Hypervelocity impact study: The effect of impact angle on crater morphology

The Space Power Institute (SPI) of Auburn University has conducted preliminary tests on the effects of impact angle on crater morphology for hypervelocity impacts. Copper target plates were set at angles of 30 deg and 60 deg from the particle flight path. For the 30 deg impact, the craters looked almost identical to earlier normal incidence impacts. The only difference found was in the apparent distribution of particle residue within the crater, and further research is needed to verify this. The 60 deg impacts showed marked differences in crater symmetry, crater lip shape, and particle residue distribution. Further research on angle effects is planned, because the particle velocities for these shots were relatively slow (7 km/s or less)

Diagram-based Analysis of Causal Systems (DACS): elucidating inter-relationships between determinants of acute lower respiratory infections among children in sub-Saharan Africa.

Effective interventions require evidence on how individual causal pathways jointly determine disease. Based on the concept of systems epidemiology, this paper develops Diagram-based Analysis of Causal Systems (DACS) as an approach to analyze complex systems, and applies it by examining the contributions of proximal and distal determinants of childhood acute lower respiratory infections (ALRI) in sub-Saharan Africa

Understanding the role of eco-evolutionary feedbacks in host-parasite coevolution

It is widely recognised that eco-evolutionary feedbacks can have important implications for evolution. However, many models of host-parasite coevolution omit eco-evolutionary feedbacks for the sake of simplicity, typically by assuming the population sizes of both species are constant. It is often difficult to determine whether the results of these models are qualitatively robust if eco-evolutionary feedbacks are included. Here, by allowing interspecific encounter probabilities to depend on population densities without otherwise varying the structure of the models, we provide a simple method that can test whether eco-evolutionary feedbacks per se affect evolutionary outcomes. Applying this approach to explicit genetic and quantitative trait models from the literature, our framework shows that qualitative changes to the outcome can be directly attributable to eco-evolutionary feedbacks. For example, shifting the dynamics between stable monomorphism or polymorphism and cycling, as well as changing the nature of the cycles. Our approach, which can be readily applied to many different models of host-parasite coevolution, offers a straightforward method for testing whether eco-evolutionary feedbacks qualitatively change coevolutionary outcomes.</p

mNCEA policy brief - PELCAP: Natural Capital in Plankton & Pelagic Habitats

This policy brief fact sheet descries ecosystem services provided by pelagic habitats and a natural capital accounting of the the economic contribution pelagic habitats provide to the UK, as estimated by the Office of National Statistics. Plankton is vital for the functioning of marine ecosystems but is hard to value monetarily. According to the Office of National Statistics, plankton in UK waters provides services valued at up to 3.4 £ billion per year. PHEG members think that this is an underestimate. This project was funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the marine arm of the Natural Capital and Ecosystem Assessment (NCEA) programme. The marine NCEA programme is leading the way in supporting Government ambition to integrate natural capital approaches into decision making for the marine environment. Find out more at https://www.gov.uk/government/publications/natural-capital-and-ecosystem-assessment-programm

A Fortran-Keras Deep Learning Bridge for Scientific Computing

Implementing artificial neural networks is commonly achieved via high-level programming languages like Python and easy-to-use deep learning libraries like Keras. These software libraries come pre-loaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful, with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model's emergent behavior to be assessed, i.e. when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of optimizer proves unexpectedly critical. This reveals many neural network architectures that produce considerable improvements in stability including some with reduced error, for an especially challenging training dataset

A Fortran-Keras Deep Learning Bridge for Scientific Computing

Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model’s emergent behavior to be assessed, i.e., when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of the optimizer proves unexpectedly critical. This in turn reveals many new neural network architectures that produce considerable improvements in climate model stability including some with reduced error, for an especially challenging training dataset

Homeostasis Patterns

Homeostasis is a regulatory mechanism that keeps a specific variable close to a set value as other variables fluctuate. The notion of homeostasis can be rigorously formulated when the model of interest is represented as an input-output network, with distinguished input and output nodes, and the dynamics of the network determines the corresponding input-output function of the system. In this context, homeostasis can be defined as an 'infinitesimal' notion, namely, the derivative of the input-output function is zero at an isolated point. Combining this approach with graph-theoretic ideas from combinatorial matrix theory provides a systematic framework for calculating homeostasis points in models and classifying the different homeostasis types in input-output networks. In this paper we extend this theory by introducing the notion of a homeostasis pattern, defined as a set of nodes, in addition to the output node, that are simultaneously infinitesimally homeostatic. We prove that each homeostasis type leads to a distinct homeostasis pattern. Moreover, we describe all homeostasis patterns supported by a given input-output network in terms of a combinatorial structure associated to the input-output network. We call this structure the homeostasis pattern network.Comment: 33 pages, 7 figures, 2 table

Global e-Readiness - For What? Readiness for e-Banking (JITD)

With the rapid diffusion of the Internet worldwide, there has been considerable interest in the e-potentials of developing countries giving rise to a 1st generation of e-Readiness studies. Moreover, e-Readiness means different things to different people, in different contexts, and for different purposes. Despite strong merits, this first generation of e-Readiness studies assumed a fixed, one-size-fits-all set of requirements, regardless of the characteristics of individual countries, the investment context, or the demands of specific applications. This feature obscures critical information for investors or policy analysts seeking to reduce uncertainties and/or make more educated decisions. But there is very little known about e-Readiness for e-Banking. In particular, based on lessons learnt to date and their implications for emerging realities of the 21st century, we designed and executed a research project with theoretical as well as practical dimensions to answer the question of e-Readiness for What, focusing specifically on e-Banking, based on the very assumption that one size can seldom, if ever, fit all. We propose and develop a conceptual framework for the "next generation" ereadiness - focusing on different e-Business applications in different economic contexts with potentially different pathways - as well as a data model - to explore e-Readiness for e-Banking in ten countries

mNCEA policy brief - Plenty more fish in the sea? Counting the cost of climate change on marine Natural Capital

This policy brief describes how predicted changes in productivity across the Atlantic will impact the amount of fish that the marine environment can support. This is bound to have important implications for marine food webs and our continued sustainable use of marine resources. Plankton form the foundation of commercially-valuable food chains to fish • Warming, stratification and reduced nutrient supply has already reduced plankton stocks • Reduced phytoplankton also means less efficient food chains • Even a modest (16-26%) continued decline in phytoplankton will magnify into a 38-55% decline in harvestable fish across the north Atlantic • Hotspots of this future decline in fish are in present-day fishing grounds • This risk-mapping approach provides a forward look for spatial protection and management This project was funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the marine arm of the Natural Capital and Ecosystem Assessment (NCEA) programme. The marine NCEA programme is leading the way in supporting Government ambition to integrate natural capital approaches into decision making for the marine environment. Find out more at https://www.gov.uk/government/publications/natural-capital-and-ecosystem-assessment-programm