Assessing 20th century climate-vegetation feedbacks of land-use change and natural vegetation dynamics in a fully coupled vegetation-climate model

This study describes the coupling of the dynamic global vegetation model (DGVM), Lund–Potsdam–Jena Model for managed land (LPJmL), with the general circulation model (GCM), Simplified Parameterizations primitivE Equation DYnamics model (SPEEDY), to study the feedbacks between land-use change and natural vegetation dynamics and climate during the 20th century. We show that anthropogenic land-use change had a stronger effect on climate than the natural vegetation's response to climate change (e.g. boreal greening). Changes in surface albedo are an important driver of the climate's response; but, especially in the (sub)tropics, changes in evapotranspiration and the corresponding changes in latent heat flux and cloud formation can be of equal importance in the opposite direction. Our study emphasizes that implementing dynamic vegetation into climate models is essential, especially at regional scales: the dynamic response of natural vegetation significantly alters the climate change that is driven by increased atmospheric greenhouse gas concentrations and anthropogenic land-use chang

Critical collapse of collisionless matter - a numerical investigation

In recent years the threshold of black hole formation in spherically symmetric gravitational collapse has been studied for a variety of matter models. In this paper the corresponding issue is investigated for a matter model significantly different from those considered so far in this context. We study the transition from dispersion to black hole formation in the collapse of collisionless matter when the initial data is scaled. This is done by means of a numerical code similar to those commonly used in plasma physics. The result is that for the initial data for which the solutions were computed, most of the matter falls into the black hole whenever a black hole is formed. This results in a discontinuity in the mass of the black hole at the onset of black hole formation.Comment: 22 pages, LaTeX, 7 figures (ps-files, automatically included using psfig

Planar Laser Induced Fluorescence Mapping of a Carbon Laser Produced Plasma

We present measurements of ion velocity distribution profiles obtained by laser induced fluorescence (LIF) on an explosive laser produced plasma (LPP). The spatio-temporal evolution of the resulting carbon ion velocity distribution was mapped by scanning through the Doppler-shifted absorption wavelengths using a tunable, diode-pumped laser. The acquisition of this data was facilitated by the high repetition rate capability of the ablation laser (1 Hz) which allowed the accumulation of thousand of laser shots in short experimental times. By varying the intensity of the LIF beam, we were able to explore the effects of fluorescence power against laser irradiance in the context of evaluating the saturation versus the non-saturation regime. The small beam size of the LIF beam led to high spatial resolution of the measurement compared to other ion velocity distribution measurement techniques, while the fast-gated operation mode of the camera detector enabled the measurement of the relevant electron transitions

Rate-dependent propagation of cardiac action potentials in a one-dimensional fiber

Action potential duration (APD) restitution, which relates APD to the preceding diastolic interval (DI), is a useful tool for predicting the onset of abnormal cardiac rhythms. However, it is known that different pacing protocols lead to different APD restitution curves (RCs). This phenomenon, known as APD rate-dependence, is a consequence of memory in the tissue. In addition to APD restitution, conduction velocity restitution also plays an important role in the spatiotemporal dynamics of cardiac tissue. We present new results concerning rate-dependent restitution in the velocity of propagating action potentials in a one-dimensional fiber. Our numerical simulations show that, independent of the amount of memory in the tissue, waveback velocity exhibits pronounced rate-dependence and the wavefront velocity does not. Moreover, the discrepancy between waveback velocity RCs is most significant for small DI. We provide an analytical explanation of these results, using a system of coupled maps to relate the wavefront and waveback velocities. Our calculations show that waveback velocity rate-dependence is due to APD restitution, not memory.Comment: 17 pages, 7 figure

MACOC: a medoid-based ACO clustering algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

Rapid extraction of genomic DNA from small plant leaf samples

Research Not

Effects of Hydrogen Annealing, Sulfur Segregation and Diffusion on the Cyclic Oxidation Resistance of Superalloys: a Review

This review is based on the phenomenon of improved oxide scale adhesion for desulfurized superalloys. The proposed adhesion mechanism involves sulfur interfacial segregation and scale-metal bond weakening. Sulfur surface segregation on superalloys is examined as a function of temperature and sulfur content and related to classical behavior predicted by the McLean isotherm. Effective desulfurization to less than 1 ppmw can be accomplished by hydrogen annealing and is governed by sulfur diffusion kinetics in nickel. Hydrogen annealing results in excellent cyclic oxidation resistance for a number of advanced superalloys. The concept of a critical sulfur content is discussed in terms of practical annealing conditions and section thicknesses

Interpretation of High-Dimensional Linear Regression: Effects of Nullspace and Regularization Demonstrated on Battery Data

High-dimensional linear regression is important in many scientific fields. This article considers discrete measured data of underlying smooth latent processes, as is often obtained from chemical or biological systems. Interpretation in high dimensions is challenging because the nullspace and its interplay with regularization shapes regression coefficients. The data's nullspace contains all coefficients that satisfy $\mathbf{Xw}=\mathbf{0}$, thus allowing very different coefficients to yield identical predictions. We developed an optimization formulation to compare regression coefficients and coefficients obtained by physical engineering knowledge to understand which part of the coefficient differences are close to the nullspace. This nullspace method is tested on a synthetic example and lithium-ion battery data. The case studies show that regularization and z-scoring are design choices that, if chosen corresponding to prior physical knowledge, lead to interpretable regression results. Otherwise, the combination of the nullspace and regularization hinders interpretability and can make it impossible to obtain regression coefficients close to the true coefficients when there is a true underlying linear model. Furthermore, we demonstrate that regression methods that do not produce coefficients orthogonal to the nullspace, such as fused lasso, can improve interpretability. In conclusion, the insights gained from the nullspace perspective help to make informed design choices for building regression models on high-dimensional data and reasoning about potential underlying linear models, which are important for system optimization and improving scientific understanding.Comment: Manuscript: 14 pages, 7 figures; Supplementary Information: 4 pages, 2 figures; Code available: https://github.com/JoachimSchaeffer/HDRegAnalytic