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This book systematically introduces readers to computational granular mechanics and its relative engineering applications. Part I describes the fundamentals, such as the generation of irregular particle shapes, contact models, macro-micro theory, DEM-FEM coupling, and solid-fluid coupling of granular materials. It also discusses the theory behind various numerical methods developed in recent years. Further, it provides the GPU-based parallel algorithm to guide the programming of DEM and examines commercial and open-source codes and software for the analysis of granular materials. Part II focuses on engineering applications, including the latest advances in sea-ice engineering, railway ballast dynamics, and lunar landers. It also presents a rational method of parameter calibration and thorough analyses of DEM simulations, which illustrate the capabilities of DEM. The computational mechanics method for granular materials can be applied widely in various engineering fields, such as rock and soil mechanics, ocean engineering and chemical process engineering.

Mechanics. --- Mechanics, Applied. --- Continuum physics. --- Ocean engineering. --- Theoretical and Applied Mechanics. --- Classical and Continuum Physics. --- Offshore Engineering. --- Deep-sea engineering --- Oceaneering --- Submarine engineering --- Underwater engineering --- Engineering --- Marine resources --- Oceanography --- Classical field theory --- Continuum physics --- Physics --- Continuum mechanics --- Applied mechanics --- Engineering, Mechanical --- Engineering mathematics --- Classical mechanics --- Newtonian mechanics --- Dynamics --- Quantum theory --- Equipment and supplies --- Granular materials --- Bulk solids --- Materials

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Paleontology --- Fossilogy --- Fossilology --- Palaeontology --- Paleontology, Zoological --- Paleozoology --- Historical geology --- Zoology --- Fossils --- Prehistoric animals in motion pictures --- Congresses --- Theses

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This important and timely text/reference reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: Describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures Examines the applications and implementations that utilize big data in cloud architectures Surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions Identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches Provides relevant theoretical frameworks, empirical research findings, and numerous case studies Discusses real-world applications of algorithms and techniques to address the challenges of big datasets This authoritative volume will be of great interest to researchers, enterprise architects, business analysts, IT infrastructure managers and application developers, who will benefit from the valuable insights offered into the adoption of architectures for big data and cloud computing. The work is also suitable as a textbook for university instructors, with the outline for a possible course structure suggested in the preface. The editors are all members of the Computing and Mathematics Department at the University of Derby, UK, where Dr. Marcello Trovati serves as a Senior Lecturer in Mathematics, Dr. Richard Hillas a Professor and Head of the Computing and Mathematics Department, Dr. Ashiq Anjum as a Professor of Distributed Computing, Dr. Shao Ying Zhu as a Senior Lecturer in Computing, and Dr. Lu Liu as a Professor of Distributed Computing. The other publications of the editors include the Springer titles Guide to Security Assurance for Cloud Computing, Guide to Cloud Computing and Cloud Computing for Enterprise Architectures.

Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics --- Cloud computing. --- Big data. --- Data sets, Large --- Large data sets --- Computer science. --- Computer communication systems. --- Mathematical statistics. --- Computer science --- Computer simulation. --- Computer Science. --- Probability and Statistics in Computer Science. --- Computer Communication Networks. --- Simulation and Modeling. --- Math Applications in Computer Science. --- Mathematics. --- Electronic data processing --- Web services --- Distributed processing --- Data sets --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Informatics --- Science --- Computer science—Mathematics. --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Network computers --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods

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This important and timely text/reference reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: Describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures Examines the applications and implementations that utilize big data in cloud architectures Surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions Identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches Provides relevant theoretical frameworks, empirical research findings, and numerous case studies Discusses real-world applications of algorithms and techniques to address the challenges of big datasets This authoritative volume will be of great interest to researchers, enterprise architects, business analysts, IT infrastructure managers and application developers, who will benefit from the valuable insights offered into the adoption of architectures for big data and cloud computing. The work is also suitable as a textbook for university instructors, with the outline for a possible course structure suggested in the preface. The editors are all members of the Computing and Mathematics Department at the University of Derby, UK, where Dr. Marcello Trovati serves as a Senior Lecturer in Mathematics, Dr. Richard Hillas a Professor and Head of the Computing and Mathematics Department, Dr. Ashiq Anjum as a Professor of Distributed Computing, Dr. Shao Ying Zhu as a Senior Lecturer in Computing, and Dr. Lu Liu as a Professor of Distributed Computing. The other publications of the editors include the Springer titles Guide to Security Assurance for Cloud Computing, Guide to Cloud Computing and Cloud Computing for Enterprise Architectures.

Operational research. Game theory --- Mathematical statistics --- Mathematics --- Computer architecture. Operating systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- stochastische analyse --- cloud computing --- vormgeving --- informatica --- statistiek --- mineralen (chemie) --- simulaties --- externe fixatie (geneeskunde --- mijnbouw --- informatietechnologie --- wiskunde --- computernetwerken

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The importance of job satisfaction has long been discussed in the human resource field. Numerous researches brought forward the ingredients of healthy jobs such as task variety, autonomy, social support, and physical demands. Nevertheless, little attention has been put to the job design process itself. What makes managers design healthy or unhealthy is a question that has seldom been addressed; the environmental influences and antecedents that affect managers’ mindset of job design is not identified yet. In order to recognize these influences, a reliable tool that is able to capture different mindsets behind the job design process is necessary. This study aims at designing an instrument that is capable to do so. A survey with nine job design scenario’s is developed. Seven of which focused on job enlargement and enrichment and two focused on job autonomy. Each scenario is further divided into three conditions: the neutral condition, the motivational condition and the efficiency condition. Job design interventions were added to the neutral condition to manipulate the job design process. Taken together, the tool still requires improvements at the current stage but it provides future researchers a prototype of a job design measurement tool.