A topic network analysis of the system turn in the environmental sciences

Scientometrics - The concept of Earth system science denotes a shift in the scientific discourse from disciplinary accounts of isolated components of the global environment towards the holistic and...     

Optimal control of molecular motion: design, implementation, and inversion

This paper reviews recent theoretical and experimental developments aimed at controlling molecular motion using tailored laser fields. Emphasis is given to seeking optimal designs for the laser controls and optimal implementation of the controls in the laboratory. Optimization on both counts provides a rigorous, flexible, and physically attractive means for obtaining the best possible control over molecular motion under any specified conditions. The theoretical design and laboratory implementation of control are best effected by a closed-loop process that draws on observations of the evolving molecular sample to steer it toward the desired target. Going beyond control, similar closed-loop laboratory learning concepts may lead to automated molecular monitors for inversion to systematically identify details of molecular Hamiltonians.     

AN IMPROVED VOLUMENOMETER*

A volumcnometer of improved design has been developed for measuring the solid volumes of specimens varying in size up to that of standard refractory bricks. The chief feature of the new design is the use of a closed-cistern, altitude-type mercurial barometer to replace the simple manometer previously used. The barometer is de signed for use in a constant temperature room (21.1°C.) where the acceleration of gravity is 980.1 cm. per sec.². Under these conditions, pressure readings are auto matically corrected to standard conditions. A vernier permits rapid readings to 0.1 mm. of mercury.     

A 1970 overview of sources of support for psychological research.

Provides information on funding sources for research activities. Grant and contract programs sponsored by various federal agencies are described, including those of the National Science Foundation, various branches of Health, Education and Welfare (the National Institute of Mental Health, the National Institute of Child Health and Human Development, the Office of Education, and the Social and Rehabilitation Service), and the Departments of Labor, Transportation, Defense, State, Justice, Housing and Urban Development, and the National Aeronautics and Space Program. Programs of 31 private foundations are also listed, including the foundation name and address, and the purposes and activities of the respective support programs. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Extraction of parameters and their error distributions from cyclic voltammograms using bootstrap resampling enhanced by solution maps: Computational study

The conventional determination of model parameter errors in least-squares regression of experimental cyclic voltammetric data assumes validity of local approximations (e.g., linearization) in the parameter space and normal distributions of the data and parameter errors. Such assumptions may not always be satisfied in practice. Bootstrap resampling techniques present a more universally applicable approach to error estimation, which until now has not been used in cyclic voltammetric studies, owing to the high costs of the required voltammogram simulations. We demonstrate that the burden of computing voltammograms can be significantly reduced by the use of high-dimensional model representation (HDMR) solution mapping techniques, thereby making it feasible to apply the bootstrap data analysis in cyclic voltammetry. We perform computational experiments with bootstrap resampling, enhanced by HDMR maps, for a typical cyclic voltammetric model (i.e., the Eqrev Cirr Eqrev reaction mechanism at a planar macroelectrode under semi-infinite, pure diffusion transport conditions). The experiments reveal that the bootstrap distributions of the estimated parameters provide a satisfactory quantification of the parameter errors and can also be used for detecting statistical correlations of the parameters.     

Volume-based large dynamic graph analysis supported by evolution provenance

We present an approach for the visualization and interactive analysis of dynamic graphs that contain a large number of time steps. A specific focus is put on the support of analyzing temporal aspects in the data. Central to our approach is a static, volumetric representation of the dynamic graph based on the concept of space-time cubes that we create by stacking the adjacency matrices of all time steps. The use of GPU-accelerated volume rendering techniques allows us to render this representation interactively. We identified four classes of analytics methods as being important for the analysis of large and complex graph data, which we discuss in detail: data views, aggregation and filtering, comparison, and evolution provenance. Implementations of the respective methods are presented in an integrated application, enabling interactive exploration and analysis of large graphs. We demonstrate the applicability, usefulness, and scalability of our approach by presenting two examples for analyzing dynamic graphs. Furthermore, we let visualization experts evaluate our analytics approach.