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MARS Applications in Geotechnical Engineering Systems: Multi-Dimension with Big Data
by Wengang ZhangThis book presents the application of a comparatively simple nonparametric regression algorithm, known as the multivariate adaptive regression splines (MARS) surrogate model, which can be used to approximate the relationship between the inputs and outputs, and express that relationship mathematically. The book first describes the MARS algorithm, then highlights a number of geotechnical applications with multivariate big data sets to explore the approach’s generalization capabilities and accuracy. As such, it offers a valuable resource for all geotechnical researchers, engineers, and general readers interested in big data analysis.
MATLAB with Applications to Engineering, Physics and Finance
by David Baez-LopezMaster the tools of MATLAB through hands-on examplesShows How to Solve Math Problems Using MATLABThe mathematical software MATLAB integrates computation, visualization, and programming to produce a powerful tool for a number of different tasks in mathematics. Focusing on the MATLAB toolboxes especially dedicated to science, finance, and engineering
MATLAB® Recipes for Earth Sciences
by Martin H. TrauthMATLAB® is used for a wide range of applications in geosciences, such as image processing in remote sensing, the generation and processing of digital elevation models and the analysis of time series. This book introduces methods of data analysis in geosciences using MATLAB, such as basic statistics for univariate, bivariate and multivariate datasets, time-series analysis, signal processing, the analysis of spatial and directional data and image analysis. The revised and updated Fourth Edition includes sixteen new sections and most chapters have greatly been expanded so that they now include a step by step discussion of all methods before demonstrating the methods with MATLAB functions. New sections include: Array Manipulation; Control Flow; Creating Graphical User Interfaces; Hypothesis Testing; Kolmogorov-Smirnov Test; Mann-Whitney Test; Ansari-Bradley Test; Detecting Abrupt Transitions in Time Series; Exporting 3D Graphics to Create Interactive Documents; Importing, Processing and Exporting LANDSAT Images; Importing and Georeferencing TERRA ASTER Images; Processing and Exporting EO-1 Hyperion Images; Image Enhancement; Correction and Rectification; Shape-Based Object Detection in Images; Discriminant Analysis; and Multiple Linear Regression. The text includes numerous examples demonstrating how MATLAB can be used on data sets from earth sciences. The book's supplementary electronic material (available online through Springer Link) includes recipes that include all the MATLAB commands featured in the book and the example data.
MATLAB® Recipes for Earth Sciences (Springer Textbooks in Earth Sciences, Geography and Environment)
by Martin H. TrauthMATLAB® is used in a wide range of geoscientific applications, such as for image processing in remote sensing, for generating and processing digital elevation models, and for analyzing time series. This book introduces methods of data analysis in the earth sciences using MATLAB, such as basic statistics for univariate, bivariate, and multivariate data sets, time series analysis, signal processing, spatial and directional data analysis, and image analysis. The text includes numerous examples demonstrating how MATLAB can be used on data sets from the earth sciences. The supplementary electronic material (available online through Springer Link) contains recipes that include all the MATLAB commands featured in the book and example data.
MATLAB® Recipes for Earth Sciences: Matlab® And Design Recipes For Earth Sciences (Springer Textbooks in Earth Sciences, Geography and Environment)
by Martin H. TrauthMATLAB® is used in a wide range of geoscientific applications, e.g. for image processing in remote sensing, for creating and processing digital elevation models, and for analyzing time series. This book introduces readers to MATLAB-based data analysis methods used in the geosciences, including basic statistics for univariate, bivariate and multivariate datasets, time-series analysis, signal processing, the analysis of spatial and directional data, and image analysis. The revised and updated Fifth Edition includes seven new sections, and the majority of the chapters have been rewritten and significantly expanded. New sections include error analysis, the problem of classical linear regression of log-transformed data, aligning stratigraphic sequences, the Normalized Difference Vegetation Index, Aitchison’s log-ratio transformation, graphical representation of spherical data, and statistics of spherical data. The book also includes numerous examples demonstrating how MATLAB can be used on datasets from the earth sciences. The supplementary electronic material (available online through SpringerLink) contains recipes that include all the MATLAB commands featured in the book and the sample data.
MATLAB®-Rezepte für die Geowissenschaften
by Martin H. TrauthTrauth, Martin H.MATLAB® - Rezepte für Geowissenschaften, 1. deutschsprachige Auflage, basierend auf der korrigierten 5. englischsprachigen AuflageMATLAB® wird in einer Vielzahl von geowissenschaftlichen Anwendungen eingesetzt, z.B. zur Bildverarbeitung in der Fernerkundung, zur Erzeugung und Verarbeitung digitaler Höhenmodelle und zur Analyse von Zeitreihen. Dieses Buch führt in Methoden der Datenanalyse in den Geowissenschaften mit MATLAB ein, wie z.B. grundlegende Statistik für univariate, bivariate und multivariate Datensätze, Zeitreihenanalyse, Signalverarbeitung, die Analyse räumlicher und gerichteter Daten und Bildanalyse. Der Text enthält zahlreiche Beispiele, die zeigen, wie MATLAB auf Datensätze aus den Geowissenschaften angewendet werden kann. Das ergänzende elektronische Material des Buches (online verfügbar über Springer Link) enthält Rezepte, die alle im Buch vorgestellten MATLAB-Befehle und die Beispieldaten enthalten. Das Buch soll Student:innen, Doktorand:innen, Postdoktorand:innen und Fachleuten helfen, schnelle Lösungen für gängige Datenanalyseprobleme in den Geowissenschaften zu finden.SystemanforderungenBenutzer:innen dieses Buches benötigen die MATLAB®-Software, die für Windows, macOS und Linux verfügbar ist. Die M-Files und Beispieldaten, die online über Springer Link verfügbar sind, sollten auf allen Plattformen laufen, ohne dass eine Modifikation erforderlich ist. Für diese Ausgabe haben wir MATLAB Version 9.11 (Release 2021b), die Bioinformatics Toolbox Version 4.15.2, die Image Processing Toolbox Version 11.4, die Mapping Toolbox Version 5.2, die Signal Processing Toolbox Version 8.7, Simulink 3D Animation Version 9.3, die Statistics and Machine Learning Toolbox Version 12.2 und die Wavelet Toolbox Version 6.0 verwendet.
MICRONATIONS
by Chad Thompson Kathy CeceriFor anyone who's ever dreamed of ruling over their own empire, here's your chance! Micronations are imaginary countries that have a lot of the same things as real ones: laws, customs, history, and their own flags, coins, and postage stamps. Micronations: Invent Your Own Country and Culture takes readers step-by-step to create their own unique realm, using examples from real nations, micronations, and fictional lands. What makes a country a country? What symbols and systems define a country and help it function? Learn about geography and government, technology and the environment, art and culture, and the literary device of "world-building" used in works like The Hobbit and Harry Potter.Activities show readers how to create authentic-looking artifacts and documents such as maps, currency, passports, a declaration of independence, and a constitution. Kids get to invent their own language, music, games, clothing, food, and holidays to fit their micronation's tradition. Whether they create a land of time travel where every city exists in a different epoch or an underwater monarchy (motto: "Bubbles, bubbles and more bubbles") whose chief export is fish, Micronations: Invent Your Own Country and Culture will engage kids' imagination and teach make-believe rulers how the real world works.
MIMO-OFDM Systems with Diversity Technique: PAPR Reduction (SpringerBriefs in Applied Sciences and Technology)
by Azlina Idris CEng Aidatul Julia Jabar Wan Norsyafizan MuhamadThis book addresses the challenges in wireless communication, particularly focusing on the high Peak-to-Average Power Ratio (PAPR) in MIMO-OFDM systems. As the demand for high-speed, reliable wireless communication continues to evolve, this book provides an in-depth exploration of Orthogonal Frequency Division Multiplexing (OFDM) and Multiple Input Multiple Output (MIMO) technologies, which are essential for modern telecommunications. Readers will find particular interest in the innovative PAPR reduction techniques discussed, which are categorized into signal distortion, coding, and probabilistic methods. These techniques not only aim to mitigate the PAPR issue but also enhance the overall efficiency of wireless systems. The book includes various illustrations, tables, and a structured improving approach, making complex concepts accessible and engaging. The main benefit for readers is the practical application of these techniques, which can lead to improved performance in wireless networks, particularly for telecommunications companies in search of optimizing the systems. This book serves as a valuable resource for researchers and practitioners, providing insights into the latest advancements in PAPR reduction and its implications for future wireless communication technologies.
MISSISSIPPI RIVER WATER QUALITY AND THE CLEAN WATER ACT: Progress, Challenges, and Opportunities
by National Research Council of the National AcademiesThe Mississippi River is, in many ways, the nation's best known and most important river system. Mississippi River water quality is of paramount importance for sustaining the many uses of the river including drinking water, recreational and commercial activities, and support for the river's ecosystems and the environmental goods and services they provide. The Clean Water Act, passed by Congress in 1972, is the cornerstone of surface water quality protection in the United States, employing regulatory and nonregulatory measures designed to reduce direct pollutant discharges into waterways. The Clean Water Act has reduced much pollution in the Mississippi River from "point sources" such as industries and water treatment plants, but problems stemming from urban runoff, agriculture, and other "non-point sources" have proven more difficult to address. This book concludes that too little coordination among the 10 states along the river has left the Mississippi River an "orphan" from a water quality monitoring and assessment perspective. Stronger leadership from the U.S. Environmental Protection Agency (EPA) is needed to address these problems. Specifically, the EPA should establish a water quality data-sharing system for the length of the river, and work with the states to establish and achieve water quality standards. The Mississippi River corridor states also should be more proactive and cooperative in their water quality programs. For this effort, the EPA and the Mississippi River states should draw upon the lengthy experience of federal-interstate cooperation in managing water quality in the Chesapeake Bay.
MOUNTAIN GEOMORPHOLOGY
by Olav Slaymaker Phil OwensMountains represent one of the most inspiring and attractive natural features on the surface of the earth. Visually, they dominate the landscape. However, the increasing realization of the fragility of mountain areas because of changes in land use, management and climate, combined with an understanding of their importance for water and other natural resources, has resulted in a growing interest in mountain environments in recent years. Hence, Mountain Geomorphology represents a timely and unique contribution to the literature. Written by a team of international experts, this book is divided into three sections, which consider historical, functional and applied mountain geomorphology from both global and local perspectives. Historical mountain geomorphology focuses on the evolution of landforms. Functional mountain geomorphology emphasises the interaction between processes and landforms, while applied mountain geomorphology concerns the interrelationships between geomorphological processes and society. Mountain Geomorphology is a valuable source of information for students studying mountain geomorphology, and also for academics and research scientists interested in mountain environments.
MRE vol 35 num 1
by The University of Chicago PressThis is volume 35 issue 1 of Marine Resource Economics. Marine Resource Economics (MRE) publishes creative and scholarly economic analyses of a range of issues related to natural resource use in the global marine environment. The scope of the journal includes conceptual and empirical investigations aimed at addressing real-world ocean and coastal policy problems. MRE is an outlet for early results and imaginative new thinking on emerging topics in the marine environment, as well as rigorous theoretical and empirical analyses of questions that have long interested economists who study the oceans. A pluralistic forum for researchers and policy makers, MRE encourages challenges to conventional paradigms and perspectives. The journal is comprised of five sections: Articles, Perspectives, Case Studies, Systematic Reviews, and Book Reviews.
MULTIDISCIPLINARY APPROACHES FOR SUSTAINABLE DEVELOPMENT: International Conference on MULTIDISCIPLINARY APPROACHES FOR SUSTAINABLE DEVELOPMENT IN SCIENCE & TECHNOLOGY
by Monica Sharma Shilpi Birla Anshuman Tripathi Jagrati Sahariya Mamta SoniIn a world where the pace of technological advancement continues to accelerate, the imperative to ensure sustainable development has never been more pressing to address the same, the 1st International Conference on Multidisciplinary Approaches for Sustainable Development in Science & Technology (MASDST - 2024), took place at Manipal University Jaipur, Rajasthan, India, from 28th to 29th March 2024. Embracing the spirit of innovation and collaboration, this conference marks a significant milestone in the pursuit of sustainable solutions for our global challenges.
Macdonald Polynomials: Commuting Family of q-Difference Operators and Their Joint Eigenfunctions (SpringerBriefs in Mathematical Physics #50)
by Masatoshi NoumiThis book is a volume of the Springer Briefs in Mathematical Physics and serves as an introductory textbook on the theory of Macdonald polynomials. It is based on a series of online lectures given by the author at the Royal Institute of Technology (KTH), Stockholm, in February and March 2021. Macdonald polynomials are a class of symmetric orthogonal polynomials in many variables. They include important classes of special functions such as Schur functions and Hall–Littlewood polynomials and play important roles in various fields of mathematics and mathematical physics. After an overview of Schur functions, the author introduces Macdonald polynomials (of type A, in the GLn version) as eigenfunctions of a q-difference operator, called the Macdonald–Ruijsenaars operator, in the ring of symmetric polynomials. Starting from this definition, various remarkable properties of Macdonald polynomials are explained, such as orthogonality, evaluation formulas, and self-duality, with emphasis on the roles of commuting q-difference operators. The author also explains how Macdonald polynomials are formulated in the framework of affine Hecke algebras and q-Dunkl operators.
Machine Learning Meets Quantum Physics (Lecture Notes in Physics #968)
by Klaus-Robert Müller Kristof T. Schütt Stefan Chmiela O. Anatole von Lilienfeld Alexandre Tkatchenko Koji TsudaDesigning molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Machine Learning and Flow Assurance in Oil and Gas Production
by Bhajan Lal Cornelius Borecho Bavoh Jai Krishna Sahith SayaniThis book is useful to flow assurance engineers, students, and industries who wish to be flow assurance authorities in the twenty-first-century oil and gas industry.The use of digital or artificial intelligence methods in flow assurance has increased recently to achieve fast results without any thorough training effectively. Generally, flow assurance covers all risks associated with maintaining the flow of oil and gas during any stage in the petroleum industry. Flow assurance in the oil and gas industry covers the anticipation, limitation, and/or prevention of hydrates, wax, asphaltenes, scale, and corrosion during operation. Flow assurance challenges mostly lead to stoppage of production or plugs, damage to pipelines or production facilities, economic losses, and in severe cases blowouts and loss of human lives. A combination of several chemical and non-chemical techniques is mostly used to prevent flow assurance issues in the industry. However, the use of models to anticipate, limit, and/or prevent flow assurance problems is recommended as the best and most suitable practice. The existing proposed flow assurance models on hydrates, wax, asphaltenes, scale, and corrosion management are challenged with accuracy and precision. They are not also limited by several parametric assumptions. Recently, machine learning methods have gained much attention as best practices for predicting flow assurance issues. Examples of these machine learning models include conventional approaches such as artificial neural network, support vector machine (SVM), least square support vector machine (LSSVM), random forest (RF), and hybrid models. The use of machine learning in flow assurance is growing, and thus, relevant knowledge and guidelines on their application methods and effectiveness are needed for academic, industrial, and research purposes.In this book, the authors focus on the use and abilities of various machine learning methods in flow assurance. Initially, basic definitions and use of machine learning in flow assurance are discussed in a broader scope within the oil and gas industry. The rest of the chapters discuss the use of machine learning in various flow assurance areas such as hydrates, wax, asphaltenes, scale, and corrosion. Also, the use of machine learning in practical field applications is discussed to understand the practical use of machine learning in flow assurance.
Machine Learning for Drone-Enabled IoT Networks: Opportunities, Developments, and Trends (Advances in Science, Technology & Innovation)
by Sara Khalifa Jahan Hassan Prasant MisraThis book aims to explore the latest developments, challenges, and opportunities in the application of machine learning techniques to enhance the performance and efficiency of IoT networks assisted by aerial unmanned vehicles (UAVs), commonly known as drones. The book aims to include cutting edge research and development on a number of areas within the topic including but not limited to: •Machine learning algorithms for drone-enabled IoT networks •Sensing and data collection with drones for IoT applications •Data analysis and processing for IoT networks assisted by drones •Energy-efficient and scalable solutions for drone-assisted IoT networks •Security and privacy issues in drone-enabled IoT networks •Emerging trends and future directions in ML for drone-assisted IoT networks.
Machine Learning for Earth Sciences: Using Python to Solve Geological Problems (Springer Textbooks in Earth Sciences, Geography and Environment)
by Maurizio PetrelliThis textbook introduces the reader to Machine Learning (ML) applications in Earth Sciences. In detail, it starts by describing the basics of machine learning and its potentials in Earth Sciences to solve geological problems. It describes the main Python tools devoted to ML, the typival workflow of ML applications in Earth Sciences, and proceeds with reporting how ML algorithms work. The book provides many examples of ML application to Earth Sciences problems in many fields, such as the clustering and dimensionality reduction in petro-volcanological studies, the clustering of multi-spectral data, well-log data facies classification, and machine learning regression in petrology. Also, the book introduces the basics of parallel computing and how to scale ML models in the cloud. The book is devoted to Earth Scientists, at any level, from students to academics and professionals.
Machine Learning for Ecology and Sustainable Natural Resource Management
by Falk Huettmann Grant R. W. Humphries Dawn R. Magness<p>Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. <p>Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: <p> <li>(1) data exploration to gain system knowledge and generate new hypotheses, <li>(2) predicting ecological patterns in space and time, <li>and (3) pattern recognition for ecological sampling.</li> <p> <p> Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.</p>
Machine Learning for Materials Discovery: Numerical Recipes and Practical Applications (Machine Intelligence for Materials Science)
by Hariprasad Kodamana Ravinder Bhattoo N. M. KrishnanFocusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect—each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials.
Machine Learning in Geomechanics 1: Overview of Machine Learning, Unervised Learning, Regression, Classification and Artificial Neural Networks (ISTE Consignment)
by Ioannis Stefanou F Lix DarveMachine learning has led to incredible achievements in many different fields of science and technology. These varied methods of machine learning all offer powerful new tools to scientists and engineers and open new paths in geomechanics. The two volumes of Machine Learning in Geomechanics aim to demystify machine learning. They present the main methods and provide examples of its applications in mechanics and geomechanics. Most of the chapters provide a pedagogical introduction to the most important methods of machine learning and uncover the fundamental notions underlying them. Building from the simplest to the most sophisticated methods of machine learning, the books give several hands-on examples of coding to assist readers in understanding both the methods and their potential and identifying possible pitfalls.
Machine Learning in Geomechanics 2: Data-Driven Modeling, Bayesian Inference, Physics- and Thermodynamics-based Artificial Neural Networks and Reinforcement Learning (ISTE Consignment)
by Ioannis Stefanou Félix DarveMachine learning has led to incredible achievements in many different fields of science and technology. These varied methods of machine learning all offer powerful new tools to scientists and engineers and open new paths in geomechanics. The two volumes of Machine Learning in Geomechanics aim to demystify machine learning. They present the main methods and provide examples of its applications in mechanics and geomechanics. Most of the chapters provide a pedagogical introduction to the most important methods of machine learning and uncover the fundamental notions underlying them. Building from the simplest to the most sophisticated methods of machine learning, the books give several hands-on examples of coding to assist readers in understanding both the methods and their potential and identifying possible pitfalls.
Machine Learning kompakt: Ein Einstieg für Studierende der Naturwissenschaften (essentials)
by Kenny Choo Eliska Greplova Mark H. Fischer Titus NeupertDieses essential befasst sich mit Anwendungen des maschinellen Lernens in verschiedenen Bereichen der Naturwissenschaften. Es behandelt geläufige Strukturen und Algorithmen, um Daten mit den Techniken des maschinellen Lernens zu analysieren. Zunächst werden Methoden eingeführt, die an klassischen statistischen Analysen andocken und auf soliderem mathematischem Fundament stehen. Die Autoren machen mit den verschiedenen Strukturen für künstliche neuronale Netzwerke vertraut und zeigen die jeweiligen Anwendungsgebiete.
Machu Picchu in Context: Interdisciplinary Approaches to the Study of Human Past
by Nicola Masini Mariusz Ziółkowski José M. BastanteThis book aims at integrating archaeology with science in order to provide additional information with respect to a traditional archaeological anthropological perspective. It sheds light on Incan culture, the relation between human frequentation and environmental changes, the Incan architecture in relation with Andean cosmovision using, for the first time, diverse technological and scientific approaches including LiDAR remote sensing, geophysics and radio carbon dating. A number of recent studies conducted by Polish, Italian and Peruvian scientific missions in Machu Picchu, Chachabamba and Cusco are presented and discussed.Chapter 5 is available open access under a Creative Commons Attribution-ShareAlike 4.0 International License via link.springer.com.
Macro-Economics of Mineral and Water Resources
by Kaulir Kisor ChatterjeeThis book highlights the indispensability of minerals, the vulnerability of humans and issues faced by governments around the world regarding the management of natural resources. It addresses the growing land-ecology-mining conflicts, energy security and water policies of different countries bringing these issues into focus and critically analyzing them. The book discusses the role of governments regarding the security-centric issues pertaining to sustainability of mineral supply and the welfare-centric aspects of sustainable development of mineral resources. The latter includes the current trends for corporate social responsibility, political viability of mining projects, industrial ethics, human health and human resource development. The Annexure I is unique: It is a list of 925 familiar consumer products and processes with the names of the minerals, metals and rocks as well as the intermediate chemicals and alloys that go into the making of that product or process alongside each. Annexure II is an up-to-date, exhaustive list of about 835 minerals, metals, rocks and intermediate chemicals and alloys and against each of them is a list of the names of the end products and processes for which they are used. These two annexures will serve as a day-to-day reference source for teachers, students and professionals concerned with minerals as well as other interested readers. The book will be useful to any university/institution with undergraduate and post-graduate teaching/research facilities and libraries in the field of geology, mining, mineral economics, planning and natural-resource management. About the Author Kaulir Kisor Chatterjee studied Applied Geology at the Indian School of Mines, Dhanbad for his post-graduate and PhD degrees. He served in the Indian Bureau of Mines for over three decades and retired in early 2004, as Chief Mineral Economist. Post-retirement, he has occupied himself mostly with writing, teaching and lecturing in various institutions of repute in India on the subject of mineral economics. Besides 50 technical papers, he has authored eight books. He has worked in various Government committees and expert groups and was involved in organization of national mineral inventory; UN Framework Classification system of mineral resources; rationalisation of the mineral taxation, royalty and mineral legislation framework in India. He has been examiner and member of selection boards of UPSC, India and is also a recognised guide of the Nagpur University for doctoral research. His resume has been included in the Marquis Who Is Who of the World and in the 2000 Outstanding Intellectuals of the 21st Century, Cambridge.
Macroeconomic Fluctuations in the Caribbean: the Role of Climatic and External Shocks
by Paul Cashin Sebastián SosaA report from the International Monetary Fund.