Sentences with phrase «equation modeling results»

Not exact matches

The mathematical symmetries of the resulting equations predict three families of particles, as described by the standard model of physics, though the third family would behave a bit differently.

Its a coupled implicit nonlinear system, and you have to model it and solve the resulting equations.

Chris C — In reviewing Raypierre's chapter, I note that he introduced his equations using a grey gas model, but my point was that he then applied the results to our own atmosphere.

The AGW «concernist's» models, based on the GCM, has to simplify their equations in order to «fit» their expected results — AKA: garbage in == garbage out!

John, There has been extensive work modeling fluids using the Boltzman equation and the results are in good agreement with Navier - Stokes continuum simulations.

I know absolutely that many FEA runs using exact «perfect» data on a «perfect» crystal or pure piece of metal machined exactly per the model dimensions under loads exactly as described by the modeled equations will yield (on average) results similar to the average of many model runs.

I had little confidence that the equations / parameters were the best ones to represent the atmosphere but know that by continually tweeking and tweeking, it resulted in the changes which moved my model in the right direction and finally showed what it was supposed to show, to get the result we needed to pass.

In the model, a series of equations represents world economic activity, the CO2 levels that activity generates, and the impact of the resulting CO2 levels.

so if I am reading it correctly the equation is based on the result of climate models.

They can't even predict the next decade, much less ten decades; despite tuning they only poorly replicate the historical climate; their equations can't be shown to converge; the number of tunable parameters is far too large for comfort; they show absolutely no skill at regional scales; their results for things they are not tuned to replicate (e.g. rainfall) are abysmal — in short they are glorified Tinkertoy ™ models which have one common characteristic... they don't work well.

The 1 - D climate model uses physically based equations to determine changes in the climate system as a result of changes in solar intensity, ice reflectance and greenhouse gas changes.

This spread results because the model equations provide a deterministic set of results that each can be different since the climate is a chaotic nonlinear system both in the model, and even more so in the real world.

I have literally had to write out differential calculus equations proving that the Earth can be modeled as a sphere, and with real - time power from the Sun, and that it makes things very hot, and that this produces wildly different results than a flat Earth requiring the invention of a greenhouse effect.

In contrast with ACR's 2010 methodology for N2O Emissions Reductions from Changes in Fertilizer Management, which incorporates site specific data into a peer - reviewed, tested and highly parameterized computer model to calculate N2O emission reductions resulting from changes in how fertilizer is applied and used, the MSU - EPRI methodology is based on empirical equations and NCR data to set conservative estimates for emission reductions.

What produced Lewandowsky's result is a statistical technique called structural - equation modelling (SEM).

In a system such as the climate, we can never include enough variables to describe the actual system on all relevant length scales (e.g. the butterfly effect — MICROSCOPIC perturbations grow exponentially in time to drive the system to completely different states over macroscopic time) so the best that we can often do is model it as a complex nonlinear set of ordinary differential equations with stochastic noise terms — a generalized Langevin equation or generalized Master equation, as it were — and average behaviors over what one hopes is a spanning set of butterfly - wing perturbations to assess whether or not the resulting system trajectories fill the available phase space uniformly or perhaps are restricted or constrained in some way.

The equation of the model can be modified to allow for air resistance and the resulting equation can be solved numerically.

Isn't that much like assigning «chaotic looking» calculated results to relativistic effects when the fundamental model equations used in the calculation do not include relativistic effects.

Simply using the calculated age factor in the multiplicative model equation will give wrong results for the climate factor for those trees.

If the model results of temperature and humidity profile vertically though the atmosphere were not correct then this would definitely affect the accuracy of the radiative transfer equations.

It's also the case that the results for the radiative transfer equations will have a certain amount of error using «band models» compared with the «line by line» (LBL) codes for all trace gases.

The radiative transfer equations as part of the modeled results have done a pretty good job of explaining the observed results but aren't exactly the same.

Miskolczi has inserted the simple result into the general model, which means, at best, it can only be applied to a «grey» atmosphere in radiative equilibrium, and at worst he has just created an equation soufflé.

Dr Miskolczi has two equations which describe the result of applying conservation of energy to the Earth and the atmosphere, the two entities in his simple model.

The analytic form of TOPMODEL equations are incorporated into the soil column framework and the resulting model is used to predict the saturated fraction of the watershed and baseflow in a consistent fashion.

The results of mediation analysis using structural equation modeling showed that maternal problems in reciprocal social behavior directly increased infantile aggression (estimate = 0.100, 95 % CI [0.011, 0.186]-RRB-, and indirectly increased infantile aggression via maternal postpartum depressive symptoms (estimate = 0.027, 95 % CI [0.010, 0.054]-RRB-, even after controlling for covariates.

In fact, one study found that social anxiety fully mediated the relation between behavioural inhibition and depression.53 Similarly, other studies, 54 revealing associations between behavioural inhibition and anxiety and depression employed structural equations modeling which found that a pathway in which behavioural inhibition results in anxiety, which in turn leads to depression, provided the best fit for the data.

RESULTS: Structural Equation Modeling revealed affective and cognitive trust in coworkers is influenced by leader - member exchange.

Results from the structural equation modeling indicated that mother's insecure adult attachment is an important determinant of maternal psychological control.

Results from a series of structural equation models, with a constructed latent variable of school attachment, using a national longitudinal study of over 9,000 U.S. adolescents and their subsequent... career choices in their late 20 s, reveal a pattern of entrepreneurial development.

Results from a series of structural equation models, with a constructed latent variable of school attachment, using a national longitudinal study of over 9,000 U.S. adolescents and their subsequent

Results from structural equation modeling revealed that work — family conflict was significantly and negatively related to marital satisfaction.

Because of skewness and the ordered categorical nature of our variables, we estimated α within a structural equation model framework, which resulted in higher α coefficients.20 Our ω reliability analyses yielded results consistent with previous studies reporting ω reliabilities for preschool and school - age SDQs.9, 16

Results from structural equation modeling indicated that self - esteem partially mediated the relation between self oriented perfectionism and behavioral self - handicapping.

Results of structural equation modeling (SEM) suggested that externalizing symptoms mediated the relationship between gender and adherence.

Structural equation modeling (SEM) allows for the simultaneous examination of the relationships between latent constructs defined by multiple measures as well as directly observed variables (e.g., gender, HbA1C) while reducing the effect of measurement error on results.

The resulting structural equation model provides further support for the revised theory of cognitive adaptation.