Density Functional Calculations On First-Row Transition Metals

The excitation energies and ionization potentials of the atoms in the first transition series are notoriously difficult to compute accurately. Errors in calculated excitation energies can range from 1--4 eV at the Hartree-Fock level, and errors as high as 1.5eV are encountered for ionization energies. In the current work we present and discuss the results of a systematic study of the first transition series using a spin-restricted Kohn-Sham density-functional method with the gradient-corrected functionals of Becke and Lee, Yang and Parr. Ionization energies are observed to be in good agreement with experiment, with a mean absolute error of approximately 0.15eV; these results are comparable to the most accurate calculations to date, the Quadratic Configuration Interaction (QCISD(T)) calculations of Raghavachari and Trucks. Excitation energies are calculated with a mean error of approximately 0.5eV, compared with \sim 1\mbox{eV} for the local density approximation and 0.1eV for QCISD(T). These gradient-corrected functionals appear to offer an attractive compromise between accuracy and computational effort.Comment: Journal of Chemical Physics, 29, LA-UR-93-425

The sciences in America, circa 1880

For many years American science in the late 19th century was regarded as an intellectual backwater. This view derived from the assumption that the health of American science at the time was equivalent to the condition of pure science, especially pure physics. However, a closer look reveals that there was considerable vitality in American scientific research, especially in the earth and life sciences. This vitality is explainable in part by the natural scientific resources of the American continent but also in part by the energy given science from religious impulses, social reformism, and practicality. Furthermore, contrary to recent assumptions, the federal government was a significant patron of American science. The portrait of American science circa 1880 advanced in this article suggests that the nation's scientific enterprise was characterized by pluralism of institutional support and motive and that such pluralism has historically been the normal mode

Tuning density profiles and mobility of inhomogeneous fluids

Density profiles are the most common measure of inhomogeneous structure in confined fluids, but their connection to transport coefficients is poorly understood. We explore via simulation how tuning particle-wall interactions to flatten or enhance the particle layering of a model confined fluid impacts its self-diffusivity, viscosity, and entropy. Interestingly, interactions that eliminate particle layering significantly reduce confined fluid mobility, whereas those that enhance layering can have the opposite effect. Excess entropy helps to understand and predict these trends.Comment: 5 pages, 3 figure

Impact of surface roughness on diffusion of confined fluids

Using event-driven molecular dynamics simulations, we quantify how the self diffusivity of confined hard-sphere fluids depends on the nature of the confining boundaries. We explore systems with featureless confining boundaries that treat particle-boundary collisions in different ways and also various types of physically (i.e., geometrically) rough boundaries. We show that, for moderately dense fluids, the ratio of the self diffusivity of a rough wall system to that of an appropriate smooth-wall reference system is a linear function of the reciprocal wall separation, with the slope depending on the nature of the roughness. We also discuss some simple practical ways to use this information to predict confined hard-sphere fluid behavior in different rough-wall systems

Development of non-dissipative numerical schemes for computational aeroacoustics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76586/1/AIAA-1993-3382-851.pd

Composition and concentration anomalies for structure and dynamics of Gaussian-core mixtures

We report molecular dynamics simulation results for two-component fluid mixtures of Gaussian-core particles, focusing on how tracer diffusivities and static pair correlations depend on temperature, particle concentration, and composition. At low particle concentrations, these systems behave like simple atomic mixtures. However, for intermediate concentrations, the single-particle dynamics of the two species largely decouple, giving rise to the following anomalous trends. Increasing either the concentration of the fluid (at fixed composition) or the mole fraction of the larger particles (at fixed particle concentration) enhances the tracer diffusivity of the larger particles, but decreases that of the smaller particles. In fact, at sufficiently high particle concentrations, the larger particles exhibit higher mobility than the smaller particles. Each of these dynamic behaviors is accompanied by a corresponding structural trend that characterizes how either concentration or composition affects the strength of the static pair correlations. Specifically, the dynamic trends observed here are consistent with a single empirical scaling law that relates an appropriately normalized tracer diffusivity to its pair-correlation contribution to the excess entropy.Comment: 5 pages, 4 figure

ECONOMICALLY OPTIMAL WILDFIRE INTERVENTION REGIMES

Wildfires in the United States result in total damages and costs that are likely to exceed billions of dollars annually. Land managers and policy makers propose higher rates of prescribed burning and other kinds of vegetation management to reduce amounts of wildfire and the risks of catastrophic losses. A wildfire public welfare maximization function, using a wildfire production function estimated using a time series model of a panel of Florida counties, is employed to simulate the publicly optimal level of prescribed burning in an example county in Florida (Volusia). Evaluation of the production function reveals that prescribed fire is not associated with reduced catastrophic wildfire risks in Volusia County Florida, indicating a short-run elasticity of -0.16 and a long-run elasticity of wildfire with respect to prescribed fire of -0.07. Stochastic dominance is used to evaluate the optimal amount of prescribed fire most likely to maximize a measure of public welfare. Results of that analysis reveal that the optimal amount of annual prescribed fire is about 3 percent (9,000 acres/year) of the total forest area, which is very close to the actual average amount of prescribed burning (12,700 acres/year) between 1994-99.Resource /Energy Economics and Policy,

Detecting rare gene transfer events in bacterial populations

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An Automatically Tuning Intrusion Detection System

An intrusion detection system (IDS) is a security layer used to detect ongoing intrusive activities in information systems. Traditionally, intrusion detection relies on extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been deployed for intrusion detection. An IDS is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current systems depends on the system operators in working out the tuning solution and in integrating it into the detection model. In this paper, an automatically tuning IDS (ATIDS) is presented. The proposed system will automatically tune the detection model on-the-fly according to the feedback provided by the system operator when false predictions are encountered. The system is evaluated using the KDDCup\u2799 intrusion detection dataset. Experimental results show that the system achieves up to 35% improvement in terms of misclassification cost when compared with a system lacking the tuning feature. If only 10% false predictions are used to tune the model, the system still achieves about 30% improvement. Moreover, when tuning is not delayed too long, the system can achieve about 20% improvement, with only 1.3% of the false predictions used to tune the model. The results of the experiments show that a practical system can be built based on ATIDS: system operators can focus on verification of predictions with low confidence, as only those predictions determined to be false will be used to tune the detection model