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Commercial floating car data (FCD) is being increasingly used as a traffic data source due to its lower cost despite concerns about its reliability. This paper focuses on the evaluation of FCD speed quality as a surrogate measure for arterial speed from different aspects. First, FCD speed is compared to video-based traffic data, collected from a specific urban road segment and assumed as ground truth in (a) descriptive evaluations, (b) speed estimation, and (c) level of service estimation. Regression analysis carried out to derive transformation function between two datasets showed a nonlinear relation with a high correlation coefficient of 0.82. Working with data along an urban corridor of 3.6 km also showed that despite some outliers, FCD was capable of detecting peak-hour queue formations as well as incident related ones. Use of transformation function on FCD speeds helped to increase its potential in urban traffic monitoring. Key words: floating car data (FCD), ground truth (GT), commercial FCD quality, level of service (LOS) estimation, regression analysis. Les donnees de vehicules flottants (DVF) commerciales sont de plus en plus utilisees comme une source de donnees de circulation en raison de leur cout plus faible en depit de preoccupations au sujet de leur fiabilite. Dans le cadre de cet article, on se penche sur l'evaluation de la qualite des DVF sur la vitesse comme mesure de substitution pour la vitesse sur les voies arterielles sous differents aspects. Tout d'abord, les DVF de la vitesse sont comparees aux donnees video sur la circulation, recueillies a partir d'un segment de route urbaine en particulier et presumees etre la realite de terrain, au niveau (a) des evaluations descriptives, (b) de l'estimation de la vitesse et (c) de l'estimation du niveau de service. L'analyse de regression effectuee pour deriver la fonction de transformation entre deux ensembles de donnees a montre une relation non lineaire avec un coefficient de correlation eleve de 0,82. En travaillant avec des donnees obtenues le long d'un couloir urbain de 3,6 km, on a egalement montre que malgre certaines valeurs aberrantes, les DVF etaient capables de detecter les files d'attente aux heures de pointe ainsi que celles liees a un incident. L'utilisation de la fonction de transformation sur les vitesses obtenues des DVF a contribue a accroitre le potentiel des DVF au niveau de la surveillance de la circulation routiere urbaine. Mots-cles : donnees de vehicules flottants (DVF), realite de terrain (RT), qualite des DVF commerciales, estimation du niveau de service, analyse de regression., 1. Introduction Accurate and reliable estimation of traffic speed or state in urban arterials is essential for traffic management and control. Traditional traffic data collection includes various sources (i.e., magnetic [...]

A theory for one-dimensional self-weight subsidence of saturated soft porous media is presented. The problem is formulated in terms of volume solid fraction using the Eulerian coordinates. Two different types of constitutive relationships between the effective stress and the volume fraction of solids are considered. The governing equations and initial and boundary conditions are nondimensionalized. Self-weight subsidence behavior of unconsolidated soils is analyzed both analytically and numerically. Two sets of exact solutions corresponding to two different compressibility relationships are obtained under steady state conditions. Experimental data available in the literature are used to validate one set of exact solutions. Numerical analysis of transient settlement that allows the movement of the top boundary is presented. The validity of the numerical technique is verified by comparing with exact solutions. The model can be used to predict transient and ultimate settlement and the void ratio distribution of soft soils subsiding under their own weight.

Abstract Transcriptional regulatory network (TRN) discovery from one method (e.g. microarray analysis, gene ontology, phylogenic similarity) does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. We develop a methodology, TRND, that integrates a preliminary TRN, microarray data, gene ontology and phylogenic similarity to accurately discover TRNs and apply the method to E. coli K12. The approach can easily be extended to include other methodologies. Although gene ontology and phylogenic similarity have been used in the context of gene-gene networks, we show that more information can be extracted when gene-gene scores are transformed to gene-transcription factor (TF) scores using a preliminary TRN. This seems to be preferable over the construction of gene-gene interaction networks in light of the observed fact that gene expression and activity of a TF made of a component encoded by that gene is often out of phase. TRND multi-method integration is found to be facilitated by the use of a Bayesian framework for each method derived from its individual scoring measure and a training set of gene/TF regulatory interactions. The TRNs we construct are in better agreement with microarray data. The number of gene/TF interactions we discover is actually double that of existing networks.

Abstract Background Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding. Results Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented. Conclusion Multiplex time series data can be used for the construction of the network of cellular processes and the calibration of the associated physicochemical parameters. We have demonstrated these concepts in the context of gene regulation understood through the analysis of gene expression microarray time series data. Casting the approach in a probabilistic framework has allowed us to address the uncertainties in gene expression microarray data. Our approach was found to be robust to error in the gene expression microarray data and mistakes in a proposed TRN.

We present a seismic waveform inversion methodology based on the Gauss–Newton method from pre-stack seismic data. The inversion employs a staggered-grid finite difference solution of the 2-D elastic wave equation in the time domain, allowing accurate simulation of all possible waves in elastic media. The partial derivatives for the Gauss–Newton method are obtained from the differential equation of the wave equation in terms of model parameters. The resulting wave equation and virtual sources from the reciprocity principle allow us to apply the Gauss–Newton method to seismic waveform inversion. The partial derivative wavefields are explicitly computed by convolution of forward wavefields propagated from each source with reciprocal wavefields from each receiver. The Gauss–Newton method for seismic waveform inversion was proposed in the 1980s but has rarely been studied. Extensive computational and memory requirements have been principal difficulties which are addressed in this work. We used different sizes of grids for the inversion, temporal windowing, approximation of virtual sources, and parallelizing computations. With numerical experiments, we show that the Gauss–Newton method has significantly higher resolving power and convergence rate over the gradient method, and demonstrate potential applications to real seismic data. [ABSTRACT FROM AUTHOR]