Evaluation of U.S. and European Concrete Pavement Noise Reduction Methods, July 2006

Highway noise is one of the most pressing of the surface characteristics issues facing the concrete paving industry. This is particularly true in urban areas, where not only is there a higher population density near major thoroughfares, but also a greater volume of commuter traffic (Sandberg and Ejsmont 2002; van Keulen 2004). To help address this issue, the National Concrete Pavement Technology Center (CP Tech Center) at Iowa State University (ISU), Federal Highway Administration (FHWA), American Concrete Pavement Association (ACPA), and other organizations have partnered to conduct a multi-part, seven-year Concrete Pavement Surface Characteristics Project. This document contains the results of Part 1, Task 2, of the ISU-FHWA project, addressing the noise issue by evaluating conventional and innovative concrete pavement noise reduction methods. The first objective of this task was to determine what if any concrete surface textures currently constructed in the United States or Europe were considered quiet, had long-term friction characteristics, could be consistently built, and were cost effective. Any specifications of such concrete textures would be included in this report. The second objective was to determine whether any promising new concrete pavement surfaces to control tire-pavement noise and friction were in the development stage and, if so, what further research was necessary. The final objective was to identify measurement techniques used in the evaluation.

Integrative Review: Concepts And Methods Used In Nursing


Integrative review (IR) has an international reputation in nursing research and evidence-based practice. This IR aimed at identifying and analyzing the concepts and methods recommended to undertaking IR in nursing. Nine information resources,including electronic databases and grey literature were searched. Seventeen studies were included. The results indicate that: primary studies were mostly from USA; it is possible to have several research questions or hypotheses and include primary studies in the review from different theoretical and methodological approaches; it is a type of review that can go beyond the analysis and synthesis of findings from primary studies allowing exploiting other research dimensions, and that presents potentialities for the development of new theories and new problems for research. Conclusion: IR is understood as a very complex type of review and it is expected to be developed using standardized and systematic methods to ensure the required rigor of scientific research and therefore the legitimacy of the established evidence.


Use of flow cytometric methods for single-cell analysis in environmental microbiology

Flow cytometry (FCM) is emerging as an important tool in environmental microbiology. Although flow cytometry applications have to date largely been restricted to certain specialized fields of microbiology, such as the bacterial cell cycle and marine phytoplankton communities, technical advances in instrumentation and methodology are leading to its increased popularity and extending its range of applications. Here we will focus on a number of recent flow cytometry developments important for addressing questions in environmental microbiology. These include (i) the study of microbial physiology under environmentally relevant conditions, (ii) new methods to identify active microbial populations and to isolate previously uncultured microorganisms, and (iii) the development of high-throughput autofluorescence bioreporter assays

The molecular basis of social behavior: models, methods and advances.

Elucidating the molecular and neural basis of complex social behaviors such as communal living, division of labor and warfare requires model organisms that exhibit these multi-faceted behavioral phenotypes. Social insects, such as ants, bees, wasps and termites, are attractive models to address this problem, with rich ecological and ethological foundations. However, their atypical systems of reproduction have hindered application of classical genetic approaches. In this review, we discuss how recent advances in social insect genomics, transcriptomics, and functional manipulations have enhanced our ability to observe and perturb gene expression, physiology and behavior in these species. Such developments begin to provide an integrated view of the molecular and cellular underpinnings of complex social behavior.

Integrated Analysis of Molecular and Clinical Prognostic Factors in Stage II/III Colon Cancer.

Background The prognostic potential of individual clinical and molecular parameters in stage II/III colon cancer has been investigated, but a thorough multivariable assessment of their relative impact is missing. Methods Tumors from patients (N = 1404) in the PETACC3 adjuvant chemotherapy trial were examined for BRAF and KRAS mutations, microsatellite instability (MSI), chromosome 18q loss of heterozygosity (18qLOH), and SMAD4 expression. Their importance in predicting relapse-free survival (RFS) and overall survival (OS) was assessed by Kaplan-Meier analyses, Cox regression models, and recursive partitioning trees. All statistical tests were two-sided. Results MSI-high status and SMAD4 focal loss of expression were identified as independent prognostic factors with better RFS (hazard ratio [HR] of recurrence = 0.54, 95% CI = 0.37 to 0.81, P = .003) and OS (HR of death = 0.43, 95% CI = 0.27 to 0.70, P = .001) for MSI-high status and worse RFS (HR = 1.47, 95% CI = 1.19 to 1.81, P < .001) and OS (HR = 1.58, 95% CI = 1.23 to 2.01, P < .001) for SMAD4 loss. 18qLOH did not have any prognostic value in RFS or OS. Recursive partitioning identified refinements of TNM into new clinically interesting prognostic subgroups. Notably, T3N1 tumors with MSI-high status and retained SMAD4 expression had outcomes similar to stage II disease. Conclusions Concomitant assessment of molecular and clinical markers in multivariable analysis is essential to confirm or refute their independent prognostic value. Including molecular markers with independent prognostic value might allow more accurate prediction of prognosis than TNM staging alone.

Estimating overidentified, nonrecursive, time-varying coefficients structural VARs

This paper provides a method to estimate time varying coefficients structuralVARs which are non-recursive and potentially overidentified. The procedureallows for linear and non-linear restrictions on the parameters, maintainsthe multi-move structure of standard algorithms and can be used toestimate structural models with different identification restrictions. We studythe transmission of monetary policy shocks and compare the results with thoseobtained with traditional methods.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification

This paper presents 3-D brain tissue classificationschemes using three recent promising energy minimizationmethods for Markov random fields: graph cuts, loopybelief propagation and tree-reweighted message passing.The classification is performed using the well knownfinite Gaussian mixture Markov Random Field model.Results from the above methods are compared with widelyused iterative conditional modes algorithm. Theevaluation is performed on a dataset containing simulatedT1-weighted MR brain volumes with varying noise andintensity non-uniformities. The comparisons are performedin terms of energies as well as based on ground truthsegmentations, using various quantitative metrics.

Speed of convergence of recursive least squares learning with ARMA perceptions

This paper fills a gap in the existing literature on least squareslearning in linear rational expectations models by studying a setup inwhich agents learn by fitting ARMA models to a subset of the statevariables. This is a natural specification in models with privateinformation because in the presence of hidden state variables, agentshave an incentive to condition forecasts on the infinite past recordsof observables. We study a particular setting in which it sufficesfor agents to fit a first order ARMA process, which preserves thetractability of a finite dimensional parameterization, while permittingconditioning on the infinite past record. We describe how previousresults (Marcet and Sargent [1989a, 1989b] can be adapted to handlethe convergence of estimators of an ARMA process in our self--referentialenvironment. We also study ``rates'' of convergence analytically and viacomputer simulation.

Recursive contracts

We obtain a recursive formulation for a general class of contractingproblems involving incentive constraints. Under these constraints,the corresponding maximization (sup) problems fails to have arecursive solution. Our approach consists of studying the Lagrangian.We show that, under standard assumptions, the solution to theLagrangian is characterized by a recursive saddle point (infsup)functional equation, analogous to Bellman's equation. Our approachapplies to a large class of contractual problems. As examples, westudy the optimal policy in a model with intertemporal participationconstraints (which arise in models of default) and intertemporalcompetitive constraints (which arise in Ramsey equilibria).