Stellar Fluxes as Probes of Convection in Stellar Atmospheres

Convection and turbulence in stellar atmospheres have a significant effect on the emergent flux from late-type stars. The theoretical advancements in convection modelling over recent years have proved challenging for the observers to obtain measurements with sufficient precision and accuracy to allow discrimination between the various predictions. An overview of the current observational techniques used to evaluate various convection theories is presented, including photometry, spectrophotometry, and spectroscopy. The results from these techniques are discussed, along with their successes and limitations. The prospects for improved observations of stellar fluxes are also given.Comment: 3 pages, 1 figure; to appear in Convection in Astrophysics, Proc. IAUS 239, F.Kupka, I.W. Roxburgh, K.L. Chan ed

Discovery of Antagonist Peptides against Bacterial Helicase-Primase Interaction in B. stearothermophilus by Reverse Yeast Three-Hybrid

SummaryDeveloping small-molecule antagonists against protein-protein interactions will provide powerful tools for mechanistic/functional studies and the discovery of new antibacterials. We have developed a reverse yeast three-hybrid approach that allows high-throughput screening for antagonist peptides against essential protein-protein interactions. We have applied our methodology to the essential bacterial helicase-primase interaction in Bacillus stearothermophilus and isolated a unique antagonist peptide. This peptide binds to the primase, thus excluding the helicase and inhibiting an essential interaction in bacterial DNA replication. We provide proof of principle that our reverse yeast three-hybrid method is a powerful “one-step” screen tool for direct high-throughput antagonist peptide selection against any protein-protein interaction detectable by traditional yeast two-hybrid systems. Such peptides will provide useful “leads” for the development of new antibacterials

Comparison and validation of three versions of a forest wind risk model

Predicting the probability of wind damage in both natural and managed forests is important for understanding forest ecosystem functioning, the environmental impact of storms and for forest risk management. We undertook a thorough validation of three versions of the hybrid-mechanistic wind risk model, ForestGALES, and a statistical logistic regression model, against observed damage in a Scottish upland conifer forest following a major storm. Statistical analysis demonstrated that increasing tree height and local wind speed during the storm were the main factors associated with increased damage levels. All models provided acceptable discrimination between damaged and undamaged forest stands but there were trade-offs between the accuracy of the mechanistic models and model bias. The two versions of the mechanistic model with the lowest bias gave very comparable overall results at the forest scale and could form part of a decision support system for managing forest wind damage risk

Variance-based sensitivity analysis of a wind risk model - Model behaviour and lessons for forest modelling

We submitted the semi-empirical, process-based wind-risk model ForestGALES to a variance-based sensitivity analysis using the method of Sobol for correlated variables proposed by Kucherenko et al. (2012). Our results show that ForestGALES is able to simulate very effectively the dynamics of wind damage to forest stands, as the model architecture reflects the significant influence of tree height, stocking density, dbh, and size of an upwind gap, on the calculations of the critical wind speeds of damage. These results highlight the importance of accurate knowledge of the values of these variables when calculating the risk of wind damage with ForestGALES. Conversely, rooting depth and soil type, i.e. the model input variables on which the empirical component of ForestGALES that describes the resistance to overturning is based, contribute only marginally to the variation in the outputs. We show that these two variables can confidently be fixed at a nominal value without significantly affecting the model's predictions. The variance-based method used in this study is equally sensitive to the accurate description of the probability distribution functions of the scrutinised variables, as it is to their correlation structure.JRC.C.3-Energy Security, Distribution and Market

Efficient Classical Simulation of Optical Quantum Circuits

We identify a broad class of physical processes in an optical quantum circuit that can be efficiently simulated on a classical computer: this class includes unitary transformations, amplification, noise, and measurements. This simulatability result places powerful constraints on the capability to realize exponential quantum speedups as well as on inducing an optical nonlinear transformation via linear optics, photodetection-based measurement and classical feedforward of measurement results, optimal cloning, and a wide range of other processes.Comment: 4 pages, published versio

Use of machine learning techniques to model wind damage to forests

This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed individually for both a small forest area containing a set of 29 permanent sample plots that were damaged in Storm Martin in December 1999, and from a much larger set of 235 forest inventory data damaged in Storm Klaus in January 2009. Both data sets are within the Landes de Gascogne Forest in Nouvelle Aquitaine, France. The models were tested both against the data from which they were developed, and against the data set from the other storm. For comparison with an earlier study using the same data, logistic regression models were also developed. In addition, the ability of machine learning techniques to substitute for a mechanistic wind damage risk model by training them with previous mechanistic model predictions was tested. All models were accurate at identifying whether trees would be damaged or not damaged but the random forests models were more accurate, had higher discriminatory power, and were almost totally unaffected by the removal of any individual input variable. However, if all information relating to a stand was removed the random forests model lost accuracy and discriminatory power. The other models were similarly affected by the removal of all site information but none of the models were affected by removal of all tree information, suggesting that damage in the Landes de Gascogne Forest occurs at stand scale and is not controlled by individual tree characteristics. The models developed with the large comprehensive database were also accurate in identifying damaged trees when applied to the small forest data damaged in the earlier storm. However, none of the models developed with the smaller forest data set could successfully discriminate between damaged and undamaged trees when applied across the whole landscape. All models were very successful in replicating the predictions of the mechanistic wind risk model and using them as a substitute for the mechanistic model predictions of critical wind speed did not affect the damage model results. Overall the results suggest that random forests provide a significant advantage over other statistical modelling techniques and the random forest models were found to be more robust in their predictions if all input variables were not available. In addition, the ability to replace the mechanistic wind damage model suggests that random forests could provide a powerful tool for damage risk assessment at the stand or single tree level over large regions and provide rapid assessment of the impact of different management strategies or be used in the development of optimised forest management with multiple objectives and constraints including the risk of wind damage

Review article: A European perspective on wind and storm damage – from the meteorological background to index-based approaches to assess impacts

Wind and windstorms cause severe damage to natural and human-made environments. Thus, wind-related risk assessment is vital for the preparation and mitigation of calamities. However, the cascade of events leading to damage depends on many factors that are environment-specific and the available methods to address wind-related damage often require sophisticated analysis and specialization. Fortunately, simple indices and thresholds are as effective as complex mechanistic models for many applications. Nonetheless, the multitude of indices and thresholds available requires a careful selection process according to the target sector. Here, we first provide a basic background on wind and storm formation and characteristics, followed by a comprehensive collection of both indices and thresholds that can be used to predict the occurrence and magnitude of wind and storm damage. We focused on five key sectors: forests, urban areas, transport, agriculture and wind-based energy production. For each sector we described indices and thresholds relating to physical properties such as topography and land cover but also to economic aspects (e.g. disruptions in transportation or energy production). In the face of increased climatic variability, the promotion of more effective analysis of wind and storm damage could reduce the impact on society and the environment

Release of multiple bubbles from cohesive sediments

Author Posting. © American Geophysical Union, 2011. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 38 (2011): L08606, doi:10.1029/2011GL046870.Methane is a strong greenhouse gas, and marine and wetland sediments constitute significant sources to the atmosphere. This flux is dominated by the release of bubbles, and quantitative prediction of this bubble flux has been elusive because of the lack of a mechanistic model. Our previous work has shown that sediments behave as elastic fracturing solids during bubble growth and rise. We now further argue that bubbles can open previously formed, partially annealed, rise tracts (fractures) and that this mechanism can account for the observed preferential release at low tides in marine settings. When this mechanical model is applied to data from Cape Lookout Bight, NC (USA), the results indicate that methanogenic bubbles released at this site do indeed follow previously formed rise tracts and that the calculated release rates are entirely consistent with the rise of multiple bubbles on tidal time scales. Our model forms a basis for making predictions of future bubble fluxes from warming sediments under the influence of climate change.This research was funded by the U.S. Office of Naval research through grants N00014‐08‐0818 and N00014‐05‐1‐0175 (project managers J. Eckman and T. Drake), the Natural Sciences and Engineering Council of Canada, and the Killam Trust (Dalhousie University)

Properties of UK-Grown Sitka Spruce: Extent and Sources of Variation.

Structural timber produced from Sitka spruce plantations in the UK typically achieves the requirements for the C16 strength class. However, very little is known about the variability of this plantation resource, or the factors that contribute to this variability. A study to benchmark the properties of the current resource found that there was a considerable amount of variation in wood properties between sites as well as between trees within a site. This benchmarking study has been complemented by studies which have investigated the impacts of genetics, precommercial thinning and rotation length on timber properties. In these studies, measurements of stress wave velocity have been made on standing trees, freshly-felled logs and sawn timber. Knowledge gained should assist in better utilisation of the current resource as well as identifyingmanagement practices that will lead to an improved future resource for structural applications

Properties of UK-Grown Sitka Spruce: Extent and Sources of Variation.

Structural timber produced from Sitka spruce plantations in the UK typically achieves the requirements for the C16 strength class. However, very little is known about the variability of this plantation resource, or the factors that contribute to this variability. A study to benchmark the properties of the current resource found that there was a considerable amount of variation in wood properties between sites as well as between trees within a site. This benchmarking study has been complemented by studies which have investigated the impacts of genetics, precommercial thinning and rotation length on timber properties. In these studies, measurements of stress wave velocity have been made on standing trees, freshly-felled logs and sawn timber. Knowledge gained should assist in better utilisation of the current resource as well as identifyingmanagement practices that will lead to an improved future resource for structural applications