AQUIFER STORAGE AND RECOVERY IN MILLVILLE, CACHE COUNTY, UTAH
AQUIFER STORAGE AND RECOVERY IN MILLVILLE, CACHE COUNTY, UTAH

Author: Paul Inkenbrandt

Publisher: Utah Geological Survey

Published: 2016-08-15

Total Pages: 94

ISBN-13:

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This study is an investigation of the feasibility of an aquifer storage and recovery project using the existing water supply infrastructure of the city of Millville, Utah. The project involved injecting water from a public water supply spring into a public water supply well. Geochemical analysis indicates that the major ion chemistry of the spring water is very similar to that of the principal aquifer, however, the spring water would likely cause minor geochemical changes in the groundwater due to oxidation. The study also showed that the injection well had elevated nitrate concentration which is likely due to septic systems in the area. Overall, the pilot tests showed that injection of water for storage would not be detrimental to the principal aquifer, which has significant storage abilities beyond the capacity of Millville’s water system; however elevated nitrate in the aquifer is a problem that should be addressed.

Machine Learning and AI in Finance
Machine Learning and AI in Finance

Author: German Creamer

Publisher: Routledge

Published: 2021-04-06

Total Pages: 206

ISBN-13: 1000372049

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The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.

Applications of Computational Intelligence in Data-Driven Trading
Applications of Computational Intelligence in Data-Driven Trading

Author: Cris Doloc

Publisher: John Wiley & Sons

Published: 2019-11-05

Total Pages: 319

ISBN-13: 1119550513

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“Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry. The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic: The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence. The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance. The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “Artificial Intelligence,” especially as it relates to the financial industry. The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic.

Small Animal Regional Anesthesia and Analgesia
Small Animal Regional Anesthesia and Analgesia

Author: Matt R. Read

Publisher: John Wiley & Sons

Published: 2024-03-26

Total Pages: 309

ISBN-13: 1119514150

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Small Animal Regional Anesthesia and Analgesia Explore regional techniques for anesthesia and analgesia in dogs and cats in this thoroughly expanded and revised edition of the most comprehensive book on the topic Small Animal Regional Anesthesia and Analgesia, Second Edition expands and updates the information in the first edition, making this a truly comprehensive and practical reference for regional anesthetic techniques in dogs and cats. Written by leading international experts in the field, this book provides an authoritative yet practical guide to using ultrasound-guided local and regional anesthesia techniques in clinical practice. Grounded in the latest scientific literature, the book presents a wealth of new or updated information, and incorporates a logical, standardized format and high-quality color images, making it easier and faster to find information about each block. Small Animal Regional Anesthesia and Analgesia, Second Edition: Provides an expanded and updated new edition of this practical, clinically-oriented resource, with step-by-step details for each procedure Features more images to support the visual aspect of learning that is necessary when using ultrasound to perform locoregional anesthesia Has been reorganized to present information based on the individual technique, rather than the general anatomical region of the body Small Animal Regional Anesthesia and Analgesia, Second Edition is a must-have reference for veterinary practitioners and specialists.

Machine Learning in Finance
Machine Learning in Finance

Author: Matthew F. Dixon

Publisher: Springer Nature

Published: 2020-07-01

Total Pages: 565

ISBN-13: 3030410684

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This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Cambridge Handbook of Anesthesiology
Cambridge Handbook of Anesthesiology

Author: Alan David Kaye

Publisher: Cambridge University Press

Published: 2023-03-31

Total Pages: 569

ISBN-13: 110895216X

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The recent advances in the clinical practice of anesthesiology have increased the need for a new state-of-the art anesthesia handbook for practicing anesthesiologists, trainees and students. This practical book provides a modern, clinically focused guide for any clinical question related to anesthesia care, presenting comprehensive, yet succinct knowledge from experts in the field. It describes the best practice in a concise yet easily digestible format and features numerous tables and figures, a bulleted points/outline format, and algorithms allowing for rapid assimilation of key information. New techniques and common procedures are also covered in detail. This up-to-date pocketbook is perfect for quick reference in the operating room, providing rapid access to evidence-based clinical content covering the full breadth of the field, including pediatric, obstetric, cardiac and regional anesthesia sections. In addition to daily clinical practice, the book serves also as a convenient companion for board review and recertification exam preparation.

Regional Anesthesia and Acute Pain Medicine
Regional Anesthesia and Acute Pain Medicine

Author: Anesthesiology Vice Chair of Clinical Operations Nabil Elkassabany

Publisher: Oxford University Press

Published: 2023-04-21

Total Pages: 617

ISBN-13: 0197518516

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Regional Anesthesia and Acute Pain Medicine: A Problem-Based Learning Approach provides a comprehensive review of regional anesthesia and acute pain medicine for medical learners to integrate theoretical knowledge into clinical practice. Its problem-based format incorporates a pool of multiple-choice questions for self-assessment. Each of its 50 case-based chapters is accompanied by questions and answers accessible online in a full practice exam. These chapters cover several areas such as pharmacology, obstetric and pediatric regional anesthesia, complex acute pain problems, anticoagulation, and regional anesthesia and complicated nerve blocks. The cases presented are also unique, as each chapter starts with a case description, usually a compilation of several actual cases, which branches out through case-based questions to increasingly complex situations. This structure is designed to create an authentic experience mirroring the nuances of a complicated clinical scenario. The discussion sections that follow offer a comprehensive approach to the chapter's subject matter, thus creating a modern, complete, and up-to-date medical review of the topic.

Machine Learning and Data Sciences for Financial Markets
Machine Learning and Data Sciences for Financial Markets

Author: Agostino Capponi

Publisher: Cambridge University Press

Published: 2023-04-30

Total Pages: 742

ISBN-13: 1316516199

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Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.

Nano-Net
Nano-Net

Author: Alexandre Schmid

Publisher: Springer Science & Business Media

Published: 2009-10-06

Total Pages: 299

ISBN-13: 3642048498

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This book constitutes the proceedings of the 4th International Conference on Nano-Networks, Nano-Net 2009, held in Lucerne, Switherland, in October 2009. The 36 invited and regular papers address the whole spectrum of Nano-Networks and spans topis like modeling, simulation, statdards, architectural aspects, novel information and graph theory aspects, device physics and interconnects, nanorobotics as well as nano-biological systems. The volume also contains the workshop on Nano-Bio-Sensing Paradigms as well as the workshop on Brain Inspired Interconnects and Circuits.