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4 edition of Selection of models to calculate the LLW source term found in the catalog.

Selection of models to calculate the LLW source term

Selection of models to calculate the LLW source term

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Published by Division of Low Level Waste Management and Decommissioning, Office of Nuclear Material Safety and Safeguards, U.S. Nuclear Regulatory Commission, Supt. of Docs., U.S. G.P.O. [distributor] in Washington, DC .
Written in English

    Subjects:
  • Radioactive waste disposal -- Mathematical models,
  • Radioisotopes -- Migration -- Mathematical models,
  • Hazardous waste sites -- Leaching -- Mathematical models

  • Edition Notes

    Statementprepared by T.M. Sullivan.
    ContributionsU.S. Nuclear Regulatory Commission. Division of Low-Level Waste Management and Decommissioning., Brookhaven National Laboratory.
    The Physical Object
    FormatMicroform
    Paginationxiii, 47, 22 p.
    Number of Pages47
    ID Numbers
    Open LibraryOL14693503M

    High short-term interest rates will attract investors who bid up the currency value over the short term. Low short-term interest rates will result in borrowing in the currency. 3) Capital Flows Approach - primarily focuses on long-term capital flows - such as those into equity investments or foreign direct investments. Myers and Majluf () in their pioneering work on pecking-order theory show that if the investors are not well informed about the information which the insiders have, the equity of that firm may be severely mispriced. In their paper they also show that if any firm wants to fund its new project by new equity then the equity can be so undervalued that the new investors will be .


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Selection of models to calculate the LLW source term Download PDF EPUB FB2

Buy Selection of models to calculate the LLW source term (SuDoc Y 3.N /) by T. Sullivan (ISBN:) from Amazon's Book Store.

Everyday low prices and free delivery on Author: T. Sullivan. Selection of models to calculate the LLW source term. [T Sullivan; U.S. Nuclear Regulatory Commission. Division of Low-Level Waste Management and Decommissioning.

@article{osti_, title = {A probabilistic source term model for LLW performance assessment}, author = {Yim, M S and Chen, M and Su, S I}, abstractNote = {Source term analysis is one of the key steps in the performance assessment of a low-level waste (LLW) disposal facility. Because of the large number of processes and parameters involved.

This report focuses on the source term for light water reactors. For the purposes of this report, the expression 'source term' is defined as follows: (a) Source term to the containment is the magnitude, physical and chemical form and timing of the release of fission products and other aerosols from core materials and primary containment.

Lippincott Williams & Wilkins (LWW) - an imprint of Wolters Kluwer - publishes scientific, technical, and medical content such as textbooks, reference works.

Regression and Model Selection Book Chapters 3 and 6. Carlos M. Carvalho The University of Texas McCombs School of Business 1. Simple Linear Regression 2. Multiple Linear Regression 3. Dummy Variables 4.

Residual Plots and Transformations 5. Variable Selection and Regularization 6. Dimension Reduction Methods. Forward selection is a very attractive approach, because it's both tractable and it gives a good sequence of models. Start with a null model.

The null model has no predictors, just one intercept (The mean over Y). Process and design capabilities: Suppliers should have up-to-date and capable products, as well as process technologies to produce the material needed. Because different manufacturing and service processes have various strengths and weaknesses, the buying organisation must be aware of these characteristics upfront.

When the buying. Ch 8 - Conducting Cost Realism Analyses Model to identify the offeror information marking of proprietary and source selection information. Government personnel must not visit any offeror or discuss the proposal with any offeror without proper Size: KB.

$\begingroup$ You should not use stepwise selection, either for standard regression or for mixed-effects models, as it is invalid. If that doesn't make sense to you / if you want to understand why, you may want to read my answer here: algorithms-for-automatic-model-selection.

$\endgroup$ – gung - Reinstate Monica ♦ May 13 '13 at Notice the grammar in the lmer function that defines the model: the term (1|Individual) is added to the model to indicate that Individual is the random term. As a technical note, the 1 indicates that an intercept is to be fitted for each level of the random variable.

models regsubsets(Fertility~., data = swiss, nvmax = 5, method = "seqrep") summary(models) Note that, the train () function [caret package] provides an easy workflow to perform stepwise selections using the leaps and the MASS packages. It has an option named method, which can take the following values:5/5(1).

Model Selection Model Evaluation Exercises. Introduction to Generalized Linear Models Outlines Part III: Count Data Generalized linear models can be tted in R using the glm function, I - to exclude a term or terms I 1 to include an intercept.

Supplier Evaluation - The first steps for effective sourcing. Introduction: The evaluation and selection of suppliers, structuring the supplier base is an important task in any organization.

It assumes utmost importance in the current scenario of global purchasing. Every Organization especially manufacturing organizations need toFile Size: KB.

Model selection in structural equation modeling (SEM) involves using selection criteria to declare one model superior and treating it as a best working hypothesis until a better model. Practitioners need a robust and reliable approach to 1) quickly determine whether the project is indeed a good DMAIC project and 2) prioritize projects to ensure resources are allocated appropriately.

A criteria-based selection matrix helps practitioners standardize the project selection process, boosting its reliability.

Cisco Catalyst Switching Family e-book; Cisco Catalyst Series ordering guide (PDF - 1 MB) Cisco Catalyst Series Switches data sheet; Enterprise networks software subscription matrix for Cisco DNA; ROI calculator. The commonly used methods of valuation can be grouped into one of three general approaches, as follows: 1.

Asset Based Approach a. Book Value Method b. Adjusted Net Asset Method i. Replacement Cost Premise ii. Liquidation Premise iii. Going Concern Premise 2. Income Approach a. Capitalization of Earnings/Cash Flows Method Size: KB. Since then I've read the book twice again and a somewhat third time (picking out my favorite parts).

I first came across the selection as one of those side adds on Goodreads that you can click on and it sent me to the harper-teen page for The Selection. I loved/love the book and I also loved/love the cover. EPRI is at the center of a quickly evolving energy landscape. With significant challenges facing the electric industry and the world, we are always seeking top talent with hands-on experience to address issues as they relate to the generation, delivery, use, management and environmental responsibility of global electricity.

ACSM is pleased to announce the release of the annual Team Physician Consensus Conference statement, which is a blend of research evidence and expert consensus providing guidance and teaching tools for physicians working in the sports medicine field, regardless of geographical location or level of team or athlete.

Model selection in mixed-model context using lmer. Ask Question Asked 8 years ago. fm4, fm5)] and then to try different random-effects specification with this model. Or would only the most comprehensive approach be valid in which I specify all possible combinations of fixed- and random-effects to compare all these model to find the best fit.

In model selection, assuming that the data arose from one of the models in competition is often somewhat unrealistic and could be misleading. However this assumption is implicitly made when using standard model selection criteria such as AIC or BIC. This \true model" assumption could lead to overestimating the model complexity in practical Cited by: 1.

Books at Amazon. The Books homepage helps you explore Earth's Biggest Bookstore without ever leaving the comfort of your couch. Here you'll find current best sellers in books, new releases in books, deals in books, Kindle eBooks, Audible audiobooks, and.

Chapter 13 Introduction to Nonlinear Regression and Neural Networks function here is: f (X,γ) =γ 0 1 +γ 1 exp(γ 2 X) () Note again that this response function is not linear in the parameters γ 0, γ 1, and γ 2.

This logistic regression model has been used in population studies to relate, for instance,File Size: KB.

Polynomial regression only captures a certain amount of curvature in a nonlinear relationship. An alternative, and often superior, approach to modeling nonlinear relationships is to use splines (P.

Bruce and Bruce ). Splines provide a way to smoothly interpolate between fixed points, called knots. Polynomial regression is computed between 5/5(4). A Refresher on Regression Analysis. by perhaps even hundreds of factors from the weather to a competitor’s promotion to the rumor of a new and improved model can impact the number.

ANSWER: TRUE The linear programming model of the production mix problem only includes constraints of the less than or equal form.

ANSWER: FALSE The linear programming model of the production scheduling process can include the impact of hiring and firing, regular and overtime pay rates, and the desire to have a constant and stable production.

Calculate the gross premium using simple criteria other than the equivalence principle. Calculate the gross premium prospective reserve using the future loss random variable. Define and calculate the gross premium retrospective reserve. State the conditions under which, in general, the prospective reserve is equal to the retrospectiveFile Size: KB.

Source term model The source-term model considered in this paper is based upon Hartley [4]. The nuclear waste is to be stored in an underground repository within a ma-trix and with a backfill of bentonite, isolated from the geological medium by engineering barriers.

The host medium will be chosen according to low. Divide the result by the sum of the weights to find the average. Once you’ve multiplied each number by its weighting factor and added the results, divide the resulting number by the sum of all the weights.

This will tell you the weighted average. For example: 98/15 = This means you slept an average of hours each night over the 66%(70).

Performance attribution, or investment performance attribution is a set of techniques that performance analysts use to explain why a portfolio's performance differed from the difference between the portfolio return and the benchmark return is known as the active active return is the component of a portfolio's performance that arises from.

PDF | On Jun 1,Daniel W. Heck and others published Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation | Find, read and cite all. Inclusive fitness is the number of offspring equivalents that an individual rears, rescues or otherwise supports through its behaviour (regardless of who begets them) An individual's own child, who carries one half of the individual's genes, is defined as one offspring equivalent.

A sibling's child, who will carry one-quarter of the individual. The officer wants to invest at least 50% of the money in short term issues and no more than 50% in high-risk issues. At least 30% of the funds should go in tax-free investments, and at least 40% of the total return should be tax free.

Creating the Linear Programming model to File Size: KB. Calculate the relative fitness of each genotype. Fitness for BB (w BB) = 1 Fitness for Bb (w Bb) = 1 Fitness for bb (w bb) = / = c) Predict quantitatively the effect of natural selection on the frequencies of phenotypes in the next generation of lupins.

First, calculate mean fitness: p2 (w BB) + 2pq (w Bb) + q 2 (w bb) = w-barFile Size: KB. The problems of regression and autoregressive model selection are closely related. Indeed, many of the proposed solutions can be applied equally well to both problems.

One of the leading selection methods, and the primary focus of this paper, is the Akaike information criterion, AIC (Akaike, ). This was designed to be an approximatelyFile Size: KB.

Search the world's most comprehensive index of full-text books. VitalSource Bookshelf is the world’s leading platform for distributing, accessing, consuming, and engaging with digital textbooks and course materials. causes through a hybrid feature selection model. From ’s, feature selection became an important subject in academic fields, such as data mining, business intelligence [8], machine learning, and pattern recognition [23,27,31,41].

At the same time, it was applied extensively to every question fields, including intrusion detection [2], genomic. 1 Models for time series Time series data A time series is a set of statistics, usually collected at regular intervals.

Time series data occur naturally in many application areas. • economics - e.g., monthly data for unemployment, hospital admissions, etc. • finance - e.g., daily exchange rate, a share price, Size: KB.

The scoring model is an objective technique: the project selection committee lists relevant criteria, weighs them according to their importance and their priorities, then adds the weighted values.

Once the scoring of these projects is completed, the project with the highest score is chosen. Payback Period is the ratio of the total cash to the Author: Avantika Monnappa.Combining predictions from linear models when training and test inputs differ Thijs van Ommen Centrum Wiskunde & Informatica Amsterdam, The Netherlands Abstract Methods for combining predictions from dif-ferent models in a supervised learning setting must somehow estimate/predict the quality of a model’s predictions at unknown future : Thijs van Ommen.