This book provides a thorough introduction to the challenge of applying mathematics in real-world scenarios. Modelling tasks rarely involve well-defined categories, and they often require multidisciplinary input from mathematics, physics, computer sciences, or engineering. In keeping with this spirit of modelling, the book includes a wealth of cross-references between the chapters and frequently points to the real-world context. The book combines classical approaches to modelling with novel areas such as soft computing methods, inverse problems, and model uncertainty. Attention is also paid to the interaction between models, data and the use of mathematical software. The reader will find a broad selection of theoretical tools for practicing industrial mathematics, including the analysis of continuum models, probabilistic and discrete phenomena, and asymptotic and sensitivity analysis.
Devoted to novel optical measurement techniques that are applied both in industry and life sciences, this book contributes a fresh perspective on the development of modern optical sensors. These sensors are often essential in detecting and controlling parameters that are important for both industrial and biomedical applications. The book provides easy access for beginners wishing to gain familiarity with the innovations of modern optics.
Mismatch negativity (MMN) is the electrophysiological change-detection response of the brain. MMN is stimulated when there is any discernible change to a repetitive sequence of sound, occurring even in the absence of attention. MMN is an automatic response and causes an involuntary attentional shift, representing a function which is of vital significance. A parallel response can also be detected in the other sensory modalities- visual, somatosensory, and olfactory. MMN occurs in different species, and across the different developmental stages, from infancy to old age. Importantly, the MMN response is affected in different cognitive brain disorders, providing an index to the severity of the disorder and consequently, a guide to the effectiveness of different treatments. MMN has become extremely popular around the world for investigating a wide range of clinical populations. It is a versatile tool for studying perception, memory, and learning functions in both the healthy and dysfunctional brain. Furthermore, being elicited irrespective of attention, it is ideal for investigating inattentive participants, such as sleeping infants or patients in a coma, whose cognitive processes are otherwise hard to access. Written by pioneers and leading authorities in the subject, this book provides an introduction to MMN and its contribution within different clinical fields: developmental disorders, neurological disorders, psychiatric disorders, and aging.
This book provides a thorough introduction to the challenge of applying mathematics in real-world scenarios. Modelling tasks rarely involve well-defined categories, and they often require multidisciplinary input from mathematics, physics, computer sciences, or engineering. In keeping with this spirit of modelling, the book includes a wealth of cross-references between the chapters and frequently points to the real-world context. The book combines classical approaches to modelling with novel areas such as soft computing methods, inverse problems, and model uncertainty. Attention is also paid to the interaction between models, data and the use of mathematical software. The reader will find a broad selection of theoretical tools for practicing industrial mathematics, including the analysis of continuum models, probabilistic and discrete phenomena, and asymptotic and sensitivity analysis.
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