4,259 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    Application of multi-scale computational techniques to complex materials systems

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    The applications of computational materials science are ever-increasing, connecting fields far beyond traditional subfields in materials science. This dissertation demonstrates the broad scope of multi-scale computational techniques by investigating multiple unrelated complex material systems, namely scandate thermionic cathodes and the metallic foam component of micrometeoroid and orbital debris (MMOD) shielding. Sc-containing scandate cathodes have been widely reported to exhibit superior properties compared to previous thermionic cathodes; however, knowledge of their precise operating mechanism remains elusive. Here, quantum mechanical calculations were utilized to map the phase space of stable, highly-faceted and chemically-complex W nanoparticles, accounting for both finite temperature and chemical environment. The precise processing conditions required to form the characteristic W nanoparticle observed experimentally were then distilled. Metallic foams, a central component of MMOD shielding, also represent a highly-complex materials system, albeit at a far higher length scale than W nanoparticles. The non-periodic, randomly-oriented constituent ligaments of metallic foams and similar materials create a significant variability in properties that is generally difficult to model. Rather than homogenizing the material such that its unique characteristic structural features are neglected, here, a stochastic modeling approach is applied that integrates complex geometric structure and utilizes continuum calculations to predict the resulting probabilistic distributions of elastic properties. Though different in many aspects, scandate cathodes and metallic foams are united by complexity that is impractical, even dangerous, to ignore and well-suited to exploration with multi-scale computational methods

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Economic and Social Consequences of the COVID-19 Pandemic in Energy Sector

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    The purpose of the Special Issue was to collect the results of research and experience on the consequences of the COVID-19 pandemic for the energy sector and the energy market, broadly understood, that were visible after a year. In particular, the impact of COVID-19 on the energy sector in the EU, including Poland, and the US was examined. The topics concerned various issues, e.g., the situation of energy companies, including those listed on the stock exchange, mining companies, and those dealing with renewable energy. The topics related to the development of electromobility, managerial competences, energy expenditure of local government units, sustainable development of energy, and energy poverty during a pandemic were also discussed

    Implementation of a real time Hough transform using FPGA technology

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    This thesis is concerned with the modelling, design and implementation of efficient architectures for performing the Hough Transform (HT) on mega-pixel resolution real-time images using Field Programmable Gate Array (FPGA) technology. Although the HT has been around for many years and a number of algorithms have been developed it still remains a significant bottleneck in many image processing applications. Even though, the basic idea of the HT is to locate curves in an image that can be parameterized: e.g. straight lines, polynomials or circles, in a suitable parameter space, the research presented in this thesis will focus only on location of straight lines on binary images. The HT algorithm uses an accumulator array (accumulator bins) to detect the existence of a straight line on an image. As the image needs to be binarized, a novel generic synchronization circuit for windowing operations was designed to perform edge detection. An edge detection method of special interest, the canny method, is used and the design and implementation of it in hardware is achieved in this thesis. As each image pixel can be implemented independently, parallel processing can be performed. However, the main disadvantage of the HT is the large storage and computational requirements. This thesis presents new and state-of-the-art hardware implementations for the minimization of the computational cost, using the Hybrid-Logarithmic Number System (Hybrid-LNS) for calculating the HT for fixed bit-width architectures. It is shown that using the Hybrid-LNS the computational cost is minimized, while the precision of the HT algorithm is maintained. Advances in FPGA technology now make it possible to implement functions as the HT in reconfigurable fabrics. Methods for storing large arrays on FPGA’s are presented, where data from a 1024 x 1024 pixel camera at a rate of up to 25 frames per second are processed

    Noncommutative Geometry and Gauge theories on AF algebras

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    Non-commutative geometry (NCG) is a mathematical discipline developed in the 1990s by Alain Connes. It is presented as a new generalization of usual geometry, both encompassing and going beyond the Riemannian framework, within a purely algebraic formalism. Like Riemannian geometry, NCG also has links with physics. Indeed, NCG provided a powerful framework for the reformulation of the Standard Model of Particle Physics (SMPP), taking into account General Relativity, in a single "geometric" representation, based on Non-Commutative Gauge Theories (NCGFT). Moreover, this accomplishment provides a convenient framework to study various possibilities to go beyond the SMPP, such as Grand Unified Theories (GUTs). This thesis intends to show an elegant method recently developed by Thierry Masson and myself, which proposes a general scheme to elaborate GUTs in the framework of NCGFTs. This concerns the study of NCGFTs based on approximately finite C∗C^*-algebras (AF-algebras), using either derivations of the algebra or spectral triples to build up the underlying differential structure of the Gauge Theory. The inductive sequence defining the AF-algebra is used to allow the construction of a sequence of NCGFTs of Yang-Mills Higgs types, so that the rank n+1n+1 can represent a grand unified theory of the rank nn. The main advantage of this framework is that it controls, using appropriate conditions, the interaction of the degrees of freedom along the inductive sequence on the AF algebra. This suggests a way to obtain GUT-like models while offering many directions of theoretical investigation to go beyond the SMPP

    Applications of Molecular Dynamics simulations for biomolecular systems and improvements to density-based clustering in the analysis

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    Molecular Dynamics simulations provide a powerful tool to study biomolecular systems with atomistic detail. The key to better understand the function and behaviour of these molecules can often be found in their structural variability. Simulations can help to expose this information that is otherwise experimentally hard or impossible to attain. This work covers two application examples for which a sampling and a characterisation of the conformational ensemble could reveal the structural basis to answer a topical research question. For the fungal toxin phalloidin—a small bicyclic peptide—observed product ratios in different cyclisation reactions could be rationalised by assessing the conformational pre-organisation of precursor fragments. For the C-type lectin receptor langerin, conformational changes induced by different side-chain protonations could deliver an explanation of the pH-dependency in the protein’s calcium-binding. The investigations were accompanied by the continued development of a density-based clustering protocol into a respective software package, which is generally well applicable for the use case of extracting conformational states from Molecular Dynamics data

    Learning to Select SAT Encodings for Pseudo-Boolean and Linear Integer Constraints

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    Many constraint satisfaction and optimisation problems can be solved effectively by encoding them as instances of the Boolean Satisfiability problem (SAT). However, even the simplest types of constraints have many encodings in the literature with widely varying performance, and the problem of selecting suitable encodings for a given problem instance is not trivial. We explore the problem of selecting encodings for pseudo-Boolean and linear constraints using a supervised machine learning approach. We show that it is possible to select encodings effectively using a standard set of features for constraint problems; however we obtain better performance with a new set of features specifically designed for the pseudo-Boolean and linear constraints. In fact, we achieve good results when selecting encodings for unseen problem classes. Our results compare favourably to AutoFolio when using the same feature set. We discuss the relative importance of instance features to the task of selecting the best encodings, and compare several variations of the machine learning method.Comment: 24 pages, 10 figures, submitted to Constraints Journal (Springer

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Low Power Memory/Memristor Devices and Systems

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    This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within
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