Analyzing dynamic intellectual capital: System-based theory and application

This research report presents an application of systems theory to evaluating intellectual capital (IC) as organization's ability for self-renewal. As renewal ability is a dynamic capability of an organization as a whole, rather than a static asset or an atomistic competence of separate individuals within the organization, it needs to be understood systemically. Consequently, renewal ability has to be measured with systemic methods that are based on a thorough conceptual analysis of systemic characteristics of organizations. The aim of this report is to demonstrate the theory and analysis methodology for grasping companies' systemic efficiency and renewal ability. The volume is divided into three parts. The first deals with the theory of organizations as self-renewing systems. In the second part, the principles of quantitative analysis of organizations are laid down. Finally, the detailed mathematics of the renewal indices are presented. We also assert that the indices produced by the analysis are an effective tool for the management and valuation of knowledge-intensive companies.

Small and Large Scale Dynamics in the Milky Way

This thesis contains dynamical analysis on four different scales: the Solar system, the Sun itself, the Solar neighbourhood, and the central region of the Milky Way galaxy. All of these topics have been handled through methods of potential theory and statistics. The central topic of the thesis is the orbits of stars in the Milky Way. An introduction into the general structure of the Milky Way is presented, with an emphasis on the evolution of the observed value for the scale-length of the Milky Way disc and the observations of two separate bars in the Milky Way. The basics of potential theory are also presented, as well as a developed potential model for the Milky Way. An implementation of the backwards restricted integration method is shown, rounding off the basic principles used in the dynamical studies of this thesis. The thesis looks at the orbit of the Sun, and its impact on the Oort cloud comets (Paper IV), showing that there is a clear link between these two dynamical systems. The statistical atypicalness of the orbit of the Sun is questioned (Paper I), concluding that there is some statistical typicalness to the orbit of the Sun, although it is not very significant. This does depend slightly on whether one includes a bar, or not, as a bar has a clear effect on the dynamical features seen in the Solar neighbourhood (Paper III). This method can be used to find the possible properties of a bar. Finally, we look at the effect of a bar on a statistical system in the Milky Way, seeing that there are not only interesting effects depending on the mass and size of the bar, but also how bars can capture disc stars (Paper II).

On Undecidable Dynamical Properties of Reversible One-Dimensional Cellular Automata

Cellular automata are models for massively parallel computation. A cellular automaton consists of cells which are arranged in some kind of regular lattice and a local update rule which updates the state of each cell according to the states of the cell's neighbors on each step of the computation. This work focuses on reversible one-dimensional cellular automata in which the cells are arranged in a two-way in_nite line and the computation is reversible, that is, the previous states of the cells can be derived from the current ones. In this work it is shown that several properties of reversible one-dimensional cellular automata are algorithmically undecidable, that is, there exists no algorithm that would tell whether a given cellular automaton has the property or not. It is shown that the tiling problem of Wang tiles remains undecidable even in some very restricted special cases. It follows that it is undecidable whether some given states will always appear in computations by the given cellular automaton. It also follows that a weaker form of expansivity, which is a concept of dynamical systems, is an undecidable property for reversible one-dimensional cellular automata. It is shown that several properties of dynamical systems are undecidable for reversible one-dimensional cellular automata. It shown that sensitivity to initial conditions and topological mixing are undecidable properties. Furthermore, non-sensitive and mixing cellular automata are recursively inseparable. It follows that also chaotic behavior is an undecidable property for reversible one-dimensional cellular automata.

Towards input/output-free modelling of linear infinite-dimensional systems in continuous time

This dissertation describes a networking approach to infinite-dimensional systems theory, where there is a minimal distinction between inputs and outputs. We introduce and study two closely related classes of systems, namely the state/signal systems and the port-Hamiltonian systems, and describe how they relate to each other. Some basic theory for these two classes of systems and the interconnections of such systems is provided. The main emphasis lies on passive and conservative systems, and the theoretical concepts are illustrated using the example of a lossless transfer line. Much remains to be done in this field and we point to some directions for future studies as well.

Methods for construction and analysis of computational models in systems biology : applications to the modelling of the heat shock response and the self-assembly of intermediate filaments

Systems biology is a new, emerging and rapidly developing, multidisciplinary research field that aims to study biochemical and biological systems from a holistic perspective, with the goal of providing a comprehensive, system- level understanding of cellular behaviour. In this way, it addresses one of the greatest challenges faced by contemporary biology, which is to compre- hend the function of complex biological systems. Systems biology combines various methods that originate from scientific disciplines such as molecu- lar biology, chemistry, engineering sciences, mathematics, computer science and systems theory. Systems biology, unlike “traditional” biology, focuses on high-level concepts such as: network, component, robustness, efficiency, control, regulation, hierarchical design, synchronization, concurrency, and many others. The very terminology of systems biology is “foreign” to “tra- ditional” biology, marks its drastic shift in the research paradigm and it indicates close linkage of systems biology to computer science. One of the basic tools utilized in systems biology is the mathematical modelling of life processes tightly linked to experimental practice. The stud- ies contained in this thesis revolve around a number of challenges commonly encountered in the computational modelling in systems biology. The re- search comprises of the development and application of a broad range of methods originating in the fields of computer science and mathematics for construction and analysis of computational models in systems biology. In particular, the performed research is setup in the context of two biolog- ical phenomena chosen as modelling case studies: 1) the eukaryotic heat shock response and 2) the in vitro self-assembly of intermediate filaments, one of the main constituents of the cytoskeleton. The range of presented approaches spans from heuristic, through numerical and statistical to ana- lytical methods applied in the effort to formally describe and analyse the two biological processes. We notice however, that although applied to cer- tain case studies, the presented methods are not limited to them and can be utilized in the analysis of other biological mechanisms as well as com- plex systems in general. The full range of developed and applied modelling techniques as well as model analysis methodologies constitutes a rich mod- elling framework. Moreover, the presentation of the developed methods, their application to the two case studies and the discussions concerning their potentials and limitations point to the difficulties and challenges one encounters in computational modelling of biological systems. The problems of model identifiability, model comparison, model refinement, model inte- gration and extension, choice of the proper modelling framework and level of abstraction, or the choice of the proper scope of the model run through this thesis.

On state and parameter estimation in chaotic systems

State-of-the-art predictions of atmospheric states rely on large-scale numerical models of chaotic systems. This dissertation studies numerical methods for state and parameter estimation in such systems. The motivation comes from weather and climate models and a methodological perspective is adopted. The dissertation comprises three sections: state estimation, parameter estimation and chemical data assimilation with real atmospheric satellite data. In the state estimation part of this dissertation, a new filtering technique based on a combination of ensemble and variational Kalman filtering approaches, is presented, experimented and discussed. This new filter is developed for large-scale Kalman filtering applications. In the parameter estimation part, three different techniques for parameter estimation in chaotic systems are considered. The methods are studied using the parameterized Lorenz 95 system, which is a benchmark model for data assimilation. In addition, a dilemma related to the uniqueness of weather and climate model closure parameters is discussed. In the data-oriented part of this dissertation, data from the Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite instrument are considered and an alternative algorithm to retrieve atmospheric parameters from the measurements is presented. The validation study presents first global comparisons between two unique satellite-borne datasets of vertical profiles of nitrogen trioxide (NO3), retrieved using GOMOS and Stratospheric Aerosol and Gas Experiment III (SAGE III) satellite instruments. The GOMOS NO3 observations are also considered in a chemical state estimation study in order to retrieve stratospheric temperature profiles. The main result of this dissertation is the consideration of likelihood calculations via Kalman filtering outputs. The concept has previously been used together with stochastic differential equations and in time series analysis. In this work, the concept is applied to chaotic dynamical systems and used together with Markov chain Monte Carlo (MCMC) methods for statistical analysis. In particular, this methodology is advocated for use in numerical weather prediction (NWP) and climate model applications. In addition, the concept is shown to be useful in estimating the filter-specific parameters related, e.g., to model error covariance matrix parameters.

Vaikutusperusteiset konseptit : EBO-, EBAO-, SOD- ja CA-käsiteanalyysi

Tutkimuksessa tarkastellaan vaikutusperusteisuuteen pohjautuvia uusia sotataitoa soveltavia konsepteja kuvaavia käsitteitä ja niiden sisältöä. Tutkimuksessa tuodaan myös esiin sellaisia syitä ja tekijöitä, jotka ovat johtaneet tai mahdollistaneet uusien konseptien synnyn ja kehittämisen. Asioita tarkastellaan nimenomaan vaikutusperusteisuuden näkökulmasta. Tarkastelun kohteena olevat käsitteet ovat EBO (Effect Based Operations), EBAO (Effect Based Approach to Operations), SOD (Systemic Operational Design) ja kokonaisvaltainen lähestymistapa, CA (Comprehensive Approach). Työn keskeisinä johtopäätöksinä esitetään seuraavat tutkimustulokset: Asevoimiin kohdistuneet rakenteelliset muutokset ovat johtaneet länsimaisissa asevoimissa joukkojen supistamiseen sekä uuden tyyppisen teknologisen toimintakyvyn ja toimintatapamallien luomiseen. Verkostokeskeinen sodankäynti ja erityisesti sen mahdollistama yhteinen tilannekuva toimivat vaikutusperusteisten konseptien mahdollistajana ja niiden ydinprosessien tukena erityisesti, kun asiaa tarkastellaan teknologiselta kannalta. Verkostosodankäynti, kuten nykypäivän talo-uselämäkin, nojaa nopeaan päätöksentekoon, kustannusten minimointiin ja teknologian luomiin mahdollisuuksiin verkottuneessa maailmassa. Taloudellisuusajattelu, tuhovaikutusten minimoinnit, omien tappioiden välttäminen ja niin edelleen, toistuvat eri yhteyksissä uusilla termeillä. Yhteinen nimittäjä on kustannustehokkuus. Mikään ei ole kuitenkaan perustavasti muuttunut. Kaikki se, mitä sodankäynti on ja on ollut, tulee säilymään.

Organisaation kehittämisen haasteet institutionalisoitumisen näkökulmasta

Pro Gradu -tutkimuksen keskeisin tavoite on ollut selvittää minkälaisia haasteita organisaatiot kohtaavat kehittäessään toimintojaan. Kontekstina tutkimuksessa on toiminut institutionalisoituminen, jolla viitataan toimintojen virallistamiseen. Tutkimus toteutettiin laadullisena ja aineistonkeräystapana käytettiin puolistrukturoituja haastatteluja. Tutkimuksen haastatteluiden kautta päästiin tutkimaan suomalaisten kuntasektoritoimijoiden muutosprosessien haasteellisuutta. Tutkimustulokset osoittivat, että haasteet organisaatiomuutosten aikana ovat hyvin moninaisia. Muutoshaasteet ovat kuitenkin luonteeltaan sellaisia, että ne voidaan kohdata keinoina muutoksen parempaan läpivientiin. Erityisesti työntekijöiden huomioiminen osana muutosprosessia korostui tämän tutkimuksen osana. Myös sosioteknisen systeemiajattelun rooli korostui oivana keinona kohdata muutoshaasteet.

Chaotic Behaviour of Active Magnetic Bearing System by Time Series Analysis

Chaotic behaviour is one of the hardest problems that can happen in nonlinear dynamical systems with severe nonlinearities. It makes the system's responses unpredictable. It makes the system's responses to behave similar to noise. In some applications it should be avoided. One of the approaches to detect the chaotic behaviour is nding the Lyapunov exponent through examining the dynamical equation of the system. It needs a model of the system. The goal of this study is the diagnosis of chaotic behaviour by just exploring the data (signal) without using any dynamical model of the system. In this work two methods are tested on the time series data collected from AMB (Active Magnetic Bearing) system sensors. The rst method is used to nd the largest Lyapunov exponent by Rosenstein method. The second method is a 0-1 test for identifying chaotic behaviour. These two methods are used to detect if the data is chaotic. By using Rosenstein method it is needed to nd the minimum embedding dimension. To nd the minimum embedding dimension Cao method is used. Cao method does not give just the minimum embedding dimension, it also gives the order of the nonlinear dynamical equation of the system and also it shows how the system's signals are corrupted with noise. At the end of this research a test called runs test is introduced to show that the data is not excessively noisy.

Sosioteknisen kuilun kaventaminen toiminnanohjausjärjestelmän ja käyttäjien välillä

Pro Gradu- tutkimuksen keskeisin tavoite on ollut selvittää, kuinka sosioteknistä kuilua kohdeyrityksen toiminnanohjausjärjestelmän ja käyttäjien välillä voitaisiin pienentää. Teo-reettisena viitekehyksenä on käytetty sosioteknistä systeemiteoriaa sekä teorioita liittyen tietoteknisen järjestelmän hyväksyntään. Toiminnanohjausjärjestelmät ovat tunnetusti välttämätön osa nykypäivää lähes kaikille yrityksille. Niiden käyttöönoton onnistumista ja käytön tehokkuutta voidaan parantaa huomioimalla sekä sosiaalinen että tekninen systeemi organisaatiossa. Sosiotekninen kuilu rakentuu kahden välttämättömän ja toisistaan riippuvaisen systeemin välille: sekä sosiaalinen systeemi eli henkilöstö ja heidän työtapansa että tekninen systeemi eli tekno-logia ja tieto on huomioitava ja aidosti sosioteknisessä muutoksessa molempia systeeme-jä muokattava. Organisaatio voi parantaa omilla toimillaan käyttäjien asennetta ja haluk-kuutta ja siten kaventaa kuilua sosiaalisen systeemin puolelta. Lisäksi teknistä systeemiä tulisi muokata paremmin vastaamaan käyttäjien toiveita, jotta kuilu kapenisi myös tekni-sen systeemin suunnasta. Tutkimus toteutettiin laadullisena ja aineistonkeräystapana käytettiin puolistrukturoituja haastatteluja kohdeyrityksessä.

Parameter estimation for Chaotic or Stochastic Dynamics

Since its discovery, chaos has been a very interesting and challenging topic of research. Many great minds spent their entire lives trying to give some rules to it. Nowadays, thanks to the research of last century and the advent of computers, it is possible to predict chaotic phenomena of nature for a certain limited amount of time. The aim of this study is to present a recently discovered method for the parameter estimation of the chaotic dynamical system models via the correlation integral likelihood, and give some hints for a more optimized use of it, together with a possible application to the industry. The main part of our study concerned two chaotic attractors whose general behaviour is diff erent, in order to capture eventual di fferences in the results. In the various simulations that we performed, the initial conditions have been changed in a quite exhaustive way. The results obtained show that, under certain conditions, this method works very well in all the case. In particular, it came out that the most important aspect is to be very careful while creating the training set and the empirical likelihood, since a lack of information in this part of the procedure leads to low quality results.

Non-Markovianity and Quantum Correlations in Qubit-Systems

In this Thesis I discuss the exact dynamics of simple non-Markovian systems. I focus on fundamental questions at the core of non-Markovian theory and investigate the dynamics of quantum correlations under non-Markovian decoherence. In the first context I present the connection between two different non-Markovian approaches, and compare two distinct definitions of non-Markovianity. The general aim is to characterize in exemplary cases which part of the environment is responsible for the feedback of information typical of non- Markovian dynamics. I also show how such a feedback of information is not always described by certain types of master equations commonly used to tackle non-Markovian dynamics. In the second context I characterize the dynamics of two qubits in a common non-Markovian reservoir, and introduce a new dynamical effect in a wellknown model, i.e., two qubits under depolarizing channels. In the first model the exact solution of the dynamics is found, and the entanglement behavior is extensively studied. The non-Markovianity of the reservoir and reservoirmediated-interaction between the qubits cause non-trivial dynamical features. The dynamical interplay between different types of correlations is also investigated. In the second model the study of quantum and classical correlations demonstrates the existence of a new effect: the sudden transition between classical and quantum decoherence. This phenomenon involves the complete preservation of the initial quantum correlations for long intervals of time of the order of the relaxation time of the system.