Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface. - Learn from 13 practical case studies using field, laboratory, and simulation data - Become knowledgeable with data science and analytics terminology relevant to subsurface characterization - Learn frameworks, concepts, and methods important for the engineer's and geoscientist's toolbox needed to support
Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization focuses on the development and application of electromagnetic measurement methodologies and their interpretation techniques for subsurface characterization. The book guides readers on how to characterize and understand materials using electromagnetic measurements, including dielectric permittivity, resistivity and conductivity measurements. This reference will be useful for subsurface engineers, petrophysicists, subsurface data analysts, geophysicists, hydrogeologists, and geoscientists who want to know how to develop tools and techniques of electromagnetic measurements and interpretation for subsurface characterization. - Includes case studies to add additional color to the presented content - Provides codes for the mechanistic modeling of multi-frequency conductivity and relative permittivity of porous geomaterials - Presents detailed descriptions of multifrequency electromagnetic data interpretation models and inversion algorithm
An Indian corporate couple’s hyper-learning journey of combining ancient wisdom and modern research. What happens when a corporate couple decides to bring their minds and efforts together to share their hyper-learning journey with the world? They embark on a magnificent adventure to distil ideas around leading more productive and healthier lives. This journey – which started as a couple’s conversation on New Years’ Eve of 2018 – turned into a national phenomenon that led Eika and Siddharth Banerjee to meet diverse experts and specialists from the fields of science, art, sports, medicine, and ancient wisdom. Eika and Siddharth’s 52RedPills is an inspiration to readers who have overscheduled and frenzied lifestyles. Written as a practical guide, this book helps you introspect and makes you eager to know more about the different walks of life. By the end of it, you will be motivated to craft your own ‘52RedPills’ journey towards a healthier, wealthier and wiser you.
Social psychiatry is a branch of psychiatry that focuses on the interpersonal and cultural context of mental disorders and mental wellbeing. This book is a comprehensive guide to social psychiatry for psychiatrists, psychologists and mental health nurses. Divided into five sections, the text begins with an overview and the basics of social psychiatry. The following sections cover social dimensions of psychiatric disorders, social interventions and therapies, and social issues and mental health. The book is presented in an easy to read format and explains both theoretical and clinical aspects of psychosocial assessment and management. The multidisciplinary text features contributions from worldwide experts, as well as diagrams and tables to enhance learning. Key points Comprehensive guide to social psychiatry Covers both theoretical and clinical aspects of psychosocial assessment and management Multidisciplinary, international author team Features diagrams and tables to enhance learning
Design, Analysis, and Manufacturing of Lightweight Composite Structures provides a thorough guide to composite materials and their applications, suitable for students of all levels, as well as those in the industry. Covering established theory as well as cutting-edge developments in the field, this book is an essential companion to anyone interested in composite materials. Discussing the mechanical properties of advanced composites and their materials, this book describes testing and evaluation, focusing on sustainability in manufacturing. Looking at how composite materials can form structural components, this book is centered around how to design and analyze these materials as appropriate to different applications. It discusses micromechanics, stiffness matrices, and numerical calculations using MATLABR, Excel, and Python. It also covers failure, applied forces, strain, and stress, alongside finite element analysis of composites. This book is suitable for students and researchers in the field of composites, mechanical design, micromechanics, mechanics of solids, and material science. It also has relevance to the automotive industry.
This book contains 50 sample papers with answers which are based on latest exam pattern given by CLAT Consortium. This books also contains previous year solved paper.
Finalist for the 2020 Lambda Literary Award in Gay Memoir/Biography A revelatory memoir about sex, oppression, and the universal struggle for justice. From his time as a child in 1960s India, Siddharth Dube knew that he was different. Reckoning with his femininity and sexuality—and his intellect—would send him on a lifelong journey of discovery: from Harvard classrooms to unsafe cruising sites; from ivory-tower think-tanks to shantytowns; from halls of power at the UN and World Bank to jail cells where sexual outcasts are brutalized. Coming of age in the earliest days of AIDS, Dube was at the frontlines when that disease made rights for gay men and for sex workers a matter of basic survival, pushing to decriminalize same-sex relations and sex work in India, both similarly outlawed under laws dating back to British colonial rule. He became a trenchant critic of the United States’ imposition of its cruel anti-prostitution policies on developing countries—an effort legitimized by leading American feminists and would-be do-gooders—warning that this was a 21st century replay of the moralistic Victorian-era campaigns that had spawned endless persecution of countless women, men, and trans individuals the world over. Profound, ferocious, and luminously written, An Indefinite Sentence is both a personal and political journey, weaving Dube’s own quest for love and self-respect with unforgettable portrayals of the struggles of some of the world’s most oppressed people, those reviled and cast out for their sexuality. Informed by a lifetime of scholarship and introspection, it is essential reading on the global debates over sexuality, gender expression, and of securing human rights and social justice in a world distorted by inequality and right-wing ascendancy.
Search structures support the fundamental data storage primitives on key-value pairs: insert a pair, delete by key, search by key, and update the value associated with a key. Concurrent search structures are parallel algorithms to speed access to search structures on multicore and distributed servers. These sophisticated algorithms perform fine-grained synchronization between threads, making them notoriously difficult to design correctly. Indeed, bugs have been found both in actual implementations and in the designs proposed by experts in peer-reviewed publications. The rapid development and deployment of these concurrent algorithms has resulted in a rift between the algorithms that can be verified by the state-of-the-art techniques and those being developed and used today. The goal of this book is to show how to bridge this gap in order to bring the certified safety of formal verification to high-performance concurrent search structures. Similar techniques and frameworks can be applied to concurrent graph and network algorithms beyond search structures.
Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization focuses on the development and application of electromagnetic measurement methodologies and their interpretation techniques for subsurface characterization. The book guides readers on how to characterize and understand materials using electromagnetic measurements, including dielectric permittivity, resistivity and conductivity measurements. This reference will be useful for subsurface engineers, petrophysicists, subsurface data analysts, geophysicists, hydrogeologists, and geoscientists who want to know how to develop tools and techniques of electromagnetic measurements and interpretation for subsurface characterization. - Includes case studies to add additional color to the presented content - Provides codes for the mechanistic modeling of multi-frequency conductivity and relative permittivity of porous geomaterials - Presents detailed descriptions of multifrequency electromagnetic data interpretation models and inversion algorithm
Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface. - Learn from 13 practical case studies using field, laboratory, and simulation data - Become knowledgeable with data science and analytics terminology relevant to subsurface characterization - Learn frameworks, concepts, and methods important for the engineer's and geoscientist's toolbox needed to support
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