3,789 research outputs found

    Understanding the effects of product architecture on technical communication in product develoment organizations

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    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

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    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

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    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 fpf_p, ({\it ii}) the onset frequency fcf_c for ML (\propto ordering velocity) and ({\it iii}) the fraction LML/LL_{ML}/L of coherently moving 3-row regions in the channel. The field dependence of these quantities shows that, at matching, where LMLL_{ML} is maximum, the pinning strength is small and the ordering velocity is low, while at mismatch, where LMLL_{ML} is small, both the pinning force and the ordering velocity are enhanced. Further, we find that fcfp2f_c \propto f_p^2, 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

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    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

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    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

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    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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