Datamodellering og simulering

This book constitutes the refereed proceedings of the 18th International Conference on Computer-Aided Systems Theory, EUROCAST 2022, held in Las Palmas de Gran Canaria, Spain, during February 20¿25, 2022. The 77 full papers included in this book were carefully reviewed and selected from 110 submissions. They were organized in topical sections as follows: Systems Theory and Applications, Theory and Applications of Metaheuristic Algorithms, Model-Based System Design, Verification and Simulation, Applications of Signal Processing Technology, Artificial Intelligence and Data Mining for Intelligent Transportation Systems and Smart Mobility, Computer Vision, Machine Learning for Image Analysis and Applications, Computer and Systems Based Methods and Electronic Technologies in Medicine, Systems in Industrial Robotics, Automation and IoT, Systems Thinking. Relevance for Technology, Science and Management Professionals.

This fully updated book explores all-new and revised protocols involving the use of in silico models, particularly with regard to pharmaceuticals. Divided into five sections, the volume covers the modeling of pharmaceuticals in the body, toxicity data for modeling purposes, in silico models for multiple endpoints, a number of platforms for evaluating pharmaceuticals, as well as an exploration of challenges, both scientific and sociological. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and implementation advice necessary for successful results. Authoritative and comprehensive, In Silico Methods for Predicting Drug Toxicity, Second Edition aims to guide the reader through the correct procedures needed to harness in silico models, a field which now touches a wide variety of research specialties.

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. Youll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what youve learned along the way.Youll learn how to:Wrangletransform your datasets into a form convenient for analysisProgramlearn powerful R tools for solving data problems with greater clarity and easeExploreexamine your data, generate hypotheses, and quickly test themModelprovide a low-dimensional summary that captures true "e;signals"e; in your datasetCommunicatelearn R Markdown for integrating prose, code, and results

The healthcare industry is starting to adopt digital twins to improve personalized medicine, healthcare organization performance, and new medicine and devices. These digital twins can create useful models based on information from wearable devices, omics, and patient records to connect the dots across processes that span patients, doctors, and healthcare organizations as well as drug and device manufacturers. Digital twins are digital representations of human physiology built on computer models. The use of digital twins in healthcare is revolutionizing clinical processes and hospital management by enhancing medical care with digital tracking and advancing modelling of the human body. These tools are of great help to researchers in studying diseases, new drugs, and medical devices. Digital Twins and Healthcare: Trends, Techniques, and Challenges facilitates the advancement and knowledge dissemination in methodologies and applications of digital twins in the healthcare and medicine fields. This book raises interest and awareness of the uses of digital twins in healthcare in the research community. Covering topics such as deep neural network, edge computing, and transfer learning method, this premier reference source is an essential resource for hospital administrators, pharmacists, medical professionals, IT consultants, students and educators of higher education, librarians, and researchers.

Deep Learning in Personalized Healthcare and Decision Support discusses the potential of deep learning technologies in the healthcare sector. The book covers the application of deep learning tools and techniques in diverse areas of healthcare, such as medical image classification, telemedicine, clinical decision support system, clinical trials, electronic health records, precision medication, Parkinson disease detection, genomics, and drug discovery. In addition, it discusses the use of DL for fraud detection and internet of things. This is a valuable resource for researchers, graduate students and healthcare professionals who are interested in learning more about deep learning applied to the healthcare sector. Although there is an increasing interest by clinicians and healthcare workers, they still lack enough knowledge to efficiently choose and make use of technologies currently available. This book fills that knowledge gap by bringing together experts from technology and clinical fields to cover the topics in depth.

Accelerating Strategic Changes for Digital Transformation in the Healthcare Industry discusses innovative conceptual frameworks, tools and solutions to tackle the challenges of mitigating major disruption caused by COVID-19 in the healthcare sector and society. It emphasizes global case studies and empirical studies, providing a comprehensive view of best lessons on digital tools to manage the health crisis. The book focuses on the role of advances in digital and collaborative technologies to offer rapid and effective tools for better health solutions for new and emerging health problems. Researchers, students, policymakers and members of the biomedical and medical fields will find this information invaluable. Specially, it pays attention to how information technologies help us in the current global health emergency and the coronavirus epidemic response, gaining more understanding of the new coronavirus and helping to contain the outbreak. In addition, it explores how these new tools and digital health solutions can support the economic and social recovery in the post-pandemic world.

The exponential growth of data combined with the need to derive real-time business value is a critical issue today. An event-driven data mesh can power real-time operational and analytical workloads, all from a single set of data product streams. With practical real-world examples, this book shows you how to successfully design and build an event-driven data mesh. Building an Event-Driven Data Mesh provides: Practical tips for iteratively building your own event-driven data mesh, including hurdles you'll experience, possible solutions, and how to obtain real value as soon as possible Solutions to pitfalls you may encounter when moving your organization from monoliths to event-driven architectures A clear understanding of how events relate to systems and other events in the same stream and across streams A realistic look at event modeling options, such as fact, delta, and command type events, including how these choices will impact your data products Best practices for handling events at scale, privacy, and regulatory compliance Advice on asynchronous communication and handling eventual consistency

The book consists of a collection of papers from a corresponding conference regarding additive manufacturing. The yearly conference used to be held in German under the title: "e;Konstruktion fur die Additive Fertigung."e; The topics are: * Design and optimization * Simulation, validation and quality assurance * Specifications, potentials and solutions

This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including adaptive observations, sensitivity analysis, parameter estimation and AI applications. The book is useful to individual researchers as well as graduate students for a reference in the field of data assimilation. 

Matthias Hisung stellt ein neuartiges Assistenzsystem zur automatisierten Fahrzeugpositionierung fur das induktive Laden (APIC) vor. Daruber hinaus wird eine neue Methode zur Detektion von magnetischen Storungen (MDMS) durch elektrische Fahrzeugkomponenten auf Basis einer Mustererkennung eingefuhrt. Das Assistenzsystem lasst sich hierbei auf unterschiedliche Fahrzeugtypen anwenden und ubernimmt fur die fahrende Person die Aufgabe der Positionierung, um eine Komfortsteigerung zu erzielen. Durch die neuartige Methode wird eine deutliche Verbesserung der Positionsermittlung und damit einhergehend eine Erhohung des Positionierungsradius fur das induktive Laden ermoglicht.

The LNCS journal Transactions on Computational Systems Biology is devoted to inter- and multidisciplinary research in the fields of computer science and life sciences and supports a paradigmatic shift in the techniques from computer and information science to cope with the new challenges arising from the systems oriented point of view of biological phenomena.This, the 13th Transactions on Computational Systems Biology volume, guest edited by Ralph-Johan Back, Ion Petre, and Erik de Vink, focuses on Computational Models for Cell Processes and features a number of carefully selected and enhanced contributions initially presented at the CompMod workshop, which took place in Eindhoven, The Netherlands, in November 2009. From different points of view and following various approaches, the papers cover a wide range of topics in systems biology, addressing the dynamics and the computational principles of this emerging field.

All About Bioinformatics: From Beginner to Expert provides readers with an overview of the fundamentals and advances in the _x001F_field of bioinformatics, as well as some future directions. Each chapter is didactically organized and includes introduction, applications, tools, and future directions to cover the topics thoroughly. The book covers both traditional topics such as biological databases, algorithms, genetic variations, static methods, and structural bioinformatics, as well as contemporary advanced topics such as high-throughput technologies, drug informatics, system and network biology, and machine learning. It is a valuable resource for researchers and graduate students who are interested to learn more about bioinformatics to apply in their research work.

Computational Modeling of Infectious Disease: With Applications in Python provides an illustrated compendium of tools and tactics for analyzing infectious diseases using cutting-edge computational methods. From simple S(E)IR models, and through time series analysis and geospatial models, this book is both a guided tour through the computational analysis of infectious diseases and a quick-reference manual. Chapters are accompanied by extensive practical examples in Python, illustrating applications from start to finish. This book is designed for researchers and practicing infectious disease forecasters, modelers, data scientists, and those who wish to learn more about analysis of infectious disease processes in the real world.--

This book constitutes the proceedings of the 15th International Symposium on Algorithmic Game Theory, SAGT 2022, which took place in Colchester, UK, in September 2022. The 31 full papers included in this book were carefully reviewed and selected from 83 submissions. They were organized in topical sections as follows: Auctions, markets and mechanism design; computational aspects in games; congestion and network creation games; data sharing and learning; social choice and stable matchings.

This book constitutes the proceedings of the 19th International Conference on Quantitative Evaluation Systems, QEST 2022, held in Warsaw, Poland, in September 2022.The 19 full papers presented together with 1 keynote paper were carefully reviewed and selected from 44 submissions. The papers are organized in the following topics: program analysis; parameter synthesis; markovian agents and population models; dynamical systems; tools; applications and automata theory; and applications.

This volume constitutes the proceedings of the 20th Asian Simulation Conference, AsiaSim 2021, held as a virtual event in November 2021.The 9 full papers presented in this volume were carefully reviewed and selected from 23 submissions. The papers are organized in topical sections on simulation and visualization; modeling and simulation of systems.

Um Frontloading bei einer Fahrbarkeitsapplikation von Fahrzeugantrieben zu unterstutzen, werden Zielwerte benotigt, die das subjektive Empfinden der Insassen beschreiben. Am Beispiel von Volllastbeschleunigungen stellt Marco Schluter eine Methode vor, die es ermoglicht, das Insassenempfinden zu prognostizieren. Auf Grundlage von Messungen an realen Fahrzeugen definiert der Autor einen Untersuchungsraum. Im Stuttgarter Fahrsimulator macht er Varianten von Beschleunigungsmanovern erlebbar, die in einer Probandenstudie bewertet werden. Hieraus leitet er eine Metrik zur Objektivierung des Empfindens ab. Abschlieend validiert Schluter diese Metrik in realen Versuchsfahrten.

This open access book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation.The eBook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by Trinity College Dublin, DARIAH-EU and the European Commission.