A General Theory of Emergence in Engineered Systems

Engineered systems are designed to satisfy specific needs and produce explainable/predictable results. But despite this intent, engineered systems don’t always do what they are designed to do once they are implemented. Some engineered systems produce properties and behaviors that are not clearly explainable or predictable by the properties of their components. This is a problem recognized in government and private sectors as having broad ranging financial and security consequences. It is also the essence of the emergence phenomena. A review of the literature reveals two significant gaps in the current body of knowledge on emergence as it pertains to engineered systems: 1) no conceptual model that reconciles conflicting aspects of emergence; and 2) no explanation of system factors and their relationships that affect the occurrence of emergence. The gaps are addressed in this dissertation through research using a methodology that incorporates rationalist inductive methods with modeling & simulation frameworks. Where other research and models of emergence focus on entity or agent behavior; the research in this dissertation takes place from a systems perspective. The focus is on system level behaviors and system factors as they pertain to the occurrence of emergent effects. Generally accepted thermodynamic principles and axioms for chemical reactions are used to develop scientific analogies for factors in engineered systems. A theory is derived consisting of six factors that are determinants in a mathematical model of a tipping point at which emergent effects will occur in engineered systems: 1) interoperability; 2) concentration of components; 3) component degrees of freedom; 4) variety of system regulators; 5) rate of information received vs transmitted by the system; and 6) relative amount of information received by the system vs a threshold for change in the system configuration. The theory and its implications are explored in simulation experiments. Other products and contributions of the research include: a) an ontology of emergence concepts; b) a unifying definition of emergence; and c) a system dynamics model of emergence in engineered systems

Fault propagation, detection and analysis in process systems

Process systems are often complicated and liable to experience faults and their effects. Faults can adversely affect the safety of the plant, its environmental impact and economic operation. As such, fault diagnosis in process systems is an active area of research and development in both academia and industry. The work reported in this thesis contributes to fault diagnosis by exploring the modelling and analysis of fault propagation and detection in process systems. This is done by posing and answering three research questions. What are the necessary ingredients of a fault diagnosis model? What information should a fault diagnosis model yield? Finally, what types of model are appropriate to fault diagnosis? To answer these questions , the assumption of the research is that the behaviour of a process system arises from the causal structure of the process system. On this basis, the research presented in this thesis develops a two-level approach to fault diagnosis based on detailed process information, and modelling and analysis techniques for representing causality. In the first instance, a qualitative approach is developed called a level 1 fusion. The level 1 fusion models the detailed causality of the system using digraphs. The level 1 fusion is a causal map of the process. Such causal maps can be searched to discover and analyse fault propagation paths through the process. By directly building on the level 1 fusion, a quantitative level 2 fusion is developed which uses a type of digraph called a Bayesian network. By associating process variables with fault variables, and using conditional probability theory, it is shown how measured effects can be used to calculate and rank the probability of candidate causes. The novel contributions are the development of a systematic approach to fault diagnosis based on modelling the chemistry, physics, and architecture of the process. It is also shown how the control and instrumentation system constrains the casualty of the process. By demonstrating how digraph models can be reversed, it is shown how both cause-to-effect and effect-to-cause analysis can be carried out. In answering the three research questions, this research shows that it is feasible to gain detailed insights into fault propagation by qualitatively modelling the physical causality of the process system. It is also shown that a qualitative fault diagnosis model can be used as the basis for a quantitative fault diagnosis modelOpen Acces

Evolutionary genomics : statistical and computational methods

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Evolutionary Genomics

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward