This book provides a complete analysis of molecular communications systems from the paradigm of TCP/IP network stack, and it exploits network theories (e.g. independent functions of a layer into a stack, addressing, flow control, error control, and traffic control) and applies them to biological systems. The authors show how these models can be applied in different areas such as industry, medicine, engineering, biochemistry, biotechnology, computer sciences, and other disciplines. The authors then explain how it is possible to obtain enormous benefits from these practices when applied in medicine, such as enhancing current treatment of diseases and reducing the side effects of drugs and improving the quality of treatment for patients. The authors show how molecular communications systems, in contrast to existing telecommunication paradigms, use molecules as information carriers. They show how sender biological nanomachines (bio-nano machines) encode data on molecules (signal molecules) and release the molecules into the environment. They go on to explain how the molecules then travel through the environment to reach the receiver bio-nano machines, where they biochemically react with the molecules to decipher information. This book is relevant to those studying telecommunications and biomedical students, engineers, masters, PhDs, and researchers.
Special Paper 498 contains 12 new scientific papers, assembled as part of an NSF-sponsored workshop in 2011. The work highlights study of persistently active volcanoes and their hazards, mostly in Central America. Such volcanoes are termed "open vents" by volcanologists, and they offer the chance to study active processes. Insight into how volcanoes work and how hazards might be mitigated are the goals of the work. Overall, the volume presents insight into hazards infrastructure collaborations and development for geoscientists and students.
Inverse problems are found in many applications, such as medical imaging, engineering, astronomy, and geophysics, among others. To solve an inverse problem is to recover an object from noisy, usually indirect observations. Solutions to inverse problems are subject to many potential sources of error introduced by approximate mathematical models, regularization methods, numerical approximations for efficient computations, noisy data, and limitations in the number of observations; thus it is important to include an assessment of the uncertainties as part of the solution. Such assessment is interdisciplinary by nature, as it requires, in addition to knowledge of the particular application, methods from applied mathematics, probability, and statistics. This book bridges applied mathematics and statistics by providing a basic introduction to probability and statistics for uncertainty quantification in the context of inverse problems, as well as an introduction to statistical regularization of inverse problems. The author covers basic statistical inference, introduces the framework of ill-posed inverse problems, and explains statistical questions that arise in their applications. An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems?includes many examples that explain techniques which are useful to address general problems arising in uncertainty quantification, Bayesian and non-Bayesian statistical methods and discussions of their complementary roles, and analysis of a real data set to illustrate the methodology covered throughout the book.
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