3,789 research outputs found
Understanding the effects of product architecture on technical communication in product develoment organizations
Title from cover. "August 2000." At head of each leaf: DRAFT - 8/28/00.Includes bibliographical references (leaves 31-34).Manuel E. Sosa, Steven D. Eppinger, Craig M. Rowles
spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models
Scientists and investigators in such diverse fields as geological and environmental sciences, ecology, forestry, disease mapping, and economics often encounter spatially referenced data collected over a fixed set of locations with coordinates (latitude-longitude, Easting-Northing etc.) in a region of study. Such point-referenced or geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific problem at hand. This requires extensive coding on the part of the user and the situation is not helped by the lack of available software for such algorithms. Here, we introduce a statistical software package, spBayes, built upon the R statistical computing platform that implements a generalized template encompassing a wide variety of Gaussian spatial process models for univariate as well as multivariate point-referenced data. We discuss the algorithms behind our package and illustrate its use with a synthetic and real data example.
Dynamic ordering and frustration of confined vortex rows studied by mode-locking experiments
The flow properties of confined vortex matter driven through disordered
mesoscopic channels are investigated by mode locking (ML) experiments. The
observed ML effects allow to trace the evolution of both the structure and the
number of confined rows and their match to the channel width as function of
magnetic field. From a detailed analysis of the ML behavior for the case of
3-rows we obtain ({\it i}) the pinning frequency , ({\it ii}) the onset
frequency for ML ( ordering velocity) and ({\it iii}) the
fraction of coherently moving 3-row regions in the channel. The
field dependence of these quantities shows that, at matching, where is
maximum, the pinning strength is small and the ordering velocity is low, while
at mismatch, where is small, both the pinning force and the ordering
velocity are enhanced. Further, we find that , consistent
with the dynamic ordering theory of Koshelev and Vinokur. The microscopic
nature of the flow and the ordering phenomena will also be discussed.Comment: 10 pages, 7 figure, submitted to PRB. Discussion has been improved
and a figure has been adde
Predictability of evolutionary trajectories in fitness landscapes
Experimental studies on enzyme evolution show that only a small fraction of
all possible mutation trajectories are accessible to evolution. However, these
experiments deal with individual enzymes and explore a tiny part of the fitness
landscape. We report an exhaustive analysis of fitness landscapes constructed
with an off-lattice model of protein folding where fitness is equated with
robustness to misfolding. This model mimics the essential features of the
interactions between amino acids, is consistent with the key paradigms of
protein folding and reproduces the universal distribution of evolutionary rates
among orthologous proteins. We introduce mean path divergence as a quantitative
measure of the degree to which the starting and ending points determine the
path of evolution in fitness landscapes. Global measures of landscape roughness
are good predictors of path divergence in all studied landscapes: the mean path
divergence is greater in smooth landscapes than in rough ones. The
model-derived and experimental landscapes are significantly smoother than
random landscapes and resemble additive landscapes perturbed with moderate
amounts of noise; thus, these landscapes are substantially robust to mutation.
The model landscapes show a deficit of suboptimal peaks even compared with
noisy additive landscapes with similar overall roughness. We suggest that
smoothness and the substantial deficit of peaks in the fitness landscapes of
protein evolution are fundamental consequences of the physics of protein
folding.Comment: 14 pages, 7 figure
Equational and Rule-Based Programming: Visualization, Reliability, and Knowledge Base Generation
This document describes developing an environment for effective use of functional/equational programs and rule-based expert systems. There are significant advantages in using these paradigms for reliability, parallelism, and accumulation of expertise in knowledge bases. The environment will make it easier to understand and use these paradigms, construct more reliable systems, and automatically enrich rule-based knowledge bases with the expertise. It will consist of the following components: (1) Visualization: for composing systems using a graphical interface and for understanding of algorithms. (2) Consistency Checking: for an equational and a rule-based language in accordance with the semantics of the languages. (3) Knowledge Base Generation and Testing: a translator that extracts expertise from existing programs and accumulates it as rules in knowledge bases; the rules are tested to enhance reliability. (4) Verification: interactive heterogeneous reasoning that consists of equational reasoning based on visual and textual information. These tools will be integrated in the proposed environment. The environment will greatly reduce the costs and increase the reliability of functional/equational and rule-based systems
Empirical Prediction of Aircraft Landing Gear Noise
This report documents a semi-empirical/semi-analytical method for landing gear noise prediction. The method is based on scaling laws of the theory of aerodynamic noise generation and correlation of these scaling laws with current available test data. The former gives the method a sound theoretical foundation and the latter quantitatively determines the relations between the parameters of the landing gear assembly and the far field noise, enabling practical predictions of aircraft landing gear noise, both for parametric trends and for absolute noise levels. The prediction model is validated by wind tunnel test data for an isolated Boeing 737 landing gear and by flight data for the Boeing 777 airplane. In both cases, the predictions agree well with data, both in parametric trends and in absolute noise levels
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