Mathematics

The first instance of pre-computer fractals was noted by the French mathematician Gaston Julia. He wondered what a complex polynomial function would look like, such as the ones named after him (in the form of z² + c, where c is a complex constant with real and imaginary parts). The idea behind this formula is that one takes the x and y coordinates of a point z, and plug them into z in the form of x + i*y, where i is the square root of -1, square this number, and then add c, a constant. Then plug the resulting pair of real and imaginary numbers back into z, run the operation again, and keep doing that until the result is greater than some number. The number of times you have to run the equations to get out of an 'orbit' not specified here can be assigned a colour and then the pixel (x,y) gets turned that colour, unless those coordinates can't get out of their orbit, in which case they are made black. Later it was Benoit Mandelbrot who used computers to produce fractals. A basic property of fractals is that they contain a large degree of self similarity, i.e., they usually contain little copies within the original, and these copies also have infinite detail. That means the more you zoom in on a fractal, the more detail you get, and this keeps going on forever and ever.

The well-written book 'Getting acquainted with fractals' by Gilbert Helmberg provides a mathematically oriented introduction to fractals, with a focus upon three types of fractals: fractals of curves, attractors for iterative function systems in the plane, and Julia sets. The presentation is on an undergraduate level, with an ample presentation of the corresponding mathematical background, e.g., linear algebra, calculus, algebra, geometry, topology, measure theory and complex analysis.

The book contains over 170 color illustrations.

This comprehensive two-volume work is devoted to the most general beginnings of mathematics. It goes back to Hausdorff’s classic Set Theory (2nd ed., 1927), where set theory and the theory of functions were expounded as the fundamental parts of mathematics in such a way that there was no need for references to other sources. Along the lines of Hausdorff’s initial work (1st ed., 1914), measure and integration theory is also included here as the third fundamental part of contemporary mathematics.The material about sets and numbers is placed in Volume 1 and the material about functions and measures is placed in Volume 2.

Contents
Fundamentals of the theory of classes, sets, and numbers
Characterization of all natural models of Neumann – Bernays – Godel and Zermelo – Fraenkel set theories
Local theory of sets as a foundation for category theory and its connection with the Zermelo – Fraenkel set theory
Compactness theorem for generalized second-order language

This comprehensive two-volume work is devoted to the most general beginnings of mathematics. It goes back to Hausdorff’s classic Set Theory (2nd ed., 1927), where set theory and the theory of functions were expounded as the fundamental parts of mathematics in such a way that there was no need for references to other sources. Along the lines of Hausdorff’s initial work (1st ed., 1914), measure and integration theory is also included here as the third fundamental part of contemporary mathematics. The material about sets and numbers is placed in Volume 1 and the material about functions and measures is placed in Volume 2.

Contents
Historical foreword on the centenary after Felix Hausdorff’s classic Set Theory
Fundamentals of the theory of functions
Fundamentals of the measure theory
Historical notes on the Riesz – Radon – Frechet problem of characterization of Radon integrals as linear functionals

In seinem wichtigen Buch "Parts of Classes" hat David Lewis eine Reduktion von ZFC auf eine Mereologie zweiter Stufe skizziert. Sein Resultat nimmt in vorliegender Rekonstruktion folgende Form an: ZFC ist in M (der klassischen Mereologie zweiter Stufe) plus "Es gibt eine stark unerreichbare Partition" parametrisiert interpretierbar. In den Beweis geht ein, dass geordnete Paare in M plus "Es gibt eine unendliche Partition" parametrisiert interpretierbar sind. Die Arbeit beleuchtet den logischen und philosophie-geschichtlichen Hintergrund von "Parts of Classes", gibt eine Einführung in die Mereologie zweiter Stufe und schließt mit einem recht einfachen Beweis für "ZFC ist (die Konsistenz von ZFC vorausgesetzt) in einer konsistenten Mereologie zweiter Stufe parametrisiert interpretierbar".

Die Wahrscheinlichkeitstheorie gehört zu den Kerndisziplinen der modernen Mathematikausbildung. Sie ist die Grundlage für alle Modelle, die „Risiko" und „Unsicherheit" einbeziehen. Dieses Lehrbuch gibt einen direkten, verlässlichen und modernen Zugang zu den wichtigsten Ergebnissen der mathematischen Wahrscheinlichkeitstheorie. Aufbauend auf dem Band „Maß & Integral" werden zunächst elementare Fragen Wahrscheinlichkeitsverteilungen, Zufallsvariable, Unabhängigkeit, bedingte Wahrscheinlichkeiten und charakteristische Funktionen – bis hin zu einfachen Grenzwertsätzen behandelt. Diese Themen werden dann um das Studium von Summen unabhängiger Zufallsvariablen – Gesetze der Großen Zahlen, Null-Eins-Gesetze, random walks, zentraler Grenzwertsatz von Lindeberg-Feller – ergänzt. Allgemeine bedingte Erwartungen, Anwendungen von charakteristischen Funktionen und eine Einführung in die Theorie unendlich teilbarer Verteilungen und der großen Abweichungen runden die Darstellung ab. In gleicher Ausstattung erscheint der Folgeband „Martingale & Prozesse".

Lösungen zu den im Buch befindlichen Übungsaufgaben unter: http://www.motapa.de/stoch/index.shtml

Allgemeine Maße und das Lebesgue-Integral gehören zu den unverzichtbaren Hilfsmitteln der modernen Analysis, der Funktionalanalysis und der Stochastik. Das vorliegende Lehrbuch bietet eine Einführung in die wesentlichen Aspekte der Theorie – Maße, Integrale, Konvergenzsätze, Parameterintegrale, Satz von Fubini –, die durch weiterführende Themen – allgemeiner Transformationssatz, Satz von Radon-Nikodým, Fouriertransformation von Maßen, topologische Maßtheorie – abgerundet wird. Mehr als 150 Übungsaufgaben (mit vollständigen Lösungen im Internet) vertiefen und erweitern den Stoff.

Die kompakte Darstellung bietet sich als Fortsetzung der Grundvorlesungen "Analysis" oder als Einstieg in die "Stochastik" an. Da nur Grundkenntnisse in Analysis und linearer Algebra vorausgesetzt werden, ist der Text auch für Studierende der Physik und Ingenieurswissenschaften sowie zum Selbststudium geeignet.

In gleicher Ausstattung erscheinen die Folgebände "Wahrscheinlichkeit" und "Martingale & Prozesse".

Lösungen zu den im Buch befindlichen Übungsaufgaben unter: http://www.motapa.de/mint/index.shtml

This textbook, now in its second revised and extended edition, presents the fundamental ideas and results of both probability theory and statistics. It comprises the material of a one-year course, which is addressed to students of mathematics and to scientists with an interest in the mathematical side of stochastics.
The stochastic concepts, models and methods are motivated by examples and then developed and analysed systematically. Some measure theory is included, but this is done at an elementary level that is in accordance with the introductory character of the book. A large number of problems, now in part with solutions, offer applications and supplements to the text.

This volume provides an overview of two of the most important examples of interacting particle systems, the contact process, and the voter model, as well as their many variants introduced in the past 50 years. These stochastic processes are organized by domains of application (epidemiology, population dynamics, ecology, genetics, sociology, econophysics, game theory) along with a flavor of the mathematical techniques developed for their analysis.

This is the first fundamental book devoted to non-Kolmogorov probability models. It provides a mathematical theory of negative probabilities, with numerous applications to quantum physics, information theory, complexity, biology and psychology. The book also presents an interesting model of cognitive information reality with flows of information probabilities, describing the process of thinking, social, and psychological phenomena.

Masatoshi Fukushima is one of the most influential probabilists of our times. His fundamental work on Dirichlet forms and Markov processes made Hilbert space methods a tool in stochastic analysis and by this he opened the way to several new developments. His impact on a new generation of probabilists can hardly be overstated.

These Selecta collect 25 of Fukushima's seminal articles published between 1967 and 2007.

This book discusses the systems of interacting particles evolving in the random media. The focus is on the study of both the finite subsystems motion and the flow, describing motion of all particles in the space. The integral characteristics of the system and mass distribution are also covered and results are illustrated with examples from turbulence theory, synchronization and DNA evolution.

This book includes discussions related to solutions of such tasks as:

• probabilistic description of the investment function;
• recovering the income function from GDP estimates;

• development of models for the economic cycles;
• selecting the time interval of pseudo-stationarity of cycles;
• estimating characteristics/parameters of cycle models;
• analysis of accuracy of model factors.

All of the above constitute the general principles of a theory explaining the phenomenon of economic cycles　and provide mathematical tools for their quantitative description. The introduced theory is applicable to macroeconomic analyses　as well as econometric estimations　of economic cycles.

This book is a concise introduction to the stochastic calculus of variations for processes with jumps. The author provides many results on this topic in a self-contained way for e.g., stochastic differential equations (SDEs) with jumps. The book also contains some applications of the stochastic calculus for processes with jumps to the control theory, mathematical finance and so. This third and entirely revised edition of the work is updated to reflect the latest developments in the theory and some applications with graphics.

This book offers an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, as well as basics of sampling distributions, estimation and hypothesis testing. As a companion for classes for engineers and scientists, the book also covers applied topics such as model building and experiment design.

Contents
Random phenomena
Probability
Random variables
Expected values
Commonly used discrete distributions
Commonly used density functions
Joint distributions
Some multivariate distributions
Collection of random variables
Sampling distributions
Estimation
Interval estimation
Tests of statistical hypotheses
Model building and regression
Design of experiments and analysis of variance
Questions and answers

This volume deals with two complementary topics. On one hand the book deals with the problem of determining the the probability distribution of a positive compound random variable, a problem which appears in the banking and insurance industries, in many areas of operational research and in reliability problems in the engineering sciences.

On the other hand, the methodology proposed to solve such problems, which is based on an application of the maximum entropy method to invert the Laplace transform of the distributions, can be applied to many other problems.

The book contains applications to a large variety of problems, including the problem of dependence of the sample data used to estimate empirically the Laplace transform of the random variable.

This book is an introduction to financial mathematics. It is intended for graduate students in mathematics and for researchers working in academia and industry.
The focus on stochastic models in discrete time has two immediate benefits. First, the probabilistic machinery is simpler, and one can discuss right away some of the key problems in the theory of pricing and hedging of financial derivatives. Second, the paradigm of a complete financial market, where all derivatives admit a perfect hedge, becomes the exception rather than the rule. Thus, the need to confront the intrinsic risks arising from market incomleteness appears at a very early stage.
The first part of the book contains a study of a simple one-period model, which also serves as a building block for later developments. Topics include the characterization of arbitrage-free markets, preferences on asset profiles, an introduction to equilibrium analysis, and monetary measures of financial risk.
In the second part, the idea of dynamic hedging of contingent claims is developed in a multiperiod framework. Topics include martingale measures, pricing formulas for derivatives, American options, superhedging, and hedging strategies with minimal shortfall risk.
This fourth, newly revised edition contains more than one hundred exercises. It also includes material on risk measures and the related issue of model uncertainty, in particular a chapter on dynamic risk measures and sections on robust utility maximization and on efficient hedging with convex risk measures.

Contents:
Part I: Mathematical finance in one period
Arbitrage theory
Preferences
Optimality and equilibrium
Monetary measures of risk
Part II: Dynamic hedging
Dynamic arbitrage theory
American contingent claims
Superhedging
Efficient hedging
Hedging under constraints
Minimizing the hedging error
Dynamic risk measures

Contents:

Part I - Estimation in regression models with errors in covariates
Measurement error models
Linear models with classical error
Polynomial regression with known variance of classical error
Nonlinear and generalized linear models

Part II Radiation risk estimation under uncertainty in exposure doses
Overview of risk models realized in program package EPICURE
Estimation of radiation risk under classical or Berkson multiplicative error in exposure doses
Radiation risk estimation for persons exposed by radioiodine as a result of the Chornobyl accident
Elements of estimating equations theory
Consistency of efficient methods
Efficient SIMEX method as a combination of the SIMEX method and the corrected score method
Application of regression calibration in the model with additive error in exposure doses

Dieses Buch ist für Anwender der Statistik mit Programmierkenntnissen. Rechnerprogramme helfen, die theoretischen Schlussfolgerungen zu verstehen. Die notwendigen und unverzichtbaren Theoreme werden allein durch Experimente illustriert.
Das Buch ist anwendungsorientiert und verzichtet weitgehend auf theoretische Ausführungen.

Inhalt:
Erzeugung von Zufallszahlen undMonte-Carlo-Experimente
Spielexperimente
Wahrscheinlichkeitsfunktionen, Dichten, Verteilungen
Punktschatzungen
Konfidenzschatzungen
Statistische Tests
Funktionstests fur Zufallszahlengeneratoren

This is the third volume of the series "Moderne Stochastik" (Modern Stochastics). As a follow-up to the volume "Wahrscheinlichkeit" (Probability Theory) it gives an intrdouction to dynamical aspects of probability theory using stochastic processes in discrete time. The first part of the book covers discrete martingales - their convergenc behaviour, optional sampling and stopping, uniform integrability and essential martingale inequalities. The power of martingale techniques is illustrated in the chapters on applications of martingales in classical probability and on the Burkholder-Davis-Gundy inequalities. The second half of the book treats random walks on Zd and Rd, their fluctuation behaviour, recurrence and transience. The last two chapters give a brief introduction to probabilistic potential theory and an outlook of further developments: Brownian motion and Donsker's invariance principle

Contents
Fair Play
Conditional Expectation
Martingale
Stopping and Localizing
Martingale Convergence
L²-Martingales
Uniformly Integrable Martingales
Some Classical Results of Probability
Elementary Inequalities for Martingales
The Burkholder–Davis–Gundy Inequalities
Random Walks on ℤd – the first steps
Fluctuations of Simple Random Walks on Z
Recurrence and Transience of General Random Walks
Random Walks and Analysis
Donsker's Invariance Principle and Brownian Motion

This book deals with analytic treatments of Markov processes. Symmetric Dirichlet forms and their associated Markov processes are important and powerful tools in the theory of Markov processes and their applications. The theory is well studied and used in various fields. In this monograph, we intend to generalize the theory to non-symmetric and time dependent semi-Dirichlet forms. By this generalization, we can cover the wide class of Markov processes and analytic theory which do not possess the dual Markov processes. In particular, under the semi-Dirichlet form setting, the stochastic calculus is not well established yet. In this monograph, we intend to give an introduction to such calculus. Furthermore, basic examples different from the symmetric cases are given. The text is written for graduate students, but also researchers.

The book presents advanced stochastic models and simulation methods for random flows and transport of particles by turbulent velocity fields and flows in porous media. Two main classes of models are constructed: (1) turbulent flows are modeled as synthetic random fields which have certain statistics and features mimicing those of turbulent fluid in the regime of interest, and (2) the models are constructed in the form of stochastic differential equations for stochastic Lagrangian trajectories of particles carried by turbulent flows.

The book is written for mathematicians, physicists, and engineers studying processes associated with probabilistic interpretation, researchers in applied and computational mathematics, in environmental and engineering sciences dealing with turbulent transport and flows in porous media, as well as nucleation, coagulation, and chemical reaction analysis under fluctuation conditions. It can be of interest for students and post-graduates studying numerical methods for solving stochastic boundary value problems of mathematical physics and dispersion of particles by turbulent flows and flows in porous media.

This is the proceedings of the "8^th IMACS Seminar on Monte Carlo Methods" held from August 29 to September 2, 2011 in Borovets, Bulgaria, and organized by the Institute of Information and Communication Technologies of the Bulgarian Academy of Sciences in cooperation with the International Association for Mathematics and Computers in Simulation (IMACS). Included are 24 papers which cover all topics presented in the sessions of the seminar: stochastic computation and complexity of high dimensional problems, sensitivity analysis, high-performance computations for Monte Carlo applications, stochastic metaheuristics for optimization problems, sequential Monte Carlo methods for large-scale problems, semiconductor devices and nanostructures.

The history of the IMACS Seminar on Monte Carlo Methods goes back to April 1997 when the first MCM Seminar was organized in Brussels:

1^st IMACS Seminar, 1997, Brussels, Belgium
2^nd IMACS Seminar, 1999, Varna, Bulgaria
3^rd IMACS Seminar, 2001, Salzburg, Austria
4^th IMACS Seminar, 2003, Berlin, Germany
5^th IMACS Seminar, 2005, Tallahassee, USA
6^th IMACS Seminar, 2007, Reading, UK
7^th IMACS Seminar, 2009, Brussels, Belgium
8^th IMACS Seminar, 2011, Borovets, Bulgaria

Interest in multilevel statistical models for social science and public health studies has been aroused dramatically since the mid-1980s. New multilevel modeling techniques are giving researchers tools for analyzing data that have a hierarchical or clustered structure. Multilevel models are now applied to a wide range of studies in sociology, population studies, education studies, psychology, economics, epidemiology, and public health.

This book covers a broad range of topics about multilevel modeling. The goal of the authors is to help students and researchers who are interested in analysis of multilevel data to understand the basic concepts, theoretical frameworks and application methods of multilevel modeling. The book is written in non-mathematical terms, focusing on the methods and application of various multilevel models, using the internationally widely used statistical software, the Statistics Analysis System (SAS^®). Examples are drawn from analysis of real-world research data. The authors focus on twolevel models in this book because it is most frequently encountered situation in real research. These models can be readily expanded to models with three or more levels when applicable. A wide range of linear and non-linear multilevel models are introduced and demonstrated.

Markov processes represent a universal model for a large variety of real life random evolutions. The wide flow of new ideas, tools, methods and applications constantly pours into the ever-growing stream of research on Markov processes that rapidly spreads over new fields of natural and social sciences, creating new streamlined logical paths to its turbulent boundary. Even if a given process is not Markov, it can be often inserted into a larger Markov one (Markovianization procedure) by including the key historic parameters into the state space.

This monograph gives a concise, but systematic and self-contained, exposition of the essentials of Markov processes, together with recent achievements, working from the "physical picture" - a formal pre-generator, and stressing the interplay between probabilistic (stochastic differential equations) and analytic (semigroups) tools.

The book will be useful to students and researchers. Part I can be used for a one-semester course on Brownian motion, Lévy and Markov processes, or on probabilistic methods for PDE. Part II mainly contains the author's research on Markov processes.

From the contents:

• Tools from Probability and Analysis
• Brownian motion
• Markov processes and martingales
• SDE, ψDE and martingale problems
• Processes in Euclidean spaces
• Processes in domains with a boundary
• Heat kernels for stable-like processes
• Continuous-time random walks and fractional dynamics
• Complex chains and Feynman integral

This textbook is devoted to the general asymptotic theory of statistical experiments. Local asymptotics for statistical models in the sense of local asymptotic (mixed) normality or local asymptotic quadraticity make up the core of the book. Numerous examples deal with classical independent and identically distributed models and with stochastic processes.

The book can be read in different ways, according to possibly different mathematical preferences of the reader. One reader may focus on the statistical theory, and thus on the chapters about Gaussian shift models, mixed normal and quadratic models, and on local asymptotics where the limit model is a Gaussian shift or a mixed normal or a quadratic experiment (LAN, LAMN, LAQ). Another reader may prefer an introduction to stochastic process models where given statistical results apply, and thus concentrate on subsections or chapters on likelihood ratio processes and some diffusion type models where LAN, LAMN or LAQ occurs. Finally, readers might put together both aspects.

The book is suitable for graduate students starting to work in statistics of stochastic processes, as well as for researchers interested in a precise introduction to this area.

In a certain sense characteristic functions and correlation functions are the same, the common underlying concept is positive definiteness. Many results in probability theory, mathematical statistics and stochastic processes can be derived by using these functions. While there are books on characteristic functions of one variable, books devoting some sections to the multivariate case, and books treating the general case of locally compact groups, interestingly there is no book devoted entirely to the multidimensional case which is extremely important for applications. This book is intended to fill this gap at least partially. It makes the basic concepts and results on multivariate characteristic and correlation functions easily accessible to both students and researchers in a comprehensive manner.

The first chapter presents basic results and should be read carefully since it is essential for the understanding of the subsequent chapters. The second chapter is devoted to correlation functions, their applications to stationary processes and some connections to harmonic analysis. In Chapter 3 we deal with several special properties, Chapter 4 is devoted to the extension problem while Chapter 5 contains a few applications. A relatively large appendix comprises topics like infinite products, functional equations, special functions or compact operators.

The book is devoted to studies of quasi-stationary phenomena in nonlinearly perturbed stochastic systems. New methods of asymptotic analysis for nonlinearly perturbed stochastic processes based on new types of asymptotic expansions for perturbed renewal equation and recurrence algorithms for construction of asymptotic expansions for Markov type processes with absorption are presented. Asymptotic expansions are given in mixed ergodic (for processes) and large deviation theorems (for absorption times) for nonlinearly perturbed regenerative processes, semi-Markov processes, and Markov chains. Applications to analysis of quasi-stationary phenomena in nonlinearly perturbed queueing systems, population dynamics and epidemic models, and for risk processes are presented. The book also contains an extended bibliography of works in the area. It is an essential reference for theoretical and applied researchers in the field of stochastic processes and their applications and may be also useful for doctoral and advanced undergraduate students.

Stochastic processes occur everywhere in the sciences, economics and engineering, and they need to be understood by (applied) mathematicians, engineers and scientists alike. This book gives a gentle introduction to Brownian motion and stochastic processes, in general. Brownian motion plays a special role, since it shaped the whole subject, displays most random phenomena while being still easy to treat, and is used in many real-life models. Im this new edition, much material is added, and there are new chapters on ''Wiener Chaos and Iterated Itô Integrals'' and ''Brownian Local Times''.

Im Laufe der Jahrhunderte wurde in der Stochastik und in der in ihr verwendeten Kombinatorik eine Vielzahl von Problemen aufgeworfen, und diese höchst geistreich gelöst. Das vorliegende Buch bietet eine Auswahl der interessantesten Probleme in ihrer historischen Abfolge. Aufgaben und Lösungen werden im historischen Kontext und in moderner Fassung vorgestellt. Dabei überrascht immer wieder, mit welchen Methoden und welcher Rechenfertigkeit in früherer Zeit diese Probleme bewältigt wurden.
Das Werk bietet Anregungen für alle Interessierten vom mathematisch gebildeten Laien bis zum professionellen Mathematiker. Viele der aufgeführten Problemstellungen hatten großen Einfluss auf die Entwicklung der Aufgabenkultur in der Stochastik. Bis heute lassen sich die Spuren in Aufgaben von Schul- und Hochschulbüchern finden.

Inhalt:
Von den Anfängen bis zum Ende des Mittelalters
Beginn der Neuzeit
1. Hälfte des 17. Jahrhunderts
HUYGENS’ Tractatus de Ratiociniis in Aleae Ludo von 1657
2. Hälfte des 17. Jahrhunderts
Beginn des 18. Jahrhunderts
JAKOB BERNOULLIs Ars Conjectandi von 1713
MONTMORTs Essay d’Analyse sur les Jeux de Hazard von 1713
NIKOLAUS BERNOULLIs Petersburger Problem von 1713
Die Jahre nach 1713 bis 1750
2. Hälfte des 18. Jahrhunderts
1. Hälfte des 19. Jahrhunderts
2. Hälfte des 19. Jahrhunderts
Das 20. Jahrhundert
Nachtrag
Lebensdaten
Literatur
Abbildungsverzeichnis
Personenregister
Sachregister

The general topic of this book is the ergodic behavior of Markov processes. A detailed introduction to methods for proving ergodicity and upper bounds for ergodic rates is presented in the first part of the book, with the focus put on weak ergodic rates, typical for Markov systems with complicated structure. The second part is devoted to the application of these methods to limit theorems for functionals of Markov processes. The book is aimed at a wide audience with a background in probability and measure theory. Some knowledge of stochastic processes and stochastic differential equations helps in a deeper understanding of specific examples.

Contents　
Part I: Ergodic Rates for Markov Chains and Processes
Markov Chains with Discrete State Spaces
General Markov Chains: Ergodicity in Total Variation
MarkovProcesseswithContinuousTime
Weak Ergodic Rates

Part II: Limit Theorems
The Law of Large Numbers and the Central Limit Theorem
Functional Limit Theorems

This book contains an introductory and comprehensive account of the theory of (symmetric) Dirichlet forms. Moreover this analytic theory is unified with the probabilistic potential theory based on symmetric Markov processes and developed further in conjunction with the stochastic analysis based on additive functional.

Since the publication of the first edition in 1994, this book has attracted constant interests from readers and is by now regarded as a standard reference for the theory of Dirichlet forms. For the present second edition, the authors not only revised the existing text, but also added sections on capacities and Sobolev type inequalities, irreducible recurrence and ergodicity, recurrence and Poincaré type inequalities, the Donsker-Varadhan type large deviation principle, as well as several new exercises with solutions.

The book addresses to researchers and graduate students who wish to comprehend the area of Dirichlet forms and symmetric Markov processes.

The book is designed to cover all major aspects of applied numerical methods, including numerical computations, solution of algebraic and transcendental equations, finite differences and interpolation, curve fitting, correlation and regression, numerical differentiation and integration, matrices and linear system of equations, numerical solution of ordinary differential equations, and numerical solution of partial differential equations. MATLAB is incorporated throughout the text and most of the problems are executed in MATLAB code. It uses a numerical problem-solving orientation with numerous examples, figures, and end of chapter exercises. Presentations are limited to very basic topics to serve as an introduction to more advanced topics.

Features:

• Integrates MATLAB throughout the text
• Includes over 600 fully-solved problems with step-by-step solutions
• Limits presentations to basic concepts of solving numerical methods

Information about the author:

Fateh Mebarek-Oudina received his PhD in 2010. He has published more than 120 papers in reputed international journals. Currently, he works as a full professor at Skikda University in Algeria and regularly serves as a reviewer for more than 250 international journals. He is ranked in the Top 2% Scientists Worldwide (2020, 2021, 2022, 2023) by Stanford University. His research work is focused on heat and mass transfer, MHD, mathematical simulation and modelling, biofluids, nanofluids, hybrid nanofluids, ternary nanofluids, microfluidics, and computational fluid dynamics.

Information abiout the book:

Mathematical modeling presented in the book is designed to help engineers understand physical systems, including magnetohydrodynamic effects on the non-Newtonian fluid flow and multiphase flow. Special attention will be given to heat transfer and entropy generation analysis on hybrid nanofluids. The process of entropy generation for nanofluid flows through porous channels will also be discussed and analyzed by means of a theoretical approach and CFD modeling. Some applications to blood-mediated gold-silver nanoparticles will be presented with detailed numerical examples. The book is designed to facilitate a more profound understanding for engineers of adopting CFD models to natural manufacturing environments. Overall, the primary objective of the book is to present mathematical modeling with CFD applications to simulate real-world engineering, industrial, and medical science problems to expose various analytical and numerical techniques and, at the same time, extend to expose researchers and academicians to the recent advancement in these diverse fields.

Mathematical optimization and machine learning are closely related. This proceedings volume of the Thematic Einstein Semester 2023 of the Berlin Mathematics Research Center MATH+ collects recent progress on their interplay in topics such as discrete optimization, nonlinear programming, optimal control, first-order methods, multilevel optimization, machine learning in optimization, physics-informed learning, and fairness in machine learning.

This book provides insights into contemporary issues and challenges concerning operational research and related mathematical modeling fundamentals, such as system reliability, scalability, and adaptability. This collaboration of academia and industry disseminates practical tools and analytics applications of mathematics in engineering and information sciences

Optimization techniques have gained popularity among system managers for making crucial decisions while meeting multiple needs. The focus of this book is the evaluation and optimization of critical decisions related to the system betterment. Each chapter presents the recent advancements and research opportunities in system assurance.

Operational Perspective of Modeling System Reliability (Research Tools for System Dynamics) is for academicians and those who need to understand the latest developments in the field of System Reliability. Along with these, anyone solving problems within the related application domains will benefit from this compilation.

This volume consists of twenty articles stemming from presentations given at the 2023 Integers Conference. They represent a variety of active areas of research in combinatorial number theory, including additive number theory, multiplicative number theory, elementary number theory, the theory of partitions, Ramsey theory, sequences, algebraic combinatorics, enumerative combinatorics, and Diophantine equations.