Authors highlight computer software in fictional story about business owner The world of computer software can be difficult to understand if one is not entrenched in it every day. In their book ¿Power of Scrum¿ (ISBN 1463578067) Jeffrey V. Sutherland, D.M. van Solingen and Eelco Rustenberg use a fictional story to help the readers to better understand the real intricacies of the frame working software, Scrum. Scrum is a software development methodology that allows companies to work more efficiently and productively. This book gives readers an easy-to-read overview of Scrum and how and why it works. It uses a narrative story to explain some of the basic do¿s and don¿ts of using Scrum while presenting only a small amount of the theory that goes into the system. The story introduces us to Mark Resting, the Chief Technology Officer (CTO) of a software company. Resting has been having some difficulty with a project for one of his company¿s major customers, LogiStrux. After meeting with the customer¿s board in London, he meets Jerry, a Scrum system expert. After discussing Scrum, Resting is convinced that some of the strategies that the system uses can help in the LogiStrux project and possibly even in some of his company¿s other areas of work. Resting invites Jerry to his company¿s headquarters in Washington, D.C. so he can show him and his employees how to implement these new methods. ¿The book is a very easy read and can be read cover to cover in less than three hours,¿ Sutherland says. ¿The characters and narrative development were created to help translate some of the theories in Scrum to a more personal, and easier to understand level.¿ Sutherland is hoping that the story and non-technical language attracts a wider audience than just those interested in technology and software. He also hopes the book will be purchased by companies and given to the associates who use Scrum so they can become more familiar with it. ¿Power of Scrum¿ is available for sale at Amazon.com.@en

Deterministic and probabilistic Profile models have been compared with hydrodynamic and morphodynamic data of laboratory and field experiments on the time scale of storms and seasons. The large-scale laboratory experiment is a pure 2D case and offers and ideal test case for cross-shore Profile models, as disturbing alongshore non-uniformities are absent. The field experiments are performed at the Eginond site (The Netherlands) during the EU-COAST3D project and represent storm time scale (Oct.-Nov. 1998) as well as seasonal time scale conditions (May 1998-Sep. 1999). The objective of the paper is to present information of coastal processes on these time scales and to assess the predictive capabilities Coastal Process-based Profile models with respect to hydrodynamics and morphodynamics at sandy beaches on the time scales of storms and seasons. Profile models can quite accurately (errors smaller than 10 represent the cross-shore significant wave height distribution in the surf zone, if the wave breaking model is properly calibrated. The wave breaking coefficient should be a function of local wave steepness and bottom slope for most accurate results. Profile models can reasonably represent the cross-shore and longshore currents (undertow) in a pure 2D case and in 3D field conditions. Profile models including cross-shore mixing effects and breaker delay effects do not produce better predictions of the longshore and cross-shore current velocities. Profile models using default settings can quite reasonably simulate the behaviour of the outer and inner bars on the storm time scale; the behaviour of the beach cannot be modelled with sufficient accuracy on the storm time scale. Profile models can reasonably simulate the post-storm onshore bar migration, provided that the near-bed orbital velocities and wave asymmetry-related sand transport are represented in a sufficiently accurate way (using non-linear wave theories). Profile models cannot simulate the beach recovery processes on the post-storm time scale, because these essentially 3D processes are not sufficiently known to be included in the models. Profile models using default settings cannot simulate the behaviour of the outer and inner bars and the beach on the seasonal time scale; the behaviour of the outer bar on the seasonal time scale can only be represented properly after tuning using measured bed profiles, The simulation of the inner bar and beach morphology on the seasonal time scale could not be improved by tuning, (C) 2002 Elsevier Science B.V All rights reserved.@en

Most numerical models are run and compared to data in a subjective manner. This paper demonstrates how model performance statistics can be used to calibrate and/or validate hydrodynamic models in a more objective way. Statistics were also used to compare model runs that used different amounts of field data in order to inform the debate about the optimum mix of modelling and measurement. The hydrodynamics around the mouth of the Teign estuary (UK) have been simulated using two coastal area numerical modelling systems. Model performance statistics were calculated to assess the accuracy of the predictions of the measured currents at a number of locations around the estuary mouth. The relative mean absolute error was used as it is applicable to vectors as well as scalars and measures all types of errors. An adjusted relative mean absolute error was also used to reduce the effect of measurement error. A classification table was adopted that categorises the results according to the size of the error. In addition, time series and scatter plots were used to judge the performance of the modelling systems. Calm conditions during a spring tide were simulated, as was a relatively large storm. The two modelling systems gave more or less equal performances when run in engineering mode (where default values were used for most of the system settings). In each case, one modelling system performed better than the other at some locations and worse than it at other locations. One model was also run using a scientific approach, where different amounts of information were used to alter the model settings and sensitivity tests were performed. The model performance statistics showed that using more data does not necessarily lead to better model predictions. New methods for incorporating data into the operation of a model need to be evaluated thoroughly before they can be used without site-specific calibration.@en