Complex system contextual framework (cscf): a grounded-theory construction for the articulation of system context in addressing complex systems problems

There is a growing body of literature addressing the challenges of transdisciplinary research – how to do it, what it is and who is doing it. At the same time there is growing discussion and awareness in international research about wicked problems and how to deal with problems such as sustainability, inequity, inequality, food (in)security, climate change and natural resource management. These problems are described as wicked since they defy complete definition, there are no final or simple solutions and any solutions are generally contested. A third body of the research literature focuses on transformational learning and knowledge creation capable of tackling contemporary social and environmental challenges. Through the study of the lived experience of transdisciplinary researchers combined with theory synthesis, this thesis contributes to further understanding of all of these inquiry areas that I propose are inseparable from the practice of transdisciplinary research. The primary aim of the thesis is to improve understanding of transdisciplinary research practice and to bring together, synthesise and test a range of frameworks that can inform and guide this practice. The guiding aspiration for my research is to access the untapped potential of transdisciplinary research practice (the practice of the researchers) to investigate wicked problems in complex systems. While the context of the thesis is research for rural development, the application of the resulting methodology is far wider, including transdisciplinary research, sustainability science and other inquiry endeavours that tackle wicked problems. Based on my own philosophical framing, one that combines constructivism with elements of critical theory, adopting a relational ontology and a pragmatist approach, I propose a relational and overarching transdisciplinary methodology in this thesis based on the following five principles: Principle number 1: A collective, inclusive approach to appreciative, contextbased problem framing is needed to embrace the richness of complexity. Principle number 2: Co-production of knowledge across the boundaries of knowledge cultures and worldviews requires an inclusive, shared language for human and social inquiry. Principle number 3: Working constructively with tension is a catalyst and foundation for transformational learning and change. Principle number 4: An iterative or recursive research inquiry process is essential for transformational learning, and for theory and practice to constructively inform each other. Principle number 5: Reflection and reflexivity (both habitual and systemic) are essential to enable the researcher to constructively capture transformational knowledge co-production. These principles guide strategies to bring together vastly different worldviews, modes of inquiry and knowledge systems to create, not empty consensus, but a rich and innovative synergy for more constructive, engaged and effective problem solving. It is relational because the research practice focuses on relationships and networks and is dynamic. Underpinning this methodology (and the conceptual framework for this thesis) is an adaptation of Christopher Alexander’s pattern language (Alexander, 1977) combined with elements of Layder’s adaptive theory (2005). These two frameworks underpin my thesis research strategy with a cyclical, adaptive research approach where theory and practice inform each other, and where I synthesise sets of provisionally universal patterns as frameworks to identify and bring together specific patterns, and relationships between patterns, to form a series of ongoing solutions to wicked societal problems. The empirical research in this thesis is based on a study of three case study research for rural development projects and the transdisciplinary researchers and participants in these project teams. Case Study 1 (seasonal climate forecasting for farming to enhance food security) is the pilot study, with Case Study 2 (family poultry production and crop integration for food security and nutrition) providing the canvas for the initial development and testing of the ideas and theory. The third case study (multi-scale climate adaptation for rice farming communities) is used to test the emergent theory and is studied in greatest depth, culminating in a detailed analysis using the principles that form the basis for the transdisciplinary methodology.

The article advances linguistic synergetics as a novel research methodology by focusing on applicability of synergetic principles to language development studies. Synergetics is a name for the science of complexity that deals with principles of emergence, self-organisation and self-regulation of complex systems. From the perspective of the synergetic approach, a human language is considered an open, dynamic, non-linear, self-organizing system with all its hierarchical subsystems and elements coherently interconnected and controlled by governing parameters. The latter are considered to be principles of grammatical structure imposing constraints on the range of structural variation permitted in a given language. Any human language, as a synergetic system, has its own set of parameters to characterize peculiarities of its structural organization. It is parameters that highlight grammatical differences between languages. From this angle language development is understood as a change of the parameter pattern of a given language system, which causes the latter to self-organize into a new state. It is assumed that at any given moment the system of a language has its own parameter pattern. Any change within this pattern is but a signal of changes of the whole synergetic system. The article focuses on the following four parameters peculiar to Old English, namely: The null subject parameter, The head directionality parameter, The reflexive domain parameter, and The question movement parameter. The article shows that the typological shift of English is based on the mechanism of changes within the parameter pattern of the language. As a result, the Old English synthetic language became the Modern English analytical language. A close examination of historical dynamics of English within its different language levels indicates that language never changes chaotically but has an underlying order determined by certain grammatical parameters of the language system. Mechanisms of self-organization of a complex system lie in the changes within its parameters. By contrast, the structural stability of the language is provided by stability of a great number of control parameters of the language mega-system.

<i>Introduction to the Modeling and Analysis of Complex Systems.</i> H. Sayama (Ed.). (2015, Open SUNY Textbooks). Free open access PDF, 498 pp. ISBN 978-1-942341-06-2 (deluxe color edition). ISBN 978-1-942341-08-6 (print edition). ISBN 978-1-942341-09-3 (ebook).

System reliability is important for systems engineers, since it is directly related to a company's reputation, customer satisfaction, and system design costs. Improving system reliability has been an important task for the system engineers and a number of studies have been published to discuss methods for improving system reliability. For this purpose sensitivity analysis and fault diagnosis has been used in various studies, where identification of significant and problematic components plays an important role. Both sensitivity analysis and fault diagnosis require understanding the system structure and component relationships; and Bayesian networks (BN) have been shown to be an effective tool for modeling the systems and quantifying the component interactions. In this study, we use BN for sensitivity analysis and fault diagnosis to improve system reliability. We focus on the complex systems, where the number of components and component interactions can be very large. In this study, we first discuss sensitivity analysis in complex systems using BN, which can be used for identification of significant system components. Sensitivity analysis using BN is concerned with the question of how sensitive system reliability is to possible changes in the nodes in BN. In this paper we demonstrate that BN can be efficiently and effectively used for sensitivity analysis in complex system reliability. This study is the first that considers component reliabilities and uses BN for sensitivity analysis in complex systems. In this paper as a part of our method for sensitivity analysis, an efficient algorithm (SA) is introduced to perform sensitivity analysis in complex systems. Our SA algorithm is based on a graph traversal algorithm that can be effectively used in BN. The SA algorithm traverses the BN through the connected nodes and evaluates the reliabilities to perform sensitivity analysis. Our method helps the systems engineers understand the cause and effect relationships between system components and their reliability and discover the key components that have significant effects on system reliability. Once the key components are identified, system structure can be revised to improve the overall system reliability. Next we discuss fault diagnosis in complex systems and show how fault diagnosis can be used to improve complex system reliability. Due to component aging and environmental factors, the system components in real-life complex systems may fail or not function as expected. Such failures may cause unprecedented changes in the system reliability values and affect the reliability of not only the failed component, but also the overall system. One important issue in complex systems is that, the system engineers must process large amounts of information before making operational decisions. Since BN combine expert knowledge of the system with probabilistic theory for construction of effective diagnosis methodologies, they have been applied to fault diagnosis in various studies. In this paper, we present a new method for fault diagnosis in complex systems. Our method uses the complex system reliability to detect the faulty components. We continuously monitor the overall system reliability value, and our fault diagnosis mechanism is only triggered when significant changes to system reliability are detected. As part of our method, an efficient search algorithm is designed specifically for BN. This algorithm is empowered with popular heuristics. In this paper, we discuss how our method can be efficiently applied to complex systems since our search algorithm needs to check only a small portion of the system's components before detecting the failed one. We believe that our method provides system engineers with invaluable information to diagnose the faulty component and improve reliability in complex systems.

System reliability is important for systems engineers, since it is directly related to a company's reputation, customer satisfaction, and system design costs. Improving system reliability has been an important task for the system engineers and a number of studies have been published to discuss methods for improving system reliability. For this purpose sensitivity analysis and fault diagnosis has been used in various studies, where identification of significant and problematic components plays an important role. Both sensitivity analysis and fault diagnosis require understanding the system structure and component relationships; and Bayesian networks (BN) have been shown to be an effective tool for modeling the systems and quantifying the component interactions. In this study, we use BN for sensitivity analysis and fault diagnosis to improve system reliability. We focus on the complex systems, where the number of components and component interactions can be very large. In this study, we first discuss sensitivity analysis in complex systems using BN, which can be used for identification of significant system components. Sensitivity analysis using BN is concerned with the question of how sensitive system reliability is to possible changes in the nodes in BN. In this paper we demonstrate that BN can be efficiently and effectively used for sensitivity analysis in complex system reliability. This study is the first that considers component reliabilities and uses BN for sensitivity analysis in complex systems. In this paper as a part of our method for sensitivity analysis, an efficient algorithm (SA) is introduced to perform sensitivity analysis in complex systems. Our SA algorithm is based on a graph traversal algorithm that can be effectively used in BN. The SA algorithm traverses the BN through the connected nodes and evaluates the reliabilities to perform sensitivity analysis. Our method helps the systems engineers understand the cause and effect relationships between system components and their reliability and discover the key components that have significant effects on system reliability. Once the key components are identified, system structure can be revised to improve the overall system reliability. Next we discuss fault diagnosis in complex systems and show how fault diagnosis can be used to improve complex system reliability. Due to component aging and environmental factors, the system components in real-life complex systems may fail or not function as expected. Such failures may cause unprecedented changes in the system reliability values and affect the reliability of not only the failed component, but also the overall system. One important issue in complex systems is that, the system engineers must process large amounts of information before making operational decisions. Since BN combine expert knowledge of the system with probabilistic theory for construction of effective diagnosis methodologies, they have been applied to fault diagnosis in various studies. In this paper, we present a new method for fault diagnosis in complex systems. Our method uses the complex system reliability to detect the faulty components. We continuously monitor the overall system reliability value, and our fault diagnosis mechanism is only triggered when significant changes to system reliability are detected. As part of our method, an efficient search algorithm is designed specifically for BN. This algorithm is empowered with popular heuristics. In this paper, we discuss how our method can be efficiently applied to complex systems since our search algorithm needs to check only a small portion of the system's components before detecting the failed one. We believe that our method provides system engineers with invaluable information to diagnose the faulty component and improve reliability in complex systems.

In organisations, competitive advantage is increasingly reliant on the alignment of sociotechnical systems with business processes. 'Socio-technical' refers to the complex systems of people, tasks and technology. Supporting this alignment is exacerbated by the speed of technological change and its relationship with organisation growth. This complexity is further aggravated in a number of ways. Organisations and/or parts of organisations are structured differently and have different approaches to change. These differences impact on their responsiveness to change, their use of technology, and its relationship to business processes. In requirements engineering, a lack of understanding of the organisational context in which change takes place has been a problem over the last decade. Eliciting requirements is complex, with requirements changing constantly. Delivered change is affected by further changing needs, as stakeholders identify new ways of using IT. Changing requirements can lead to mismatches between tasks, technology and people. Relations and their alignment can be compromised. We contribute to understanding this complex domain by presenting an approach which engages with stakeholders/users in the early stages of the requirements elicitation process. The two expressions of the approach are derived from the literature and 19 real-world studies. They are referred to as Conceptual Framework and Change Frame. Both support a problem-centred focus on context analysis when reasoning about changing technology in business processes. The framework provides structures, techniques, notation and terminology. These represent, describe, and analyse the context in which change takes place, in the present and over time. The Change Frame combines an extension of the framework with an organisation pattern. It facilitates representing, describing and analysing change, across the strategic/operation area of an organisation. A known pattern of solution is provided, for the recurring change problem of representing an organisation-wide change in different organisation locations. Chapter 4 shows the conceptual framework in the context of a real-world study, and chapter 6 uses a real-world use/case scenario to illustrate the change frame. Both chapters show support for understanding change, through client/customer and stakeholder/users reasoning about the implications of change.

Signals that evolve over multiple variables or indices occur in all fields of science and engineering. Measurements of the distribution of temperature across the globe during a certain period of time are an example of such a signal. Multivariable systems describe the evolution of signals over a spatial-temporal domain. The mathematical equations involved in such a description are called a model and this model dictates which values the signals can obtain as a function of time and space. In an industrial production setting, such mathematical models may be used to monitor the process or determine the control action required to reach a certain set-point. Since their evolution is over both space and time, multi-variable systems are described by Partial Differential Equations (PDEs). Generally, it is not the signals or systems themselves one is interested in, but the information they carry. The main numerical tools to extract system trajectories from the PDE description are Finite Element (FE) methods. FE models allow simulation of the model via a discretization scheme. The main problem with FE models is their complexity, which leads to large simulation time, making them not suitable for applications such as on-line monitoring of the process or model-based control design. Model reduction techniques aim to derive lowcomplexity replacement models from complex process models, in the setting of this work, from FE models. The approximations are achieved by projection on lower-dimensional subspaces of the signals and their dynamic laws. This work considers the computation of empirical projection spaces for signals and systems evolving over multi-dimensional domains. Formally, signal approximation may be viewed as a low-rank approximation problem. Whenever the signal under consideration is a function of multiple variables, low-rank approximations can be obtained via multi-linear functionals, tensors. It has been explained in this work that approximation of multi-variable systems also boils down to low-rank approximation problems.The first problem under consideration was that of finding low-rank approximations to tensors. For order-2 tensors, matrices, this problem is well understood. Generalization of these results to higher-order tensors is not straightforward. Finding tensor decompositions that allow suitable approximations after truncation is an active area of research. In this work a concept of rank for tensors, referred to as multi-linear or modal rank, has been considered. A new method has been defined to obtain modal rank decompositions to tensors, referred to as Tensor Singular Value Decomposition (TSVD). Properties of the TSVD that reflect its sparsity structure have been derived and low-rank approximation error bounds have been obtained for certain specific cases. An adaptation of the TSVD method has been proposed that may give better approximation results when not all modal directions are approximated. A numerical algorithm has been presented for the computation of the (dedicated) TSVD, which with a small adaptation can also be used to compute successive rank-one approximation to tensors. Finally, a simulation example has been included which demonstrates the methods proposed in this work and compares them to a well-known existing method. The concepts that were introduced and discussed with regard to signal approximation have been used in a system approximation context.We have considered the well-known model reduction method of Proper Orthogonal Decompositions (POD). We have shown how the basis functions inferred from the TSVD can be used to define projection spaces in POD. This adaptation is both a generalization and a restriction. It is a generalization because it allows POD to be used in a scalable fashion for problems with an arbitrary number of dependent and independent variables. However, it is also a restriction, since the projection spaces require a Cartesian product structure of the domain. The model reduction method that is thus obtained has been demonstrated on a benchmark example from chemical engineering. This application shows that the method is indeed feasible, and that the accuracy is comparable to existing methods for this example. In the final part of the thesis the problem of reconstruction and approximation of multi-dimensional signals was considered. Specifically, the problem of sampling and signal reconstruction for multi-variable signals with non-uniformly distributed sensors on a Cartesian domain has been considered. The central question of this chapter was that of finding a reconstruction of the original signal from its samples. A specific reconstruction map has been examined and conditions for exact reconstruction have been presented. In case that exact reconstruction was not possible, we have derived an expression for the reconstruction error.

The monograph reveals the basics of complexity theory and methods for assessing complexity. The concept of complexity consideration is based on the analysis of complexity as a common attribute in processes and systems. The monograph describes the main methods for assessing different types of complexity. The concept of considering complexity in this monograph is also based on the fact that complexity is a comparative characteristic. It is given on a relative scale of difficulty. Therefore, complexity must be defined on a relative scale of “simplicity-complexity.” This concept motivates the consideration and analysis of the concept of “simplicity” as a complement to the concept of “complexity”. These concepts set the scale of complexity. The monograph provides a comparative analysis of the related concepts of simplicity and complexity. Three methods for assessing complexity are described: expert assessment of complexity, assessment of complexity using mathematical metrics, comparative assessment of complexity based on the theory of comparative analysis. The monograph contains a taxonomy of the main types of complexity. The content of the main types of complexity is revealed in detail: descriptive complexity, system complexity, modeling complexity, computational complexity. algorithmic complexity, deterministic complexity. Specific cognitive difficulties are described in detail. For cognitive complexity, special assessment methods are used. An interpretation of the concept of cognitive filter is given. Complexity is associated with the concept of complex systems. In most monographs on complex systems, the complexity aspect has not been considered or is viewed in a simplified manner. This monograph examines complexity as a characteristic of complex systems and the basis for their classification. Emergence is described as a characteristic of the complexity of systems and complex processes. The monograph contains a taxonomy of complex systems with characteristics of the complexity of different systems. Complex data systems have been explored. An analysis of organizational complex systems is given. Various types of complex ergatic systems have been described. An analysis of complex technical systems is given. Self-developing complex systems are described. autopoiesis of a complex organizational and technical system has been studied as a principle of systems development. Cyber-physical systems are described as an example of the development of complex systems. The monograph is intended for specialists in the field of computer science, systems analysis, artificial intelligence and philosophy of information.

This research examines the handling complexity aspects of conceptual design. Contemporary consensus suggests vessel design must consider new market requirements such as greater emphasis on environmental performance, a larger degree of uncertainty in terms of contract horizon, and the need for reliability of multiple operations assessed during early stages. Consequently, the industry has experienced development on many levels of ship design, from advanced subsystems (e.g., a wide range of machinery congurations), to vessels with demanding operations (e.g., modern oshore support vessels), to incorporation of eet assessment in early stages. Designers face a number of new technologies - usually representing greater investment - to obtain improved energy eciency and exibility regarding multi-faceted, future scenarios in which the vessel must operate. This large number of options results in an increase in the amount of information that should be considered to understand important aspects of the ship during the conceptual phase. This thesis is based on a systems engineering perspective to approach these kinds of developments, especially recent theories combining complexity theory in engineering.This thesis reviews current methods and approaches that deal with conceptual ship design and its complexity aspects. Based on this review, three research questions are proposed. First, which general complex systems theory premises can be used to dene complexity in conceptual ship design? Second, what general principles for organizing and simplifying complexity t the conceptual ship design task? Third, what methods eciently handle primary complexity aspects during conceptual ship design?The results of this study are the identification of the general principle of handling complexity, based on decomposition and encapsulation, as a strategy to manage relevant information during conceptual design, and proposing a five-aspect taxonomy to characterize and classify complexity in conceptual ship design. The taxonomy categorizes five aspects of conceptual ship design. The structural aspect relates to arrangement and interrelationships of the physical parts in the ship. The behavioral aspect derives from form-function mapping. The external circumstances to which the ship is subjected are captured in the contextual aspect. Uncertainties in future scenarios and expected/unexpected changes over time relate to the temporal aspect. The perceptual aspect relates to how various stakeholders perceive the value they receive from a design through the operational life cycle of the vessel.A discussion of both traditional and novel techniques to handle each of the aspects is presented. Focus is given to methods able to handle the three extended aspects (i.e., contextual, temporal, and perceptual). The goal of the study is to designate ship design as a complex system problem, developing and improving methods capable of handling primary complexity aspects during the conceptual phase.The primary contribution is characterization of conceptual ship design as a complex systems engineering task. Decomposition and encapsulation is presented as a general principle to handle complexity during the conceptual phase of ship design. More importantly, it identifies the intelligent encapsulation allowed by the five-aspect taxonomy, with implementation and development of methods to handle each aspect. Structural and behavioral aspects are investigated, merging traditional and novel techniques. Epoch-era analysis and a ship design deployment problem are used to tackle contextual and temporal aspects. The perceptual aspect is discussed through complex value robustness, and integration and concurrent assessment of all five aspects is handled theoretically through the responsive systems comparison method.This thesis consists of two parts. The first contains an introductory chapter presenting the background, the research questions, state-of-the-art conceptual ship design, ship as a complex system, information growth in ship design and complexity in a systems engineering framework, the research approach, a timeline of the research, initial results of a study of complexity aspects, results relevant to answering the three research questions, discussion of contributions, concluding remarks, and future research. The second part contains the five papers, in which individual results and contributions are discussed in more detail.

In popular dialogues, describing a system as "complex" is often the point of resignation, inferring that the system cannot be sufficiently described, predicted nor managed. Transport networks, management infrastructure and supply chain logistics are all often described in this way. Academic dialogues have begun to explore the collective behaviors of complex systems to define a complex system specifically as an adaptive one; i.e. a system that demonstrates 'self organising' principles and 'emergent' properties. Based upon the key principles of interaction and emergence in relation to adaptive and self organising systems in cultural artifacts and processes, this paper will argue that complex systems are cultural systems. By introducing generic principles of complex systems, and looking at the exploration of such principles in art, design and media research, this paper argues that a science of cultural systems as part of complex systems theory is the post modern science for the digital age. Furthermore, that such a science was predicated by post structuralism and has been manifest in art, design and media practice since the late 1960s.

A central question in complexity theory is how large-scale phenomena, such as such as self-organisation, perpetual novelty, and sustained diversity, emerge. Complex systems can be understood as networks of interacting components. The focus of this research is the role that the properties of such networks play in self-organisation and emergence in complex systems. Based on the previously known concept of Dual Phase Evolution (DPE), I propose a theoretical framework, within which recurrent phase transitions in network connectivity underlie emergent phenomena in many systems. This DPE framework extends and refines the original concept. Networks can exist in two general connectivity phases: well connected and poorly connected. DPE relates each of the two connectivity phases and the transition events between them to typical system dynamics. I analyse empirical and experimental evidence from published studies in areas as diverse as physics, biology, socio-economics, mathematics and computer science. The analysis implies that DPE is widespread and operates in many kinds of complex systems, where it drives emergence and self-organisation. What is more, the analysis uncovers hitherto unstudied deep similarities and common underlying processes between different complex systems. To further understand the theoretical concepts of the DPE framework, I apply DPE in studies of mechanisms behind particular emergent properties in several types of complex systems: Seeking to better understand the emergence of novelty and diversity in ecosystems, I develop and study an individual-based simulation model of adaptive radiation (speciation) in landscapes. Simulation results imply that recurrent external disturbances facilitate perpetual novelty and diversity in landscape populations through two complementary mechanisms: One mechanism constitutes recurrent DPE phase changes in landscape connectivity on several levels. The other mechanism is alteration of the environment in disturbed areas leading to modified selection regimes. As a result of the simulation studies of landscape evolution, I develop a new genetic model that combines the advantages of two existing genetic models. The new model allows individual-based simulation studies of genetics on holey fineness landscapes (HFLs). Such fitness landscapes result from biochemical constraints to genetic viability and have previously only been studied analytically. Simulation studies of reproductive isolation uncover that when HFLs are considered, common predictions about maintenance of reproductive isolation in migrating populations change. Results also show that HFL-genetics can facilitate the emergence of stable hybrid populations, and the evolution of social selection though reinforcement. Continuing to study and apply DPE, I investigate how DPE processes can lead to the emergence of important network topologies. Using simulations models, I demonstrate two possible mechanisms behind emergent connectivity phase transitions without facilitation by external stimuli. A study of social network models reveals simple mechanisms that lead to structures typical of some real social networks and points towards general principles for emergence of important topologies such as modularity. A study of a network model of co-operations in markets reveals further mechanisms behind the emergence of complex and hierarchical modularity. Generative models for scale-free networks, that are ubiquitous in many natural systems, are well known, however, such models apply to growing networks. I propose and examine a generative model for scale-free topologies that can account for some scale-free networks of constant size found in nature. A wider context for DPE as a framework for reasoning about complexity is provided by examining the relationship between DPE and other established concepts such as Self-Organised Criticality and the Adaptive Cycle. In conclusion, DPE complements other established theories. In general, network-theoretical approaches, such as DPE, are powerful paradigms in understanding complexity. This thesis shows that recurrent changes in connectivity of component interaction networks constitute a broad mechanism for emergence and self-organisation in complex systems, and demonstrates this mechanism in several specific biological and socio-economic systems.

An RDF model is similar to a directed labeled graph (DLG) [Lassila and Swick, 1999].However, it differs from a classical DLG since its definition allows for multiple edges between

Software is pervading our society more and more and is becoming increasingly complex. At the same time, software quality demands remain at the same, high level. Model-driven engineering (MDE) is a software engineering paradigm that aims at dealing with this increasing software complexity and improving productivity and quality. Models play a pivotal role in MDE. The purpose of using models is to raise the level of abstraction at which software is developed to a level where concepts of the domain in which the software has to be applied, i.e., the target domain, can be expressed e??ectively. For that purpose, domain-speci??c languages (DSLs) are employed. A DSL is a language with a narrow focus, i.e., it is aimed at providing abstractions speci??c to the target domain. This makes that the application of models developed using DSLs is typically restricted to describing concepts existing in that target domain. Reuse of models such that they can be applied for di??erent purposes, e.g., analysis and code generation, is one of the challenges that should be solved by applying MDE. Therefore, model transformations are typically applied to transform domain-speci??c models to other (equivalent) models suitable for di??erent purposes. A model transformation is a mapping from a set of source models to a set of target models de??ned as a set of transformation rules. MDE is gradually being adopted by industry. Since MDE is becoming more and more important, model transformations are becoming more prominent as well. Model transformations are in many ways similar to traditional software artifacts. Therefore, they need to adhere to similar quality standards as well. The central research question discoursed in this thesis is therefore as follows. How can the quality of model transformations be assessed and improved, in particular with respect to development and maintenance? Recall that model transformations facilitate reuse of models in a software development process. We have developed a model transformation that enables reuse of analysis models for code generation. The semantic domains of the source and target language of this model transformation are so far apart that straightforward transformation is impossible, i.e., a semantic gap has to be bridged. To deal with model transformations that have to bridge a semantic gap, the semantics of the source and target language as well as possible additional requirements should be well understood. When bridging a semantic gap is not straightforward, we recommend to address a simpli??ed version of the source metamodel ??rst. Finally, the requirements on the transformation may, if possible, be relaxed to enable automated model transformation. Model transformations that need to transform between models in di??erent semantic domains are expected to be more complex than those that merely transform syntax. The complexity of a model transformation has consequences for its quality. Quality, in general, is a subjective concept. Therefore, quality can be de??ned in di??erent ways. We de??ned it in the context of model transformation. A model transformation can either be considered as a transformation de??nition or as the process of transforming a source model to a target model. Accordingly, model transformation quality can be de??ned in two di??erent ways. The quality of the de??nition is referred to as its internal quality. The quality of the process of transforming a source model to a target model is referred to as its external quality. There are also two ways to assess the quality of a model transformation (both internal and external). It can be assessed directly, i.e., by performing measurements on the transformation de??nition, or indirectly, i.e., by performing measurements in the environment of the model transformation. We mainly focused on direct assessment of internal quality. However, we also addressed external quality and indirect assessment. Given this de??nition of quality in the context of model transformations, techniques can be developed to assess it. Software metrics have been proposed for measuring various kinds of software artifacts. However, hardly any research has been performed on applying metrics for assessing the quality of model transformations. For four model transformation formalisms with di??fferent characteristics, viz., for ASF+SDF, ATL, Xtend, and QVTO, we de??ned sets of metrics for measuring model transformations developed with these formalisms. While these metric sets can be used to indicate bad smells in the code of model transformations, they cannot be used for assessing quality yet. A relation has to be established between the metric sets and attributes of model transformation quality. For two of the aforementioned metric sets, viz., the ones for ASF+SDF and for ATL, we conducted an empirical study aiming at establishing such a relation. From these empirical studies we learned what metrics serve as predictors for di??erent quality attributes of model transformations. Metrics can be used to quickly acquire insights into the characteristics of a model transformation. These insights enable increasing the overall quality of model transformations and thereby also their maintainability. To support maintenance, and also development in a traditional software engineering process, visualization techniques are often employed. For model transformations this appears as a feasible approach as well. Currently, however, there are few visualization techniques available tailored towards analyzing model transformations. One of the most time-consuming processes during software maintenance is acquiring understanding of the software. We expect that this holds for model transformations as well. Therefore, we presented two complementary visualization techniques for facilitating model transformation comprehension. The ??rst-technique is aimed at visualizing the dependencies between the components of a model transformation. The second technique is aimed at analyzing the coverage of the source and target metamodels by a model transformation. The development of the metric sets, and in particular the empirical studies, have led to insights considering the development of model transformations. Also, the proposed visualization techniques are aimed at facilitating the development of model transformations. We applied the insights acquired from the development of the metric sets as well as the visualization techniques in the development of a chain of model transformations that bridges a number of semantic gaps. We chose to solve this transformational problem not with one model transformation, but with a number of smaller model transformations. This should lead to smaller transformations, which are more understandable. The language on which the model transformations are de??ned, was subject to evolution. In particular the coverage visualization proved to be bene??cial for the co-evolution of the model transformations. Summarizing, we de??ned quality in the context of model transformations and addressed the necessity for a methodology to assess it. Therefore, we de??ned metric sets and performed empirical studies to validate whether they serve as predictors for model transformation quality. We also proposed a number of visualizations to increase model transformation comprehension. The acquired insights from developing the metric sets and the empirical studies, as well as the visualization tools, proved to be bene??cial for developing model transformations.

Understand-Compute-Adapt: Neural Networks for Intelligent Agents

Hysteretic and nonlinear dielectric behaviour in ferroelectric ceramics has been of interest since 1950s, when these materials found application in various electronic devices. Presently, these phenomena concern with important areas of science, technology and engineering. In particular, nonlinearity and hysteresis are the key factors in performance, precision and accuracy of modern devices. Many theoretical and experimental studies have been aimed at understanding the origins of hysteresis and nonlinearity in ferroelectrics. Nowadays, there are several models that describe major contributions to nonlinearity and hysteresis on phenomenological, microscopical or statistical levels. These models have a limited area of applicability due to the complexity of physical processes occurring in real materials. Empirically, hysteresis and nonlinearity in ferroelectrics can be controlled by softening and hardening of the material. This is the case of most widely used ferroelectric, lead zirconate titanate (PZT). The soft compositions possess large electro-mechanical coefficients but also large hysteresis and nonlinearity while the opposite is true for the hard compositions. After fifty years since introduction of these materials, the mechanisms of softening and hardening remain poorly understood. The present study is aimed at a better understanding of the processes leading to hardening and softening of Pb(Zr,Ti)O3 ceramics in order to verify the key principles required for a more universal physical model of hysteresis and nonlinearity. Based on the present state of knowledge, such model should consider domain wall contribution to nonlinear and hysteretic polarization response and at the same time account for hardening and softening of the ferroelectric. For this purpose the well known lead zirconate titanate (PZT) ceramics doped with various concentrations of niobium (soft materials) or iron (hard materials) are chosen as a prototype of the ferroelectric system. The starting hypothesis of the thesis' approach is that the softening and hardening are a result of electrostatic arrangement of charged defects in the ceramic bulk: the hard materials are characterized by the ordered and the soft by disordered defects. The thesis then develops in detail the idea that hardening-softening transitions in a ferroelectric system may occur under the influence of (i) dopants, depending on their type and concentration, (ii) a cyclically applied electric field, (iii) a thermal treatment, and (iv) time. The transition from microscopic order to microscopic disorder is confirmed experimentally using carefully analyzed phenomenological parameters of the macroscopic hysteresis and nonlinearity. Among the nonlinear and hysteretic parameters characterizing the polarization response of a ferroelectric material, some (e.g., third harmonic of polarization) are shown to be particularly sensitive to the softness and hardness of ferroelectric system and thus may serve as the characteristics of ferroelectric hardening-softening transitions. Contribution of domain walls to hysteresis and nonlinearity is analyzed in terms of domain wall energy potential and degree of ordering of pinning centres. It is shown that two existing models characterizing hard (V-potential) and soft (random potential) materials are ideal, limiting cases and that some real materials are described by an intermediary case, which can evolve with time and under influence of external factors. The dielectric characterization performed at wide range of frequencies has revealed an increase of the apparent frequency dispersion of the dielectric permittivity with the transition from the hard to soft state in PZT ceramics. The investigation of dielectric response over a wide temperature range has revealed the profound presence of hopping conductivity in iron doped PZT ceramics below the Curie temperatures and its absence in niobium doped PZT ceramics. The role of hopping charged species in ferroelectric hardening – softening transitions is analyzed and discussed. The thesis is organized in the following way. A brief introduction (Chapter 1) and a literature review of the theoretical description of domain wall contribution to dielectric nonlinearity and hysteresis in ferroelectrics (Chapter 2) is followed by the thesis outline and discussion of a unified model of hysteresis and nonlinearity in ferroelectrics with ordered and disordered states of domain wall pinning centres (Chapter 3). Processing of ceramics is described in Chapter 4 and mathematical and experimental background for the dielectric spectroscopy study in Chapter 5. The results and discussion of detailed experimental studies of polarization response in ferroelectric PZT ceramics under subswitching and switching conditions are given in Chapters 6 and 7. The summary of the main results and conclusions are given in the last thesis section.