Designing and Supporting Computer Networks, CCNA Discovery Learning Guide is the official supplemental textbook for the Designing and Supporting Computer Networks course in the Cisco® Networking Academy® CCNA® Discovery curriculum version 4. In this course, the last of four in the new curriculum, you progress through a variety of case studies and role-playing exercises, which include gathering requirements, designing basic networks, establishing proof-of-concept, and performing project management tasks. In addition, within the context of a pre-sales support position, you learn lifecycle services, including upgrades, competitive analyses, and system integration. The Learning Guide, written and edited by instructors, is designed as a portable desk reference to use anytime, anywhere to reinforce the material from the course and organize your time. The Learning Guide’s features help you focus on important concepts to succeed in this course: Chapter Objectives—Review core concepts by answering the focus questions listed at the beginning of each chapter. Key Terms—Refer to the lists of networking vocabulary introduced and highlighted in context in each chapter. The Glossary defines each key term. Summary of Activities and Labs—Maximize your study time with this complete list of all associated exercises at the end of each chapter. Check Your Understanding—Evaluate your readiness with the end-of-chapter questions that match the style of questions you see in the online course quizzes. The answer key explains each answer. Challenge Questions and Activities—Apply a deeper understanding of the concepts with these challenging end-of-chapter questions and activities. The answer key explains each answer. Hands-on Labs—Master the practical, hands-on skills of the course by performing all the tasks in the course labs included in Part II of the Learning Guide. Portfolio Documents—Develop a professional network design portfolio as you work through real-life case studies. All the course portfolio documents and support materials are provided for you in this Learning Guide and on the CD-ROM. How To—Look for this icon to study the steps you need to learn to perform certain tasks. Interactive Activities—Reinforce your understanding of topics with exercises from the online course identified throughout the book with this icon. The files for these activities are on the accompanying CD-ROM. Packet Tracer Activities—Explore and visualize networking concepts using Packet Tracer exercises interspersed throughout some chapters. The files for these activities are on the accompanying CD-ROM. Packet Tracer v4.1 software developed by Cisco is available separately. Hands-on Labs—Master the practical, hands-on skills of the course by working through all 71 labs in this course included in Part II of the book. The labs are an integral part of the CCNA Discovery curriculum—review the core text and the lab material to prepare for all your exams. Companion CD-ROM **See instructions within the ebook on how to get access to the files from the CD-ROM that accompanies this print book.** The CD-ROM includes Interactive Activities Packet Tracer Activity files All Portfolio documents IT Career Information Taking Notes Lifelong Learning This book is part of the Cisco Networking Academy Series from Cisco Press®. Books in this series support and complement the Cisco Networking Academy curriculum.

Frontiers in Drug Design and Discovery is a book series devoted to publishing the latest and the most important advances in drug design and discovery. Eminent scientists have contributed chapters focused on all areas of rational drug design and drug discovery including medicinal chemistry, in-silico drug design, combinatorial chemistry, high-throughput screening, drug targets, and structure-activity relationships. This book series should prove to be of interest to all pharmaceutical scientists who are involved in research in drug design and discovery and who wish to keep abreast of rapid and important developments in the field.

Machine learning methods are changing the way we design and discover new materials. This book provides an overview of approaches successfully used in addressing materials problems (alloys, ferroelectrics, dielectrics) with a focus on probabilistic methods, such as Gaussian processes, to accurately estimate density functions. The authors, who have extensive experience in this interdisciplinary field, discuss generalizations where more than one competing material property is involved or data with differing degrees of precision/costs or fidelity/expense needs to be considered.

Strategies to reduce medical uncertainty and build evidence have become critical to the advancement of medical knowledge and modern medical practice. As new techniques and strategies have arisen, so has the need for a current reference work. Drug Discovery and Design examines the latest research in the development of these new strategies. Some of the topics covered include angiotensin converting enzyme inhibitors, HIV protease inhibitors, PPAR agonists for diabetes, and glucan synthase antifungal agents.

This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader.

Agent-based modeling and simulation (ABMS), a way to simulate a large number of choices by individual actors, is one of the most exciting practical developments in business modeling since the invention of relational databases. It represents a new way to understand data and generate information that has never been available before--a way for businesses to view the future and to understand and anticipate the likely effects of their decisions on their markets and industries. It thus promises to have far-reaching effects on the way that businesses in many areas use computers to support practical decision-making. Managing Business Complexity is the first complete business-oriented agent-based modeling and simulation resource. It has three purposes: first, to teach readers how to think about ABMS, that is, about agents and their interactions; second, to teach readers how to explain the features and advantages of ABMS to other people and third, to teach readers how to actually implement ABMS by building agent-based simulations. It is intended to be a complete ABMS resource, accessible to readers who haven't had any previous experience in building agent-based simulations, or any other kinds of models, for that matter. It is also a collection of ABMS business applications resources, all assembled in one place for the first time. In short, Managing Business Complexity addresses who needs ABMS and why, where and when ABMS can be applied to the everyday business problems that surround us, and how specifically to build these powerful agent-based models.

Typical timelines to go from discovery to impact in the advanced materials sector are between 10 to 30 years. Advances in robotics and artificial intelligence are poised to accelerate the discovery and development of new materials dramatically. This book is a primer for any materials scientist looking to future-proof their careers and get ahead of the disruption that artificial intelligence and robotic automation is just starting to unleash. It is meant to be an overview of how we can use these disruptive technologies to augment and supercharge our abilities to discover new materials that will solve world’s biggest challenges. Written by world leading experts on accelerated materials discovery from academia (UC Berkeley, Caltech, UBC, Cornell, etc.), industry (Toyota Research Institute, Citrine Informatics) and national labs (National Research Council of Canada, Lawrence Berkeley National Labs).