Mesoscopic physics has made great strides in the last few years. It is an area of research that is attractive to many graduate students of theoretical condensed matter physics. The techniques that are needed to understand it go beyond the conventional perturbative approaches that still form the bulk of the graduate lectures that are given to students. Even when the non-perturbative techniques are presented, they often are presented within an abstract context. It is important to have lectures given by experts in the field, which present both theory and experiment in an illuminating and inspiring way, so that the impact of new methodology on novel physics is clear. It is an apt time to have such a volume since the field has reached a level of maturity. The pedagogical nature of the articles and the variety of topics makes it an important resource for newcomers to the field. The topics range from the newly emerging area of quantum computers and quantum information using Josephson junctions to the formal mathematical methods of conformal field theory which are applied to the understanding of Luttinger liquids.

Electrons which interact strongly can give rise to non-trivial ground states such as superconductivity, quantum Hall states and magnetism. Both their theory and application are discussed in a pedagogical way for quantum information in mesoscopic superconducting devices, skyrmions and magnetism in two dimensional electron gases, transport in quantum wires, metal-insulator transitions and spin electronics.

Many physical phenomena are described by nonlinear evolution equation. Those that are integrable provide various mathematical methods, presented by experts in this tutorial book, to find special analytic solutions to both integrable and partially integrable equations. The direct method to build solutions includes the analysis of singularities a la Painleve, Lie symmetries leaving the equation invariant, extension of the Hirota method, construction of the nonlinear superposition formula. The main inverse method described here relies on the bi-hamiltonian structure of integrable equations. The book also presents some extension to equations with discrete independent and dependent variables.
The different chapters face from different points of view the theory of exact solutions and of the complete integrability of nonlinear evolution equations. Several examples and applications to concrete problems allow the reader to experience directly the power of the different machineries involved."

The last two decades have witnessed an enormous growth with regard to ap plications of information theoretic framework in areas of physical, biological, engineering and even social sciences. In particular, growth has been spectac ular in the field of information technology, soft computing, nonlinear systems and molecular biology. Claude Shannon in 1948 laid the foundation of the field of information theory in the context of communication theory. It is in deed remarkable that his framework is as relevant today as was when he 1 proposed it. Shannon died on Feb 24, 2001. Arun Netravali observes "As if assuming that inexpensive, high-speed processing would come to pass, Shan non figured out the upper limits on communication rates. First in telephone channels, then in optical communications, and now in wireless, Shannon has had the utmost value in defining the engineering limits we face." Shannon introduced the concept of entropy. The notable feature of the entropy frame work is that it enables quantification of uncertainty present in a system. In many realistic situations one is confronted only with partial or incomplete information in the form of moment, or bounds on these values etc.; and it is then required to construct a probabilistic model from this partial information. In such situations, the principle of maximum entropy provides a rational ba sis for constructing a probabilistic model. It is thus necessary and important to keep track of advances in the applications of maximum entropy principle to ever expanding areas of knowledge."

Most of the interesting and difficult problems in statistical mechanics arise when the constituent particles of the system interact with each other with pair or multipartiele energies. The types of behaviour which occur in systems because of these interactions are referred to as cooperative phenomena giving rise in many cases to phase transitions. This book and its companion volume (Lavis and Bell 1999, referred to in the text simply as Volume 1) are princi pally concerned with phase transitions in lattice systems. Due mainly to the insights gained from scaling theory and renormalization group methods, this subject has developed very rapidly over the last thirty years. ' In our choice of topics we have tried to present a good range of fundamental theory and of applications, some of which reflect our own interests. A broad division of material can be made between exact results and ap proximation methods. We have found it appropriate to inelude some of our discussion of exact results in this volume and some in Volume 1. Apart from this much of the discussion in Volume 1 is concerned with mean-field theory. Although this is known not to give reliable results elose to a critical region, it often provides a good qualitative picture for phase diagrams as a whole. For complicated systems some kind of mean-field method is often the only tractable method available. In this volume our main concern is with scaling theory, algebraic methods and the renormalization group."

Fully Tuned Radial Basis Function Neural Networks for Flight Control presents the use of the Radial Basis Function (RBF) neural networks for adaptive control of nonlinear systems with emphasis on flight control applications. A Lyapunov synthesis approach is used to derive the tuning rules for the RBF controller parameters in order to guarantee the stability of the closed loop system. Unlike previous methods that tune only the weights of the RBF network, this book presents the derivation of the tuning law for tuning the centers, widths, and weights of the RBF network, and compares the results with existing algorithms. It also includes a detailed review of system identification, including indirect and direct adaptive control of nonlinear systems using neural networks. Fully Tuned Radial Basis Function Neural Networks for Flight Control is an excellent resource for professionals using neural adaptive controllers for flight control applications.

Intelligent Multimedia Multi-Agent Systems focuses on building intelligent successful systems. The book adopts a human-centered approach and considers various pragmatic issues and problems in areas like intelligent systems, software engineering, multimedia databases, electronic commerce, data mining, enterprise modeling and human-computer interaction for developing a human-centered virtual machine. The authors describe an ontology of the human-centered virtual machine which includes four components: activity-centered analysis component, problem solving adapter component, transformation agent component, and multimedia based interpretation component. These four components capture the external and internal planes of the system development spectrum. They integrate the physical, social and organizational reality on the external plane with stakeholder goals, tasks and incentives, and organization culture on the internal plane. The human-centered virtual machine and its four components are used for developing intelligent multimedia multi-agent systems in areas like medical decision support and health informatics, medical image retrieval, e-commerce, face detection and annotation, internet games and sales recruitment. The applications in these areas help to expound various aspects of the human-centered virtual machine including, human-centered domain modeling, distributed intelligence and communication, perceptual and cognitive task modeling, component based software development, and multimedia based data modeling. Further, the applications described in the book employ various intelligent technologies like neural networks, fuzzy logic and knowledge based systems, software engineering artifacts like agents and objects, internet technologies like XML and multimedia artifacts like image, audio, video and text.

Recent years have witnessed a rapid development of active control of various mechanical systems. With increasingly strict requirements for control speed and system performance, the unavoidable time delays in both controllers and actuators have become a serious problem. For instance, all digital controllers, analogue anti aliasing and reconstruction filters exhibit a certain time delay during operation, and the hydraulic actuators and human being interaction usually show even more significant time delays. These time delays, albeit very short in most cases, often deteriorate the control performance or even cause the instability of the system, be cause the actuators may feed energy at the moment when the system does not need it. Thus, the effect of time delays on the system performance has drawn much at tention in the design of robots, active vehicle suspensions, active tendons for tall buildings, as well as the controlled vibro-impact systems. On the other hand, the properly designed delay control may improve the performance of dynamic sys tems. For instance, the delayed state feedback has found its applications to the design of dynamic absorbers, the linearization of nonlinear systems, the control of chaotic oscillators, etc. Most controlled mechanical systems with time delays can be modeled as the dynamic systems described by a set of ordinary differential equations with time delays."

TheconferenceChanceinPhysics: FoundationsandPerspectiveswasheldfrom 29thNovemberto3rdDecember1999inIschia, Italy. Itwassponsoredbythe IstitutoItalianoPerGliStudiFiloso?ciinNaples, bytheDeutscheForschun- gemeinschaft(DFG), andbytheSocietaItalianaDiFondamentiDellaFisica. SponsoringbytheInternationalSchoolforAdvancedStudies(ISAS)ofTrieste, Italy, madethecompilationofthisvolumepossible;thefundingbytheIs- tutoItalianoPerGliStudiFiloso?ciwascrucialfortheconferenceandisvery gratefullyacknowledged. TheIstitutomanagedtoprovideauniqueatmosphere foraninterdisciplinarymeeting, andtheseproceedingsre?ectindeedthevery friendlybutneverthelessintenseandneverendingdiscussionsononeofthemost debatedissuesofscience: probability, andinparticularprobabilityinphysics. Wegratefullyacknowlegdetheorganisationalworkaswellastheeditorialwork donebyoursecretaryofthemeetingPhDstudentRoderichTumulka. Themeetingwasintendedtostimulaterenewedre?ectiononthefundam- talandpracticalaspectsofprobabilityinphysics, inparticularthefoundations ofstatisticalsechanics, theprobabilityinthefoundationsofquantummech- ics, thealgebraicviewofprobabilityandthephilosophyofprobabilityinits interrelationwithphysics. Questionslikewhatprobabilityis, orwhatitisabout, orhowprobability entersphysicsareofasubtlekind. Theyaredi?cultinvariousways, often mixedupwiththeenormouscomplexityandtheinescapablelackofmat- maticalrigorinthephysicalapplication, orwiththefoundationalproblemsof quantummechanics, wheretheprobabilisticignoranceconcerningthevaluesof certainphysicalquantitieshasevenbeenelevatedtoamatterofprinciple. At present, theunderstandingofprobabilityinphysicsisalmostaspersonalasthe understandingofquantumtheory. Theaimoftheconferencewasthustofocusonideasaboutprobabilityin physics, itsmeaninganditsphilosophicalimplications, byreviewingthedi?erent facetsofprobabilityinphysicsinitsmodernsettingsandbytakingintoaccount modernquantumtheorieswithoutobservers, wheretheoriginofprobabilityis notmysti?edbydogmatism. Thereviewsweregiveninone-hourtalks, andthediscussionswereheldin theformofroundtables, whereshortercontributionswerealsogiven. Thespeakerswereaskednottodilutethemainthemesoftheconference withtechnicalitiesandtofocussharplyontheissueofprobability. Thiswas VI Preface takentoheartbyallspeakersandthemeetingthusprovedverysuccessful. The contributionsinthisvolumeconsequentlyfocusonconceptualissues, andthey makeworthwhilereadingforspecialistsinthe?eldoffoundationsaswellasfor nonspecialists, becauseextensivetechnicalpriorknowledgeisnotrequired. The contributionshavebeenleftintheordertheywerediscussedinthemeeting, whichprovedtobeaverynaturalone: 1. ClassicalStatisticalMechanics, whereBoltzmann'sunderstandingofstat- ticalmechnanicsasarisingfromkineticgastheoryisreviewedandputinto modernperspectives, withanoutlookonrelativisticstatisticalmechanics. Therelativelackofemphasisonthee?ectofchaoticbehaviouronthefo- dationsofprobabilityisnoteworthy. 2. QuantumMechanics, wherewereviewthoseontologicalquantumtheories, thathavebeenmostseriouslydiscussedintherecentyears. Amongthese areadeterministictheory(Bohmianmechanics)andboththeintrinsically randomtheoriesofwavepacketreductionandtheoperator-basedconsistent (decoherent)histories. Itstartswiththe"orthodox"view, againwith- phasisontheprobabilisticaspectsofthesetheories. 3. Chaoticsystems, wherethedynamicalaspectsforthefoundationsofpro- bilityinphysicsareadressed. 4. PhilosophyofProbability, wheretheissuesoftheearliersectionsarefurther scrutinizedonphilosophicalgrounds. Thesecontributionshavenoabstracts. T

Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq Theory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

This book presents a theory for unconventional superconductivity driven by spin excitations. Using the Hubbard Hamiltonian and a self-consistent treatment of the spin excitations, the interplay between magnetism and superconductivity in various unconventional superconductors is discussed. In particular, the monograph applies this theory for Cooper-pairing due to the exchange of spin fluctuations to the case of singlet pairing in hole- and electron-doped high-Tc superconductors, and to triplet pairing in
Sr2RuO4. Within the framework of a generalized Eliashberg-like treatment, calculations of both many normal and superconducting properties as well as elementary excitations are performed. The results are related to the phase diagrams of the materials which reflect
the interaction between magnetism and superconductivity.

obtained are still severely limited to low Reynolds numbers (about only one decade better than direct numerical simulations), and the interpretation of such calculations for complex, curved geometries is still unclear. It is evident that a lot of work (and a very significant increase in available computing power) is required before such methods can be adopted in daily's engineering practice. I hope to l"Cport on all these topics in a near future. The book is divided into six chapters, each. chapter in subchapters, sections and subsections. The first part is introduced by Chapter 1 which summarizes the equations of fluid mechanies, it is developed in C apters 2 to 4 devoted to the construction of turbulence models. What has been called "engineering methods" is considered in Chapter 2 where the Reynolds averaged equations al"C established and the closure problem studied ( 1-3). A first detailed study of homogeneous turbulent flows follows ( 4). It includes a review of available experimental data and their modeling. The eddy viscosity concept is analyzed in 5 with the l"Csulting alar-transport equation models such as the famous K-e model. Reynolds stl"Css models (Chapter 4) require a preliminary consideration of two-point turbulence concepts which are developed in Chapter 3 devoted to homogeneous turbulence. We review the two-point moments of velocity fields and their spectral transforms ( 1), their general dynamics ( 2) with the particular case of homogeneous, isotropie turbulence ( 3) whel"C the so-called Kolmogorov's assumptions are discussed at length."

This book contains the courses given at the Third School on Statistical Physics and Cooperative Systems held at Santiago, Chile, from 14th to 18th December 1992. The main idea of this periodic school was to bring together scientists work with recent trends in Statistical Physics. More precisely ing on subjects related related with non linear phenomena, dynamical systems, ergodic theory, cellular au tomata, symbolic dynamics, large deviation theory and neural networks. Scientists working in these subjects come from several areas: mathematics, biology, physics, computer science, electrical engineering and artificial intelligence. Recently, a very important cross-fertilization has taken place with regard to the aforesaid scientific and technological disciplines, so as to give a new approach to the research whose common core remains in statistical physics. Each contribution is devoted to one or more of the previous subjects. In most cases they are structured as surveys, presenting at the same time an original point of view about the topic and showing mostly new results. The expository text of Fran"

In this monograph, the density ?uctuation theory of transport coe?cients of simple and complex liquids is described together with the kinetic theory of liquids, the generic van der Waals equation of state, and the modi?ed free volume theory. The latter two theories are integral parts of the density ?- tuation theory, which enables us to calculate the density and temperature dependence of transport coe?cients of liquids from intermolecular forces. The terms nanoscience and bioscience are the catch phrases currently in fashion in science. It seems that much of the fundamentals remaining unsolved or poorly understood in the science of condensed matter has been overshadowed by the frenzy over the more glamorous disciplines of the former, shunned by novices, and are on the verge of being forgotten. The transport coe?cients of liquids and gases and related thermophysical properties of matter appear to be one such area in the science of macroscopic properties of molecular systems and statisticalmechanicsofcondensedmatter. Evennano-andbiomaterials, h- ever, cannot be fully and appropriately understood without ?rm grounding and foundations in the macroscopic and molecular theories of transport pr- ertiesandrelatedthermophysicalpropertiesofmatterinthecondensedphase. Oneisstilldealingwithsystemsmadeupofnotafewparticlesbutamultitude of them, often too many to count, to call them few-body problems that can be understoodwithoutthehelpofstatisticalmechanicsandmacroscopicphysics. In the density ?uctuation theory of transport coe?cients, the basic approach taken is quite di?erent from the approaches taken in the conventional kinetic theories of gases and liquids

This book covers a new explanation of the origin of Hamiltonian chaos and its quantitative characterization. The author focuses on two main areas: Riemannian formulation of Hamiltonian dynamics, providing an original viewpoint about the relationship between geodesic instability and curvature properties of the mechanical manifolds; and a topological theory of thermodynamic phase transitions, relating topology changes of microscopic configuration space with the generation of singularities of thermodynamic observables. The book contains numerous illustrations throughout and it will interest both mathematicians and physicists.

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-a-vis several widely used classifiers, including neural networks. "

In the last two decades remarkable progress has been made in understanding and describing tunneling processes in complex systems in terms of classical trajectories. This book introduces recent concepts and achievements. There is particular emphasis on a dynamical formulation and relations to specific systems in mesoscopic, molecular, atomic and nuclear physics.

This completely revised edition of the classical book on Statistical Mechanics covers the basic concepts of equilibrium and non-equilibrium statistical physics. In addition to a deductive approach to equilibrium statistics and thermodynamics based on a single hypothesis this book treats the most important elements of non-equilibrium phenomena. Intermediate calculations are presented in complete detail. Problems at the end of each chapter help students to consolidate their understanding of the material. Beyond the fundamentals, this text demonstrates the breadth of the field and its great variety of applications.

Intended for self-study, this second volume presents a systematic approach for deriving model equations of planar and spatial mechanisms. The necessary theoretical foundations have been laid in the first volume. The focus is on the application of the modeling methodology to various examples of rigid-body mechanisms, simple planar ones as well as more challenging spatial problems. A rich variety of joint models, active constraints, as well as active and passive force elements is treated. The book is intended for self-study by working engineers and students concerned with the control of mechanical systems, i.e. robotics, mechatronics, vehicles, and machine tools. Its examples can be used as models for university lectures.

This book examines life not from the reductionist point of view, but rather asks the questions: what are the universal properties of living systems, and how can one construct from there a phenomenological theory of life that leads naturally to complex processes such as reproductive cellular systems, evolution and differentiation? The presentation is relatively non-technical to appeal to a broad spectrum of students and researchers.

Condensed-matter physics plays an ever increasing role in photonics, electronic and atomic collisions research. Dispersion (Dynamics and Relaxation) includes scattering/collisions in the gaseous phase. It also includes thermal agitation, tunneling and relaxation in the liquid and solid phases. Classical mechanics, classical statistical mechanics, classical relativity and quantum mechanics are all implicated. 'Semiclassical' essentially means that there is a large or asymptotic real parameter. 'Semiclassical' can also mean 'classical with first-order quantal correction', based on an exponentiated Liouville series commencing with a simple pole in the -plane, being Planck's reduced constant and coming with all the attendant connection problems associated with the singularity at the turning or transition point and with the Stokes phenomenon. Equally, ' semiclassical' can mean 'electrons described quantally and the heavy particles classically'. This latter gives rise to the so-called impact parameter method based on a pre-assigned classical trajectory.

With evermore sophisticated experiments, it has become equally more important to test theory over a wider range of parameters. For instance, at low impact energies in heavy-particle collisions, the inverse velocity is a large parameter; in single-domain ferromagnetism, thermal agitation (including Brownian motion and continuous-time random walks) is faced with a barrier of height 'sigma', a possibly large parameter. Methods of solution include phase-integral analysis, integral transforms and change-of-dependent variable. We shall consider the Schrodinger time-independent and time-dependent equations, the Dirac equation, the Fokker Planck equation, the Langevin equation and the equations of Einstein's classical general relativity equations.

There is an increasing tendency among physicists to decry applied mathematics and theoretical physics in favour of computational blackboxes. One may say applied mathematics concerns hard sums and products (and their inverses) but unless one can simplify and sum infinite series of products of infinite series, can one believe the results of a computer program? The era of the polymath has passed; this book proposal aims to show the relevance to, and impact of theory on, laboratory scientists."

In this book, we approach neurophysiology at the interface of neurology and clinical neurophysiology. The medical disciplines of the nervous system, n- rology and clinical neurophysiology, rest heavily on other sciences, notably cellular biology, neuro-anatomy, neuro-physiology, applied physics and ma- ematical biology. Existing medical textbooks on neurophysiology, neurology and clinical neurophysiology are an excellent source of the phenomenology of various principles and diseases. Here, we choose to elucidate some of the under- ing physiological, physical processes and experimental methods, intended for a broad audience - medical residents and students, as well as students in the emerging area of medical technical sciences. We feel that a good understanding of fundamentals may signi?cantly - hance insight into various aspects of clinical neurology and clinical neu- physiology. This book, therefore, is focused on a selection of clinical signs and symptoms to highlight basic principles of neurology, (neuro-)physiology and neuroanatomy. While we believe this text to be of interest to medical students or residents in neurology or clinical neurophysiology, we speci?cally aim at students - terested in contributing to new developments and innovations in neurology and clinical neurophysiology. These students are involved with patients, even though they are not trained for routine patient care.

Computational Mechanics of the Classical Guitar describes a new dynamic paradigm in instrument acoustics based on time-dependent transient analysis and simulation of complete musical instruments. It describes the current state of theoretical and experimental research into the guitar for engineers, instrument makers and musicians. This includes a summary of the basic equations for the mechanics of vibrating bodies and a presentation of the FDM (finite difference method) model with which the true vibrational behaviour of the instrument as an entire system can be understood for the first time. This monograph presents various new theoretical and experimental results and insights into guitar playing such as the coupling between the strings and the top plate or a description of the finger noise made when the fingers slide over the strings before plucking.

social network analysis has been an established eld since the 1950s; in computer and information sciences, in biology, and of course in mathematics (graph theory) networks are central representations of objects and methods (De Nooy, forthc- ing). More detailed bibliometric studies have examined the individual, cognitive, and institutional composition of complex network theory (Morris and Yen 2004), and social network theory (Otte and Rousseau 2002). Among the more impor- .. tant pieces of literature are Borner et al. (2007), Bornholdt and Schuster (2003), Buchanan (2002), Dorogovtsev and Mendes (2003), Otte and Rousseau (2002), Newman (2003), and Watts (1999, 2004). Of these, Borner .. et al. (2007) stand out because they have most recently re-examined network science, considering it as a possible innovation in information science. All the reviews mentioned include efforts to build bridges between different scienti c disciplines and specialties. In this book we draw particular attention to the link between evolutionary economics and statistical physics. Despite this impressive development, claims that an entirely new science has been created (Barabasi ' 2002) have nevertheless been the subject of criticism. - depth analyses of a subset of "complex networks" contributions (1991-2003) have shown that the notion of "complex networks" was already prevalent in a number of different elds before it became practically a "brand name" or the popular label for a new specialty area in physics, or a new cross-disciplinary paradigm.

In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, that even today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail.

The ?eld of applied nonlinear dynamics has attracted scientists and engineers across many different disciplines to develop innovative ideas and methods to study c- plex behavior exhibited by relatively simple systems. Examples include: population dynamics, ?uidization processes, applied optics, stochastic resonance, ?ocking and ?ightformations, lasers, andmechanicalandelectricaloscillators. Acommontheme among these and many other examples is the underlying universal laws of nonl- ear science that govern the behavior, in space and time, of a given system. These laws are universal in the sense that they transcend the model-speci?c features of a system and so they can be readily applied to explain and predict the behavior of a wide ranging phenomena, natural and arti?cial ones. Thus the emphasis in the past decades has been in explaining nonlinear phenomena with signi?cantly less att- tion paid to exploiting the rich behavior of nonlinear systems to design and fabricate new devices that can operate more ef?ciently. Recently, there has been a series of meetings on topics such as Experimental Chaos, Neural Coding, and Stochastic Resonance, which have brought together many researchers in the ?eld of nonlinear dynamics to discuss, mainly, theoretical ideas that may have the potential for further implementation. In contrast, the goal of the 2007 ICAND (International Conference on Applied Nonlinear Dynamics) was focused more sharply on the implementation of theoretical ideas into actual - vices and system