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Powell Stephen G.: Business Analytics

Business Analytics

The Art of Modeling With Spreadsheets

Due to the hierarchical organization of morphology in soft materials and their slow dynamics, a single modelling technique does not suffice to simulate them. The wide range of modelling approaches available span many time and length scales, making it challenging for newcomers to the field to know how to critically assess the tools and to determine which is most appropriate for any given problem. This book provides a concise and clear description of a variety of simulation methods to model these ubiquitous materials. The list of techniques includes numerical and molecular modelling ones and covers several time and length scales. Along with the fundamental concepts of the theory behind the methods, a comprehensive set of examples taken from the broad pool of soft materials is included. These exemplify how, thanks to the increased computational resources nowadays available to almost any research group, computational methods have become a powerful tool to sit alongside other experimental characterizations and show their increasing relevance for the manufacturing sector. Chapters illustrate how modelling techniques can be used to aid interpretation of experimental data, and how experiments can be used to parameterise models. In addition to enabling informed decisions to be made about the modelling tools to adopt for a given problem, the book will enable those who might already be experts in one technique to transition to other tools more easily. This will become increasingly important as multiscale tools become increasingly sophisticated and sufficiently well developed to be used by more casual users of simulation tools. Bringing together all these modelling approaches and applications into one coherent volume, Computational Methods for the Multiscale Modelling of Soft Matter provides a one-stop resource that is written primarily for postgraduate students and researchers in materials science, computational physics and chemists and chemical engineering interested in learning about simulation methods for soft materials as polymers, surfactants, and colloids.

Computational Methods for the Multiscale Modelling of Soft Matter

Advances in Chemical Engineering

Photocatalytic Degradation of Pharmaceutical Wastewater: Volume 65

MATLAB Scientific Plotting and Data Analysis combines the author's extensive experience in data analysis and scientific plotting to provide detailed explanations of the methods and techniques for using MATLAB. The book is divided into three parts, consisting of 12 chapters. The first part covers the basics of MATLAB, including the operating environment, data types and basic operations, file operations, and programming. The second part focuses on MATLAB's data visualization capabilities, with topics such as figure window information, 2D plotting, 3D plotting, specialized plotting, and handle graphics objects. The third part covers descriptive data analysis, interpolation and fitting, regression analysis, and optimization problem solving. Furthermore, this book provides links to over 200 examples of teaching videos and hands-on exercise resource files, enhancing readers' learning efficiency.

MATLAB Scientific Plotting and Data Analysis

Agricultural Insights from Space: Machine Learning Applications in Satellite Data Analysis seamlessly integrates theoretical knowledge with practical applications, presenting cutting-edge research alongside real-world examples. This book leverages geospatial technology and Artificial Intelligence to address various challenges in agriculture. Readers will find practical examples and case studies demonstrating how machine learning and deep learning techniques can extract valuable insights from remote sensing data, optimizing agricultural processes. Highlighting the significance of satellite data, the book explores the benefits of leveraging space-based information for enhancing agricultural practices. The book emphasizes the importance of geospatial intelligence and AI technologies in monitoring and managing agricultural activities. It inspires readers to envision a future where these innovative approaches lead to more productive agricultural environments and healthier growth for future generations.

Machine Learning Applications in Satellite Data Analysis

Agricultural Insights from Space

The Digital Doctor: How Digital Health Can Transform Healthcare discusses digital health and demonstrates the appropriateness of each technology using an evidence-based approach. It serves as a comprehensive summary on current, evidence-based digital health applications, future novel digital health technologies (e.g., mobile health, blockchain, web3.0), as well as some of the current challenges and future directions for digital health within the various medical subspecialties. This book is a comprehensive review of digital health for clinicians, researchers, bioinformatic students, biomedical engineers interested in this topic.

How Digital Health Can Transform Healthcare

The Digital Doctor

Woodhead Publishing Series in Civil and Structural Engineering

A growing use of membranes for construction purposes has recently been encouraged by their environmentally friendly characteristics. To offer a timely and extensive, research-grounded compendium on structures made with fabrics and foils, the most relevant facets of the state of the art are covered in two sister titles. After a moving remembrance of Marc Malinowski, a great promoter of membrane structures, Membranes for Structures - Fundamentals begins with a brief historical review of the development of design tools for membrane structures. Part One follows, discussing design fundamentals in the light of recent experiences and modern technologies. Part Two updates knowledge of products and materials, with a special focus on fire performance, the incorporation of photovoltaic cells, and ETFE. Part Three deals with other considerations related to design and installation, such as acoustic performance, post-failure stability, wind loads and lifecycle analysis, with special mentions of textile halls and biogas containers. A look into the future is proposed in Part Four, where some interesting, present-day research on ageing and applications is included. The well-amalgamated contributions, authored by international experts, produce a comprehensive reference to keep students and researchers as well as industry professionals and various other stakeholders abreast with the latest advancements.

Fundamentals

Membranes for Structures

Earth Observation

Remote Sensing, Big Data, and GeoAI: Exploring Applications with Geospatial Insights is an in-depth analysis of the transformative power of AI and Big Data in remote sensing. This book provides readers with the knowledge and tools to utilize these technologies to enhance decision-making and analysis. Starting from fundamental concepts, it progresses to advanced applications, offering accessible explanations and real-world examples to bridge the gap between theory and practice. The book uses a structured format to balance theoretical knowledge with immersive case studies, giving readers a deeper understanding of practical implications. It also covers ethical and legal considerations, making it an invaluable resource for researchers, professionals, and students keen on using AI and Big Data techniques in remote sensing to solve complex geospatial challenges.

Exploring Applications with Geospatial Insights

Remote Sensing, Big Data, and GeoAI

Digital Transformation and Equitable Global Health: A Future-Ready Perspective presents a collective body of knowledge and global experiences that demonstrate current status and future trends in the use of exponential technologies and their potential for poverty reduction, improving health outcomes, strengthening health systems, and transforming traditional development aid structures. The book uses a translational innovation perspective to guide the reader-regardless of their area of expertise-on the rationale behind the co-creation of human-centered, affordable, and sustainable digital solutions. It addresses the interest of professionals from multiple areas (e.g., technology, health, social development, global financing), and it is a valuable resource for professionals, social scientists, practitioners, researchers, instructors, and undergraduate and graduate students interested in understanding the challenges and complexities of global public health and the applied uses of health technologies for equitable access to primary health care and universal health coverage.

A Future-Ready Perspective

Digital Transformation and Equitable Global Health

Geospatial Information and Services for Climate Resilient Disaster Management explores how cutting-edge technology can shape our response to climate-related disasters. Leading experts uncover the indispensable role that geospatial information and services can play in disaster preparedness, response, and recovery. The book opens with an overview of climate resilient disaster management and the application of geospatial technology. The chapters next undertake a thorough examination of early warning systems, risk assessment, vulnerability mapping, resource allocation, and disaster response. Following from those vital tools and applications, the editors then evaluate climate adaptation planning and community engagement in the light of geospatial technology. The book wraps up with real-world case studies and success stories along with a discussion of current challenges and future trends. Readers concerned with the rising connections between natural disaster and climate change will be inspired by this inside view of the latest innovations in geospatial information and services.

Geospatial Information and Services for Climate Resilient Disaster Management

Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems: Prediction Models Exploiting Well-Log Information explores machine and deep learning models for subsurface geological prediction problems commonly encountered in applied resource evaluation and reservoir characterization tasks. The book provides insights into how the performance of ML/DL models can be optimized-and sparse datasets of input variables enhanced and/or rescaled-to improve prediction performances. A variety of topics are covered, including regression models to estimate total organic carbon from well-log data, predicting brittleness indexes in tight formation sequences, trapping mechanisms in potential sub-surface carbon storage reservoirs, and more. Each chapter includes its own introduction, summary, and nomenclature sections, along with one or more case studies focused on prediction model implementation related to its topic.

Prediction Models Exploiting Well-Log Information

Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems

Bridge the gap between biology and cutting-edge machine learning techniques with this essential guide. Train and interpret deep learning models and answer fundamental biological questions. Whether your background is in biology, machine learning, or software engineering, this book empowers you with the practical skills needed for your deep learning journey. Through step-by-step recipes, authors Natasha Latysheva and Charles Ravarani teach you to train models for tasks like understanding gene regulatory logic, uncovering insights about protein families, and exploring subcellular localization. Each chapter provides self-contained mini-projects that serve as templates for approaching complex biological problems. By the end of this book, you'll be able to: <ul> <li>Approach biological problems with a deep learning perspective </li><li>Train and interpret deep learning models to explore complex biological data </li><li>Leverage Python code and iPython notebooks to extend your projects </li><li>Transform your insights into actionable solutions in medicine, genomics, and biotechnology </li></ul>

Harness AI to Solve Real-World Biology Problems

Deep Learning for Biology

Deep Learning for Earth Observation and Climate Monitoring bridges the gap between deep learning and the Earth sciences, offering cutting-edge techniques and applications that are transforming our understanding of the environment. With a focus on practical scenarios, this book introduces readers to the fundamental concepts of deep learning, from classification and image segmentation to anomaly detection and domain adaptability. The book includes practical discussion on regression, parameter retrieval, forecasting, and interpolation, among other topics. With a solid foundational theory, real-world examples, and example codes, it provides a full understanding of how intelligent systems can be applied to enhance Earth observation and especially climate monitoring. This book allows readers to apply learning representations, unsupervised deep learning, and physics-aware models to Earth observation data, enabling them to leverage the power of deep learning to fully utilize the wealth of environmental data from satellite technologies.

Deep Learning for Earth Observation and Climate Monitoring

Business Analytics

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