background knowledge necessary to grasp and to apply a model to their own research. We aim to provide a comprehensive and expandable simulation tool to visualizing tumor growth. This novel Web-based application offers the advantage of a user-friendly graphical interface with several manipulable input variables to correlate different aspects of tumor growth. By refining model parameters we highlight the significance of heterogeneous intercellular interactions on tumor progression. Within this paper we present the implementation of the Cellular Potts Model graphically presented through Cytoscape.js within a Web application. The tool is available under the MIT license at https://github.com/davcem/cpm-cytoscape and http://styx.cgv.tugraz.at:8080/cpm-cytoscape/ . In-silico methods overcome the lack of wet experimental possibilities and as dry method succeed in terms of reduction, refinement and replacement of animal experimentation, also known as the 3R principles. Our visualization approach to simulation allows for more flexible usage and easy extension to facilitate understanding and gain novel insight. We believe that biomedical research in general and research on tumor growth in particular will benefit from the systems biology perspective.
Variable importance in latent variable regression models

NARCIS (Netherlands)

Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.

2014-01-01

The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable
Modeling Math Growth Trajectory--An Application of Conventional Growth Curve Model and Growth Mixture Model to ECLS K-5 Data

Science.gov (United States)

Lu, Yi

2016-01-01

To model students' math growth trajectory, three conventional growth curve models and three growth mixture models are applied to the Early Childhood Longitudinal Study Kindergarten-Fifth grade (ECLS K-5) dataset in this study. The results of conventional growth curve model show gender differences on math IRT scores. When holding socio-economic…
United States geological survey's reserve-growth models and their implementation

Science.gov (United States)

Klett, T.R.

2005-01-01

The USGS has developed several mathematical models to forecast reserve growth of fields both in the United States (U.S.) and the world. The models are based on historical reserve growth patterns of fields in the U.S. The patterns of past reserve growth are extrapolated to forecast future reserve growth. Changes of individual field sizes through time are extremely variable, therefore, the reserve growth models take on a statistical approach whereby volumetric changes for populations of fields are used in the models. Field age serves as a measure of the field-development effort that is applied to promote reserve growth. At the time of the USGS World Petroleum Assessment 2000, a reserve growth model for discovered fields of the world was not available. Reserve growth forecasts, therefore, were made based on a model of historical reserve growth of fields of the U.S. To test the feasibility of such an application, reserve growth forecasts were made of 186 giant oil fields of the world (excluding the U.S. and Canada). In addition, forecasts were made for these giant oil fields subdivided into those located in and outside of Organization of Petroleum Exporting Countries (OPEC). The model provided a reserve-growth forecast that closely matched the actual reserve growth that occurred from 1981 through 1996 for the 186 fields as a whole, as well as for both OPEC and non-OPEC subdivisions, despite the differences in reserves definition among the fields of the U.S. and the rest of the world. ?? 2005 International Association for Mathematical Geology.
Growth and energy nexus in Europe revisited: Evidence from a fixed effects political economy model

International Nuclear Information System (INIS)

Menegaki, Angeliki N.; Ozturk, Ilhan

2013-01-01

This is an empirical study on the causal relationship between economic growth and energy for 26 European countries in a multivariate panel framework over the period 1975–2009 using a two-way fixed effects model and including greenhouse gas emissions, capital, fossil energy consumption, Herfindahl index (political competition) and number of years the government chief executive stays in office (political stability) as independent variables in the model. Empirical results confirm bidirectional causality between growth and political stability, capital and political stability, capital and fossil energy consumption. Whether political stability favors the implementation of growth or leads to corruption demands further research. - Highlights: • Economic growth and energy for 26 European countries is examined. • Two-way fixed effects model with political economy variables is employed. • Bidirectional causality is observed between growth and political stability
Effects of Population Growth and Climate Variability on Sustainable Groundwater in Mali, West Africa

Directory of Open Access Journals (Sweden)

Alexandra Lutz

2010-12-01

Full Text Available Groundwater is increasingly relied on as a source of potable water in developing countries, but factors such as population growth, development, and climate variability, pose potential challenges for ongoing sustainable supply. The effect of these factors on the groundwater system was considered in four scenarios using a numerical model to represent the Bani area of Mali, West Africa. By 2040, population growth, climate variability, and development as urbanization, agriculture, and industry creates scenarios in which groundwater extraction is an increasingly larger percentage of the groundwater system. Consumption from agriculture and industry increases extraction rates from less than 1 to 3.8% of mean annual precipitation, which will likely affect the groundwater system. For instance, concentrated pumping in local areas may result in water level declines. The results of this study contribute to an ongoing evaluation of sustainable groundwater resources in West Africa.
Nationwide Macroeconomic Variables and the Growth Rate of Bariatric Surgeries in Brazil.

Science.gov (United States)

Cazzo, Everton; Ramos, Almino Cardoso; Pareja, José Carlos; Chaim, Elinton Adami

2018-06-06

The effect of nationwide economic issues on the necessary expansion in the number of bariatric procedures remains unclear. This study aims to determine whether there are correlations between the growth rate in the number of bariatric surgeries and the major macroeconomic variables over time in Brazil. It is a nationwide analysis regarding the number of bariatric surgeries in Brazil and the main national macroeconomic variables from 2003 through 2016: gross domestic product (GDP), inflation rate, and the unemployment rate, as well as the evolution in the number of registered bariatric surgeons. There were significant positive correlations of the growth rate of surgeries with the early variations of the GDP (R = 0.5558; p = 0.04863) and of the overall health expenditure per capita (R = 0.78322; p = 0.00259). The growth rate of the number of bariatric surgeries was not correlated with the unemployment and inflation rates, as well as with the growth rate of available bariatric surgeons. There were direct relationships between the growth rate of bariatric surgeries and the evolutions of the GDP and health care expenditure per capita. These variables appear to influence the nationwide offer of bariatric surgery.
MODELING THE GROWTH OF EUCALYPTUS PLANTS BASED ON THE THERMAL SUM

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Aline Santana de Oliveira

Full Text Available ABSTRACT Among the environmental variables that affect the growth and development of plants, the air temperature is of great importance. In this context, the objectives of this work were to model the growth of eucalyptus seedlings in terms of accumulated degree-days during the production process and model validation. The study was conducted in the forest research nursery of the Department of Forestry, located in Viçosa (MG, during the periods of 08/02/2011 to 28/04/2011 and 03/08/2012 to 01/11/2012, making it possible to contemplate seasonal variations in the production cycle. The monitored variables were shoot height, stem diameter, leaf area, root length and fresh and dry biomass. Results showed that it took 1065 degree-days for the production of seedlings and sigmoidal models obtained showed high correlation and Willmott coefficients, indicating good performance for estimating the growth and development of eucalyptus seedlings. This tool has great potential for planning and monitoring the production of eucalyptus seedlings in nurseries.
A new population growth map with variable coefficients

International Nuclear Information System (INIS)

Jannussis, A.

1986-01-01

In the present paper it is investigated a simple population growth map with variable coefficients. Moreover, it is studied the new population map of the form xsub(j+1) = axsub(j) (1/(1 + bxsub(j)) -1/(1 + cxsub(j))), c not= b, j = 0, 1,..., which is transformed in an equivalent logistic map
Modelling of strongly coupled particle growth and aggregation

International Nuclear Information System (INIS)

Gruy, F; Touboul, E

2013-01-01

The mathematical modelling of the dynamics of particle suspension is based on the population balance equation (PBE). PBE is an integro-differential equation for the population density that is a function of time t, space coordinates and internal parameters. Usually, the particle is characterized by a unique parameter, e.g. the matter volume v. PBE consists of several terms: for instance, the growth rate and the aggregation rate. So, the growth rate is a function of v and t. In classical modelling, the growth and the aggregation are independently considered, i.e. they are not coupled. However, current applications occur where the growth and the aggregation are coupled, i.e. the change of the particle volume with time is depending on its initial value v 0 , that in turn is related to an aggregation event. As a consequence, the dynamics of the suspension does not obey the classical Von Smoluchowski equation. This paper revisits this problem by proposing a new modelling by using a bivariate PBE (with two internal variables: v and v 0 ) and by solving the PBE by means of a numerical method and Monte Carlo simulations. This is applied to a physicochemical system with a simple growth law and a constant aggregation kernel.
Variable elasticity of substituition in a discrete time Solow–Swan growth model with differential saving

International Nuclear Information System (INIS)

Brianzoni, Serena; Mammana, Cristiana; Michetti, Elisabetta

2012-01-01

Highlights: ► One dimensional piecewise smooth map: border collision bifurcations. ► Numerical simulations: complex dynamics. ► Ves production function in the solow–swan growth model and comparison with the ces production function. - Abstract: We study the dynamics shown by the discrete time neoclassical one-sector growth model with differential savings as in Bohm and Kaas while assuming VES production function in the form given by Revankar . It is shown that the model can exhibit unbounded endogenous growth despite the absence of exogenous technical change and the presence of non-reproducible factors if the elasticity of substitution is greater than one. We then consider parameters range related to non-trivial dynamics (i.e. the elasticity of substitution in less than one and shareholders save more than workers) and we focus on local and global bifurcations causing the transition to more and more complex asymptotic dynamics. In particular, as our map is non-differentiable in a subset of the states space, we show that border collision bifurcations occur. Several numerical simulations support the analysis.

Product unit neural network models for predicting the growth limits of Listeria monocytogenes.

Science.gov (United States)

Valero, A; Hervás, C; García-Gimeno, R M; Zurera, G

2007-08-01

A new approach to predict the growth/no growth interface of Listeria monocytogenes as a function of storage temperature, pH, citric acid (CA) and ascorbic acid (AA) is presented. A linear logistic regression procedure was performed and a non-linear model was obtained by adding new variables by means of a Neural Network model based on Product Units (PUNN). The classification efficiency of the training data set and the generalization data of the new Logistic Regression PUNN model (LRPU) were compared with Linear Logistic Regression (LLR) and Polynomial Logistic Regression (PLR) models. 92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%. On the other hand predictions of LRPU were closer to data observed which permits to design proper formulations in minimally processed foods. This novel methodology can be applied to predictive microbiology for describing growth/no growth interface of food-borne microorganisms such as L. monocytogenes. The optimal balance is trying to find models with an acceptable interpretation capacity and with good ability to fit the data on the boundaries of variable range. The results obtained conclude that these kinds of models might well be very a valuable tool for mathematical modeling.
Revealing the Driving Forces of Mid-Cities Urban Growth Patterns Using Spatial Modeling: a Case Study of Los Ángeles, Chile

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Mauricio I. Aguayo

2007-06-01

Full Text Available City growth and changes in land-use patterns cause various important social and environmental impacts. To understand the spatial and temporal dynamics of these processes, the factors that drive urban development must be identified and analyzed, especially those factors that can be used to predict future changes and their potential environmental effects. Our objectives were to quantify the relationship between urban growth and its driving forces and to predict the spatial growth pattern based on historical land-use changes for the city of Los Ángeles in central Chile. This involved the analysis of images from 1978, 1992, and 1998 and characterization of the spatial pattern of land-use change; the construction of digital coverage in GIS; the selection of predictive variables through univariate analysis; the construction of logistic regression models using growth vs. nongrowth for 1978-1992 as the dependent variable; and the prediction of the probability of land-use change by applying the regression model to the 1992-1998 period. To investigate the influence of spatial scale, we constructed several sets of models that contained (1 only distance variables, e.g., distance to highways; (2 only scale-dependent density variables, e.g., density of urban area within a 600-m radius; (3 both distance and density variables; and (4 both distance and density variables at several spatial scales. The environmental variables were included in all models. The combination of distance and density variables at several scales is required to appropriately capture the multiscale urban growth process. The best models correctly predict ~90% of the observed land-use changes for 1992-1998. The distance to access roads, densities of the urban road system and urbanized area at various scales, and soil type were the strongest predictors of the growth pattern. Other variables were less important or not significant in explaining the urban growth process. Our approach, which
Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

Science.gov (United States)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.
Robust Determinants of Growth in Asian Developing Economies: A Bayesian Panel Data Model Averaging Approach

OpenAIRE

LEON-GONZALEZ, Roberto; VINAYAGATHASAN, Thanabalasingam

2013-01-01

This paper investigates the determinants of growth in the Asian developing economies. We use Bayesian model averaging (BMA) in the context of a dynamic panel data growth regression to overcome the uncertainty over the choice of control variables. In addition, we use a Bayesian algorithm to analyze a large number of competing models. Among the explanatory variables, we include a non-linear function of inflation that allows for threshold effects. We use an unbalanced panel data set of 27 Asian ...
Another brick in the cell wall: biosynthesis dependent growth model.

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Adelin Barbacci

Full Text Available Expansive growth of plant cell is conditioned by the cell wall ability to extend irreversibly. This process is possible if (i a tensile stress is developed in the cell wall due to the coupling effect between turgor pressure and the modulation of its mechanical properties through enzymatic and physicochemical reactions and if (ii new cell wall elements can be synthesized and assembled to the existing wall. In other words, expansive growth is the result of coupling effects between mechanical, thermal and chemical energy. To have a better understanding of this process, models must describe the interplay between physical or mechanical variable with biological events. In this paper we propose a general unified and theoretical framework to model growth in function of energy forms and their coupling. This framework is based on irreversible thermodynamics. It is then applied to model growth of the internodal cell of Chara corallina modulated by changes in pressure and temperature. The results describe accurately cell growth in term of length increment but also in term of cell pectate biosynthesis and incorporation to the expanding wall. Moreover, the classical growth model based on Lockhart's equation such as the one proposed by Ortega, appears as a particular and restrictive case of the more general growth equation developed in this paper.
Variable effects of radiological contrast media on thrombus growth in a rabbit jugular vein thrombosis model

NARCIS (Netherlands)

Levi, M. [=Marcel M.; Biemond, B. J.; Sturk, A.; Hoek, J.; ten Cate, J. W.

1991-01-01

We studied the effect of an ionic high osmolar contrast medium (Ioxitalamate), an ionic low osmolar contrast medium (Ioxaglate) and various nonionic low osmolar contrast media (Iopamidol, Iopromide and Iohexol) on thrombus growth in a rabbit jugular vein thrombosis model. Thrombus growth was
Institution, Financial Sector, and Economic Growth: Use The Institutions As An Instrument Variable

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Albertus Girik Allo

2016-06-01

Full Text Available Institution has been investigated having indirect role on economic growth. This paper aims to evaluate whether the quality of institution matters for economic growth. By applying institution as instrumental variable at Foreign Direct Investment (FDI, quality of institution significantly influence economic growth. This study applies two set of data period, namely 1985-2013 and 2000-2013, available online in the World Bank (WB. The first data set, 1985-2013 is used to estimate the role of financial sector on economic growth, focuses on 67 countries. The second data set, 2000-2013 determine the role of institution on financial sector and economic growth by applying 2SLS estimation method. We define institutional variables as set of indicators: Control of Corruption, Political Stability and Absence of Violence, and Voice and Accountability provide declining impact of FDI to economic growth.
Regression models for linking patterns of growth to a later outcome: infant growth and childhood overweight

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Andrew K. Wills

2016-04-01

Full Text Available Abstract Background Regression models are widely used to link serial measures of anthropometric size or changes in size to a later outcome. Different parameterisations of these models enable one to target different questions about the effect of growth, however, their interpretation can be challenging. Our objective was to formulate and classify several sets of parameterisations by their underlying growth pattern contrast, and to discuss their utility using an expository example. Methods We describe and classify five sets of model parameterisations in accordance with their underlying growth pattern contrast (conditional growth; being bigger v being smaller; becoming bigger and staying bigger; growing faster v being bigger; becoming and staying bigger versus being bigger. The contrasts are estimated by including different sets of repeated measures of size and changes in size in a regression model. We illustrate these models in the setting of linking infant growth (measured on 6 occasions: birth, 6 weeks, 3, 6, 12 and 24 months in weight-for-height-for-age z-scores to later childhood overweight at 8y using complete cases from the Norwegian Childhood Growth study (n = 900. Results In our expository example, conditional growth during all periods, becoming bigger in any interval and staying bigger through infancy, and being bigger from birth were all associated with higher odds of later overweight. The highest odds of later overweight occurred for individuals who experienced high conditional growth or became bigger in the 3 to 6 month period and stayed bigger, and those who were bigger from birth to 24 months. Comparisons between periods and between growth patterns require large sample sizes and need to consider how to scale associations to make comparisons fair; with respect to the latter, we show one approach. Conclusion Studies interested in detrimental growth patterns may gain extra insight from reporting several sets of growth pattern
On the ""early-time"" evolution of variables relevant to turbulence models for the Rayleigh-Taylor instability

Energy Technology Data Exchange (ETDEWEB)

Rollin, Bertrand [Los Alamos National Laboratory; Andrews, Malcolm J [Los Alamos National Laboratory

2010-01-01

We present our progress toward setting initial conditions in variable density turbulence models. In particular, we concentrate our efforts on the BHR turbulence model for turbulent Rayleigh-Taylor instability. Our approach is to predict profiles of relevant variables before fully turbulent regime and use them as initial conditions for the turbulence model. We use an idealized model of mixing between two interpenetrating fluids to define the initial profiles for the turbulence model variables. Velocities and volume fractions used in the idealized mixing model are obtained respectively from a set of ordinary differential equations modeling the growth of the Rayleigh-Taylor instability and from an idealization of the density profile in the mixing layer. A comparison between predicted profiles for the turbulence model variables and profiles of the variables obtained from low Atwood number three dimensional simulations show reasonable agreement.
Modeling the variability of shapes of a human placenta.

Science.gov (United States)

Yampolsky, M; Salafia, C M; Shlakhter, O; Haas, D; Eucker, B; Thorp, J

2008-09-01

Placentas are generally round/oval in shape, but "irregular" shapes are common. In the Collaborative Perinatal Project data, irregular shapes were associated with lower birth weight for placental weight, suggesting variably shaped placentas have altered function. (I) Using a 3D one-parameter model of placental vascular growth based on Diffusion Limited Aggregation (an accepted model for generating highly branched fractals), models were run with a branching density growth parameter either fixed or perturbed at either 5-7% or 50% of model growth. (II) In a data set with detailed measures of 1207 placental perimeters, radial standard deviations of placental shapes were calculated from the umbilical cord insertion, and from the centroid of the shape (a biologically arbitrary point). These two were compared to the difference between the observed scaling exponent and the Kleiber scaling exponent (0.75), considered optimal for vascular fractal transport systems. Spearman's rank correlation considered pcentroid) was associated with differences from the Kleiber exponent (p=0.006). A dynamical DLA model recapitulates multilobate and "star" placental shapes via changing fractal branching density. We suggest that (1) irregular placental outlines reflect deformation of the underlying placental fractal vascular network, (2) such irregularities in placental outline indicate sub-optimal branching structure of the vascular tree, and (3) this accounts for the lower birth weight observed in non-round/oval placentas in the Collaborative Perinatal Project.
Modelling the Growth and Volatility in Daily International Mass Tourism to Peru

OpenAIRE

Jose Angelo Divino; Michael McAleer

2009-01-01

Peru is a South American country that is divided into two parts by the Andes Mountains. The rich historical, cultural and geographic diversity has led to the inclusion of ten Peruvian sites on UNESCO’s World Heritage List. For the potential negative impacts of mass tourism on the environment, and hence on future international tourism demand, to be managed appropriately require modelling growth rates and volatility adequately. The paper models the growth rate and volatility (or the variability...
Modelling the co-evolution of indirect genetic effects and inherited variability.

Science.gov (United States)

Marjanovic, Jovana; Mulder, Han A; Rönnegård, Lars; Bijma, Piter

2018-03-28

When individuals interact, their phenotypes may be affected not only by their own genes but also by genes in their social partners. This phenomenon is known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, however, competition not only affects trait levels of individuals, but also inflates variability of trait values among individuals. In the field of quantitative genetics, the variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Such studies, however, consider only the genetic effect of the focal individual on trait variability and do not make a connection to competition. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, the current quantitative genetic models of IGE and inherited variability do not allow for such a relationship. The lack of quantitative genetic models that connect IGEs to inherited variability limits our understanding of the potential of variability to respond to selection, both in nature and agriculture. Models of trait levels, for example, show that IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to variability of trait values. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of the genetic and environmental effects of the focal individual and of the difference in trait value between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait, which is a measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. In contrast to the existing quantitative genetic models, our model allows for co-evolution of
Modelling the growth of Populus species using Ecosystem Demography (ED) model

Science.gov (United States)

Wang, D.; Lebauer, D. S.; Feng, X.; Dietze, M. C.

2010-12-01

Hybrid poplar plantations are an important source being evaluated for biomass production. Effective management of such plantations requires adequate growth and yield models. The Ecosystem Demography model (ED) makes predictions about the large scales of interest in above- and belowground ecosystem structure and the fluxes of carbon and water from a description of the fine-scale physiological processes. In this study, we used a workflow management tool, the Predictive Ecophysiological Carbon flux Analyzer (PECAn), to integrate literature data, field measurement and the ED model to provide predictions of ecosystem functioning. Parameters for the ED ensemble runs were sampled from the posterior distribution of ecophysiological traits of Populus species compiled from the literature using a Bayesian meta-analysis approach. Sensitivity analysis was performed to identify the parameters which contribute the most to the uncertainties of the ED model output. Model emulation techniques were used to update parameter posterior distributions using field-observed data in northern Wisconsin hybrid poplar plantations. Model results were evaluated with 5-year field-observed data in a hybrid poplar plantation at New Franklin, MO. ED was then used to predict the spatial variability of poplar yield in the coterminous United States (United States minus Alaska and Hawaii). Sensitivity analysis showed that root respiration, dark respiration, growth respiration, stomatal slope and specific leaf area contribute the most to the uncertainty, which suggests that our field measurements and data collection should focus on these parameters. The ED model successfully captured the inter-annual and spatial variability of the yield of poplar. Analyses in progress with the ED model focus on evaluating the ecosystem services of short-rotation woody plantations, such as impacts on soil carbon storage, water use, and nutrient retention.
A MODEL OF ECONOMIC GROWTH WITH PUBLIC FINANCE: DYNAMICS AND ANALYTIC SOLUTION

Directory of Open Access Journals (Sweden)

Oliviero Antonio Carboni

2013-01-01

Full Text Available This paper studies the equilibrium dynamics of a growth model with public finance where two different allocations of public resources are considered. The model simultaneously determines the optimal shares of consumption, capital accumulation, taxes and composition of the two different public expenditures which maximize a representative household's lifetime utilities in a centralized economy. The analysis supplies a closed form solution. Moreover, with one restriction on the parameters ( we fully determine the solutions path for all variables of the model and determine the conditions for balanced growth.
Inventory implications of using sampling variances in estimation of growth model coefficients

Science.gov (United States)

Albert R. Stage; William R. Wykoff

2000-01-01

Variables based on stand densities or stocking have sampling errors that depend on the relation of tree size to plot size and on the spatial structure of the population, ignoring the sampling errors of such variables, which include most measures of competition used in both distance-dependent and distance-independent growth models, can bias the predictions obtained from...
An Attempt To Estimate The Contribution Of Variability Of Wetland Extent On The Variability Of The Atmospheric Methane Growth Rate In The Years 1993-2000.

Science.gov (United States)

Ringeval, B.; de Noblet-Ducoudre, N.; Prigent, C.; Bousquet, P.

2006-12-01

The atmospheric methane growth rate presents lots of seasonal and year-to-year variations. Large uncertainties still exist in the relative part of differents sources and sinks on these variations. We have considered, in this study, the main natural sources of methane and the supposed main variable source, i.e. wetlands, and tried to simulate the variations of their emissions considering the variability of the wetland extent and of the climate. For this study, we use the methane emission model of Walter et al. (2001) and the quantification of the flooded areas for the years 1993-2000 obtained with a suite of satellite observations by Prigent et al. (2001). The data necessary to the Walter's model are obtained with simulation of a dynamic global vegetation model ORCHIDEE (Krinner et al. (2005)) constrained by the NCC climate data (Ngo-Duc et al. (2005)) and after imposing a water-saturated soil to approach productivity of wetlands. We calculate global annual methane emissions from wetlands to be 400 Tg per year, that is higher than previous results obtained with fixed wetland extent. Simulations are realised to estimate the part of variability in the emissions explained by the variability of the wetland extent. It seems that the year-to-year emission variability is mainly explained by the interannual variability of wetland extent. The seasonnal variability is explained for 75% in the tropics and only for 40% in the north of 30°N by variability of wetlands extend. Finally, we compare results with a top-down approach of Bousquet et al.(2006).
Developing a dynamic growth model for teak plantations in India

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Vindhya Prasad Tewari

2014-05-01

Full Text Available Background Tectona grandis (teak is one of the most important tropical timber speciesoccurring naturally in India. Appropriate growth models, based on advanced modeling techniques,are not available but are necessary for the successful management of teak stands in the country.Long-term forest planning requires mathematical models, and the principles of Dynamical SystemTheory provide a solid foundation for these. Methods The state-space approach makes it possible to accommodate disturbances and avarying environment. In this paper, an attempt has been made to develop a dynamic growthmodel based on the limited data, consisting of three annual measurements, collected from 22 teak sample plots in Karnataka, Southern India. Results A biologically consistent whole-stand growth model has been presented which uses thestate-space approach for modelling rates of change of three state-variables viz., dominant height,stems per hectare and stand basal area. Moreover, the model includes a stand volume equationas an output function to estimate this variable at any point in time. Transition functions werefitted separately and simultaneously. Moreover, a continuous autoregressive error structure isalso included in the modelling process. For fitting volume equation, generalized method of moments was used to get efficient parameter estimates under heteroscedastic conditions. Conclusions A simple model containing few free parameters performed well and is particularlywell suited to situations where available data is scarce.
TAX COMPOSITION AND ECONOMIC GROWTH. A PANEL-MODEL APPROACH FOR EASTERN EUROPE

Directory of Open Access Journals (Sweden)

MURA PETRU-OVIDIU

2015-03-01

Full Text Available In this paper, we investigate the impact of tax composition on economic growth, based on a panel-model approach. The dataset includes six East-European countries and covers the period 1995-2012. Specifically, the study explores the relative impact of different components of tax revenue (direct and indirect tax revenue, as percentage of total tax revenue on economic growth. The paper adds marginally to the empirical literature, showing how the two types of tax revenue influence economic growth in Eastern Europe, under an extended set of economic and sociopolitical control variables. The most important empirical output, for the 6 investigated East-European countries during 1995-2012, suggests that direct taxes are significant and negatively correlated with economic growth, while indirect taxes exert a positive influence on the dependent variable, though insignificant. As for the control variables, it seems that only freedom from corruption and political stability have a significant impact on economic growth. The study suggests that the design of tax systems in Eastern European countries is in accordance with the Commission’s priorities regarding its growth-friendliness. As for policy implications, governments should continue shifting the tax burden away from labour on to tax bases linked to consumption, property, and combating pollution, with potential positive effects both for growth and for fighting against tax evasion.
Urban tree growth modeling

Science.gov (United States)

E. Gregory McPherson; Paula J. Peper

2012-01-01

This paper describes three long-term tree growth studies conducted to evaluate tree performance because repeated measurements of the same trees produce critical data for growth model calibration and validation. Several empirical and process-based approaches to modeling tree growth are reviewed. Modeling is more advanced in the fields of forestry and...
Modeling lodgepole pine radial growth relative to climate and genetics using universal growth-trend response functions.

Science.gov (United States)

McLane, Sierra C; LeMay, Valerie M; Aitken, Sally N

2011-04-01

Forests strongly affect Earth's carbon cycles, making our ability to forecast forest-productivity changes associated with rising temperatures and changes in precipitation increasingly critical. In this study, we model the influence of climate on annual radial growth using lodgepole pine (Pinus contorta) trees grown for 34 years in a large provenance experiment in western Canada. We use a random-coefficient modeling approach to build universal growth-trend response functions that simultaneously incorporate the impacts of different provenance and site climates on radial growth trends under present and future annual (growth-year), summer, and winter climate regimes. This approach provides new depth to traditional quantitative genetics population response functions by illustrating potential changes in population dominance over time, as well as indicating the age and size at which annual growth begins declining for any population growing in any location under any present or future climate scenario within reason, given the ages and climatic conditions sampled. Our models indicate that lodgepole pine radial-growth levels maximize between 3.9 degrees and 5.1 degrees C mean growth-year temperature. This translates to productivity declining by the mid-21st century in southern and central British Columbia (BC), while increasing beyond the 2080s in northern BC and Yukon, as temperatures rise. Relative to summer climate indices, productivity is predicted to decline continuously through the 2080s in all locations, while relative to winter climate variables, the opposite trend occurs, with the growth increases caused by warmer winters potentially offsetting the summer losses. Trees from warmer provenances, i.e., from the center of the species range, perform best in nearly all of our present and future climate-scenario models. We recommend that similar models be used to analyze population growth trends relative to annual and intra-annual climate in other large-scale provenance

A fuzzy mathematical model of West Java population with logistic growth model

Science.gov (United States)

Nurkholipah, N. S.; Amarti, Z.; Anggriani, N.; Supriatna, A. K.

2018-03-01

In this paper we develop a mathematics model of population growth in the West Java Province Indonesia. The model takes the form as a logistic differential equation. We parameterize the model using several triples of data, and choose the best triple which has the smallest Mean Absolute Percentage Error (MAPE). The resulting model is able to predict the historical data with a high accuracy and it also able to predict the future of population number. Predicting the future population is among the important factors that affect the consideration is preparing a good management for the population. Several experiment are done to look at the effect of impreciseness in the data. This is done by considering a fuzzy initial value to the crisp model assuming that the model propagates the fuzziness of the independent variable to the dependent variable. We assume here a triangle fuzzy number representing the impreciseness in the data. We found that the fuzziness may disappear in the long-term. Other scenarios also investigated, such as the effect of fuzzy parameters to the crisp initial value of the population. The solution of the model is obtained numerically using the fourth-order Runge-Kutta scheme.
Insights into the variability of nucleated amyloid polymerization by a minimalistic model of stochastic protein assembly

Energy Technology Data Exchange (ETDEWEB)

Eugène, Sarah, E-mail: Sarah.Eugene@inria.fr; Doumic, Marie, E-mail: Philippe.Robert@inria.fr, E-mail: Marie.Doumic@inria.fr [INRIA de Paris, 2 Rue Simone Iff, CS 42112, 75589 Paris Cedex 12 (France); Sorbonne Universités, UPMC Université Pierre et Marie Curie, UMR 7598, Laboratoire Jacques-Louis Lions, F-75005 Paris (France); Xue, Wei-Feng, E-mail: W.F.Xue@kent.ac.uk [School of Biosciences, University of Kent, Canterbury, Kent CT2 7NJ (United Kingdom); Robert, Philippe, E-mail: Philippe.Robert@inria.fr [INRIA de Paris, 2 Rue Simone Iff, CS 42112, 75589 Paris Cedex 12 (France)

2016-05-07

Self-assembly of proteins into amyloid aggregates is an important biological phenomenon associated with human diseases such as Alzheimer’s disease. Amyloid fibrils also have potential applications in nano-engineering of biomaterials. The kinetics of amyloid assembly show an exponential growth phase preceded by a lag phase, variable in duration as seen in bulk experiments and experiments that mimic the small volumes of cells. Here, to investigate the origins and the properties of the observed variability in the lag phase of amyloid assembly currently not accounted for by deterministic nucleation dependent mechanisms, we formulate a new stochastic minimal model that is capable of describing the characteristics of amyloid growth curves despite its simplicity. We then solve the stochastic differential equations of our model and give mathematical proof of a central limit theorem for the sample growth trajectories of the nucleated aggregation process. These results give an asymptotic description for our simple model, from which closed form analytical results capable of describing and predicting the variability of nucleated amyloid assembly were derived. We also demonstrate the application of our results to inform experiments in a conceptually friendly and clear fashion. Our model offers a new perspective and paves the way for a new and efficient approach on extracting vital information regarding the key initial events of amyloid formation.
Age structure and capital dilution effects in neo-classical growth models.

Science.gov (United States)

Blanchet, D

1988-01-01

Economists often over estimate capital dilution effects when applying neoclassical growth models which use age structured population and depreciation of capital stock. This occurs because capital stock is improperly characterized. A standard model which assumes a constant depreciation of capital intimates that a population growth rate equal to a negative constant savings ratio is preferable to any higher growth rate. Growth rates which are lower than a negative constant savings ratio suggest an ever growing capital/labor ratio and an ever growing standard of living, even if people do not save. This is suggested because the natural reduction of the capital stock through depreciation is slower than the population decrease which is simply unrealistic. This model overlooks the fact that low or negative growth rates result in an ageing of the capital stock, and this ageing subsequently results in an increase of the overall rate of capital depreciation. In that overly simplistic model, depreciation was assumed independent of the age of the captial stock. Incorporating depreciation as a variable into a model allows a more symmetric treatment of capital. Using models with heterogenous capital, this article explores what occurs when more than 1 kind of capital good is involved in production and when these various captial goods have different lengths of life. Applying economic models, it also examines what occurs when the length of life of capital may vary. These variations correct the negative impact that population growth can have on per capital production and consumption.
Growth Modeling with Non-Ignorable Dropout: Alternative Analyses of the STAR*D Antidepressant Trial

Science.gov (United States)

Muthén, Bengt; Asparouhov, Tihomir; Hunter, Aimee; Leuchter, Andrew

2011-01-01

This paper uses a general latent variable framework to study a series of models for non-ignorable missingness due to dropout. Non-ignorable missing data modeling acknowledges that missingness may depend on not only covariates and observed outcomes at previous time points as with the standard missing at random (MAR) assumption, but also on latent variables such as values that would have been observed (missing outcomes), developmental trends (growth factors), and qualitatively different types of development (latent trajectory classes). These alternative predictors of missing data can be explored in a general latent variable framework using the Mplus program. A flexible new model uses an extended pattern-mixture approach where missingness is a function of latent dropout classes in combination with growth mixture modeling using latent trajectory classes. A new selection model allows not only an influence of the outcomes on missingness, but allows this influence to vary across latent trajectory classes. Recommendations are given for choosing models. The missing data models are applied to longitudinal data from STAR*D, the largest antidepressant clinical trial in the U.S. to date. Despite the importance of this trial, STAR*D growth model analyses using non-ignorable missing data techniques have not been explored until now. The STAR*D data are shown to feature distinct trajectory classes, including a low class corresponding to substantial improvement in depression, a minority class with a U-shaped curve corresponding to transient improvement, and a high class corresponding to no improvement. The analyses provide a new way to assess drug efficiency in the presence of dropout. PMID:21381817
Frost Growth and Densification on a Flat Surface in Laminar Flow with Variable Humidity

Science.gov (United States)

Kandula, M.

2012-01-01

Experiments are performed concerning frost growth and densification in laminar flow over a flat surface under conditions of constant and variable humidity. The flat plate test specimen is made of aluminum-6031, and has dimensions of 0.3 mx0.3 mx6.35 mm. Results for the first variable humidity case are obtained for a plate temperature of 255.4 K, air velocity of 1.77 m/s, air temperature of 295.1 K, and a relative humidity continuously ranging from 81 to 54%. The second variable humidity test case corresponds to plate temperature of 255.4 K, air velocity of 2.44 m/s, air temperature of 291.8 K, and a relative humidity ranging from 66 to 59%. Results for the constant humidity case are obtained for a plate temperature of 263.7 K, air velocity of 1.7 m/s, air temperature of 295 K, and a relative humidity of 71.6 %. Comparisons of the data with the author's frost model extended to accommodate variable humidity suggest satisfactory agreement between the theory and the data for both constant and variable humidity.
Spatial variability in growth-increment chronologies of long-lived freshwater mussels: Implications for climate impacts and reconstructions

Science.gov (United States)

Black, Bryan A.; Dunham, Jason B.; Blundon, Brett W.; Raggon, Mark F.; Zima, Daniela

2010-01-01

Estimates of historical variability in river ecosystems are often lacking, but long-lived freshwater mussels could provide unique opportunities to understand past conditions in these environments. We applied dendrochronology techniques to quantify historical variability in growth-increment widths in valves (shells) of western pearlshell freshwater mussels (Margaritifera falcata). A total of 3 growth-increment chronologies, spanning 19 to 26 y in length, were developed. Growth was highly synchronous among individuals within each site, and to a lesser extent, chronologies were synchronous among sites. All 3 chronologies negatively related to instrumental records of stream discharge, while correlations with measures of water temperature were consistently positive but weaker. A reconstruction of stream discharge was performed using linear regressions based on a mussel growth chronology and the regional Palmer Drought Severity Index (PDSI). Models based on mussel growth and PDSI yielded similar coefficients of prediction (R2Pred) of 0.73 and 0.77, respectively, for predicting out-ofsample observations. From an ecological perspective, we found that mussel chronologies provided a rich source of information for understanding climate impacts. Responses of mussels to changes in climate and stream ecosystems can be very site- and process-specific, underscoring the complex nature of biotic responses to climate change and the need to understand both regional and local processes in projecting climate impacts on freshwater species.
Influence of Environmental Variables on Gambierdiscus spp. (Dinophyceae Growth and Distribution.

Directory of Open Access Journals (Sweden)

Yixiao Xu

Full Text Available Benthic dinoflagellates in the genus Gambierdiscus produce the ciguatoxin precursors responsible for the occurrence of ciguatera toxicity. The prevalence of ciguatera toxins in fish has been linked to the presence and distribution of toxin-producing species in coral reef ecosystems, which is largely determined by the presence of suitable benthic habitat and environmental conditions favorable for growth. Here using single factor experiments, we examined the effects of salinity, irradiance, and temperature on growth of 17 strains of Gambierdiscus representing eight species/phylotypes (G. belizeanus, G. caribaeus, G. carolinianus, G. carpenteri, G. pacificus, G. silvae, Gambierdiscus sp. type 4-5, most of which were established from either Marakei Island, Republic of Kiribati, or St. Thomas, United States Virgin Island (USVI. Comparable to prior studies, growth rates fell within the range of 0-0.48 divisions day(-1. In the salinity and temperature studies, Gambierdiscus responded in a near Gaussian, non-linear manner typical for such studies, with optimal and suboptimal growth occurring in the range of salinities of 25 and 45 and 21.0 and 32.5°C. In the irradiance experiment, no mortality was observed; however, growth rates at 55 μmol photons · m(-2 · s(-1 were lower than those at 110-400 μmol photons · m(-2 · s(-1. At the extremes of the environmental conditions tested, growth rates were highly variable, evidenced by large coefficients of variability. However, significant differences in intraspecific growth rates were typically found only at optimal or near-optimal growth conditions. Polynomial regression analyses showed that maximum growth occurred at salinity and temperature levels of 30.1-38.5 and 23.8-29.2°C, respectively. Gambierdiscus growth patterns varied among species, and within individual species: G. belizeanus, G. caribaeus, G. carpenteri, and G. pacificus generally exhibited a wider range of tolerance to environmental
Growth hormone responsiveness: peak stimulated growth hormone levels and other variables in idiopathic short stature (ISS): data from the National Cooperative Growth Study.

Science.gov (United States)

Moore, Wayne V; Dana, Ken; Frane, James; Lippe, Barbara

2008-09-01

In children with idiopathic short stature (ISS), growth hormone (GH) response to a provocative test will be inversely related to the first year response to hGH and be a variable accounting for a degree of responsiveness. Because high levels of GH are a characteristic of GH insensitivity, such as in Laron syndrome, it is possible that a high stimulated GH is associated with a lower first year height velocity among children diagnosed as having ISS. We examined the relationship between the peak stimulated GH levels in 3 ISS groups; GH >10 -40 ng/mL and the first year growth response to rhGH therapy. We also looked at 8 other predictor variables (age, sex, height SDS, height age, body mass index (BMI), bone age, dose, and SDS deficit from target parental height. Multiple regression analysis with the first year height as the dependent variable and peak stimulated GH was the primary endpoint. The predictive value of adding each of the other variables was then assessed. Mean change in height velocity was similar among the three groups, with a maximum difference among the groups of 0.6 cm/yr. There was a small but statistically significant correlation (r=-0.12) between the stimulated GH and first year height velocity. The small correlation between first year growth response and peak GH is not clinically relevant in defining GH resistance. No cut off level by peak GH could be determined to enhance the usefulness of this measure to predict response. Baseline age was the only clinically significant predictor, R-squared, 6.4%. All other variables contributed less than an additional 2% to the R-squared.
Oscillations in a Growth Model with Capital, Technology and Environment with Exogenous Shocks

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Wei-Bin Zhang

2015-07-01

Full Text Available This paper generalizes the dynamic growth model with wealth accumulation, technological change and environmental change by Zhang (2012 by making all the parameters as time-dependent parameters. The model treats physical capital accumulation, knowledge creation and utilization, and environmental change as endogenous variables. It synthesizes the basic ideas of the neoclassical growth theory, Arrow’s learning-by-doing model and the traditional dynamic models of environmental change within a comprehensive framework. The behavior of the household is described with an alternative approach to household behavior. We simulated the model to demonstrate existence of equilibrium points, motion of the dynamic system, and oscillations due to different exogenous shocks.
Among-tree variability and feedback effects result in different growth responses to climate change at the upper treeline in the Swiss Alps.

Science.gov (United States)

Jochner, Matthias; Bugmann, Harald; Nötzli, Magdalena; Bigler, Christof

2017-10-01

Upper treeline ecotones are important life form boundaries and particularly sensitive to a warming climate. Changes in growth conditions at these ecotones have wide-ranging implications for the provision of ecosystem services in densely populated mountain regions like the European Alps. We quantify climate effects on short- and long-term tree growth responses, focusing on among-tree variability and potential feedback effects. Although among-tree variability is thought to be substantial, it has not been considered systematically yet in studies on growth-climate relationships. We compiled tree-ring data including almost 600 trees of major treeline species ( Larix decidua , Picea abies , Pinus cembra , and Pinus mugo ) from three climate regions of the Swiss Alps. We further acquired tree size distribution data using unmanned aerial vehicles. To account for among-tree variability, we employed information-theoretic model selections based on linear mixed-effects models (LMMs) with flexible choice of monthly temperature effects on growth. We isolated long-term trends in ring-width indices (RWI) in interaction with elevation. The LMMs revealed substantial amounts of previously unquantified among-tree variability, indicating different strategies of single trees regarding when and to what extent to invest assimilates into growth. Furthermore, the LMMs indicated strongly positive temperature effects on growth during short summer periods across all species, and significant contributions of fall ( L. decidua ) and current year's spring ( L. decidua , P. abies ). In the longer term, all species showed consistently positive RWI trends at highest elevations, but different patterns with decreasing elevation. L. decidua exhibited even negative RWI trends compared to the highest treeline sites, whereas P. abies , P. cembra , and P. mugo showed steeper or flatter trends with decreasing elevation. This does not only reflect effects of ameliorated climate conditions on tree
Key variables influencing patterns of lava dome growth and collapse

Science.gov (United States)

Husain, T.; Elsworth, D.; Voight, B.; Mattioli, G. S.; Jansma, P. E.

2013-12-01

Lava domes are conical structures that grow by the infusion of viscous silicic or intermediate composition magma from a central volcanic conduit. Dome growth can be characterized by repeated cycles of growth punctuated by collapse, as the structure becomes oversized for its composite strength. Within these cycles, deformation ranges from slow long term deformation to sudden deep-seated collapses. Collapses may range from small raveling failures to voluminous and fast-moving pyroclastic flows with rapid and long-downslope-reach from the edifice. Infusion rate and magma rheology together with crystallization temperature and volatile content govern the spatial distribution of strength in the structure. Solidification, driven by degassing-induced crystallization of magma leads to the formation of a continuously evolving frictional talus as a hard outer shell. This shell encapsulates the cohesion-dominated soft ductile core. Here we explore the mechanics of lava dome growth and failure using a two-dimensional particle-dynamics model. This meshless model follows the natural evolution of a brittle carapace formed by loss of volatiles and rheological stiffening and avoids difficulties of hour-glassing and mesh-entangelment typical in meshed models. We test the fidelity of the model against existing experimental and observational models of lava dome growth. The particle-dynamics model follows the natural development of dome growth and collapse which is infeasible using simple analytical models. The model provides insight into the triggers that lead to the transition in collapse mechasnism from shallow flank collapse to deep seated sector collapse. Increase in material stiffness due to decrease in infusion rate results in the transition of growth pattern from endogenous to exogenous. The material stiffness and strength are strongly controlled by the magma infusion rate. Increase in infusion rate decreases the time available for degassing induced crystallization leading to a
Lévy-based growth models

DEFF Research Database (Denmark)

Jónsdóttir, Kristjana Ýr; Schmiegel, Jürgen; Jensen, Eva Bjørn Vedel

2008-01-01

In the present paper, we give a condensed review, for the nonspecialist reader, of a new modelling framework for spatio-temporal processes, based on Lévy theory. We show the potential of the approach in stochastic geometry and spatial statistics by studying Lévy-based growth modelling of planar o...... objects. The growth models considered are spatio-temporal stochastic processes on the circle. As a by product, flexible new models for space–time covariance functions on the circle are provided. An application of the Lévy-based growth models to tumour growth is discussed....
Population growth is a variable open to change

Science.gov (United States)

Potts, M.

2016-12-01

The absolute number of people and the rate of population growth have an impact on climate mitigation, adaptation and possible conflict. Half the pregnancies in the US are unintended. Robust quantitative evidence from California demonstrates that improving access to family planning is the single most cost-effective way of mitigating our carbon footprint. Globally, there are 80 million unintended pregnancies annually. Many non-evidence barriers deprive women of the information and means required to separate sex from childbearing. Between 1960 and 1990, meeting the need for family planning led to a rapid fall in family size in much of Asia. Since 1990, funding for family planning has collapsed and fertility decline has stalled. The UN projects that by 2100 global population will increase by 3.8 billion (equal to world population in 1975). 80% of this growth will be in Africa. Studies project that climate change will undermine crop yields in parts of Africa, especially the Sahel. A high ratio of young males to the rest of the population is a risk factor in conflict. Today, only 1% of overseas assistance is allocated to family planning. Based on analysis of the past, doubling that investment would accelerate fertility decline, facilitating climate mitigation and adaptation, and possibly reducing conflict. Population and family planning were pushed off the international agenda by unacceptably and tragic episodes of coercion in China and India. However, there is compelling data that when voluntary family planning is widely available then family size can fall rapidly, as occurred in the Islamic Republic of Iran, where fertility fell more rapidly than in any other country in history. Family planning is listening to what women want not telling people want to do. Population growth is a variable open to change in a human rights framework. Population and family planning are variables relevant to the scientific agenda of the AGU.
Comparison of the Effects of Environmental Parameters on the Growth Variability of Vibrio parahaemolyticus Coupled with Strain Sources and Genotypes Analyses.

Science.gov (United States)

Liu, Bingxuan; Liu, Haiquan; Pan, Yingjie; Xie, Jing; Zhao, Yong

2016-01-01

Microbial growth variability plays an important role on food safety risk assessment. In this study, the growth kinetic characteristics corresponding to maximum specific growth rate (μmax) of 50 V. parahaemolyticus isolates from different sources and genotypes were evaluated at different temperatures (10, 20, 30, and 37°C) and salinity (0.5, 3, 5, 7, and 9%) using the automated turbidimetric system Bioscreen C. The results demonstrated that strain growth variability increased as the growth conditions became more stressful both in terms of temperature and salinity. The coefficient of variation (CV) of μmax for temperature was larger than that for salinity, indicating that the impact of temperature on strain growth variability was greater than that of salinity. The strains isolated from freshwater aquatic products had more conspicuous growth variations than those from seawater. Moreover, the strains with tlh (+) /tdh (+) /trh (-) exhibited higher growth variability than tlh (+) /tdh (-) /trh (-) or tlh (+) /tdh (-) /trh (+), revealing that gene heterogeneity might have possible relations with the growth variability. This research illustrates that the growth environments, strain sources as well as genotypes have impacts on strain growth variability of V. parahaemolyticus, which can be helpful for incorporating strain variability in predictive microbiology and microbial risk assessment.
MODEL-ASSISTED ESTIMATION OF THE GENETIC VARIABILITY IN PHYSIOLOGICAL PARAMETERS RELATED TO TOMATO FRUIT GROWTH UNDER CONTRASTED WATER CONDITIONS

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Dario Constantinescu

2016-12-01

Full Text Available Drought stress is a major abiotic stres threatening plant and crop productivity. In case of fleshy fruits, understanding Drought stress is a major abiotic stress threatening plant and crop productivity. In case of fleshy fruits, understanding mechanisms governing water and carbon accumulations and identifying genes, QTLs and phenotypes, that will enable trade-offs between fruit growth and quality under Water Deficit (WD condition is a crucial challenge for breeders and growers. In the present work, 117 recombinant inbred lines of a population of Solanum lycopersicum were phenotyped under control and WD conditions. Plant water status, fruit growth and composition were measured and data were used to calibrate a process-based model describing water and carbon fluxes in a growing fruit as a function of plant and environment. Eight genotype-dependent model parameters were estimated using a multiobjective evolutionary algorithm in order to minimize the prediction errors of fruit dry and fresh mass throughout fruit development. WD increased the fruit dry matter content (up to 85 % and decreased its fresh weight (up to 60 %, big fruit size genotypes being the most sensitive. The mean normalized root mean squared errors of the predictions ranged between 16-18 % in the population. Variability in model genotypic parameters allowed us to explore diverse genetic strategies in response to WD. An interesting group of genotypes could be discriminated in which i the low loss of fresh mass under WD was associated with high active uptake of sugars and low value of the maximum cell wall extensibility, and ii the high dry matter content in control treatment (C was associated with a slow decrease of mass flow. Using 501 SNP markers genotyped across the genome, a QTL analysis of model parameters allowed to detect three main QTLs related to xylem and phloem conductivities, on chromosomes 2, 4 and 8. The model was then applied to design ideotypes with high dry matter
Wood phenology, not carbon input, controls the interannual variability of wood growth in a temperate oak forest.

Science.gov (United States)

Delpierre, Nicolas; Berveiller, Daniel; Granda, Elena; Dufrêne, Eric

2016-04-01

Although the analysis of flux data has increased our understanding of the interannual variability of carbon inputs into forest ecosystems, we still know little about the determinants of wood growth. Here, we aimed to identify which drivers control the interannual variability of wood growth in a mesic temperate deciduous forest. We analysed a 9-yr time series of carbon fluxes and aboveground wood growth (AWG), reconstructed at a weekly time-scale through the combination of dendrometer and wood density data. Carbon inputs and AWG anomalies appeared to be uncorrelated from the seasonal to interannual scales. More than 90% of the interannual variability of AWG was explained by a combination of the growth intensity during a first 'critical period' of the wood growing season, occurring close to the seasonal maximum, and the timing of the first summer growth halt. Both atmospheric and soil water stress exerted a strong control on the interannual variability of AWG at the study site, despite its mesic conditions, whilst not affecting carbon inputs. Carbon sink activity, not carbon inputs, determined the interannual variations in wood growth at the study site. Our results provide a functional understanding of the dependence of radial growth on precipitation observed in dendrological studies. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.
Robust Growth Determinants

OpenAIRE

Doppelhofer, Gernot; Weeks, Melvyn

2011-01-01

This paper investigates the robustness of determinants of economic growth in the presence of model uncertainty, parameter heterogeneity and outliers. The robust model averaging approach introduced in the paper uses a flexible and parsi- monious mixture modeling that allows for fat-tailed errors compared to the normal benchmark case. Applying robust model averaging to growth determinants, the paper finds that eight out of eighteen variables found to be significantly related to economic growth ...
Assessment of the interaction of variables in the intergranular stress corrosion crack growth rate behavior of Alloys 600, 82, and 182

International Nuclear Information System (INIS)

Paraventi, D.J.; Moshier, W.C.

2007-01-01

SCC testing of Alloy 600 and its weld metals has demonstrated that temperature, stress intensity factor (K), dissolved hydrogen, and yield strength all play a role on crack growth in deaerated, hydrogenated water. Typically, each variable has been modeled independently. However, some of these variables interact, which can affect crack growth predictions. In particular, testing has demonstrated several important interactions, including final annealing temperature and K, cold work and dissolved hydrogen, and orientation and cold work. The annealing temperature influences the K dependence of Alloy 600, with lower temperature anneals decreasing the influence of stress on growth. The response to cold work varies as a function of processing method and orientation, with crack growth in the processing direction having a stronger yield strength dependence than crack growth perpendicular to the processing direction. The effect of hydrogen has been found to be related to electrochemical potential, with the most susceptible condition occurring near the Ni/NiO phase transition. However, cold worked Alloy 600 maintains the peak susceptibility at low hydrogen conditions. (author)
Kinetic Modeling of Corn Fermentation with S. cerevisiae Using a Variable Temperature Strategy

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Augusto C. M. Souza

2018-04-01

Full Text Available While fermentation is usually done at a fixed temperature, in this study, the effect of having a controlled variable temperature was analyzed. A nonlinear system was used to model batch ethanol fermentation, using corn as substrate and the yeast Saccharomyces cerevisiae, at five different fixed and controlled variable temperatures. The lower temperatures presented higher ethanol yields but took a longer time to reach equilibrium. Higher temperatures had higher initial growth rates, but the decay of yeast cells was faster compared to the lower temperatures. However, in a controlled variable temperature model, the temperature decreased with time with the initial value of 40 ∘ C. When analyzing a time window of 60 h, the ethanol production increased 20% compared to the batch with the highest temperature; however, the yield was still 12% lower compared to the 20 ∘ C batch. When the 24 h’ simulation was analyzed, the controlled model had a higher ethanol concentration compared to both fixed temperature batches.
Kinetic Modeling of Corn Fermentation with S. cerevisiae Using a Variable Temperature Strategy.

Science.gov (United States)

Souza, Augusto C M; Mousaviraad, Mohammad; Mapoka, Kenneth O M; Rosentrater, Kurt A

2018-04-24

While fermentation is usually done at a fixed temperature, in this study, the effect of having a controlled variable temperature was analyzed. A nonlinear system was used to model batch ethanol fermentation, using corn as substrate and the yeast Saccharomyces cerevisiae , at five different fixed and controlled variable temperatures. The lower temperatures presented higher ethanol yields but took a longer time to reach equilibrium. Higher temperatures had higher initial growth rates, but the decay of yeast cells was faster compared to the lower temperatures. However, in a controlled variable temperature model, the temperature decreased with time with the initial value of 40 ∘ C. When analyzing a time window of 60 h, the ethanol production increased 20% compared to the batch with the highest temperature; however, the yield was still 12% lower compared to the 20 ∘ C batch. When the 24 h’ simulation was analyzed, the controlled model had a higher ethanol concentration compared to both fixed temperature batches.

Towards Sustainable Growth Business Models

Energy Technology Data Exchange (ETDEWEB)

Kamp-Roelands, N.; Balkenende, J.P.; Van Ommen, P.

2012-03-15

The Dutch Sustainable Growth Coalition (DSGC) has the following objectives: The DSGC aims to pro-actively drive sustainable growth business models along three lines: (1) Shape. DSGC member companies aim to connect economic profitability with environmental and social progress on the basis of integrated sustainable growth business models; (2) Share. DSGC member companies aim for joint advocacy of sustainable growth business models both internationally and nationally; and (3) Stimulate. DSGC member companies aim to stimulate and influence the policy debate on enabling sustainable growth - with a view to finding solutions to the environmental and social challenges we are facing. This is their first report. The vision, actions and mission of DSGC are documented in the Manifesto in Chapter 2 of this publication. Chapter 3 contains an overview of key features of an integrated sustainable growth business model and the roadmap towards such a model. In Chapter 4, project examples of DSGC members are presented, providing insight into the hands-on reality of implementing the good practices. Chapter 5 offers an overview of how the Netherlands provides an enabling environment for sustainable growth business models. Chapter 6 offers the key conclusions.
Confounding of three binary-variables counterfactual model

OpenAIRE

Liu, Jingwei; Hu, Shuang

2011-01-01

Confounding of three binary-variables counterfactual model is discussed in this paper. According to the effect between the control variable and the covariate variable, we investigate three counterfactual models: the control variable is independent of the covariate variable, the control variable has the effect on the covariate variable and the covariate variable affects the control variable. Using the ancillary information based on conditional independence hypotheses, the sufficient conditions...
Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

Science.gov (United States)

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19
Long-term relationships of major macro-variables in a resource-related economic model of Australia

International Nuclear Information System (INIS)

Harvie, Charles; Hoa, T. van

1993-01-01

The paper reports the results of a simple cointegration analysis applied to bivariate causality models using data on resource output, oil prices, terms of trade, current account and output growth to investigate the long-term relationships among these major macroeconomic aggregates in a resource-related economic model of Australia. For the period 1960-1990, the empirical evidence indicates that these five macro-variables, as formulated in our model, are not random walks. In addition, resource production and oil prices are significantly cointegrated, and they are also significantly cointegrated with the current account, terms of trade and economic growth. These findings provide support to the long-term adjustments foundation of our resource-related model. (author)
Using Remote Sensing Mapping and Growth Response to Environmental Variability to Aide Aquatic Invasive Plant Management

Science.gov (United States)

Bubenheim, David L.; Schlick, Greg; Genovese, Vanessa; Wilson, Kenneth D.

2018-01-01

Management of aquatic weeds in complex watersheds and river systems present many challenges to assessment, planning and implementation of management practices for floating and submerged aquatic invasive plants. The Delta Region Areawide Aquatic Weed Project (DRAAWP), a USDA sponsored area-wide project, is working to enhance planning, decision-making and operational efficiency in the California Sacramento-San Joaquin Delta. Satellite and airborne remote sensing are used map (area coverage and biomass density), direct operations, and assess management impacts on plant communities. Archived satellite records enable review of results following previous climate and management events and aide in developing long-term strategies. Examples of remote sensing aiding effectiveness of aquatic weed management will be discussed as well as areas for potential technological improvement. Modeling at local and watershed scales using the SWAT modeling tool provides insight into land-use effects on water quality (described by Zhang in same Symposium). Controlled environment growth studies have been conducted to quantify the growth response of invasive aquatic plants to water quality and other environmental factors. Environmental variability occurs across a range of time scales from long-term climate and seasonal trends to short-term water flow mediated variations. Response time for invasive species response are examined at time scales of weeks, day, and hours using a combination of study duration and growth assessment techniques to assess water quality, temperature (air and water), nitrogen, phosphorus, and light effects. These provide response parameters for plant growth models in response to the variation and interact with management and economic models associated with aquatic weed management. Plant growth models are to be informed by remote sensing and applied spatially across the Delta to balance location and type of aquatic plant, growth response to altered environments and
A Generalized Stability Analysis of the AMOC in Earth System Models: Implication for Decadal Variability and Abrupt Climate Change

Energy Technology Data Exchange (ETDEWEB)

Fedorov, Alexey V. [Yale Univ., New Haven, CT (United States)

2015-01-14

The central goal of this research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) as related to climate variability and abrupt climate change within a hierarchy of climate models ranging from realistic ocean models to comprehensive Earth system models. Generalized Stability Analysis, a method that quantifies the transient and asymptotic growth of perturbations in the system, is one of the main approaches used throughout this project. The topics we have explored range from physical mechanisms that control AMOC variability to the factors that determine AMOC predictability in the Earth system models, to the stability and variability of the AMOC in past climates.
Modeling and control of greenhouse crop growth

CERN Document Server

Rodríguez, Francisco; Guzmán, José Luis; Ramírez-Arias, Armando

2015-01-01

A discussion of challenges related to the modeling and control of greenhouse crop growth, this book presents state-of-the-art answers to those challenges. The authors model the subsystems involved in successful greenhouse control using different techniques and show how the models obtained can be exploited for simulation or control design; they suggest ideas for the development of physical and/or black-box models for this purpose. Strategies for the control of climate- and irrigation-related variables are brought forward. The uses of PID control and feedforward compensators, both widely used in commercial tools, are summarized. The benefits of advanced control techniques—event-based, robust, and predictive control, for example—are used to improve on the performance of those basic methods. A hierarchical control architecture is developed governed by a high-level multiobjective optimization approach rather than traditional constrained optimization and artificial intelligence techniques. Reference trajector...
Hopf Bifurcation of a Delayed Epidemic Model with Information Variable and Limited Medical Resources

Directory of Open Access Journals (Sweden)

Caijuan Yan

2014-01-01

Full Text Available We consider SIR epidemic model in which population growth is subject to logistic growth in absence of disease. We get the condition for Hopf bifurcation of a delayed epidemic model with information variable and limited medical resources. By analyzing the corresponding characteristic equations, the local stability of an endemic equilibrium and a disease-free equilibrium is discussed. If the basic reproduction ratio ℛ01, we obtain sufficient conditions under which the endemic equilibrium E* of system is locally asymptotically stable. And we also have discussed the stability and direction of Hopf bifurcations. Numerical simulations are carried out to explain the mathematical conclusions.
NONLINEAR MODELS FOR DESCRIPTION OF CACAO FRUIT GROWTH WITH ASSUMPTION VIOLATIONS

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JOEL AUGUSTO MUNIZ

2017-01-01

Full Text Available Cacao (Theobroma cacao L. is an important fruit in the Brazilian economy, which is mainly cultivated in the southern State of Bahia. The optimal stage for harvesting is a major factor for fruit quality and the knowledge on its growth curves can help, especially in identifying the ideal maturation stage for harvesting. Nonlinear regression models have been widely used for description of growth curves. However, several studies in this subject do not consider the residual analysis, the existence of a possible dependence between longitudinal observations, or the sample variance heterogeneity, compromising the modeling quality. The objective of this work was to compare the fit of nonlinear regression models, considering residual analysis and assumption violations, in the description of the cacao (clone Sial-105 fruit growth. The data evaluated were extracted from Brito and Silva (1983, who conducted the experiment in the Cacao Research Center, Ilheus, State of Bahia. The variables fruit length, diameter and volume as a function of fruit age were studied. The use of weighting and incorporation of residual dependencies was efficient, since the modeling became more consistent, improving the model fit. Considering the first-order autoregressive structure, when needed, leads to significant reduction in the residual standard deviation, making the estimates more reliable. The Logistic model was the most efficient for the description of the cacao fruit growth.
Nonlinear Growth Models as Measurement Models: A Second-Order Growth Curve Model for Measuring Potential.

Science.gov (United States)

McNeish, Daniel; Dumas, Denis

2017-01-01

Recent methodological work has highlighted the promise of nonlinear growth models for addressing substantive questions in the behavioral sciences. In this article, we outline a second-order nonlinear growth model in order to measure a critical notion in development and education: potential. Here, potential is conceptualized as having three components-ability, capacity, and availability-where ability is the amount of skill a student is estimated to have at a given timepoint, capacity is the maximum amount of ability a student is predicted to be able to develop asymptotically, and availability is the difference between capacity and ability at any particular timepoint. We argue that single timepoint measures are typically insufficient for discerning information about potential, and we therefore describe a general framework that incorporates a growth model into the measurement model to capture these three components. Then, we provide an illustrative example using the public-use Early Childhood Longitudinal Study-Kindergarten data set using a Michaelis-Menten growth function (reparameterized from its common application in biochemistry) to demonstrate our proposed model as applied to measuring potential within an educational context. The advantage of this approach compared to currently utilized methods is discussed as are future directions and limitations.
Institution, Financial Sector, and Economic Growth: Use The Institutions As An Instrument Variable

OpenAIRE

Albertus Girik Allo

2016-01-01

Institution has been investigated having indirect role on economic growth. This paper aims to evaluate whether the quality of institution matters for economic growth. By applying institution as instrumental variable at Foreign Direct Investment (FDI), quality of institution significantly influence economic growth. This study applies two set of data period, namely 1985-2013 and 2000-2013, available online in the World Bank (WB). The first data set, 1985-2013 is used to estimate the role of fin...
A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change.

Directory of Open Access Journals (Sweden)

M Irfan Ashraf

Full Text Available Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model. Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm(2 5-year(-1 and volume: 0.0008 m(3 5-year(-1. Model variability described by root mean squared error (RMSE in basal area prediction was 40.53 cm(2 5-year(-1 and 0.0393 m(3 5-year(-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence
Development of a dynamic growth-death model for Escherichia coli O157:H7 in minimally processed leafy green vegetables.

Science.gov (United States)

McKellar, Robin C; Delaquis, Pascal

2011-11-15

Escherichia coli O157:H7, an occasional contaminant of fresh produce, can present a serious health risk in minimally processed leafy green vegetables. A good predictive model is needed for Quantitative Risk Assessment (QRA) purposes, which adequately describes the growth or die-off of this pathogen under variable temperature conditions experienced during processing, storage and shipping. Literature data on behaviour of this pathogen on fresh-cut lettuce and spinach was taken from published graphs by digitization, published tables or from personal communications. A three-phase growth function was fitted to the data from 13 studies, and a square root model for growth rate (μ) as a function of temperature was derived: μ=(0.023*(Temperature-1.20))(2). Variability in the published data was incorporated into the growth model by the use of weighted regression and the 95% prediction limits. A log-linear die-off function was fitted to the data from 13 studies, and the resulting rate constants were fitted to a shifted lognormal distribution (Mean: 0.013; Standard Deviation, 0.010; Shift, 0.001). The combined growth-death model successfully predicted pathogen behaviour under both isothermal and non-isothermal conditions when compared to new published data. By incorporating variability, the resulting model is an improvement over existing ones, and is suitable for QRA applications. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.
The Impact of Key Monetary Variables on the Economic Growth of the CEMAC Zone

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Forgha Godfrey NJIMANTED

2016-09-01

Full Text Available This study seeks to empirically explore the impact of key monetary policy variables on the economic growth in the CEMAC zone from the period of 1981 to 2015. Carried out using the Ex post facto research design based on the principal components selection approach, the study interacts money supply, interest rate, economic growth, and inflation rate, among themselves and their lagged values using the Vector Auto-regressive (VAR analytical technique. The Classical quantity theory of money, the Cambridge Cash Balanced, the liquidity preference theory and the Monetarists as theoretical frameworks were explored to appreciate the time trends of the selected variables on the economic growth of the CEMAC zone. Based on the (VAR methodology, the study reveals that key monetary policy variables influence economic growth of the CEMAC zone in different ways with inflation rate as the impact factor. On the basis of the above findings and the evidence from other studies, lending and inflation rate generated substantial destabilizing impacts on the economic growth, suggesting that the monetary authorities should play a critical role in creating an enabling environment for growth. The determination of the optimal lending rate should reflect the overall internal rate of returns in the productive sectors with due attention to market fundamentals. In line with this, the Central Bank of CEMAC should be given complete instrumental autonomy to operate depending on a set of in-built targets by the individual countries of the zone. Effective monetary targeting and accommodating monetary policies should be designed and implemented as the need arises with little or no political motives.
Reactor scale modeling of multi-walled carbon nanotube growth

International Nuclear Information System (INIS)

Lombardo, Jeffrey J.; Chiu, Wilson K.S.

2011-01-01

As the mechanisms of carbon nanotube (CNT) growth becomes known, it becomes important to understand how to implement this knowledge into reactor scale models to optimize CNT growth. In past work, we have reported fundamental mechanisms and competing deposition regimes that dictate single wall carbon nanotube growth. In this study, we will further explore the growth of carbon nanotubes with multiple walls. A tube flow chemical vapor deposition reactor is simulated using the commercial software package COMSOL, and considered the growth of single- and multi-walled carbon nanotubes. It was found that the limiting reaction processes for multi-walled carbon nanotubes change at different temperatures than the single walled carbon nanotubes and it was shown that the reactions directly governing CNT growth are a limiting process over certain parameters. This work shows that the optimum conditions for CNT growth are dependent on temperature, chemical concentration, and the number of nanotube walls. Optimal reactor conditions have been identified as defined by (1) a critical inlet methane concentration that results in hydrogen abstraction limited versus hydrocarbon adsorption limited reaction kinetic regime, and (2) activation energy of reaction for a given reactor temperature and inlet methane concentration. Successful optimization of a CNT growth processes requires taking all of those variables into account.
A mean-field game economic growth model

KAUST Repository

Gomes, Diogo A.

2016-08-05

Here, we examine a mean-field game (MFG) that models the economic growth of a population of non-cooperative, rational agents. In this MFG, agents are described by two state variables - the capital and consumer goods they own. Each agent seeks to maximize his/her utility by taking into account statistical data about the whole population. The individual actions drive the evolution of the players, and a market-clearing condition determines the relative price of capital and consumer goods. We study the existence and uniqueness of optimal strategies of the agents and develop numerical methods to compute these strategies and the equilibrium price.
Variable effects of climate on forest growth in relation to climate extremes, disturbance, and forest dynamics.

Science.gov (United States)

Itter, Malcolm S; Finley, Andrew O; D'Amato, Anthony W; Foster, Jane R; Bradford, John B

2017-06-01

Changes in the frequency, duration, and severity of climate extremes are forecast to occur under global climate change. The impacts of climate extremes on forest productivity and health remain difficult to predict due to potential interactions with disturbance events and forest dynamics-changes in forest stand composition, density, size and age structure over time. Such interactions may lead to non-linear forest growth responses to climate involving thresholds and lag effects. Understanding how forest dynamics influence growth responses to climate is particularly important given stand structure and composition can be modified through management to increase forest resistance and resilience to climate change. To inform such adaptive management, we develop a hierarchical Bayesian state space model in which climate effects on tree growth are allowed to vary over time and in relation to past climate extremes, disturbance events, and forest dynamics. The model is an important step toward integrating disturbance and forest dynamics into predictions of forest growth responses to climate extremes. We apply the model to a dendrochronology data set from forest stands of varying composition, structure, and development stage in northeastern Minnesota that have experienced extreme climate years and forest tent caterpillar defoliation events. Mean forest growth was most sensitive to water balance variables representing climatic water deficit. Forest growth responses to water deficit were partitioned into responses driven by climatic threshold exceedances and interactions with insect defoliation. Forest growth was both resistant and resilient to climate extremes with the majority of forest growth responses occurring after multiple climatic threshold exceedances across seasons and years. Interactions between climate and disturbance were observed in a subset of years with insect defoliation increasing forest growth sensitivity to water availability. Forest growth was particularly
Variable effects of climate on forest growth in relation to climate extremes, disturbance, and forest dynamics

Science.gov (United States)

Itter, Malcolm S.; Finley, Andrew O.; D'Amato, Anthony W.; Foster, Jane R.; Bradford, John B.

2017-01-01

Changes in the frequency, duration, and severity of climate extremes are forecast to occur under global climate change. The impacts of climate extremes on forest productivity and health remain difficult to predict due to potential interactions with disturbance events and forest dynamics—changes in forest stand composition, density, size and age structure over time. Such interactions may lead to non-linear forest growth responses to climate involving thresholds and lag effects. Understanding how forest dynamics influence growth responses to climate is particularly important given stand structure and composition can be modified through management to increase forest resistance and resilience to climate change. To inform such adaptive management, we develop a hierarchical Bayesian state space model in which climate effects on tree growth are allowed to vary over time and in relation to past climate extremes, disturbance events, and forest dynamics. The model is an important step toward integrating disturbance and forest dynamics into predictions of forest growth responses to climate extremes. We apply the model to a dendrochronology data set from forest stands of varying composition, structure, and development stage in northeastern Minnesota that have experienced extreme climate years and forest tent caterpillar defoliation events. Mean forest growth was most sensitive to water balance variables representing climatic water deficit. Forest growth responses to water deficit were partitioned into responses driven by climatic threshold exceedances and interactions with insect defoliation. Forest growth was both resistant and resilient to climate extremes with the majority of forest growth responses occurring after multiple climatic threshold exceedances across seasons and years. Interactions between climate and disturbance were observed in a subset of years with insect defoliation increasing forest growth sensitivity to water availability. Forest growth was particularly
Variability in urban soils influences the health and growth of native tree seedlings

Science.gov (United States)

Clara C. Pregitzer; Nancy F. Sonti; Richard A. Hallett

2016-01-01

Reforesting degraded urban landscapes is important due to the many benefits urban forests provide. Urban soils are highly variable, yet little is known about how this variability in urban soils influences tree seedling performance and survival. We conducted a greenhouse study to assess health, growth, and survival of four native tree species growing in native glacial...
Effect of specimen geometry on the variability in fatigue crack growth rate

International Nuclear Information System (INIS)

Tsuji, Hirokazu; Nakajima, Hajime; Kondo, Tatsuo

1982-02-01

Fatigue crack growth tests on SA 533 grade B class 1 steel were conducted in air with both contoured double cantilever beam (CDCB) specimens and compact-tension (CT) specimens for comparison, which corresponded to the ΔK constant and ΔK increasing fatigue tests respectively. The variability of the measured values was examined statistically, and possible sources of the determined variability were discussed. The variability in the ΔK increasing fatigue tests with the CT specimens was found to be substantially greater than that in the ΔK constant fatigue tests with the CDCB specimens employed in the present study. In addition, the width of the scatter as well as in the degree of deviation from the expected linearity in da/dN versus ΔK plots were found to be varied depending on the level of ΔK in the CT specimen. Based on the results, a conclusion was drawn that constant ΔK type tests should be preferred in the tests where accuracy and reproducibility of crack growth rate measurement was of particular importance. (author)

Predictive model of Amorphophallus muelleri growth in some agroforestry in East Java by multiple regression analysis

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BUDIMAN

2012-01-01

Full Text Available Budiman, Arisoesilaningsih E. 2012. Predictive model of Amorphophallus muelleri growth in some agroforestry in East Java by multiple regression analysis. Biodiversitas 13: 18-22. The aims of this research was to determine the multiple regression models of vegetative and corm growth of Amorphophallus muelleri Blume in some age variations and habitat conditions of agroforestry in East Java. Descriptive exploratory research method was conducted by systematic random sampling at five agroforestries on four plantations in East Java: Saradan, Bojonegoro, Nganjuk and Blitar. In each agroforestry, we observed A. muelleri vegetative and corm growth on four growing age (1, 2, 3 and 4 years old respectively as well as environmental variables such as altitude, vegetation, climate and soil conditions. Data were analyzed using descriptive statistics to compare A. muelleri habitat in five agroforestries. Meanwhile, the influence and contribution of each environmental variable to the growth of A. muelleri vegetative and corm were determined using multiple regression analysis of SPSS 17.0. The multiple regression models of A. muelleri vegetative and corm growth were generated based on some characteristics of agroforestries and age showed high validity with R2 = 88-99%. Regression model showed that age, monthly temperatures, percentage of radiation and soil calcium (Ca content either simultaneously or partially determined the growth of A. muelleri vegetative and corm. Based on these models, the A. muelleri corm reached the optimal growth after four years of cultivation and they will be ready to be harvested. Additionally, the soil Ca content should reach 25.3 me.hg-1 as Sugihwaras agroforestry, with the maximal radiation of 60%.
Effects of temperature on domain-growth kinetics of fourfold-degenerate (2×1) ordering in Ising models

DEFF Research Database (Denmark)

Høst-Madsen, Anders; Shah, Peter Jivan; Hansen, Torben

1987-01-01

Computer-simulation techniques are used to study the domain-growth kinetics of (2×1) ordering in a two-dimensional Ising model with nonconserved order parameter and with variable ratio α of next-nearest- and nearest-neighbor interactions. At zero temperature, persistent growth characterized...
Eutrophication Modeling Using Variable Chlorophyll Approach

International Nuclear Information System (INIS)

Abdolabadi, H.; Sarang, A.; Ardestani, M.; Mahjoobi, E.

2016-01-01

In this study, eutrophication was investigated in Lake Ontario to identify the interactions among effective drivers. The complexity of such phenomenon was modeled using a system dynamics approach based on a consideration of constant and variable stoichiometric ratios. The system dynamics approach is a powerful tool for developing object-oriented models to simulate complex phenomena that involve feedback effects. Utilizing stoichiometric ratios is a method for converting the concentrations of state variables. During the physical segmentation of the model, Lake Ontario was divided into two layers, i.e., the epilimnion and hypolimnion, and differential equations were developed for each layer. The model structure included 16 state variables related to phytoplankton, herbivorous zooplankton, carnivorous zooplankton, ammonium, nitrate, dissolved phosphorus, and particulate and dissolved carbon in the epilimnion and hypolimnion during a time horizon of one year. The results of several tests to verify the model, close to 1 Nash-Sutcliff coefficient (0.98), the data correlation coefficient (0.98), and lower standard errors (0.96), have indicated well-suited model’s efficiency. The results revealed that there were significant differences in the concentrations of the state variables in constant and variable stoichiometry simulations. Consequently, the consideration of variable stoichiometric ratios in algae and nutrient concentration simulations may be applied in future modeling studies to enhance the accuracy of the results and reduce the likelihood of inefficient control policies.
Predicting Madura cattle growth curve using non-linear model

Science.gov (United States)

Widyas, N.; Prastowo, S.; Widi, T. S. M.; Baliarti, E.

2018-03-01

Madura cattle is Indonesian native. It is a composite breed that has undergone hundreds of years of selection and domestication to reach nowadays remarkable uniformity. Crossbreeding has reached the isle of Madura and the Madrasin, a cross between Madura cows and Limousine semen emerged. This paper aimed to compare the growth curve between Madrasin and one type of pure Madura cows, the common Madura cattle (Madura) using non-linear models. Madura cattles are kept traditionally thus reliable records are hardly available. Data were collected from small holder farmers in Madura. Cows from different age classes (5years) were observed, and body measurements (chest girth, body length and wither height) were taken. In total 63 Madura and 120 Madrasin records obtained. Linear model was built with cattle sub-populations and age as explanatory variables. Body weights were estimated based on the chest girth. Growth curves were built using logistic regression. Results showed that within the same age, Madrasin has significantly larger body compared to Madura (plogistic models fit better for Madura and Madrasin cattle data; with the estimated MSE for these models were 39.09 and 759.28 with prediction accuracy of 99 and 92% for Madura and Madrasin, respectively. Prediction of growth curve using logistic regression model performed well in both types of Madura cattle. However, attempts to administer accurate data on Madura cattle are necessary to better characterize and study these cattle.
Harvesting Atlantic Cod under Climate Variability

Science.gov (United States)

Oremus, K. L.

2016-12-01

Previous literature links the growth of a fishery to climate variability. This study uses an age-structured bioeconomic model to compare optimal harvest in the Gulf of Maine Atlantic cod fishery under a variable climate versus a static climate. The optimal harvest path depends on the relationship between fishery growth and the interest rate, with higher interest rates dictating greater harvests now at the cost of long-term stock sustainability. Given the time horizon of a single generation of fishermen under assumptions of a static climate, the model finds that the economically optimal management strategy is to harvest the entire stock in the short term and allow the fishery to collapse. However, if the biological growth of the fishery is assumed to vary with climate conditions, such as the North Atlantic Oscillation, there will always be pulses of high growth in the stock. During some of these high-growth years, the growth of the stock and its economic yield can exceed the growth rate of the economy even under high interest rates. This implies that it is not economically optimal to exhaust the New England cod fishery if NAO is included in the biological growth function. This finding may have theoretical implications for the management of other renewable yet exhaustible resources whose growth rates are subject to climate variability.
Variable selection and model choice in geoadditive regression models.

Science.gov (United States)

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.
Estimation of Paddy Rice Variables with a Modified Water Cloud Model and Improved Polarimetric Decomposition Using Multi-Temporal RADARSAT-2 Images

Directory of Open Access Journals (Sweden)

Zhi Yang

2016-10-01

Full Text Available Rice growth monitoring is very important as rice is one of the staple crops of the world. Rice variables as quantitative indicators of rice growth are critical for farming management and yield estimation, and synthetic aperture radar (SAR has great advantages for monitoring rice variables due to its all-weather observation capability. In this study, eight temporal RADARSAT-2 full-polarimetric SAR images were acquired during rice growth cycle and a modified water cloud model (MWCM was proposed, in which the heterogeneity of the rice canopy in the horizontal direction and its phenological changes were considered when the double-bounce scattering between the rice canopy and the underlying surface was firstly considered as well. Then, three scattering components from an improved polarimetric decomposition were coupled with the MWCM, instead of the backscattering coefficients. Using a genetic algorithm, eight rice variables were estimated, such as the leaf area index (LAI, rice height (h, and the fresh and dry biomass of ears (Fe and De. The accuracy validation showed the MWCM was suitable for the estimation of rice variables during the whole growth season. The validation results showed that the MWCM could predict the temporal behaviors of the rice variables well during the growth cycle (R2 > 0.8. Compared with the original water cloud model (WCM, the relative errors of rice variables with the MWCM were much smaller, especially in the vegetation phase (approximately 15% smaller. Finally, it was discussed that the MWCM could be used, theoretically, for extensive applications since the empirical coefficients in the MWCM were determined in general cases, but more applications of the MWCM are necessary in future work.
A predictability study of Lorenz's 28-variable model as a dynamical system

Science.gov (United States)

Krishnamurthy, V.

1993-01-01

The dynamics of error growth in a two-layer nonlinear quasi-geostrophic model has been studied to gain an understanding of the mathematical theory of atmospheric predictability. The growth of random errors of varying initial magnitudes has been studied, and the relation between this classical approach and the concepts of the nonlinear dynamical systems theory has been explored. The local and global growths of random errors have been expressed partly in terms of the properties of an error ellipsoid and the Liapunov exponents determined by linear error dynamics. The local growth of small errors is initially governed by several modes of the evolving error ellipsoid but soon becomes dominated by the longest axis. The average global growth of small errors is exponential with a growth rate consistent with the largest Liapunov exponent. The duration of the exponential growth phase depends on the initial magnitude of the errors. The subsequent large errors undergo a nonlinear growth with a steadily decreasing growth rate and attain saturation that defines the limit of predictability. The degree of chaos and the largest Liapunov exponent show considerable variation with change in the forcing, which implies that the time variation in the external forcing can introduce variable character to the predictability.
The impact of atmospheric deposition and climate on forest growth in Europe using two empirical modelling approaches

Science.gov (United States)

Dobbertin, M.; Solberg, S.; Laubhann, D.; Sterba, H.; Reinds, G. J.; de Vries, W.

2009-04-01

Most recent studies show increasing forest growth in central Europe, rather than a decline as was expected due to negative effects of air pollution. While nitrogen deposition, increasing temperature and change in forest management are discussed as possible causes, quantification of the various environmental factors has rarely been undertaken. In our study, we used data from several hundreds of intensive monitoring plots from the ICP Forests network in Europe, ranging from northern Finland to Spain and southern Italy. Five-year growth data for the period 1994-1999 were available from roughly 650 plots to examine the influence of environmental factors on forest growth. Evaluations focused on the influence of nitrogen, sulphur and acid deposition, temperature, precipitation and drought. Concerning the latter meteorological variables we used the deviation from the long-term (30 years) mean. The study included the main tree species common beech (Fagus sylvatica), sessile or pedunculate oak (Quercus petraea and Q. robur), Scots pine (Pinus sylvestris) and Norway spruce (Picea abies). Two very different approaches were used. In the first approach an individual tree-based regression model was applied (Laubhahn et al., 2009), while in the second approach a stand-based model was applied (Solberg et al., 2009). The individual tree-based model had measured basal area increment of each individual tree as a growth response variable and tree size (diameter at breast height), tree competition (basal area of larger trees and stand density index), site factors (e.g. soil C/N ratio, temperature), and environmental factors (e.g. temperature change compared to long-term average, nitrogen and sulphur deposition) as influencing parameters. In the stand-growth model, stem volume increment was used as the growth response variable, after filtering out the expected growth. Expected growth was modelled as a function of site productivity, stand age and a stand density index. Relative volume
Variability in age and size at maturation, reproductive longevity, and long-term growth dynamics for Kemp's ridley sea turtles in the Gulf of Mexico.

Directory of Open Access Journals (Sweden)

Larisa Avens

Full Text Available Effective management of protected sea turtle populations requires knowledge not only of mean values for demographic and life-history parameters, but also temporal and spatial trends, variability, and underlying causes. For endangered Kemp's ridley sea turtles (Lepidochelys kempii, the need for baseline information of this type has been emphasized during attempts to understand causes underlying the recent truncation in the recovery trajectory for nesting females. To provide insight into variability in age and size at sexual maturation (ASM and SSM and long-term growth patterns likely to influence population trends, we conducted skeletochronological analysis of humerus bones from 333 Kemp's ridleys stranded throughout the Gulf of Mexico (GOM from 1993 to 2010. Ranges of possible ASMs (6.8 to 21.8 yr and SSMs (53.3 to 68.3 cm straightline carapace length (SCL estimated using the "rapprochement" skeletal growth mark associated with maturation were broad, supporting incorporation of a maturation schedule in Kemp's ridley population models. Mean ASMs estimated from rapprochement and by fitting logistic, generalized additive mixed, and von Bertalanffy growth models to age and growth data ranged from 11 to 13 yr; confidence intervals for the logistic model predicted maturation of 95% of the population between 11.9 and 14.8 yr. Early juvenile somatic growth rates in the GOM were greater than those previously reported for the Atlantic, indicating potential for differences in maturation trajectories between regions. Finally, long-term, significant decreases in somatic growth response were found for both juveniles and adults, which could influence recruitment to the reproductive population and observed nesting population trends.
A Core Language for Separate Variability Modeling

DEFF Research Database (Denmark)

Iosif-Lazăr, Alexandru Florin; Wasowski, Andrzej; Schaefer, Ina

2014-01-01

Separate variability modeling adds variability to a modeling language without requiring modifications of the language or the supporting tools. We define a core language for separate variability modeling using a single kind of variation point to define transformations of software artifacts in object...... hierarchical dependencies between variation points via copying and flattening. Thus, we reduce a model with intricate dependencies to a flat executable model transformation consisting of simple unconditional local variation points. The core semantics is extremely concise: it boils down to two operational rules...
Individualism in plant populations: using stochastic differential equations to model individual neighbourhood-dependent plant growth.

Science.gov (United States)

Lv, Qiming; Schneider, Manuel K; Pitchford, Jonathan W

2008-08-01

We study individual plant growth and size hierarchy formation in an experimental population of Arabidopsis thaliana, within an integrated analysis that explicitly accounts for size-dependent growth, size- and space-dependent competition, and environmental stochasticity. It is shown that a Gompertz-type stochastic differential equation (SDE) model, involving asymmetric competition kernels and a stochastic term which decreases with the logarithm of plant weight, efficiently describes individual plant growth, competition, and variability in the studied population. The model is evaluated within a Bayesian framework and compared to its deterministic counterpart, and to several simplified stochastic models, using distributional validation. We show that stochasticity is an important determinant of size hierarchy and that SDE models outperform the deterministic model if and only if structural components of competition (asymmetry; size- and space-dependence) are accounted for. Implications of these results are discussed in the context of plant ecology and in more general modelling situations.
Modelling planktic foraminifer growth and distribution using an ecophysiological multi-species approach

Directory of Open Access Journals (Sweden)

F. Lombard

2011-04-01

Full Text Available We present an eco-physiological model reproducing the growth of eight foraminifer species (Neogloboquadrina pachyderma, Neogloboquadrina incompta, Neogloboquadrina dutertrei, Globigerina bulloides, Globigerinoides ruber, Globigerinoides sacculifer, Globigerinella siphonifera and Orbulina universa. By using the main physiological rates of foraminifers (nutrition, respiration, symbiotic photosynthesis, this model estimates their growth as a function of temperature, light availability, and food concentration. Model parameters are directly derived or calibrated from experimental observations and only the influence of food concentration (estimated via Chlorophyll-a concentration was calibrated against field observations. Growth rates estimated from the model show positive correlation with observed abundance from plankton net data suggesting close coupling between individual growth and population abundance. This observation was used to directly estimate potential abundance from the model-derived growth. Using satellite data, the model simulate the dominant foraminifer species with a 70.5% efficiency when compared to a data set of 576 field observations worldwide. Using outputs of a biogeochemical model of the global ocean (PISCES instead of satellite images as forcing variables gives also good results, but with lower efficiency (58.9%. Compared to core tops observations, the model also correctly reproduces the relative worldwide abundance and the diversity of the eight species when using either satellite data either PISCES results. This model allows prediction of the season and water depth at which each species has its maximum abundance potential. This offers promising perspectives for both an improved quantification of paleoceanographic reconstructions and for a better understanding of the foraminiferal role in the marine carbon cycle.
Assessing allometric models to predict vegetative growth of mango (Mangifera indica; Anacardiaceae) at the current-year branch scale.

Science.gov (United States)

Normand, Frédéric; Lauri, Pierre-Éric

2012-03-01

Accurate and reliable predictive models are necessary to estimate nondestructively key variables for plant growth studies such as leaf area and leaf, stem, and total biomass. Predictive models are lacking at the current-year branch scale despite the importance of this scale in plant science. We calibrated allometric models to estimate leaf area and stem and branch (leaves + stem) mass of current-year branches, i.e., branches several months old studied at the end of the vegetative growth season, of four mango cultivars on the basis of their basal cross-sectional area. The effects of year, site, and cultivar were tested. Models were validated with independent data and prediction accuracy was evaluated with the appropriate statistics. Models revealed a positive allometry between dependent and independent variables, whose y-intercept but not the slope, was affected by the cultivar. The effects of year and site were negligible. For each branch characteristic, cultivar-specific models were more accurate than common models built with pooled data from the four cultivars. Prediction quality was satisfactory but with data dispersion around the models, particularly for large values. Leaf area and stem and branch mass of mango current-year branches could be satisfactorily estimated on the basis of branch basal cross-sectional area with cultivar-specific allometric models. The results suggested that, in addition to the heteroscedastic behavior of the variables studied, model accuracy was probably related to the functional plasticity of branches in relation to the light environment and/or to the number of growth units composing the branches.
Contributions of cell growth and biochemical reactions to nongenetic variability of cells.

NARCIS (Netherlands)

Schwabe, A.; Bruggeman, F.J.

2014-01-01

Cell-to-cell variability in the molecular composition of isogenic, steady-state growing cells arises spontaneously from the inherent stochasticity of intracellular biochemical reactions and cell growth. Here, we present a general decomposition of the total variance in the copy number per cell of a
Latent Growth and Dynamic Structural Equation Models.

Science.gov (United States)

Grimm, Kevin J; Ram, Nilam

2018-05-07

Latent growth models make up a class of methods to study within-person change-how it progresses, how it differs across individuals, what are its determinants, and what are its consequences. Latent growth methods have been applied in many domains to examine average and differential responses to interventions and treatments. In this review, we introduce the growth modeling approach to studying change by presenting different models of change and interpretations of their model parameters. We then apply these methods to examining sex differences in the development of binge drinking behavior through adolescence and into adulthood. Advances in growth modeling methods are then discussed and include inherently nonlinear growth models, derivative specification of growth models, and latent change score models to study stochastic change processes. We conclude with relevant design issues of longitudinal studies and considerations for the analysis of longitudinal data.
Testing mechanistic models of growth in insects.

Science.gov (United States)

Maino, James L; Kearney, Michael R

2015-11-22

Insects are typified by their small size, large numbers, impressive reproductive output and rapid growth. However, insect growth is not simply rapid; rather, insects follow a qualitatively distinct trajectory to many other animals. Here we present a mechanistic growth model for insects and show that increasing specific assimilation during the growth phase can explain the near-exponential growth trajectory of insects. The presented model is tested against growth data on 50 insects, and compared against other mechanistic growth models. Unlike the other mechanistic models, our growth model predicts energy reserves per biomass to increase with age, which implies a higher production efficiency and energy density of biomass in later instars. These predictions are tested against data compiled from the literature whereby it is confirmed that insects increase their production efficiency (by 24 percentage points) and energy density (by 4 J mg(-1)) between hatching and the attainment of full size. The model suggests that insects achieve greater production efficiencies and enhanced growth rates by increasing specific assimilation and increasing energy reserves per biomass, which are less costly to maintain than structural biomass. Our findings illustrate how the explanatory and predictive power of mechanistic growth models comes from their grounding in underlying biological processes. © 2015 The Author(s).
Size-dependent standard deviation for growth rates: empirical results and theoretical modeling.

Science.gov (United States)

Podobnik, Boris; Horvatic, Davor; Pammolli, Fabio; Wang, Fengzhong; Stanley, H Eugene; Grosse, I

2008-05-01

We study annual logarithmic growth rates R of various economic variables such as exports, imports, and foreign debt. For each of these variables we find that the distributions of R can be approximated by double exponential (Laplace) distributions in the central parts and power-law distributions in the tails. For each of these variables we further find a power-law dependence of the standard deviation sigma(R) on the average size of the economic variable with a scaling exponent surprisingly close to that found for the gross domestic product (GDP) [Phys. Rev. Lett. 81, 3275 (1998)]. By analyzing annual logarithmic growth rates R of wages of 161 different occupations, we find a power-law dependence of the standard deviation sigma(R) on the average value of the wages with a scaling exponent beta approximately 0.14 close to those found for the growth of exports, imports, debt, and the growth of the GDP. In contrast to these findings, we observe for payroll data collected from 50 states of the USA that the standard deviation sigma(R) of the annual logarithmic growth rate R increases monotonically with the average value of payroll. However, also in this case we observe a power-law dependence of sigma(R) on the average payroll with a scaling exponent beta approximately -0.08 . Based on these observations we propose a stochastic process for multiple cross-correlated variables where for each variable (i) the distribution of logarithmic growth rates decays exponentially in the central part, (ii) the distribution of the logarithmic growth rate decays algebraically in the far tails, and (iii) the standard deviation of the logarithmic growth rate depends algebraically on the average size of the stochastic variable.
Size-dependent standard deviation for growth rates: Empirical results and theoretical modeling

Science.gov (United States)

Podobnik, Boris; Horvatic, Davor; Pammolli, Fabio; Wang, Fengzhong; Stanley, H. Eugene; Grosse, I.

2008-05-01

We study annual logarithmic growth rates R of various economic variables such as exports, imports, and foreign debt. For each of these variables we find that the distributions of R can be approximated by double exponential (Laplace) distributions in the central parts and power-law distributions in the tails. For each of these variables we further find a power-law dependence of the standard deviation σ(R) on the average size of the economic variable with a scaling exponent surprisingly close to that found for the gross domestic product (GDP) [Phys. Rev. Lett. 81, 3275 (1998)]. By analyzing annual logarithmic growth rates R of wages of 161 different occupations, we find a power-law dependence of the standard deviation σ(R) on the average value of the wages with a scaling exponent β≈0.14 close to those found for the growth of exports, imports, debt, and the growth of the GDP. In contrast to these findings, we observe for payroll data collected from 50 states of the USA that the standard deviation σ(R) of the annual logarithmic growth rate R increases monotonically with the average value of payroll. However, also in this case we observe a power-law dependence of σ(R) on the average payroll with a scaling exponent β≈-0.08 . Based on these observations we propose a stochastic process for multiple cross-correlated variables where for each variable (i) the distribution of logarithmic growth rates decays exponentially in the central part, (ii) the distribution of the logarithmic growth rate decays algebraically in the far tails, and (iii) the standard deviation of the logarithmic growth rate depends algebraically on the average size of the stochastic variable.
Phenology and growth adjustments of oil palm (Elaeis guineensis) to photoperiod and climate variability.

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Legros, S; Mialet-Serra, I; Caliman, J-P; Siregar, F A; Clément-Vidal, A; Dingkuhn, M

2009-11-01

Oil palm flowering and fruit production show seasonal maxima whose causes are unknown. Drought periods confound these rhythms, making it difficult to analyse or predict dynamics of production. The present work aims to analyse phenological and growth responses of adult oil palms to seasonal and inter-annual climatic variability. Two oil palm genotypes planted in a replicated design at two sites in Indonesia underwent monthly observations during 22 months in 2006-2008. Measurements included growth of vegetative and reproductive organs, morphology and phenology. Drought was estimated from climatic water balance (rainfall - potential evapotranspiration) and simulated fraction of transpirable soil water. Production history of the same plants for 2001-2005 was used for inter-annual analyses. Drought was absent at the equatorial Kandista site (0 degrees 55'N) but the Batu Mulia site (3 degrees 12'S) had a dry season with variable severity. Vegetative growth and leaf appearance rate fluctuated with drought level. Yield of fruit, a function of the number of female inflorescences produced, was negatively correlated with photoperiod at Kandista. Dual annual maxima were observed supporting a recent theory of circadian control. The photoperiod-sensitive phases were estimated at 9 (or 9 + 12 x n) months before bunch maturity for a given phytomer. The main sensitive phase for drought effects was estimated at 29 months before bunch maturity, presumably associated with inflorescence sex determination. It is assumed that seasonal peaks of flowering in oil palm are controlled even near the equator by photoperiod response within a phytomer. These patterns are confounded with drought effects that affect flowering (yield) with long time-lag. Resulting dynamics are complex, but if the present results are confirmed it will be possible to predict them with models.

Modeling the dynamics of urban growth using multinomial logistic regression: a case study of Jiayu County, Hubei Province, China

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Nong, Yu; Du, Qingyun; Wang, Kun; Miao, Lei; Zhang, Weiwei

2008-10-01

Urban growth modeling, one of the most important aspects of land use and land cover change study, has attracted substantial attention because it helps to comprehend the mechanisms of land use change thus helps relevant policies made. This study applied multinomial logistic regression to model urban growth in the Jiayu county of Hubei province, China to discover the relationship between urban growth and the driving forces of which biophysical and social-economic factors are selected as independent variables. This type of regression is similar to binary logistic regression, but it is more general because the dependent variable is not restricted to two categories, as those previous studies did. The multinomial one can simulate the process of multiple land use competition between urban land, bare land, cultivated land and orchard land. Taking the land use type of Urban as reference category, parameters could be estimated with odds ratio. A probability map is generated from the model to predict where urban growth will occur as a result of the computation.
Spatial models reveal the microclimatic buffering capacity of old-growth forests.

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Frey, Sarah J K; Hadley, Adam S; Johnson, Sherri L; Schulze, Mark; Jones, Julia A; Betts, Matthew G

2016-04-01

Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed independent test data well (r = 0.87). As expected, elevation strongly predicted temperatures, but vegetation and microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decreased by 2.5°C across the observed gradient in old-growth structure. These cooling effects across a gradient in forest structure are of similar magnitude to 50-year forecasts of the Intergovernmental Panel on Climate Change and therefore have the potential to mitigate climate warming at local scales. Management strategies to conserve old-growth characteristics and to curb current rates of primary forest loss could maintain microrefugia, enhancing biodiversity persistence in mountainous systems under climate warming.
Variability in age and size at maturation, reproductive longevity, and long-term growth dynamics for Kemp's ridley sea turtles in the Gulf of Mexico

Science.gov (United States)

Goshe, Lisa R.; Coggins, Lewis; Shaver, Donna J.; Higgins, Ben; Landry, Andre M.; Bailey, Rhonda

2017-01-01

Effective management of protected sea turtle populations requires knowledge not only of mean values for demographic and life-history parameters, but also temporal and spatial trends, variability, and underlying causes. For endangered Kemp’s ridley sea turtles (Lepidochelys kempii), the need for baseline information of this type has been emphasized during attempts to understand causes underlying the recent truncation in the recovery trajectory for nesting females. To provide insight into variability in age and size at sexual maturation (ASM and SSM) and long-term growth patterns likely to influence population trends, we conducted skeletochronological analysis of humerus bones from 333 Kemp’s ridleys stranded throughout the Gulf of Mexico (GOM) from 1993 to 2010. Ranges of possible ASMs (6.8 to 21.8 yr) and SSMs (53.3 to 68.3 cm straightline carapace length (SCL)) estimated using the “rapprochement” skeletal growth mark associated with maturation were broad, supporting incorporation of a maturation schedule in Kemp’s ridley population models. Mean ASMs estimated from rapprochement and by fitting logistic, generalized additive mixed, and von Bertalanffy growth models to age and growth data ranged from 11 to 13 yr; confidence intervals for the logistic model predicted maturation of 95% of the population between 11.9 and 14.8 yr. Early juvenile somatic growth rates in the GOM were greater than those previously reported for the Atlantic, indicating potential for differences in maturation trajectories between regions. Finally, long-term, significant decreases in somatic growth response were found for both juveniles and adults, which could influence recruitment to the reproductive population and observed nesting population trends. PMID:28333937
Sociological explanations of economic growth.

Science.gov (United States)

Marsh, R M

1988-01-01

Even if questions of how resources are distributed within and between societies are the main concern, it is necessary to continue to grapple with the issue of the causes of economic growth since economic growth and level of development continue to be among the most important causes of inequality, poverty, unemployment, and the quality of life. This paper's dependent variable is the economic growth rate of 55 less developed countries (LDCs) over 2 time periods. 1970-78 and 1965-84. The causal model consists of control variables--level of development and domestic investment in 1965--and a variety of independent variables drawn from major sociological theories of economic growth published during the last 3 decades. Multiple regression analysis shows that, net of the effects of the 2 control variables, the variables which have the strongest effect on economic growth are: 1) direct foreign investment, which has a negative effect, 2) the proportion of the population in military service, and 3) the primary school enrollment ratio, both of which have positive effects on economic growth. On the other hand, variables drawn from some theories receive no empirical support. The mass media of communications, ethnolinguistic heterogeneity, democracy and human rights, income inequality, and state-centric theory's key variable, state strength, all fail to show any significant impact on economic growth rates when the control variables and the significant independent variables are held constant. The theoretical implications of these findings are discussed.
Bayesian modeling of measurement error in predictor variables

NARCIS (Netherlands)

Fox, Gerardus J.A.; Glas, Cornelis A.W.

2003-01-01

It is shown that measurement error in predictor variables can be modeled using item response theory (IRT). The predictor variables, that may be defined at any level of an hierarchical regression model, are treated as latent variables. The normal ogive model is used to describe the relation between
Layered growth model and epitaxial growth structures for SiCAlN alloys

International Nuclear Information System (INIS)

Liu Zhaoqing; Ni Jun; Su Xiaoao; Dai Zhenhong

2009-01-01

Epitaxial growth structures for (SiC) 1-x (AlN) x alloys are studied using a layered growth model. First-principle calculations are used to determine the parameters in the layered growth model. The phase diagrams of epitaxial growth are given. There is a rich variety of the new metastable polytype structures at x=1/6 ,1/5 ,1/4 ,1/3 , and 1/2 in the layered growth phase diagrams. We have also calculated the electronic properties of the short periodical SiCAlN alloys predicted by our layered growth model. The results show that various ordered structures of (SiC) 1-x (AlN) x alloys with the band gaps over a wide range are possible to be synthesized by epitaxial growth.
Modelling planktic foraminifer growth and distribution using an ecophysiological multi-species approach

DEFF Research Database (Denmark)

Lombard, Fabien; Labeyrie, L.; Michel, E.

2011-01-01

We present an eco-physiological model reproducing the growth of eight foraminifer species (Neogloboquadrina pachyderma, Neogloboquadrina incompta, Neogloboquadrina dutertrei, Globigerina bulloides, Globigerinoides ruber, Globigerinoides sacculifer, Globigerinella siphonifera and Orbulina universa...... in the marine carbon cycle....... ocean (PISCES) instead of satellite images as forcing variables gives also good results, but with lower efficiency (58.9%). Compared to core tops observations, the model also correctly reproduces the relative worldwide abundance and the diversity of the eight species when using either satellite data...
Stochastic ontogenetic growth model

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West, B. J.; West, D.

2012-02-01

An ontogenetic growth model (OGM) for a thermodynamically closed system is generalized to satisfy both the first and second law of thermodynamics. The hypothesized stochastic ontogenetic growth model (SOGM) is shown to entail the interspecies allometry relation by explicitly averaging the basal metabolic rate and the total body mass over the steady-state probability density for the total body mass (TBM). This is the first derivation of the interspecies metabolic allometric relation from a dynamical model and the asymptotic steady-state distribution of the TBM is fit to data and shown to be inverse power law.
Poisson Growth Mixture Modeling of Intensive Longitudinal Data: An Application to Smoking Cessation Behavior

Science.gov (United States)

Shiyko, Mariya P.; Li, Yuelin; Rindskopf, David

2012-01-01

Intensive longitudinal data (ILD) have become increasingly common in the social and behavioral sciences; count variables, such as the number of daily smoked cigarettes, are frequently used outcomes in many ILD studies. We demonstrate a generalized extension of growth mixture modeling (GMM) to Poisson-distributed ILD for identifying qualitatively…
Modeling Growth and Yield of Schizolobium amazonicum under Different Spacings

Directory of Open Access Journals (Sweden)

Gilson Fernandes da Silva

2013-01-01

Full Text Available This study aimed to present an approach to model the growth and yield of the species Schizolobium amazonicum (Paricá based on a study of different spacings located in Pará, Brazil. Whole-stand models were employed, and two modeling strategies (Strategies A and B were tested. Moreover, the following three scenarios were evaluated to assess the accuracy of the model in estimating total and commercial volumes at five years of age: complete absence of data (S1; available information about the variables basal area, site index, dominant height, and number of trees at two years of age (S2; and this information available at five years of age (S3. The results indicated that the 3 × 2 spacing has a higher mortality rate than normal, and, in general, greater spacing corresponds to larger diameter and average height and smaller basal area and volume per hectare. In estimating the total and commercial volumes for the three scenarios tested, Strategy B seems to be the most appropriate method to estimate the growth and yield of Paricá plantations in the study region, particularly because Strategy A showed a significant bias in its estimates.
RELATIONSHIP BETWEEN CLIMATE VARIABLES, TRUNK GROWTH RATE AND WOOD DENSITY OF Eucalyptus grandis W. Mill ex Maiden TREES

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Carlos Roberto Sette Jr

2016-04-01

Full Text Available ABSTRACT Climatic conditions stimulates the cambial activity of plants, and cause significant changes in trunk diameter growth and wood characteristics. The objective of this study was to evaluate the influence of climate variables in the diameter growth rate of the stem and the wood density of Eucalyptus grandis trees in different classes of the basal area. A total of 25 Eucalyptus trees at 22 months of age were selected according to the basal area distribution. Dendrometer bands were installed at the height of 1.30 meters (DBH to monitor the diameter growth every 14 days, for 26 months. After measuring growth, the trees were felled and wood discs were removed at the DBH level to determine the radial density profile through x-ray microdensitometry and then re-scale the average values every 14 days. Climatic variables for the monitoring period were obtained and grouped every 14 days. The effect of the climate variables was determined by maximum and minimum growth periods in assessing trunk growth. These growth periods were related with precipitation, average temperature and relative air humidity. The re-scaled wood density values, calculated using the radial growth of the tree trunks measured accurately with steel dendrometers, enabled the determination of the relationship of small changes in wood density and the effect of the climatic variations and growth rate of eucalyptus tree trunks. A high sensitivity of the wood density to variation in precipitation levels was found.
Traditional and alternative nonlinear models for estimating the growth of Morada Nova sheep

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Laaina de Andrade Souza

2013-09-01

Full Text Available In the present study, alternative and traditional nonlinear models to describe growth curves of Morada Nova sheep reared in the state of Bahia, Brazil, were applied. The nonlinear models were: Schnute, Mitscherlich, Gompertz, Logistic, Meloun I Meloun II, III Meloun, Gamito and Meloun IV. The model adjustment was evaluated by using: Adjusted Coefficient of Determination (R²aj, Akaike Information Criterion (AIC, Bayesian Information Criterion (BIC, Mean Squared Error of Prediction (MEP and Coefficient of Determination of Prediction (R²p. The selection of the best model was based on cluster analysis, using the evaluators as variables. Six out of the nine tested models converged, while Meloun I and Meloun IV were equally effective in explaining animal growth, without significant influence of sex or type of parturition over the curve parameters. The models Meloun I and IV have the best adjustment and reveal a remarkable reduction of weight gain after 150 days of age, which indicates special attention should be given to feeding at this stage.
Severe linear growth retardation in rural Zambian children: the influence of biological variables.

NARCIS (Netherlands)

Hautvast, J.L.A.; Tolboom, J.J.M.; Kaftwembe, E.M.; Musonda, R.M.; Mwanakasale, V.; Staveren, W.A. van; Hof, M.A. van 't; Sauerwein, R.W.; Willems, J.L.; Monnens, L.A.H.

2000-01-01

BACKGROUND: The prevalence of stunting in preschool children in Zambia is high; stunting has detrimental effects on concurrent psychomotor development and later working capacity. OBJECTIVE: Our objective was to investigate biological variables that may contribute to linear growth retardation in
Lack of evidence for an association between hemodynamic variables and hematoma growth in spontaneous intracerebral hemorrhage.

Science.gov (United States)

Jauch, Edward C; Lindsell, Christopher J; Adeoye, Opeolu; Khoury, Jane; Barsan, William; Broderick, Joseph; Pancioli, Arthur; Brott, Thomas

2006-08-01

Early hematoma expansion in spontaneous intracerebral hemorrhage (ICH) is associated with worse clinical outcome. We hypothesized that hemodynamic parameters are associated with the increase in hematoma volume owing to their relationship to blood vessel wall stresses. We performed a post hoc analysis of clinical and computed tomography (CT) data from patients enrolled in a prospective observational study of ICH patients presenting within 3 hours from symptom onset. Hematoma volumes were measured at hospital arrival and at 1 and 20 hours from presentation. Blood pressure and heart rate, recorded at 19 time points between presentation and 20 hours, were used to derive hemodynamic variables. Multivariable logistic-regression models were constructed to assess the relation between hemodynamic parameters and hematoma growth, adjusted for clinical covariates. From the original study, 98 patients underwent baseline and 1-hour CT scans; of these, 65 had 20-hour CT scans. Substantial hematoma growth was observed in 28% within the first hour. Of the 65 patients not undergoing surgery within 20 hours, 37% experienced hematoma growth by 20 hours. Neither baseline or peak hemodynamic parameters nor changes in hemodynamic parameters were significantly associated with hematoma growth at either 1 or 20 hours. We found no blood pressure or heart rate parameters, individually or in combination, that were associated with hematoma growth. Our data suggest the influence of hemodynamic parameters on vessel wall stress to be an unlikely target for intervention in reducing the risk of early hematoma growth in ICH.
Handbook of latent variable and related models

CERN Document Server

Lee, Sik-Yum

2011-01-01

This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.- Covers a wide class of important models- Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data- Includes illustrative examples with real data sets from business, education, medicine, public health and sociology.- Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.
A structurally based analytic model of growth and biomass dynamics in single species stands of conifers

Science.gov (United States)

Robin J. Tausch

2015-01-01

A theoretically based analytic model of plant growth in single species conifer communities based on the species fully occupying a site and fully using the site resources is introduced. Model derivations result in a single equation simultaneously describes changes over both, different site conditions (or resources available), and over time for each variable for each...
Fatigue Crack and Delamination Growth in Fibre Metal Laminates under Variable Amplitude Loading

NARCIS (Netherlands)

Khan, S.

2013-01-01

This thesis presents the investigation into the fatigue propagation and delamination growth of Fibre Metal Laminates under variable amplitude loading. As explained in the first chapter, the motivation of the research is twofold: first, to obtain a clear understanding and detailed characterization of
Resolving structural variability in network models and the brain.

Directory of Open Access Journals (Sweden)

Florian Klimm

2014-03-01

Full Text Available Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity do not in general simultaneously display a second (e.g., hierarchy. This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful
Probabilistic model for the spoilage wine yeast Dekkera bruxellensis as a function of pH, ethanol and free SO2 using time as a dummy variable.

Science.gov (United States)

Sturm, M E; Arroyo-López, F N; Garrido-Fernández, A; Querol, A; Mercado, L A; Ramirez, M L; Combina, M

2014-01-17

The present study uses a probabilistic model to determine the growth/no growth interfaces of the spoilage wine yeast Dekkera bruxellensis CH29 as a function of ethanol (10-15%, v/v), pH (3.4-4.0) and free SO2 (0-50 mg/l) using time (7, 14, 21 and 30 days) as a dummy variable. The model, built with a total of 756 growth/no growth data obtained in a simile wine medium, could have application in the winery industry to determine the wine conditions needed to inhibit the growth of this species. Thereby, at 12.5% of ethanol and pH 3.7 for a growth probability of 0.01, it is necessary to add 30 mg/l of free SO2 to inhibit yeast growth for 7 days. However, the concentration of free SO2 should be raised to 48 mg/l to achieve a probability of no growth of 0.99 for 30 days under the same wine conditions. Other combinations of environmental variables can also be determined using the mathematical model depending on the needs of the industry. Copyright © 2013 Elsevier B.V. All rights reserved.
Modeling biological tissue growth: discrete to continuum representations.

Science.gov (United States)

Hywood, Jack D; Hackett-Jones, Emily J; Landman, Kerry A

2013-09-01

There is much interest in building deterministic continuum models from discrete agent-based models governed by local stochastic rules where an agent represents a biological cell. In developmental biology, cells are able to move and undergo cell division on and within growing tissues. A growing tissue is itself made up of cells which undergo cell division, thereby providing a significant transport mechanism for other cells within it. We develop a discrete agent-based model where domain agents represent tissue cells. Each agent has the ability to undergo a proliferation event whereby an additional domain agent is incorporated into the lattice. If a probability distribution describes the waiting times between proliferation events for an individual agent, then the total length of the domain is a random variable. The average behavior of these stochastically proliferating agents defining the growing lattice is determined in terms of a Fokker-Planck equation, with an advection and diffusion term. The diffusion term differs from the one obtained Landman and Binder [J. Theor. Biol. 259, 541 (2009)] when the rate of growth of the domain is specified, but the choice of agents is random. This discrepancy is reconciled by determining a discrete-time master equation for this process and an associated asymmetric nonexclusion random walk, together with consideration of synchronous and asynchronous updating schemes. All theoretical results are confirmed with numerical simulations. This study furthers our understanding of the relationship between agent-based rules, their implementation, and their associated partial differential equations. Since tissue growth is a significant cellular transport mechanism during embryonic growth, it is important to use the correct partial differential equation description when combining with other cellular functions.

The Effect of Macroeconomic Variables on Value-Added Agriculture: Approach of Vector Autoregresive Bayesian Model (BVAR

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E. Pishbahar

2015-05-01

Full Text Available There are different ideas and opinions about the effects of macroeconomic variables on real and nominal variables. To answer the question of whether changes in macroeconomic variables as a political tool is useful over a business cycle, understanding the effect of macroeconomic variables on economic growth is important. In the present study, the Bayesian Vector autoregresive model and seasonality data for the years between 1991 and 2013 was used to determine the impact of monetary policy on value-added agriculture. Predicts of Vector autoregresive model are usually divertaed due to a lot of parameters in the model. Bayesian vector autoregresive model estimates more reliable predictions due to reducing the number of included parametrs and considering the former models. Compared to the Vector Autoregressive model, the coefficients are estimated more accurately. Based on the results of RMSE in this study, previous function Nrmal-Vyshart was identified as a suitable previous disteribution. According to the results of the impulse response function, the sudden effects of shocks in macroeconomic variables on the value added in agriculture and domestic venture capital are stable. The effects on the exchange rates, tax revenues and monetary will bemoderated after 7, 5 and 4periods. Monetary policy shocks ,in the first half of the year, increased the value added of agriculture, while in the second half of the year had a depressing effect on the value added.
On a Versatile Stochastic Growth Model

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Samiur Arif

2012-06-01

Full Text Available Growth phenomena are ubiquitous and pervasive not only in biology and the medical sciences, but also in economics, marketing and the computer and social sciences. We introduce a three-parameter version of the classic pure-birth process growth model when suitably instantiated, can be used to model growth phenomena in many seemingly unrelated application domains. We point out that the model is computationally attractive since it admits of conceptually simple, closed form solutions for the time-dependent probabilities.
Spatial and temporal variability in growth of southern flounder (Paralichthys lethostigma)

Science.gov (United States)

Midway, Stephen R.; Wagner, Tyler; Arnott, Stephen A.; Biondo, Patrick; Martinez-Andrade, Fernando; Wadsworth, Thomas F.

2015-01-01

Delineation of stock structure is important for understanding the ecology and management of many fish populations, particularly those with wide-ranging distributions and high levels of harvest. Southern flounder (Paralichthys lethostigma) is a popular commercial and recreational species along the southeast Atlantic coast and Gulf of Mexico, USA. Recent studies have provided genetic and otolith morphology evidence that the Gulf of Mexico and Atlantic Ocean stocks differ. Using age and growth data from four states (Texas, Alabama, South Carolina, and North Carolina) we expanded upon the traditional von Bertalanffy model in order to compare growth rates of putative geographic stocks of southern flounder. We improved the model fitting process by adding a hierarchical Bayesian framework to allow each parameter to vary spatially or temporally as a random effect, as well as log transforming the three model parameters (L∞, K, andt0). Multiple comparisons of parameters showed that growth rates varied (even within states) for females, but less for males. Growth rates were also consistent through time, when long-term data were available. Since within-basin populations are thought to be genetically well-mixed, our results suggest that consistent small-scale environmental conditions (i.e., within estuaries) likely drive growth rates and should be considered when developing broader scale management plans.
Viscoelastic model of tungsten 'fuzz' growth

International Nuclear Information System (INIS)

Krasheninnikov, S I

2011-01-01

A viscoelastic model of fuzz growth is presented. The model describes the main features of tungsten fuzz observed in experiments. It gives estimates of fuzz growth rate and temperature range close to experimental ones.
Does Black’s Hypothesis for Output Variability Hold for Mexico?

OpenAIRE

Macri, Joseph; Sinha, Dipendra

2007-01-01

Using two data series, namely GDP and the index of industrial production, we study the relationship between output variability and the growth rate of output. Ng-Perron unit root test shows that the growth rate of GDP is non-stationary but the growth rate of industrial output is stationary. Thus, we use the ARCH-M model for the monthly data of industrial output. A number of specifications (with and without a dummy variable) are used. In all cases, the results show that output variability has a...
Non-Linear Relationship between Economic Growth and CO₂ Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models.

Science.gov (United States)

Wang, Zheng-Xin; Hao, Peng; Yao, Pei-Yi

2017-12-13

The non-linear relationship between provincial economic growth and carbon emissions is investigated by using panel smooth transition regression (PSTR) models. The research indicates that, on the condition of separately taking Gross Domestic Product per capita (GDPpc), energy structure (Es), and urbanisation level (Ul) as transition variables, three models all reject the null hypothesis of a linear relationship, i.e., a non-linear relationship exists. The results show that the three models all contain only one transition function but different numbers of location parameters. The model taking GDPpc as the transition variable has two location parameters, while the other two models separately considering Es and Ul as the transition variables both contain one location parameter. The three models applied in the study all favourably describe the non-linear relationship between economic growth and CO₂ emissions in China. It also can be seen that the conversion rate of the influence of Ul on per capita CO₂ emissions is significantly higher than those of GDPpc and Es on per capita CO₂ emissions.
Stochastic modeling of thermal fatigue crack growth

CERN Document Server

Radu, Vasile

2015-01-01

The book describes a systematic stochastic modeling approach for assessing thermal-fatigue crack-growth in mixing tees, based on the power spectral density of temperature fluctuation at the inner pipe surface. It shows the development of a frequency-temperature response function in the framework of single-input, single-output (SISO) methodology from random noise/signal theory under sinusoidal input. The frequency response of stress intensity factor (SIF) is obtained by a polynomial fitting procedure of thermal stress profiles at various instants of time. The method, which takes into account the variability of material properties, and has been implemented in a real-world application, estimates the probabilities of failure by considering a limit state function and Monte Carlo analysis, which are based on the proposed stochastic model. Written in a comprehensive and accessible style, this book presents a new and effective method for assessing thermal fatigue crack, and it is intended as a concise and practice-or...
Mathematical modeling of microbial growth in milk

Directory of Open Access Journals (Sweden)

Jhony Tiago Teleken

2011-12-01

Full Text Available A mathematical model to predict microbial growth in milk was developed and analyzed. The model consists of a system of two differential equations of first order. The equations are based on physical hypotheses of population growth. The model was applied to five different sets of data of microbial growth in dairy products selected from Combase, which is the most important database in the area with thousands of datasets from around the world, and the results showed a good fit. In addition, the model provides equations for the evaluation of the maximum specific growth rate and the duration of the lag phase which may provide useful information about microbial growth.
Variable Selection for Regression Models of Percentile Flows

Science.gov (United States)

Fouad, G.

2017-12-01

Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high
Resolving the Complex Genetic Basis of Phenotypic Variation and Variability of Cellular Growth.

Science.gov (United States)

Ziv, Naomi; Shuster, Bentley M; Siegal, Mark L; Gresham, David

2017-07-01

In all organisms, the majority of traits vary continuously between individuals. Explaining the genetic basis of quantitative trait variation requires comprehensively accounting for genetic and nongenetic factors as well as their interactions. The growth of microbial cells can be characterized by a lag duration, an exponential growth phase, and a stationary phase. Parameters that characterize these growth phases can vary among genotypes (phenotypic variation), environmental conditions (phenotypic plasticity), and among isogenic cells in a given environment (phenotypic variability). We used a high-throughput microscopy assay to map genetic loci determining variation in lag duration and exponential growth rate in growth rate-limiting and nonlimiting glucose concentrations, using segregants from a cross of two natural isolates of the budding yeast, Saccharomyces cerevisiae We find that some quantitative trait loci (QTL) are common between traits and environments whereas some are unique, exhibiting gene-by-environment interactions. Furthermore, whereas variation in the central tendency of growth rate or lag duration is explained by many additive loci, differences in phenotypic variability are primarily the result of genetic interactions. We used bulk segregant mapping to increase QTL resolution by performing whole-genome sequencing of complex mixtures of an advanced intercross mapping population grown in selective conditions using glucose-limited chemostats. We find that sequence variation in the high-affinity glucose transporter HXT7 contributes to variation in growth rate and lag duration. Allele replacements of the entire locus, as well as of a single polymorphic amino acid, reveal that the effect of variation in HXT7 depends on genetic, and allelic, background. Amplifications of HXT7 are frequently selected in experimental evolution in glucose-limited environments, but we find that HXT7 amplifications result in antagonistic pleiotropy that is absent in naturally
Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

Science.gov (United States)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.
How to get rid of W: a latent variables approach to modelling spatially lagged variables

NARCIS (Netherlands)

Folmer, H.; Oud, J.

2008-01-01

In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are
How to get rid of W : a latent variables approach to modelling spatially lagged variables

NARCIS (Netherlands)

Folmer, Henk; Oud, Johan

2008-01-01

In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are
Internal variability of a 3-D ocean model

Directory of Open Access Journals (Sweden)

Bjarne Büchmann

2016-11-01

Full Text Available The Defence Centre for Operational Oceanography runs operational forecasts for the Danish waters. The core setup is a 60-layer baroclinic circulation model based on the General Estuarine Transport Model code. At intervals, the model setup is tuned to improve ‘model skill’ and overall performance. It has been an area of concern that the uncertainty inherent to the stochastical/chaotic nature of the model is unknown. Thus, it is difficult to state with certainty that a particular setup is improved, even if the computed model skill increases. This issue also extends to the cases, where the model is tuned during an iterative process, where model results are fed back to improve model parameters, such as bathymetry.An ensemble of identical model setups with slightly perturbed initial conditions is examined. It is found that the initial perturbation causes the models to deviate from each other exponentially fast, causing differences of several PSUs and several kelvin within a few days of simulation. The ensemble is run for a full year, and the long-term variability of salinity and temperature is found for different regions within the modelled area. Further, the developing time scale is estimated for each region, and great regional differences are found – in both variability and time scale. It is observed that periods with very high ensemble variability are typically short-term and spatially limited events.A particular event is examined in detail to shed light on how the ensemble ‘behaves’ in periods with large internal model variability. It is found that the ensemble does not seem to follow any particular stochastic distribution: both the ensemble variability (standard deviation or range as well as the ensemble distribution within that range seem to vary with time and place. Further, it is observed that a large spatial variability due to mesoscale features does not necessarily correlate to large ensemble variability. These findings bear
Modeling volcano growth on the Island of Hawaii: deep-water perspectives

Science.gov (United States)

Lipman, Peter W.; Calvert, Andrew T.

2013-01-01

Recent ocean-bottom geophysical surveys, dredging, and dives, which complement surface data and scientific drilling at the Island of Hawaii, document that evolutionary stages during volcano growth are more diverse than previously described. Based on combining available composition, isotopic age, and geologically constrained volume data for each of the component volcanoes, this overview provides the first integrated models for overall growth of any Hawaiian island. In contrast to prior morphologic models for volcano evolution (preshield, shield, postshield), growth increasingly can be tracked by age and volume (magma supply), defining waxing alkalic, sustained tholeiitic, and waning alkalic stages. Data and estimates for individual volcanoes are used to model changing magma supply during successive compositional stages, to place limits on volcano life spans, and to interpret composite assembly of the island. Volcano volumes vary by an order of magnitude; peak magma supply also varies sizably among edifices but is challenging to quantify because of uncertainty about volcano life spans. Three alternative models are compared: (1) near-constant volcano propagation, (2) near-equal volcano durations, (3) high peak-tholeiite magma supply. These models define inconsistencies with prior geodynamic models, indicate that composite growth at Hawaii peaked ca. 800–400 ka, and demonstrate a lower current rate. Recent age determinations for Kilauea and Kohala define a volcano propagation rate of 8.6 cm/yr that yields plausible inception ages for other volcanoes of the Kea trend. In contrast, a similar propagation rate for the less-constrained Loa trend would require inception of Loihi Seamount in the future and ages that become implausibly large for the older volcanoes. An alternative rate of 10.6 cm/yr for Loa-trend volcanoes is reasonably consistent with ages and volcano spacing, but younger Loa volcanoes are offset from the Kea trend in age-distance plots. Variable magma flux
Generalized latent variable modeling multilevel, longitudinal, and structural equation models

CERN Document Server

Skrondal, Anders; Rabe-Hesketh, Sophia

2004-01-01

This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models.
Dynamic of vapor bubble growth in fields of variable pressure

International Nuclear Information System (INIS)

Pedroso, H.K.

1982-01-01

A mathematical model for the description of the growth from an initial nucleus of a vapor bubble imersed in liquid, subjected to a loss of pressure is presented. The model is important for analysing LOCA (Loss of Coolant Acident) in P.W.R. type reactors. Several simplifications were made in the phenomenum governing equations. With such simplifications the heat diffusion equation became the determining factor for the bubble growth, and the problem was reduced to solve the heat diffusion equation for semi infinite solid whose surface temperature is a well known function of time (it is supposed that the surface temperature is equal to the saturation temperature of the liquid at the system pressure at a given moment). The model results in an analytical expression for the bubble radius as a function of time. Comparisons with experimental data and previous models were made, with reasonable agreement. (author) [pt
Organic Growth Improvement of Indonesian Logistics Companies (A Conceptual Model: Contribution of Strategic Management, Transformational Leadership, and Knowledge Management to Corporate Entrepreneurship and Its Impact on Organic Growth

Directory of Open Access Journals (Sweden)

Darjat Sudrajat

2015-05-01

Full Text Available Indonesian logistics companies needed performance improvement (particularly in organic growth for increasing their competitiveness. Based on previous researches that in order to increase organic growth, it could be conducted through developing corporate entrepreneurship, namely the activities that enhance company’s ability to innovate, take risk and seize market opportunities. The purpose of this paper tried to explore the relationships among variables, namely organic growth (OG, corporate entrepreneurship (CE, transformational leadership (TL, knowledge management (KM, and strategic management (SM. Therefore, this research used causal-explanatory study to explain relationships among the variables. The results of this research were concluded that TL, KM, and SM have contributions to corporate entrepreneurship and organicgrowth. The relationships could be constructed in a conceptual model that could be verified through further research.
Modeling Root Growth, Crop Growth and N Uptake of Winter Wheat Based on SWMS_2D: Model and Validation

Directory of Open Access Journals (Sweden)

Dejun Yang

Full Text Available ABSTRACT Simulations for root growth, crop growth, and N uptake in agro-hydrological models are of significant concern to researchers. SWMS_2D is one of the most widely used physical hydrologically related models. This model solves equations that govern soil-water movement by the finite element method, and has a public access source code. Incorporating key agricultural components into the SWMS_2D model is of practical importance, especially for modeling some critical cereal crops such as winter wheat. We added root growth, crop growth, and N uptake modules into SWMS_2D. The root growth model had two sub-models, one for root penetration and the other for root length distribution. The crop growth model used was adapted from EU-ROTATE_N, linked to the N uptake model. Soil-water limitation, nitrogen limitation, and temperature effects were all considered in dry-weight modeling. Field experiments for winter wheat in Bouwing, the Netherlands, in 1983-1984 were selected for validation. Good agreements were achieved between simulations and measurements, including soil water content at different depths, normalized root length distribution, dry weight and nitrogen uptake. This indicated that the proposed new modules used in the SWMS_2D model are robust and reliable. In the future, more rigorous validation should be carried out, ideally under 2D situations, and attention should be paid to improve some modules, including the module simulating soil N mineralization.
Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

Science.gov (United States)

Henson, Robert A.; Templin, Jonathan L.; Willse, John T.

2009-01-01

This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…

Testing R&D-Based Endogenous Growth Models

DEFF Research Database (Denmark)

Kruse-Andersen, Peter Kjær

2017-01-01

R&D-based growth models are tested using US data for the period 1953-2014. A general growth model is developed which nests the model varieties of interest. The model implies a cointegrating relationship between multifactor productivity, research intensity, and employment. This relationship...... is estimated using cointegrated VAR models. The results provide evidence against the widely used fully endogenous variety and in favor of the semi-endogenous variety. Forecasts based on the empirical estimates suggest that the slowdown in US productivity growth will continue. Particularly, the annual long...
On the explaining-away phenomenon in multivariate latent variable models.

Science.gov (United States)

van Rijn, Peter; Rijmen, Frank

2015-02-01

Many probabilistic models for psychological and educational measurements contain latent variables. Well-known examples are factor analysis, item response theory, and latent class model families. We discuss what is referred to as the 'explaining-away' phenomenon in the context of such latent variable models. This phenomenon can occur when multiple latent variables are related to the same observed variable, and can elicit seemingly counterintuitive conditional dependencies between latent variables given observed variables. We illustrate the implications of explaining away for a number of well-known latent variable models by using both theoretical and real data examples. © 2014 The British Psychological Society.
A model for strain hardening, recovery, recrystallization and grain growth with applications to forming processes of nickel base alloys

Energy Technology Data Exchange (ETDEWEB)

Riedel, Hermann, E-mail: hermann.riedel@iwm.fraunhofer.de [Fraunhofer Institute for Materials Mechanics, Wöhlerstr. 11, 79108 Freiburg (Germany); Svoboda, Jiri, E-mail: svobj@ipm.cz [Institute of Physics of Materials, Academy of Science of the Czech Republic, Zizkova 22, Brno (Czech Republic)

2016-05-17

An ensemble of n spherical grains is considered, each of which is characterized by its radius r{sub i} and by a hardening variable a{sub i}. The hardening variable obeys a Chaboche-type evolution equation with dynamic and static recovery. The grain growth law includes the usual contribution of the grain boundary energy, a term for the stored energy associated with the hardening variable, and the Zener pinning force exerted by particles on the migrating grain boundaries. New grains develop by recrystallization in grains whose stored energy density exceeds a critical value. The growth or shrinkage of the particles, which restrain grain boundary migration, obeys a thermodynamic/kinetic evolution equation. This set of first order differential equations for r{sub i}, a{sub i} and the particle radius is integrated numerically. Fictitious model parameters for a virtual nickel base alloy are used to demonstrate the properties and capabilities of the model. For a real nickel alloy, model parameters are adjusted using measured stress-strain curves, as well as recrystallized volume fractions and grain size distributions. Finally the model with adjusted parameters is applied to a forming process with complex temperature-strain rate histories.
Predicting company growth using logistic regression and neural networks

Directory of Open Access Journals (Sweden)

Marijana Zekić-Sušac

2016-12-01

Full Text Available The paper aims to establish an efficient model for predicting company growth by leveraging the strengths of logistic regression and neural networks. A real dataset of Croatian companies was used which described the relevant industry sector, financial ratios, income, and assets in the input space, with a dependent binomial variable indicating whether a company had high-growth if it had annualized growth in assets by more than 20% a year over a three-year period. Due to a large number of input variables, factor analysis was performed in the pre -processing stage in order to extract the most important input components. Building an efficient model with a high classification rate and explanatory ability required application of two data mining methods: logistic regression as a parametric and neural networks as a non -parametric method. The methods were tested on the models with and without variable reduction. The classification accuracy of the models was compared using statistical tests and ROC curves. The results showed that neural networks produce a significantly higher classification accuracy in the model when incorporating all available variables. The paper further discusses the advantages and disadvantages of both approaches, i.e. logistic regression and neural networks in modelling company growth. The suggested model is potentially of benefit to investors and economic policy makers as it provides support for recognizing companies with growth potential, especially during times of economic downturn.
Response of the mean global vegetation distribution to interannual climate variability

Energy Technology Data Exchange (ETDEWEB)

Notaro, Michael [University of Wisconsin-Madison, Center for Climatic Research, Madison, WI (United States)

2008-06-15

The impact of interannual variability in temperature and precipitation on global terrestrial ecosystems is investigated using a dynamic global vegetation model driven by gridded climate observations for the twentieth century. Contrasting simulations are driven either by repeated mean climatology or raw climate data with interannual variability included. Interannual climate variability reduces net global vegetation cover, particularly over semi-arid regions, and favors the expansion of grass cover at the expense of tree cover, due to differences in growth rates, fire impacts, and interception. The area burnt by global fires is substantially enhanced by interannual precipitation variability. The current position of the central United States' ecotone, with forests to the east and grasslands to the west, is largely attributed to climate variability. Among woody vegetation, climate variability supports expanded deciduous forest growth and diminished evergreen forest growth, due to difference in bioclimatic limits, leaf longevity, interception rates, and rooting depth. These results offer insight into future ecosystem distributions since climate models generally predict an increase in climate variability and extremes. (orig.)
Instrumental Variables in the Long Run

DEFF Research Database (Denmark)

Casey, Gregory; Klemp, Marc Patrick Brag

2017-01-01

In the study of long-run economic growth, it is common to use historical or geographical variables as instruments for contemporary endogenous regressors. We study the interpretation of these conventional instrumental variable (IV) regressions in a general, yet simple, framework. Our aim...... quantitative implications for the field of long-run economic growth. We also use our framework to examine related empirical techniques. We find that two prominent regression methodologies - using gravity-based instruments for trade and including ancestry-adjusted variables in linear regression models - have...... is to estimate the long-run causal effect of changes in the endogenous explanatory variable. We find that conventional IV regressions generally cannot recover this parameter of interest. To estimate this parameter, therefore, we develop an augmented IV estimator that combines the conventional regression...
Kinetic Model of Growth of Arthropoda Populations

Science.gov (United States)

Ershov, Yu. A.; Kuznetsov, M. A.

2018-05-01

Kinetic equations were derived for calculating the growth of crustacean populations ( Crustacea) based on the biological growth model suggested earlier using shrimp ( Caridea) populations as an example. The development cycle of successive stages for populations can be represented in the form of quasi-chemical equations. The kinetic equations that describe the development cycle of crustaceans allow quantitative prediction of the development of populations depending on conditions. In contrast to extrapolation-simulation models, in the developed kinetic model of biological growth the kinetic parameters are the experimental characteristics of population growth. Verification and parametric identification of the developed model on the basis of the experimental data showed agreement with experiment within the error of the measurement technique.
Araucaria growth response to solar and climate variability in South Brazil

Science.gov (United States)

Prestes, Alan; Klausner, Virginia; Rojahn da Silva, Iuri; Ojeda-González, Arian; Lorensi, Caren

2018-05-01

In this work, the Sun-Earth-climate relationship is studied using tree growth rings of Araucaria angustifolia (Bertol.) O. Kuntze collected in the city of Passo Fundo, located in the state of Rio Grande do Sul (RS), Brazil. These samples were previously studied by Rigozo et al. (2008); however, their main interest was to search for the solar periodicities in the tree-ring width mean time series without interpreting the rest of the periodicities found. The question arises as to what are the drivers related to those periodicities. For this reason, the classical method of spectral analysis by iterative regression and wavelet methods are applied to find periodicities and trends present in each tree-ring growth, in Southern Oscillation Index (SOI), and in annual mean temperature anomaly between the 24 and 44° S. In order to address the aforementioned question, this paper discusses the correlation between the growth rate of the tree rings with temperature and SOI. In each tree-ring growth series, periods between 2 and 7 years were found, possibly related to the El Niño/La Niña phenomena, and a ˜ 23-year period was found, which may be related to temperature variation. These novel results might represent the tree-ring growth response to local climate conditions during its lifetime, and to nonlinear coupling between the Sun and the local climate variability responsible to the regional climate variations.
Structural equation modeling of latent growth curves of weight gain among treated tuberculosis patients.

Directory of Open Access Journals (Sweden)

Mahalingam Vasantha

Full Text Available Tuberculosis still remains a major public health problem even though it is treatable and curable. Weight gain measurement during anti tuberculosis (TB treatment period is an important component to assess the progress of TB patients. In this study, Latent Growth Models (LGMs were implemented in a longitudinal design to predict the change in weight of TB patients who were given three different regimens under randomized controlled clinical trial for anti-TB treatment. Linear and Quadratic LGMs were fitted using Mplus software. The age, sex and treatment response of the TB patients were used as time invariant independent variables of the growth trajectories. The quadratic trend was found to be better in explaining the changes in weight without grouping than the quadratic model for three group comparisons. A significant increase in the change of weight over time was identified while a significant quadratic effect indicated that weights were sustained over time. The growth rate was similar in both the groups. The treatment response had significant association with the growth rate of weight scores of the patients.
Differential model of macroeconomic growth with endogenic cyclicity

Directory of Open Access Journals (Sweden)

Mikhail I. Geraskin

2017-09-01

Full Text Available Objective to elaborate a mathematical model of economic growth taking into account the cyclical nature of macroeconomic dynamics with the model parameters based on the Russian economy statistics. Methods economic and mathematical modeling system analysis regression factor analysis econometric time series analysis. Results the article states that under unstable economic growth in Russia forecasting of strategic prospects of the Russian economy is one of the topical directions of scientific studies. Furthermore construction of predictive models should be based on multiple factors taking into account such basic concepts as the neoKeynesian HarrodDomar model Ramsey ndash Cass ndash Koopmans model S. V. Dubovskiyrsquos concept as well as the neoclassical growth model by R. Solow. They served as the basis for developing a multifactor differential economic growth model which is a modification of the neoclassical growth model by R. Solow taking into account the laborsaving and capitalsaving forms of scientifictechnical progress and the Keynesian concept of investment. The model parameters are determined based on the dynamics of actual GDP employment fixed assets and investments in fixed assets for 19652016 in Russia on the basis of official statistics. The generalized model showed the presence of longwave fluctuations that are not detected during the individual periods modeling. The cyclical nature of macroeconomic dynamics with a period of 54 years was found which corresponds to the parameters of long waves by N. D. Kondratiev. Basing on the model the macroeconomic growth forecast was generated which shows that after 2020 the increase of scientifictechnical progress will be negative. Scientific novelty a model is proposed of the scientifictechnical progress indicator showing the growth rate of the capital productivity ratio to the saving rate a differential model of macroeconomic growth is obtained which endogenously takes cyclicity into account
Size, Value and Business Cycle Variables. The Three-Factor Model and Future Economic Growth: Evidence from an Emerging Market

Directory of Open Access Journals (Sweden)

Fahad Ali

2018-02-01

Full Text Available The paper empirically investigates three different methods to construct factors and identifies some pitfalls that arise in the application of Fama-French’s three-factor model to the Pakistani stock returns. We find that the special features in Pakistan significantly affect size and value factors and also influence the explanatory power of the three-factor model. Additionally, the paper examines the ability of the three factors to predict the future growth of Pakistan’s economy. Using monthly data of both financial and non-financial companies between 2002 and 2016, the article empirically investigates and finds that: (1 size and book-to-market factors exist in the Pakistani stock market, two mimic portfolios SMB and HML generate a return of 9.15% and 12.27% per annum, respectively; (2 adding SMB and HML factors into the model meaningfully increases the explanatory power of the model; and (3 the model’s factors, except for value factor, predict future gross domestic product (GDP growth of Pakistan and remain robust. Our results are robust across sub-periods, risk regimes, and under three different methods of constructing the factors.
Precipitation variability inferred from the annual growth and isotopic composition of tropical trees

Science.gov (United States)

Ballantyne, A. P.; Baker, P. A.; Chambers, J. Q.; Villalba, R.

2005-12-01

Here we demonstrate that annual growth and isotopic ratios in tropical trees are responsive to seasonal and annual precipitation variability. We identify several regions of tropical South America characterized by significant relationships between oxygen isotopic ratios (δ 18O) in precipitation and precipitation amount (r = -0.82). Many of these regions are also sensitive to inter-annual variability in the South American Monsoon modulated by the El Niño Southern Oscillation (ENSO). The effectiveness of δ 18O and annual growth of tropical trees as a precipitation proxy is validated by high-resolution sampling of a Tachigali vermelho tree growing near Manaus, Brazil (3.1° S, 60.0° S). Growth in Tachigali spp. was highly correlated with both precipitation and cellulose δ 18O (r = 0.60) and precipitation amount was significantly correlated with δ 18O at a lag of approximately one month (r = 0.56). We also report a multi-proxy record spanning 180 years from Cedrela odorata growing in the Peruvian Amazon near Puerto Maldonado (12.6° S, 69.2° W) revealing a significant relationship between cellulose and monsoon precipitation over the region (r = -0.33). A 150-year record obtained from Polylepis tarapacana growing at Volcan Granada in Northern Argentina (22.0° S, 66.0° W) is also reported with a significant relationship between local monsoon precipitation and a regionally derived ring width index (r = 0.38). Although no significant relationship was revealed between cellulose δ 18O and precipitation in this taxa at this location, separate radii within the same tree revealed a significantly coherent δ 18O signal (r = 0.38). We compared our proxy chronologies with monsoon precipitation reanalysis data for tropical South America, which revealed key features of the South American Monsoon and their sensitivity to ENSO variability.
Singular vector decomposition of the internal variability of the Canadian Regional Climate Model

Energy Technology Data Exchange (ETDEWEB)

Diaconescu, Emilia Paula; Laprise, Rene [University of Quebec at Montreal (UQAM), Department of Earth and Atmospheric Sciences, Canadian Network for Regional Climate Modelling and Diagnostics, P.O. Box 8888, Montreal, QC (Canada); Centre ESCER (Etude et Simulation du Climat a l' Echelle Regionale), Montreal, QC (Canada); Zadra, Ayrton [University of Quebec at Montreal (UQAM), Department of Earth and Atmospheric Sciences, Canadian Network for Regional Climate Modelling and Diagnostics, P.O. Box 8888, Montreal, QC (Canada); Environment Canada, Meteorological Research Division, Montreal, QC (Canada); Centre ESCER (Etude et Simulation du Climat a l' Echelle Regionale), Montreal, QC (Canada)

2012-03-15

Previous studies have shown that Regional Climate Models (RCM) internal variability (IV) fluctuates in time depending on synoptic events. This study focuses on the physical understanding of episodes with rapid growth of IV. An ensemble of 21 simulations, differing only in their initial conditions, was run over North America using version 5 of the Canadian RCM (CRCM). The IV is quantified in terms of energy of CRCM perturbations with respect to a reference simulation. The working hypothesis is that IV is arising through rapidly growing perturbations developed in dynamically unstable regions. If indeed IV is triggered by the growth of unstable perturbations, a large proportion of the CRCM perturbations must project onto the most unstable singular vectors (SVs). A set of ten SVs was computed to identify the orthogonal set of perturbations that provide the maximum growth with respect to the dry total-energy norm during the course of the CRCM ensemble of simulations. CRCM perturbations were then projected onto the subspace of SVs. The analysis of one episode of rapid growth of IV is presented in detail. It is shown that a large part of the IV growth is explained by initially small-amplitude unstable perturbations represented by the ten leading SVs, the SV subspace accounting for over 70% of the CRCM IV growth in 36 h. The projection on the leading SV at final time is greater than the projection on the remaining SVs and there is a high similarity between the CRCM perturbations and the leading SV after 24-36 h tangent-linear model integration. The vertical structure of perturbations revealed that the baroclinic conversion is the dominant process in IV growth for this particular episode. (orig.)
Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield.

Directory of Open Access Journals (Sweden)

Meike T Wortel

2018-02-01

Full Text Available Microbes may maximize the number of daughter cells per time or per amount of nutrients consumed. These two strategies correspond, respectively, to the use of enzyme-efficient or substrate-efficient metabolic pathways. In reality, fast growth is often associated with wasteful, yield-inefficient metabolism, and a general thermodynamic trade-off between growth rate and biomass yield has been proposed to explain this. We studied growth rate/yield trade-offs by using a novel modeling framework, Enzyme-Flux Cost Minimization (EFCM and by assuming that the growth rate depends directly on the enzyme investment per rate of biomass production. In a comprehensive mathematical model of core metabolism in E. coli, we screened all elementary flux modes leading to cell synthesis, characterized them by the growth rates and yields they provide, and studied the shape of the resulting rate/yield Pareto front. By varying the model parameters, we found that the rate/yield trade-off is not universal, but depends on metabolic kinetics and environmental conditions. A prominent trade-off emerges under oxygen-limited growth, where yield-inefficient pathways support a 2-to-3 times higher growth rate than yield-efficient pathways. EFCM can be widely used to predict optimal metabolic states and growth rates under varying nutrient levels, perturbations of enzyme parameters, and single or multiple gene knockouts.
Linear latent variable models: the lava-package

DEFF Research Database (Denmark)

Holst, Klaus Kähler; Budtz-Jørgensen, Esben

2013-01-01

are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation......An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features...
Testing linear growth rate formulas of non-scale endogenous growth models

NARCIS (Netherlands)

Ziesemer, Thomas

2017-01-01

Endogenous growth theory has produced formulas for steady-state growth rates of income per capita which are linear in the growth rate of the population. Depending on the details of the models, slopes and intercepts are positive, zero or negative. Empirical tests have taken over the assumption of
Gait variability: methods, modeling and meaning

Directory of Open Access Journals (Sweden)

Hausdorff Jeffrey M

2005-07-01

Full Text Available Abstract The study of gait variability, the stride-to-stride fluctuations in walking, offers a complementary way of quantifying locomotion and its changes with aging and disease as well as a means of monitoring the effects of therapeutic interventions and rehabilitation. Previous work has suggested that measures of gait variability may be more closely related to falls, a serious consequence of many gait disorders, than are measures based on the mean values of other walking parameters. The Current JNER series presents nine reports on the results of recent investigations into gait variability. One novel method for collecting unconstrained, ambulatory data is reviewed, and a primer on analysis methods is presented along with a heuristic approach to summarizing variability measures. In addition, the first studies of gait variability in animal models of neurodegenerative disease are described, as is a mathematical model of human walking that characterizes certain complex (multifractal features of the motor control's pattern generator. Another investigation demonstrates that, whereas both healthy older controls and patients with a higher-level gait disorder walk more slowly in reduced lighting, only the latter's stride variability increases. Studies of the effects of dual tasks suggest that the regulation of the stride-to-stride fluctuations in stride width and stride time may be influenced by attention loading and may require cognitive input. Finally, a report of gait variability in over 500 subjects, probably the largest study of this kind, suggests how step width variability may relate to fall risk. Together, these studies provide new insights into the factors that regulate the stride-to-stride fluctuations in walking and pave the way for expanded research into the control of gait and the practical application of measures of gait variability in the clinical setting.
Multi-wheat-model ensemble responses to interannual climatic variability

DEFF Research Database (Denmark)

Ruane, A C; Hudson, N I; Asseng, S

2016-01-01

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and ......-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.......We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and we...... evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal...
A deterministic model for the growth of non-conducting electrical tree structures

International Nuclear Information System (INIS)

Dodd, S J

2003-01-01

Electrical treeing is of interest to the electrical generation, transmission and distribution industries as it is one of the causes of insulation failure in electrical machines, switchgear and transformer bushings. In this paper a deterministic electrical tree growth model is described. The model is based on electrostatics and local electron avalanches to model partial discharge activity within the growing tree structure. Damage to the resin surrounding the tree structure is dependent on the local electrostatic energy dissipation by partial discharges within the tree structure and weighted by the magnitudes of the local electric fields in the resin surrounding the tree structure. The model is successful in simulating the formation of branched structures without the need of a random variable, a requirement of previous stochastic models. Instability in the spatial development of partial discharges within the tree structure takes the role of the stochastic element as used in previous models to produce branched tree structures. The simulated electrical trees conform to the experimentally observed behaviour; tree length versus time and electrical tree growth rate as a function of applied voltage for non-conducting electrical trees. The phase synchronous partial discharge activity and the spatial distribution of emitted light from the tree structure are also in agreement with experimental data for non-conducting trees as grown in a flexible epoxy resin and in polyethylene. The fact that similar tree growth behaviour is found using pure amorphous (epoxy resin) and semicrystalline (polyethylene) materials demonstrate that neither annealed or quenched noise, representing material inhomogeneity, is required for the formation of irregular branched structures (electrical trees). Instead, as shown in this paper, branched growth can occur due to the instability of individual discharges within the tree structure
Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

Science.gov (United States)

Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos

2016-01-01

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

Are revised models better models? A skill score assessment of regional interannual variability

Science.gov (United States)

Sperber, Kenneth R.; Participating AMIP Modelling Groups

1999-05-01

Various skill scores are used to assess the performance of revised models relative to their original configurations. The interannual variability of all-India, Sahel and Nordeste rainfall and summer monsoon windshear is examined in integrations performed under the experimental design of the Atmospheric Model Intercomparison Project. For the indices considered, the revised models exhibit greater fidelity at simulating the observed interannual variability. Interannual variability of all-India rainfall is better simulated by models that have a more realistic rainfall climatology in the vicinity of India, indicating the beneficial effect of reducing systematic model error.
Graphene growth process modeling: a physical-statistical approach

Science.gov (United States)

Wu, Jian; Huang, Qiang

2014-09-01

As a zero-band semiconductor, graphene is an attractive material for a wide variety of applications such as optoelectronics. Among various techniques developed for graphene synthesis, chemical vapor deposition on copper foils shows high potential for producing few-layer and large-area graphene. Since fabrication of high-quality graphene sheets requires the understanding of growth mechanisms, and methods of characterization and control of grain size of graphene flakes, analytical modeling of graphene growth process is therefore essential for controlled fabrication. The graphene growth process starts with randomly nucleated islands that gradually develop into complex shapes, grow in size, and eventually connect together to cover the copper foil. To model this complex process, we develop a physical-statistical approach under the assumption of self-similarity during graphene growth. The growth kinetics is uncovered by separating island shapes from area growth rate. We propose to characterize the area growth velocity using a confined exponential model, which not only has clear physical explanation, but also fits the real data well. For the shape modeling, we develop a parametric shape model which can be well explained by the angular-dependent growth rate. This work can provide useful information for the control and optimization of graphene growth process on Cu foil.
Taking the pulse of mountains: Ecosystem responses to climatic variability

Science.gov (United States)

Fagre, Daniel B.; Peterson, David L.; Hessl, Amy E.

2003-01-01

An integrated program of ecosystem modeling and field studies in the mountains of the Pacific Northwest (U.S.A.) has quantified many of the ecological processes affected by climatic variability. Paleoecological and contemporary ecological data in forest ecosystems provided model parameterization and validation at broad spatial and temporal scales for tree growth, tree regeneration and treeline movement. For subalpine tree species, winter precipitation has a strong negative correlation with growth; this relationship is stronger at higher elevations and west-side sites (which have more precipitation). Temperature affects tree growth at some locations with respect to length of growing season (spring) and severity of drought at drier sites (summer). Furthermore, variable but predictable climate-growth relationships across elevation gradients suggest that tree species respond differently to climate at different locations, making a uniform response of these species to future climatic change unlikely. Multi-decadal variability in climate also affects ecosystem processes. Mountain hemlock growth at high-elevation sites is negatively correlated with winter snow depth and positively correlated with the winter Pacific Decadal Oscillation (PDO) index. At low elevations, the reverse is true. Glacier mass balance and fire severity are also linked to PDO. Rapid establishment of trees in subalpine ecosystems during this century is increasing forest cover and reducing meadow cover at many subalpine locations in the western U.S.A. and precipitation (snow depth) is a critical variable regulating conifer expansion. Lastly, modeling potential future ecosystem conditions suggests that increased climatic variability will result in increasing forest fire size and frequency, and reduced net primary productivity in drier, east-side forest ecosystems. As additional empirical data and modeling output become available, we will improve our ability to predict the effects of climatic change
Coevolution of variability models and related software artifacts

DEFF Research Database (Denmark)

Passos, Leonardo; Teixeira, Leopoldo; Dinztner, Nicolas

2015-01-01

models coevolve with other artifact types, we study a large and complex real-world variant-rich software system: the Linux kernel. Specifically, we extract variability-coevolution patterns capturing changes in the variability model of the Linux kernel with subsequent changes in Makefiles and C source...
Grain-growth law during Stage 1 sintering of materials

International Nuclear Information System (INIS)

He Zeming; Ma, J.

2002-01-01

This work investigates the grain-growth behaviour of powder compact during Stage 1 sintering (<90{%} theoretical density). It is widely accepted that grain size is an important state variable in the constitutive modelling in material sintering. However, it is noted that all the existing grain-growth laws proposed in the literature do not incorporate the effect of externally applied stress independently. In this work, a grain-growth law with externally applied stress as a variable was proposed. Alumina powders were forge-sintered at different applied stresses to examine the proposed grain-growth relationship. The proposed grain-growth law was then applied to model the grain-growth process on the sinter forging of tool steel. It is shown that the present proposed grain-growth law provides a good description on the experimental results. (author)
Variable selection in Logistic regression model with genetic algorithm.

Science.gov (United States)

Zhang, Zhongheng; Trevino, Victor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, Arumugam Manivanna; Zhang, Ping; Gorunescu, Florin; Subha, Velappan; Dai, Songshi

2018-02-01

Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.
Growth curve models and statistical diagnostics

CERN Document Server

Pan, Jian-Xin

2002-01-01

Growth-curve models are generalized multivariate analysis-of-variance models. These models are especially useful for investigating growth problems on short times in economics, biology, medical research, and epidemiology. This book systematically introduces the theory of the GCM with particular emphasis on their multivariate statistical diagnostics, which are based mainly on recent developments made by the authors and their collaborators. The authors provide complete proofs of theorems as well as practical data sets and MATLAB code.
Energy Production and Regional Economic Growth in China: A More Comprehensive Analysis Using a Panel Model

Directory of Open Access Journals (Sweden)

Yaobin Liu

2013-03-01

Full Text Available China has witnessed a fast economic growth in the recent two decades. However, the heavy energy exploitation seems to show a negative relation to regional economic growth. Thus, the issue is whether the energy production is a curse or blessing for the regional economic growth in China. The present study deploys a comprehensive approach to rigorously prove the validity of a proposed panel data model that includes a second generation panel unit root test and panel cointegration and a spatial panel model. The results from the second generation panel unit root test and panel cointegration allowing for cross-sectional dependences show the differenced series are stationary and there exists a cointegration relationship among these variables for all sub-regions. The results from the spatial panel data model support the conjecture of the spatial dependent and show that there is a “resource curse” only for the Western region and Central region in China.
Modeling and Design Optimization of Variable-Speed Wind Turbine Systems

Directory of Open Access Journals (Sweden)

Ulas Eminoglu

2014-01-01

Full Text Available As a result of the increase in energy demand and government subsidies, the usage of wind turbine system (WTS has increased dramatically. Due to the higher energy production of a variable-speed WTS as compared to a fixed-speed WTS, the demand for this type of WTS has increased. In this study, a new method for the calculation of the power output of variable-speed WTSs is proposed. The proposed model is developed from the S-type curve used for population growth, and is only a function of the rated power and rated (nominal wind speed. It has the advantage of enabling the user to calculate power output without using the rotor power coefficient. Additionally, by using the developed model, a mathematical method to calculate the value of rated wind speed in terms of turbine capacity factor and the scale parameter of the Weibull distribution for a given wind site is also proposed. Design optimization studies are performed by using the particle swarm optimization (PSO and artificial bee colony (ABC algorithms, which are applied into this type of problem for the first time. Different sites such as Northern and Mediterranean sites of Europe have been studied. Analyses for various parameters are also presented in order to evaluate the effect of rated wind speed on the design parameters and produced energy cost. Results show that proposed models are reliable and very useful for modeling and optimization of WTSs design by taking into account the wind potential of the region. Results also show that the PSO algorithm has better performance than the ABC algorithm for this type of problem.
An adaptive ARX model to estimate the RUL of aluminum plates based on its crack growth

Science.gov (United States)

Barraza-Barraza, Diana; Tercero-Gómez, Víctor G.; Beruvides, Mario G.; Limón-Robles, Jorge

2017-01-01

A wide variety of Condition-Based Maintenance (CBM) techniques deal with the problem of predicting the time for an asset fault. Most statistical approaches rely on historical failure data that might not be available in several practical situations. To address this issue, practitioners might require the use of self-starting approaches that consider only the available knowledge about the current degradation process and the asset operating context to update the prognostic model. Some authors use Autoregressive (AR) models for this purpose that are adequate when the asset operating context is constant, however, if it is variable, the accuracy of the models can be affected. In this paper, three autoregressive models with exogenous variables (ARX) were constructed, and their capability to estimate the remaining useful life (RUL) of a process was evaluated following the case of the aluminum crack growth problem. An existing stochastic model of aluminum crack growth was implemented and used to assess RUL estimation performance of the proposed ARX models through extensive Monte Carlo simulations. Point and interval estimations were made based only on individual history, behavior, operating conditions and failure thresholds. Both analytic and bootstrapping techniques were used in the estimation process. Finally, by including recursive parameter estimation and a forgetting factor, the ARX methodology adapts to changing operating conditions and maintain the focus on the current degradation level of an asset.
Mathematical analysis of a model for the growth of the bovine corpus luteum

KAUST Repository

Prokopiou, Sotiris A.

2013-12-13

The corpus luteum (CL) is an ovarian tissue that grows in the wound space created by follicular rupture. It produces the progesterone needed in the uterus to maintain pregnancy. Rapid growth of the CL and progesterone transport to the uterus require angiogenesis, the creation of new blood vessels from pre-existing ones, a process which is regulated by proteins that include fibroblast growth factor 2 (FGF2). In this paper we develop a system of time-dependent ordinary differential equations to model CL growth. The dependent variables represent FGF2, endothelial cells (ECs), luteal cells, and stromal cells (like pericytes), by assuming that the CL volume is a continuum of the three cell types. We assume that if the CL volume exceeds that of the ovulated follicle, then growth is inhibited. This threshold volume partitions the system dynamics into two regimes, so that the model may be classified as a Filippov (piecewise smooth) system. We show that normal CL growth requires an appropriate balance between the growth rates of luteal and stromal cells. We investigate how angiogenesis influences CL growth by considering how the system dynamics depend on the dimensionless EC proliferation rate, {Mathematical expression}. We find that weak (low {Mathematical expression}) or strong (high {Mathematical expression}) angiogenesis leads to \\'pathological\\' CL growth, since the loss of CL constituents compromises progesterone production or delivery. However, for intermediate values of {Mathematical expression}, normal CL growth is predicted. The implications of these results for cow fertility are also discussed. For example, inadequate angiogenesis has been linked to infertility in dairy cows. © 2013 Springer-Verlag Berlin Heidelberg.
Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination.

Science.gov (United States)

Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

2017-08-22

The calibration performance of Partial Least Squares regression (PLS) can be improved by eliminating uninformative variables. For PLS, many variable elimination methods have been developed. One is the Uninformative-Variable Elimination for PLS (UVE-PLS). However, the number of variables retained by UVE-PLS is usually still large. In UVE-PLS, variable elimination is repeated as long as the root mean squared error of cross validation (RMSECV) is decreasing. The set of variables in this first local minimum is retained. In this paper, a modification of UVE-PLS is proposed and investigated, in which UVE is repeated until no further reduction in variables is possible, followed by a search for the global RMSECV minimum. The method is called Global-Minimum Error Uninformative-Variable Elimination for PLS, denoted as GME-UVE-PLS or simply GME-UVE. After each iteration, the predictive ability of the PLS model, built with the remaining variable set, is assessed by RMSECV. The variable set with the global RMSECV minimum is then finally selected. The goal is to obtain smaller sets of variables with similar or improved predictability than those from the classical UVE-PLS method. The performance of the GME-UVE-PLS method is investigated using four data sets, i.e. a simulated set, NIR and NMR spectra, and a theoretical molecular descriptors set, resulting in twelve profile-response (X-y) calibrations. The selective and predictive performances of the models resulting from GME-UVE-PLS are statistically compared to those from UVE-PLS and 1-step UVE, one-sided paired t-tests. The results demonstrate that variable reduction with the proposed GME-UVE-PLS method, usually eliminates significantly more variables than the classical UVE-PLS, while the predictive abilities of the resulting models are better. With GME-UVE-PLS, a lower number of uninformative variables, without a chemical meaning for the response, may be retained than with UVE-PLS. The selectivity of the classical UVE method
Growth, unemployment and wage inertia

OpenAIRE

Raurich, Xavier; Sorolla, Valeri

2014-01-01

We introduce wage setting via efficiency wages in the neoclassical one-sector growth model to study the growth effects of wage inertia. We compare the dynamic equilibrium of an economy with wage inertia with the equilibrium of an economy without wage inertia. We show that wage inertia affects the long run employment rate and that the transitional dynamics of the main economic variables will be different because wages are a state variable when wage inertia is introduced. In particular, we show...
On the estimation of the volatility-growth link

DEFF Research Database (Denmark)

Launov, Andrey; Posch, Olaf; Wälde, Klaus

It is common practice to estimate the volatility-growth link by specifying a standard growth equation such that the variance of the error term appears as an explanatory variable in this growth equation. The variance in turn is modelled by a second equation. Hardly any of existing applications...... of this framework includes exogenous controls in this second variance equation. Our theoretical …ndings suggest that the absence of relevant explanatory variables in the variance equation leads to a biased and inconsistent estimate of the volatility-growth link. Our simulations show that this effect is large. Once...... the appropriate controls are included in the variance equation consistency is restored. In short, we suggest that the variance equation must include relevant control variables to estimate the volatility-growth link....
Design of Plant Gas Exchange Experiments in a Variable Pressure Growth Chamber

Science.gov (United States)

Corey, Kenneth A.

1996-01-01

Sustainable human presence in extreme environments such as lunar and martian bases will require bioregenerative components to human life support systems where plants are used for generation of oxygen, food, and water. Reduced atmospheric pressures will be used to minimize mass and engineering requirements. Few studies have assessed the metabolic and developmental responses of plants to reduced pressure and varied oxygen atmospheres. The first tests of hypobaric pressures on plant gas exchange and biomass production at the Johnson Space Center will be initiated in January 1996 in the Variable Pressure Growth Chamber (VPGC), a large, closed plant growth chamber rated for 10.2 psi. Experiments were designed and protocols detailed for two complete growouts each of lettuce and wheat to generate a general database for human life support requirements and to answer questions about plant growth processes in reduced pressure and varied oxygen environments. The central objective of crop growth studies in the VPGC is to determine the influence of reduced pressure and reduced oxygen on the rates of photosynthesis, dark respiration, evapotranspiration and biomass production of lettuce and wheat. Due to the constraint of one experimental unit, internal controls, called pressure transients, will be used to evaluate rates of CO2 uptake, O2 evolution, and H2O generation. Pressure transients will give interpretive power to the results of repeated growouts at both reduced and ambient pressures. Other experiments involve the generation of response functions to partial pressures of O2 and CO2 and to light intensity. Protocol for determining and calculating rates of gas exchange have been detailed. In order to build these databases and implement the necessary treatment combinations in short time periods, specific requirements for gas injections and removals have been defined. A set of system capability checks will include determination of leakage rates conducted prior to the actual crop
Growth and renewable energy in Europe: A random effect model with evidence for neutrality hypothesis

International Nuclear Information System (INIS)

Menegaki, Angeliki N.

2011-01-01

This is an empirical study on the causal relationship between economic growth and renewable energy for 27 European countries in a multivariate panel framework over the period 1997-2007 using a random effect model and including final energy consumption, greenhouse gas emissions and employment as additional independent variables in the model. Empirical results do not confirm causality between renewable energy consumption and GDP, although panel causality tests unfold short-run relationships between renewable energy and greenhouse gas emissions and employment. The estimated cointegration factor refrains from unity, indicating only a weak, if any, relationship between economic growth and renewable energy consumption in Europe, suggesting evidence of the neutrality hypothesis, which can partly be explained by the uneven and insufficient exploitation of renewable energy sources across Europe.
Modelling asymmetric growth in crowded plant communities

DEFF Research Database (Denmark)

Damgaard, Christian

2010-01-01

A class of models that may be used to quantify the effect of size-asymmetric competition in crowded plant communities by estimating a community specific degree of size-asymmetric growth for each species in the community is suggested. The model consists of two parts: an individual size......-asymmetric growth part, where growth is assumed to be proportional to a power function of the size of the individual, and a term that reduces the relative growth rate as a decreasing function of the individual plant size and the competitive interactions from other plants in the neighbourhood....
Modeling Population Growth and Extinction

Science.gov (United States)

Gordon, Sheldon P.

2009-01-01

The exponential growth model and the logistic model typically introduced in the mathematics curriculum presume that a population grows exclusively. In reality, species can also die out and more sophisticated models that take the possibility of extinction into account are needed. In this article, two extensions of the logistic model are considered,…
Variability in growth rates of larval haddock in the northern North Sea

DEFF Research Database (Denmark)

Gallego, A.; Heath, M.R.; Basford, D.J.

1999-01-01

of the spring plankton production bloom, and a likely explanation for the absence of environmental effects on larval growth was high food availability and larval feeding rates. Nevertheless, differences in growth were observed between cohorts, with larvae hatched later in the spring displaying higher growth...... at age than those hatched earlier. Particle-tracking modelling suggested that differences in temperature history between cohorts, on their own or compounded by a potential interaction between temperature and the development of plankton production, may explain the higher growth rate of the larvae hatched...
Modeling of Hybrid Growth Wastewater Bio-reactor

International Nuclear Information System (INIS)

EI Nashaei, S.; Garhyan, P.; Prasad, P.; Abdel Halim, H.S.; Ibrahim, G.

2004-01-01

The attached/suspended growth mixed reactors are considered one of the recently tried approaches to improve the performance of the biological treatment by increasing the volume of the accumulated biomass in terms of attached growth as well as suspended growth. Moreover, the domestic WW can be easily mixed with a high strength non-hazardous industrial wastewater and treated together in these bio-reactors if the need arises. Modeling of Hybrid hybrid growth wastewater reactor addresses the need of understanding the rational of such system in order to achieve better design and operation parameters. This paper aims at developing a heterogeneous mathematical model for hybrid growth system considering the effect of diffusion, external mass transfer, and power input to the system in a rational manner. The model will be based on distinguishing between liquid/solid phase (bio-film and bio-floc). This model would be a step ahead to the fine tuning the design of hybrid systems based on the experimental data of a pilot plant to be implemented in near future

Financial development and investment market integration: An approach of underlying financial variables & indicators for corporate governance growth empirical approach

Directory of Open Access Journals (Sweden)

Vojinovič Borut

2005-01-01

Full Text Available Financial development is correlated with several underlying regulatory variables (such as indicators of investor protection, market transparency variables for corporate governance growth and rules for capital market development, which are under the control of national legislators and EU directives. This paper provides estimates of the relationship between financial market development and corporate growth and assesses the impact of financial market integration on this relationship with reference to European Union (EU countries. The regression results obtained using this panel support the hypothesis that financial development promotes growth particularly in industries that are more financially dependent on external finance. For policy purposes, analyzing changes in these regulatory variables may be a more interesting exercise than analyzing integration of the financial systems themselves. Since assuming that EU countries will raise its regulatory and legal standards to the U.S. standards appears unrealistic, in this case we examine a scenario where EU countries raise their standards to the highest current EU standard.
The Validation of the Mixedwood Growth Model (MGM for Use in Forest Management Decision Making

Directory of Open Access Journals (Sweden)

Mike Bokalo

2013-01-01

Full Text Available We evaluated the Mixedwood Growth Model (MGM at a whole model scale for pure and mixed species stands of aspen and white spruce in the western boreal forest. MGM is an individual tree-based, distance-independent growth model, designed to evaluate growth and yield implications relating to the management of white spruce, black spruce, aspen, lodgepole pine, and mixedwood stands in Alberta, British Columbia, Saskatchewan, and Manitoba. Our validation compared stand-level model predictions against re-measured data (volume, basal area, diameter at breast height (DBH, average and top height and density from permanent sample plots using combined analysis of residual plots, bias statistics, efficiency and an innovative application of the equivalence test. For state variables, the model effectively simulated juvenile and mature stages of stand development for both pure and mixed species stands of aspen and white spruce in Alberta. MGM overestimates increment in older stands likely due to age-related pathology and weather-related stand damage. We identified underestimates of deciduous density and volume in Saskatchewan. MGM performs well for increment in postharvest stands less than 30 years of age. These results illustrate the comprehensive application of validation metrics to evaluate a complex model, and provide support for the use of MGM in management planning.
From Transition Systems to Variability Models and from Lifted Model Checking Back to UPPAAL

DEFF Research Database (Denmark)

Dimovski, Aleksandar; Wasowski, Andrzej

2017-01-01

efficient lifted (family-based) model checking for real-time variability models. This reduces the cost of maintaining specialized family-based real-time model checkers. Real-time variability models can be model checked using the standard UPPAAL. We have implemented abstractions as syntactic source...
Economic Growth Models Transition

Directory of Open Access Journals (Sweden)

Coralia Angelescu

2006-03-01

Full Text Available The transitional recession in countries of Eastern Europe has been much longer than expected. The legacy and recent policy mistakes have both contributed to the slow progress. As structural reforms and gradual institution building have taken hold, the post-socialist economics have started to recover, with some leading countries building momentum toward faster growth. There is a possibility that in wider context of globalization several of these emerging market economies will be able to catch up with the more advanced industrial economies in a matter of one or two generations. Over the past few years, most candidate countries have made progress in the transition to a competitive market economy, macroeconomic stabilization and structural reform. However their income levels have remained far below those in the Member States. Measured by per capita income in purchasing power standards, there has been a very limited amount of catching up over the past fourteen years. Prior, the distinctions between Solow-Swan model and endogenous growth model. The interdependence between transition and integration are stated in this study. Finally, some measures of macroeconomic policy for sustainable growth are proposed in correlation with real macroeconomic situation of the Romanian economy. Our study would be considered the real convergence for the Romanian economy and the recommendations for the adequate policies to achieve a fast real convergence and sustainable growth.
Economic Growth Models Transition

Directory of Open Access Journals (Sweden)

Coralia Angelescu

2006-01-01

Full Text Available The transitional recession in countries of Eastern Europe has been much longer than expected. The legacy and recent policy mistakes have both contributed to the slow progress. As structural reforms and gradual institution building have taken hold, the post-socialist economics have started to recover, with some leading countries building momentum toward faster growth. There is a possibility that in wider context of globalization several of these emerging market economies will be able to catch up with the more advanced industrial economies in a matter of one or two generations. Over the past few years, most candidate countries have made progress in the transition to a competitive market economy, macroeconomic stabilization and structural reform. However their income levels have remained far below those in the Member States. Measured by per capita income in purchasing power standards, there has been a very limited amount of catching up over the past fourteen years. Prior, the distinctions between Solow-Swan model and endogenous growth model. The interdependence between transition and integration are stated in this study. Finally, some measures of macroeconomic policy for sustainable growth are proposed in correlation with real macroeconomic situation of the Romanian economy. Our study would be considered the real convergence for the Romanian economy and the recommendations for the adequate policies to achieve a fast real convergence and sustainable growth.
Recent advances in modelling creep crack growth

International Nuclear Information System (INIS)

Riedel, H.

1988-08-01

At the time of the previous International Conference on Fracture, the C* integral had long been recognized as a promising load parameter for correlating crack growth rates in creep-ductile materials. The measured crack growth rates as a function of C* and of the temperature could be understood on the basis of micromechanical models. The distinction between C*-controlled and K I -controlled creep crack growth had been clarified and first attempts had been made to describe creep crack growth in the transient regime between elastic behavior and steady-state creep. This paper describes the progress in describing transient crack growth including the effect of primary creep. The effect of crack-tip geometry changes by blunting and by crack growth on the crack-tip fields and on the validity of C* is analyzed by idealizing the growing-crack geometry by a sharp notch and using recent solutions for the notch-tip fields. A few new three-dimensional calculations of C* are cited and important theoretical points are emphasized regarding the three-dimensional fields at crack tips. Finally, creep crack growth is described by continuum-damage models for which similarity solutions can be obtained. Crack growth under small-scale creep conditions turns out to be difficult to understand. Slightly different models yield very different crack growth rates. (orig.) With 4 figs
On the ""early-time"" evolution of variables relevant to turbulence models for Rayleigh-Taylor instability

Energy Technology Data Exchange (ETDEWEB)

Rollin, Bertrand [Los Alamos National Laboratory; Andrews, Malcolm J [Los Alamos National Laboratory

2010-01-01

We present our progress toward setting initial conditions in variable density turbulence models. In particular, we concentrate our efforts on the BHR turbulence model for turbulent Rayleigh-Taylor instability. Our approach is to predict profiles of relevant parameters before the fully turbulent regime and use them as initial conditions for the turbulence model. We use an idealized model of the mixing between two interpenetrating fluids to define the initial profiles for the turbulence model parameters. Velocities and volume fractions used in the idealized mixing model are obtained respectively from a set of ordinary differential equations modeling the growth of the Rayleigh-Taylor instability and from an idealization of the density profile in the mixing layer. A comparison between predicted initial profiles for the turbulence model parameters and initial profiles of the parameters obtained from low Atwood number three dimensional simulations show reasonable agreement.
Recruitment success and growth variability of mugilids in a West African estuary impacted by climate change

Science.gov (United States)

Trape, S.; Durand, J.-D.; Vigliola, L.; Panfili, J.

2017-11-01

With the persistence of a drought since the late 1960s, some West African estuaries became permanently reversed in term of salinity gradient and hypersaline waters are present in their upstream part (salinity >60). To understand the mechanisms regulating fish recruitment intensity in these estuaries and evaluate the consequences of freshwater shortages on juvenile habitat quality, a growth study was conducted in the Saloum hypersaline estuary (Senegal). The Mugilidae fish family, highly representative of estuarine environments, was targeted and several species sampled (Chelon dumerili, Mugil bananensis and M. cf. curema sp. M). Juveniles were sampled monthly all the year round in three areas of the estuary exhibiting strongly contrasted habitat conditions. Otolith sections were used to estimate the ages, reconstruct growth trajectories, estimate the duration of the oceanic larval phase, and evaluate juvenile growth variability along the salinity gradient. Analyses revealed that the temporal recruitment variability of C. dumerili, with 2 annual cohorts, was not mainly induced by growth-selection mechanisms, but probably more by predation pressures. Juveniles exhibited significantly faster growth rates in the lower salinity suggesting that benthic food availability was a strong factor controlling habitat quality of early juveniles. Salinity had also a clear impact when reducing the growth in hypersaline conditions and/or selecting slower growing individuals. Moderate freshwater inputs positively affected the nursery function of the estuary for mugilids by enhancing the productivity of the first trophic levels. In a long term, the global change could have an impact of the mugilid fishery and its management.
Spatial variability and parametric uncertainty in performance assessment models

International Nuclear Information System (INIS)

Pensado, Osvaldo; Mancillas, James; Painter, Scott; Tomishima, Yasuo

2011-01-01

The problem of defining an appropriate treatment of distribution functions (which could represent spatial variability or parametric uncertainty) is examined based on a generic performance assessment model for a high-level waste repository. The generic model incorporated source term models available in GoldSim ® , the TDRW code for contaminant transport in sparse fracture networks with a complex fracture-matrix interaction process, and a biosphere dose model known as BDOSE TM . Using the GoldSim framework, several Monte Carlo sampling approaches and transport conceptualizations were evaluated to explore the effect of various treatments of spatial variability and parametric uncertainty on dose estimates. Results from a model employing a representative source and ensemble-averaged pathway properties were compared to results from a model allowing for stochastic variation of transport properties along streamline segments (i.e., explicit representation of spatial variability within a Monte Carlo realization). We concluded that the sampling approach and the definition of an ensemble representative do influence consequence estimates. In the examples analyzed in this paper, approaches considering limited variability of a transport resistance parameter along a streamline increased the frequency of fast pathways resulting in relatively high dose estimates, while those allowing for broad variability along streamlines increased the frequency of 'bottlenecks' reducing dose estimates. On this basis, simplified approaches with limited consideration of variability may suffice for intended uses of the performance assessment model, such as evaluation of site safety. (author)
Pollution externalities in a Schumpeterian growth model

OpenAIRE

Koesler, Simon

2010-01-01

This paper extends a standard Schumpeterian growth model to include an environmental dimension. Thereby, it explicitly links the pollution intensity of economic activity to technological progress. In a second step, it investigates the effect of pollution on economic growth under the assumption that pollution intensities are related to technological progress. Several conclusions emerge from the model. In equilibrium, the economy follows a balanced growth path. The effect of pollution on the ec...
Mechanistic model for microbial growth on hydrocarbons

Energy Technology Data Exchange (ETDEWEB)

Mallee, F M; Blanch, H W

1977-12-01

Based on available information describing the transport and consumption of insoluble alkanes, a mechanistic model is proposed for microbial growth on hydrocarbons. The model describes the atypical growth kinetics observed, and has implications in the design of large scale equipment for single cell protein (SCP) manufacture from hydrocarbons. The model presents a framework for comparison of the previously published experimental kinetic data.
Value function in economic growth model

Science.gov (United States)

Bagno, Alexander; Tarasyev, Alexandr A.; Tarasyev, Alexander M.

2017-11-01

Properties of the value function are examined in an infinite horizon optimal control problem with an unlimited integrand index appearing in the quality functional with a discount factor. Optimal control problems of such type describe solutions in models of economic growth. Necessary and sufficient conditions are derived to ensure that the value function satisfies the infinitesimal stability properties. It is proved that value function coincides with the minimax solution of the Hamilton-Jacobi equation. Description of the growth asymptotic behavior for the value function is provided for the logarithmic, power and exponential quality functionals and an example is given to illustrate construction of the value function in economic growth models.
A two-dimensional continuum model of biofilm growth incorporating fluid flow and shear stress based detachment

KAUST Repository

Duddu, Ravindra

2009-05-01

We present a two-dimensional biofilm growth model in a continuum framework using an Eulerian description. A computational technique based on the eXtended Finite Element Method (XFEM) and the level set method is used to simulate the growth of the biofilm. The model considers fluid flow around the biofilm surface, the advection-diffusion and reaction of substrate, variable biomass volume fraction and erosion due to the interfacial shear stress at the biofilm-fluid interface. The key assumptions of the model and the governing equations of transport, biofilm kinetics and biofilm mechanics are presented. Our 2D biofilm growth results are in good agreement with those obtained by Picioreanu et al. (Biotechnol Bioeng 69(5):504-515, 2000). Detachment due to erosion is modeled using two continuous speed functions based on: (a) interfacial shear stress and (b) biofilm height. A relation between the two detachment models in the case of a 1D biofilm is established and simulated biofilm results with detachment in 2D are presented. The stress in the biofilm due to fluid flow is evaluated and higher stresses are observed close to the substratum where the biofilm is attached. © 2008 Wiley Periodicals, Inc.
A Model of Controlled Growth

Science.gov (United States)

Bressan, Alberto; Lewicka, Marta

2018-03-01

We consider a free boundary problem for a system of PDEs, modeling the growth of a biological tissue. A morphogen, controlling volume growth, is produced by specific cells and then diffused and absorbed throughout the domain. The geometric shape of the growing tissue is determined by the instantaneous minimization of an elastic deformation energy, subject to a constraint on the volumetric growth. For an initial domain with C}^{2,α boundary, our main result establishes the local existence and uniqueness of a classical solution, up to a rigid motion.
Trajectories and models of individual growth

Directory of Open Access Journals (Sweden)

Arseniy Karkach

2006-11-01

Full Text Available It has long been recognized that the patterns of growth play an important role in the evolution of age trajectories of fertility and mortality (Williams, 1957. Life history studies would benefit from a better understanding of strategies and mechanisms of growth, but still no comparative research on individual growth strategies has been conducted. Growth patterns and methods have been shaped by evolution and a great variety of them are observed. Two distinct patterns - determinate and indeterminate growth - are of a special interest for these studies since they present qualitatively different outcomes of evolution. We attempt to draw together studies covering growth in plant and animal species across a wide range of phyla focusing primarily on the noted qualitative features. We also review mathematical descriptions of growth, namely empirical growth curves and growth models, and discuss the directions of future research.
A day in the life of fish larvae: modeling foraging and growth using quirks.

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Klaus B Huebert

Full Text Available This article introduces "Quirks," a generic, individual-based model synthesizing over 40 years of empirical and theoretical insights into the foraging behavior and growth physiology of marine fish larvae. In Quirks, different types of larvae are defined by a short list of their biological traits, and all foraging and growth processes (including the effects of key environmental factors are modeled following one unified set of mechanistic rules. This approach facilitates ecologically meaningful comparisons between different species and environments. We applied Quirks to model young exogenously feeding larvae of four species: 5.5-mm European anchovy (Engraulis encrasicolus, 7-mm Atlantic cod (Gadus morhua, 13-mm Atlantic herring (Clupea harengus, and 7-mm European sprat (Sprattus sprattus. Modeled growth estimates explained the majority of variability among 53 published empirical growth estimates, and displayed very little bias: 0.65% ± 1.2% d(-1 (mean ± standard error. Prey organisms of ∼ 67% the maximum ingestible prey length were optimal for all larval types, in terms of the expected ingestion per encounter. Nevertheless, the foraging rate integrated over all favorable prey sizes was highest when smaller organisms made up >95% of the prey biomass under the assumption of constant normalized size spectrum slopes. The overall effect of turbulence was consistently negative, because its detrimental influence on prey pursuit success exceeded its beneficial influence on prey encounter rate. Model sensitivity to endogenous traits and exogenous environmental factors was measured and is discussed in depth. Quirks is free software and open source code is provided.
Modeling error distributions of growth curve models through Bayesian methods.

Science.gov (United States)

Zhang, Zhiyong

2016-06-01

Growth curve models are widely used in social and behavioral sciences. However, typical growth curve models often assume that the errors are normally distributed although non-normal data may be even more common than normal data. In order to avoid possible statistical inference problems in blindly assuming normality, a general Bayesian framework is proposed to flexibly model normal and non-normal data through the explicit specification of the error distributions. A simulation study shows when the distribution of the error is correctly specified, one can avoid the loss in the efficiency of standard error estimates. A real example on the analysis of mathematical ability growth data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 is used to show the application of the proposed methods. Instructions and code on how to conduct growth curve analysis with both normal and non-normal error distributions using the the MCMC procedure of SAS are provided.
Preliminary Multi-Variable Cost Model for Space Telescopes

Science.gov (United States)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. This paper reviews the methodology used to develop space telescope cost models; summarizes recently published single variable models; and presents preliminary results for two and three variable cost models. Some of the findings are that increasing mass reduces cost; it costs less per square meter of collecting aperture to build a large telescope than a small telescope; and technology development as a function of time reduces cost at the rate of 50% per 17 years.
Structural modelling of economic growth: Technological changes

Directory of Open Access Journals (Sweden)

Sukharev Oleg

2016-01-01

Full Text Available Neoclassical and Keynesian theories of economic growth assume the use of Cobb-Douglas modified functions and other aggregate econometric approaches to growth dynamics modelling. In that case explanations of economic growth are based on the logic of the used mathematical ratios often including the ideas about aggregated values change and factors change a priori. The idea of assessment of factor productivity is the fundamental one among modern theories of economic growth. Nevertheless, structural parameters of economic system, institutions and technological changes are practically not considered within known approaches, though the latter is reflected in the changing parameters of production function. At the same time, on the one hand, the ratio of structural elements determines the future value of the total productivity of the factors and, on the other hand, strongly influences the rate of economic growth and its mode of innovative dynamics. To put structural parameters of economic system into growth models with the possibility of assessment of such modes under conditions of interaction of new and old combinations is an essential step in the development of the theory of economic growth/development. It allows forming stimulation policy of economic growth proceeding from the structural ratios and relations recognized for this economic system. It is most convenient in such models to use logistic functions demonstrating the resource change for old and new combination within the economic system. The result of economy development depends on starting conditions, and on institutional parameters of velocity change of resource borrowing in favour of a new combination and creation of its own resource. Model registration of the resource is carried out through the idea of investments into new and old combinations.
A comparison of scope for growth (SFG) and dynamic energy budget (DEB) models applied to the blue mussel ( Mytilus edulis)

Science.gov (United States)

Filgueira, Ramón; Rosland, Rune; Grant, Jon

2011-11-01

Growth of Mytilus edulis was simulated using individual based models following both Scope For Growth (SFG) and Dynamic Energy Budget (DEB) approaches. These models were parameterized using independent studies and calibrated for each dataset by adjusting the half-saturation coefficient of the food ingestion function term, XK, a common parameter in both approaches related to feeding behavior. Auto-calibration was carried out using an optimization tool, which provides an objective way of tuning the model. Both approaches yielded similar performance, suggesting that although the basis for constructing the models is different, both can successfully reproduce M. edulis growth. The good performance of both models in different environments achieved by adjusting a single parameter, XK, highlights the potential of these models for (1) producing prospective analysis of mussel growth and (2) investigating mussel feeding response in different ecosystems. Finally, we emphasize that the convergence of two different modeling approaches via calibration of XK, indicates the importance of the feeding behavior and local trophic conditions for bivalve growth performance. Consequently, further investigations should be conducted to explore the relationship of XK to environmental variables and/or to the sophistication of the functional response to food availability with the final objective of creating a general model that can be applied to different ecosystems without the need for calibration.

Vulnerability of white spruce tree growth in interior Alaska in response to climate variability: dendrochronological, demographic, and experimental perspectives

Science.gov (United States)

A.D. McGuire; R.W. Ruess; A. Lloyd; J. Yarie; J.S. Clein; G.P. Juday

2010-01-01

This paper integrates dendrochronological, demographic, and experimental perspectives to improve understanding of the response of white spruce (Picea glauca (Moench) Voss) tree growth to climatic variability in interior Alaska. The dendrochronological analyses indicate that climate warming has led to widespread declines in white spruce growth...
gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

Directory of Open Access Journals (Sweden)

Benjamin Hofner

2016-10-01

Full Text Available Generalized additive models for location, scale and shape are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we use a data set on stunted growth in India. In addition to the specification and application of the model itself, we present a variety of convenience functions, including methods for tuning parameter selection, prediction and visualization of results. The package gamboostLSS is available from the Comprehensive R Archive Network (CRAN at https://CRAN.R-project.org/package=gamboostLSS.
Probabilistic modeling of fatigue crack growth in Ti-6Al-4V

International Nuclear Information System (INIS)

Soboyejo, W.O.; Shen, W.; Soboyejo, A.B.O.

2001-01-01

This paper presents the results of a combined experimental and analytical study of the probabilistic nature of fatigue crack growth in Ti-6Al-4V. A simple experimental fracture mechanics framework is presented for the determination of statistical fatigue crack growth parameters from two fatigue tests. The experimental studies show that the variabilities in long fatigue crack growth rate data and the Paris coefficient are well described by the log-normal distributions. The variabilities in the Paris exponent are also shown to be well characterized by a normal distribution. The measured statistical distributions are incorporated into a probabilistic fracture mechanics framework for the estimation of material reliability. The implications of the results are discussed for the probabilistic analysis of fatigue crack growth in engineering components and structures. (orig.)
Analysis models for variables associated with breastfeeding duration

Directory of Open Access Journals (Sweden)

Edson Theodoro dos S. Neto

2013-09-01

Full Text Available OBJECTIVE To analyze the factors associated with breastfeeding duration by two statistical models. METHODS A population-based cohort study was conducted with 86 mothers and newborns from two areas primary covered by the National Health System, with high rates of infant mortality in Vitória, Espírito Santo, Brazil. During 30 months, 67 (78% children and mothers were visited seven times at home by trained interviewers, who filled out survey forms. Data on food and sucking habits, socioeconomic and maternal characteristics were collected. Variables were analyzed by Cox regression models, considering duration of breastfeeding as the dependent variable, and logistic regression (dependent variables, was the presence of a breastfeeding child in different post-natal ages. RESULTS In the logistic regression model, the pacifier sucking (adjusted Odds Ratio: 3.4; 95%CI 1.2-9.55 and bottle feeding (adjusted Odds Ratio: 4.4; 95%CI 1.6-12.1 increased the chance of weaning a child before one year of age. Variables associated to breastfeeding duration in the Cox regression model were: pacifier sucking (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.3 and bottle feeding (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.5. However, protective factors (maternal age and family income differed between both models. CONCLUSIONS Risk and protective factors associated with cessation of breastfeeding may be analyzed by different models of statistical regression. Cox Regression Models are adequate to analyze such factors in longitudinal studies.
A conceptual model for the growth, persistence, and blooming behavior of the benthic mat-forming diatom Didymosphenia geminata (Invited)

Science.gov (United States)

Cullis, J. D.; Gillis, C.; Bothwell, M.; Kilroy, C.; Packman, A. I.; Hassan, M. A.

2010-12-01

The nuisance diatom Didymosphenia geminata (didymo) presents an ecological paradox. How can this benthic algae produce such large amounts of biomass in cold, fast flowing, low nutrient streams? The aim of this paper is to present a conceptual model for the growth, persistence, and blooming behavior of this benthic mat-forming diatom that may help to explain this paradox. The conceptual model highlights the importance of distinguishing between mat thickness and cell growth. It presents evidence gathered from a range of existing studies around the world to support the proposed relationship between growth and light, nutrients and temperature as well as the importance of flood events and bed disturbance in mat removal. It is anticipated that this conceptual model will not only help in identifying the key controlling variables and set a framework for future studies but also support the future management of this nuisance algae. Summary of the conceptual model for didymo growth showing the proposed relationships for the growth of cells and mats with nutrients, radiation and water temperature and the dependence of removal on bed shear stress and the potential for physical bed disturbance.
Modeling stability of growth between mathematics and science achievement during middle and high school.

Science.gov (United States)

Ma, Xin; Ma, Lingling

2004-04-01

In this study, the authors introduced a multivariate multilevel model to estimate the consistency among students and schools in the rates of growth between mathematics and science achievement during the entire middle and high school years with data from the Longitudinal Study of American Youth (LSAY). There was no evident consistency in the rates of growth between mathematics and science achievement among students, and this inconsistency was not much influenced by student characteristics and school characteristics. However, there was evident consistency in the average rates of growth between mathematics and science achievement among schools, and this consistency was influenced by student characteristics and school characteristics. Major school-level variables associated with parental involvement did not show any significant impacts on consistency among either students or schools. Results call for educational policies that promote collaboration between mathematics and science departments or teachers.
Bayesian approach to errors-in-variables in regression models

Science.gov (United States)

Rozliman, Nur Aainaa; Ibrahim, Adriana Irawati Nur; Yunus, Rossita Mohammad

2017-05-01

In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.
Understanding and forecasting polar stratospheric variability with statistical models

Directory of Open Access Journals (Sweden)

C. Blume

2012-07-01

Full Text Available The variability of the north-polar stratospheric vortex is a prominent aspect of the middle atmosphere. This work investigates a wide class of statistical models with respect to their ability to model geopotential and temperature anomalies, representing variability in the polar stratosphere. Four partly nonstationary, nonlinear models are assessed: linear discriminant analysis (LDA; a cluster method based on finite elements (FEM-VARX; a neural network, namely the multi-layer perceptron (MLP; and support vector regression (SVR. These methods model time series by incorporating all significant external factors simultaneously, including ENSO, QBO, the solar cycle, volcanoes, to then quantify their statistical importance. We show that variability in reanalysis data from 1980 to 2005 is successfully modeled. The period from 2005 to 2011 can be hindcasted to a certain extent, where MLP performs significantly better than the remaining models. However, variability remains that cannot be statistically hindcasted within the current framework, such as the unexpected major warming in January 2009. Finally, the statistical model with the best generalization performance is used to predict a winter 2011/12 with warm and weak vortex conditions. A vortex breakdown is predicted for late January, early February 2012.
Model uncertainty in growth empirics

NARCIS (Netherlands)

Prüfer, P.

2008-01-01

This thesis applies so-called Bayesian model averaging (BMA) to three different economic questions substantially exposed to model uncertainty. Chapter 2 addresses a major issue of modern development economics: the analysis of the determinants of pro-poor growth (PPG), which seeks to combine high
Gaussian Mixture Model of Heart Rate Variability

Science.gov (United States)

Costa, Tommaso; Boccignone, Giuseppe; Ferraro, Mario

2012-01-01

Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters. PMID:22666386
Verification of models for ballistic movement time and endpoint variability.

Science.gov (United States)

Lin, Ray F; Drury, Colin G

2013-01-01

A hand control movement is composed of several ballistic movements. The time required in performing a ballistic movement and its endpoint variability are two important properties in developing movement models. The purpose of this study was to test potential models for predicting these two properties. Twelve participants conducted ballistic movements of specific amplitudes using a drawing tablet. The measured data of movement time and endpoint variability were then used to verify the models. This study was successful with Hoffmann and Gan's movement time model (Hoffmann, 1981; Gan and Hoffmann 1988) predicting more than 90.7% data variance for 84 individual measurements. A new theoretically developed ballistic movement variability model, proved to be better than Howarth, Beggs, and Bowden's (1971) model, predicting on average 84.8% of stopping-variable error and 88.3% of aiming-variable errors. These two validated models will help build solid theoretical movement models and evaluate input devices. This article provides better models for predicting end accuracy and movement time of ballistic movements that are desirable in rapid aiming tasks, such as keying in numbers on a smart phone. The models allow better design of aiming tasks, for example button sizes on mobile phones for different user populations.
A Discrete Fracture Network Model with Stress-Driven Nucleation and Growth

Science.gov (United States)

Lavoine, E.; Darcel, C.; Munier, R.; Davy, P.

2017-12-01

The realism of Discrete Fracture Network (DFN) models, beyond the bulk statistical properties, relies on the spatial organization of fractures, which is not issued by purely stochastic DFN models. The realism can be improved by injecting prior information in DFN from a better knowledge of the geological fracturing processes. We first develop a model using simple kinematic rules for mimicking the growth of fractures from nucleation to arrest, in order to evaluate the consequences of the DFN structure on the network connectivity and flow properties. The model generates fracture networks with power-law scaling distributions and a percentage of T-intersections that are consistent with field observations. Nevertheless, a larger complexity relying on the spatial variability of natural fractures positions cannot be explained by the random nucleation process. We propose to introduce a stress-driven nucleation in the timewise process of this kinematic model to study the correlations between nucleation, growth and existing fracture patterns. The method uses the stress field generated by existing fractures and remote stress as an input for a Monte-Carlo sampling of nuclei centers at each time step. Networks so generated are found to have correlations over a large range of scales, with a correlation dimension that varies with time and with the function that relates the nucleation probability to stress. A sensibility analysis of input parameters has been performed in 3D to quantify the influence of fractures and remote stress field orientations.
Causal relationships between energy consumption, foreign direct investment and economic growth: Fresh evidence from dynamic simultaneous-equations models

International Nuclear Information System (INIS)

Omri, Anis; Kahouli, Bassem

2014-01-01

This paper examines the interrelationships between energy consumption, foreign direct investment and economic growth using dynamic panel data models in simultaneous-equations for a global panel consisting of 65 countries. The time component of our dataset is 1990–2011 inclusive. To make the panel data analysis more homogenous, we also investigate this interrelationship for a number of sub-panels which are constructed based on the income level of countries. In this way, we end up with three income panels; namely, high income, middle income, and low income panels. In the empirical part, we draw on the growth theory and augment the classical growth model, which consists of capital stock, labor force and inflation, with foreign direct investment and energy. Generally, we show mixed results about the interrelationship between energy consumption, FDI and economic growth. - Highlights: • We examine the energy–FDI–growth nexus for a global panel of 65 countries. • Dynamic simultaneous-equation panel data models are used to address this issue. • We also investigate this nexus for three sub-panels which are constructed based on the income level of countries. • We show mixed results about the interrelationship between the three variables
Re-investigating the electricity consumption and economic growth nexus in Portugal

International Nuclear Information System (INIS)

Tang, Chor Foon; Shahbaz, Muhammad; Arouri, Mohamed

2013-01-01

In the previous decades, a number of studies have been conducted to analyse the causal relationship between electricity consumption and economic growth in the Portuguese economy. However, the evidence remains controversial because the previous studies do not provide clear causality evidence. This might be attributed to the omitted variables bias because most previous studies only focus on the relationship between electricity consumption and economic growth in a bi-variate model. This paper attempts to re-investigate the relationship between electricity consumption and economic growth in Portugal using a multivariate model. Based on the bounds testing approach to cointegration and the Granger causality test within the vector error-correction model (VECM), our empirical results confirm the presence of cointegration among the variables. Moreover, there is evidence of bi-directional causality between electricity consumption and economic growth in the short- and long-run. This suggests that energy is an important source of economic growth in Portugal. Therefore, energy conservation policies should not be implemented because it would deteriorate the process of economic growth and development of the Portuguese economy. - Highlights: • Electricity consumption and economic growth series in Portugal are cointegrated. • There is evidence of feedback effects between the two variables. • Energy is an important source of economic growth in Portugal
Latent variable modeling%建立隐性变量模型

Institute of Scientific and Technical Information of China (English)

蔡力

2012-01-01

@@ A latent variable model, as the name suggests,is a statistical model that contains latent, that is, unobserved, variables.Their roots go back to Spearman's 1904 seminal work[1] on factor analysis,which is arguably the first well-articulated latent variable model to be widely used in psychology, mental health research, and allied disciplines.Because of the association of factor analysis with early studies of human intelligence, the fact that key variables in a statistical model are, on occasion, unobserved has been a point of lingering contention and controversy.The reader is assured, however, that a latent variable,defined in the broadest manner, is no more mysterious than an error term in a normal theory linear regression model or a random effect in a mixed model.
Climate-induced interannual variability of marine primary and export production in three global coupled climate carbon cycle models

Directory of Open Access Journals (Sweden)

B. Schneider

2008-04-01

Full Text Available Fully coupled climate carbon cycle models are sophisticated tools that are used to predict future climate change and its impact on the land and ocean carbon cycles. These models should be able to adequately represent natural variability, requiring model validation by observations. The present study focuses on the ocean carbon cycle component, in particular the spatial and temporal variability in net primary productivity (PP and export production (EP of particulate organic carbon (POC. Results from three coupled climate carbon cycle models (IPSL, MPIM, NCAR are compared with observation-based estimates derived from satellite measurements of ocean colour and results from inverse modelling (data assimilation. Satellite observations of ocean colour have shown that temporal variability of PP on the global scale is largely dominated by the permanently stratified, low-latitude ocean (Behrenfeld et al., 2006 with stronger stratification (higher sea surface temperature; SST being associated with negative PP anomalies. Results from all three coupled models confirm the role of the low-latitude, permanently stratified ocean for anomalies in globally integrated PP, but only one model (IPSL also reproduces the inverse relationship between stratification (SST and PP. An adequate representation of iron and macronutrient co-limitation of phytoplankton growth in the tropical ocean has shown to be the crucial mechanism determining the capability of the models to reproduce observed interactions between climate and PP.
Galactic models with variable spiral structure

International Nuclear Information System (INIS)

James, R.A.; Sellwood, J.A.

1978-01-01

A series of three-dimensional computer simulations of disc galaxies has been run in which the self-consistent potential of the disc stars is supplemented by that arising from a small uniform Population II sphere. The models show variable spiral structure, which is more pronounced for thin discs. In addition, the thin discs form weak bars. In one case variable spiral structure associated with this bar has been seen. The relaxed discs are cool outside resonance regions. (author)
Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes

Science.gov (United States)

Leite, Walter L.; Stapleton, Laura M.

2011-01-01

In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…
Modeling the growth boundary of Listeria monocytogenes in ready-to-eat cooked meat products as a function of the product salt, moisture, potassium lactate, and sodium diacetate concentrations.

Science.gov (United States)

Legan, J D; Seman, D L; Milkowski, A L; Hirschey, J A; Vandeven, M H

2004-10-01

A central composite response surface design was used to determine the time to growth of Listeria monocytogenes as a function of four continuous variables: added sodium chloride (0.8 to 3.6%), sodium diacetate (0 to 0.2%), potassium lactate syrup (60% [wt/wt]; 0.25 to 9.25%), and finished-product moisture (45.5 to 83.5%) in ready-to-eat cured meat products. The design was repeated for ready-to-eat uncured meat products giving a fifth categorical variable for cure status. Products were stored at 4 degrees C. The results were modeled using a generalized regression approach. All five main effects, six two-factor interactions, and two quadratic terms were statistically significant. The model was used to show the boundary between growth and no-growth conditions at 4 degrees C using contour plots of time to growth. It was validated using independent challenge studies of cured and uncured products. Generally, the model predicted well, particularly for cured products, where it will be useful for establishing conditions that prevent the growth of L. monocytogenes. For uncured products, there was good agreement overall between predicted and observed times to growth, but the model is less thoroughly validated than for cured products. The model should initially only be used for screening of formulations likely to prevent growth of Listeria monocytogenes in uncured products, with recommendations subject to confirmation by challenge studies.
Modeling urban fire growth

International Nuclear Information System (INIS)

Waterman, T.E.; Takata, A.N.

1983-01-01

The IITRI Urban Fire Spread Model as well as others of similar vintage were constrained by computer size and running costs such that many approximations/generalizations were introduced to reduce program complexity and data storage requirements. Simplifications were introduced both in input data and in fire growth and spread calculations. Modern computational capabilities offer the means to introduce greater detail and to examine its practical significance on urban fire predictions. Selected portions of the model are described as presently configured, and potential modifications are discussed. A single tract model is hypothesized which permits the importance of various model details to be assessed, and, other model applications are identified

A variable-order fractal derivative model for anomalous diffusion

Directory of Open Access Journals (Sweden)

Liu Xiaoting

2017-01-01

Full Text Available This paper pays attention to develop a variable-order fractal derivative model for anomalous diffusion. Previous investigations have indicated that the medium structure, fractal dimension or porosity may change with time or space during solute transport processes, results in time or spatial dependent anomalous diffusion phenomena. Hereby, this study makes an attempt to introduce a variable-order fractal derivative diffusion model, in which the index of fractal derivative depends on temporal moment or spatial position, to characterize the above mentioned anomalous diffusion (or transport processes. Compared with other models, the main advantages in description and the physical explanation of new model are explored by numerical simulation. Further discussions on the dissimilitude such as computational efficiency, diffusion behavior and heavy tail phenomena of the new model and variable-order fractional derivative model are also offered.
Neo-logistic model for the growth of bacteria

OpenAIRE

Tashiro, Tohru; Yoshimura, Fujiko

2017-01-01

We propose a neo-logistic model that can describe bacterial growth data precisely. This model is not derived by modifying the logistic model formally, but by incorporating the synthesis of inducible enzymes into the logistic model indirectly. Therefore, the meaning of the parameters of the neo-logistic model becomes physically clear. The neo-logistic model can approximate bacterial growth better than models previously presented, and predict the order of the saturated number of bacteria in the...
Higher-dimensional cosmological model with variable gravitational ...

Indian Academy of Sciences (India)

We have studied five-dimensional homogeneous cosmological models with variable and bulk viscosity in Lyra geometry. Exact solutions for the field equations have been obtained and physical properties of the models are discussed. It has been observed that the results of new models are well within the observational ...
Non-linear Growth Models in Mplus and SAS

Science.gov (United States)

Grimm, Kevin J.; Ram, Nilam

2013-01-01

Non-linear growth curves or growth curves that follow a specified non-linear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this paper we describe how a variety of sigmoid curves can be fit using the Mplus structural modeling program and the non-linear mixed-effects modeling procedure NLMIXED in SAS. Using longitudinal achievement data collected as part of a study examining the effects of preschool instruction on academic gain we illustrate the procedures for fitting growth models of logistic, Gompertz, and Richards functions. Brief notes regarding the practical benefits, limitations, and choices faced in the fitting and estimation of such models are included. PMID:23882134
A literature survey on energy-growth nexus

International Nuclear Information System (INIS)

Ozturk, Ilhan

2010-01-01

This paper provides a survey of the recent progress in the literature of energy consumption-economic growth and electricity consumption-economic growth causality nexus. The survey highlights that most empirical studies focus on either testing the role of energy (electricity) in stimulating economic growth or examining the direction of causality between these two variables. Although the positive role of energy on growth has become a stylized fact, there are some methodological reservations about the results from these empirical studies. A general observation from these studies is that the literature produced conflicting results and there is no consensus neither on the existence nor on the direction of causality between energy consumption (electricity consumption) and economic growth. As a policy implication, to avoid from conflicting and unreliable results, the authors may use the autoregressive distributed lags bounds test, two-regime threshold co-integration models, panel data approach and multivariate models including new variables (such as: real gross fixed capital formation, labor force, carbon dioxide emissions, population, exchange rates, interest rates, etc.). Thus, the authors should focus more on the new approaches and perspectives rather than by employing usual methods based on a set of common variables for different countries and different intervals of time.
Modelling diameter growth, mortality and recruitment of trees in ...

African Journals Online (AJOL)

Modelling diameter growth, mortality and recruitment of trees in miombo woodlands of Tanzania. ... Individual tree diameter growth and mortality models, and area-based recruitment models were developed. ... AJOL African Journals Online.
Forest growth modeling in the Southern Region, National Forest System

International Nuclear Information System (INIS)

Belcher, D.M.

1988-01-01

This paper discusses an attempt to combine individual tree growth models and stand level growth models currently available for the Region into one computer program. Operation of the program is explained and growth models are included
Multi-scale climate modelling over Southern Africa using a variable-resolution global model

CSIR Research Space (South Africa)

Engelbrecht, FA

2011-12-01

Full Text Available -mail: fengelbrecht@csir.co.za Multi-scale climate modelling over Southern Africa using a variable-resolution global model FA Engelbrecht1, 2*, WA Landman1, 3, CJ Engelbrecht4, S Landman5, MM Bopape1, B Roux6, JL McGregor7 and M Thatcher7 1 CSIR Natural... improvement. Keywords: multi-scale climate modelling, variable-resolution atmospheric model Introduction Dynamic climate models have become the primary tools for the projection of future climate change, at both the global and regional scales. Dynamic...
Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai

Science.gov (United States)

Shafizadeh-Moghadam, Hossein; Helbich, Marco

2015-03-01

The rapid growth of megacities requires special attention among urban planners worldwide, and particularly in Mumbai, India, where growth is very pronounced. To cope with the planning challenges this will bring, developing a retrospective understanding of urban land-use dynamics and the underlying driving-forces behind urban growth is a key prerequisite. This research uses regression-based land-use change models - and in particular non-spatial logistic regression models (LR) and auto-logistic regression models (ALR) - for the Mumbai region over the period 1973-2010, in order to determine the drivers behind spatiotemporal urban expansion. Both global models are complemented by a local, spatial model, the so-called geographically weighted logistic regression (GWLR) model, one that explicitly permits variations in driving-forces across space. The study comes to two main conclusions. First, both global models suggest similar driving-forces behind urban growth over time, revealing that LRs and ALRs result in estimated coefficients with comparable magnitudes. Second, all the local coefficients show distinctive temporal and spatial variations. It is therefore concluded that GWLR aids our understanding of urban growth processes, and so can assist context-related planning and policymaking activities when seeking to secure a sustainable urban future.
Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery

Directory of Open Access Journals (Sweden)

Dong-Wook Kim

2018-04-01

Full Text Available Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide range of growth rates. Remotely sensed digital imagery data were collected for test fields at approximately one-week intervals using a UAV platform equipped with an RGB digital camera flying at 2 m/s at 20 m above ground. Radiometric calibrations for the RGB band sensors were performed on every UAV flight using standard calibration panels to minimize the effect of ever-changing light conditions on the RGB images. Vegetation fractions (VFs of crops in each region of interest from the mosaicked ortho-images were calculated as the ratio of pixels classified as crops segmented using the Otsu threshold method and a vegetation index of excess green (ExG. Plant heights (PHs were estimated using the structure from motion (SfM algorithm to create 3D surface models from crop canopy data. Multiple linear regression equations consisting of three predictor variables (VF, PH, and VF × PH and four different response variables (fresh weight, leaf length, leaf width, and leaf count provided good fits with coefficients of determination (R2 ranging from 0.66 to 0.90. The validation results using a dataset of crop growth obtained in a different year also showed strong linear relationships (R2 > 0.76 between the developed regression models and standard methods, confirming that the models make it possible to use UAV-RGB images for quantifying spatial and temporal variability in biophysical properties of Chinese cabbage and white radish over the growing season.
Self-regulation and recall: growth curve modeling of intervention outcomes for older adults.

Science.gov (United States)

West, Robin L; Hastings, Erin C

2011-12-01

Memory training has often been supported as a potential means to improve performance for older adults. Less often studied are the characteristics of trainees that benefit most from training. Using a self-regulatory perspective, the current project examined a latent growth curve model to predict training-related gains for middle-aged and older adult trainees from individual differences (e.g., education), information processing skills (strategy use) and self-regulatory factors such as self-efficacy, control, and active engagement in training. For name recall, a model including strategy usage and strategy change as predictors of memory gain, along with self-efficacy and self-efficacy change, showed comparable fit to a more parsimonious model including only self-efficacy variables as predictors. The best fit to the text recall data was a model focusing on self-efficacy change as the main predictor of memory change, and that model showed significantly better fit than a model also including strategy usage variables as predictors. In these models, overall performance was significantly predicted by age and memory self-efficacy, and subsequent training-related gains in performance were best predicted directly by change in self-efficacy (text recall), or indirectly through the impact of active engagement and self-efficacy on gains (name recall). These results underscore the benefits of targeting self-regulatory factors in intervention programs designed to improve memory skills.
Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

Energy Technology Data Exchange (ETDEWEB)

Cole, Wesley [National Renewable Energy Lab. (NREL), Golden, CO (United States); Frew, Bethany [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mai, Trieu [National Renewable Energy Lab. (NREL), Golden, CO (United States); Sun, Yinong [National Renewable Energy Lab. (NREL), Golden, CO (United States); Bistline, John [Electric Power Research Inst. (EPRI), Knoxville, TN (United States); Blanford, Geoffrey [Electric Power Research Inst. (EPRI), Knoxville, TN (United States); Young, David [Electric Power Research Inst. (EPRI), Knoxville, TN (United States); Marcy, Cara [U.S. Energy Information Administration, Washington, DC (United States); Namovicz, Chris [U.S. Energy Information Administration, Washington, DC (United States); Edelman, Risa [US Environmental Protection Agency (EPA), Washington, DC (United States); Meroney, Bill [US Environmental Protection Agency (EPA), Washington, DC (United States); Sims, Ryan [US Environmental Protection Agency (EPA), Washington, DC (United States); Stenhouse, Jeb [US Environmental Protection Agency (EPA), Washington, DC (United States); Donohoo-Vallett, Paul [Dept. of Energy (DOE), Washington DC (United States)

2017-11-01

Long-term capacity expansion models of the U.S. electricity sector have long been used to inform electric sector stakeholders and decision-makers. With the recent surge in variable renewable energy (VRE) generators — primarily wind and solar photovoltaics — the need to appropriately represent VRE generators in these long-term models has increased. VRE generators are especially difficult to represent for a variety of reasons, including their variability, uncertainty, and spatial diversity. This report summarizes the analyses and model experiments that were conducted as part of two workshops on modeling VRE for national-scale capacity expansion models. It discusses the various methods for treating VRE among four modeling teams from the Electric Power Research Institute (EPRI), the U.S. Energy Information Administration (EIA), the U.S. Environmental Protection Agency (EPA), and the National Renewable Energy Laboratory (NREL). The report reviews the findings from the two workshops and emphasizes the areas where there is still need for additional research and development on analysis tools to incorporate VRE into long-term planning and decision-making. This research is intended to inform the energy modeling community on the modeling of variable renewable resources, and is not intended to advocate for or against any particular energy technologies, resources, or policies.
Bridging process-based and empirical approaches to modeling tree growth

Science.gov (United States)

Harry T. Valentine; Annikki Makela; Annikki Makela

2005-01-01

The gulf between process-based and empirical approaches to modeling tree growth may be bridged, in part, by the use of a common model. To this end, we have formulated a process-based model of tree growth that can be fitted and applied in an empirical mode. The growth model is grounded in pipe model theory and an optimal control model of crown development. Together, the...
Sources and Impacts of Modeled and Observed Low-Frequency Climate Variability

Science.gov (United States)

Parsons, Luke Alexander

Here we analyze climate variability using instrumental, paleoclimate (proxy), and the latest climate model data to understand more about the sources and impacts of low-frequency climate variability. Understanding the drivers of climate variability at interannual to century timescales is important for studies of climate change, including analyses of detection and attribution of climate change impacts. Additionally, correctly modeling the sources and impacts of variability is key to the simulation of abrupt change (Alley et al., 2003) and extended drought (Seager et al., 2005; Pelletier and Turcotte, 1997; Ault et al., 2014). In Appendix A, we employ an Earth system model (GFDL-ESM2M) simulation to study the impacts of a weakening of the Atlantic meridional overturning circulation (AMOC) on the climate of the American Tropics. The AMOC drives some degree of local and global internal low-frequency climate variability (Manabe and Stouffer, 1995; Thornalley et al., 2009) and helps control the position of the tropical rainfall belt (Zhang and Delworth, 2005). We find that a major weakening of the AMOC can cause large-scale temperature, precipitation, and carbon storage changes in Central and South America. Our results suggest that possible future changes in AMOC strength alone will not be sufficient to drive a large-scale dieback of the Amazonian forest, but this key natural ecosystem is sensitive to dry-season length and timing of rainfall (Parsons et al., 2014). In Appendix B, we compare a paleoclimate record of precipitation variability in the Peruvian Amazon to climate model precipitation variability. The paleoclimate (Lake Limon) record indicates that precipitation variability in western Amazonia is 'red' (i.e., increasing variability with timescale). By contrast, most state-of-the-art climate models indicate precipitation variability in this region is nearly 'white' (i.e., equally variability across timescales). This paleo-model disagreement in the overall
Reactive burn models and ignition & growth concept

Directory of Open Access Journals (Sweden)

Shaw M.S.

2011-01-01

Full Text Available Plastic-bonded explosives are heterogeneous materials. Experimentally, shock initiation is sensitive to small amounts of porosity, due to the formation of hot spots (small localized regions of high temperature. This leads to the Ignition & Growth concept, introduced by LeeTarver in 1980, as the basis for reactive burn models. A homo- genized burn rate needs to account for three meso-scale physical effects: (i the density of active hot spots or burn centers; (ii the growth of the burn fronts triggered by the burn centers; (iii a geometric factor that accounts for the overlap of deflagration wavelets from adjacent burn centers. These effects can be combined and the burn model defined by specifying the reaction progress variable λ = g(s as a function of a dimensionless reaction length s(t = rbc/ℓbc, rather than by specifying an explicit burn rate. The length scale ℓbc(Ps = [Nbc(Ps]−1/3 is the average distance between burn centers, where Nbc is the number density of burn centers activated by the lead shock. The reaction length rbc(t = ∫t0 D(P(t′dt′ is the distance the burn front propagates from a single burn center, where D(P is the deflagration speed as a function of the local pressure and t is the time since the shock arrival. A key implementation issue is how to determine the lead shock strength in conjunction with a shock capturing scheme. We have developed a robust algorithm for this purpose based on the Hugoniot jump condition for the energy. The algorithm utilizes the time dependence of density, pressure and energy within each cell. The method is independent of the numerical dissipation used for shock capturing. It is local and can be used in one or more space dimensions. The burn model has a small number of parameters which can be calibrated to fit velocity gauge data from shock initiation experiments.
Posttraumatic Growth in Populations with Posttraumatic Stress Disorder-A Systematic Review on Growth-Related Psychological Constructs and Biological Variables.

Science.gov (United States)

Schubert, Christine F; Schmidt, Ulrike; Rosner, Rita

2016-11-01

Posttraumatic growth (PTG) and Posttraumatic Stress Disorder (PTSD) are possible consequences of trauma. PTG is supposed to emerge from cognitive processes and can have functional and dysfunctional aspects. This systematic review aims to identify and evaluate publications assessing PTG in adults diagnosed with PTSD in order to analyse the relationship between both constructs, how PTG is related to specific psychological variables and if there are biological variables linked to PTG. This extended review evaluates the quality of measures applied and is the first to study PTG only in populations meeting full PTSD criteria. In addition, the relationship between PTG and other relevant constructs, such as openness, optimism and social support, is explored. Our systematic literature search identified 140 studies of which 19 fulfilled our inclusion criteria; most of them used the Post-Traumatic Growth Inventory. Results indicate that trauma survivors with PTSD exhibit more PTG than those without PTSD and that PTG can be intensified during the therapeutic process whereat it is unclear whether PTG is a desirable outcome of PTSD therapy. Positive correlations between PTG and PTSD are reported. For diagnosed populations, we could not find strong evidence of a quadratic relationship between PTG and PTSD, although some studies support this hypothesis. Findings regarding the association of PTG with psychological variables are heterogeneous. Only one study focused on PTG as well as on biological variables (salivary cortisol) but did not discuss possible links between these two so far unconnected research fields in PTSD. Copyright © 2015 John Wiley & Sons, Ltd. Trauma survivors with PTSD develop more PTG than those without PTSD, it remains unclear whether PTSD and PTG are curvilinearly related. PTG can be enhanced through PTSD therapy, nevertheless one must not assume that PTG is a favorable treatment outcome since we do not know if the development of PTG during therapy promotes
Growth of cortical neuronal network in vitro: Modeling and analysis

International Nuclear Information System (INIS)

Lai, P.-Y.; Jia, L. C.; Chan, C. K.

2006-01-01

We present a detailed analysis and theoretical growth models to account for recent experimental data on the growth of cortical neuronal networks in vitro [Phys. Rev. Lett. 93, 088101 (2004)]. The experimentally observed synchronized firing frequency of a well-connected neuronal network is shown to be proportional to the mean network connectivity. The growth of the network is consistent with the model of an early enhanced growth of connection, but followed by a retarded growth once the synchronized cluster is formed. Microscopic models with dominant excluded volume interactions are consistent with the observed exponential decay of the mean connection probability as a function of the mean network connectivity. The biological implications of the growth model are also discussed
The necessity of connection structures in neural models of variable binding.

Science.gov (United States)

van der Velde, Frank; de Kamps, Marc

2015-08-01

In his review of neural binding problems, Feldman (Cogn Neurodyn 7:1-11, 2013) addressed two types of models as solutions of (novel) variable binding. The one type uses labels such as phase synchrony of activation. The other ('connectivity based') type uses dedicated connections structures to achieve novel variable binding. Feldman argued that label (synchrony) based models are the only possible candidates to handle novel variable binding, whereas connectivity based models lack the flexibility required for that. We argue and illustrate that Feldman's analysis is incorrect. Contrary to his conclusion, connectivity based models are the only viable candidates for models of novel variable binding because they are the only type of models that can produce behavior. We will show that the label (synchrony) based models analyzed by Feldman are in fact examples of connectivity based models. Feldman's analysis that novel variable binding can be achieved without existing connection structures seems to result from analyzing the binding problem in a wrong frame of reference, in particular in an outside instead of the required inside frame of reference. Connectivity based models can be models of novel variable binding when they possess a connection structure that resembles a small-world network, as found in the brain. We will illustrate binding with this type of model with episode binding and the binding of words, including novel words, in sentence structures.
A data-model synthesis to explain variability in calcification observed during a CO2 perturbation mesocosm experiment

Science.gov (United States)

Krishna, Shubham; Schartau, Markus

2017-04-01

The effect of ocean acidification on growth and calcification of the marine algae Emiliania huxleyi was investigated in a series of mesocosm experiments where enclosed water volumes that comprised a natural plankton community were exposed to different carbon dioxide (CO2) concentrations. Calcification rates observed during those experiments were found to be highly variable, even among replicate mesocosms that were subject to similar CO2 perturbations. Here, data from an ocean acidification mesocosm experiment are reanalysed with an optimality-based dynamical plankton model. According to our model approach, cellular calcite formation is sensitive to variations in CO2 at the organism level. We investigate the temporal changes and variability in observations, with a focus on resolving observed differences in total alkalinity and particulate inorganic carbon (PIC). We explore how much of the variability in the data can be explained by variations of the initial conditions and by the level of CO2 perturbation. Nine mesocosms of one experiment were sorted into three groups of high, medium, and low calcification rates and analysed separately. The spread of the three optimised ensemble model solutions captures most of the observed variability. Our results show that small variations in initial abundance of coccolithophores and the prevailing physiological acclimation states generate differences in calcification that are larger than those induced by ocean acidification. Accordingly, large deviations between optimal mass flux estimates of carbon and of nitrogen are identified even between mesocosms that were subject to similar ocean acidification conditions. With our model-based data analysis we document how an ocean acidification response signal in calcification can be disentangled from the observed variability in PIC.
Balanced growth path solutions of a Boltzmann mean field game model for knowledge growth

KAUST Repository

Burger, Martin

2016-11-18

In this paper we study balanced growth path solutions of a Boltzmann mean field game model proposed by Lucas and Moll [15] to model knowledge growth in an economy. Agents can either increase their knowledge level by exchanging ideas in learning events or by producing goods with the knowledge they already have. The existence of balanced growth path solutions implies exponential growth of the overall production in time. We prove existence of balanced growth path solutions if the initial distribution of individuals with respect to their knowledge level satisfies a Pareto-tail condition. Furthermore we give first insights into the existence of such solutions if in addition to production and knowledge exchange the knowledge level evolves by geometric Brownian motion.

Modeling the effects of ozone on soybean growth and yield.

Science.gov (United States)

Kobayashi, K; Miller, J E; Flagler, R B; Heck, W W

1990-01-01

A simple mechanistic model was developed based on an existing growth model in order to address the mechanisms of the effects of ozone on growth and yield of soybean [Glycine max. (L.) Merr. 'Davis'] and interacting effects of other environmental stresses. The model simulates daily growth of soybean plants using environmental data including shortwave radiation, temperature, precipitation, irrigation and ozone concentration. Leaf growth, dry matter accumulation, water budget, nitrogen input and seed growth linked to senescence and abscission of leaves are described in the model. The effects of ozone are modeled as reduced photosynthate production and accelerated senescence. The model was applied to the open-top chamber experiments in which soybean plants were exposed to ozone under two levels of soil moisture regimes. After calibrating the model to the growth data and seed yield, goodness-of-fit of the model was tested. The model fitted well for top dry weight in the vegetative growth phase and also at maturity. The effect of ozone on seen yield was also described satisfactorily by the model. The simulation showed apparent interaction between the effect of ozone and soil moisture stress on the seed yield. The model revealed that further work is needed concerning the effect of ozone on the senescence process and the consequences of alteration of canopy microclimate by the open-top chambers.
A Non-Gaussian Spatial Generalized Linear Latent Variable Model

KAUST Repository

Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.

2012-01-01

We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.
A Non-Gaussian Spatial Generalized Linear Latent Variable Model

KAUST Repository

Irincheeva, Irina

2012-08-03

We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.
Multimodel ensembles of wheat growth

DEFF Research Database (Denmark)

Martre, Pierre; Wallach, Daniel; Asseng, Senthold

2015-01-01

, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24...
Plant Growth Modeling Using L-System Approach and Its Visualization

Directory of Open Access Journals (Sweden)

Atris Suyantohadi

2011-05-01

Full Text Available The visualizationof plant growth modeling using computer simulation has rarely been conducted with Lindenmayer System (L-System approach. L-System generally has been used as framework for improving and designing realistic modeling on plant growth. It is one kind of tools for representing plant growth based on grammar sintax and mathematic formulation. This research aimed to design modeling and visualizing plant growth structure generated using L-System. The environment on modeling design used three dimension graphic on standart OpenGL format. The visualization on system design has been developed by some of L-System grammar, and the output graphic on three dimension reflected on plant growth as a virtual plant growth system. Using some of samples on grammar L-System rules for describing of the charaterictics of plant growth, the visualization of structure on plant growth has been resulted and demonstrated.
Error analysis in predictive modelling demonstrated on mould data.

Science.gov (United States)

Baranyi, József; Csernus, Olívia; Beczner, Judit

2014-01-17

The purpose of this paper was to develop a predictive model for the effect of temperature and water activity on the growth rate of Aspergillus niger and to determine the sources of the error when the model is used for prediction. Parallel mould growth curves, derived from the same spore batch, were generated and fitted to determine their growth rate. The variances of replicate ln(growth-rate) estimates were used to quantify the experimental variability, inherent to the method of determining the growth rate. The environmental variability was quantified by the variance of the respective means of replicates. The idea is analogous to the "within group" and "between groups" variability concepts of ANOVA procedures. A (secondary) model, with temperature and water activity as explanatory variables, was fitted to the natural logarithm of the growth rates determined by the primary model. The model error and the experimental and environmental errors were ranked according to their contribution to the total error of prediction. Our method can readily be applied to analysing the error structure of predictive models of bacterial growth models, too. © 2013.
Variability of Pinus halepensis Mill. Essential Oils and Their Antioxidant Activities Depending on the Stage of Growth During Vegetative Cycle.

Science.gov (United States)

Djerrad, Zineb; Djouahri, Abderrahmane; Kadik, Leila

2017-04-01

The impact of growth stages during vegetative cycle (B 0 - B 5 ) on chemical composition and antioxidant activities of Pinus halepensis Mill. needles essential oils was investigated for the first time. GC and GC/MS analyses pointed to a quantitative variability of components; terpene hydrocarbons derivatives, represented by α-pinene (8.5 - 12.9%), myrcene (17.5 - 21.6%), p-cymene (7.9 - 11.9%) and (Z)-β-caryophyllene (17.3 - 21.2%) as major components, decreased from 88.9% at B 0 growth stage to 66.9% at B 5 growth stage, whereas oxygenated derivatives, represented by caryophyllene oxide (5.4 - 12.6%) and terpinen-4-ol (0.4 - 3.3%) as major components, increased from 7% at B 0 growth stage to 28.4% at B 5 growth stage. Furthermore, our findings showed that essential oil of P. halepensis needles collected at B 5 growth stage possess higher antioxidant activities by four different testing systems than those collected at B 0 - B 4 growth stages. This highlighted variability led to conclude that we should select essential oils to be investigated carefully depending on growth stage, in order to have the highest effectiveness of essential oil in terms of biological activities for human health purposes. © 2017 Wiley-VHCA AG, Zurich, Switzerland.
Non-rigid image registration using bone growth model

DEFF Research Database (Denmark)

Bro-Nielsen, Morten; Gramkow, Claus; Kreiborg, Sven

1997-01-01

Non-rigid registration has traditionally used physical models like elasticity and fluids. These models are very seldom valid models of the difference between the registered images. This paper presents a non-rigid registration algorithm, which uses a model of bone growth as a model of the change...... between time sequence images of the human mandible. By being able to register the images, this paper at the same time contributes to the validation of the growth model, which is based on the currently available medical theories and knowledge...
China and India: Openness, Trade and Effects on Economic Growth

Directory of Open Access Journals (Sweden)

Marelli, Enrico

2011-06-01

Full Text Available The purpose of this paper is to analyse the economic growth of China and India in terms of their integration in the global economy. We begin with a discussion of some stylized facts concerning their recent economic growth, the most significant institutional reforms, with particular reference to trade relations, and their impact on their economic development. We then propose a descriptive analysis of economic growth, opening up of the economies and trade specialisation, by comparing the features and trends of the two countries (by considering trade and foreign direct investment data. We have also estimated some econometric relations between economic growth and trade/openness, with the addition of control variables (such as the gross fixed capital formation. We initially used a panel data model for the two countries, to be estimated with fixed effects; to test for reverse causality, we re-estimated the fixed effects model by 2SLS (with the inclusion of specific instrumental variables. The effect on economic growth (in terms of GDP per capita of our variables of interest - Openness and FDI - remains positive and statistically significant in all specifications, which confirms our findings even if we treat these variables as endogenous variables. The results prove the positive growth effects, for the two countries, of opening up and integrating in the world economy. Note that the robust growth of these two "giants" has contained the initial impact of the recent global crisis and is now sustaining the recovery of the entire world economy. Other policy relevant implications are discussed in the concluding section.
Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity.

Science.gov (United States)

Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

2011-10-31

The calibration performance of partial least squares for one response variable (PLS1) can be improved by elimination of uninformative variables. Many methods are based on so-called predictive variable properties, which are functions of various PLS-model parameters, and which may change during the variable reduction process. In these methods variable reduction is made on the variables ranked in descending order for a given variable property. The methods start with full spectrum modelling. Iteratively, until a specified number of remaining variables is reached, the variable with the smallest property value is eliminated; a new PLS model is calculated, followed by a renewed ranking of the variables. The Stepwise Variable Reduction methods using Predictive-Property-Ranked Variables are denoted as SVR-PPRV. In the existing SVR-PPRV methods the PLS model complexity is kept constant during the variable reduction process. In this study, three new SVR-PPRV methods are proposed, in which a possibility for decreasing the PLS model complexity during the variable reduction process is build in. Therefore we denote our methods as PPRVR-CAM methods (Predictive-Property-Ranked Variable Reduction with Complexity Adapted Models). The selective and predictive abilities of the new methods are investigated and tested, using the absolute PLS regression coefficients as predictive property. They were compared with two modifications of existing SVR-PPRV methods (with constant PLS model complexity) and with two reference methods: uninformative variable elimination followed by either a genetic algorithm for PLS (UVE-GA-PLS) or an interval PLS (UVE-iPLS). The performance of the methods is investigated in conjunction with two data sets from near-infrared sources (NIR) and one simulated set. The selective and predictive performances of the variable reduction methods are compared statistically using the Wilcoxon signed rank test. The three newly developed PPRVR-CAM methods were able to retain
[Effects of growth hormone treatment on anthropometrics, metabolic risk, and body composition variables in small for gestational age patients].

Science.gov (United States)

Aurensanz Clemente, Esther; Samper Villagrasa, Pilar; Ayerza Casas, Ariadna; Ruiz Frontera, Pablo; Bueno Lozano, Olga; Moreno Aznar, Luis Alberto; Bueno Lozano, Gloria

2017-05-01

Small for gestational age (SGA) children without catch-up growth can benefit from treatment with growth hormone (rhGH). However, they should be monitored very closely because they are at increased risk of metabolic syndrome. A group of 28 SGA children with a mean age of 8.79 years and undergoing treatment with rhGH were selected for evaluation. Over the course of 4 years, an annual evaluation was performed on the anthropometric variables (weight, height, body mass index [BMI], growth rate, blood pressure and waist perimeter), metabolic risk variables (glycaemia, glycosylated haemoglobin, cholesterol ratio, insulinaemia, insulin-like growth factor 1[IGF1], IGF binding protein-3 [IGFBP-3], IGF1/IGFBP3 ratio, and HOMA index), and body composition variables. Treatment with rhGH was associated with a significant increase in height (-2.76±.11 SD to -1.53±.17 SD, P=.000), weight (-1.50±.09 SD to -1.21±.13 SD; P=.016), and growth rate (-1.43±.35 SD to .41±.41 SD; P=.009), without a corresponding change in the BMI. Insulinaemia (9.33±1.93mU/ml to 16.55±1.72mU/ml; P=.044) and the HOMA index (3.63±.76 to 6.43±.67; P=.042) increased, approaching insulin resistance levels. No changes were observed in the lipid profile. Body composition changes were observed, with a significant increase in lean mass (73.19±1.26 to 78.74±1.31; P=.037), and a reduction of fat mass (26.81±1.26 to 21.26±1.31; P=.021). Treatment with rhGH is effective for improving anthropometric variables in SGA patients who have not experienced a catch-up growth. It also produces changes in body composition, which may lead to a reduction in risk of metabolic syndrome. However, some insulin resistance was observed. It is important to follow up this patient group in order to find out whether these changes persist into adulthood. Copyright © 2016 Asociación Española de Pediatría. Publicado por Elsevier España, S.L.U. All rights reserved.
IMPACT OF TRADE OPENNESS ON OUTPUT GROWTH: CO INTEGRATION AND ERROR CORRECTION MODEL APPROACH

Directory of Open Access Journals (Sweden)

Asma Arif

2012-01-01

Full Text Available This study analyzed the long run relationship between trade openness and output growth for Pakistan using annual time series data for 1972-2010. This study follows the Engle and Granger co integration analysis and error correction approach to analyze the long run relationship between the two variables. The Error Correction Term (ECT for output growth and trade openness is significant at 5% level of significance and indicates a positive long run relation between the variables. This study has also analyzed the causality between trade openness and output growth by using granger causality test. The results of granger causality show that there is a bi-directional significant relationship between trade openness and economic growth.
Global terrestrial isoprene emission models: sensitivity to variability in climate and vegetation

Directory of Open Access Journals (Sweden)

A. Arneth

2011-08-01

Full Text Available Due to its effects on the atmospheric lifetime of methane, the burdens of tropospheric ozone and growth of secondary organic aerosol, isoprene is central among the biogenic compounds that need to be taken into account for assessment of anthropogenic air pollution-climate change interactions. Lack of process-understanding regarding leaf isoprene production as well as of suitable observations to constrain and evaluate regional or global simulation results add large uncertainties to past, present and future emissions estimates. Focusing on contemporary climate conditions, we compare three global isoprene models that differ in their representation of vegetation and isoprene emission algorithm. We specifically aim to investigate the between- and within model variation that is introduced by varying some of the models' main features, and to determine which spatial and/or temporal features are robust between models and different experimental set-ups. In their individual standard configurations, the models broadly agree with respect to the chief isoprene sources and emission seasonality, with maximum monthly emission rates around 20–25 Tg C, when averaged by 30-degree latitudinal bands. They also indicate relatively small (approximately 5 to 10 % around the mean interannual variability of total global emissions. The models are sensitive to changes in one or more of their main model components and drivers (e.g., underlying vegetation fields, climate input which can yield increases or decreases in total annual emissions of cumulatively by more than 30 %. Varying drivers also strongly alters the seasonal emission pattern. The variable response needs to be interpreted in view of the vegetation emission capacities, as well as diverging absolute and regional distribution of light, radiation and temperature, but the direction of the simulated emission changes was not as uniform as anticipated. Our results highlight the need for modellers to evaluate their
Spatiotemporal Dynamics and Spatial Determinants of Urban Growth in Suzhou, China

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Ling Zhang

2017-03-01

Full Text Available This paper analyzes the spatiotemporal dynamics of urban growth and models its spatial determinants in China through a case study of Suzhou, a rapidly industrializing and globalizing city. We conducted spatial analysis on land use data derived from multi-temporal remote sensing images of Suzhou from 1986 to 2008. Three urban growth types, namely infilling, edge-expansion, and leapfrog, were identified. We used landscape metrics to quantify the temporal trend of urban growth in Suzhou. During these 22 years, Suzhou’s urbanization changed from bottom-up rural urbanization to city-based top-down urban expansion. The underlying mechanism changed from TVE (town village enterprise driven rural industrialization to FDI (foreign direct investment driven development zone fever. Furthermore, we employed both global and local logistic regressions to model the probability of urban land conversion against a set of spatial variables. The global logistic regression model found the significance of proximity, neighborhood conditions, and socioeconomic factors. The logistic geographically weighted regression (GWR model improved the global regression model with better model goodness-of-fit and higher prediction accuracy. More importantly, the local parameter estimates of variables enabled us to exam spatial variations of the influences of variables on urban growth in Suzhou.
Growth and variability in health plan premiums in the individual insurance market before the Affordable Care Act.

Science.gov (United States)

Gruber, Jonathan

2014-06-01

Before we can evaluate the impact of the Affordable Care Act on health insurance premiums in the individual market, it is critical to understand the pricing trends of these premiums before the implementation of the law. Using rates of increase in the individual insurance market collected from state regulators, this issue brief documents trends in premium growth in the pre-ACA period. From 2008 to 2010, premiums grew by 10 percent or more per year. This growth was also highly variable across states, and even more variable across insurance plans within states. The study suggests that evaluating trends in premiums requires looking across a broad array of states and plans, and that policymakers must examine how present and future changes in premium rates compare with the more than 10 percent per year premium increases in the years preceding health reform.
A mathematical model of microalgae growth in cylindrical photobioreactor

Science.gov (United States)

Bakeri, Noorhadila Mohd; Jamaian, Siti Suhana

2017-08-01

Microalgae are unicellular organisms, which exist individually or in chains or groups but can be utilized in many applications. Researchers have done various efforts in order to increase the growth rate of microalgae. Microalgae have a potential as an effective tool for wastewater treatment, besides as a replacement for natural fuel such as coal and biodiesel. The growth of microalgae can be estimated by using Geider model, which this model is based on photosynthesis irradiance curve (PI-curve) and focused on flat panel photobioreactor. Therefore, in this study a mathematical model for microalgae growth in cylindrical photobioreactor is proposed based on the Geider model. The light irradiance is the crucial part that affects the growth rate of microalgae. The absorbed photon flux will be determined by calculating the average light irradiance in a cylindrical system illuminated by unidirectional parallel flux and considering the cylinder as a collection of differential parallelepipeds. Results from this study showed that the specific growth rate of microalgae increases until the constant level is achieved. Therefore, the proposed mathematical model can be used to estimate the rate of microalgae growth in cylindrical photobioreactor.
Modeling of carbon sequestration in coal-beds: A variable saturated simulation

International Nuclear Information System (INIS)

Liu Guoxiang; Smirnov, Andrei V.

2008-01-01

Storage of carbon dioxide in deep coal seams is a profitable method to reduce the concentration of green house gases in the atmosphere while the methane as a byproduct can be extracted during carbon dioxide injection into the coal seam. In this procedure, the key element is to keep carbon dioxide in the coal seam without escaping for a long term. It is depended on many factors such as properties of coal basin, fracture state, phase equilibrium, etc., especially the porosity, permeability and saturation of the coal seam. In this paper, a variable saturation model was developed to predict the capacity of carbon dioxide sequestration and coal-bed methane recovery. This variable saturation model can be used to track the saturation variability with the partial pressures change caused by carbon dioxide injection. Saturation variability is a key factor to predict the capacity of carbon dioxide storage and methane recovery. Based on this variable saturation model, a set of related variables including capillary pressure, relative permeability, porosity, coupled adsorption model, concentration and temperature equations were solved. From results of the simulation, historical data agree with the variable saturation model as well as the adsorption model constructed by Langmuir equations. The Appalachian basin, as an example, modeled the carbon dioxide sequestration in this paper. The results of the study and the developed models can provide the projections for the CO 2 sequestration and methane recovery in coal-beds within different regional specifics
Salmonella Typhimurium and Staphylococcus aureus dynamics in/on variable (micro)structures of fish-based model systems at suboptimal temperatures.

Science.gov (United States)

Baka, Maria; Verheyen, Davy; Cornette, Nicolas; Vercruyssen, Stijn; Van Impe, Jan F

2017-01-02

The limited knowledge concerning the influence of food (micro)structure on microbial dynamics decreases the accuracy of the developed predictive models, as most studies have mainly been based on experimental data obtained in liquid microbiological media or in/on real foods. The use of model systems has a great potential when studying this complex factor. Apart from the variability in (micro)structural properties, model systems vary in compositional aspects, as a consequence of their (micro)structural variation. In this study, different experimental food model systems, with compositional and physicochemical properties similar to fish patés, are developed to study the influence of food (micro)structure on microbial dynamics. The microbiological safety of fish products is of major importance given the numerous cases of salmonellosis and infections attributed to staphylococcus toxins. The model systems understudy represent food (micro)structures of liquids, aqueous gels, emulsions and gelled emulsions. The growth/inactivation dynamics and a modelling approach of combined growth and inactivation of Salmonella Typhimurium and Staphylococcus aureus, related to fish products, are investigated in/on these model systems at temperatures relevant to fish products' common storage (4°C) and to abuse storage temperatures (8 and 12°C). ComBase (http://www.combase.cc/) predictions compared with the maximum specific growth rate (μ max ) values estimated by the Baranyi and Roberts model in the current study indicated that the (micro)structure influences the microbial dynamics. Overall, ComBase overestimated microbial growth at the same pH, a w and storage temperature. Finally, the storage temperature had also an influence on how much each model system affected the microbial dynamics. Copyright Â© 2016. Published by Elsevier B.V.
Modeling the growth of individuals in plant populations: local density variation in a strand population of Xanthium strumarium (Asteraceae).

Science.gov (United States)

Weiner, J; Kinsman, S; Williams, S

1998-11-01

We studied the growth of individual Xanthium strumarium plants growing at four naturally occurring local densities on a beach in Maine: (1) isolated plants, (2) pairs of plants ≤1 cm apart, (3) four plants within 4 cm of each other, and (4) discrete dense clumps of 10-39 plants. A combination of nondestructive measurements every 2 wk and parallel calibration harvests provided very good estimates of the growth in aboveground biomass of over 400 individual plants over 8 wk and afforded the opportunity to fit explicit growth models to 293 of them. There was large individual variation in growth and resultant size within the population and within all densities. Local crowding played a role in determining plant size within the population: there were significant differences in final size between all densities except pairs and quadruples, which were almost identical. Overall, plants growing at higher densities were more variable in growth and final size than plants growing at lower densities, but this was due to increased variation among groups (greater variation in local density and/or greater environmental heterogeneity), not to increased variation within groups. Thus, there was no evidence of size asymmetric competition in this population. The growth of most plants was close to exponential over the study period, but half the plants were slightly better fit by a sigmoidal (logistic) model. The proportion of plants better fit by the logistic model increased with density and with initial plant size. The use of explicit growth models over several growth intervals to describe stand development can provide more biological content and more statistical power than "growth-size" methods that analyze growth intervals separately.
Localisation in a Growth Model with Interaction

Science.gov (United States)

Costa, M.; Menshikov, M.; Shcherbakov, V.; Vachkovskaia, M.

2018-05-01

This paper concerns the long term behaviour of a growth model describing a random sequential allocation of particles on a finite cycle graph. The model can be regarded as a reinforced urn model with graph-based interaction. It is motivated by cooperative sequential adsorption, where adsorption rates at a site depend on the configuration of existing particles in the neighbourhood of that site. Our main result is that, with probability one, the growth process will eventually localise either at a single site, or at a pair of neighbouring sites.

Housing and Economic Growth Nexus in Nigeria: Data-Based Evidence

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Andy Titus OKWU

2017-06-01

Full Text Available Housing is considered as one of the cardinalmeasures of the state of an economy. This paperemployed data-based evidence to explore housingsector-economic growth relationship in Nigeriaduring 1980-2015. Choice variables were realestate business services (REBS, building constructioninvestments (BCI, property rights index(PRI and human labor (L engaged in the sector.Anchored on perceived interactions amongthe variables, articulated conceptual modelpreceded an analytic model modifi ed from theendogenous growth model of economic theory.Graphical and econometric techniques were employedto analyze the data sets on the variablesfor trends in time series values of the variables;and the effects of the housing sector variableson growth of the economy. The results showedthat housing services delivery had long-run relationshipand signifi cantly spurred growth of theeconomy. Further, housing services delivery andgrowth of the economy had high speed adjustmentcoeffi cient to long-run equilibrium growthpath under stable structural housing sector servicesdelivery and appropriate human labor mixparticipation. Therefore, the paper concludedthat housing services enhanced growth of theeconomy, and emphasized the need for appropriatehuman, capital and fi nancial policies forthe sector to engender sustainable growth anddevelopment of the Nigerian economy.
Mediterranean climate modelling: variability and climate change scenarios

International Nuclear Information System (INIS)

Somot, S.

2005-12-01

Air-sea fluxes, open-sea deep convection and cyclo-genesis are studied in the Mediterranean with the development of a regional coupled model (AORCM). It accurately simulates these processes and their climate variabilities are quantified and studied. The regional coupling shows a significant impact on the number of winter intense cyclo-genesis as well as on associated air-sea fluxes and precipitation. A lower inter-annual variability than in non-coupled models is simulated for fluxes and deep convection. The feedbacks driving this variability are understood. The climate change response is then analysed for the 21. century with the non-coupled models: cyclo-genesis decreases, associated precipitation increases in spring and autumn and decreases in summer. Moreover, a warming and salting of the Mediterranean as well as a strong weakening of its thermohaline circulation occur. This study also concludes with the necessity of using AORCMs to assess climate change impacts on the Mediterranean. (author)
Growth rate in the dynamical dark energy models

International Nuclear Information System (INIS)

Avsajanishvili, Olga; Arkhipova, Natalia A.; Samushia, Lado; Kahniashvili, Tina

2014-01-01

Dark energy models with a slowly rolling cosmological scalar field provide a popular alternative to the standard, time-independent cosmological constant model. We study the simultaneous evolution of background expansion and growth in the scalar field model with the Ratra-Peebles self-interaction potential. We use recent measurements of the linear growth rate and the baryon acoustic oscillation peak positions to constrain the model parameter α that describes the steepness of the scalar field potential. (orig.)
Growth rate in the dynamical dark energy models.

Science.gov (United States)

Avsajanishvili, Olga; Arkhipova, Natalia A; Samushia, Lado; Kahniashvili, Tina

Dark energy models with a slowly rolling cosmological scalar field provide a popular alternative to the standard, time-independent cosmological constant model. We study the simultaneous evolution of background expansion and growth in the scalar field model with the Ratra-Peebles self-interaction potential. We use recent measurements of the linear growth rate and the baryon acoustic oscillation peak positions to constrain the model parameter [Formula: see text] that describes the steepness of the scalar field potential.
Translating hydrologically-relevant variables from the ice sheet model SICOPOLIS to the Greenland Analog Project hydrologic modeling domain

Science.gov (United States)

Vallot, Dorothée; Applegate, Patrick; Pettersson, Rickard

2013-04-01

Projecting future climate and ice sheet development requires sophisticated models and extensive field observations. Given the present state of our knowledge, it is very difficult to say what will happen with certainty. Despite the ongoing increase in atmospheric greenhouse gas concentrations, the possibility that a new ice sheet might form over Scandinavia in the far distant future cannot be excluded. The growth of a new Scandinavian Ice Sheet would have important consequences for buried nuclear waste repositories. The Greenland Analogue Project, initiated by the Swedish Nuclear Fuel and Waste Management Company (SKB), is working to assess the effects of a possible future ice sheet on groundwater flow by studying a constrained domain in Western Greenland by field measurements (including deep bedrock drilling in front of the ice sheet) combined with numerical modeling. To address the needs of the GAP project, we interpolated results from an ensemble of ice sheet model runs to the smaller and more finely resolved modeling domain used in the GAP project's hydrologic modeling. Three runs have been chosen with three fairly different positive degree-day factors among those that reproduced the modern ice margin at the borehole position. The interpolated results describe changes in hydrologically-relevant variables over two time periods, 115 ka to 80 ka, and 20 ka to 1 ka. In the first of these time periods, the ice margin advances over the model domain; in the second time period, the ice margin retreats over the model domain. The spatially-and temporally dependent variables that we treated include the ice thickness, basal melting rate, surface mass balance, basal temperature, basal thermal regime (frozen or thawed), surface temperature, and basal water pressure. The melt flux is also calculated.
Predictive and Descriptive CoMFA Models: The Effect of Variable Selection.

Science.gov (United States)

Sepehri, Bakhtyar; Omidikia, Nematollah; Kompany-Zareh, Mohsen; Ghavami, Raouf

2018-01-01

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
Studying historical occupational careers with multilevel growth models

Directory of Open Access Journals (Sweden)

Wiebke Schulz

2010-10-01

Full Text Available In this article we propose to study occupational careers with historical data by using multilevel growth models. Historical career data are often characterized by a lack of information on the timing of occupational changes and by different numbers of observations of occupations per individual. Growth models can handle these specificities, whereas standard methods, such as event history analyses can't. We illustrate the use of growth models by studying career success of men and women, using data from the Historical Sample of the Netherlands. The results show that the method is applicable to male careers, but causes trouble when analyzing female careers.
Tax Evasion in a Model of Endogenous Growth

OpenAIRE

Been-Lon Chen

2003-01-01

This paper integrates tax evasion into a standard AK growth model with public capital. In the model, the government optimizes the tax rate, while individuals optimize tax evasion. It studies tax rate, tax evasion and economic growth, and compares them with otherwise identical economies except those without tax evasion. It inquires into the effects of three government policies on tax rate, tax evasion, and economic growth. It finds that an increase in both unit cost of tax evasion and punishme...
Cellular Automaton Modeling of Dendritic Growth Using a Multi-grid Method

International Nuclear Information System (INIS)

Natsume, Y; Ohsasa, K

2015-01-01

A two-dimensional cellular automaton model with a multi-grid method was developed to simulate dendritic growth. In the present model, we used a triple-grid system for temperature, solute concentration and solid fraction fields as a new approach of the multi-grid method. In order to evaluate the validity of the present model, we carried out simulations of single dendritic growth, secondary dendrite arm growth, multi-columnar dendritic growth and multi-equiaxed dendritic growth. From the results of the grid dependency from the simulation of single dendritic growth, we confirmed that the larger grid can be used in the simulation and that the computational time can be reduced dramatically. In the simulation of secondary dendrite arm growth, the results from the present model were in good agreement with the experimental data and the simulated results from a phase-field model. Thus, the present model can quantitatively simulate dendritic growth. From the simulated results of multi-columnar and multi-equiaxed dendrites, we confirmed that the present model can perform simulations under practical solidification conditions. (paper)
Nonlinear Growth Models in M"plus" and SAS

Science.gov (United States)

Grimm, Kevin J.; Ram, Nilam

2009-01-01

Nonlinear growth curves or growth curves that follow a specified nonlinear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this article we describe how a variety of sigmoid curves can be fit using the M"plus" structural modeling program and the nonlinear…
Development and validation of a prediction model for measurement variability of lung nodule volumetry in patients with pulmonary metastases.

Science.gov (United States)

Hwang, Eui Jin; Goo, Jin Mo; Kim, Jihye; Park, Sang Joon; Ahn, Soyeon; Park, Chang Min; Shin, Yeong-Gil

2017-08-01

To develop a prediction model for the variability range of lung nodule volumetry and validate the model in detecting nodule growth. For model development, 50 patients with metastatic nodules were prospectively included. Two consecutive CT scans were performed to assess volumetry for 1,586 nodules. Nodule volume, surface voxel proportion (SVP), attachment proportion (AP) and absolute percentage error (APE) were calculated for each nodule and quantile regression analyses were performed to model the 95% percentile of APE. For validation, 41 patients who underwent metastasectomy were included. After volumetry of resected nodules, sensitivity and specificity for diagnosis of metastatic nodules were compared between two different thresholds of nodule growth determination: uniform 25% volume change threshold and individualized threshold calculated from the model (estimated 95% percentile APE). SVP and AP were included in the final model: Estimated 95% percentile APE = 37.82 · SVP + 48.60 · AP-10.87. In the validation session, the individualized threshold showed significantly higher sensitivity for diagnosis of metastatic nodules than the uniform 25% threshold (75.0% vs. 66.0%, P = 0.004) CONCLUSION: Estimated 95% percentile APE as an individualized threshold of nodule growth showed greater sensitivity in diagnosing metastatic nodules than a global 25% threshold. • The 95 % percentile APE of a particular nodule can be predicted. • Estimated 95 % percentile APE can be utilized as an individualized threshold. • More sensitive diagnosis of metastasis can be made with an individualized threshold. • Tailored nodule management can be provided during nodule growth follow-up.
Variable-Structure Control of a Model Glider Airplane

Science.gov (United States)

Waszak, Martin R.; Anderson, Mark R.

2008-01-01

A variable-structure control system designed to enable a fuselage-heavy airplane to recover from spin has been demonstrated in a hand-launched, instrumented model glider airplane. Variable-structure control is a high-speed switching feedback control technique that has been developed for control of nonlinear dynamic systems.
Monitoring small pioneer trees in the forest-tundra ecotone: using multi-temporal airborne laser scanning data to model height growth.

Science.gov (United States)

Hauglin, Marius; Bollandsås, Ole Martin; Gobakken, Terje; Næsset, Erik

2017-12-08

Monitoring of forest resources through national forest inventory programmes is carried out in many countries. The expected climate changes will affect trees and forests and might cause an expansion of trees into presently treeless areas, such as above the current alpine tree line. It is therefore a need to develop methods that enable the inclusion of also these areas into monitoring programmes. Airborne laser scanning (ALS) is an established tool in operational forest inventories, and could be a viable option for monitoring tasks. In the present study, we used multi-temporal ALS data with point density of 8-15 points per m 2 , together with field measurements from single trees in the forest-tundra ecotone along a 1500-km-long transect in Norway. The material comprised 262 small trees with an average height of 1.78 m. The field-measured height growth was derived from height measurements at two points in time. The elapsed time between the two measurements was 4 years. Regression models were then used to model the relationship between ALS-derived variables and tree heights as well as the height growth. Strong relationships between ALS-derived variables and tree heights were found, with R 2 values of 0.93 and 0.97 for the two points in time. The relationship between the ALS data and the field-derived height growth was weaker, with R 2 values of 0.36-0.42. A cross-validation gave corresponding results, with root mean square errors of 19 and 11% for the ALS height models and 60% for the model relating ALS data to single-tree height growth.
Export and Economic Growth in the West Balkan Countries

Directory of Open Access Journals (Sweden)

Florentina Xhelili Krasniqi

2017-09-01

Full Text Available The aim of this paper is to explore the effects of exports and other variables (foreign direct investment, remittances, capital formation, and labour force on economic growth in West Balkan countries (Albania, Kosovo, Macedonia, Montenegro, Bosnia and Herzegovina and Serbia. This study utilizes a strongly balanced panel data over the 2005-2015 period for Western Balkan countries using the ordinary least squares method (OLS, ie Pooled regression model to evaluate the parameters. The relationship between export and economic growth has turned to be statistically significant and positively related for the countries under the study. Results also indicate the statistically significant positive relationship between economic growth and other variables included in the model such is remittances, capital formation, and labor. The relationship between economic growth and foreign direct investment has turned out to be statistically insignificant and negatively related.
Export and Economic Growth in the Case of the Manufacturing Industry: Panel Data Analysis of Developing Countries

Directory of Open Access Journals (Sweden)

Emine Kılavuz

2012-01-01

Full Text Available The correlation between growth in export and economic growth, which is called “Export-led Growth Hypothesis” in the literature, is still a current issue in both the theoretical and empirical literature. In the present study, the effect of different classifications of export and import on economic growth in 22 developing countries in the 1998–2006 period was tested based on two models, via panel data analysis. According to the results of the first model, the analysis of which included variables such as high and low-tech manufacturing industry exports, investment and population, it was found that only two variables, high-tech manufacturing industry export and investment, have a positive and significant effect on growth. In addition to the first model which included the analysis of all variables, the second model investigated the effect of high and low-tech manufacturing industry imports on growth. The findings revealed that only high-tech manufacturing industry export, investment and low-tech manufacturing industry import have a positive and significant effect on growth.
Effects of constant and cyclical thermal regimes on growth and feeding of juvenile cutthroat trout of variable sizes

Science.gov (United States)

Meeuwig, M.H.; Dunham, J.B.; Hayes, J.P.; Vinyard, G.L.

2004-01-01

The effects of constant (12, 18, and 24 A?C) and cyclical (daily variation of 15a??21 and 12a??24 A?C) thermal regimes on the growth and feeding of Lahontan cutthroat trout (Oncorhynchus clarki henshawi) of variable sizes were examined. Higher constant temperatures (i.e., 24 A?C) and more variable daily temperatures (i.e., 12a??24 A?C daily cycle) negatively affected growth rates. As fish mass increased (from 0.24 to 15.52 g) the effects of different thermal regimes on mass growth became more pronounced. Following 14 days exposure to the thermal regimes, feeding rates of individual fish were assessed during acute exposure (40 min) to test temperatures of 12, 18, and 24 A?C. Feeding rate was depressed during acute exposure to 24 A?C, but was not significantly affected by the preceding thermal regime. Our results indicate that even brief daily exposure to higher temperatures (e.g., 24 A?C) can have considerable sublethal effects on cutthroat trout, and that fish size should be considered when examining the effects of temperature.
Computational Fluid Dynamics Modeling of a Supersonic Nozzle and Integration into a Variable Cycle Engine Model

Science.gov (United States)

Connolly, Joseph W.; Friedlander, David; Kopasakis, George

2015-01-01

This paper covers the development of an integrated nonlinear dynamic simulation for a variable cycle turbofan engine and nozzle that can be integrated with an overall vehicle Aero-Propulso-Servo-Elastic (APSE) model. A previously developed variable cycle turbofan engine model is used for this study and is enhanced here to include variable guide vanes allowing for operation across the supersonic flight regime. The primary focus of this study is to improve the fidelity of the model's thrust response by replacing the simple choked flow equation convergent-divergent nozzle model with a MacCormack method based quasi-1D model. The dynamic response of the nozzle model using the MacCormack method is verified by comparing it against a model of the nozzle using the conservation element/solution element method. A methodology is also presented for the integration of the MacCormack nozzle model with the variable cycle engine.
Partitioning of the variance in the growth parameters of Erwinia carotovora on vegetable products.

Science.gov (United States)

Shorten, P R; Membré, J-M; Pleasants, A B; Kubaczka, M; Soboleva, T K

2004-06-01

The objective of this paper was to estimate and partition the variability in the microbial growth model parameters describing the growth of Erwinia carotovora on pasteurised and non-pasteurised vegetable juice from laboratory experiments performed under different temperature-varying conditions. We partitioned the model parameter variance and covariance components into effects due to temperature profile and replicate using a maximum likelihood technique. Temperature profile and replicate were treated as random effects and the food substrate was treated as a fixed effect. The replicate variance component was small indicating a high level of control in this experiment. Our analysis of the combined E. carotovora growth data sets used the Baranyi primary microbial growth model along with the Ratkowsky secondary growth model. The variability in the microbial growth parameters estimated from these microbial growth experiments is essential for predicting the mean and variance through time of the E. carotovora population size in a product supply chain and is the basis for microbiological risk assessment and food product shelf-life estimation. The variance partitioning made here also assists in the management of optimal product distribution networks by identifying elements of the supply chain contributing most to product variability. Copyright 2003 Elsevier B.V.
Analytical Model for LLC Resonant Converter With Variable Duty-Cycle Control

DEFF Research Database (Denmark)

Shen, Yanfeng; Wang, Huai; Blaabjerg, Frede

2016-01-01

are identified and discussed. The proposed model enables a better understanding of the operation characteristics and fast parameter design of the LLC converter, which otherwise cannot be achieved by the existing simulation based methods and numerical models. The results obtained from the proposed model......In LLC resonant converters, the variable duty-cycle control is usually combined with a variable frequency control to widen the gain range, improve the light-load efficiency, or suppress the inrush current during start-up. However, a proper analytical model for the variable duty-cycle controlled LLC...... converter is still not available due to the complexity of operation modes and the nonlinearity of steady-state equations. This paper makes the efforts to develop an analytical model for the LLC converter with variable duty-cycle control. All possible operation models and critical operation characteristics...
Does more energy consumption bolster economic growth? An application of the nonlinear threshold regression model

International Nuclear Information System (INIS)

Huang, B.-N.; Hwang, M.J.; Yang, C.W.

2008-01-01

This paper separates data extending from 1971 to 2002 into the energy crisis period (1971-1980) and the post-energy crisis period (1981-2000) for 82 countries. The cross-sectional data (yearly averages) in these two periods are used to investigate the nonlinear relationships between energy consumption growth and economic growth when threshold variables are used. If threshold variables are higher than certain optimal threshold levels, there is either no significant relationship or else a significant negative relationship between energy consumption and economic growth. However, when these threshold variables are lower than certain optimal levels, there is a significant positive relationship between the two. In 48 out of the 82 countries studied, none of the four threshold variables is found to be higher than the optimal levels. It is inferred that these 48 countries should adopt a more aggressive energy policy. As for the other 34 countries, at least one threshold variable is higher than the optimal threshold level and thus these countries should adopt energy policies with varying degrees of conservation based on the number of threshold variables that are higher than the optimal threshold levels

Stochastic modelling of avascular tumour growth and therapy

International Nuclear Information System (INIS)

Sahoo, S; Sahoo, A; Shearer, S F C

2011-01-01

In this paper, a generalized stochastic model for the growth of avascular tumours is presented. This model captures the dynamical evolution of avascular tumour cell subpopulations by incorporating Gaussian white noise into the growth rate of the mitotic function. This work generalizes the deterministic model proposed by Sherratt and Chaplain (2001 J. Math. Biol. 43 291) where they formulated a tumour model in an in vivo setting, in terms of continuum densities of proliferating, quiescent and necrotic cells. Detailed simulations of our model show that the inclusion of Gaussian noise in the original model of Sherratt and Chaplain substantially distorts the overall structure of the density profiles in addition to reducing the speed of tumour growth. Within this stochastic carcinogenesis framework the action of therapy is also investigated by replacing Gaussian white noise with a therapy term. We compare a constant therapy protocol with a logarithmic time-dependent protocol. Our results predict that a logarithmic therapy is more effective than the constant therapy protocol.
Surface-bounded growth modeling applied to human mandibles

DEFF Research Database (Denmark)

Andresen, Per Rønsholt

1999-01-01

This thesis presents mathematical and computational techniques for three dimensional growth modeling applied to human mandibles. The longitudinal shape changes make the mandible a complex bone. The teeth erupt and the condylar processes change direction, from pointing predominantly backward...... of the common features. 3.model the process that moves the matched points (growth modeling). A local shape feature called crest line has shown itself to be structurally stable on mandibles. Registration of crest lines (from different mandibles) results in a sparse deformation field, which must be interpolated...... old mandible based on the 3 month old scan. When using successively more recent scans as basis for the model the error drops to 2.0 mm for the 11 years old scan. Thus, it seems reasonable to assume that the mandibular growth is linear....
Selection, calibration, and validation of models of tumor growth.

Science.gov (United States)

Lima, E A B F; Oden, J T; Hormuth, D A; Yankeelov, T E; Almeida, R C

2016-11-01

This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as "model agnostic" in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology ( in vivo ). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction-diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory
A mathematical model of the growth of uterine myomas.

Science.gov (United States)

Chen, C Y; Ward, J P

2014-12-01

Uterine myomas or fibroids are common, benign smooth muscle tumours that can grow to 10 cm or more in diameter and are routinely removed surgically. They are typically slow- growing, well-vascularised, spherical tumours that, on a macro-scale, are a structurally uniform, hard elastic material. We present a multi-phase mathematical model of a fully vascularised myoma growing within a surrounding elastic tissue. Adopting a continuum approach, the model assumes the conservation of mass and momentum of four phases, namely cells/collagen, extracellular fluid, arterial and venous phases. The cell/collagen phase is treated as a poro-elastic material, based on a linear stress-strain relationship, and Darcy's law is applied to describe flow in the extracellular fluid and the two vascular phases. The supply of extracellular fluid is dependent on the capillary flow rate and mean capillary pressure expressed in terms of the arterial and venous pressures. Cell growth and division is limited to the myoma domain and dependent on the local stress in the material. The resulting model consists of a system of nonlinear partial differential equations with two moving boundaries. Numerical solutions of the model successfully reproduce qualitatively the clinically observed three-phase "fast-slow-fast" growth profile that is typical for myomas. The results suggest that this growth profile requires stress-induced resistance to growth by the surrounding tissue and a switch-like cell growth response to stress. Analysis of large-time solutions reveal that while there is a functioning vasculature throughout the myoma, exponential growth results, otherwise power-law growth is predicted. An extensive survey of the effect of parameters on model solutions is also presented, and in particular, the enhanced growth caused by factors such as oestrogen is predicted by the model.
Classification criteria of syndromes by latent variable models

DEFF Research Database (Denmark)

Petersen, Janne

2010-01-01

patient's characteristics. These methods may erroneously reduce multiplicity either by combining markers of different phenotypes or by mixing HALS with other processes such as aging. Latent class models identify homogenous groups of patients based on sets of variables, for example symptoms. As no gold......The thesis has two parts; one clinical part: studying the dimensions of human immunodeficiency virus associated lipodystrophy syndrome (HALS) by latent class models, and a more statistical part: investigating how to predict scores of latent variables so these can be used in subsequent regression...... standard exists for diagnosing HALS the normally applied diagnostic models cannot be used. Latent class models, which have never before been used to diagnose HALS, make it possible, under certain assumptions, to: statistically evaluate the number of phenotypes, test for mixing of HALS with other processes...
Growth Kinetics and Modeling of Direct Oxynitride Growth with NO-O2 Gas Mixtures

Energy Technology Data Exchange (ETDEWEB)

Everist, Sarah; Nelson, Jerry; Sharangpani, Rahul; Smith, Paul Martin; Tay, Sing-Pin; Thakur, Randhir

1999-05-03

We have modeled growth kinetics of oxynitrides grown in NO-O₂ gas mixtures from first principles using modified Deal-Grove equations. Retardation of oxygen diffusion through the nitrided dielectric was assumed to be the dominant growth-limiting step. The model was validated against experimentally obtained curves with good agreement. Excellent uniformity, which exceeded expected walues, was observed.
Parameter Estimates in Differential Equation Models for Population Growth

Science.gov (United States)

Winkel, Brian J.

2011-01-01

We estimate the parameters present in several differential equation models of population growth, specifically logistic growth models and two-species competition models. We discuss student-evolved strategies and offer "Mathematica" code for a gradient search approach. We use historical (1930s) data from microbial studies of the Russian biologist,…
Biopsychosocial determinants of pregnancy length and fetal growth.

Science.gov (United States)

St-Laurent, Jennifer; De Wals, Philippe; Moutquin, Jean-Marie; Niyonsenga, Theophile; Noiseux, Manon; Czernis, Loretta

2008-05-01

The causes and mechanisms related to preterm delivery and intrauterine growth restriction are poorly understood. Our objective was to assess the direct and indirect effects of psychosocial and biomedical factors on the duration of pregnancy and fetal growth. A self-administered questionnaire was distributed to pregnant women attending prenatal ultrasound clinics in nine hospitals in the Montérégie region in the province of Quebec, Canada, from November 1997 to May 1998. Prenatal questionnaires were linked with birth certificates. Theoretical models explaining pregnancy length and fetal growth were developed and tested, using path analysis. In order to reduce the number of variables from the questionnaire, a principal component analysis was performed, and the three most important new dimensions were retained as explanatory variables in the final models. Data were available for 1602 singleton pregnancies. The biophysical score, covering both maternal age and the pre-pregnancy body mass index, was the only variable statistically associated with pregnancy length. Smoking, obstetric history, maternal health and biophysical indices were direct predictors of fetal growth. Perceived stress, social support and self-esteem were not directly related to pregnancy outcomes, but were determinants of smoking and the above-mentioned biomedical variables. More studies are needed to identify the mechanisms by which adverse psychosocial factors are translated into adverse biological effects.
Mudcake growth: Model and implications

KAUST Repository

Liu, Q.; Santamarina, Carlos

2017-01-01

cementing, and to prevent partial differential sticking. We developed a robust mud cake growth model for water-based mud based on wide stress-range constitutive equations within a Lagrangian reference system to avoid non-natural moving boundary solutions
Growth potential and habitat requirements of endangered age-0 pallid sturgeon (Scaphirhynchus albus) in the Missouri River, USA, determined using a individual-based model framework

Science.gov (United States)

Deslauriers, David; Heironimus, Laura B.; Rapp, Tobias; Graeb, Brian D. S.; Klumb, Robert A.; Chipps, Steven R.

2018-01-01

An individual-based model framework was used to evaluate growth potential of the federally endangered pallid sturgeon (Scaphirhynchus albus) in the Missouri River. The model, developed for age-0 sturgeon, combines information on functional feeding response, bioenergetics and swimming ability to regulate consumption and growth within a virtual foraging arena. Empirical data on water temperature, water velocity and prey density were obtained from three sites in the Missouri River and used as inputs in the model to evaluate hypotheses concerning factors affecting pallid sturgeon growth. The model was also used to evaluate the impacts of environmental heterogeneity and water velocity on individual growth variability, foraging success and dispersal ability. Growth was simulated for a period of 100 days using 100 individuals (first feeding; 19 mm and 0.035 g) per scenario. Higher growth was shown to occur at sites where high densities of Ephemeroptera and Chironomidae larvae occurred throughout the growing season. Highly heterogeneous habitats (i.e., wide range of environmental conditions) and moderate water velocities (0.3 m/s) were also found to positively affect growth rates. The model developed here provides an important management and conservation tool for evaluating growth hypotheses and(or) identifying habitats in the Missouri River that are favourable to age-0 pallid sturgeon growth.
Growth Modeling of Aspergillus niger Strains Isolated from Citrus Fruit as a Function of Temperature on a Synthetic Medium from Lime (Citrus latifolia T.) Pericarp.

Science.gov (United States)

Sandoval-Contreras, T; Marín, S; Villarruel-López, A; Gschaedler, A; Garrido-Sánchez, L; Ascencio, F

2017-07-01

Molds are responsible for postharvest spoilage of citrus fruits. The objective of this study was to evaluate the effect of temperature on growth rate and the time to visible growth of Aspergillus niger strains isolated from citrus fruits. The growth of these strains was studied on agar lime medium (AL) at different temperatures, and growth rate was estimated using the Baranyi and Roberts model (Int. J. Food Microbiol. 23:277-294, 1994). The Rosso et al. cardinal model with inflexion (L. Rosso, J. R. Lobry, S. Bajard, and J. P. Flandrois, J. Theor. Biol. 162:447-463, 1993) was used as a secondary model to describe the effect of temperature on growth rate and the lag phase. We hypothesized that the same model could be used to calculate the time for the mycelium to become visible (t v ) by substituting the lag phase (1/λ and 1/λ opt ) with the time to visible colony (1/t v -opt and 1/t v ), respectively, in the Rosso et al. High variability was observed at suboptimal conditions. Extremes of temperature of growth for A. niger seem to have a normal variability. For the growth rate and time t v , the model was satisfactorily compared with results of previous studies. An external validation was performed in lime fruits; the bias and accuracy factors were 1.3 and 1.5, respectively, for growth rate and 0.24 and 3.72, respectively, for the appearance time. The discrepancy may be due to the influence of external factors. A. niger grows significantly more slowly on lime fruit than in culture medium, probably because the nutrients are more easily available in medium than in fruits, where the peel consistency may be a physical barrier. These findings will help researchers understand the postharvest behavior of mold on lime fruits, host-pathogen interactions, and environmental conditions infecting fruit and also help them develop guidelines for future work in the field of predictive mycology to improve models for control of postharvest fungi.
Response of wheat restricted-tillering and vigorous growth traits to variables of climate change.

Science.gov (United States)

Dias de Oliveira, Eduardo A; Siddique, Kadambot H M; Bramley, Helen; Stefanova, Katia; Palta, Jairo A

2015-02-01

The response of wheat to the variables of climate change includes elevated CO2, high temperature, and drought which vary according to the levels of each variable and genotype. Independently, elevated CO2, high temperature, and terminal drought affect wheat biomass and grain yield, but the interactive effects of these three variables are not well known. The aim of this study was to determine the effects of elevated CO2 when combined with high temperature and terminal drought on the high-yielding traits of restricted-tillering and vigorous growth. It was hypothesized that elevated CO2 alone, rather than combined with high temperature, ameliorates the effects of terminal drought on wheat biomass and grain yield. It was also hypothesized that wheat genotypes with more sink capacity (e.g. high-tillering capacity and leaf area) have more grain yield under combined elevated CO2, high temperature, and terminal drought. Two pairs of sister lines with contrasting tillering and vigorous growth were grown in poly-tunnels in a four-factor completely randomized split-plot design with elevated CO2 (700 µL L(-1)), high day time temperature (3 °C above ambient), and drought (induced from anthesis) in all combinations to test whether elevated CO2 ameliorates the effects of high temperature and terminal drought on biomass accumulation and grain yield. For biomass and grain yield, only main effects for climate change variables were significant. Elevated CO2 significantly increased grain yield by 24-35% in all four lines and terminal drought significantly reduced grain yield by 16-17% in all four lines, while high temperature (3 °C above the ambient) had no significant effect. A trade-off between yield components limited grain yield in lines with greater sink capacity (free-tillering lines). This response suggests that any positive response to predicted changes in climate will not overcome the limitations imposed by the trade-off in yield components. © 2014 Commonwealth of
R.M. Solow Adjusted Model of Economic Growth

Directory of Open Access Journals (Sweden)

Ion Gh. Rosca

2007-05-01

Full Text Available Besides the models of M. Keynes, R.F. Harrod, E. Domar, D. Romer, Ramsey-Cass-Koopmans etc., the R.M. Solow model is part of the category which characterizes the economic growth. The paper proposes the study of the R.M. Solow adjusted model of economic growth, while the adjustment consisting in the model adaptation to the Romanian economic characteristics. The article is the first one from a three paper series dedicated to the macroeconomic modelling theme, using the R.M. Solow model, such as: “Measurement of the economic growth and extensions of the R.M. Solow adjusted model” and “Evolution scenarios at the Romanian economy level using the R.M. Solow adjusted model”. The analysis part of the model is based on the study of the equilibrium to the continuous case with some interpretations of the discreet one, by using the state diagram. The optimization problem at the economic level is also used; it is built up of a specified number of representative consumers and firms in order to reveal the interaction between these elements.
Fixed transaction costs and modelling limited dependent variables

NARCIS (Netherlands)

Hempenius, A.L.

1994-01-01

As an alternative to the Tobit model, for vectors of limited dependent variables, I suggest a model, which follows from explicitly using fixed costs, if appropriate of course, in the utility function of the decision-maker.
A Finite Element Model for Mixed Porohyperelasticity with Transport, Swelling, and Growth.

Directory of Open Access Journals (Sweden)

Michelle Hine Armstrong

Full Text Available The purpose of this manuscript is to establish a unified theory of porohyperelasticity with transport and growth and to demonstrate the capability of this theory using a finite element model developed in MATLAB. We combine the theories of volumetric growth and mixed porohyperelasticity with transport and swelling (MPHETS to derive a new method that models growth of biological soft tissues. The conservation equations and constitutive equations are developed for both solid-only growth and solid/fluid growth. An axisymmetric finite element framework is introduced for the new theory of growing MPHETS (GMPHETS. To illustrate the capabilities of this model, several example finite element test problems are considered using model geometry and material parameters based on experimental data from a porcine coronary artery. Multiple growth laws are considered, including time-driven, concentration-driven, and stress-driven growth. Time-driven growth is compared against an exact analytical solution to validate the model. For concentration-dependent growth, changing the diffusivity (representing a change in drug fundamentally changes growth behavior. We further demonstrate that for stress-dependent, solid-only growth of an artery, growth of an MPHETS model results in a more uniform hoop stress than growth in a hyperelastic model for the same amount of growth time using the same growth law. This may have implications in the context of developing residual stresses in soft tissues under intraluminal pressure. To our knowledge, this manuscript provides the first full description of an MPHETS model with growth. The developed computational framework can be used in concert with novel in-vitro and in-vivo experimental approaches to identify the governing growth laws for various soft tissues.
A Finite Element Model for Mixed Porohyperelasticity with Transport, Swelling, and Growth.

Science.gov (United States)

Armstrong, Michelle Hine; Buganza Tepole, Adrián; Kuhl, Ellen; Simon, Bruce R; Vande Geest, Jonathan P

2016-01-01

The purpose of this manuscript is to establish a unified theory of porohyperelasticity with transport and growth and to demonstrate the capability of this theory using a finite element model developed in MATLAB. We combine the theories of volumetric growth and mixed porohyperelasticity with transport and swelling (MPHETS) to derive a new method that models growth of biological soft tissues. The conservation equations and constitutive equations are developed for both solid-only growth and solid/fluid growth. An axisymmetric finite element framework is introduced for the new theory of growing MPHETS (GMPHETS). To illustrate the capabilities of this model, several example finite element test problems are considered using model geometry and material parameters based on experimental data from a porcine coronary artery. Multiple growth laws are considered, including time-driven, concentration-driven, and stress-driven growth. Time-driven growth is compared against an exact analytical solution to validate the model. For concentration-dependent growth, changing the diffusivity (representing a change in drug) fundamentally changes growth behavior. We further demonstrate that for stress-dependent, solid-only growth of an artery, growth of an MPHETS model results in a more uniform hoop stress than growth in a hyperelastic model for the same amount of growth time using the same growth law. This may have implications in the context of developing residual stresses in soft tissues under intraluminal pressure. To our knowledge, this manuscript provides the first full description of an MPHETS model with growth. The developed computational framework can be used in concert with novel in-vitro and in-vivo experimental approaches to identify the governing growth laws for various soft tissues.
Effect of inter-annual variability in pasture growth and irrigation response on farm productivity and profitability based on biophysical and farm systems modelling.

Science.gov (United States)

Vogeler, Iris; Mackay, Alec; Vibart, Ronaldo; Rendel, John; Beautrais, Josef; Dennis, Samuel

2016-09-15

Farm system and nutrient budget models are increasingly being used in analysis to inform on farm decision making and evaluate land use policy options at regional scales. These analyses are generally based on the use of average annual pasture yields. In New Zealand (NZ), like in many countries, there is considerable inter-annual variation in pasture growth rates, due to climate. In this study a modelling approach was used to (i) include inter-annual variability as an integral part of the analysis and (ii) test the approach in an economic analysis of irrigation in a case study within the Hawkes Bay Region of New Zealand. The Agricultural Production Systems Simulator (APSIM) was used to generate pasture dry matter yields (DMY) for 20 different years and under both dryland and irrigation. The generated DMY were linked to outputs from farm-scale modelling for both Sheep and Beef Systems (Farmaxx Pro) and Dairy Systems (Farmax® Dairy Pro) to calculate farm production over 20 different years. Variation in DMY and associated livestock production due to inter-annual variation in climate was large, with a coefficient of variations up to 20%. Irrigation decreased this inter-annual variation. On average irrigation, with unlimited available water, increased income by $831 to 1195/ha, but when irrigation was limited to 250mm/ha/year income only increased by $525 to 883/ha. Using pasture responses in individual years to capturing the inter-annual variation, rather than the pasture response averaged over 20years resulted in lower financial benefits. In the case study income from irrigation based on an average year were 10 to >20% higher compared with those obtained from individual years. Copyright © 2016 Elsevier B.V. All rights reserved.
Random-growth urban model with geographical fitness

Science.gov (United States)

Kii, Masanobu; Akimoto, Keigo; Doi, Kenji

2012-12-01

This paper formulates a random-growth urban model with a notion of geographical fitness. Using techniques of complex-network theory, we study our system as a type of preferential-attachment model with fitness, and we analyze its macro behavior to clarify the properties of the city-size distributions it predicts. First, restricting the geographical fitness to take positive values and using a continuum approach, we show that the city-size distributions predicted by our model asymptotically approach Pareto distributions with coefficients greater than unity. Then, allowing the geographical fitness to take negative values, we perform local coefficient analysis to show that the predicted city-size distributions can deviate from Pareto distributions, as is often observed in actual city-size distributions. As a result, the model we propose can generate a generic class of city-size distributions, including but not limited to Pareto distributions. For applications to city-population projections, our simple model requires randomness only when new cities are created, not during their subsequent growth. This property leads to smooth trajectories of city population growth, in contrast to other models using Gibrat’s law. In addition, a discrete form of our dynamical equations can be used to estimate past city populations based on present-day data; this fact allows quantitative assessment of the performance of our model. Further study is needed to determine appropriate formulas for the geographical fitness.
Modelling the growth of a methanotrophic biofilm

DEFF Research Database (Denmark)

Arcangeli, J.-P.; Arvin, E.

1999-01-01

This article discusses the growth of methanotrophic biofilms. Several independent biofilm growths scenarios involving different inocula were examined. Biofilm growth, substrate removal and product formation were monitored throughout the experiments. Based on the oxygen consumption it was concluded...... that heterotrophs and nitrifiers co-existed with methanotrophs in the biofilm. Heterotrophic biomass grew on soluble polymers formed by the hydrolysis of dead biomass entrapped in the biofilm. Nitrifier populations developed because of the presence of ammonia in the mineral medium. Based on these experimental...... was performed on this model. It indicated that the most influential parameters were those related to the biofilm (i.e. density; solid-volume fraction; thickness). This suggests that in order to improve the model, further research regarding the biofilm structure and composition is needed....
Modelling of tomato stem diameter growth rate based on physiological responses

International Nuclear Information System (INIS)

Li, L.; Tan, J.; Lv, T.

2017-01-01

The stem diameter is an important parameter describing the growth of tomato plant during vegetative growth stage. A stem diameter growth model was developed to predict the response of plant growth under different conditions. By analyzing the diurnal variations of stem diameter in tomato (Solanum lycopersicum L.), it was found that the stem diameter measured at 3:00 am was the representative value as the daily basis of tomato stem diameter. Based on the responses of growth rate in stem diameter to light and temperature, a linear regression relationship was applied to establish the stem diameter growth rate prediction model for the vegetative growth stage in tomato and which was further validated by experiment. The root mean square error (RMSE) and relative error (RE) were used to test the correlation between measured and modeled stem diameter variations. Results showed that the model can be used in prediction for stem diameter growth rate at vegetative growth stage in tomato. (author)

Simulation modeling on the growth of firm's safety management capability

Institute of Scientific and Technical Information of China (English)

LIU Tie-zhong; LI Zhi-xiang

2008-01-01

Aiming to the deficiency of safety management measure, established simulation model about firm's safety management capability(FSMC) based on organizational learning theory. The system dynamics(SD) method was used, in which level and rate system, variable equation and system structure flow diagram was concluded. Simulation model was verified from two aspects: first, model's sensitivity to variable was tested from the gross of safety investment and the proportion of safety investment; second, variables dependency was checked up from the correlative variable of FSMC and organizational learning. The feasibility of simulation model is verified though these processes.
Sparse modeling of spatial environmental variables associated with asthma.

Science.gov (United States)

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.
Estimation of Staphylococcus aureus growth parameters from turbidity data: characterization of strain variation and comparison of methods.

Science.gov (United States)

Lindqvist, R

2006-07-01

Turbidity methods offer possibilities for generating data required for addressing microorganism variability in risk modeling given that the results of these methods correspond to those of viable count methods. The objectives of this study were to identify the best approach for determining growth parameters based on turbidity data and use of a Bioscreen instrument and to characterize variability in growth parameters of 34 Staphylococcus aureus strains of different biotypes isolated from broiler carcasses. Growth parameters were estimated by fitting primary growth models to turbidity growth curves or to detection times of serially diluted cultures either directly or by using an analysis of variance (ANOVA) approach. The maximum specific growth rates in chicken broth at 17 degrees C estimated by time to detection methods were in good agreement with viable count estimates, whereas growth models (exponential and Richards) underestimated growth rates. Time to detection methods were selected for strain characterization. The variation of growth parameters among strains was best described by either the logistic or lognormal distribution, but definitive conclusions require a larger data set. The distribution of the physiological state parameter ranged from 0.01 to 0.92 and was not significantly different from a normal distribution. Strain variability was important, and the coefficient of variation of growth parameters was up to six times larger among strains than within strains. It is suggested to apply a time to detection (ANOVA) approach using turbidity measurements for convenient and accurate estimation of growth parameters. The results emphasize the need to consider implications of strain variability for predictive modeling and risk assessment.
Evaluating two model reduction approaches for large scale hedonic models sensitive to omitted variables and multicollinearity

DEFF Research Database (Denmark)

Panduro, Toke Emil; Thorsen, Bo Jellesmark

2014-01-01

Hedonic models in environmental valuation studies have grown in terms of number of transactions and number of explanatory variables. We focus on the practical challenge of model reduction, when aiming for reliable parsimonious models, sensitive to omitted variable bias and multicollinearity. We...
A Hierarchical Analysis of Tree Growth and Environmental Drivers Across Eastern US Temperate Forests

Science.gov (United States)

Mantooth, J.; Dietze, M.

2014-12-01

Improving predictions of how forests in the eastern United States will respond to future global change requires a better understanding of the drivers of variability in tree growth rates. Current inventory data lack the temporal resolution to characterize interannual variability, while existing growth records lack the extent required to assess spatial scales of variability. Therefore, we established a network of forest inventory plots across ten sites across the eastern US, and measured growth in adult trees using increment cores. Sites were chosen to maximize climate space explored, while within sites, plots were spread across primary environmental gradients to explore landscape-level variability in growth. Using the annual growth record available from tree cores, we explored the responses of trees to multiple environmental covariates over multiple spatial and temporal scales. We hypothesized that within and across sites growth rates vary among species, and that intraspecific growth rates increase with temperature along a species' range. We also hypothesized that trees show synchrony in growth responses to landscape-scale climatic changes. Initial analyses of growth increments indicate that across sites, trees with intermediate shade tolerance, e.g. Red Oak (Quercus rubra), tend to have the highest growth rates. At the site level, there is evidence for synchrony in response to large-scale climatic events (e.g. prolonged drought and above average temperatures). However, growth responses to climate at the landscape scale have yet to be detected. Our current analysis utilizes hierarchical Bayesian state-space modeling to focus on growth responses of adult trees to environmental covariates at multiple spatial and temporal scales. This predictive model of tree growth currently incorporates observed effects at the individual, plot, site, and landscape scale. Current analysis using this model shows a potential slowing of growth in the past decade for two sites in the
Variability in growth of Vachellia nilotica provenances tested in the Sudano-Sahelian zone of Niger

DEFF Research Database (Denmark)

Larwanou, Mahamane; Issa, Rabiou; Saadou, Mahamane

2014-01-01

A provenance trial of Vachellia nilotica (Acacia nilotica) was conducted in Niger in order to assess variability in growth among 10 provenances from Africa (subsp. adstringens from Niger, Senegal and Cameroun) and outside the continent (subsp. indica from Pakistan and Yemen). Tree height, diameter...... at breast height and crown diameter were measured 15 years after trial establishment. Comparison of blocks located at slightly different elevation showed that trees had better survival and growth at the lower sites. The African provenances had better survival and total basal area than provenances from Yemen...
A literature review on growth models and strategies: The missing link in entrepreneurial growth

Directory of Open Access Journals (Sweden)

Syed Fida Hussain Shah

2013-08-01

Full Text Available This study focuses on the importance of growth models, growth strategies, role of knowledge management system in the formulation of effective strategy for the enterprises following growth. Choice of an appropriate growth strategy is at the heart of any successful entrepreneurial venture. Selection of a strategy may be effective for one entrepreneur while it is not for other. Choice of Growth Strategy depends on various different factors, organisational context and environment which may vary from enterprise to enterprise. Resource based view is very important consideration for the entrepreneurs on the path of growth. Evaluation of all kind of resources helps them to grow their enterprises successfully. Selection of an appropriate growth strategy allows the entrepreneurs in overcoming growth challenges and avoiding the growth reversals and setbacks.
Local variability in growth and reproduction of Salix arctica in the High Arctic

Directory of Open Access Journals (Sweden)

Noémie Boulanger-Lapointe

2016-06-01

Full Text Available Arctic terrestrial ecosystems are heterogeneous because of the strong influences of microtopography, soil moisture and snow accumulation on vegetation distribution. The interaction between local biotic and abiotic factors and global climate patterns will influence species responses to climate change. Salix arctica (Arctic willow is a structuring species, ubiquitous and widespread, and as such is one of the most important shrub species in the High Arctic. In this study, we measured S. arctica reproductive effort, early establishment, survival and growth in the Zackenberg valley, north-east Greenland. We sampled four plant communities that varied with respect to snow conditions, soil moisture, nutrient content and plant composition. We found large variability in reproductive effort and success with total catkin density ranging from 0.6 to 66 catkins/m2 and seedling density from <1 to 101 seedlings/m2. There were also major differences in crown area increment (4–23 cm2/year and stem radial growth (40–74 µm/year. The snowbed community, which experienced a recent reduction in snow cover, supported young populations with high reproductive effort, establishment and growth. Soil nutrient content and herbivore activity apparently did not strongly constrain plant reproduction and growth, but competition by Cassiope tetragona and low soil moisture may inhibit performance. Our results show that local environmental factors, such as snow accumulation, have a significant impact on tundra plant response to climate change and will affect the understanding of regional vegetation response to climate change.
Increased spruce tree growth in Central Europe since 1960s.

Science.gov (United States)

Cienciala, Emil; Altman, Jan; Doležal, Jiří; Kopáček, Jiří; Štěpánek, Petr; Ståhl, Göran; Tumajer, Jan

2018-04-01

Tree growth response to recent environmental changes is of key interest for forest ecology. This study addressed the following questions with respect to Norway spruce (Picea abies, L. Karst.) in Central Europe: Has tree growth accelerated during the last five decades? What are the main environmental drivers of the observed tree radial stem growth and how much variability can be explained by them? Using a nationwide dendrochronological sampling of Norway spruce in the Czech Republic (1246 trees, 266 plots), novel regional tree-ring width chronologies for 40(±10)- and 60(±10)-year old trees were assembled, averaged across three elevation zones (break points at 500 and 700m). Correspondingly averaged drivers, including temperature, precipitation, nitrogen (N) deposition and ambient CO 2 concentration, were used in a general linear model (GLM) to analyze the contribution of these in explaining tree ring width variability for the period from 1961 to 2013. Spruce tree radial stem growth responded strongly to the changing environment in Central Europe during the period, with a mean tree ring width increase of 24 and 32% for the 40- and 60-year old trees, respectively. The indicative General Linear Model analysis identified CO 2 , precipitation during the vegetation season, spring air temperature (March-May) and N-deposition as the significant covariates of growth, with the latter including interactions with elevation zones. The regression models explained 57% and 55% of the variability in the two tree ring width chronologies, respectively. Growth response to N-deposition showed the highest variability along the elevation gradient with growth stimulation/limitation at sites below/above 700m. A strong sensitivity of stem growth to CO 2 was also indicated, suggesting that the effect of rising ambient CO 2 concentration (direct or indirect by increased water use efficiency) should be considered in analyses of long-term growth together with climatic factors and N
A conjugate of an anti-midkine single-chain variable fragment to doxorubicin inhibits tumor growth

Energy Technology Data Exchange (ETDEWEB)

Zhao, Shuli [Immunology and Reproductive Biology Laboratory, Medical School & State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing (China); Nanjing Affiliated First Hospital, Nanjing Medical University, Nanjing (China); Zhao, Guangfeng; Xie, Hao; Huang, Yahong [Immunology and Reproductive Biology Laboratory, Medical School & State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing (China); Hou, Yayi [Immunology and Reproductive Biology Laboratory, Medical School & State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing (China); Jiangsu Key Laboratory of Molecular Medicine, Nanjing University, Nanjing (China)

2012-01-27

Doxorubicin (DOX) was conjugated to a single-chain variable fragment (scFv) against human midkine (MK), and the conjugate (scFv-DOX) was used to target the chemotherapeutic agent to a mouse solid tumor model in which the tumor cells expressed high levels of human MK. The His-tagged recombinant scFv was expressed in bacteria, purified by metal affinity chromatography, and then conjugated to DOX using oxidative dextran (Dex) as a linker. The molecular formula of this immunoconjugate was scFv(Dex){sub 1.3}(DOX){sub 20}. In vitro apoptosis assays showed that the scFv-DOX conjugate was more cytotoxic against MK-transfected human adenocarcinoma cells (BGC823-MK) than untransfected cells (55.3 ± 2.4 vs 22.4 ± 3.8%) for three independent experiments. Nude mice bearing BGC823-MK solid tumors received scFv-DOX or equivalent doses of scFv + DOX for 2 weeks and tumor growth was more effectively inhibited by the scFv-DOX conjugate than by scFv + DOX (51.83% inhibition vs 40.81%). Histological analysis of the tumor tissues revealed that the highest levels of DOX accumulated in tumors from mice treated with scFv-DOX and this resulted in more extensive tumor cell death than in animals treated with the equivalent dose of scFv + DOX. These results show that the scFv-DOX conjugate effectively inhibited tumor growth in vivo and suggest that antigen-specific scFv may be competent drug-carriers.
A conjugate of an anti-midkine single-chain variable fragment to doxorubicin inhibits tumor growth

International Nuclear Information System (INIS)

Zhao, Shuli; Zhao, Guangfeng; Xie, Hao; Huang, Yahong; Hou, Yayi

2012-01-01

Doxorubicin (DOX) was conjugated to a single-chain variable fragment (scFv) against human midkine (MK), and the conjugate (scFv-DOX) was used to target the chemotherapeutic agent to a mouse solid tumor model in which the tumor cells expressed high levels of human MK. The His-tagged recombinant scFv was expressed in bacteria, purified by metal affinity chromatography, and then conjugated to DOX using oxidative dextran (Dex) as a linker. The molecular formula of this immunoconjugate was scFv(Dex) 1.3 (DOX) 20 . In vitro apoptosis assays showed that the scFv-DOX conjugate was more cytotoxic against MK-transfected human adenocarcinoma cells (BGC823-MK) than untransfected cells (55.3 ± 2.4 vs 22.4 ± 3.8%) for three independent experiments. Nude mice bearing BGC823-MK solid tumors received scFv-DOX or equivalent doses of scFv + DOX for 2 weeks and tumor growth was more effectively inhibited by the scFv-DOX conjugate than by scFv + DOX (51.83% inhibition vs 40.81%). Histological analysis of the tumor tissues revealed that the highest levels of DOX accumulated in tumors from mice treated with scFv-DOX and this resulted in more extensive tumor cell death than in animals treated with the equivalent dose of scFv + DOX. These results show that the scFv-DOX conjugate effectively inhibited tumor growth in vivo and suggest that antigen-specific scFv may be competent drug-carriers
Energy consumption and economic growth in China: A multivariate causality test

International Nuclear Information System (INIS)

Wang Yuan; Wang Yichen; Zhou Jing; Zhu Xiaodong; Lu Genfa

2011-01-01

This study takes a fresh look at the direction of causality between energy consumption and economic growth in China during the period from 1972 to 2006, using a multivariate cointegration approach. Given the weakness associated with the bivariate causality framework, the current study performs a multivariate causality framework by incorporating capital and labor variables into the model between energy consumption and economic growth based on neo-classical aggregate production theory. Using the recently developed autoregressive distributed lag (ARDL) bounds testing approach, a long-run equilibrium cointegration relationship has been found to exist between economic growth and the explanatory variables: energy consumption, capital and employment. Empirical results reveal that the long-run parameter of energy consumption on economic growth in China is approximately 0.15, through a long-run static solution of the estimated ARDL model, and that for the short-run is approximately 0.12 by the error correction model. The study also indicates the existence of short-run and long-run causality running from energy consumption, capital and employment to economic growth. The estimation results imply that energy serves as an important source of economic growth, thus more vigorous energy use and economic development strategies should be adopted for China. - Highlights: → Cointegration is only present when real GDP is the dependent variable. →The long-run causality running from energy consumption to economic growth. →China is an energy dependent economy.
Adjusting the Stems Regional Forest Growth Model to Improve Local Predictions

Science.gov (United States)

W. Brad Smith

1983-01-01

A simple procedure using double sampling is described for adjusting growth in the STEMS regional forest growth model to compensate for subregional variations. Predictive accuracy of the STEMS model (a distance-independent, individual tree growth model for Lake States forests) was improved by using this procedure
Variability aware compact model characterization for statistical circuit design optimization

Science.gov (United States)

Qiao, Ying; Qian, Kun; Spanos, Costas J.

2012-03-01

Variability modeling at the compact transistor model level can enable statistically optimized designs in view of limitations imposed by the fabrication technology. In this work we propose an efficient variabilityaware compact model characterization methodology based on the linear propagation of variance. Hierarchical spatial variability patterns of selected compact model parameters are directly calculated from transistor array test structures. This methodology has been implemented and tested using transistor I-V measurements and the EKV-EPFL compact model. Calculation results compare well to full-wafer direct model parameter extractions. Further studies are done on the proper selection of both compact model parameters and electrical measurement metrics used in the method.
Interannual modes of variability of Southern Hemisphere atmospheric circulation in CMIP3 models

International Nuclear Information System (INIS)

Grainger, S; Frederiksen, C S; Zheng, X

2010-01-01

The atmospheric circulation acts as a bridge between large-scale sources of climate variability, and climate variability on regional scales. Here a statistical method is applied to monthly mean Southern Hemisphere 500hPa geopotential height to separate the interannual variability of the seasonal mean into intraseasonal and slowly varying (time scales of a season or longer) components. Intraseasonal and slow modes of variability are estimated from realisations of models from the Coupled Model Intercomparison Project Phase 3 (CMIP3) twentieth century coupled climate simulation (20c3m) and are evaluated against those estimated from reanalysis data. The intraseasonal modes of variability are generally well reproduced across all CMIP3 20c3m models for both Southern Hemisphere summer and winter. The slow modes are in general less well reproduced than the intraseasonal modes, and there are larger differences between realisations than for the intraseasonal modes. New diagnostics are proposed to evaluate model variability. It is found that differences between realisations from each model are generally less than inter-model differences. Differences between model-mean diagnostics are found. The results obtained are applicable to assessing the reliability of changes in atmospheric circulation variability in CMIP3 models and for their suitability for further studies of regional climate variability.
Influence of lake surface area and total phosphorus on annual bluegill growth in small impoundments of central Georgia

Science.gov (United States)

Jennings, Cecil A.; Sundmark, Aaron P.

2017-01-01

The relationships between environmental variables and the growth rates of fishes are important and rapidly expanding topics in fisheries ecology. We used an informationtheoretic approach to evaluate the influence of lake surface area and total phosphorus on the age-specific growth rates of Lepomis macrochirus (Bluegill) in 6 small impoundments in central Georgia. We used model averaging to create composite models and determine the relative importance of the variables within each model. Results indicated that surface area was the most important factor in the models predicting growth of Bluegills aged 1–4 years; total phosphorus was also an important predictor for the same age-classes. These results suggest that managers can use water quality and lake morphometry variables to create predictive models specific to their waterbody or region to help develop lake-specific management plans that select for and optimize local-level habitat factors for enhancing Bluegill growth.
A novel methodology improves reservoir characterization models using geologic fuzzy variables

Energy Technology Data Exchange (ETDEWEB)

Soto B, Rodolfo [DIGITOIL, Maracaibo (Venezuela); Soto O, David A. [Texas A and M University, College Station, TX (United States)

2004-07-01

One of the research projects carried out in Cusiana field to explain its rapid decline during the last years was to get better permeability models. The reservoir of this field has a complex layered system that it is not easy to model using conventional methods. The new technique included the development of porosity and permeability maps from cored wells following the same trend of the sand depositions for each facie or layer according to the sedimentary facie and the depositional system models. Then, we used fuzzy logic to reproduce those maps in three dimensions as geologic fuzzy variables. After multivariate statistical and factor analyses, we found independence and a good correlation coefficient between the geologic fuzzy variables and core permeability and porosity. This means, the geologic fuzzy variable could explain the fabric, the grain size and the pore geometry of the reservoir rock trough the field. Finally, we developed a neural network permeability model using porosity, gamma ray and the geologic fuzzy variable as input variables. This model has a cross-correlation coefficient of 0.873 and average absolute error of 33% compared with the actual model with a correlation coefficient of 0.511 and absolute error greater than 250%. We tested different methodologies, but this new one showed dramatically be a promiser way to get better permeability models. The use of the models have had a high impact in the explanation of well performance and workovers, and reservoir simulation models. (author)
A size-structured model of bacterial growth and reproduction.

Science.gov (United States)

Ellermeyer, S F; Pilyugin, S S

2012-01-01

We consider a size-structured bacterial population model in which the rate of cell growth is both size- and time-dependent and the average per capita reproduction rate is specified as a model parameter. It is shown that the model admits classical solutions. The population-level and distribution-level behaviours of these solutions are then determined in terms of the model parameters. The distribution-level behaviour is found to be different from that found in similar models of bacterial population dynamics. Rather than convergence to a stable size distribution, we find that size distributions repeat in cycles. This phenomenon is observed in similar models only under special assumptions on the functional form of the size-dependent growth rate factor. Our main results are illustrated with examples, and we also provide an introductory study of the bacterial growth in a chemostat within the framework of our model.
Plasticity models of material variability based on uncertainty quantification techniques

Energy Technology Data Exchange (ETDEWEB)

Jones, Reese E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Rizzi, Francesco [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Boyce, Brad [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Templeton, Jeremy Alan [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Ostien, Jakob [Sandia National Lab. (SNL-CA), Livermore, CA (United States)

2017-11-01

The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.
Climate variability and demand growth as drivers of water scarcity in the Turkwel river basin: a bottom-up risk assessment of a data-sparse basin in Kenya

Science.gov (United States)

Hirpa, F. A.; Dyer, E.; Hope, R.; Dadson, S. J.

2017-12-01

Sustainable water management and allocation are essential for maintaining human well-being, sustaining healthy ecosystems, and supporting steady economic growth. The Turkwel river basin, located in north-western Kenya, experiences a high level of water scarcity due to its arid climate, high rainfall variability, and rapidly growing water demand. However, due to sparse hydro-climatic data and limited literature, the water resources system of the basin has been poorly understood. Here we apply a bottom-up climate risk assessment method to estimate the resilience of the basin's water resources system to growing demand and climate stressors. First, using a water resource system model and historical climate data, we construct a climate risk map that depicts the way in which the system responds to climate change and variability. Then we develop a set of water demand scenarios to identify the conditions that potentially lead to the risk of unmet water demand and groundwater depletion. Finally, we investigate the impact of climate change and variability by stress testing these development scenarios against historically strong El Niño/Southern Oscillation (ENSO) years and future climate projections from multiple Global Circulation Models (GCMs). The results reveal that climate variability and increased water demand are the main drivers of water scarcity in the basin. Our findings show that increases in water demand due to expanded irrigation and population growth exert the strongest influence on the ability of the system to meet water resource supply requirements, and in all cases considered increase the impacts of droughts caused by future climate variability. Our analysis illustrates the importance of combining analysis of future climate risks with other development decisions that affect water resources planning. Policy and investment decisions which maximise water use efficiency in the present day are likely to impart resilience to climate change and variability under a

Modeling Urban Spatial Growth in Mountainous Regions of Western China

Directory of Open Access Journals (Sweden)

Guoping Huang

2017-08-01

Full Text Available The scale and speed of urbanization in the mountainous regions of western China have received little attention from researchers. These cities are facing rapid population growth and severe environmental degradation. This study analyzed historical urban growth trends in this mountainous region to better understand the interaction between the spatial growth pattern and the mountainous topography. Three major factors—slope, accessibility, and land use type—were studied in light of their relationships with urban spatial growth. With the analysis of historical data as the basis, a conceptual urban spatial growth model was devised. In this model, slope, accessibility, and land use type together create resistance to urban growth, while accessibility controls the sequence of urban development. The model was tested and evaluated using historical data. It serves as a potential tool for planners to envision and assess future urban growth scenarios and their potential environmental impacts to make informed decisions.
Efficient Business Service Consumption by Customization with Variability Modelling

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Michael Stollberg

2010-07-01

Full Text Available The establishment of service orientation in industry determines the need for efficient engineering technologies that properly support the whole life cycle of service provision and consumption. A central challenge is adequate support for the efficient employment of komplex services in their individual application context. This becomes particularly important for large-scale enterprise technologies where generic services are designed for reuse in several business scenarios. In this article we complement our work regarding Service Variability Modelling presented in a previous publication. There we presented an approach for the customization of services for individual application contexts by creating simplified variants, based on model-driven variability management. That work presents our revised service variability metamodel, new features of the variability tools and an applicability study, which reveals that substantial improvements on the efficiency of standard business service consumption under both usability and economic aspects can be achieved.
Stochastic growth logistic model with aftereffect for batch fermentation process

Energy Technology Data Exchange (ETDEWEB)

Rosli, Norhayati; Ayoubi, Tawfiqullah [Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang (Malaysia); Bahar, Arifah; Rahman, Haliza Abdul [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia); Salleh, Madihah Md [Department of Biotechnology Industry, Faculty of Biosciences and Bioengineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)

2014-06-19

In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits.
Stochastic growth logistic model with aftereffect for batch fermentation process

Science.gov (United States)

Rosli, Norhayati; Ayoubi, Tawfiqullah; Bahar, Arifah; Rahman, Haliza Abdul; Salleh, Madihah Md

2014-06-01

In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits.
Stochastic growth logistic model with aftereffect for batch fermentation process

International Nuclear Information System (INIS)

Rosli, Norhayati; Ayoubi, Tawfiqullah; Bahar, Arifah; Rahman, Haliza Abdul; Salleh, Madihah Md

2014-01-01

In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits
Characterisation of Growth Variability and Mycelial Compatibility of Botrytis Cinerea Isolates Originated from Apple and Strawberry in Lithuania

Directory of Open Access Journals (Sweden)

Rasiukevičiūtė Neringa

2017-06-01

Full Text Available Botrytis cinerea Pers.:Fr. is a widespread necrotrophic pathogen causing grey mould on many economically important horticultural crops. The variability in various B. cinerea populations is known to be very high. Despite the economic importance, the variability of B. cinerea has not been investigated previously on fruit crops in Lithuania. The aim of the study was to characterise the variability of B. cinerea strains isolated from strawberry and apple in different growth conditions on various agar media and to assess mycelial compatibility among the isolates. Larger colony diameter after four days of incubation was observed for isolates from strawberry on potato dextrose and beer universal agars in 24 h dark or light regime, followed by pectin agar in 24 h light. Similarly, the maximum radial growth of the isolates from apple was on potato dextrose agar (dark, followed by beer universal agar (dark and light, after four days of incubation at 20 °C. In the mycelial compatibility tests, barrage formation was evident in mycelial contacts between several isolates, indicating their vegetative incompatibility. The tests revealed that 76% were compatible and 24% were incompatible among investigated strains.
Modelling and predicting growth of psychrotolerant pseudomonads in milk and cottage cheese

DEFF Research Database (Denmark)

Martinez Rios, Veronica; Østergaard, Nina Bjerre; Rosshaug, Per Sand

.43. The acceptable simulation zone method showed the new model for cottage cheese to successfully predict growth of psychrotolerant pseudomonads at both constant and dynamic temperature storage conditions. The new models can be used together with the Food Spoilage and Safety Predictor (FSSP) software to predict......Mathematical models were developed and evaluated for growth of psychrotolerant pseudomonads in chilled milk and cottage cheese with cultured cream dressing. The mathematical models include the effect of temperature, pH, NaCl, lactic acid and sorbic acid. A simplified cardinal parameter growth model...... was developed based on growth in broth. Subsequently, the reference growth rate parameter (μref at 25 °C) was fitted to a total of 35 growth rates from cottage cheese with cultured cream dressing. Growth rate models for milk and cottage cheese were evaluated by comparison with data from literature and new...
Brexit and the Politics of UK Growth Models

DEFF Research Database (Denmark)

Rosamond, Ben

2018-01-01

Brexit has reopened and repoliticized the debate about future growth models for the UK economy. This contribution argues that this debate is built around historically specific path dependencies that reflect the particular character of public debate about British political economy, while also...... particular attention to the importance of the politics of support. It suggests that recent debate about growth models has been largely subsumed within the politics of Brexit, which has politicized that debate, albeit through the emergent political economy frames that Brexit has provoked. The paper explores...... suggesting that the debate around Brexit takes place at a very distinctive moment in the history of democratic capitalism in Europe. This combination gives the renewed politicization a specific and perhaps perverse character. The paper considers how we should approach debates about growth models, paying...
Flower Power: Sunflowers as a Model for Logistic Growth

Science.gov (United States)

Fernandez, Eileen; Geist, Kristi A.

2011-01-01

Logistic growth displays an interesting pattern: It starts fast, exhibiting the rapid growth characteristic of exponential models. As time passes, it slows in response to constraints such as limited resources or reallocation of energy. The growth continues to slow until it reaches a limit, called capacity. When the growth describes a population,…
Longitudinal associations between exercise identity and exercise motivation: A multilevel growth curve model approach.

Science.gov (United States)

Ntoumanis, N; Stenling, A; Thøgersen-Ntoumani, C; Vlachopoulos, S; Lindwall, M; Gucciardi, D F; Tsakonitis, C

2018-02-01

Past work linking exercise identity and exercise motivation has been cross-sectional. This is the first study to model the relations between different types of exercise identity and exercise motivation longitudinally. Understanding the dynamic associations between these sets of variables has implications for theory development and applied research. This was a longitudinal survey study. Participants were 180 exercisers (79 men, 101 women) from Greece, who were recruited from fitness centers and were asked to complete questionnaires assessing exercise identity (exercise beliefs and role-identity) and exercise motivation (intrinsic, identified, introjected, external motivation, and amotivation) three times within a 6 month period. Multilevel growth curve modeling examined the role of motivational regulations as within- and between-level predictors of exercise identity, and a model in which exercise identity predicted exercise motivation at the within- and between-person levels. Results showed that within-person changes in intrinsic motivation, introjected, and identified regulations were positively and reciprocally related to within-person changes in exercise beliefs; intrinsic motivation was also a positive predictor of within-person changes in role-identity but not vice versa. Between-person differences in the means of predictor variables were predictive of initial levels and average rates of change in the outcome variables. The findings show support to the proposition that a strong exercise identity (particularly exercise beliefs) can foster motivation for behaviors that reinforce this identity. We also demonstrate that such relations can be reciprocal overtime and can depend on the type of motivation in question as well as between-person differences in absolute levels of these variables. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Evaluating Non-Linear Regression Models in Analysis of Persian Walnut Fruit Growth

Directory of Open Access Journals (Sweden)

I. Karamatlou

2016-02-01

Full Text Available Introduction: Persian walnut (Juglans regia L. is a large, wind-pollinated, monoecious, dichogamous, long lived, perennial tree cultivated for its high quality wood and nuts throughout the temperate regions of the world. Growth model methodology has been widely used in the modeling of plant growth. Mathematical models are important tools to study the plant growth and agricultural systems. These models can be applied for decision-making anddesigning management procedures in horticulture. Through growth analysis, planning for planting systems, fertilization, pruning operations, harvest time as well as obtaining economical yield can be more accessible.Non-linear models are more difficult to specify and estimate than linear models. This research was aimed to studynon-linear regression models based on data obtained from fruit weight, length and width. Selecting the best models which explain that fruit inherent growth pattern of Persian walnut was a further goal of this study. Materials and Methods: The experimental material comprising 14 Persian walnut genotypes propagated by seed collected from a walnut orchard in Golestan province, Minoudasht region, Iran, at latitude 37◦04’N; longitude 55◦32’E; altitude 1060 m, in a silt loam soil type. These genotypes were selected as a representative sampling of the many walnut genotypes available throughout the Northeastern Iran. The age range of walnut trees was 30 to 50 years. The annual mean temperature at the location is16.3◦C, with annual mean rainfall of 690 mm.The data used here is the average of walnut fresh fruit and measured withgram/millimeter/day in2011.According to the data distribution pattern, several equations have been proposed to describesigmoidal growth patterns. Here, we used double-sigmoid and logistic–monomolecular models to evaluate fruit growth based on fruit weight and4different regression models in cluding Richards, Gompertz, Logistic and Exponential growth for evaluation
Empirical testing of Kotler's high-performance factors to increase sales growth

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Oren Dayan

2010-12-01

Full Text Available Purpose and/or objectives: The primary objective of this study is to empirically test Kotler's (2003 high-performance model which ensures an increase in sales growth. More specifically, the study explores the influence of process variables (as measured by marketing strategies, resources management (as measured by the management of labour, materials, machines, information technology and energy and organisational variables (as measured by TQM and organisational culture on sales growth in the food, motorcar and high-technology manufacturing industries. Problem investigated Various research studies suggest that the managers of firms are continuously challenged in their attempts to increase their sales (Morre, 2007; Pauwels, Silva Risso, Srinivasan & Hanssens, 2004: 142-143; Gray & Hayes, 2007: 1. Kotler (2003 suggests a model that leads to a high performing business. The question is posed as to whether this model can be used to increase sales growth in all businesses. This study seeks to develop a generic model to increase sales growth across industries by using an adapted version of Kotler's (2003 high-performance model. The study investigates the application of this adapted model on the food, motorcar and high-technology manufacturing industries. Design and/or methodology and/or approach: An empirical causal research design that includes 770 marketing and product development practitioners from multinational food, motorcar and high-technology manufacturing firms, was used in this study. A response rate of 76.1% was achieved as only 571 useable questionnaires were returned. The internal reliability and discriminant validity of the measuring instrument were assessed by the calculation of Cronbach alpha coefficients and the conducting an exploratory factor analysis respectively. Structural Equation Modelling SEM was used to statistically test the relationships between the independent variables (marketing strategies, resource management, TQM and
Thermal models pertaining to continental growth

International Nuclear Information System (INIS)

Morgan, P.; Ashwal, L.

1988-01-01

Thermal models are important to understanding continental growth as the genesis, stabilization, and possible recycling of continental crust are closely related to the tectonic processes of the earth which are driven primarily by heat. The thermal energy budget of the earth was slowly decreasing since core formation, and thus the energy driving the terrestrial tectonic engine was decreasing. This fundamental observation was used to develop a logic tree defining the options for continental growth throughout earth history
Models of the Economic Growth and their Relevance

Directory of Open Access Journals (Sweden)

Nicolae MOROIANU

2012-06-01

Full Text Available Until few years ago, the economic growth was something perfect normal, part of an era marked by the transformation speed. Normality itself has been transformed and we currently are influenced by other rules, unknown yet, which should answer the question: “How do we return to the economic growth?” The economic growth and the models aiming to solve this problem concern the economic history even since its beginnings. In this paper we would like to find out what is the relevance that the well-known macroeconomic models still have and which might be their applicability level in a framework created by a black swan event type.
Generalized Network Psychometrics : Combining Network and Latent Variable Models

NARCIS (Netherlands)

Epskamp, S.; Rhemtulla, M.; Borsboom, D.

2017-01-01

We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between
Stochastic Differential Equation-Based Flexible Software Reliability Growth Model

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P. K. Kapur

2009-01-01

Full Text Available Several software reliability growth models (SRGMs have been developed by software developers in tracking and measuring the growth of reliability. As the size of software system is large and the number of faults detected during the testing phase becomes large, so the change of the number of faults that are detected and removed through each debugging becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. In such a situation, we can model the software fault detection process as a stochastic process with continuous state space. In this paper, we propose a new software reliability growth model based on Itô type of stochastic differential equation. We consider an SDE-based generalized Erlang model with logistic error detection function. The model is estimated and validated on real-life data sets cited in literature to show its flexibility. The proposed model integrated with the concept of stochastic differential equation performs comparatively better than the existing NHPP-based models.
Investigation of various growth mechanisms of solid tumour growth within the linear-quadratic model for radiotherapy

International Nuclear Information System (INIS)

McAneney, H; O'Rourke, S F C

2007-01-01

The standard linear-quadratic survival model for radiotherapy is used to investigate different schedules of radiation treatment planning to study how these may be affected by different tumour repopulation kinetics between treatments. The laws for tumour cell repopulation include the logistic and Gompertz models and this extends the work of Wheldon et al (1977 Br. J. Radiol. 50 681), which was concerned with the case of exponential re-growth between treatments. Here we also consider the restricted exponential model. This has been successfully used by Panetta and Adam (1995 Math. Comput. Modelling 22 67) in the case of chemotherapy treatment planning.Treatment schedules investigated include standard fractionation of daily treatments, weekday treatments, accelerated fractionation, optimized uniform schedules and variation of the dosage and α/β ratio, where α and β are radiobiological parameters for the tumour tissue concerned. Parameters for these treatment strategies are extracted from the literature on advanced head and neck cancer, prostate cancer, as well as radiosensitive parameters. Standardized treatment protocols are also considered. Calculations based on the present analysis indicate that even with growth laws scaled to mimic initial growth, such that growth mechanisms are comparable, variation in survival fraction to orders of magnitude emerged. Calculations show that the logistic and exponential models yield similar results in tumour eradication. By comparison the Gompertz model calculations indicate that tumours described by this law result in a significantly poorer prognosis for tumour eradication than either the exponential or logistic models. The present study also shows that the faster the tumour growth rate and the higher the repair capacity of the cell line, the greater the variation in outcome of the survival fraction. Gaps in treatment, planned or unplanned, also accentuate the differences of the survival fraction given alternative growth
Can Geostatistical Models Represent Nature's Variability? An Analysis Using Flume Experiments

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Scheidt, C.; Fernandes, A. M.; Paola, C.; Caers, J.

2015-12-01

The lack of understanding in the Earth's geological and physical processes governing sediment deposition render subsurface modeling subject to large uncertainty. Geostatistics is often used to model uncertainty because of its capability to stochastically generate spatially varying realizations of the subsurface. These methods can generate a range of realizations of a given pattern - but how representative are these of the full natural variability? And how can we identify the minimum set of images that represent this natural variability? Here we use this minimum set to define the geostatistical prior model: a set of training images that represent the range of patterns generated by autogenic variability in the sedimentary environment under study. The proper definition of the prior model is essential in capturing the variability of the depositional patterns. This work starts with a set of overhead images from an experimental basin that showed ongoing autogenic variability. We use the images to analyze the essential characteristics of this suite of patterns. In particular, our goal is to define a prior model (a minimal set of selected training images) such that geostatistical algorithms, when applied to this set, can reproduce the full measured variability. A necessary prerequisite is to define a measure of variability. In this study, we measure variability using a dissimilarity distance between the images. The distance indicates whether two snapshots contain similar depositional patterns. To reproduce the variability in the images, we apply an MPS algorithm to the set of selected snapshots of the sedimentary basin that serve as training images. The training images are chosen from among the initial set by using the distance measure to ensure that only dissimilar images are chosen. Preliminary investigations show that MPS can reproduce fairly accurately the natural variability of the experimental depositional system. Furthermore, the selected training images provide
Use of an uncertainty analysis for genome-scale models as a prediction tool for microbial growth processes in subsurface environments.

Science.gov (United States)

Klier, Christine

2012-03-06

The integration of genome-scale, constraint-based models of microbial cell function into simulations of contaminant transport and fate in complex groundwater systems is a promising approach to help characterize the metabolic activities of microorganisms in natural environments. In constraint-based modeling, the specific uptake flux rates of external metabolites are usually determined by Michaelis-Menten kinetic theory. However, extensive data sets based on experimentally measured values are not always available. In this study, a genome-scale model of Pseudomonas putida was used to study the key issue of uncertainty arising from the parametrization of the influx of two growth-limiting substrates: oxygen and toluene. The results showed that simulated growth rates are highly sensitive to substrate affinity constants and that uncertainties in specific substrate uptake rates have a significant influence on the variability of simulated microbial growth. Michaelis-Menten kinetic theory does not, therefore, seem to be appropriate for descriptions of substrate uptake processes in the genome-scale model of P. putida. Microbial growth rates of P. putida in subsurface environments can only be accurately predicted if the processes of complex substrate transport and microbial uptake regulation are sufficiently understood in natural environments and if data-driven uptake flux constraints can be applied.
Fatigue Crack Propagation Under Variable Amplitude Loading Analyses Based on Plastic Energy Approach

Directory of Open Access Journals (Sweden)

Sofiane Maachou

2014-04-01

Full Text Available Plasticity effects at the crack tip had been recognized as “motor” of crack propagation, the growth of cracks is related to the existence of a crack tip plastic zone, whose formation and intensification is accompanied by energy dissipation. In the actual state of knowledge fatigue crack propagation is modeled using crack closure concept. The fatigue crack growth behavior under constant amplitude and variable amplitude loading of the aluminum alloy 2024 T351 are analyzed using in terms energy parameters. In the case of VAL (variable amplitude loading tests, the evolution of the hysteretic energy dissipated per block is shown similar with that observed under constant amplitude loading. A linear relationship between the crack growth rate and the hysteretic energy dissipated per block is obtained at high growth rates. For lower growth rates values, the relationship between crack growth rate and hysteretic energy dissipated per block can represented by a power law. In this paper, an analysis of fatigue crack propagation under variable amplitude loading based on energetic approach is proposed.

Post-traumatic growth in parents after infants' neonatal intensive care unit hospitalisation.

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Aftyka, Anna; Rozalska-Walaszek, Ilona; Rosa, Wojciech; Rybojad, Beata; Karakuła-Juchnowicz, Hanna

2017-03-01

To determine the incidence and severity of post-traumatic growth in a group of parents of children hospitalised in the intensive care unit in the past. A premature birth or a birth with life-threatening conditions is a traumatic event for the parents and may lead to a number of changes, some of which are positive, known as post-traumatic growth. The survey covered 106 parents of 67 infants aged 3-12 months. An original questionnaire and standardised research tools were used in the study: Impact Event Scale - Revised, Perceived Stress Scale, COPE Inventory: Positive Reinterpretation and Growth, Coping Inventory for Stressful Situations, Post-traumatic Growth Inventory and Parent and Infant Characteristic Questionnaire. Due to a stepwise backward variables selection, we found three main factors that explain post-traumatic growth: post-traumatic stress symptoms, positive reinterpretation and growth and dichotomic variable infants' survival. This model explained 29% of the post-traumatic growth variation. Similar models that were considered separately for mothers and fathers showed no significantly better properties. Post-traumatic growth was related to a lesser extent to sociodemographic variables or the stressor itself, and related to a far greater extent to psychological factors. Our study highlights the fact that post-traumatic growth in the parents of neonates hospitalised in the neonatal intensive care units remains under-evaluated. © 2016 John Wiley & Sons Ltd.
FDI, Economic Growth, Energy Consumption & Environmental Nexus in Bangladesh

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Sandip SARKER

2016-04-01

Full Text Available This paper attempts to investigate the relationship among economic growth, energy consumption, CO2 emission, FDI and natural gas usage in Bangladesh through co-integration and Vector Error Correction model (VECM over the period 1978 to 2010. Using ADP unit root tests it is found that all the four variables are integrated in first difference. The Johansen co-integration tests indicate that there is existence of long-run relationship among the variables. The VECM long run causality model indicates that there is a long run causality running from energy consumption and natural gas usage by industrial sector to GDP as well as from CO2 emission to FDI. Likewise in the short run a causal relationships have also been found among the variables. Moreover our model is found be error free based on several statistical test. Our results provide important policy suggestions regarding our foreign direct investment, environmental issues and economic growth nexus in Bangladesh.
Equations of bark thickness and volume profiles at different heights with easy-measurement variables

Energy Technology Data Exchange (ETDEWEB)

Cellini, J. M.; Galarza, M.; Burns, S. L.; Martinez-Pastur, G. J.; Lencinas, M. V.

2012-11-01

The objective of this work was to develop equations of thickness profile and bark volume at different heights with easy-measurement variables, taking as a study case Nothofagus pumilio forests, growing in different site qualities and growth phases in Southern Patagonia. Data was collected from 717 harvested trees. Three models were fitted using multiple, non-lineal regression and generalized linear model, by stepwise methodology, iteratively reweighted least squares method for maximum likelihood estimation and Marquardt algorithm. The dependent variables were diameter at 1.30 m height (DBH), relative height (RH) and growth phase (GP). The statistic evaluation was made through the adjusted determinant coefficient (r2-adj), standard error of the estimation (SEE), mean absolute error and residual analysis. All models presented good fitness with a significant correlation with the growth phase. A decrease in the thickness was observed when the relative height increase. Moreover, a bark coefficient was made to calculate volume with and without bark of individual trees, where significant differences according to site quality of the stands and DBH class of the trees were observed. It can be concluded that the prediction of bark thickness and bark coefficient is possible using DBH, height, site quality and growth phase, common and easy measurement variables used in forest inventories. (Author) 23 refs.
Mathematical foundations of the dendritic growth models.

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Villacorta, José A; Castro, Jorge; Negredo, Pilar; Avendaño, Carlos

2007-11-01

At present two growth models describe successfully the distribution of size and topological complexity in populations of dendritic trees with considerable accuracy and simplicity, the BE model (Van Pelt et al. in J. Comp. Neurol. 387:325-340, 1997) and the S model (Van Pelt and Verwer in Bull. Math. Biol. 48:197-211, 1986). This paper discusses the mathematical basis of these models and analyzes quantitatively the relationship between the BE model and the S model assumed in the literature by developing a new explicit equation describing the BES model (a dendritic growth model integrating the features of both preceding models; Van Pelt et al. in J. Comp. Neurol. 387:325-340, 1997). In numerous studies it is implicitly presupposed that the S model is conditionally linked to the BE model (Granato and Van Pelt in Brain Res. Dev. Brain Res. 142:223-227, 2003; Uylings and Van Pelt in Network 13:397-414, 2002; Van Pelt, Dityatev and Uylings in J. Comp. Neurol. 387:325-340, 1997; Van Pelt and Schierwagen in Math. Biosci. 188:147-155, 2004; Van Pelt and Uylings in Network. 13:261-281, 2002; Van Pelt, Van Ooyen and Uylings in Modeling Dendritic Geometry and the Development of Nerve Connections, pp 179, 2000). In this paper we prove the non-exactness of this assumption, quantify involved errors and determine the conditions under which the BE and S models can be separately used instead of the BES model, which is more exact but considerably more difficult to apply. This study leads to a novel expression describing the BE model in an analytical closed form, much more efficient than the traditional iterative equation (Van Pelt et al. in J. Comp. Neurol. 387:325-340, 1997) in many neuronal classes. Finally we propose a new algorithm in order to obtain the values of the parameters of the BE model when this growth model is matched to experimental data, and discuss its advantages and improvements over the more commonly used procedures.
Predicting growth of the healthy infant using a genome scale metabolic model.

Science.gov (United States)

Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

2017-01-01

An estimated 165 million children globally have stunted growth, and extensive growth data are available. Genome scale metabolic models allow the simulation of molecular flux over each metabolic enzyme, and are well adapted to analyze biological systems. We used a human genome scale metabolic model to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body composition. The model corroborates the finding that essential amino and fatty acids do not limit growth, but that energy is the main growth limiting factor. Disruptions to the supply and demand of energy markedly affected the predicted growth, indicating that elevated energy expenditure may be detrimental. The model was used to simulate the metabolic effect of mineral deficiencies, and showed the greatest growth reduction for deficiencies in copper, iron, and magnesium ions which affect energy production through oxidative phosphorylation. The model and simulation method were integrated to a platform and shared with the research community. The growth model constitutes another step towards the complete representation of human metabolism, and may further help improve the understanding of the mechanisms underlying stunting.
Testing the growth links of emerging economies: Croatia in a growing world economy

NARCIS (Netherlands)

Ziesemer, Thomas

2017-01-01

We estimate a dynamic simultaneous equation model for 16 variables of the Croatian economy in order to test the links of growth with education, R&D, trade, savings and FDI. In order to motivate the choice of variables we review the related theories of growth and look at the relevant data. Permanent
Solar Cycle Variability Induced by Tilt Angle Scatter in a Babcock-Leighton Solar Dynamo Model

Science.gov (United States)

Karak, Bidya Binay; Miesch, Mark

2017-09-01

We present results from a three-dimensional Babcock-Leighton (BL) dynamo model that is sustained by the emergence and dispersal of bipolar magnetic regions (BMRs). On average, each BMR has a systematic tilt given by Joy’s law. Randomness and nonlinearity in the BMR emergence of our model produce variable magnetic cycles. However, when we allow for a random scatter in the tilt angle to mimic the observed departures from Joy’s law, we find more variability in the magnetic cycles. We find that the observed standard deviation in Joy’s law of {σ }δ =15^\\circ produces a variability comparable to the observed solar cycle variability of ˜32%, as quantified by the sunspot number maxima between 1755 and 2008. We also find that tilt angle scatter can promote grand minima and grand maxima. The time spent in grand minima for {σ }δ =15^\\circ is somewhat less than that inferred for the Sun from cosmogenic isotopes (about 9% compared to 17%). However, when we double the tilt scatter to {σ }δ =30^\\circ , the simulation statistics are comparable to the Sun (˜18% of the time in grand minima and ˜10% in grand maxima). Though the BL mechanism is the only source of poloidal field, we find that our simulations always maintain magnetic cycles even at large fluctuations in the tilt angle. We also demonstrate that tilt quenching is a viable and efficient mechanism for dynamo saturation; a suppression of the tilt by only 1°-2° is sufficient to limit the dynamo growth. Thus, any potential observational signatures of tilt quenching in the Sun may be subtle.
Internal variability in a regional climate model over West Africa

Energy Technology Data Exchange (ETDEWEB)

Vanvyve, Emilie; Ypersele, Jean-Pascal van [Universite catholique de Louvain, Institut d' astronomie et de geophysique Georges Lemaitre, Louvain-la-Neuve (Belgium); Hall, Nicholas [Laboratoire d' Etudes en Geophysique et Oceanographie Spatiales/Centre National d' Etudes Spatiales, Toulouse Cedex 9 (France); Messager, Christophe [University of Leeds, Institute for Atmospheric Science, Environment, School of Earth and Environment, Leeds (United Kingdom); Leroux, Stephanie [Universite Joseph Fourier, Laboratoire d' etude des Transferts en Hydrologie et Environnement, BP53, Grenoble Cedex 9 (France)

2008-02-15

Sensitivity studies with regional climate models are often performed on the basis of a few simulations for which the difference is analysed and the statistical significance is often taken for granted. In this study we present some simple measures of the confidence limits for these types of experiments by analysing the internal variability of a regional climate model run over West Africa. Two 1-year long simulations, differing only in their initial conditions, are compared. The difference between the two runs gives a measure of the internal variability of the model and an indication of which timescales are reliable for analysis. The results are analysed for a range of timescales and spatial scales, and quantitative measures of the confidence limits for regional model simulations are diagnosed for a selection of study areas for rainfall, low level temperature and wind. As the averaging period or spatial scale is increased, the signal due to internal variability gets smaller and confidence in the simulations increases. This occurs more rapidly for variations in precipitation, which appear essentially random, than for dynamical variables, which show some organisation on larger scales. (orig.)
Mechanical model for filament buckling and growth by phase ordering.

Science.gov (United States)

Rey, Alejandro D; Abukhdeir, Nasser M

2008-02-05

A mechanical model of open filament shape and growth driven by phase ordering is formulated. For a given phase-ordering driving force, the model output is the filament shape evolution and the filament end-point kinematics. The linearized model for the slope of the filament is the Cahn-Hilliard model of spinodal decomposition, where the buckling corresponds to concentration fluctuations. Two modes are predicted: (i) sequential growth and buckling and (ii) simultaneous buckling and growth. The relation among the maximum buckling rate, filament tension, and matrix viscosity is given. These results contribute to ongoing work in smectic A filament buckling.
Impulsive synchronization and parameter mismatch of the three-variable autocatalator model

International Nuclear Information System (INIS)

Li, Yang; Liao, Xiaofeng; Li, Chuandong; Huang, Tingwen; Yang, Degang

2007-01-01

The synchronization problems of the three-variable autocatalator model via impulsive control approach are investigated; several theorems on the stability of impulsive control systems are also investigated. These theorems are then used to find the conditions under which the three-variable autocatalator model can be asymptotically controlled to the equilibrium point. This Letter derives some sufficient conditions for the stabilization and synchronization of a three-variable autocatalator model via impulsive control with varying impulsive intervals. Furthermore, we address the chaos quasi-synchronization in the presence of single-parameter mismatch. To illustrate the effectiveness of the new scheme, several numerical examples are given
Hyperbolastic modeling of tumor growth with a combined treatment of iodoacetate and dimethylsulphoxide

International Nuclear Information System (INIS)

Eby, Wayne M; Tabatabai, Mohammad A; Bursac, Zoran

2010-01-01

An understanding of growth dynamics of tumors is important in understanding progression of cancer and designing appropriate treatment strategies. We perform a comparative study of the hyperbolastic growth models with the Weibull and Gompertz models, which are prevalently used in the field of tumor growth. The hyperbolastic growth models H1, H2, and H3 are applied to growth of solid Ehrlich carcinoma under several different treatments. These are compared with results from Gompertz and Weibull models for the combined treatment. The growth dynamics of the solid Ehrlich carcinoma with the combined treatment are studied using models H1, H2, and H3, and the models are highly accurate in representing the growth. The growth dynamics are also compared with the untreated tumor, the tumor treated with only iodoacetate, and the tumor treated with only dimethylsulfoxide, and the combined treatment. The hyperbolastic models prove to be effective in representing and analyzing the growth dynamics of the solid Ehrlich carcinoma. These models are more precise than Gompertz and Weibull and show less error for this data set. The precision of H3 allows for its use in a comparative analysis of tumor growth rates between the various treatments
Variable cycle control model for intersection based on multi-source information

Science.gov (United States)

Sun, Zhi-Yuan; Li, Yue; Qu, Wen-Cong; Chen, Yan-Yan

2018-05-01

In order to improve the efficiency of traffic control system in the era of big data, a new variable cycle control model based on multi-source information is presented for intersection in this paper. Firstly, with consideration of multi-source information, a unified framework based on cyber-physical system is proposed. Secondly, taking into account the variable length of cell, hysteresis phenomenon of traffic flow and the characteristics of lane group, a Lane group-based Cell Transmission Model is established to describe the physical properties of traffic flow under different traffic signal control schemes. Thirdly, the variable cycle control problem is abstracted into a bi-level programming model. The upper level model is put forward for cycle length optimization considering traffic capacity and delay. The lower level model is a dynamic signal control decision model based on fairness analysis. Then, a Hybrid Intelligent Optimization Algorithm is raised to solve the proposed model. Finally, a case study shows the efficiency and applicability of the proposed model and algorithm.
Has growth mixture modeling improved our understanding of how early change predicts psychotherapy outcome?

Science.gov (United States)

Koffmann, Andrew

2017-03-02

Early change in psychotherapy predicts outcome. Seven studies have used growth mixture modeling [GMM; Muthén, B. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class-latent growth modeling. In L. M. Collins & A. G. Sawyers (Eds.), New methods for the analysis of change (pp. 291-322). Washington, DC: American Psychological Association] to identify patient classes based on early change but have yielded conflicting results. Here, we review the earlier studies and apply GMM to a new data set. In a university-based training clinic, 251 patients were administered the Outcome Questionnaire-45 [Lambert, M. J., Hansen, N. B., Umphress, V., Lunnen, K., Okiishi, J., Burlingame, G., … Reisinger, C. W. (1996). Administration and scoring manual for the Outcome Questionnaire (OQ 45.2). Wilmington, DE: American Professional Credentialing Services] at each psychotherapy session. We used GMM to identify class structure based on change in the first six sessions and examined trajectories as predictors of outcome. The sample was best described as a single class. There was no evidence of autoregressive trends in the data. We achieved better fit to the data by permitting latent variables some degree of kurtosis, rather than to assume multivariate normality. Treatment outcome was predicted by the amount of early improvement, regardless of initial level of distress. The presence of sudden early gains or losses did not further improve outcome prediction. Early improvement is an easily computed, powerful predictor of psychotherapy outcome. The use of GMM to investigate the relationship between change and outcome is technically complex and computationally intensive. To date, it has not been particularly informative.
Hidden Markov latent variable models with multivariate longitudinal data.

Science.gov (United States)

Song, Xinyuan; Xia, Yemao; Zhu, Hongtu

2017-03-01

Cocaine addiction is chronic and persistent, and has become a major social and health problem in many countries. Existing studies have shown that cocaine addicts often undergo episodic periods of addiction to, moderate dependence on, or swearing off cocaine. Given its reversible feature, cocaine use can be formulated as a stochastic process that transits from one state to another, while the impacts of various factors, such as treatment received and individuals' psychological problems on cocaine use, may vary across states. This article develops a hidden Markov latent variable model to study multivariate longitudinal data concerning cocaine use from a California Civil Addict Program. The proposed model generalizes conventional latent variable models to allow bidirectional transition between cocaine-addiction states and conventional hidden Markov models to allow latent variables and their dynamic interrelationship. We develop a maximum-likelihood approach, along with a Monte Carlo expectation conditional maximization (MCECM) algorithm, to conduct parameter estimation. The asymptotic properties of the parameter estimates and statistics for testing the heterogeneity of model parameters are investigated. The finite sample performance of the proposed methodology is demonstrated by simulation studies. The application to cocaine use study provides insights into the prevention of cocaine use. © 2016, The International Biometric Society.
Development and validation of an extensive growth and growth boundary model for psychrotolerant Lactobacillus spp. in seafood and meat products

DEFF Research Database (Denmark)

Mejlholm, Ole; Dalgaard, Paw

2013-01-01

A new and extensive growth and growth boundary model for psychrotolerant Lactobacillus spp. was developed and validated for processed and unprocessed products of seafood and meat. The new model was developed by refitting and expanding an existing cardinal parameter model for growth and the growth...... of psychrotolerant Lactobacillus spp. was clearly demonstrated. The new model can be used to predict growth of psychrotolerant Lactobacillus spp. in seafood and meat products e.g. prediction of the time to a critical cell concentration of bacteria is considered useful for establishing the shelf life. In addition...... boundary of lactic acid bacteria (LAB) in processed seafood (O. Mejlholm and P. Dalgaard, J. Food Prot. 70. 2485–2497, 2007). Initially, to estimate values for the maximum specific growth rate at the reference temperature of 25°C (μref) and the theoretical minimum temperature that prevents growth...
Modeling and simulation of Si crystal growth from melt

Energy Technology Data Exchange (ETDEWEB)

Liu, Lijun; Liu, Xin; Li, Zaoyang [National Engineering Research Center for Fluid Machinery and Compressors, School of Energy and Power Engineering, Xi' an Jiaotong University, Xi' an, Shaanxi 710049 (China); Miyazawa, Hiroaki; Nakano, Satoshi; Kakimoto, Koichi [Research Institute for Applied Mechanics, Kyushu University, Kasuga 816-8580 (Japan)

2009-07-01

A numerical simulator was developed with a global model of heat transfer for any crystal growth taking place at high temperature. Convective, conductive and radiative heat transfers in the furnace are solved together in a conjugated way by a finite volume method. A three-dimensional (3D) global model was especially developed for simulation of heat transfer in any crystal growth with 3D features. The model enables 3D global simulation be conducted with moderate requirement of computer resources. The application of this numerical simulator to a CZ growth and a directional solidification process for Si crystals, the two major production methods for crystalline Si for solar cells, was introduced. Some typical results were presented, showing the importance and effectiveness of numerical simulation in analyzing and improving these kinds of Si crystal growth processes from melt. (copyright 2009 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
Seasonal and interannual variability of the water exchange in the Turkish Straits System estimated by modelling

Directory of Open Access Journals (Sweden)

V. MADERICH

2015-07-01

Full Text Available A chain of simple linked models is used to simulate the seasonal and interannual variability of the Turkish Straits System. This chain includes two-layer hydraulic models of the Bosphorus and Dardanelles straits simulating the exchange in terms of level and density difference along each strait, and a one-dimensional area averaged layered model of the Marmara Sea. The chain of models is complemented also by the similar layered model of the Black Sea proper and by a one-layer Azov Sea model with the Kerch Strait. This linked chain of models is used to study the seasonal and interannual variability of the system in the period 1970-2009. The salinity of the Black Sea water flowing into the Aegean Sea increases by approximately 1.7 times through entrainment from the lower layer. The flow entering into the lower layer of the Dardanelles Strait from the Aegean Sea is reduced by nearly 80% when it reaches the Black Sea. In the seasonal scale, a maximal transport in the upper layer and minimal transport in the bottom layer are during winter/spring for the Bosphorus and in spring for the Dardanelles Strait, whereas minimal transport in upper layer and maximal undercurrent are during the summer for the Bosphorus Strait and autumn for the Dardanelles Strait. The increase of freshwater flux into the Black Sea in interannual time scales (41 m3s-1 per year is accompanied by a more than twofold growth of the Dardanelles outflow to the North Aegean (102 m3s-1 per year.
A variable resolution nonhydrostatic global atmospheric semi-implicit semi-Lagrangian model

Science.gov (United States)

Pouliot, George Antoine

2000-10-01

The objective of this project is to develop a variable-resolution finite difference adiabatic global nonhydrostatic semi-implicit semi-Lagrangian (SISL) model based on the fully compressible nonhydrostatic atmospheric equations. To achieve this goal, a three-dimensional variable resolution dynamical core was developed and tested. The main characteristics of the dynamical core can be summarized as follows: Spherical coordinates were used in a global domain. A hydrostatic/nonhydrostatic switch was incorporated into the dynamical equations to use the fully compressible atmospheric equations. A generalized horizontal variable resolution grid was developed and incorporated into the model. For a variable resolution grid, in contrast to a uniform resolution grid, the order of accuracy of finite difference approximations is formally lost but remains close to the order of accuracy associated with the uniform resolution grid provided the grid stretching is not too significant. The SISL numerical scheme was implemented for the fully compressible set of equations. In addition, the generalized minimum residual (GMRES) method with restart and preconditioner was used to solve the three-dimensional elliptic equation derived from the discretized system of equations. The three-dimensional momentum equation was integrated in vector-form to incorporate the metric terms in the calculations of the trajectories. Using global re-analysis data for a specific test case, the model was compared to similar SISL models previously developed. Reasonable agreement between the model and the other independently developed models was obtained. The Held-Suarez test for dynamical cores was used for a long integration and the model was successfully integrated for up to 1200 days. Idealized topography was used to test the variable resolution component of the model. Nonhydrostatic effects were simulated at grid spacings of 400 meters with idealized topography and uniform flow. Using a high
Aerosol Drug Delivery During Noninvasive Positive Pressure Ventilation: Effects of Intersubject Variability and Excipient Enhanced Growth

Science.gov (United States)

Walenga, Ross L.; Kaviratna, Anubhav; Hindle, Michael

2017-01-01

Abstract Background: Nebulized aerosol drug delivery during the administration of noninvasive positive pressure ventilation (NPPV) is commonly implemented. While studies have shown improved patient outcomes for this therapeutic approach, aerosol delivery efficiency is reported to be low with high variability in lung-deposited dose. Excipient enhanced growth (EEG) aerosol delivery is a newly proposed technique that may improve drug delivery efficiency and reduce intersubject aerosol delivery variability when coupled with NPPV. Materials and Methods: A combined approach using in vitro experiments and computational fluid dynamics (CFD) was used to characterize aerosol delivery efficiency during NPPV in two new nasal cavity models that include face mask interfaces. Mesh nebulizer and in-line dry powder inhaler (DPI) sources of conventional and EEG aerosols were both considered. Results: Based on validated steady-state CFD predictions, EEG aerosol delivery improved lung penetration fraction (PF) values by factors ranging from 1.3 to 6.4 compared with conventional-sized aerosols. Furthermore, intersubject variability in lung PF was very high for conventional aerosol sizes (relative differences between subjects in the range of 54.5%–134.3%) and was reduced by an order of magnitude with the EEG approach (relative differences between subjects in the range of 5.5%–17.4%). Realistic in vitro experiments of cyclic NPPV demonstrated similar trends in lung delivery to those observed with the steady-state simulations, but with lower lung delivery efficiencies. Reaching the lung delivery efficiencies reported with the steady-state simulations of 80%–90% will require synchronization of aerosol administration during inspiration and reducing the size of the EEG aerosol delivery unit. Conclusions: The EEG approach enabled high-efficiency lung delivery of aerosols administered during NPPV and reduced intersubject aerosol delivery variability by an order of magnitude. Use of an in
Aerosol Drug Delivery During Noninvasive Positive Pressure Ventilation: Effects of Intersubject Variability and Excipient Enhanced Growth.

Science.gov (United States)

Walenga, Ross L; Longest, P Worth; Kaviratna, Anubhav; Hindle, Michael

2017-06-01

Nebulized aerosol drug delivery during the administration of noninvasive positive pressure ventilation (NPPV) is commonly implemented. While studies have shown improved patient outcomes for this therapeutic approach, aerosol delivery efficiency is reported to be low with high variability in lung-deposited dose. Excipient enhanced growth (EEG) aerosol delivery is a newly proposed technique that may improve drug delivery efficiency and reduce intersubject aerosol delivery variability when coupled with NPPV. A combined approach using in vitro experiments and computational fluid dynamics (CFD) was used to characterize aerosol delivery efficiency during NPPV in two new nasal cavity models that include face mask interfaces. Mesh nebulizer and in-line dry powder inhaler (DPI) sources of conventional and EEG aerosols were both considered. Based on validated steady-state CFD predictions, EEG aerosol delivery improved lung penetration fraction (PF) values by factors ranging from 1.3 to 6.4 compared with conventional-sized aerosols. Furthermore, intersubject variability in lung PF was very high for conventional aerosol sizes (relative differences between subjects in the range of 54.5%-134.3%) and was reduced by an order of magnitude with the EEG approach (relative differences between subjects in the range of 5.5%-17.4%). Realistic in vitro experiments of cyclic NPPV demonstrated similar trends in lung delivery to those observed with the steady-state simulations, but with lower lung delivery efficiencies. Reaching the lung delivery efficiencies reported with the steady-state simulations of 80%-90% will require synchronization of aerosol administration during inspiration and reducing the size of the EEG aerosol delivery unit. The EEG approach enabled high-efficiency lung delivery of aerosols administered during NPPV and reduced intersubject aerosol delivery variability by an order of magnitude. Use of an in-line DPI device that connects to the NPPV mask appears to be a

Growth of farmed blue mussels ( Mytilus edulis L.) in a Norwegian coastal area; comparison of food proxies by DEB modeling

Science.gov (United States)

Handå, Aleksander; Alver, Morten; Edvardsen, Christian Vik; Halstensen, Stein; Olsen, Anders Johny; Øie, Gunvor; Reitan, Kjell Inge; Olsen, Yngvar; Reinertsen, Helge

2011-11-01

Seston variables and growth of the blue mussel ( Mytilus edulis L.) were measured during the growth season from March to October in three suspended longline farms in Central Norway; one in the inner part of Åfjorden (63° 56' N, 10° 11' E) and two in Inner and Outer Koet, respectively (63° 49' N, 9° 42' and 47' E). Four seston variables were used as alternative input values in a Dynamic Energy Budget (DEB) model to compare their suitability as food proxies for predicting mussel growth: 1; total particulate matter (TPM), 2; particulate organic matter (POM), 3; organic content (OC) and 4; chlorophyll a (chl a). Mean TPM and POM measured 6.1 and 1.9 mg L - 1 in Åfjorden, 10.3 and 4.2 mg L - 1 in Inner Koet, and 10.5 and 4.6 mg L - 1 in Outer Koet, respectively, resulting in a mean OC of 32, 41 and 44% in Åfjorden and Inner and Outer Koet, respectively. Mean chl a measured 1.6 μg L - 1 in Åfjorden, 3.1 μg L - 1 in Inner Koet, and 1.6 μg L - 1 in Outer Koet. Average length growth was 0.20% day - 1 in medium sized mussels (24-36 mm) in Åfjorden and 0.08% day - 1 in large mussels (40-55 mm) in Inner and Outer Koet. Mean standardized soft tissue dry weight ranged between 250 and 390 mg in Åfjorden, 600 and 1175 in Inner Koet, and 600 and 960 mg in Outer Koet, and showed a seasonal pattern independent of growth in length with scattered spawnings. The model showed the best match for a single criterion for growth in both length and soft tissue dry weight for different food proxies depending on location. TPM gave the best match in Åfjorden, while chl a and POM gave the best match in Inner and Outer Koet, respectively. For Åfjorden, growth in length decreased markedly at the end of the sampling period, and this decrease was not reproduced by the model for any of the food proxies. For Inner and Outer Koet, agreement between measured and modeled length was quite good for the optimal choices of food proxy, with clear variations between the proxies for both farms. The
Variable Fidelity Aeroelastic Toolkit - Structural Model, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — The proposed innovation is a methodology to incorporate variable fidelity structural models into steady and unsteady aeroelastic and aeroservoelastic analyses in...
The Properties of Model Selection when Retaining Theory Variables

DEFF Research Database (Denmark)

Hendry, David F.; Johansen, Søren

Economic theories are often fitted directly to data to avoid possible model selection biases. We show that embedding a theory model that specifies the correct set of m relevant exogenous variables, x{t}, within the larger set of m+k candidate variables, (x{t},w{t}), then selection over the second...... set by their statistical significance can be undertaken without affecting the estimator distribution of the theory parameters. This strategy returns the theory-parameter estimates when the theory is correct, yet protects against the theory being under-specified because some w{t} are relevant....
Asset growth strategy and bank performance in Nigeria | Toby ...

African Journals Online (AJOL)

This study examines the nature of the relationship between asset growth rate and growth in such output variables as total cost, total income and net profit In the Nigerian banking industry. Based on the data of 25 quoted Nigerian banks, three linear regression models were estimated complemented by descriptive data ...
Leptin administration affects growth and skeletal development in a rat intrauterine growth restriction model: preliminary study.

Science.gov (United States)

Bar-El Dadon, Shimrit; Shahar, Ron; Katalan, Vered; Monsonego-Ornan, Efrat; Reifen, Ram

2011-09-01

Skeletal abnormalities are one of the hallmarks of growth delay during gestation. The aim of this study was to determine changes induced by leptin in skeletal growth and development in a rat model of intrauterine growth retardation (IUGR) and to elucidate the possible underlying mechanisms. Intrauterine growth retardation was induced prepartum and the effects of leptin to mothers prenatally or to offspring postnatally were studied. Radii were harvested and tested mechanically and structurally. Tibias were evaluated for growth-plate morphometry. On day 40 postpartum, total bone length and mineral density and tibial growth-plate width and numbers of cells within its zones of offspring treated with leptin were significantly greater than in the control group. Postnatal leptin administration in an IUGR model improves the structural properties and elongation rate of bone. These findings could pave the way to preventing some phenotypic presentations of IUGR. Copyright © 2011 Elsevier Inc. All rights reserved.
Modelling breast cancer tumour growth for a stable disease population.

Science.gov (United States)

Isheden, Gabriel; Humphreys, Keith

2017-01-01

Statistical models of breast cancer tumour progression have been used to further our knowledge of the natural history of breast cancer, to evaluate mammography screening in terms of mortality, to estimate overdiagnosis, and to estimate the impact of lead-time bias when comparing survival times between screen detected cancers and cancers found outside of screening programs. Multi-state Markov models have been widely used, but several research groups have proposed other modelling frameworks based on specifying an underlying biological continuous tumour growth process. These continuous models offer some advantages over multi-state models and have been used, for example, to quantify screening sensitivity in terms of mammographic density, and to quantify the effect of body size covariates on tumour growth and time to symptomatic detection. As of yet, however, the continuous tumour growth models are not sufficiently developed and require extensive computing to obtain parameter estimates. In this article, we provide a detailed description of the underlying assumptions of the continuous tumour growth model, derive new theoretical results for the model, and show how these results may help the development of this modelling framework. In illustrating the approach, we develop a model for mammography screening sensitivity, using a sample of 1901 post-menopausal women diagnosed with invasive breast cancer.
Relationship between health services, socioeconomic variables and inadequate weight gain among Brazilian children.

Science.gov (United States)

de Souza, A C; Peterson, K E; Cufino, E; Gardner, J; Craveiro, M V; Ascherio, A

1999-01-01

This ecological analysis assessed the relative contribution of behavioural, health services and socioeconomic variables to inadequate weight gain in infants (0-11 months) and children (12-23 months) in 140 municipalities in the State of Ceara, north-east Brazil. To assess the total effect of selected variables, we fitted three unique sets of multivariate linear regression models to the prevalence of inadequate weight gain in infants and in children. The final predictive models included variables from the three sets. Findings showed that participation in growth monitoring and urbanization were inversely and significantly associated with the prevalence of inadequate weight gain in infants, accounting for 38.3% of the variation. Female illiteracy rate, participation in growth monitoring and degree of urbanization were all positively associated with prevalence of inadequate weight gain in children. Together, these factors explained 25.6% of the variation. Our results suggest that efforts to reduce the average municipality-specific female illiteracy rate, in combination with participation in growth monitoring, may be effective in reducing municipality-level prevalence of inadequate weight gain in infants and children in Ceara.
Evaluating the Predictive Value of Growth Prediction Models

Science.gov (United States)

Murphy, Daniel L.; Gaertner, Matthew N.

2014-01-01

This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…
Optimization modeling of U.S. renewable electricity deployment using local input variables

Science.gov (United States)

Bernstein, Adam

For the past five years, state Renewable Portfolio Standard (RPS) laws have been a primary driver of renewable electricity (RE) deployments in the United States. However, four key trends currently developing: (i) lower natural gas prices, (ii) slower growth in electricity demand, (iii) challenges of system balancing intermittent RE within the U.S. transmission regions, and (iv) fewer economical sites for RE development, may limit the efficacy of RPS laws over the remainder of the current RPS statutes' lifetime. An outsized proportion of U.S. RE build occurs in a small number of favorable locations, increasing the effects of these variables on marginal RE capacity additions. A state-by-state analysis is necessary to study the U.S. electric sector and to generate technology specific generation forecasts. We used LP optimization modeling similar to the National Renewable Energy Laboratory (NREL) Renewable Energy Development System (ReEDS) to forecast RE deployment across the 8 U.S. states with the largest electricity load, and found state-level RE projections to Year 2031 significantly lower than thoseimplied in the Energy Information Administration (EIA) 2013 Annual Energy Outlook forecast. Additionally, the majority of states do not achieve their RPS targets in our forecast. Combined with the tendency of prior research and RE forecasts to focus on larger national and global scale models, we posit that further bottom-up state and local analysis is needed for more accurate policy assessment, forecasting, and ongoing revision of variables as parameter values evolve through time. Current optimization software eliminates much of the need for algorithm coding and programming, allowing for rapid model construction and updating across many customized state and local RE parameters. Further, our results can be tested against the empirical outcomes that will be observed over the coming years, and the forecast deviation from the actuals can be attributed to discrete parameter
Life insurance, financial development and economic growth in South Africa: An application of the autoregressive distributed lag model

Directory of Open Access Journals (Sweden)

Athenia Bongani Sibindi

2014-12-01

Full Text Available The life insurance sector may contribute to economic growth by its very mechanism of savings mobilisation and thereby performing an intermediation role in the economy. This ensures that capital is provided to deficient units who are in need of capital to finance their working capital requirements and invest in technology thereby resulting in an increase in output. In this way, it could be argued that life insurance development spurs financial development. In this article we investigate the causal relationship between the life insurance sector, financial development and economic growth in South Africa for the period 1990 to 2012 by applying the ARDL bounds testing procedure. We make use of life insurance density as the proxy for life insurance development, real per capita growth domestic product as the proxy for economic growth and real broad money per capita as the proxy for financial development. We test for cointegration amongst the variables by applying the bounds test and then proceed to test for Granger causality based on the error correction model. Our results confirm that the variables are cointegrated and move in tandem to each other in the long-run. The results also indicate that the direction of causality runs from the economy to the life insurance sector in the short-run which is consistent with the “demand-following” insurance-growth hypothesis. There is also evidence of bidirectional Granger causality running from the economy to financial development and vice versa, both in the long-run and short-run. The results also reveal that life insurance complements financial development in bringing about economic growth further lending credence to the “complementarity” hypothesis
Optimal Patent Life in a Variety-Expansion Growth Model

OpenAIRE

Lin, Hwan C.

2013-01-01

This paper presents more channels through which the optimal patent life is determined in a R&D-based endogenous growth model that permits growth of new varieties of consumer goods over time. Its modeling features include an endogenous hazard rate facing incumbent monopolists, the prevalence of research congestion, and the aggregate welfare importance of product differentiation. As a result, a patent’s effective life is endogenized and less than its legal life. The model is calibrated to a glo...
Effects of growth reducer and nitrogen fertilization on morphological variables, SPAD index, interception of radiation and productivity of wheat

OpenAIRE

Elvis Felipe Elli; Braulio Otomar Caron; Sandro Luis Petter Medeiros; Elder Eloy; Gean Charles Monteiro; Denise Schmidt

2015-01-01

ABSTRACT The objective of this study was to evaluate the effect of growth reducer and nitrogen fertilization on morphological variables, SPAD index, radiation interception, and grain yield of three cultivars of wheat. The experimental design was a randomized block in factorial scheme 3x5x2, with three cultivars (Mestre, Iguaçú and Itaipú), five nitrogen doses (0, 40, 80, 120, 160 Kg ha-1), and application or no application of a growth reducer, with three replications. The following characteri...
Nonlinear model-based control of the Czochralski process III: Proper choice of manipulated variables and controller parameter scheduling

Science.gov (United States)

Neubert, M.; Winkler, J.

2012-12-01

This contribution continues an article series [1,2] about the nonlinear model-based control of the Czochralski crystal growth process. The key idea of the presented approach is to use a sophisticated combination of nonlinear model-based and conventional (linear) PI controllers for tracking of both, crystal radius and growth rate. Using heater power and pulling speed as manipulated variables several controller structures are possible. The present part tries to systematize the properties of the materials to be grown in order to get unambiguous decision criteria for a most profitable choice of the controller structure. For this purpose a material specific constant M called interface mobility and a more process specific constant S called system response number are introduced. While the first one summarizes important material properties like thermal conductivity and latent heat the latter one characterizes the process by evaluating the average axial thermal gradients at the phase boundary and the actual growth rate at which the crystal is grown. Furthermore these characteristic numbers are useful for establishing a scheduling strategy for the PI controller parameters in order to improve the controller performance. Finally, both numbers give a better understanding of the general thermal system dynamics of the Czochralski technique.
RELATIONSHIP BETWEEN POLITICAL INSTABILITY AND ECONOMIC GROWTH: THE CASE OF TURKEY (1987-2006

Directory of Open Access Journals (Sweden)

SELİM ŞANLISOY

2013-06-01

Full Text Available Economy and policy are always in mutual interaction. Especially the fact that sharp judgement between economical variables and democratization cannot be put forward shows that the relationship between political instability and economical variables should be analysed. In recent studies political stability or instability variables including more comprehensive variables instead of democratization variables have been used. The main aim of this study is to analyse the effects of political instability often experienced in Turkey’s conditions on economic growth. Here in terms of the analysis of variables (data time series a model with single equation was established and within the framework of this model some analyses were carried out with the help of main and control variables. To the findings, in accordance with the literature, it is verified that there is a reverse relationship between political instability and economic growth in Turkey and in the light of the findings, some policy suggestions are also made.
Residual Structures in Latent Growth Curve Modeling

Science.gov (United States)

Grimm, Kevin J.; Widaman, Keith F.

2010-01-01

Several alternatives are available for specifying the residual structure in latent growth curve modeling. Two specifications involve uncorrelated residuals and represent the most commonly used residual structures. The first, building on repeated measures analysis of variance and common specifications in multilevel models, forces residual variances…
Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations.

Science.gov (United States)

Deng, Chenhui; Plan, Elodie L; Karlsson, Mats O

2016-06-01

Parameter variation in pharmacometric analysis studies can be characterized as within subject parameter variability (WSV) in pharmacometric models. WSV has previously been successfully modeled using inter-occasion variability (IOV), but also stochastic differential equations (SDEs). In this study, two approaches, dynamic inter-occasion variability (dIOV) and adapted stochastic differential equations, were proposed to investigate WSV in pharmacometric count data analysis. These approaches were applied to published count models for seizure counts and Likert pain scores. Both approaches improved the model fits significantly. In addition, stochastic simulation and estimation were used to explore further the capability of the two approaches to diagnose and improve models where existing WSV is not recognized. The results of simulations confirmed the gain in introducing WSV as dIOV and SDEs when parameters vary randomly over time. Further, the approaches were also informative as diagnostics of model misspecification, when parameters changed systematically over time but this was not recognized in the structural model. The proposed approaches in this study offer strategies to characterize WSV and are not restricted to count data.
Influences of variables on ship collision probability in a Bayesian belief network model

International Nuclear Information System (INIS)

Hänninen, Maria; Kujala, Pentti

2012-01-01

The influences of the variables in a Bayesian belief network model for estimating the role of human factors on ship collision probability in the Gulf of Finland are studied for discovering the variables with the largest influences and for examining the validity of the network. The change in the so-called causation probability is examined while observing each state of the network variables and by utilizing sensitivity and mutual information analyses. Changing course in an encounter situation is the most influential variable in the model, followed by variables such as the Officer of the Watch's action, situation assessment, danger detection, personal condition and incapacitation. The least influential variables are the other distractions on bridge, the bridge view, maintenance routines and the officer's fatigue. In general, the methods are found to agree on the order of the model variables although some disagreements arise due to slightly dissimilar approaches to the concept of variable influence. The relative values and the ranking of variables based on the values are discovered to be more valuable than the actual numerical values themselves. Although the most influential variables seem to be plausible, there are some discrepancies between the indicated influences in the model and literature. Thus, improvements are suggested to the network.
Otolith shape variability and associated body growth differences in giant grenadier, Albatrossia pectoralis.

Science.gov (United States)

Rodgveller, Cara J; Hutchinson, Charles E; Harris, Jeremy P; Vulstek, Scott C; Guthrie, Charles M

2017-01-01

Fish stocks can be defined by differences in their distribution, life history, and genetics. Managing fish based on stock structure is integral to successful management of a species because fishing may affect stocks disproportionately. Genetic and environmental differences can affect the shape and growth of otoliths and these differences may be indicative of stock structure. To investigate the potential for speciation or stock structure in giant grenadier, Albatrossia pectoralis, we quantified the shape of female giant grenadier otoliths and compared body growth rates for fish with three otolith shapes; shape types were classified visually by an experienced giant grenadier age reader, and were not defined by known distribution or life history differences. We found extreme variation in otolith shape among individuals; however, the shapes were a gradation and not clearly defined into three groups. The two more extreme shapes, visually defined as "hatchet" and "comb", were discernable based on principal component analyses of elliptical Fourier descriptors, and the "mixed" shape overlapped both of the extreme shapes. Fish with hatchet-shaped otoliths grew faster than fish with comb-shaped otoliths. A genetic test (cytochrome c oxidase 1 used by the Fish Barcode of Life Initiative) showed almost no variability among samples, indicating that the samples were all from one species. The lack of young specimens makes it difficult to link otolith shape and growth difference to life history. In addition, shape could not be correlated with adult movement patterns because giant grenadiers experience 100% mortality after capture and, therefore, cannot be tagged and released. Despite these limitations, the link between body growth and otolith shape indicates measurable differences that deserve more study.
Forest Growth and Yield Models Viewed From a Different Perspective

Science.gov (United States)

Jeffery C. Goelz

2002-01-01

Typically, when different forms of growth and yield models are considered, they are grouped into convenient discrete classes. As a heuristic device, I chose to use a contrasting perspective, that all growth and yield models are diameter distribution models that merely differ in regard to which diameter distribution is employed and how the distribution is projected to...
A generalized diffusion model for growth of nanoparticles synthesized by colloidal methods.

Science.gov (United States)

Wen, Tianlong; Brush, Lucien N; Krishnan, Kannan M

2014-04-01

A nanoparticle growth model is developed to predict and guide the syntheses of monodisperse colloidal nanoparticles in the liquid phase. The model, without any a priori assumptions, is based on the Fick's law of diffusion, conservation of mass and the Gibbs-Thomson equation for crystal growth. In the limiting case, this model reduces to the same expression as the currently accepted model that requires the assumption of a diffusion layer around each nanoparticle. The present growth model bridges the two limiting cases of the previous model i.e. complete diffusion controlled and adsorption controlled growth of nanoparticles. Specifically, the results show that a monodispersion of nanoparticles can be obtained both with fast monomer diffusion and with surface reaction under conditions of small diffusivity to surface reaction constant ratio that results is growth 'focusing'. This comprehensive description of nanoparticle growth provides new insights and establishes the required conditions for fabricating monodisperse nanoparticles critical for a wide range of applications. Copyright © 2013 Elsevier Inc. All rights reserved.

Uncertainty and variability in computational and mathematical models of cardiac physiology.

Science.gov (United States)

Mirams, Gary R; Pathmanathan, Pras; Gray, Richard A; Challenor, Peter; Clayton, Richard H

2016-12-01

Mathematical and computational models of cardiac physiology have been an integral component of cardiac electrophysiology since its inception, and are collectively known as the Cardiac Physiome. We identify and classify the numerous sources of variability and uncertainty in model formulation, parameters and other inputs that arise from both natural variation in experimental data and lack of knowledge. The impact of uncertainty on the outputs of Cardiac Physiome models is not well understood, and this limits their utility as clinical tools. We argue that incorporating variability and uncertainty should be a high priority for the future of the Cardiac Physiome. We suggest investigating the adoption of approaches developed in other areas of science and engineering while recognising unique challenges for the Cardiac Physiome; it is likely that novel methods will be necessary that require engagement with the mathematics and statistics community. The Cardiac Physiome effort is one of the most mature and successful applications of mathematical and computational modelling for describing and advancing the understanding of physiology. After five decades of development, physiological cardiac models are poised to realise the promise of translational research via clinical applications such as drug development and patient-specific approaches as well as ablation, cardiac resynchronisation and contractility modulation therapies. For models to be included as a vital component of the decision process in safety-critical applications, rigorous assessment of model credibility will be required. This White Paper describes one aspect of this process by identifying and classifying sources of variability and uncertainty in models as well as their implications for the application and development of cardiac models. We stress the need to understand and quantify the sources of variability and uncertainty in model inputs, and the impact of model structure and complexity and their consequences for
Comparison between two meshless methods based on collocation technique for the numerical solution of four-species tumor growth model

Science.gov (United States)

Dehghan, Mehdi; Mohammadi, Vahid

2017-03-01

As is said in [27], the tumor-growth model is the incorporation of nutrient within the mixture as opposed to being modeled with an auxiliary reaction-diffusion equation. The formulation involves systems of highly nonlinear partial differential equations of surface effects through diffuse-interface models [27]. Simulations of this practical model using numerical methods can be applied for evaluating it. The present paper investigates the solution of the tumor growth model with meshless techniques. Meshless methods are applied based on the collocation technique which employ multiquadrics (MQ) radial basis function (RBFs) and generalized moving least squares (GMLS) procedures. The main advantages of these choices come back to the natural behavior of meshless approaches. As well as, a method based on meshless approach can be applied easily for finding the solution of partial differential equations in high-dimension using any distributions of points on regular and irregular domains. The present paper involves a time-dependent system of partial differential equations that describes four-species tumor growth model. To overcome the time variable, two procedures will be used. One of them is a semi-implicit finite difference method based on Crank-Nicolson scheme and another one is based on explicit Runge-Kutta time integration. The first case gives a linear system of algebraic equations which will be solved at each time-step. The second case will be efficient but conditionally stable. The obtained numerical results are reported to confirm the ability of these techniques for solving the two and three-dimensional tumor-growth equations.
Natural climate variability in a coupled model

International Nuclear Information System (INIS)

Zebiak, S.E.; Cane, M.A.

1990-01-01

Multi-century simulations with a simplified coupled ocean-atmosphere model are described. These simulations reveal an impressive range of variability on decadal and longer time scales, in addition to the dominant interannual el Nino/Southern Oscillation signal that the model originally was designed to simulate. Based on a very large sample of century-long simulations, it is nonetheless possible to identify distinct model parameter sensitivities that are described here in terms of selected indices. Preliminary experiments motivated by general circulation model results for increasing greenhouse gases suggest a definite sensitivity to model global warming. While these results are not definitive, they strongly suggest that coupled air-sea dynamics figure prominently in global change and must be included in models for reliable predictions
Geologic Variable Associated with Height of Yellow-Poplar Stand in the Bald Mountains of North Carolina

Science.gov (United States)

W. Henry McNab; Carl E. Merschat

1990-01-01

Quartz grain size and mylonitization, geologic variables determined fromrocks on sites, were associated with total height of yellow-poplar (Liriodendron tulipifera L.) standsand may be of value as independent variables in modeling tree growth from site characteristics. A predictive model containing quartz grain site and stand age accounted for about 54% of the...
The Importance of Considering the Temporal Distribution of Climate Variables for Ecological-Economic Modeling to Calculate the Consequences of Climate Change for Agriculture

Science.gov (United States)

Plegnière, Sabrina; Casper, Markus; Hecker, Benjamin; Müller-Fürstenberger, Georg

2014-05-01

The basis of many models to calculate and assess climate change and its consequences are annual means of temperature and precipitation. This method leads to many uncertainties especially at the regional or local level: the results are not realistic or too coarse. Particularly in agriculture, single events and the distribution of precipitation and temperature during the growing season have enormous influences on plant growth. Therefore, the temporal distribution of climate variables should not be ignored. To reach this goal, a high-resolution ecological-economic model was developed which combines a complex plant growth model (STICS) and an economic model. In this context, input data of the plant growth model are daily climate values for a specific climate station calculated by the statistical climate model (WETTREG). The economic model is deduced from the results of the plant growth model STICS. The chosen plant is corn because corn is often cultivated and used in many different ways. First of all, a sensitivity analysis showed that the plant growth model STICS is suitable to calculate the influences of different cultivation methods and climate on plant growth or yield as well as on soil fertility, e.g. by nitrate leaching, in a realistic way. Additional simulations helped to assess a production function that is the key element of the economic model. Thereby the problems when using mean values of temperature and precipitation in order to compute a production function by linear regression are pointed out. Several examples show why a linear regression to assess a production function based on mean climate values or smoothed natural distribution leads to imperfect results and why it is not possible to deduce a unique climate factor in the production function. One solution for this problem is the additional consideration of stress indices that show the impairment of plants by water or nitrate shortage. Thus, the resulting model takes into account not only the ecological
Contrasting growth forecasts across the geographical range of Scots pine due to altitudinal and latitudinal differences in climatic sensitivity.

Science.gov (United States)

Matías, Luis; Linares, Juan C; Sánchez-Miranda, Ángela; Jump, Alistair S

2017-10-01

Ongoing changes in global climate are altering ecological conditions for many species. The consequences of such changes are typically most evident at the edge of a species' geographical distribution, where differences in growth or population dynamics may result in range expansions or contractions. Understanding population responses to different climatic drivers along wide latitudinal and altitudinal gradients is necessary in order to gain a better understanding of plant responses to ongoing increases in global temperature and drought severity. We selected Scots pine (Pinus sylvestris L.) as a model species to explore growth responses to climatic variability (seasonal temperature and precipitation) over the last century through dendrochronological methods. We developed linear models based on age, climate and previous growth to forecast growth trends up to year 2100 using climatic predictions. Populations were located at the treeline across a latitudinal gradient covering the northern, central and southernmost populations and across an altitudinal gradient at the southern edge of the distribution (treeline, medium and lower elevations). Radial growth was maximal at medium altitude and treeline of the southernmost populations. Temperature was the main factor controlling growth variability along the gradients, although the timing and strength of climatic variables affecting growth shifted with latitude and altitude. Predictive models forecast a general increase in Scots pine growth at treeline across the latitudinal distribution, with southern populations increasing growth up to year 2050, when it stabilizes. The highest responsiveness appeared at central latitude, and moderate growth increase is projected at the northern limit. Contrastingly, the model forecasted growth declines at lowland-southern populations, suggesting an upslope range displacement over the coming decades. Our results give insight into the geographical responses of tree species to climate change
The growth of optically variable features on banknotes

Science.gov (United States)

Lancaster, Ian M.; Mitchell, Astrid

2004-06-01

Public verification features are part of a matrix of security features on banknotes which allow the authenticity of legitimate banknotes to be established. They are characterised by being overt and easy to verify -- no examination tool or equipment is required even though the devices themselves are invariably highly sophisticated. Recent developments, though, combine overt and covert elements which may reqire inspection tools. Traditionally, banknote issuers were reluctant to involve the general public in the checking of banknotes, preferring to rely on those employed to handle them, experts and machinery to authenticate the various (normally undisclosed) features. This has now changed as the ability to counterfeit has moved from those highly skilled in printing to anyone with a scanner and computer -- the incidence of counterfeiting has grown exponentially in the last decade. Three techniques for what can be categorized as public verification features have been used for banknotes for many decades and continue to provide a barrier to counterfeiting: (1) the optical effects of watermarks; (2) the appearance and tactile characteristics of cylinder mould-made paper; (3) the tactile characteristics of intaglio print. Since the 1980s the emergence of threads and optically variable features have added to the available features which can be utilized on banknotes for public verification purposes. OVDs fall broadly into the two categories of diffraction and color shift. Products which utilize the former include holograms, kinegrams and other devices originated with similar techniques and bearing a variety of proprietary names, but collectively known as diffractive optically variable image devices (DOVIDs). All share the fundamental characteristic of changing in appearance according to the viewing angle, providing an effective barrier to the increasingly common use of digital reprographic technology as a counterfeiting tool as well as a simple means for verification by the
SUSTAINABILITY OF ECONOMIC GROWTH AND INEQUALITY IN INCOMES DISTRIBUTION

Directory of Open Access Journals (Sweden)

Bogdan Ion Boldea

2012-07-01

Full Text Available The problem of inequality in incomes distribution is a present one, much discussed. Economic growth is considered an essential force to reduce the level of poverty by increasing the labor demand and finally the wages within the economy. But the extent to which poverty is reduced as a result of economic growth depends mostly on the initial inequalities in income and on how the distribution of income changes with economic growth. A lot of researches are focused on studying the evolution of inequality in incomes distribution and others have attempted to explore the relationship between income inequality and economic growth. There are also studies which try to identify the main factors which have impact on inequality in incomes distribution. The objective of this study is to put in discussion another possible factor that affects the variability on inequality of incomes distribution â€“ economic growth variability. As background research, until now, we did not find any studies which are investigating this possible relation between inequality of incomes distribution and economic growth variability. To provide some empirical evidences for a positive impact of social output volatility on inequality of incomesâ€™ distribution we are involving a small sample of 27 developing countries for an observation time span between 1995 and 2006. The values of the Gini coefficient reported in World Income Inequality Database are used as dependent variable. As a first step in testing our research hypothesis, we are involving a static panel data model with pooled ordinary least squares (OLS, fixed effects (FE and random effects (RE estimators. The F statistics tests the null hypothesis of same specific effects for all countries. If we accept the null hypothesis, we could use the OLS estimator. The Hausman test can decide which model is better: random effects (RE versus fixed effects (FE. The FE model was selected because it avoids the inconsistency due to
Model for the growth of the world airline network

Science.gov (United States)

Verma, T.; Araújo, N. A. M.; Nagler, J.; Andrade, J. S.; Herrmann, H. J.

2016-06-01

We propose a probabilistic growth model for transport networks which employs a balance between popularity of nodes and the physical distance between nodes. By comparing the degree of each node in the model network and the World Airline Network (WAN), we observe that the difference between the two is minimized for α≈2. Interestingly, this is the value obtained for the node-node correlation function in the WAN. This suggests that our model explains quite well the growth of airline networks.
An endogenous growth model with embodied energy-saving technical change

International Nuclear Information System (INIS)

Van Zon, A.; Yetkiner, I. H.

2003-01-01

In this paper, we extend the Romer [Journal of Political Economy 98 (Part 2) (1990) S271] model in two ways. First we include energy consumption of intermediates. Second, intermediates become heterogeneous due to endogenous energy-saving technical change. We show that the resulting model can still generate steady state growth, but the growth rate depends negatively on the growth of real energy prices. The reason is that real energy price rises will lower the profitability of using new intermediate goods, and hence, the profitability of doing research, and therefore have a negative impact on growth. We also show that the introduction of an energy tax that is recycled in the form of an R and D subsidy may increase growth. We conclude that in order to have energy efficiency growth and output growth under rising real energy prices, a combination of R and D and energy policy is called for
Using Calculus to Model the Growth of L. Plantarum Bacteria

Directory of Open Access Journals (Sweden)

Erin Carey

2009-01-01

Full Text Available Experimental data for the growth of Lactobacillus plantarum bacteria have been obtained over time, creating the need for mathematical means to model this data. We use the Gompertz model because it is a sigmoid function for a time series, where growth is slowest at the start and end of a time period. The Gompertz model is especially useful because it defines specific parameters that characterize the S-shaped curve. In addition, the Gompertz model uses relative growth, which is the logarithm of the given population compared to the initial population. This reflects the fact that bacteria grow exponentially. The important parameters that were found were the lag time and the asymptote.
Applying a particle filtering technique for canola crop growth stage estimation in Canada

Science.gov (United States)

Sinha, Abhijit; Tan, Weikai; Li, Yifeng; McNairn, Heather; Jiao, Xianfeng; Hosseini, Mehdi

2017-10-01

Accurate crop growth stage estimation is important in precision agriculture as it facilitates improved crop management, pest and disease mitigation and resource planning. Earth observation imagery, specifically Synthetic Aperture Radar (SAR) data, can provide field level growth estimates while covering regional scales. In this paper, RADARSAT-2 quad polarization and TerraSAR-X dual polarization SAR data and ground truth growth stage data are used to model the influence of canola growth stages on SAR imagery extracted parameters. The details of the growth stage modeling work are provided, including a) the development of a new crop growth stage indicator that is continuous and suitable as the state variable in the dynamic estimation procedure; b) a selection procedure for SAR polarimetric parameters that is sensitive to both linear and nonlinear dependency between variables; and c) procedures for compensation of SAR polarimetric parameters for different beam modes. The data was collected over three crop growth seasons in Manitoba, Canada, and the growth model provides the foundation of a novel dynamic filtering framework for real-time estimation of canola growth stages using the multi-sensor and multi-mode SAR data. A description of the dynamic filtering framework that uses particle filter as the estimator is also provided in this paper.
Computer simulation of grain growth in HAZ

Science.gov (United States)

Gao, Jinhua

Two different models for Monte Carlo simulation of normal grain growth in metals and alloys were developed. Each simulation model was based on a different approach to couple the Monte Carlo simulation time to real time-temperature. These models demonstrated the applicability of Monte Carlo simulation to grain growth in materials processing. A grain boundary migration (GBM) model coupled the Monte Carlo simulation to a first principle grain boundary migration model. The simulation results, by applying this model to isothermal grain growth in zone-refined tin, showed good agreement with experimental results. An experimental data based (EDB) model coupled the Monte Carlo simulation with grain growth kinetics obtained from the experiment. The results of the application of the EDB model to the grain growth during continuous heating of a beta titanium alloy correlated well with experimental data. In order to acquire the grain growth kinetics from the experiment, a new mathematical method was developed and utilized to analyze the experimental data on isothermal grain growth. Grain growth in the HAZ of 0.2% Cu-Al alloy was successfully simulated using the EDB model combined with grain growth kinetics obtained from the experiment and measured thermal cycles from the welding process. The simulated grain size distribution in the HAZ was in good agreement with experimental results. The pinning effect of second phase particles on grain growth was also simulated in this work. The simulation results confirmed that by introducing the variable R, degree of contact between grain boundaries and second phase particles, the Zener pinning model can be modified as${D/ r} = {K/{Rf}}$where D is the pinned grain size, r the mean size of second phase particles, K a constant, f the area fraction (or the volume fraction in 3-D) of second phase.
Re-Examining the Finance-Growth Nexus: Empirical Evidence from Indonesia

Directory of Open Access Journals (Sweden)

M. Shabri Abd. Majid

2007-06-01

Full Text Available This paper empirically examines the short- and long-run relationships between financial development and economic growth during the post-1997 financial crisis in Indonesia by employing a battery of times-series techniques, such as Autoregressive Dis-tributed Lag (ARDL model, vector error correction model (VECM, variance decompositions (VDCs, and impulse-response functions (IRFs. Based on the ARDL (2, 0, 1, 2 model, the study finds that there exists a long-run equilibrium between economic growth and financial depth, share of investment, and inflation. In the long run, inflation is found to be the only variable which significantly (negatively affects economic growth, implying a crucial role of maintaining a low rate of inflation in promoting the economic growth in the country. As for the dynamic causalities among the variables, the study finds the bidirectional causation between economic growth and investment, while the unidirectional causation is only found running from financial depth to investment. The finding of independence between economic growth and financial development supports the view of “the independent hypothesis” of Lucas (1988. Finally, based on VDCs and IRFs, the study documents that the variations in the economic growth respond more to shocks in the price stability (inflation, followed by investment and financial development. Our findings indicate that if policy makers want to promote growth, attention should be focused on long-run policies, i.e., maintaining the low rate of inflation.
The Balance-of-Payments-Constrained Growth Model and the Limits to Export-Led Growth

Directory of Open Access Journals (Sweden)

Robert A. Blecker

2000-12-01

Full Text Available This paper discusses how A. P. Thirlwall's model of balance-of-payments-constrained growth can be adapted to analyze the idea of a "fallacy of composition" in the export-led growth strategy of many developing countries. The Deaton-Muellbauer model of the Almost Ideal Demand System (AIDS is used to represent the adding-up constraints on individual countries' exports, when they are all trying to export competing products to the same foreign markets (i.e. newly industrializing countries are exporting similar types of manufactured goods to the OECD countries. The relevance of the model to the recent financial crises in developing countries and policy alternatives for redirecting development strategies are also discussed.
Using a laboratory-based growth model to estimate mass- and temperature-dependent growth parameters across populations of juvenile Chinook Salmon

Science.gov (United States)

Perry, Russell W.; Plumb, John M.; Huntington, Charles

2015-01-01

To estimate the parameters that govern mass- and temperature-dependent growth, we conducted a meta-analysis of existing growth data from juvenile Chinook Salmon Oncorhynchus tshawytscha that were fed an ad libitum ration of a pelleted diet. Although the growth of juvenile Chinook Salmon has been well studied, research has focused on a single population, a narrow range of fish sizes, or a narrow range of temperatures. Therefore, we incorporated the Ratkowsky model for temperature-dependent growth into an allometric growth model; this model was then fitted to growth data from 11 data sources representing nine populations of juvenile Chinook Salmon. The model fit the growth data well, explaining 98% of the variation in final mass. The estimated allometric mass exponent (b) was 0.338 (SE = 0.025), similar to estimates reported for other salmonids. This estimate of b will be particularly useful for estimating mass-standardized growth rates of juvenile Chinook Salmon. In addition, the lower thermal limit, optimal temperature, and upper thermal limit for growth were estimated to be 1.8°C (SE = 0.63°C), 19.0°C (SE = 0.27°C), and 24.9°C (SE = 0.02°C), respectively. By taking a meta-analytical approach, we were able to provide a growth model that is applicable across populations of juvenile Chinook Salmon receiving an ad libitum ration of a pelleted diet.
Modelling grain growth in the framework of Rational Extended Thermodynamics

International Nuclear Information System (INIS)

Kertsch, Lukas; Helm, Dirk

2016-01-01

Grain growth is a significant phenomenon for the thermomechanical processing of metals. Since the mobility of the grain boundaries is thermally activated and energy stored in the grain boundaries is released during their motion, a mutual interaction with the process conditions occurs. To model such phenomena, a thermodynamic framework for the representation of thermomechanical coupling phenomena in metals including a microstructure description is required. For this purpose, Rational Extended Thermodynamics appears to be a useful tool. We apply an entropy principle to derive a thermodynamically consistent model for grain coarsening due to the growth and shrinkage of individual grains. Despite the rather different approaches applied, we obtain a grain growth model which is similar to existing ones and can be regarded as a thermodynamic extension of that by Hillert (1965) to more general systems. To demonstrate the applicability of the model, we compare our simulation results to grain growth experiments in pure copper by different authors, which we are able to reproduce very accurately. Finally, we study the implications of the energy release due to grain growth on the energy balance. The present unified approach combining a microstructure description and continuum mechanics is ready to be further used to develop more elaborate material models for complex thermo-chemo-mechanical coupling phenomena. (paper)
Modelling grain growth in the framework of Rational Extended Thermodynamics

Science.gov (United States)

Kertsch, Lukas; Helm, Dirk

2016-05-01

Grain growth is a significant phenomenon for the thermomechanical processing of metals. Since the mobility of the grain boundaries is thermally activated and energy stored in the grain boundaries is released during their motion, a mutual interaction with the process conditions occurs. To model such phenomena, a thermodynamic framework for the representation of thermomechanical coupling phenomena in metals including a microstructure description is required. For this purpose, Rational Extended Thermodynamics appears to be a useful tool. We apply an entropy principle to derive a thermodynamically consistent model for grain coarsening due to the growth and shrinkage of individual grains. Despite the rather different approaches applied, we obtain a grain growth model which is similar to existing ones and can be regarded as a thermodynamic extension of that by Hillert (1965) to more general systems. To demonstrate the applicability of the model, we compare our simulation results to grain growth experiments in pure copper by different authors, which we are able to reproduce very accurately. Finally, we study the implications of the energy release due to grain growth on the energy balance. The present unified approach combining a microstructure description and continuum mechanics is ready to be further used to develop more elaborate material models for complex thermo-chemo-mechanical coupling phenomena.
Modeling plasma-assisted growth of graphene-carbon nanotube hybrid

International Nuclear Information System (INIS)

Tewari, Aarti

2016-01-01

A theoretical model describing the growth of graphene-CNT hybrid in a plasma medium is presented. Using the model, the growth of carbon nanotube (CNT) on a catalyst particle and thereafter the growth of the graphene on the CNT is studied under the purview of plasma sheath and number density kinetics of different plasma species. It is found that the plasma parameter such as ion density; gas ratios and process parameter such as source power affect the CNT and graphene dimensions. The variation in growth rates of graphene and CNT under different plasma power, gas ratios, and ion densities is analyzed. Based on the results obtained, it can be concluded that higher hydrocarbon ion densities and gas ratios of hydrocarbon to hydrogen favor the growth of taller CNTs and graphene, respectively. In addition, the CNT tip radius reduces with hydrogen ion density and higher plasma power favors graphene with lesser thickness. The present study can help in better understanding of the graphene-CNT hybrid growth in a plasma medium.
Modeling plasma-assisted growth of graphene-carbon nanotube hybrid

Energy Technology Data Exchange (ETDEWEB)

Tewari, Aarti [Department of Applied Physics, Delhi Technological University, Shahbad Daulatpur, Bawana Road, Delhi 110 042 (India)

2016-08-15

A theoretical model describing the growth of graphene-CNT hybrid in a plasma medium is presented. Using the model, the growth of carbon nanotube (CNT) on a catalyst particle and thereafter the growth of the graphene on the CNT is studied under the purview of plasma sheath and number density kinetics of different plasma species. It is found that the plasma parameter such as ion density; gas ratios and process parameter such as source power affect the CNT and graphene dimensions. The variation in growth rates of graphene and CNT under different plasma power, gas ratios, and ion densities is analyzed. Based on the results obtained, it can be concluded that higher hydrocarbon ion densities and gas ratios of hydrocarbon to hydrogen favor the growth of taller CNTs and graphene, respectively. In addition, the CNT tip radius reduces with hydrogen ion density and higher plasma power favors graphene with lesser thickness. The present study can help in better understanding of the graphene-CNT hybrid growth in a plasma medium.

AMOC decadal variability in Earth system models: Mechanisms and climate impacts

Energy Technology Data Exchange (ETDEWEB)

Fedorov, Alexey [Yale Univ., New Haven, CT (United States)

2017-09-06

This is the final report for the project titled "AMOC decadal variability in Earth system models: Mechanisms and climate impacts". The central goal of this one-year research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) within a hierarchy of climate models ranging from realistic ocean GCMs to Earth system models. The AMOC is a key element of ocean circulation responsible for oceanic transport of heat from low to high latitudes and controlling, to a large extent, climate variations in the North Atlantic. The questions of the AMOC stability, variability and predictability, directly relevant to the questions of climate predictability, were at the center of the research work.
Higher-dimensional cosmological model with variable gravitational ...

Indian Academy of Sciences (India)

variable G and bulk viscosity in Lyra geometry. Exact solutions for ... a comparative study of Robertson–Walker models with a constant deceleration .... where H is defined as H =(˙A/A)+(1/3)( ˙B/B) and β0,H0 are representing present values of β ...
A coupled physical-biological model of the Northern Gulf of Mexico shelf: model description, validation and analysis of phytoplankton variability

Directory of Open Access Journals (Sweden)

K. Fennel

2011-07-01

Full Text Available The Texas-Louisiana shelf in the Northern Gulf of Mexico receives large inputs of nutrients and freshwater from the Mississippi/Atchafalaya River system. The nutrients stimulate high rates of primary production in the river plume, which contributes to the development of a large and recurring hypoxic area in summer, but the mechanistic links between hypoxia and river discharge of freshwater and nutrients are complex as the accumulation and vertical export of organic matter, the establishment and maintenance of vertical stratification, and the microbial degradation of organic matter are controlled by a non-linear interplay of factors. Unraveling these interactions will have to rely on a combination of observations and models. Here we present results from a realistic, 3-dimensional, physical-biological model with focus on a quantification of nutrient-stimulated phytoplankton growth, its variability and the fate of this organic matter. We demonstrate that the model realistically reproduces many features of observed nitrate and phytoplankton dynamics including observed property distributions and rates. We then contrast the environmental factors and phytoplankton source and sink terms characteristic of three model subregions that represent an ecological gradient from eutrophic to oligotrophic conditions. We analyze specifically the reasons behind the counterintuitive observation that primary production in the light-limited plume region near the Mississippi River delta is positively correlated with river nutrient input, and find that, while primary production and phytoplankton biomass are positively correlated with nutrient load, phytoplankton growth rate is not. This suggests that accumulation of biomass in this region is not primarily controlled bottom up by nutrient-stimulation, but top down by systematic differences in the loss processes.
Development and validation of extensive growth and growth boundary models for psychrotolerant pseudomonads in seafood, meat and vegetable products

DEFF Research Database (Denmark)

Martinez Rios, Veronica; Dalgaard, Paw

Extensive growth and growth boundary models were developed and validated for psychrotolerant pseudomonads growing in seafood, meat and vegetable products. The new models were developed by expanding anexisting cardinal parameter-type model for growth of pseudomonads in milk (Martinez-Rios et al......, when observed and predicted μmax -values were compared. Thus, on average μmax -values for seafood and meat products were overestimated by 14%. Additionally, the reference growth rate parameter μref25˚C was calibrated by fitting the model to 21 μmax -values in vegetable products. This resulted in a μref......25˚C -value of 0.54 1/h. The calibrated vegetable model wassuccessfully validated using 51 μmax -values for psychrotolerant pseudomonads in vegetables. Average bias and accuracy factor values of 1.24 and 1.38 were obtained, respectively. Lag time models were developed by using relative lag times from...
Modeling temporal and spatial variability of traffic-related air pollution: Hourly land use regression models for black carbon

Science.gov (United States)

Dons, Evi; Van Poppel, Martine; Kochan, Bruno; Wets, Geert; Int Panis, Luc

2013-08-01

Land use regression (LUR) modeling is a statistical technique used to determine exposure to air pollutants in epidemiological studies. Time-activity diaries can be combined with LUR models, enabling detailed exposure estimation and limiting exposure misclassification, both in shorter and longer time lags. In this study, the traffic related air pollutant black carbon was measured with μ-aethalometers on a 5-min time base at 63 locations in Flanders, Belgium. The measurements show that hourly concentrations vary between different locations, but also over the day. Furthermore the diurnal pattern is different for street and background locations. This suggests that annual LUR models are not sufficient to capture all the variation. Hourly LUR models for black carbon are developed using different strategies: by means of dummy variables, with dynamic dependent variables and/or with dynamic and static independent variables. The LUR model with 48 dummies (weekday hours and weekend hours) performs not as good as the annual model (explained variance of 0.44 compared to 0.77 in the annual model). The dataset with hourly concentrations of black carbon can be used to recalibrate the annual model, resulting in many of the original explaining variables losing their statistical significance, and certain variables having the wrong direction of effect. Building new independent hourly models, with static or dynamic covariates, is proposed as the best solution to solve these issues. R2 values for hourly LUR models are mostly smaller than the R2 of the annual model, ranging from 0.07 to 0.8. Between 6 a.m. and 10 p.m. on weekdays the R2 approximates the annual model R2. Even though models of consecutive hours are developed independently, similar variables turn out to be significant. Using dynamic covariates instead of static covariates, i.e. hourly traffic intensities and hourly population densities, did not significantly improve the models' performance.
Modelling growth curves of Nigerian indigenous normal feather ...

African Journals Online (AJOL)

This study was conducted to predict the growth curve parameters using Bayesian Gompertz and logistic models and also to compare the two growth function in describing the body weight changes across age in Nigerian indigenous normal feather chicken. Each chick was wing-tagged at day old and body weights were ...
Variable selection for mixture and promotion time cure rate models.

Science.gov (United States)

Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng

2016-11-16

Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing. © The Author(s) 2016.
A laboratory-calibrated model of coho salmon growth with utility for ecological analyses

Science.gov (United States)

Manhard, Christopher V.; Som, Nicholas A.; Perry, Russell W.; Plumb, John M.

2018-01-01

We conducted a meta-analysis of laboratory- and hatchery-based growth data to estimate broadly applicable parameters of mass- and temperature-dependent growth of juvenile coho salmon (Oncorhynchus kisutch). Following studies of other salmonid species, we incorporated the Ratkowsky growth model into an allometric model and fit this model to growth observations from eight studies spanning ten different populations. To account for changes in growth patterns with food availability, we reparameterized the Ratkowsky model to scale several of its parameters relative to ration. The resulting model was robust across a wide range of ration allocations and experimental conditions, accounting for 99% of the variation in final body mass. We fit this model to growth data from coho salmon inhabiting tributaries and constructed ponds in the Klamath Basin by estimating habitat-specific indices of food availability. The model produced evidence that constructed ponds provided higher food availability than natural tributaries. Because of their simplicity (only mass and temperature are required as inputs) and robustness, ration-varying Ratkowsky models have utility as an ecological tool for capturing growth in freshwater fish populations.
The relationship between size, growth and profitability of commercial banks

NARCIS (Netherlands)

Shehzad, C. T.; De Haan, J.; Scholtens, B.

2013-01-01

Using a dynamic panel model for more than 15 000 banks from 148 countries from 1988 to 2010, we investigate the interaction between size, growth and profitability of banks. For our total sample, we cannot reject the hypotheses that the variability of bank profitability and the level and variability
Mathematical model for predicting molecular-beam epitaxy growth rates for wafer production

International Nuclear Information System (INIS)

Shi, B.Q.

2003-01-01

An analytical mathematical model for predicting molecular-beam epitaxy (MBE) growth rates is reported. The mathematical model solves the mass-conservation equation for liquid sources in conical crucibles and predicts the growth rate by taking into account the effect of growth source depletion on the growth rate. Assumptions made for deducing the analytical model are discussed. The model derived contains only one unknown parameter, the value of which can be determined by using data readily available to MBE growers. Procedures are outlined for implementing the model in MBE production of III-V compound semiconductor device wafers. Results from use of the model to obtain targeted layer compositions and thickness of InP-based heterojunction bipolar transistor wafers are presented
Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review

Science.gov (United States)

Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed

2017-05-01

Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.
Preparation of sustained release matrix pellets by melt agglomeration in the fluidized bed: influence of formulation variables and modelling of agglomerate growth.

Science.gov (United States)

Pauli-Bruns, Anette; Knop, Klaus; Lippold, Bernhard C

2010-03-01

The one-step preparation of sustained release matrix pellets, using a melting procedure in a fluidized bed apparatus, was tested in a 2(3) full factorial design of experiments, using microcrystalline wax as lipophilic binder, theophylline as model drug and talc as additional matrix forming agent. The three influence parameters were (A) size of binder particles, (B) fraction of theophylline in solid particles and (C) fraction of microcrystalline wax in formulation. The response variables were agglomerate size and size distribution, dissolution time, agglomerate crush resistance, sphericity, yield and porosity. Nearly spherical pellets comprising a smooth, closed surface could be obtained with the used method, exhibiting the hollow core typical for the immersion and layering mechanism. The reproducibility was very good concerning all responses. The size of agglomerates is proportional to the size of the binder particles, which serve as cores for pellet formation in the molten state in the fluidized bed. Additionally, the agglomerate size is influenced by the volume of the solid particles in relation to the binder particles, with more solid particles leading to larger agglomerates and vice versa. Dissolution times vary in a very wide range, resulting from the interplay between amount of drug in relation to the meltable matrix substance microcrystalline wax and the non-meltable matrix substance talc. The change of binder particle size does not lead to a structural change of the matrix; both dissolution times and porosity are not significantly altered. Agglomerate crush resistance is low due to the hollow core of the pellets. However, it is significantly increased if the volume fraction of microcrystalline wax in the matrix is high, which means that the matrix is mechanically better stabilized. A theoretical model has been established to quantitatively explain agglomerate growth and very good accordance of the full particle size distributions between predicted and
Model Predictive Control of a Nonlinear System with Known Scheduling Variable

DEFF Research Database (Denmark)

Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

2012-01-01

Model predictive control (MPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Consequently...... the control problem of the nonlinear system is simplied into a quadratic programming. Wind turbine is chosen as the case study and we choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon....
Multilevel models for longitudinal data

OpenAIRE

Fiona Steele

2008-01-01

Repeated measures and repeated events data have a hierarchical structure which can be analysed by using multilevel models. A growth curve model is an example of a multilevel random-coefficients model, whereas a discrete time event history model for recurrent events can be fitted as a multilevel logistic regression model. The paper describes extensions to the basic growth curve model to handle auto-correlated residuals, multiple-indicator latent variables and correlated growth processes, and e...
A geometric model for magnetizable bodies with internal variables

Directory of Open Access Journals (Sweden)

Restuccia, L

2005-11-01

Full Text Available In a geometrical framework for thermo-elasticity of continua with internal variables we consider a model of magnetizable media previously discussed and investigated by Maugin. We assume as state variables the magnetization together with its space gradient, subjected to evolution equations depending on both internal and external magnetic fields. We calculate the entropy function and necessary conditions for its existence.
Orchestrated structure evolution: modeling growth-regulated nanomanufacturing

Energy Technology Data Exchange (ETDEWEB)

Abbasi, Shaghayegh; Boehringer, Karl F [Department of Electrical Engineering, University of Washington, Seattle, WA 98195-2500 (United States); Kitayaporn, Sathana; Schwartz, Daniel T, E-mail: karlb@washington.edu [Department of Chemical Engineering, University of Washington, Seattle, WA 98195-2500 (United States)

2011-04-22

Orchestrated structure evolution (OSE) is a scalable manufacturing method that combines the advantages of top-down (tool-directed) and bottom-up (self-propagating) approaches. The method consists of a seed patterning step that defines where material nucleates, followed by a growth step that merges seeded islands into the final patterned thin film. We develop a model to predict the completed pattern based on a computationally efficient approximate Green's function solution of the diffusion equation plus a Voronoi diagram based approach that defines the final grain boundary structure. Experimental results rely on electron beam lithography to pattern the seeds, followed by the mass transfer limited growth of copper via electrodeposition. The seed growth model is compared with experimental results to quantify nearest neighbor seed-to-seed interactions as well as how seeds interact with the pattern boundary to impact the local growth rate. Seed-to-seed and seed-to-pattern interactions are shown to result in overgrowth of seeds on edges and corners of the shape, where seeds have fewer neighbors. We explore how local changes to the seed location can be used to improve the patterning quality without increasing the manufacturing cost. OSE is shown to enable a unique set of trade-offs between the cost, time, and quality of thin film patterning.
Combination of a higher-tier flow-through system and population modeling to assess the effects of time-variable exposure of isoproturon on the green algae Desmodesmus subspicatus and Pseudokirchneriella subcapitata.

Science.gov (United States)

Weber, Denis; Schaefer, Dieter; Dorgerloh, Michael; Bruns, Eric; Goerlitz, Gerhard; Hammel, Klaus; Preuss, Thomas G; Ratte, Hans Toni

2012-04-01

A flow-through system was developed to investigate the effects of time-variable exposure of pesticides on algae. A recently developed algae population model was used for simulations supported and verified by laboratory experiments. Flow-through studies with Desmodesmus subspicatus and Pseudokirchneriella subcapitata under time-variable exposure to isoproturon were performed, in which the exposure patterns were based on the results of FOrum for Co-ordination of pesticide fate models and their USe (FOCUS) model calculations for typical exposure situations via runoff or drain flow. Different types of pulsed exposure events were realized, including a whole range of repeated pulsed and steep peaks as well as periods of constant exposure. Both species recovered quickly in terms of growth from short-term exposure and according to substance dissipation from the system. Even at a peak 10 times the maximum predicted environmental concentration of isoproturon, only transient effects occurred on algae populations. No modified sensitivity or reduced growth was observed after repeated exposure. Model predictions of algal growth in the flow-through tests agreed well with the experimental data. The experimental boundary conditions and the physiological properties of the algae were used as the only model input. No calibration or parameter fitting was necessary. The combination of the flow-through experiments with the algae population model was revealed to be a powerful tool for the assessment of pulsed exposure on algae. It allowed investigating the growth reduction and recovery potential of algae after complex exposure, which is not possible with standard laboratory experiments alone. The results of the combined approach confirm the beneficial use of population models as supporting tools in higher-tier risk assessments of pesticides. Copyright © 2012 SETAC.
Modeling of dislocation dynamics in germanium Czochralski growth

Science.gov (United States)

Artemyev, V. V.; Smirnov, A. D.; Kalaev, V. V.; Mamedov, V. M.; Sidko, A. P.; Podkopaev, O. I.; Kravtsova, E. D.; Shimansky, A. F.

2017-06-01

Obtaining very high-purity germanium crystals with low dislocation density is a practically difficult problem, which requires knowledge and experience in growth processes. Dislocation density is one of the most important parameters defining the quality of germanium crystal. In this paper, we have performed experimental study of dislocation density during 4-in. germanium crystal growth using the Czochralski method and comprehensive unsteady modeling of the same crystal growth processes, taking into account global heat transfer, melt flow and melt/crystal interface shape evolution. Thermal stresses in the crystal and their relaxation with generation of dislocations within the Alexander-Haasen model have been calculated simultaneously with crystallization dynamics. Comparison to experimental data showed reasonable agreement for the temperature, interface shape and dislocation density in the crystal between calculation and experiment.
Examples of EOS Variables as compared to the UMM-Var Data Model

Science.gov (United States)

Cantrell, Simon; Lynnes, Chris

2016-01-01

In effort to provide EOSDIS clients a way to discover and use variable data from different providers, a Unified Metadata Model for Variables is being created. This presentation gives an overview of the model and use cases we are handling.
The Effect of Government Expenditures, Private Investment and Labor on Economic Growth in Pidie District

Directory of Open Access Journals (Sweden)

Munzir AG

2018-02-01

Full Text Available This study aims to determine the effect of government spending, private investment, and labor on economic growth in Pidie District, Data analyzed from 2000-2016, using multiple linear regression model. The results of research on government spending, private investment and labor both simultaneously and partially have a positive and significant impact on economic growth in Pidie District. Variations of government expenditure variables, private investment and labor are able to explain the variation of economic growth in Pidie District by 48,7 percent and the rest of 51,3 percent influenced by other variables. Labor is the most dominant variable of influence on economic growth in Pidie District. Private investment is the least influence variable to economic growth in Pidie District. The need for a policy that could make private government investment spending, and labor increases simultaneously so it is likely to have a positive impact on improving economic growth in Pidie District.

Statistically Enhanced Model of In Situ Oil Sands Extraction Operations: An Evaluation of Variability in Greenhouse Gas Emissions.

Science.gov (United States)

Orellana, Andrea; Laurenzi, Ian J; MacLean, Heather L; Bergerson, Joule A

2018-02-06

Greenhouse gas (GHG) emissions associated with extraction of bitumen from oil sands can vary from project to project and over time. However, the nature and magnitude of this variability have yet to be incorporated into life cycle studies. We present a statistically enhanced life cycle based model (GHOST-SE) for assessing variability of GHG emissions associated with the extraction of bitumen using in situ techniques in Alberta, Canada. It employs publicly available, company-reported operating data, facilitating assessment of inter- and intraproject variability as well as the time evolution of GHG emissions from commercial in situ oil sands projects. We estimate the median GHG emissions associated with bitumen production via cyclic steam stimulation (CSS) to be 77 kg CO 2 eq/bbl bitumen (80% CI: 61-109 kg CO 2 eq/bbl), and via steam assisted gravity drainage (SAGD) to be 68 kg CO 2 eq/bbl bitumen (80% CI: 49-102 kg CO 2 eq/bbl). We also show that the median emissions intensity of Alberta's CSS and SAGD projects have been relatively stable from 2000 to 2013, despite greater than 6-fold growth in production. Variability between projects is the single largest source of variability (driven in part by reservoir characteristics) but intraproject variability (e.g., startups, interruptions), is also important and must be considered in order to inform research or policy priorities.
Phase-field model of vapor-liquid-solid nanowire growth

Science.gov (United States)

Wang, Nan; Upmanyu, Moneesh; Karma, Alain

2018-03-01

We present a multiphase-field model to describe quantitatively nanowire growth by the vapor-liquid-solid (VLS) process. The free-energy functional of this model depends on three nonconserved order parameters that distinguish the vapor, liquid, and solid phases and describe the energetic properties of various interfaces, including arbitrary forms of anisotropic γ plots for the solid-vapor and solid-liquid interfaces. The evolution equations for those order parameters describe basic kinetic processes including the rapid (quasi-instantaneous) equilibration of the liquid catalyst to a droplet shape with constant mean curvature, the slow incorporation of growth atoms at the droplet surface, and crystallization within the droplet. The standard constraint that the sum of the phase fields equals unity and the conservation of the number of catalyst atoms, which relates the catalyst volume to the concentration of growth atoms inside the droplet, are handled via separate Lagrange multipliers. An analysis of the model is presented that rigorously maps the phase-field equations to a desired set of sharp-interface equations for the evolution of the phase boundaries under the constraint of force balance at three-phase junctions (triple points) given by the Young-Herring relation that includes torque term related to the anisotropy of the solid-liquid and solid-vapor interface excess free energies. Numerical examples of growth in two dimensions are presented for the simplest case of vanishing crystalline anisotropy and the more realistic case of a solid-liquid γ plot with cusped minima corresponding to two sets of (10 ) and (11 ) facets. The simulations reproduce many of the salient features of nanowire growth observed experimentally, including growth normal to the substrate with tapering of the side walls, transitions between different growth orientations, and crawling growth along the substrate. They also reproduce different observed relationships between the nanowire growth
An interactive environmental model for economic growth: evidence from a panel of countries.

Science.gov (United States)

Ramakrishnan, Suresh; Hishan, Sanil S; Nabi, Agha Amad; Arshad, Zeeshan; Kanjanapathy, Malini; Zaman, Khalid; Khan, Faisal

2016-07-01

This study aims to determine an interactive environmental model for economic growth that would be supported by the "sustainability principles" across the globe. The study examines the relationship between environmental pollutants (i.e., carbon dioxide emission, sulfur dioxide emission, mono-nitrogen oxide, and nitrous oxide emission); population growth; energy use; trade openness; per capita food production; and it's resulting impact on the real per capita GDP and sectoral growth (i.e., share of agriculture, industry, and services in GDP) in a panel of 34 high-income OECD, high-income non-OECD, and Europe and Central Asian countries, for the period of 1995-2014. The results of the panel fixed effect regression show that per capita GDP are influenced by sulfur dioxide emission, population growth, and per capita food production variability, while energy and trade openness significantly increases per capita income of the region. The results of the panel Seemingly Unrelated Regression (SUR) show that carbon dioxide emission significantly decreases the share of agriculture and industry in GDP, while it further supports the share of services sector to GDP. Both the sulfur dioxide and mono-nitrogen oxide emission decreases the share of services in GDP; nitrous oxide decreases the share of industry in GDP; while mono-nitrogen oxide supports the industrial activities. The following key growth-specific results has been obtained from the panel SUR estimation, i.e., (i) Both the food production per capita and trade openness significantly associated with the increasing share of agriculture, (ii) food production and energy use significantly increases the service sectors' productivity; (iii) food production decreases the industrial activities; (iv) trade openness decreases the share of services to GDP while it supports the industrial share to GDP; and finally, (v) energy demand decreases along with the increase agricultural share in the region. The results emphasize the need for
Speech-discrimination scores modeled as a binomial variable.

Science.gov (United States)

Thornton, A R; Raffin, M J

1978-09-01

Many studies have reported variability data for tests of speech discrimination, and the disparate results of these studies have not been given a simple explanation. Arguments over the relative merits of 25- vs 50-word tests have ignored the basic mathematical properties inherent in the use of percentage scores. The present study models performance on clinical tests of speech discrimination as a binomial variable. A binomial model was developed, and some of its characteristics were tested against data from 4120 scores obtained on the CID Auditory Test W-22. A table for determining significant deviations between scores was generated and compared to observed differences in half-list scores for the W-22 tests. Good agreement was found between predicted and observed values. Implications of the binomial characteristics of speech-discrimination scores are discussed.
Optimal variable-grid finite-difference modeling for porous media

International Nuclear Information System (INIS)

Liu, Xinxin; Yin, Xingyao; Li, Haishan

2014-01-01

Numerical modeling of poroelastic waves by the finite-difference (FD) method is more expensive than that of acoustic or elastic waves. To improve the accuracy and computational efficiency of seismic modeling, variable-grid FD methods have been developed. In this paper, we derived optimal staggered-grid finite difference schemes with variable grid-spacing and time-step for seismic modeling in porous media. FD operators with small grid-spacing and time-step are adopted for low-velocity or small-scale geological bodies, while FD operators with big grid-spacing and time-step are adopted for high-velocity or large-scale regions. The dispersion relations of FD schemes were derived based on the plane wave theory, then the FD coefficients were obtained using the Taylor expansion. Dispersion analysis and modeling results demonstrated that the proposed method has higher accuracy with lower computational cost for poroelastic wave simulation in heterogeneous reservoirs. (paper)
Site and stand analysis for growth prediction of Eucalyptus grandis ...

African Journals Online (AJOL)

The integration of site information with that of tree growth is of special importance in Zululand, where sustainable supply of timber is essential for local processing and export commitments. Site prediction growth models need to be based on easily attainable input variables that are suitable for operational implementation by ...
Comparison of climate envelope models developed using expert-selected variables versus statistical selection

Science.gov (United States)

Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable
A model analysis of climate and CO2 controls on tree growth in a semi-arid woodland

Science.gov (United States)

Li, G.; Harrison, S. P.; Prentice, I. C.

2015-03-01

We used a light-use efficiency model of photosynthesis coupled with a dynamic carbon allocation and tree-growth model to simulate annual growth of the gymnosperm Callitris columellaris in the semi-arid Great Western Woodlands, Western Australia, over the past 100 years. Parameter values were derived from independent observations except for sapwood specific respiration rate, fine-root turnover time, fine-root specific respiration rate and the ratio of fine-root mass to foliage area, which were estimated by Bayesian optimization. The model reproduced the general pattern of interannual variability in radial growth (tree-ring width), including the response to the shift in precipitation regimes that occurred in the 1960s. Simulated and observed responses to climate were consistent. Both showed a significant positive response of tree-ring width to total photosynthetically active radiation received and to the ratio of modeled actual to equilibrium evapotranspiration, and a significant negative response to vapour pressure deficit. However, the simulations showed an enhancement of radial growth in response to increasing atmospheric CO2 concentration (ppm) ([CO2]) during recent decades that is not present in the observations. The discrepancy disappeared when the model was recalibrated on successive 30-year windows. Then the ratio of fine-root mass to foliage area increases by 14% (from 0.127 to 0.144 kg C m-2) as [CO2] increased while the other three estimated parameters remained constant. The absence of a signal of increasing [CO2] has been noted in many tree-ring records, despite the enhancement of photosynthetic rates and water-use efficiency resulting from increasing [CO2]. Our simulations suggest that this behaviour could be explained as a consequence of a shift towards below-ground carbon allocation.
Parameter estimation of variable-parameter nonlinear Muskingum model using excel solver

Science.gov (United States)

Kang, Ling; Zhou, Liwei

2018-02-01

Abstract . The Muskingum model is an effective flood routing technology in hydrology and water resources Engineering. With the development of optimization technology, more and more variable-parameter Muskingum models were presented to improve effectiveness of the Muskingum model in recent decades. A variable-parameter nonlinear Muskingum model (NVPNLMM) was proposed in this paper. According to the results of two real and frequently-used case studies by various models, the NVPNLMM could obtain better values of evaluation criteria, which are used to describe the superiority of the estimated outflows and compare the accuracies of flood routing using various models, and the optimal estimated outflows by the NVPNLMM were closer to the observed outflows than the ones by other models.
Gompertzian stochastic model with delay effect to cervical cancer growth

International Nuclear Information System (INIS)

Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti; Bahar, Arifah

2015-01-01

In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits
Gompertzian stochastic model with delay effect to cervical cancer growth

Energy Technology Data Exchange (ETDEWEB)

Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti [Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang (Malaysia); Bahar, Arifah [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor and UTM Centre for Industrial and Applied Mathematics (UTM-CIAM), Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)

2015-02-03

In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits.
A stress driven growth model for soft tissue considering biological availability

International Nuclear Information System (INIS)

Oller, S; Bellomo, F J; Nallim, L G; Armero, F

2010-01-01

Some of the key factors that regulate growth and remodeling of tissues are fundamentally mechanical. However, it is important to take into account the role of bioavailability together with the stresses and strains in the processes of normal or pathological growth. In this sense, the model presented in this work is oriented to describe the growth of soft biological tissue under 'stress driven growth' and depending on the biological availability of the organism. The general theoretical framework is given by a kinematic formulation in large strain combined with the thermodynamic basis of open systems. The formulation uses a multiplicative decomposition of deformation gradient, splitting it in a growth part and visco-elastic part. The strains due to growth are incompatible and are controlled by an unbalanced stresses related to a homeostatic state. Growth implies a volume change with an increase of mass maintaining constant the density. One of the most interesting features of the proposed model is the generation of new tissue taking into account the contribution of mass to the system controlled through biological availability. Because soft biological tissues in general have a hierarchical structure with several components (usually a soft matrix reinforced with collagen fibers), the developed growth model is suitable for the characterization of the growth of each component. This allows considering a different behavior for each of them in the context of a generalized theory of mixtures. Finally, we illustrate the response of the model in case of growth and atrophy with an application example.
Modeling of multibranched crosslike crack growth

International Nuclear Information System (INIS)

Canessa, E.; Tanatar, B.

1991-06-01

Multibranched crosslike crack patterns formed in concentrically loaded square plates are studied in terms of fractal geometry, where the associated fractal dimension d f is calculated for their characterization. We apply simplest deterministic and stochastic approaches at a phenomenological level in an attempt to find generic features as guidelines for future experimental and theoretical work. The deterministic model for fracture propagation we apply, which is a variant of the discretized Laplace approach for randomly ramified fractal cracks proposed by Takayasu, reproduces the basic ingredients of observed complex fracture patters. The stochastic model, although is not strictly a model for crack propagation, is based on diffusion-limited aggregation (DLA) for fractal growth and produces slightly more realistic assessment of the crosslike growth of the cracks in asymmetric multibranches. Nevertheless, this simple ad-hoc DLA-version for modeling the present phenomena as well as the deterministic approach for fracture propagation give fractal dimensionality for the fracture pattern in accord with our estimations made from recent experimental data. It is found that there is a crossover of two fractal dimensions, corresponding to the core (higher d f ) and multibranched crosslike (lower D f ) regions, that contains loops, that are interpreted as representing different symmetry regions within the square plates of finite size. (author). 26 refs, 5 figs
Human Capital and Economic Growth - How Strong is the Nexus?

Directory of Open Access Journals (Sweden)

Marinko Škare

2016-08-01

Full Text Available The link between human capital and economic growth still remains unexplained because of the measurement issues connected to the human capital stock. This study investigates the link between human capital stock and economic growth using inclusive wealth index and ratio of engaged to actively disengaged employees as proxy for human capital stock. Data from the global workplace and inclusive wealth reports are used in order to provide an international comparison of the link between human capital and inclusive wealth. Cross country comparison show human capital largerly contribute to the inclusive wealth formation. Formal education is important but also motivating working environment is needed to achieve sustainable economic growth. The finding further indicates that standard human capital growth model should be revised taking into the account variables addressing sustainable growth (not just growth and environmental variables (work conditions affecting human capital stock. Countries encouraging investments in the development of individuals both through formal education and inspiring work environments achieve higher sustainable economic growth
Growth and Poverty

DEFF Research Database (Denmark)

Arndt, Channing; Leyaro, Vincent; Mahrt, Kristi

2017-01-01

This chapter considers the evolution of welfare of the Tanzanian population using a multi-dimensional approach. It also employs a detailed economy-wide model of the Tanzanian economy to explore growth and monetary poverty reduction scenarios from 2007 to 2015. This approach permits assessment...... of the coherence of observed trends in macroeconomic variables and projects consumption poverty outcomes to 2015. In the multi-dimensional approach, we find that real gains have been achieved. On monetary poverty, our model broadly reproduces key macroeconomic features of the past eight years. We find...... that published consumption poverty reductions for 2007 to 2011/12 from the most recent assessment fall within a reasonable to optimistic range. And, the simulations generate broader based growth across the income distribution compared with the recent assessment. Looking forward, the simulations from 2012 to 2105...
BehavePlus fire modeling system, version 5.0: Variables

Science.gov (United States)

Patricia L. Andrews

2009-01-01

This publication has been revised to reflect updates to version 4.0 of the BehavePlus software. It was originally published as the BehavePlus fire modeling system, version 4.0: Variables in July, 2008.The BehavePlus fire modeling system is a computer program based on mathematical models that describe wildland fire behavior and effects and the...
A smart growth evaluation model based on data envelopment analysis

Science.gov (United States)

Zhang, Xiaokun; Guan, Yongyi

2018-04-01

With the rapid spread of urbanization, smart growth (SG) has attracted plenty of attention from all over the world. In this paper, by the establishment of index system for smart growth, data envelopment analysis (DEA) model was suggested to evaluate the SG level of the current growth situation in cities. In order to further improve the information of both radial direction and non-radial detection, we introduced the non-Archimedean infinitesimal to form C2GS2 control model. Finally, we evaluated the SG level in Canberra and identified a series of problems, which can verify the applicability of the model and provide us more improvement information.
Computational modeling of chemical reactions and interstitial growth and remodeling involving charged solutes and solid-bound molecules.

Science.gov (United States)

Ateshian, Gerard A; Nims, Robert J; Maas, Steve; Weiss, Jeffrey A

2014-10-01

Mechanobiological processes are rooted in mechanics and chemistry, and such processes may be modeled in a framework that couples their governing equations starting from fundamental principles. In many biological applications, the reactants and products of chemical reactions may be electrically charged, and these charge effects may produce driving forces and constraints that significantly influence outcomes. In this study, a novel formulation and computational implementation are presented for modeling chemical reactions in biological tissues that involve charged solutes and solid-bound molecules within a deformable porous hydrated solid matrix, coupling mechanics with chemistry while accounting for electric charges. The deposition or removal of solid-bound molecules contributes to the growth and remodeling of the solid matrix; in particular, volumetric growth may be driven by Donnan osmotic swelling, resulting from charged molecular species fixed to the solid matrix. This formulation incorporates the state of strain as a state variable in the production rate of chemical reactions, explicitly tying chemistry with mechanics for the purpose of modeling mechanobiology. To achieve these objectives, this treatment identifies the specific theoretical and computational challenges faced in modeling complex systems of interacting neutral and charged constituents while accommodating any number of simultaneous reactions where reactants and products may be modeled explicitly or implicitly. Several finite element verification problems are shown to agree with closed-form analytical solutions. An illustrative tissue engineering analysis demonstrates tissue growth and swelling resulting from the deposition of chondroitin sulfate, a charged solid-bound molecular species. This implementation is released in the open-source program FEBio ( www.febio.org ). The availability of this framework may be particularly beneficial to optimizing tissue engineering culture systems by examining the
Statistical analysis of corn yields responding to climate variability at various spatio-temporal resolutions

Science.gov (United States)

Jiang, H.; Lin, T.

2017-12-01

Rain-fed corn production systems are subject to sub-seasonal variations of precipitation and temperature during the growing season. As each growth phase has varied inherent physiological process, plants necessitate different optimal environmental conditions during each phase. However, this temporal heterogeneity towards climate variability alongside the lifecycle of crops is often simplified and fixed as constant responses in large scale statistical modeling analysis. To capture the time-variant growing requirements in large scale statistical analysis, we develop and compare statistical models at various spatial and temporal resolutions to quantify the relationship between corn yield and weather factors for 12 corn belt states from 1981 to 2016. The study compares three spatial resolutions (county, agricultural district, and state scale) and three temporal resolutions (crop growth phase, monthly, and growing season) to characterize the effects of spatial and temporal variability. Our results show that the agricultural district model together with growth phase resolution can explain 52% variations of corn yield caused by temperature and precipitation variability. It provides a practical model structure balancing the overfitting problem in county specific model and weak explanation power in state specific model. In US corn belt, precipitation has positive impact on corn yield in growing season except for vegetative stage while extreme heat attains highest sensitivity from silking to dough phase. The results show the northern counties in corn belt area are less interfered by extreme heat but are more vulnerable to water deficiency.
An individual-based growth and competition model for coastal redwood forest restoration

Science.gov (United States)

van Mantgem, Phillip J.; Das, Adrian J.

2014-01-01

Thinning treatments to accelerate coastal redwood forest stand development are in wide application, but managers have yet to identify prescriptions that might best promote Sequoia sempervirens (Lamb. ex D. Don) Endl. (redwood) growth. The creation of successful thinning prescriptions would be aided by identifying the underlying mechanisms governing how individual tree growth responds to competitive environments in coastal redwood forests. We created a spatially explicit individual-based model of tree competition and growth parameterized using surveys of upland redwood forests at Redwood National Park, California. We modeled competition for overstory trees (stems ≥ 20 cm stem diameter at breast height, 1.37 m (dbh)) as growth reductions arising from sizes, distances, and species identity of competitor trees. Our model explained up to half of the variation in individual tree growth, suggesting that neighborhood crowding is an important determinant of growth in this forest type. We used our model to simulate the effects of novel thinning prescriptions (e.g., 40% stand basal area removal) for redwood forest restoration, concluding that these treatments could lead to substantial growth releases, particularly for S. sempervirens. The results of this study, along with continued improvements to our model, will help to determine spacing and species composition that best encourage growth.

Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling

Science.gov (United States)

Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

2017-03-01

The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP - Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM - the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the
Modeling Turbulent Combustion for Variable Prandtl and Schmidt Number

Science.gov (United States)

Hassan, H. A.

2004-01-01

This report consists of two abstracts submitted for possible presentation at the AIAA Aerospace Science Meeting to be held in January 2005. Since the submittal of these abstracts we are continuing refinement of the model coefficients derived for the case of a variable Turbulent Prandtl number. The test cases being investigated are a Mach 9.2 flow over a degree ramp and a Mach 8.2 3-D calculation of crossing shocks. We have developed an axisymmetric code for treating axisymmetric flows. In addition the variable Schmidt number formulation was incorporated in the code and we are in the process of determining the model constants.
Photosynthesis driven crop growth models for greenhouse cultivation; advances and bottlenecks.

NARCIS (Netherlands)

Challa, H.; Heuvelink, E.

1996-01-01

In recent years considerable progress has been made in modelling growth of green-house crops. Nevertheless, the share of research in this field compared to crop modelling in general is only a few percent. Yet, crop growth models have a great potential for greenhouse production systems, because they
Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling

Science.gov (United States)

Chen, Jie; Li, Chao; Brissette, François P.; Chen, Hua; Wang, Mingna; Essou, Gilles R. C.

2018-05-01

Bias correction is usually implemented prior to using climate model outputs for impact studies. However, bias correction methods that are commonly used treat climate variables independently and often ignore inter-variable dependencies. The effects of ignoring such dependencies on impact studies need to be investigated. This study aims to assess the impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling. To this end, a joint bias correction (JBC) method which corrects the joint distribution of two variables as a whole is compared with an independent bias correction (IBC) method; this is considered in terms of correcting simulations of precipitation and temperature from 26 climate models for hydrological modeling over 12 watersheds located in various climate regimes. The results show that the simulated precipitation and temperature are considerably biased not only in the individual distributions, but also in their correlations, which in turn result in biased hydrological simulations. In addition to reducing the biases of the individual characteristics of precipitation and temperature, the JBC method can also reduce the bias in precipitation-temperature (P-T) correlations. In terms of hydrological modeling, the JBC method performs significantly better than the IBC method for 11 out of the 12 watersheds over the calibration period. For the validation period, the advantages of the JBC method are greatly reduced as the performance becomes dependent on the watershed, GCM and hydrological metric considered. For arid/tropical and snowfall-rainfall-mixed watersheds, JBC performs better than IBC. For snowfall- or rainfall-dominated watersheds, however, the two methods behave similarly, with IBC performing somewhat better than JBC. Overall, the results emphasize the advantages of correcting the P-T correlation when using climate model-simulated precipitation and temperature to assess the impact of climate change on watershed
Eye growth and myopia development: Unifying theory and Matlab model.

Science.gov (United States)

Hung, George K; Mahadas, Kausalendra; Mohammad, Faisal

2016-03-01

The aim of this article is to present an updated unifying theory of the mechanisms underlying eye growth and myopia development. A series of model simulation programs were developed to illustrate the mechanism of eye growth regulation and myopia development. Two fundamental processes are presumed to govern the relationship between physiological optics and eye growth: genetically pre-programmed signaling and blur feedback. Cornea/lens is considered to have only a genetically pre-programmed component, whereas eye growth is considered to have both a genetically pre-programmed and a blur feedback component. Moreover, based on the Incremental Retinal-Defocus Theory (IRDT), the rate of change of blur size provides the direction for blur-driven regulation. The various factors affecting eye growth are shown in 5 simulations: (1 - unregulated eye growth): blur feedback is rendered ineffective, as in the case of form deprivation, so there is only genetically pre-programmed eye growth, generally resulting in myopia; (2 - regulated eye growth): blur feedback regulation demonstrates the emmetropization process, with abnormally excessive or reduced eye growth leading to myopia and hyperopia, respectively; (3 - repeated near-far viewing): simulation of large-to-small change in blur size as seen in the accommodative stimulus/response function, and via IRDT as well as nearwork-induced transient myopia (NITM), leading to the development of myopia; (4 - neurochemical bulk flow and diffusion): release of dopamine from the inner plexiform layer of the retina, and the subsequent diffusion and relay of neurochemical cascade show that a decrease in dopamine results in a reduction of proteoglycan synthesis rate, which leads to myopia; (5 - Simulink model): model of genetically pre-programmed signaling and blur feedback components that allows for different input functions to simulate experimental manipulations that result in hyperopia, emmetropia, and myopia. These model simulation programs
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

Science.gov (United States)

Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.
Student Engagement and Classroom Variables in Improving Mathematics Achievement

Science.gov (United States)

Park, So-Young

2005-01-01

The study explored how much student engagement and classroom variables predicted student achievement in mathematics. Since students were nested within a classroom, hierarchical linear modeling (HLM) was employed for the analysis. The results indicated that student engagement had positive effects on student academic growth per month in math after…
A Model for Positively Correlated Count Variables

DEFF Research Database (Denmark)

Møller, Jesper; Rubak, Ege Holger

2010-01-01

An α-permanental random field is briefly speaking a model for a collection of non-negative integer valued random variables with positive associations. Though such models possess many appealing probabilistic properties, many statisticians seem unaware of α-permanental random fields...... and their potential applications. The purpose of this paper is to summarize useful probabilistic results, study stochastic constructions and simulation techniques, and discuss some examples of α-permanental random fields. This should provide a useful basis for discussing the statistical aspects in future work....
Interacting ghost dark energy models with variable G and Λ

Science.gov (United States)

Sadeghi, J.; Khurshudyan, M.; Movsisyan, A.; Farahani, H.

2013-12-01

In this paper we consider several phenomenological models of variable Λ. Model of a flat Universe with variable Λ and G is accepted. It is well known, that varying G and Λ gives rise to modified field equations and modified conservation laws, which gives rise to many different manipulations and assumptions in literature. We will consider two component fluid, which parameters will enter to Λ. Interaction between fluids with energy densities ρ1 and ρ2 assumed as Q = 3Hb(ρ1+ρ2). We have numerical analyze of important cosmological parameters like EoS parameter of the composed fluid and deceleration parameter q of the model.
Representing general theoretical concepts in structural equation models: The role of composite variables

Science.gov (United States)

Grace, J.B.; Bollen, K.A.

2008-01-01

Structural equation modeling (SEM) holds the promise of providing natural scientists the capacity to evaluate complex multivariate hypotheses about ecological systems. Building on its predecessors, path analysis and factor analysis, SEM allows for the incorporation of both observed and unobserved (latent) variables into theoretically-based probabilistic models. In this paper we discuss the interface between theory and data in SEM and the use of an additional variable type, the composite. In simple terms, composite variables specify the influences of collections of other variables and can be helpful in modeling heterogeneous concepts of the sort commonly of interest to ecologists. While long recognized as a potentially important element of SEM, composite variables have received very limited use, in part because of a lack of theoretical consideration, but also because of difficulties that arise in parameter estimation when using conventional solution procedures. In this paper we present a framework for discussing composites and demonstrate how the use of partially-reduced-form models can help to overcome some of the parameter estimation and evaluation problems associated with models containing composites. Diagnostic procedures for evaluating the most appropriate and effective use of composites are illustrated with an example from the ecological literature. It is argued that an ability to incorporate composite variables into structural equation models may be particularly valuable in the study of natural systems, where concepts are frequently multifaceted and the influence of suites of variables are often of interest. ?? Springer Science+Business Media, LLC 2007.
Inter-model variability and biases of the global water cycle in CMIP3 coupled climate models

International Nuclear Information System (INIS)

Liepert, Beate G; Previdi, Michael

2012-01-01

Observed changes such as increasing global temperatures and the intensification of the global water cycle in the 20th century are robust results of coupled general circulation models (CGCMs). In spite of these successes, model-to-model variability and biases that are small in first order climate responses, however, have considerable implications for climate predictability especially when multi-model means are used. We show that most climate simulations of the 20th and 21st century A2 scenario performed with CMIP3 (Coupled Model Inter-comparison Project Phase 3) models have deficiencies in simulating the global atmospheric moisture balance. Large biases of only a few models (some biases reach the simulated global precipitation changes in the 20th and 21st centuries) affect the multi-model mean global moisture budget. An imbalanced flux of −0.14 Sv exists while the multi-model median imbalance is only −0.02 Sv. Moreover, for most models the detected imbalance changes over time. As a consequence, in 13 of the 18 CMIP3 models examined, global annual mean precipitation exceeds global evaporation, indicating that there should be a ‘leaking’ of moisture from the atmosphere whereas for the remaining five models a ‘flooding’ is implied. Nonetheless, in all models, the actual atmospheric moisture content and its variability correctly increases during the course of the 20th and 21st centuries. These discrepancies therefore imply an unphysical and hence ‘ghost’ sink/source of atmospheric moisture in the models whose atmospheres flood/leak. The ghost source/sink of moisture can also be regarded as atmospheric latent heating/cooling and hence as positive/negative perturbation of the atmospheric energy budget or non-radiative forcing in the range of −1 to +6 W m −2 (median +0.1 W m −2 ). The inter-model variability of the global atmospheric moisture transport from oceans to land areas, which impacts the terrestrial water cycle, is also quite high and ranges
Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

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Fereydoun Naghibi

2016-12-01

Full Text Available This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA using the artificial bee colony (ABC optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was
How ocean lateral mixing changes Southern Ocean variability in coupled climate models

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Pradal, M. A. S.; Gnanadesikan, A.; Thomas, J. L.

2016-02-01

The lateral mixing of tracers represents a major uncertainty in the formulation of coupled climate models. The mixing of tracers along density surfaces in the interior and horizontally within the mixed layer is often parameterized using a mixing coefficient ARedi. The models used in the Coupled Model Intercomparison Project 5 exhibit more than an order of magnitude range in the values of this coefficient used within the Southern Ocean. The impacts of such uncertainty on Southern Ocean variability have remained unclear, even as recent work has shown that this variability differs between different models. In this poster, we change the lateral mixing coefficient within GFDL ESM2Mc, a coarse-resolution Earth System model that nonetheless has a reasonable circulation within the Southern Ocean. As the coefficient varies from 400 to 2400 m2/s the amplitude of the variability varies significantly. The low-mixing case shows strong decadal variability with an annual mean RMS temperature variability exceeding 1C in the Circumpolar Current. The highest-mixing case shows a very similar spatial pattern of variability, but with amplitudes only about 60% as large. The suppression of mixing is larger in the Atlantic Sector of the Southern Ocean relatively to the Pacific sector. We examine the salinity budgets of convective regions, paying particular attention to the extent to which high mixing prevents the buildup of low-saline waters that are capable of shutting off deep convection entirely.
Experimental design and estimation of growth rate distributions in size-structured shrimp populations

International Nuclear Information System (INIS)

Banks, H T; Davis, Jimena L; Ernstberger, Stacey L; Hu, Shuhua; Artimovich, Elena; Dhar, Arun K

2009-01-01

We discuss inverse problem results for problems involving the estimation of probability distributions using aggregate data for growth in populations. We begin with a mathematical model describing variability in the early growth process of size-structured shrimp populations and discuss a computational methodology for the design of experiments to validate the model and estimate the growth-rate distributions in shrimp populations. Parameter-estimation findings using experimental data from experiments so designed for shrimp populations cultivated at Advanced BioNutrition Corporation are presented, illustrating the usefulness of mathematical and statistical modeling in understanding the uncertainty in the growth dynamics of such populations
A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses

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Vasdekis, Vassilis G. S.; Cagnone, Silvia; Moustaki, Irini

2012-01-01

The paper proposes a composite likelihood estimation approach that uses bivariate instead of multivariate marginal probabilities for ordinal longitudinal responses using a latent variable model. The model considers time-dependent latent variables and item-specific random effects to be accountable for the interdependencies of the multivariate…
Sensitivity Analysis of a Riparian Vegetation Growth Model

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Michael Nones

2016-11-01

Full Text Available The paper presents a sensitivity analysis of two main parameters used in a mathematic model able to evaluate the effects of changing hydrology on the growth of riparian vegetation along rivers and its effects on the cross-section width. Due to a lack of data in existing literature, in a past study the schematization proposed here was applied only to two large rivers, assuming steady conditions for the vegetational carrying capacity and coupling the vegetal model with a 1D description of the river morphology. In this paper, the limitation set by steady conditions is overcome, imposing the vegetational evolution dependent upon the initial plant population and the growth rate, which represents the potential growth of the overall vegetation along the watercourse. The sensitivity analysis shows that, regardless of the initial population density, the growth rate can be considered the main parameter defining the development of riparian vegetation, but it results site-specific effects, with significant differences for large and small rivers. Despite the numerous simplifications adopted and the small database analyzed, the comparison between measured and computed river widths shows a quite good capability of the model in representing the typical interactions between riparian vegetation and water flow occurring along watercourses. After a thorough calibration, the relatively simple structure of the code permits further developments and applications to a wide range of alluvial rivers.
ECONOMIC GROWTH AND IMPACT FACTORS IN COUNTRIES OF CENTRAL AND EASTERN EUROPE

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Bogdan Florin FILIP

2015-07-01

Full Text Available This paper starts from the premise that the performance of the economies of different countries, respectively their economic growth, is sinthtically expressed by the GDP growth indicator, whose dynamics evolves under the impact of a variety of determining factors, including some of financial-monetary nature. Thus, there are highlighted specific causal linkages and influences of economic and financial factors represented by certain indicators (inflation, unemployment, exports as percentage of GDP, imports as percentage of GDP, domestic credit as percentage of GDP, non-performing loans rate to GDP growth rate, by using econometric methods. Much of the paper is focused on on shaping an econometric model in which GDP growth rate is dependent variable and the other mentioned indicators are impact factors, respectively determinant variables. Along the mentioned determining factors, in our model is evaluated also the impact of the manifestation of the recent financial crisis, considering it as an additional determinant dummy variable. By processing the data for a group of countries of Central and Eastern Europe over the period 2000-2013, there result findings on the impact of each of the determining factors on the economic growth in the countries concerned and are formulated the appropriate assessments and conclusions.
Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

International Nuclear Information System (INIS)

Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

2017-01-01

Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction. (paper)
On the growth and dissemination laws in a mathematical model of metastatic growth

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Benzekry Sébastien

2015-01-01

Full Text Available Metastasis represents one of the main clinical challenge in cancer treatment since it is associated with the majority of deaths. Recent technological advances allow quantification of the dynamics of the process by means of noninvasive techniques such as longitudinal tracking of bioluminescent cells. The metastatic process was simplified here into two essential components – dissemination and colonization – which were mathematically formalized in terms of simple quantitative laws. The resulting mathematical model was confronted to in vivo experimental data of spontaneous metastasis after primary tumor resection. We discuss how much information can be inferred from confrontation of theories to the data with emphasis on identifiability issues. It is shown that two mutually exclusive assumptions for the secondary growth law (namely same or different from the primary tumor growth law could fit equally well the data. Similarly, the fractal dimension coeffcient in the dissemination law could not be uniquely determined from data on total metastatic burden only. Together, these results delimitate the range of information that can be recovered from fitting data of metastatic growth to already simplified mathematical models.
Effects of γ-irradiation of garden rose seeds on yield growth anddevelopment of plants. Stufy of relationship between radiation variability of survival rate, growth and developement of plants

International Nuclear Information System (INIS)

Zykov, K.I.; Klimenko, Z.K.

1994-01-01

Correlations between the yield of seedings in green house, survival rate, growth and development in an outdoor plot with rigid agroecological conditions were studied, when variability of these indices was due to γ-irradiation of seeds in different modes. The results obtained allow us to suppose that when a heterogeneous population of garden roses exposed to high doses significantly reduced their germination ability, a selection of ecologically stable, well growing and developing genotypes can take place. It is accounted for direct relation between radioresistance of the seeds and their genetically conditioned ecological stability and ability of seedings to good growth and development

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