While the negative effects of urban poverty are well documented, the everyday experiences of urban residents are often absent or secondary in urban studies research. The Cost of Being Poor rectifies this problem by examining both the noneconomic and the often-overlooked economic costs faced by residents of poor urban neighborhoods in Gary, Indiana. Using census, regional, and local data, and in-depth interviews with the residents of Gary, Sandra L. Barnes argues that many people incur costs resulting from the dual dilemma of being poor and residing in a poor urban area. She explores how factors such as race/ethnicity, neighborhood type, and location influence residents' views, coping strategies, and unconventional approaches toward making ends meet. Well written and accessible, this study of Gary's poor urban neighborhoods offers broad findings that apply to other similarly impoverished Rust Belt cities.

The book takes a closer look at the theoretical and empirical basis for a competence-based view of models and modeling in science learning and science education research. Current thinking about models and modeling is reflected. The focus lies on the development of modeling competence in science education, and on philosophical aspects, including perspectives on nature of science. The book explores, interprets, and discusses models and modeling from the perspective of different theoretical frameworks and empirical results. The extent to which these frameworks can be integrated into a competence-based approach for science education is discussed. In addition, the book provides practical guidance by outlining evidence-based approaches to diagnosing and promoting modeling competence. The aim is to convey a strong understanding of models and modeling for professions such as teacher educators, science education researchers, teachers, and scientists. Different methods for the diagnosis and assessment of modeling competence are presented and discussed with regard to their potential and limitations. The book provides evidence-based ideas about how teachers can be supported in teaching with models and modeling implementing a competence-based approach and, thus, how students can develop their modeling competence. Based on the findings, research challenges for the future are identified.

Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you’ll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This expanded second edition—updated for Cassandra 3.0—provides the technical details and practical examples you need to put this database to work in a production environment. Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra’s non-relational design, with special attention to data modeling. If you’re a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra’s speed and flexibility. Understand Cassandra’s distributed and decentralized structure Use the Cassandra Query Language (CQL) and cqlsh—the CQL shell Create a working data model and compare it with an equivalent relational model Develop sample applications using client drivers for languages including Java, Python, and Node.js Explore cluster topology and learn how nodes exchange data Maintain a high level of performance in your cluster Deploy Cassandra on site, in the Cloud, or with Docker Integrate Cassandra with Spark, Hadoop, Elasticsearch, Solr, and Lucene

Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage.

The recent introduction of two European index options on the FTSE Eurotrack 100 and the Eurotop 100 is evidence of a demand from investors to hedge pan-European risk. The FTSE Eurotrack 100 was designed to closely resemble the longer established and widely quoted Morgan Stanley European index. The Eurotrack 100 covers a hundred companies in eleven countries in continental Europe. The index is denominated in DM and' a breakdown by value into the different countries covered is given in figure 1. Capitalisation weights for Figure 1 FT-SE Eurotrack 100 Index Norway mark Germany Italy Switzerland France Netherlands Another recently introduced European index is the Eurotop 100 index denominated in EeUs, this index contains twenty two UK companies which represent 27% by value of this index. The attraction of investments in these indices is that they provide a basis for weighted exposure to Europe, investors can then build on this 240 basis by investment in individual countries. The multinational context of the universe of shares defined by this index raises some new questions for the selection of portfolios, whether the portfolios are chosen for absolute performance or to track the index. Various possible objectives of portfolio selection will be discussed, in all cases the crucial role of the covariance matrix of returns is clear. The extra source of risk present in a multinational portfolio is the combination of country risk coupled with foreign exchange risk. Two models of the return covariance matrix are proposed and examined.