Computer Methods for Tolerance Design


Book Description

This book describes recent research advances and computer tools that can be applied in the determination of geometric tolerances. A framework for tolerance synthesis is developed and used with artificial intelligence techniques to provide computer methods for both analysis and synthesis of geometric tolerance specifications. Tolerance primitives, based on a sound theory of tolerancing, are used to represent tolerance relationships or links between geometric entities and functional requirements. Algorithms are developed for the determination of boundedness and the measurement of sufficiency. A detailed constraint network is used to represent tolerance relations for a part under design and provide for the composition of tolerance specifications.




Margins of Tolerance


Book Description

The characters in ¿Margins of Tolerance¿ are journeying. On vacation, in transit, or in flux, they seek to discover their place, within and outside of their relationships, while learning to navigate their way through a minefield of challenges both external and internal. These gripping tales, some riotously funny, others heart-wrenching, give us a broad picture of the state of gay identity in the cultural kaleidoscope of the 21st century.




Rift-lines within european regulatory framework for biosimilars when taking heterogeneity and variation during lifecycle of the reference biologic and the biosimilar into account


Book Description

Biopharmaceutical medicinal products (biologics) represent a huge financial market. Thus upon patent protection expiry of the innovator (reference) biologic there is interest from industry to gain a portion of this market by launching a 'similar' biologic at a reduced development cost, thus boosting potential gains. The EMA responded to this desire and lead the guidance process with industry on the topic of biosimilars. Based on the experience gained with biosimilars in the past, the EMA started to introduce a second generation series of guidance documents, which take into account the past, current and possibly future challenges of biosimilars. Those proposals were evaluated by EMA and partially incorporated into new guidance documents. This work highlights the challenges and risks associated with biosimilar submissions for large and complex bio-molecules such antibodies. Results: There are unaddressed questions for the regulator with regard to the unsolved dynamic of heterogeneity and variations of the quality profile, which have potential implications on safety and efficacy. This is neglected and not taken into account seriously enough by the stakeholders. Solution: Further, the only (in my view) progressive way to deal with such foreseeable situations from the biosimilar developer’s point of view is to incorporate a design space.







Research in the History of Economic Thought and Methodology


Book Description

Research in the History of Economic Thought and Methodology (RHETM) is a book series dedicated to an interdisciplinary approach to a broad range of topics related to the history and methodology of economics.




Spaces of Tolerance


Book Description

Spaces of Tolerance addresses the topic of tolerance in architectural production. Through examining the boundaries of where discourses, practices and designs are considered publishable (suitable to be made public) or not, the book exposes criteria and cultures which censor architecture so as to offer ways that architecture can be more inclusive and diverse for society at large. The contributors to the book discuss: disciplinary tolerances and constraints related to architecture and its interdisciplinary exchanges and modes of working; physical, spatial, temporal and digital tolerance in material assemblages and production between drawing and building; and social, cultural and political tolerance and threats contingent on geography and history. This timely book aims to look at extremities, margins and marginality to explore acceptable levels – and their fluctuations – in deviation and divergence. Chapters in the book involve ungendering, unacculturating (in disciplinary terms) and diversifying the architectural practitioner, writer, editor, reviewer, and reader, and retooling the instruments and tactics of architectural practice and theory. They argue that tolerance in interdisciplinary research in architecture can cultivate more diverse and productive conversations. The chapters in this book were originally published as a special issue of the journal Architecture and Culture.




Lectures on the Theory of Socialist Planning


Book Description




Your Nostradamus Factor


Book Description

You Can Predict the Future Throughout the centuries the future has been seen in dreams, in visions, and by seers. But you don't have to be a prophet or a visionary to predict what the future holds. Now, Ingo Swann explains how to understand and use the future-predicting ability that lies within. He identifies the different ways the future is revealed: Spontaneous forewarnings during dreams Spontaneous alerts that happen when awake Forewarnings and alerts communally experienced by numbers of people while sleeping or while awake Consciously controlled future-seeing achieved by seers Ingo Swann also presents fascinating, documented examples of prophecies that came true, ranging from those that foresaw the sinking of the Titanic to his own prediction of the fall of the Berlin Wall. Your Nostradamus Factor explains how you can develop your ability to foresee the future by: • Overcoming blocks to future-seeing • Tracking a particular subject and testing your predictions • Using astrology to help see the future, and • Paying close attention to your dreams. With the millennium right around the corner, Swann also offers startling predictions for the future of the environment, the economy, science, and society. (originally published in 1993)




Machine Learning in the AWS Cloud


Book Description

Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You’ll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you’ll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. • Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building • Discover common neural network frameworks with Amazon SageMaker • Solve computer vision problems with Amazon Rekognition • Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.




Machine Learning for iOS Developers


Book Description

Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple’s ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book’s clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models—both pre-trained and user-built—with Apple’s CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers: Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming Develop skills in data acquisition and modeling, classification, and regression. Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS) Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreML Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps.