In this empirical analysis Sara Polier investigates forward-looking external search strategies and their impact on the value contribution of corporate foresight. Based on a mixed method approach combining a quantitative and qualitative analysis of large, R&D-intensive firms, the findings reveal a general positive influence of different search strategies with respect to the scope (i.e. breadth, depth, and distance) and direction of search (i.e. market, science, and intermediary-driven) on the role of foresight as a driver for innovation. This relationship is found to be mediated by a firm’s exploratory learning capability, which appears to facilitate the effective transfer of external future-related knowledge into valuable outputs.
Rural roads constitute the most dangerous road category with regard to the number of fatal accidents. In order to increase traffic safety on rural roads it is necessary to take into account not only their inherent properties but also their effect on behaviour. Gert Weller develops a psychological model for driving on rural roads which is validated in three empirical steps: laboratory, simulator and driving experiments. His results provide insight into the possibilities of how driving behaviour on rural roads can be influenced and give practical guidance for the enhancement of rural road safety. The book is written for psychologists in the fields of traffic psychology and human factors research, traffic engineers, road planners as well as for political decision makers in traffic planning departments.
'What makes this collection unusual and refreshing is that it is not the more common ‘Festschrift’ written by specialists for specialists, but a broad set of topical summaries and analyses addressed to a wide readership of particle physicists. Inevitably, some of the sections are more advanced in their treatment than others, but most of the material will be accessible and helpful to researchers at all levels, and in particular to those working on experiments at CERN, where Altarelli spent many years in the theory group. It is hard to do justice to the varied contents of this excellent collection … I can only recommend that anyone involved in particle research should turn to the web for a full description of the richness of material that is included here … There is something here for everyone, and much for most. I’m sure Altarelli would have been pleased with that! The Editors are to be complimented for their initiative in making this unique volume possible.'Contemporary PhysicsGuido Altarelli was a leading figure in 20th century particle physics. His scientific contributions and leadership played a key role in the development of the Standard Model of fundamental interactions, as well as the current search for new physics beyond it, both at and beyond CERN. This book is a collection of original contributions, at the cutting edge of scientific research, by some of the leading theoretical and experimental high-energy physicists currently in the field. These were inspired by Guido's ideas, whether directly or indirectly. This book is ideal for researchers looking to keep up with the latest developments in high-energy physics.
Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts About This Book Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily Who This Book Is For Although budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status , will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure. What You Will Learn Master the steps involved in the predictive modeling process Grow your expertise in using R and its diverse range of packages Learn how to classify predictive models and distinguish which models are suitable for a particular problem Understand steps for tidying data and improving the performing metrics Recognize the assumptions, strengths, and weaknesses of a predictive model Understand how and why each predictive model works in R Select appropriate metrics to assess the performance of different types of predictive model Explore word embedding and recurrent neural networks in R Train models in R that can work on very large datasets In Detail R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R. Style and approach This book takes a step-by-step approach in explaining the intermediate to advanced concepts in predictive analytics. Every concept is explained in depth, supplemented with practical examples applicable in a real-world setting.
This title shows system administrators how to put together a system that can support RAID, install Linux software RAID or a Linux support hardware RAID card, and to build a high-performance file system.
The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. This approach is able to utilize complex, diverse and high-dimensional data sets, which often occur in manufacturing applications, and to integrate the important process intra- and interrelations. The approach has been evaluated using three scenarios from different manufacturing domains (aviation, chemical and semiconductor). The results, which are reported in detail in this book, confirmed that it is possible to incorporate implicit process intra- and interrelations on both a process and programme level by applying SVM-based feature ranking. In practice, this method can be used to identify the most important process parameters and state characteristics, the so-called state drivers, of a manufacturing system. Given the increasing availability of data and information, this selection support can be directly utilized in, e.g., quality monitoring and advanced process control. Importantly, the method is neither limited to specific products, manufacturing processes or systems, nor by specific quality concepts.
Author: Ingrid B. Potts, Douglas W. Harwood, Jessica M. Hutton, Chris A. Fees, Karin M. Bauer, Lindsay M. Lucas, Christopher S. Kinzel and Robert J. Frazier
This report from the second Strategic Highway Research Program (SHRP 2), which is administered by the Transportation Research Board of the National Academies, focuses on geometric design treatments that can be used to reduce delays due to nonrecurrent congestion.
In this Special Issue on human health engineering, we invited submissions exploring recent contributions to the field of human health engineering, which is the technology used for monitoring the physical or mental health status of individuals in a variety of applications. Contributions focused on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main aspects of the monitoring and control engineering scheme applied to human health applications, including papers focusing on measuring/sensing physiological variables, contributions describing research on the modelling of biological signals, papers highlighting health monitoring applications, and finally examples of control applications for human health. In comparison to biomedical engineering, the field of human health engineering also covers applications on healthy humans (e.g., sports, sleep, and stress) and thus not only contributes to develop technology for curing patients or supporting chronically ill people, but also more generally for disease prevention and optimizing human well-being.
