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A Simple Model of GC x GC Separations

A Simple Model of GC x GC Separations

A Simple Model of GC x GC Separations John V. Seeley Oakland University 3/6/07 Model Goals Generation of a “Simplified Chromatogram” from: 1-D retention times Linear free energy relationship parameters Retention indices Utility of the “Simplified Chromatogram”

By Audrey
(718 views)

Studio Web is not . . .

Studio Web is not . . .

Studio Web is not . . . a portal For information on PAWS: The Future of Web Portals 2:45 p.m. - 3:45 p.m. Kachina. c. Studio Web is . . . a new approach to web site development and management 28 web sites – 8 months – 2 FTE. c. Agenda. Defining the problem Introducing Studio Web

By Renfred
(230 views)

AIM/Material Model Features, model and necessary data

AIM/Material Model Features, model and necessary data

AIM/Material Model Features, model and necessary data. Toshihiko MASUI and Ashish RANA National Institute for Environmental Studies Session 4: Asia-Pacific Integrated Model (AIM): Introduction to Component Models (Cont.) APEIS Capacity Building Workshop on

By Melvin
(279 views)

Calibration Techniques

Calibration Techniques

Calibration Techniques. 1. Calibration Curve Method 2. Standard Additions Method 3. Internal Standard Method. Calibration Curve Method. Most convenient when a large number of similar samples are to be analyzed. Most common technique. Facilitates calculation of Figures of Merit.

By niveditha
(513 views)

Solving Linear Programming Problems Shubhagata Roy

Solving Linear Programming Problems Shubhagata Roy

Solving Linear Programming Problems Shubhagata Roy.

By oshin
(298 views)

Model Order Reduction

Model Order Reduction

Model Order Reduction. Bo Hu Mixed Signal CAD Electrical Engineering Department University of Washington. Outline. Overview of the problem Linear Model Order Reduction Non-linear Model Order reduction Reference. The Problem. Slow to simulate. x(t). u(t). y(t). N is Large. reduce.

By emily
(581 views)

California Sediment Quality Objectives Bioaccumulation Methods

California Sediment Quality Objectives Bioaccumulation Methods

California Sediment Quality Objectives Bioaccumulation Methods. A Presentation to the SQO Scientific Steering Committee July 27, 2005. Presentation Summary. Background and Conceptual Model Three Lines of Evidence Technical Issues With Each Line of Evidence. Conceptual Model.

By Antony
(291 views)

Predicting Depression Occurrence Using Classification Algorithm in Data Mining

Predicting Depression Occurrence Using Classification Algorithm in Data Mining

Predicting Depression Occurrence Using Classification Algorithm in Data Mining. Abdur Rahman Department of Statistics Shahjalal University of Science and Technology Sylhet , Bangladesh E-mail: airdipu@gmail.com. Introduction. Universal definition of old age is elusive

By tessa
(359 views)

Hash Tables

Hash Tables

Hash Tables. 0. . 1. 025-61-0001. 2. 981-10-0002. 3. . 4. 451-22-0004. Recall the Map ADT. Map ADT methods: get (k): if the map M has an entry with key k, return its assoiciated value; else, return null

By lainey
(253 views)

Exploratory Data Analysis: Two Variables

Exploratory Data Analysis: Two Variables

Exploratory Data Analysis: Two Variables . FPP 7-9. Exploratory data analysis: two variables. There are three combinations of variables we must consider. We do so in the following order 1 qualitative/categorical, 1 quantitative variables Side-by-side box plots, counts, etc.

By susane
(618 views)

Elastic Behavior

Elastic Behavior

Elastic Behavior. s = E e Strain, e, is linearly proportional to stress E = elasticity or Young’s modulus Rock values of E are generally in GPa. Retrn to text. Rock Type . Modulus of Elasticity. - . (MPa x 1000). Limestone . 3-27. Dolomite . 7-15. Limestone (very hard) . 70.

By astin
(310 views)

Single phase flow in porous media: Darcy’s law

Single phase flow in porous media: Darcy’s law

Single phase flow in porous media: Darcy’s law. Oil or gas reservoir. Sandstone reservoir. Limestone reservoir. Porosity. Rock matrix. Pore space. Rock Matrix and Pore Space. Typical Pore Structure. Pore Structure. Porosity in Sandstone. Pore Throat. Pores Provide the

By parry
(3381 views)

ECG Filtering

ECG Filtering

ECG Filtering. T-61.181 – Biomedical Signal Processing Presentation 11.11.2004 Matti Aksela ( matti.aksela@hut.fi ). Contents. Very brief introduction to ECG Some common ECG Filtering tasks Baseline wander filtering Power line interference filtering Muscle noise filtering Summary.

By destiny
(881 views)

Effective Connectivity

Effective Connectivity

Effective Connectivity. Lee Harrison. Wellcome Department of Imaging Neuroscience, University College London, UK. SPM Short Course, May 2004. Outline. Motivation & concepts Models of effective connectivity An example. Outline. Motivation & concepts

By elkan
(448 views)

Chapter 6: Statistical Inference: n-gram Models over Sparse Data

Chapter 6: Statistical Inference: n-gram Models over Sparse Data

Chapter 6: Statistical Inference: n-gram Models over Sparse Data. TDM Seminar Jonathan Henke http://www.sims.berkeley.edu/~jhenke/Tdm/TDM-Ch6.ppt. Basic Idea:. Examine short sequences of words How likely is each sequence?

By gamma
(231 views)

Rotation

Rotation

y. x. Rotation. Imagine watching a spinning bicycle wheel: How would you describe the position of a point (a reflector, for example) on this wheel with time? You could keep track of the ( x , y ) coordinates of this point: . ( x , y ). y. x. Angular Position. q.

By bryony
(957 views)

13. 1: Statistical Review

13. 1: Statistical Review

13. 1: Statistical Review. Uchechukwu Ofoegbu Temple University. Measure of Location. Arithmetic mean : the sum of the individual data points ( y i ) divided by the number of points n : Median : the midpoint of a group of data.

By ipo
(156 views)

Faster shortest path algorithms for planar graphs

Faster shortest path algorithms for planar graphs

Faster shortest path algorithms for planar graphs. Algorithms seminar 2009 By Ety Khaitsin. Linear time algorithm for single source shortest paths in planar graphs with non-negative lengths. Based on: Separators -> Division -> Recursive division Upgraded Dijkstra.

By elam
(193 views)

Linear Programming CISC5835, Algorithms for Big Data CIS, Fordham Univ.

Linear Programming CISC5835, Algorithms for Big Data CIS, Fordham Univ.

Linear Programming CISC5835, Algorithms for Big Data CIS, Fordham Univ. Instructor: X. Zhang. Linear Programming. In a linear programming problem, there is a set of variables , and we want to assign real values to them so as to

By chipo
(294 views)

CHAPTER 1 2: From Crypto-Theory to Crypto-Practice I

CHAPTER 1 2: From Crypto-Theory to Crypto-Practice I

CHAPTER 1 2: From Crypto-Theory to Crypto-Practice I. SHIFT REGISTERS The first practical approach to ONE-TIME PAD cryptosystem. IV054. Basic idea: to use a short key, called “seed'' with a pseudorandom generator to generate as long key as needed.

By rod
(805 views)

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