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This book provides a rich overview and takes a closer look at the current state of theory and practice in the field of sustainable business models. The chapters in this book examine and analyze existing and new approaches towards sustainable business models and showcase the implementation of sustainable business through both quantitative and qualitative studies, including several case studies and many practical examples. It approaches these issues from the standpoints of diverse business disciplines to yield new insights and ideas that are relevant from both an academic and professional perspective. In its essence, the book examines how firms' value creation processes can be driven by sustainability and social responsibility and how this impacts business and society. Readers will find a range of sustainable business models that have been employed and are being pioneered in various industries around the globe - which are thoroughly investigated and discussed, and put into a comprehensive conceptual framework.

This work opens with an accessible introduction to computer networks, providing general definitions of commonly used terms in networking. This is followed by a detailed description of the OSI model, including the concepts of connection-oriented and connectionless communications. The text carefully elaborates the specific functions of each layer, along with what is expected of protocols operating at each layer. Next, the journey of a single packet, from source to destination, is described in detail. The final chapter is devoted to the TCP/IP model, beginning with a discussion of IP protocols and the supporting ARP, RARP and In ARP protocols. The work also discusses the TCP and UDP protocols operating at the transport layer and the application layer protocols HTTP, DNS, FTP, TFTP, SMTP, POP3 and Telnet. Important facts and definitions are highlighted in gray boxes found throughout the text.

This book presents in their basic form the most important models of computation, their programming paradigms, and their mathematical descriptions, both concrete and abstract. The authors also describe important techniques for reasoning on them and for proving some essential properties. After preliminary chapters that introduce the notions of structure and meaning, semantic methods, inference rules, and logic programming, the authors arrange their chapters into parts on IMP, a simple imperative language; HOFL, a higher-order functional language, concurrent systems, and probabilistic systems.§§The book will be useful for undergraduate and graduate courses on theoretical computer science.§

The Brian Friel Papers at the National Library of Ireland are a record of a life's work in progress. They represent a way of working and of making art over a period spanning more than fifty years. This book is the first of its kind in its attempt to interrogate the role of the Brian Friel Papers in Friel's legacy as a working artist with a richly developed creative practice. By means of an unprecedented focus on Friel's artistic process, Kuczynska asks not only how and by whom Friel was being influenced and inspired, but also how and for whom Friel's praxis might come to be an inspiration. Combining forensic archival scholarship with original, collaborative practice-based research, this study remains focused on the 'how' of influence, showcasing an approach to literary archives that foregrounds live practices of access in the spirit of creative encounter. Whether uncovering forgotten source materials for Friel's plays or working with current practitioners in the arts, Kuczynska reveals how an approach to literary archives grounded in artistic practice might provide the tools for setting a major creative legacy not in stone but rather in motion.

This book presents an exciting collection of contributions based on the workshop "Bringing Maths to Life" held October 27-29, 2014 in Naples, Italy. The state-of-the art research in biology and the statistical and analytical challenges facing huge masses of data collection are treated in this Work. Specific topics explored in depth surround the sessions and special invited sessions of the workshop and include genetic variability via differential expression, molecular dynamics and modeling, complex biological systems viewed from quantitative models, and microscopy images processing, to name several.§§In depth discussions of the mathematical analysis required to extract insights from complex bodies of biological datasets, to aid development in the field novel algorithms, methods and software tools for genetic variability, molecular dynamics, and complex biological systems are presented in this book.§§Researchers and graduate students in biology, life science, and mathematics/statistics will find the content useful as it addresses existing challenges in identifying the gaps between mathematical modeling and biological research. The shared solutions will aid and promote further collaboration between life sciences and mathematics.§

This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. For many years now, standard Brownian motion has been (and still remains) a popular model of randomness used to investigate processes in the natural sciences, financial markets, and the economy. The substantial limitation in the use of stochastic diffusion models with Brownian motion is due to the fact that the motion has independent increments, and, therefore, the random noise it generates is "white," i.e., uncorrelated. However, many processes in the natural sciences, computer networks and financial markets have long-term or short-term dependences, i.e., the correlations of random noise in these processes are non-zero, and slowly or rapidly decrease with time. In particular, models of financial markets demonstrate various kinds of memory and usually this memory is modeled by fractional Brownian diffusion. Therefore, the book constructs diffusion models with memory and provides simple and suitable parameter estimation methods in these models, making it a valuable resource for all researchers in this field.

The emphasis in this book is placed on general models (Markov chains, random fields, random graphs), universal methods (the probabilistic method, the coupling method, the Stein-Chen method, martingale methods, the method of types) and versatile tools (Chernoff's bound, Hoeffding's inequality, Holley's inequality) whose domain of application extends far beyond the present text. Although the examples treated in the book relate to the possible applications, in the communication and computing sciences, in operations research and in physics, this book is in the first instance concerned with theory.The level of the book is that of a beginning graduate course. It is self-contained, the prerequisites consisting merely of basic calculus (series) and basic linear algebra (matrices). The reader is not assumed to be trained in probability since the first chapters give in considerable detail the background necessary to understand the rest of the book.

This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement financial models. This text will show students how to obtain publicly available data, manipulate such data, implement the models, and generate typical output expected for a particular analysis.This text aims to overcome several common obstacles in teaching financial modeling. First, most texts do not provide students with enough information to allow them to implement models from start to finish. In this book, we walk through each step in relatively more detail and show intermediate R output to help students make sure they are implementing the analyses correctly. Second, most books deal with sanitized or clean data that have been organized to suit a particular analysis. Consequently, many students do not know how to deal with real-world data or know how to apply simple data manipulation techniques to get the real-world data into a usable form. This book will expose students to the notion of data checking and make them aware of problems that exist when using real-world data. Third, most classes or texts use expensive commercial software or toolboxes. In this text, we use R to analyze financial data and implement models. R and the accompanying packages used in the text are freely available; therefore, any code or models we implement do not require any additional expenditure on the part of the student.Demonstrating rigorous techniques applied to real-world data, this text covers a wide spectrum of timely and practical issues in financial modeling, including return and risk measurement, portfolio management, options pricing, and fixed income analysis.