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Boolean Networks provide a simple yet effective approach to the analysis of many complex dynamical systems. And yet, this approach is not currently utilized as widely as possible in various fields. Whether due to lack of knowledge about Boolean Networks or lack of proper modeling software, researchers outside of mathematics may not be able to use this tool that could potentially help with solutions in their own field of knowledge. The main goal of this thesis is to explain the basics of Cellular Automaton and Boolean Networks in a way that requires little mathematical background, making the concepts of these topics accessible to many. Recognizing that visual aides and key to deeper understanding of these concepts, we provide a brief overview of some of the currently available visualization software; asw well as explore some potential additions and propose some possible modifications to these visualization approaches. Furthermore, we outline the basics of fractals and fractal dimensions, and explore how Boolean Networks may be thought of within this framework. We detail the calculation of fractal dimentions of certain Boolean Networks and examine the correlation between fractal dimension and some aspects of the network dynamics. Specifically, we explore the relationship between fractal dimensions and Wolfram's 4 classes of Elementary Cellular Automaton behavior. Our findings match and complement previous work on the topic, and are an indication that fractal dimensions are a meaningful measure of network dynamics, the implication of this being that it may be possible to quantify complex networks using simple measures. The goal is to improve communication surrounding the discourse of Boolean Networks and their utility both among non-mathematicians, and between mathematicians and others. Some applications for Boolean Networks are detailed, including suggested modeling software and use of new findings for these fields.
Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling. - Offers a clear, interdisciplinary guide to serve researchers in a variety of fields, including hazards, land surveying, remote sensing, cartography, geophysics, geology, natural resources, environment and geography - Provides an overview, methods and case studies for each application - Expresses concepts and methods at an appropriate level for both students and new users to learn by example
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'Aquatic Food Webs' provides a current synthesis of theoretical and empirical food web research. The textbook is suitable for graduate level students as well as professional researchers in community, ecosystem, and theoretical ecology, in aquatic ecology, and in conservation biology.
Introductio to bioinformatics. Overview of structural bioinformatics. Database warehousing in bioinformatics. Modeling for bioinformatics. Pattern matching for motifs. Visualization and fractal analysis of biological sequences. Microarray data analysis.
Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. Applications to biology, chemistry, linguistics, and data analysis are emphasized. The book is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science, machine learning, artificial intelligence, computational and systems biology, cognitive science, computational linguistics, and mathematical chemistry. It may also be used as a supplementary textbook in graduate-level seminars on structural graph analysis, complex networks, or network-based machine learning methods.
Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.
Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of these complex entities. Further, this book introduces a contribution to the definition of reference models for Collaborative Networks. Collaborative Networks: Reference Modeling provides valuable elements for researchers, PhD students, engineers, managers, and leading practitioners interested in collaborative systems and networked society.
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
This Java-built "Visualization Toolkit (VTK)" will enable readers to represent any set of data--medical, scientific, or financial--in 3D. Users will learn to build 3D Java applets with the VTK software on the CD-ROM. The book covers Web applications like VRML, Java, and Java3D.