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For prediction

The third edition of "Properties of Gases and Liquids" by Reid et al. (1977) lists useful group contribution methods for predicting critical properties. Contributions to the second... [Pg.36]

Before suggesting an approach for predicting the minimum number of shells for an entire network, a more convenient method for determining the number of shells in a single unit must first be found. Adopting the design criterion given by Eq. (7.13) as the basis, then any need for trial and error can be eliminated, since an explicit... [Pg.225]

Other techniques for predicting the cetane number rely on chemical analysis (Glavinceski et al., 1984) (Pande et al., 1990). Gas phase chromatography can be used, as can NMR or even mass spectrometry (refer to 3.2.1.l.b and 3.2.2.2). [Pg.220]

The spectroscopic methods, NMR and mass spectrometry for predicting cetane numbers have been established from correlations of a large number of samples. The NMR of carbon 13 or proton (see Chapter 3) can be employed. In terms of ease of operation, analysis time (15 minutes), accuracy of prediction (1.4 points average deviation from the measured number), it is... [Pg.220]

Forecasting of time series behavior using lead time data (data obtained during current experiment) for prediction of the material response to the similar actions and loads in future or of testing results for twin material specimens during lead time . [Pg.188]

Calculations of mutual locations of poles and zeros for these TF models allow to trace dynamics of moving of the parameters (poles and zeros) under increasing loads. Their location regarding to the unit circle could be used for prediction of stability of the system (material behavior) or the process stationary state (absence of AE burst ) [7]. [Pg.192]

Use of One-Dimensional Skin-Effect Equations for Predicting Remote Field Characteristics Materials Evaluation Vol.47 / Jan.89... [Pg.317]

This work presents the theoretieal results and their experimental verifications concerning two possible methods for predicting the material discontinuities shape and severity. The methods are developed for the case of the eddy current transducer with orthogonal coils, for two situations for long crack-tjfpe discontinuities, a metod based on the geometrical diffraction has been used, while in the ease of short discontinuities the holographic method is prefered. [Pg.373]

Maintenance of process installation is still a necessity to realise high process reliability. Infrared thermography is becoming more and more an useful tool for predictive maintenance in the process and electrical industry. [Pg.399]

Why do you think the Cassie equation Eq. X-27 might work better than Eq. X-28 for predicting the contact angle as a function of surface polarity ... [Pg.380]

Kaptein s rule for the multiplet effect is useful for predicting the phase of each transition, and it is similar to... [Pg.1600]

Aqvist, J., Medina, C., Samuelsson, J. -E. A new method for predicting binding affinity in computer-aided drug design. Prot. Eng. 7 (1994) 385-391... [Pg.162]

One task of data analysis is to establish a model which quantitatively describes the relationships between data variables and can then be used for prediction. [Pg.446]

A counter-propagation network is a method for supervised learning which can be used for prediction, It has a two-layer architecture where each netiron in the upper layer, the Kohonen layer, has a corresponding netiron in the lower layer, the output layer (sec Figure 9-21). A trained counter-propagation network can be used as a look-up tabic a neuron in one layer is used as a pointer to the other layer. [Pg.459]

During training the input layer is adapted as in a regular Kohonen network, i.c., the winning neuron is determined only on the basis of the input values. But in contra.st to the training of a Kohonen network, the output layer is also adapted, which gives an opportunity to use the network for prediction. [Pg.460]

Prediction implies the generation of unknown properties. On the basis of example data, a model is established which is able to relate an object to its property. This model can then be used for predicting values for new data vectors. [Pg.473]

A counter-propagation neural network is a method for supervised learning which can be used for predictions. [Pg.481]

The spectral signals are assigned to the HOSE codes that represent the corresponding carbon atom. This approach has been used to create algorithms that allow the automatic creation of "substructure-sub-spectrum databases that are now used in systems for predicting chemical structures directly from NMR. [Pg.519]

The reliability of the in silico models will be improved and their scope for predictions will be broader as soon as more reliable experimental data are available. However, there is the paradox of predictivity versus diversity. The greater the chemical diversity in a data set, the more difficult is the establishment of a predictive structure-activity relationship. Otherwise, a model developed based on compounds representing only a small subspace of the chemical space has no predictivity for compounds beyond its boundaries. [Pg.616]

Equations (8.21) still contain too many adjustable parameters to be of much value for predictive purposes, and Feng and Stewart propose three simpler special cases which may be of practical value. [Pg.74]

Gibson K D and H A Scheraga 1987. Revised Algorithms for the Build-up Procedure for Predicting lAotein Conformations by Energy Minimization, journal of Computational Chemistry 8 826-834. [Pg.523]

First-principles Methods for Predicting Protein Structure... [Pg.533]

Gamier J, D Osguthorpe and B Robson 1978. Analysis of the Accuracy and ImpUcatiotrs of Simple Mel for Predicting the Secondary Structure of Globular Proteins. Journal of Mokadar Biology 120 97-i... [Pg.575]

Aqvist J, C Medina and J-E Samuelsson 1994. A New Method for Predicting Binding Affinity Computer-aided Drug Design. Protein Engineering 7 385-391. [Pg.649]


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See also in sourсe #XX -- [ Pg.948 ]




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A Practice Tutorial for Active Site Prediction Using MOE

A Practice Tutorial for Predicting Active Site Using SiteMap

A Thermodynamic Approach for Predicting Physical-chemical Properties

Accounting for Dynamical Electron Correlation An Important Step Towards Accurate Predictions

Active Site-Directed Pose Prediction Programs for Efficient Filtering of Molecules

An Equilibrium-Based Model for Predicting Potential Ammonia Volatilization from Soil

Analytical Methods for Predicting and Reducing Human Error

Animal Models of Disease for Future Toxicity Predictions

Approach for predicting

Brief overview of the methods for predicting and

Cell-based assays, for toxicity prediction

Challenges for Early, Predictive Genotoxicity Testing

Comparison between measured and theoretically predicted results for micromixing time

Computational Models for Prediction of Intestinal Permeability

Computational Models for the Prediction of Aerosol Dispersion

Computer model for prediction

Concept of Creep and Fatigue Life Prediction for Polymer Composites

Conclusions and predictions for the future

Critical Assessment of Techniques for Protein Structure Prediction

Critical Assessment of Techniques for Protein Structure Prediction (CASP

Curve-fit equation for predicting core temperature in Elkos

Determining Properties that Drive eD2M Predictions for a Series

Developing models for predicting

Equations for Slump Predictions

Equations for Strength Predictions

Example Polymorph Prediction for Estrone

Examples for Reaction Predictions

First principles method for predicting protein

First-principles Methods for Predicting Protein Structure

For prediction of aquatic toxicity

Forced air cooling for Elkos life prediction

Free Online Tools for Active Site Prediction

General Tips for Predicting Products

Hadley Centre for Climate Prediction

In Silico Methods for Prediction of Phototoxicity - (Q)SAR Models

Inferences for Predicted Functions

Inscribed polygon method, for predicting

Integrated Methods for the Prediction of Binding Sites

Intervals for the Predicted Response

Learning for Protein Structure and Function Prediction

Machine Learning Models for Predictive Studies

Methods for Predicting Drug Metabolism

Methods for Structure Prediction

Methods for predicting absorption

Model Predictions for Void Growth

Model acceptance criteria for the time-domain technique predictability

Model acceptance for transfer-function-based technique predictability

Modeling of Chain Dynamics and Predictions for NMR Measurands

Models for Predicting Battery Behavior

Models for Prediction of Absorption

Models for Prediction of Incipient Boiling Heat Flux and Wall Superheat

Models for Prediction of Volume

Models for performance prediction

Models for predicting functions

Mouse Population-Based Toxicology for Personalized Medicine and Improved Safety Prediction

Oscillatory Measurements for Prediction of Creaming

PASS (Prediction of Activity Spectra for

Predicted K values for

Predicting Substrate Properties for P-Glycoprotein

Predicting Toxicology — Deductive Estimation of Risk from Existing Knowledge (DEREK) for Windows

Predicting cosolvency for pharmaceutical and environmental applications

Predicting the Mechanism of Action for Narcotic and Reactive Compounds

Predicting the Mechanism of Action for Polar and Nonpolar Narcotic Compounds

Predicting the Site of Cleavage for Acyl Transfers from Esters

Prediction approaches, for

Prediction of Fatigue Strength for Arbitrary Stress Ratios

Prediction of Optimum Conditions for New Substrates in the Willgerodt-Kindler Reaction

Prediction of pressure gradient for flow through packed beds

Prediction of the Shift Factors for Viscoelastic Liquids

Prediction of the Shift Factors for Viscoelastic Solids

Prediction procedure for

Prediction statistics for wine color

Prediction statistics for wine color measures

Predictions for Proteins with Known 3D Structure

Predictions for hydrogen storage in carbon nanostructures coated with light transition metals

Predictions for polymers in oxidative

Predictions for polymers in oxidative environments

Predictions for polymers in oxidative excess

Predictions for the twenty-first century

Predictive Design Tools for the Performance Imperative

Predictive Design for the Cost Imperative

Predictive Methods for Organic Spectral Data Simulation

Predictive Modeling Approaches for Assessing Human Lead Exposure

Pressure Drop Prediction for Slurries Exhibiting Bingham Plastic Rheology

QSARs for Predicting Cation Toxicity, Bioconcentration, Biosorption, and Binding

Quantum Chemistry Methods for the Prediction of Molecular Thermochemistry

Quantum Mechanical Methods for Predicting Nonlinear Optical Properties

Reasons for predicting the productivity of organic grassland

Resampling Methods for Prediction Error Assessment and Model Selection

Scale CFD—Solutions for High Predictability and

Semiempirical Methods for Predicting Thermodynamic Properties and Kinetic Parameters

System for Predictive Error Analysis and Reduction (SPEAR)

System for predictive error analysis and reduction

Technique for Human Error Rate Prediction

Technique for Human Error Rate Prediction THERP)

Testing for false positive predictions in membrane and soluble proteins of crystallographically known structure

The Challenge of Affinity Prediction Scoring Functions for Structure-Based Virtual Screening

The Expert System for Metabolism Prediction in Drug Design and Discovery

The Inscribed Polygon Method for Predicting Aromaticity

The Need for Prediction

Theoretical Analysis for Shear Prediction in Stirred Cell

Theoretical Predictions for Ga Oxonitride Compounds

Thermodynamic Models for the Prediction of Petroleum-Fluid Phase Behaviour

Thumb Rules for Spectral Data Handling and Prediction

Tools for Predictions and Modeling

Tools for Risk Prediction

Tutorial Developing Models for Solubility Prediction with 18 Topological Descriptors

Two Guiding Principles for Predicting Relative Acidities

Understanding and predicting stiffness in advanced fibre-reinforced polymer (FRP) composites for structural applications

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