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1.

図書
図書
1. Solid particle erosion : occurrence, prediction and control
Ilmar Kleis, Priit Kulu
出版情報: London : Springer, c2008  xii, 206 p ; 24 cm
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Notation
Experimental Study of Erosion Characteristics / 1：
Laboratory Equipment Used in Erosion Research / 1.1：
Dependence of Erosion on Particle Velocity / 1.2：
Dependence of Erosion on Impact Angle / 1.3：
Dependence of Erosion on Particle Size / 1.4：
Influence of Particle Concentration / 1.5：
Effect of Abrasive Mixtures and Liquid Additives on Erosion / 1.6：
Effect of Mixtures of Uniform Granularity / 1.6.1：
Effect of Fine-grained Solid Additives on Abrasion / 1.6.2：
Abrasion by Industrial Dusts / 1.6.3：
Effect of Liquid Additives / 1.6.4：
Influence of Temperature on Erosion / 1.7：
Erosion of Surface by Grazing Particles / 1.8：
References / 1.9：
Research into the Physical Mechanism of Erosion / 2：
Changes in the Macro-and Microgeometry of a Wearing Surface / 2.1：
Stress Distribution and Structural Changes in Target Material Surface Layer / 2.2：
Fragmentation of Abrasive Particles and Adhesion of the Latter to the Surface / 2.3：
Development of Theories of Collision and Erosion / 2.4：
Hypothesis of a Constant Specific Energy; Dynamic Hardness / 3.1：
Experimental and Theoretical Determination of the Coefficient of Restitution / 3.2：
Analytical Determination of Indentation Load in Terms of Impact Energy / 3.3：
Mathematical Models for Force Calculation / 3.3.1：
Comparison of Calculated and Experimental Results / 3.3.2：
Conclusions / 3.3.3：
Theoretical Treatment of Erosion / 3.4：
A Short Survey of Erosion Theory / 3.4.1：
Erosion by Plastic Contact / 3.4.2：
Energetic Erosion Theory / 3.4.2.1：
Verification and Modification of Energetic Erosion Theory / 3.4.2.2：
Erosion by Brittle Behaviour / 3.4.3：
Modelling of Wear / 3.4.3.1：
Verification of the Model / 3.4.3.2：
Calculation of Erosive Wear of Composite Materials / 3.4.4：
Prediction of Relative Erosion Resistance / 3.5：
Erosion Resistance of Powder Materials and Coatings / 3.6：
Groups and Properties of Wear Resistant Materials and Coatings / 4.1：
Erosion Resistance of Advanced Ceramic Materials and Coatings / 4.2：
Erosion Resistance of Ceramic-Metal Composites and Coatings at Room Temperature / 4.3：
Erosion of Ceramic-Metal Composites / 4.3.1：
Erosion of Coatings / 4.3.2：
Erosion Resistance of Ceramic-Metal Materials and Coatings at Elevated Temperatures / 4.4：
Criteria for Erosive Wear Resistant Material and Coating Selection / 4.4.1：
Tribological Criteria / 4.5.1：
Structural Criteria / 4.5.2：
Qualitative Criteria / 4.5.3：
Improvement of Erosion Resistance of Industrial Equipment / 4.6：
Fans and Exhausters / 5.1：
Influence of Geometrical Parameters of the Rotor on the Erosion Rate / 5.1.1：
Design Methods for Reducing Erosion of Rotors / 5.1.2：
Disintegrators / 5.2：
Use of Disintegrators in the Building Industry / 5.2.1：
Disintegrator as a Machine for Treatment of Different Materials by Collision / 5.2.2：
Application of Wear Resistant Materials and Coatings in Disintegrators / 5.2.3：
Improvement of Disintegrator Design / 5.2.4：
Cyclones for Ash Separation / 5.3：
Cyclone Working Conditions / 5.3.1：
Determination of the Impact Parameters of Erosive Particles / 5.3.2：
Drying Line Equipment at Peat-Briquette Works / 5.4：
Disintegrator as a Device for Milling of Mineral Ores / 5.5：
Materials to be Studied / 5.5.1：
Grindability and Abrasivity of Mineral Materials and Ores / 5.5.2：
Prediction of Relative Erosion Resistance of the Grinding Media / 5.5.3：
Index / 5.6：
Notation
Experimental Study of Erosion Characteristics / 1：
Laboratory Equipment Used in Erosion Research / 1.1：
2.

図書
図書
2. Advanced techniques for assessment surface topography : development of a basis for 3D surface texture standards "surfstand" (: pbk.)
Liam Blunt, Xiangqian Jiang
出版情報: London : Kogan Page Science, c2003  vi, 355 p. ; 30 cm
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Introduction: The History and Current State of 3D Surface Characterisation / Liam Blunt1.：
Characterisation / Part 1：
Numerical Parameters for Characterisation of Topography / Xiangqian Jiang2.：
Novel Areal Characterisation Techniques / Paul J. Scott3.：
Advanced Gaussian Filters / Stefan Brinkman ; Horst Bodschwinna4.：
Multi-scalar Filtration Methodologies / 5.：
Instrumentation / Part 2：
Calibration Procedures for Stylus and Optical Instrumentation / Jean Francois Ville6.：
Calibration Procedures for Atomic Force Microscopes / Anders Kuhle7.：
Case Studies / Part 3：
The Interrelationship of 3D Surface Characterisation Techniques with Standardised 2D Techniques / Robert Ohlsson ; Bengt Goran Rosen ; John Westberg8.：
Applications of Numerical Parameters and Filtration / 9.：
Functionality and Characterisation of Textured Sheet Steel Products / Micheal Vermeulen ; Henrik Hobleke10.：
Characterisation of Automotive Engine Bore Performance using 3D Surface Metrology / 11.：
Future Developments / Part 4：
Surface Texture Knowledge Support--ISM / 12.：
Future Developments in Surface Metrology / 13.：
Index
Introduction: The History and Current State of 3D Surface Characterisation / Liam Blunt1.：
Characterisation / Part 1：
Numerical Parameters for Characterisation of Topography / Xiangqian Jiang2.：
3.

図書
図書
3. Information modeling and relational databases. 2nd ed.
Terry Halpin, Tony Morgan
出版情報: Burlington : Morgan Kaufman Publishers, c2008  xxvi, 943 p. ; 24 cm
シリーズ名: The Morgan Kaufmann series in data management systems
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Introduction / 1：
Information Modeling / 1.1：
Modeling Approaches / 1.2：
Some Historical Background / 1.3：
The Relevant Skills / 1.4：
Summary / 1.5：
Information Levels and Frameworks / 2：
Four Information Levels / 2.1：
The Conceptual Level / 2.2：
Database Design Example / 2.3：
Development Frameworks / 2.4：
Conceptual Modeling: First Steps / 2.5：
Conceptual Modeling Language Criteria / 3.1：
Conceptual Schema Design Procedure / 3.2：
CSDP Step 1: From Examples to Elementary Facts / 3.3：
CSDP Step 2: Draw Fact Types, and Populate / 3.4：
CSDP Step 3: Trim Schema / 3.5：
Note Basic Derivations
Uniqueness Constraints / 3.6：
Arity Check / 4.1 CSDP Step 4: Uniqueness Constraints：
Mandatory Roles / 4.2 Uniqueness Constraints on Unaries and Binaries：
Introduction to CSDP Step 5 / 5.1：
Mandatory and Optional Roles / 5.2：
Reference Schemes / 5.3：
Case Study: A Compact Disc Retailer / 5.4：
Logical Derivation Check / 5.5：
Value, Set-Comparison and Subtype Constraints / 5.6：
CSDP Step 6: Value, Set-Comparison and Subtype constraints / 6.1：
Basic Set Theory / 6.2：
Value Constraints and Independent Objects / 6.3：
Subset, Equality, and Exclusion Constraints / 6.4：
Subtyping / 6.5：
Generalization of Object Types / 6.6：
Other Constraints and Final Checks / 6.7：
CSDP Step 7: Other Constraints and Final Checks / 7.1：
Occurrence Frequencies / 7.2：
Ring Constraints / 7.3：
Other Constraints and Rules / 7.4：
Final Checks / 7.5：
Entity Relationship Modeling / 7.6：
Overview of ER / 8.1：
Barker notation / 8.2：
Information Engineering notation / 8.3：
IDEF1X / 8.4：
Mapping from ORM to ER / 8.5：
Data Modeling in UML / 8.6：
Object-Orientation / 9.1：
Attributes / 9.3：
Associations / 9.4：
Set-Comparison constraints / 9.5：
Other Constraints and Derivation Rules / 9.6：
Mapping from ORM to UML / 9.8：
Advanced Modeling Issues / 9.9：
Join Constraints / 10.1：
Deontic Rules / 10.2：
Temporality / 10.3：
Collection Types / 10.4：
Nominalization and Objectification / 10.5：
Open/Closed World Semantics / 10.6：
Higher-Order Types / 10.7：
Relational Mapping / 10.8：
Implementing a Conceptual Schema / 11.1：
Relational Schemas / 11.2：
Relational Mapping Procedure / 11.3：
Advanced Mapping Aspects / 11.4：
Data Manipulation with Relational Languages / 11.5：
Relational Algebra / 12.1：
Relational Database Systems / 12.2：
SQL: Historical and Structural Overview / 12.3：
SQL: Identifiers and Data Types / 12.4：
SQL: Choosing Columns, Rows, and Order / 12.5：
SQL: Joins / 12.6：
SQL: In, Between, Like, and Null Operators / 12.7：
SQL: Union and Simple Subqueries / 12.8：
SQL: Scalar Operators and Bag Functions / 12.9：
SQL: Grouping / 12.10：
SQL: Correlated and Existential Subqueries / 12.11：
SQL: Recursive Queries / 12.12：
SQL: Updating Table Populations / 12.13：
SQL: Other Useful Constructs / 12.14：
Using Other Database Objects / 12.15：
SQL: Data Definition / 13.1：
SQL: User Defined Functions / 13.2：
SQL: Views and Computed Columns / 13.3：
SQL: Triggers / 13.4：
SQL: Stored Procedures / 13.5：
SQL: Indexes / 13.6：
Other Objects / 13.7：
Exploiting 3GLs / 13.8：
Exploiting XML / 13.9：
Security and Meta-Data / 13.10：
Concurrency / 13.11：
Schema Transformations / 13.12：
Schema Equivalence and Optimization / 14.1：
Predicate Specialization and Generalization / 14.2：
Nesting, Coreferencing, and Flattening / 14.3：
Other Transformations / 14.4：
Introduction / 1：
Information Modeling / 1.1：
Modeling Approaches / 1.2：
4.

図書
図書
4. Process analysis and improvement : tools and techniques
Marvin S. Seppanen, Sameer Kumar, Charu Chandra
出版情報: New York : McGraw-Hill/Irwin, c2005  xvii, 366 p. ; 26 cm.
シリーズ名: The Irwin/McGraw-Hill series in operations and decision sciences
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Introduction to Process Analysis and Improvement / Chapter 1：
Process Analysis and Improvement Using Visio / Chapter 2：
Applications Using Visio / Chapter 3：
Data Management and Analysis Using Excel / Chapter 4：
Applications Using Excel / Chapter 5：
Process Simulation Using Arena / Chapter 6：
Applications Using Arena / Chapter 7：
Visual Basic for Applications: Computer Based Tools Integration / Chapter 8：
Process Analysis and Improvement Application: Customer Service Center / Chapter 9：
Process Analysis and Improvement / Chapter 10：
Student Process Analysis and Improvement Projects / Chapter 11：
Future of Computer Based Tools for Process Analysis and Improvement / Chapter 12：
Introduction to Process Analysis and Improvement / Chapter 1：
Process Analysis and Improvement Using Visio / Chapter 2：
Applications Using Visio / Chapter 3：
5.

図書
図書
5. Nanoscience and nanotechnology on quantum particles : 15-17 December 2003 Tokyo Institute of Technology
RLNR, Tokyo Institute of Technology
出版情報: Tokyo : [Tokyo Institute of Technology], [2003]  x, 79 p ; 30 cm
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6.

図書
図書
6. Data analysis and graphics using R : an example-based approach
John Maindonald and John Braun
出版情報: Cambridge, UK : Cambridge University Press, 2003  xxiii, 362 p., [4] p. of plates ; 26 cm
シリーズ名: Cambridge series on statistical and probabilistic mathematics
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Preface
A Chapter by Chapter Summary
A Brief Introduction to R / 1：
A Short R Session / 1.1：
R must be installed! / 1.1.1：
Using the console (or command line) window / 1.1.2：
Reading data from a file / 1.1.3：
Entry of data at the command line / 1.1.4：
Online help / 1.1.5：
Quitting R / 1.1.6：
The Uses of R / 1.2：
The R Language / 1.3：
R objects / 1.3.1：
Retaining objects between sessions / 1.3.2：
Vectors in R / 1.4：
Concatenation--joining vector objects / 1.4.1：
Subsets of vectors / 1.4.2：
Patterned data / 1.4.3：
Missing values / 1.4.4：
Factors / 1.4.5：
Data Frames / 1.5：
Variable names / 1.5.1：
Applying a function to the columns of a data frame / 1.5.2：
Data frames and matrices / 1.5.3：
Identification of rows that include missing values / 1.5.4：
R Packages / 1.6：
Data sets that accompany R packages / 1.6.1：
Looping / 1.7：
R Graphics / 1.8：
The function plot () and allied functions / 1.8.1：
Identification and location on the figure region / 1.8.2：
Plotting mathematical symbols / 1.8.3：
Row by column layouts of plots / 1.8.4：
Graphs--additional notes / 1.8.5：
Additional Points on the Use of R in This Book / 1.9：
Further Reading / 1.10：
Exercises / 1.11：
Styles of Data Analysis / 2：
Revealing Views of the Data / 2.1：
Views of a single sample / 2.1.1：
Patterns in grouped data / 2.1.2：
Patterns in bivariate data--the scatterplot / 2.1.3：
Multiple variables and times / 2.1.4：
Lattice (trellis style) graphics / 2.1.5：
What to look for in plots / 2.1.6：
Data Summary / 2.2：
Mean and median / 2.2.1：
Standard deviation and inter-quartile range / 2.2.2：
Correlation / 2.2.3：
Statistical Analysis Strategies / 2.3：
Helpful and unhelpful questions / 2.3.1：
Planning the formal analysis / 2.3.2：
Changes to the intended plan of analysis / 2.3.3：
Recap / 2.4：
Statistical Models / 2.5：
Regularities / 3.1：
Mathematical models / 3.1.1：
Models that include a random component / 3.1.2：
Smooth and rough / 3.1.3：
The construction and use of models / 3.1.4：
Model formulae / 3.1.5：
Distributions: Models for the Random Component / 3.2：
Discrete distributions / 3.2.1：
Continuous distributions / 3.2.2：
The Uses of Random Numbers / 3.3：
Simulation / 3.3.1：
Sampling from populations / 3.3.2：
Model Assumptions / 3.4：
Random sampling assumptions--independence / 3.4.1：
Checks for normality / 3.4.2：
Checking other model assumptions / 3.4.3：
Are non-parametric methods the answer? / 3.4.4：
Why models matter--adding across contingency tables / 3.4.5：
An Introduction to Formal Inference / 3.5：
Standard Errors / 4.1：
Population parameters and sample statistics / 4.1.1：
Assessing accuracy--the standard error / 4.1.2：
Standard errors for differences of means / 4.1.3：
The standard error of the median / 4.1.4：
Resampling to estimate standard errors: bootstrapping / 4.1.5：
Calculations Involving Standard Errors: the t-Distribution / 4.2：
Confidence Intervals and Hypothesis Tests / 4.3：
One- and two-sample intervals and tests for means / 4.3.1：
Confidence intervals and tests for proportions / 4.3.2：
Confidence intervals for the correlation / 4.3.3：
Contingency Tables / 4.4：
Rare and endangered plant species / 4.4.1：
Additional notes / 4.4.2：
One-Way Unstructured Comparisons / 4.5：
Displaying means for the one-way layout / 4.5.1：
Multiple comparisons / 4.5.2：
Data with a two-way structure / 4.5.3：
Presentation issues / 4.5.4：
Response Curves / 4.6：
Data with a Nested Variation Structure / 4.7：
Degrees of freedom considerations / 4.7.1：
General multi-way analysis of variance designs / 4.7.2：
Resampling Methods for Tests and Confidence Intervals / 4.8：
The one-sample permutation test / 4.8.1：
The two-sample permutation test / 4.8.2：
Bootstrap estimates of confidence intervals / 4.8.3：
Further Comments on Formal Inference / 4.9：
Confidence intervals versus hypothesis tests / 4.9.1：
If there is strong prior information, use it! / 4.9.2：
Regression with a Single Predictor / 4.10：
Fitting a Line to Data / 5.1：
Lawn roller example / 5.1.1：
Calculating fitted values and residuals / 5.1.2：
Residual plots / 5.1.3：
The analysis of variance table / 5.1.4：
Outliers, Influence and Robust Regression / 5.2：
Standard Errors and Confidence Intervals / 5.3：
Confidence intervals and tests for the slope / 5.3.1：
SEs and confidence intervals for predicted values / 5.3.2：
Implications for design / 5.3.3：
Regression versus Qualitative ANOVA Comparisons / 5.4：
Assessing Predictive Accuracy / 5.5：
Training/test sets, and cross-validation / 5.5.1：
Cross-validation--an example / 5.5.2：
Bootstrapping / 5.5.3：
A Note on Power Transformations / 5.6：
Size and Shape Data / 5.7：
Allometric growth / 5.7.1：
There are two regression lines! / 5.7.2：
The Model Matrix in Regression / 5.8：
Methodological References / 5.9：
Multiple Linear Regression / 5.11：
Basic Ideas: Book Weight and Brain Weight Examples / 6.1：
Omission of the intercept term / 6.1.1：
Diagnostic plots / 6.1.2：
Further investigation of influential points / 6.1.3：
Example: brain weight / 6.1.4：
Multiple Regression Assumptions and Diagnostics / 6.2：
Influential outliers and Cook's distance / 6.2.1：
Component plus residual plots / 6.2.2：
Further types of diagnostic plot / 6.2.3：
Robust and resistant methods / 6.2.4：
A Strategy for Fitting Multiple Regression Models / 6.3：
Preliminaries / 6.3.1：
Model fitting / 6.3.2：
An example--the Scottish hill race data / 6.3.3：
Measures for the Comparison of Regression Models / 6.4：
R[superscript 2] and adjusted R[superscript 2] / 6.4.1：
AIC and related statistics / 6.4.2：
How accurately does the equation predict? / 6.4.3：
An external assessment of predictive accuracy / 6.4.4：
Interpreting Regression Coefficients--the Labor Training Data / 6.5：
Problems with Many Explanatory Variables / 6.6：
Variable selection issues / 6.6.1：
Principal components summaries / 6.6.2：
Multicollinearity / 6.7：
A contrived example / 6.7.1：
The variance inflation factor (VIF) / 6.7.2：
Remedying multicollinearity / 6.7.3：
Multiple Regression Models--Additional Points / 6.8：
Confusion between explanatory and dependent variables / 6.8.1：
Missing explanatory variables / 6.8.2：
The use of transformations / 6.8.3：
Non-linear methods--an alternative to transformation? / 6.8.4：
Exploiting the Linear Model Framework / 6.9：
Levels of a Factor--Using Indicator Variables / 7.1：
Example--sugar weight / 7.1.1：
Different choices for the model matrix when there are factors / 7.1.2：
Polynomial Regression / 7.2：
Issues in the choice of model / 7.2.1：
Fitting Multiple Lines / 7.3：
Methods for Passing Smooth Curves through Data / 7.4：
Scatterplot smoothing--regression splines / 7.4.1：
Other smoothing methods / 7.4.2：
Generalized additive models / 7.4.3：
Smoothing Terms in Multiple Linear Models / 7.5：
Logistic Regression and Other Generalized Linear Models / 7.6：
Generalized Linear Models / 8.1：
Transformation of the expected value on the left / 8.1.1：
Noise terms need not be normal / 8.1.2：
Log odds in contingency tables / 8.1.3：
Logistic regression with a continuous explanatory variable / 8.1.4：
Logistic Multiple Regression / 8.2：
A plot of contributions of explanatory variables / 8.2.1：
Cross-validation estimates of predictive accuracy / 8.2.2：
Logistic Models for Categorical Data--an Example / 8.3：
Poisson and Quasi-Poisson Regression / 8.4：
Data on aberrant crypt foci / 8.4.1：
Moth habitat example / 8.4.2：
Residuals, and estimating the dispersion / 8.4.3：
Ordinal Regression Models / 8.5：
Exploratory analysis / 8.5.1：
Proportional odds logistic regression / 8.5.2：
Other Related Models / 8.6：
Loglinear models / 8.6.1：
Survival analysis / 8.6.2：
Transformations for Count Data / 8.7：
Multi-level Models, Time Series and Repeated Measures / 8.8：
Introduction / 9.1：
Example--Survey Data, with Clustering / 9.2：
Alternative models / 9.2.1：
Instructive, though faulty, analyses / 9.2.2：
Predictive accuracy / 9.2.3：
A Multi-level Experimental Design / 9.3：
The ANOVA table / 9.3.1：
Expected values of mean squares / 9.3.2：
The sums of squares breakdown / 9.3.3：
The variance components / 9.3.4：
The mixed model analysis / 9.3.5：
Different sources of variance--complication or focus of interest? / 9.3.6：
Within and between Subject Effects--an Example / 9.4：
Time Series--Some Basic Ideas / 9.5：
Preliminary graphical explorations / 9.5.1：
The autocorrelation function / 9.5.2：
Autoregressive (AR) models / 9.5.3：
Autoregressive moving average (ARMA) models--theory / 9.5.4：
Regression Modeling with Moving Average Errors--an Example / 9.6：
Repeated Measures in Time--Notes on the Methodology / 9.7：
The theory of repeated measures modeling / 9.7.1：
Correlation structure / 9.7.2：
Different approaches to repeated measures analysis / 9.7.3：
Further Notes on Multi-level Modeling / 9.8：
An historical perspective on multi-level models / 9.8.1：
Meta-analysis / 9.8.2：
Tree-based Classification and Regression / 9.9：
The Uses of Tree-based Methods / 10.1：
Problems for which tree-based regression may be used / 10.1.1：
Tree-based regression versus parametric approaches / 10.1.2：
Summary of pluses and minuses / 10.1.3：
Detecting Email Spam--an Example / 10.2：
Choosing the number of splits / 10.2.1：
Terminology and Methodology / 10.3：
Choosing the split--regression trees / 10.3.1：
Within and between sums of squares / 10.3.2：
Choosing the split--classification trees / 10.3.3：
The mechanics of tree-based regression--a trivial example / 10.3.4：
Assessments of Predictive Accuracy / 10.4：
Cross-validation / 10.4.1：
The training/test set methodology / 10.4.2：
Predicting the future / 10.4.3：
A Strategy for Choosing the Optimal Tree / 10.5：
Cost-complexity pruning / 10.5.1：
Prediction error versus tree size / 10.5.2：
Detecting Email Spam--the Optimal Tree / 10.6：
The one-standard-deviation rule / 10.6.1：
Interpretation and Presentation of the rpart Output / 10.7：
Data for female heart attack patients / 10.7.1：
Printed Information on Each Split / 10.7.2：
Additional Notes / 10.8：
Multivariate Data Exploration and Discrimination / 10.9：
Multivariate Exploratory Data Analysis / 11.1：
Scatterplot matrices / 11.1.1：
Principal components analysis / 11.1.2：
Discriminant Analysis / 11.2：
Example--plant architecture / 11.2.1：
Classical Fisherian discriminant analysis / 11.2.2：
Logistic discriminant analysis / 11.2.3：
An example with more than two groups / 11.2.4：
Principal Component Scores in Regression / 11.3：
Propensity Scores in Regression Comparisons--Labor Training Data / 11.4：
The R System--Additional Topics / 11.5：
Graphs in R / 12.1：
Functions--Some Further Details / 12.2：
Common useful functions / 12.2.1：
User-written R functions / 12.2.2：
Functions for working with dates / 12.2.3：
Data input and output / 12.3：
Input / 12.3.1：
Data output / 12.3.2：
Factors--Additional Comments / 12.4：
Missing Values / 12.5：
Lists and Data Frames / 12.6：
Data frames as lists / 12.6.1：
Reshaping data frames; reshape () / 12.6.2：
Joining data frames and vectors--cbind () / 12.6.3：
Conversion of tables and arrays into data frames / 12.6.4：
Merging data frames--merge () / 12.6.5：
The function sapply () and related functions / 12.6.6：
Splitting vectors and data frames into lists--split () / 12.6.7：
Matrices and Arrays / 12.7：
Outer products / 12.7.1：
Arrays / 12.7.2：
Classes and Methods / 12.8：
Printing and summarizing model objects / 12.8.1：
Extracting information from model objects / 12.8.2：
Data-bases and Environments / 12.9：
Workspace management / 12.9.1：
Function environments, and lazy evaluation / 12.9.2：
Manipulation of Language Constructs / 12.10：
Epilogue--Models / 12.11：
S-PLUS Differences / Appendix：
References
Index of R Symbols and Functions
Index of Terms
Index of Names
Preface
A Chapter by Chapter Summary
A Brief Introduction to R / 1：
7.

図書
図書
7. Data-driven techniques in speech synthesis
edited by R.I. Damper
出版情報: Dordrecht : Kluwer Academic Publishers, c2001  xviii, 316 p. ; 25 cm
シリーズ名: Telecommunications technology & application series
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8.

学位論文
学位
8. Formation of atomospheric mutagen by heterogeneous reaction of benzo〔a〕pyrene or triphenylene on airborne particles in NO2-Air in the Presence of O3 or under light irradiation
石井聡子
出版情報: 東京 : 東京工業大学, 2001
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9.

学位論文
学位
9. 資源要求を考慮した計算機システムの性能評価法の研究
木下俊之
出版情報: 東京 : 東京工業大学, 2001
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10.

学位論文
学位
10. Scalable visualization and navigation support techniques for data flow visual program excution
志築文太郎
出版情報: 東京 : 東京工業大学, 2001
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