Science Communication with Dinosaurs

Dinosaurs successfully conjure images of lost worlds and forgotten lives. Our understanding of these iconic, extinct animals now comes from many disciplines, not just the science of palaeontology. In recent years palaeontology has benefited from the application of new and existing techniques from physics, biology, chemistry, engineering, but especially computational science. The application of computers in palaeontology is highlighted in this chapter as a key area of development in studying fossils. The advances in high performance computing (HPC) have greatly aided and abetted multiple disciplines and technologies that are now feeding paleontological research, especially when dealing with large and complex data sets. We also give examples of how such multidisciplinary research can be used to communicate not only specific discoveries in palaeontology, but also the methods and ideas, from interrelated disciplines to wider audiences. Dinosaurs represent a useful vehicle that can help enable wider public engagement, communicating complex science in digestible chunks.

Improving Computational Models and Practices

Since infectious diseases pose a significant risk to human health many countries aim to control their spread. Public health bodies faced with a disease threat must understand the disease’s progression and its transmission process. From this understanding it is possible to evaluate public health interventions intended to decrease impacts on the population. Commonly, contingency planning has been achieved through epidemiological studies and the use of relatively simple models. However, computational methods increasingly allow more complex, and potentially more realistic, simulations of various scenarios of the control of the spread of disease. However, understanding computational results from more sophisticated models can pose considerable challenges. A case study of a system combining a complex infectious disease model with interactive visualization and computational steering tools shows some of the opportunities this approach offers to infectious disease control.

Visualization

This chapter focuses on state of the art at the intersection of visualization and CSE. From understanding current trends it looks to future applications for these technologies. Some background is provided into visualization and its relation with CSE as well as with software and hardware frameworks that visualization systems depend on. Important emerging research areas are identified, including: interactive simulation and computational steering; collaborative, remote visualization and visualization services; VR technologies for visualization; user experience and assessment; teaching and serious gaming; communicating science to the public; ultra-scale visualization; and computational aesthetics. This should present the readers with real possibilities for CSE no matter what their disciplinary background.

Mesh Morphing and Smoothing by Means of Radial Basis Functions (RBF)

Radial Basis Functions (RBF) mesh morphing, its theoretical basis, its numerical implementation, and its use for the solution of industrial problems, mainly in Computer Aided Engineering (CAE), are introduced. RBF theory is presented showing the mathematical framework for a basic RBF fit, its MathCAD implementation, and its usage. The equations required for a 2D case comparing RBF smoothing and pseudosolid smoothing based on Finite Elements Method (FEM) structural solution are given; RBF exhibits excellent performance and produces high quality meshes even for very large deformations. The industrial application of RBF morphing to Computational Fluid Dynamics (CFD) is covered presenting the RBF Morph software, its implementation, and a description of its working principles and performance. Practical examples include: physical problems that use CFD, shape parameterisation strategy, and modelling guidelines for setting-up a well posed RBF problem. Future directions explored are: transient shape evolution, fluid structure interaction modelling, and shape parameterization in multi-physics, multi-objective design optimization.

Opportunities and Challenges in Porting a Parallel Code from a Tightly-Coupled System to the Distributed EU Grid, Enabling Grids for E-sciencE

The computational architecture of Enabling Grids for E-sciencE is introduced as it made our code porting very challenging, and the discussion presented is directly applicable to EGEE users. The final solution to the code poring problem is proposed, and its performance is examined. The solution to this problem be generally faced in the other large scale computation and so is applicable to users of other HPC facilities. This chapter gives a hint to those who have difficulties in applications with heavy data Input/Output (I/O) under the computational environment whose weak point is the data I/O.

High-Performance Customizable Computing

Many accelerator-based computers have demonstrated that they can be faster and more energy-efficient than traditional high-performance multi-core computers. Two types of programmable accelerators are available in high-performance computing: general-purpose accelerators such as GPUs, and customizable accelerators such as FPGAs, although general-purpose accelerators have received more attention. This chapter reviews the state-of-the-art and current trends of high-performance customizable computers (HPCC) and their use in Computational Science and Engineering (CSE). A top-down approach is used to be more accessible to the non-specialists. The “top view” is provided by a taxonomy of customizable computers. This abstract view is accompanied with a performance comparison of common CSE applications on HPCC systems and high-performance microprocessor-based computers. The “down view” examines software development, describing how CSE applications are programmed on HPCC computers. Additionally, a cost analysis and an example illustrate the origin of the benefits. Finally, the future of the high-performance customizable computing is analyzed.

Some Illustrations of Information Geometry in Biology and Physics

Many real processes have stochastic features which seem to be representable in some intuitive sense as `close to Poisson’, `nearly random’, `nearly uniform’ or with binary variables `nearly independent’. Each of those particular reference states, defined by an equation, is unstable in the formal sense, but it is passed through or hovered about by the observed process. Information geometry gives precise meaning for nearness and neighbourhood in a state space of processes, naturally quantifying proximity of a process to a particular state via an information theoretic metric structure on smoothly parametrized families of probability density functions. We illustrate some aspects of the methodology through case studies: inhomogeneous statistical evolutionary rate processes for epidemics, amino acid spacings along protein chains, constrained disordering of crystals, distinguishing nearby signal distributions and testing pseudorandom number generators.

Developing Software for a Scientific Community

There are significant challenges in developing scientific software for a broad community. In this chapter, we discuss how these challenges are somewhat different both from those encountered when a scientist end-user developer develops software to address a very specific scientific problem of his/her own, and from those encountered in many commercial developments. However, many developers of scientific community software are steeped in the culture of either scientific end-user or commercial development. As we shall discuss herein, neither background provides sufficient experience so as to meet the challenges of developing software for a scientific community. We make various proposals as to which development approaches, methods, techniques and tools might be useful in this context, and just as importantly, which might not.

Artificial Neural Network Modelling of Sequencing Batch Reactor Performance

Sequencing batch reactor (SBR) is a versatile, eco-friendly, and cost-saving process for the biological treatment of nutrient-rich wastewater, at varying loading rates. The performance of a laboratory-scale SBR was monitored to ascertain the chemical oxygen demand (COD) and total nitrogen (T-N) removals under four different operating conditions, by varying the operating time for the nitrification/denitrification steps, i.e., the cycle times. A multi-layered neural network was developed using COD, T-N, carbon to nitrogen ratio (C/N), aeration time, and mixed liquor suspended solids concentration (MLSS) data. This chapter compares the neural simulation results to the experimental results and extracts information on the significant factors affecting SBR performance. The application of artificial neural networks to biological processes such as SBR is a relatively new technique in wastewater and water quality management, and the results presented herein indicate the promising start of the adoption of computational science in this domain of research.

Visualizing Indicators of Debt Crises in a Lower Dimension

Since the 1980s, two severe global waves of sovereign defaults have occurred in less developed countries (LDCs): the LDC defaults in the 1980s and the LDC defaults at the turn of the 21st century. To date, the topic is contemporary, while the forecasting and monitoring results of debt crises are still at a preliminary stage. This chapter explores whether the application of the Self-Organizing Map (SOM), a neural network-based visualization tool, facilitates the monitoring of multidimensional financial data. Thus, this chapter presents a SOM model for visualizing the evolution of sovereign debt crises’ indicators. The results of this chapter indicate that the SOM is a feasible tool for visualization of early warning signals of sovereign defaults.