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Wear is one of the main reasons mechanical components and materials become inoperable, rendering enormous costs to society over time. Estimating wear allows engineers to predict the useful life of modern mechanical elements, reduce the costs of inoperability, or obtain optimal designs (i.e. selecting proper materials, shapes, and surface finishing according to mechanical conditions and durability) to reduce the impact of wear.Wear in Advanced Engineering Applications and Materials presents recent computational and practical research studying damage and wear in advanced engineering applications and materials. As such, this book covers numerical formulations based on the finite element method (FEM) - and the boundary element method (BEM) - as well as theoretical and experimental research to predict the wear response or life-limiting failure of engineering applications.
Quantum Computing for the Brain argues that the brain is the killer application for quantum computing. No other system is as complex, as multidimensional in time and space, as dynamic, as less well-understood, as of peak interest, and as in need of three-dimensional modeling as it functions in real-life, as the brain.Quantum computing has emerged as a platform suited to contemporary data processing needs, surpassing classical computing and supercomputing. This book shows how quantum computing''s increased capacity to model classical data with quantum states and the ability to run more complex permutations of problems can be employed in neuroscience applications such as neural signaling and synaptic integration. State-of-the-art methods are discussed such as quantum machine learning, tensor networks, Born machines, quantum kernel learning, wavelet transforms, Rydberg atom arrays, ion traps, boson sampling, graph-theoretic models, quantum optical machine learning, neuromorphic architectures, spiking neural networks, quantum teleportation, and quantum walks.Quantum Computing for the Brain is a comprehensive one-stop resource for an improved understanding of the converging research frontiers of foundational physics, information theory, and neuroscience in the context of quantum computing.
Existing literature focuses on the alleged merits of the Stirling engine. These are indeed latent but, decades on, remain to be fully realised. This is despite the fact that Stirling and other closed-cycle prime-movers offer a contribution to an ultra-low carbon economy. By contrast with solar panels, the initial manufacture of Stirling engines makes no demands on scarce or exotic raw materials. Further, calculating embodied carbon per kWh favours the Stirling engine by a wide margin.However, the reader expecting to find the Stirling engine promoted as a panacea for energy problems may be surprised to find the reverse. Stirling and Thermal-Lag Engines reflects upon the fact that there is more to be gained by approaching its subject as a problem than as a solution. The Achilles heel of the Stirling engine is a low numerical value of specific work, defined as work per cycle per swept volume per unit of charge pressure and conventionally denoted Beale number NB. Measured values remain unimproved since 1818, quantified here for the first time at 2% of the NB of the modern internal combustion engine! The low figure is traced to incomplete utilisation of the working gas. Only a small percentage of the charge gas - if any - is processed through a complete cycle, i.e., between temperature extremes.The book offers ready-made tools including a simplified algorithm for particle trajectory map construction; an author-patented mechanism delivering optimised working-gas distribution; flow and heat transfer data re-acquired in context and an illustrated re-derivation of the academically respected Method of Characteristics which now copes with shock formation and flow-area discontinuities. All formulations are presented in sufficient detail to allow the reader to 'pick up and run' with them using the data offered in the book.The various strands are drawn together in a comprehensively engineered design of an internally focusing solar Stirling engine, presented in a form allowing a reader with access to basic machining facilities to construct one.The sun does not always shine. But neither will the oil always flow. This new title offers an entrée to technology appropriate to the 21st century.
Aimed at master's and Ph. D. students with a limited background in chemistry.
Artificial Intelligence and Innovation Management contributes to the ongoing debate among innovation scholars and practitioners focusing on the potential impact of Artificial Intelligence (AI) on the ways companies and organizations do business, operate and innovate. It considers AI as a source of innovation both in terms of innovation within the field of AI itself (AI innovation) and in terms of how it enables or disrupts innovation in other fields (AI-driven innovation). The book''s content is driven by several important conclusions:AI has a great potential for innovation, but this potential is associated with significant challenges.There are different views on how to implement AI in innovation management but little agreement and guidance on how to do it.AI not only has the potential to produce radically new innovations, but also to rethink innovation management in general.Deeper knowledge about the innovative impact of AI technologies would be highly relevant to both practitioners and academics within the field of Innovation Management (IM).It is therefore both necessary and timely to explore the different aspects of the relationship between AI and IM.The contributors to this book include both scholars and practitioners from multiple countries and different types of institutions. They were selected based on their ability to provide a relevant distinctive perspective on the relationship between AI and IM; the degree of their professional engagement with the field; their ability to contribute to the thematic and contextual diversity of the contributions; and their ability to provide actionable insights for both innovation scholars and practitioners.Helena Blackbright (Mälardalen University, Sweden) and Stoyan Tanev (Carleton University, Canada) are chairing the Special Interest Group on AI and IM at the International Society for Professional Innovation Management (https://www.ispim-innovation.com/).
Amongst the famous planetary inhabitants of our solar system there is an entire ecosystem of smaller, less recognised bodies in the form of comets and "minor" planets.
What is the secret of successful collective action that enables communities to maintain common-pool resources, withstand disasters and make effective decisions?
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