Surface determination of the air-earth electrical current density using co-located sensors of different geometry

A vertical conduction current flows in the atmosphere as a result of the global atmospheric electric circuit. The current at the surface consists of the conduction current and a locally generated displacement current, which are often approximately equal in magnitude. A method of separating the two currents using two collectors of different geometry is investigated. The picoammeters connected to the collectors have a RC time constant of approximately 3 s, permitting the investigation of higher frequency air-earth current changes than previously achieved. The displacement current component of the air-earth current derived from the instrument agrees with calculations using simultaneous data from a co-located fast response electric field mill. The mean value of the nondisplacement current measured over 9 h was 1.76 +/- 0.002 pA m(-2). (c) 2006 American Institute of Physics.

Dynamic scaling of bred vectors in spatially extended chaotic systems

We unfold a profound relationship between the dynamics of finite-size perturbations in spatially extended chaotic systems and the universality class of Kardar-Parisi-Zhang (KPZ). We show how this relationship can be exploited to obtain a complete theoretical description of the bred vectors dynamics. The existence of characteristic length/time scales, the spatial extent of spatial correlations and how to time it, and the role of the breeding amplitude are all analyzed in the light of our theory. Implications to weather forecasting based on ensembles of initial conditions are also discussed.

The expected imprint of flux rope geometry on suprathermal electrons in magnetic clouds

Magnetic clouds are a subset of interplanetary coronal mass ejections characterized by a smooth rotation in the magnetic field direction, which is interpreted as a signature of a magnetic flux rope. Suprathermal electron observations indicate that one or both ends of a magnetic cloud typically remain connected to the Sun as it moves out through the heliosphere. With distance from the axis of the flux rope, out toward its edge, the magnetic field winds more tightly about the axis and electrons must traverse longer magnetic field lines to reach the same heliocentric distance. This increased time of flight allows greater pitch-angle scattering to occur, meaning suprathermal electron pitch-angle distributions should be systematically broader at the edges of the flux rope than at the axis. We model this effect with an analytical magnetic flux rope model and a numerical scheme for suprathermal electron pitch-angle scattering and find that the signature of a magnetic flux rope should be observable with the typical pitch-angle resolution of suprathermal electron data provided ACE's SWEPAM instrument. Evidence of this signature in the observations, however, is weak, possibly because reconnection of magnetic fields within the flux rope acts to intermix flux tubes.

Dynamic load balancing in parallel KD-tree k-means

One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting geometrical constraints and an efficient data structure, notably a multidimensional binary search tree (KD-Tree). These techniques allow to reduce the number of distance computations the algorithm performs at each iteration. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient k-Means variants in parallel computing environments. In this work, we provide a parallel formulation of the KD-Tree based k-Means algorithm for distributed memory systems and address its load balancing issue. Three solutions have been developed and tested. Two approaches are based on a static partitioning of the data set and a third solution incorporates a dynamic load balancing policy.

Stages of organizational transformation in transition economies: A dynamic capabilities approach

How do organizations previously dominated by the state develop dynamic capabilities that would support their growth in a competitive market economy? We develop a theoretical framework of organizational transformation that explains the processes by which organizations learn and develop dynamic capabilities in transition economies. Specifically, the framework theorizes about the importance of, and inter-relationships between, leadership, organizational learning, dynamic capabilities, and performance over three stages of transformation. Propositions derived from this framework explain the pre-conditions enabling organizational learning, the linkages between types of learning and functions of dynamic capabilities, and the feedback from dynamic capabilities to organizational learning that allows firms in transition economies to regain their footing and build long-term competitive advantage. We focus on transition contexts, where these processes have been magnified and thus offer new insights into strategizing in radically altered environments.

Dynamic causal modeling applied to fMRI data shows high reliability

Sensitivity, specificity, and reproducibility are vital to interpret neuroscientific results from functional magnetic resonance imaging (fMRI) experiments. Here we examine the scan–rescan reliability of the percent signal change (PSC) and parameters estimated using Dynamic Causal Modeling (DCM) in scans taken in the same scan session, less than 5 min apart. We find fair to good reliability of PSC in regions that are involved with the task, and fair to excellent reliability with DCM. Also, the DCM analysis uncovers group differences that were not present in the analysis of PSC, which implies that DCM may be more sensitive to the nuances of signal changes in fMRI data.

Influence of adsorption geometry in the heterogeneous enantioselective catalytic hydrogenation of a prototypical enone

Asymmetric catalysis is of paramount importance in organic synthesis and, in current practice, is achieved by means of homogeneous catalysts. The ability to catalyze such reactions heterogeneously would have a major impact both in the research laboratory and in the production of fine chemicals and pharmaceuticals, yet heterogeneous asymmetric hydrogenation of C═C bonds remains hardly explored. Very recently, we demonstrated how chiral ligands that anchor robustly to the surface of Pd nanoparticles promote asymmetric catalytic hydrogenation: ligand rigidity and stereochemistry emerged as key factors. Here, we address a complementary question: how does the enone reactant adsorb on the metal surface, and what implications does this have for the enantiodifferentiating interaction with the surface-tethered chiral modifiers? A reaction model is proposed, which correctly predicts the identity of the enantiomer experimentally observed in excess.