Application of autoregressive spectral analysis to missing data problems

Time series solutions for spectral analysis in missing data problems use reconstruction of the missing data, or a maximum likelihood approach that analyzes only the available measured data. Maximum likelihood estimation yields the most accurate spectra. An approximate maximum likelihood algorithm is presented that uses only previous observations falling in a finite interval to compute the likelihood, instead of all previous observations. The resulting nonlinear estimation algorithm requires no user-provided initial solution, is suited for order selection, and can give very accurate spectra even if less than 10% of the data remains.     

Finite sample properties of ARMA order selection

The cost of order selection is defined as the loss in model quality due to selection. It is the difference between the quality of the best of all available candidate models that have been estimated from a finite sample of N observations and the quality of the model that is actually selected. The order selection criterion itself has an influence on the cost because of the penalty factor for each additionally selected parameter. Also, the number of competitive candidate models for the selection is important. The number of candidates is, of itself, small for the nested and hierarchical autoregressive/moving average (ARMA) models. However, intentionally reducing the number of selection candidates can be beneficial in combined ARMA(p,q) models, where two separate model orders are involved: the AR order p and the MA order q. The selection cost can be diminished by creating a nested sequence of ARMA(r,r-1) models. Moreover, not evaluating every combination (p,q) of the orders considerably reduces the required computation time. The disadvantage may be that the true ARMA(p,q) model is no longer among the nested candidate models. However, in finite samples, this disadvantage is largely compensated for by the reduction in the cost of order selection by considering fewer candidates. Thus, the quality of the selected model remains acceptable with only hierarchically nested ARMA(r,r-1) models as candidates.     

Disc filtration for separation of flocs from a moving bed bio-film reactor.

A Discfilter with 10 and 18 microm filter openings, respectively, was placed in parallel to a flotation plant for separation of biological flocs from a post-denitrifying Kaldnes Moving Bed Process, the last treatment step at the municipal wastewater treatment plant at Sj?unda, Malm?, Sweden. The effluent concentrations from the 10 and 18 microm filter were 2-5 and 2-8 mg SS L(-1), respectively, which is comparable to, or better than, the flotation plant. Comparison with experiences from activated sludge plants shows that the Discfilter works especially well after the Kaldnes process. Particle size distribution (PSD) studies show that particles larger than the filter openings of 10 and 18 microm are separated with approximately 90% efficiency, whereas most of the smaller particles pass the filter. This fact indicates that the major particle separation mechanism is physical blocking. These findings point to the possibility of improving the prediction of the separation efficiency by combining measurements of turbidity and suspended solids with particle size analysis.     

It takes two to mimic: Behavioral consequences of self-construals.

The present studies demonstrated the moderation of self-construal orientation on mimicry. Recent research has indicated that an interdependent self-construal is associated with assimilation of the other to the self whereas an independent self-construal is associated with minimizing the influence of others on the self (H. R. Markus & S. Kitayama, 1991; D. Stapel & W. Koomen, 2001). Therefore, the authors hypothesized that an interdependent self-construal would be associated with more mimicry than an independent self-construal. When self-construal orientations were experimentally primed. as in Studies 1 and 2, independent self-construals produced less nonconscious mimicry than interdependent self-construals. When self-construals were examined as cultural differences with either a chronically dominant independent (Americans) or interdependent (Japanese) construal of the self, these results were replicated. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Challenges in the Application of Stochastic Modal Identification Methods to a Cable-Stayed Bridge

This paper presents an analysis of the data collected in the ambient vibration test of the International Guadiana cable-stayed Bridge, which links Portugal and Spain, based on different output-only identification techniques: peak-picking, frequency domain decomposition, covariance-driven stochastic subspace identification, and data-driven stochastic subspace identification. The purpose of the analysis is to compare the performance of the four techniques and evaluate their efficiency in dealing with specific challenges involved in the modal identification of the tested cable-stayed bridge, namely the existence of closely spaced modes, the perturbation produced by the local vibration of stay-cables, and the variation of modal damping coefficients with wind velocity. The identified natural frequencies and mode shapes are compared with the corresponding modal parameters provided by a previously developed numerical model. Additionally, the variability of some modal damping coefficients is related with the variation of the wind characteristics and associated with a component of aerodynamic damping.     

Critical Shear Stress of Bimodal Sediment in Sand-Gravel Rivers

A new model for the critical shear stress and the transport of graded sediment is presented. The model is based on the size distribution of the bed surface and can be used to compute sediment transport rates in numerical simulations with an active layer model. This model makes a distinction between unimodal and bimodal sediments. It is assumed that all size fractions of unimodal sediments have the same critical shear stress while there is selective transport for the gravel fractions of bimodal sediments. A recently published laboratory transport data set is used to calibrate our model.     

Pretreatment of Highly Polluted Pharmaceutical Waste Broth by Wet Air Oxidation

The preoxidation of a highly polluted waste pharmaceutical fermentation broth using wet air oxidation (WAO) has been studied as a possible method for the effective removal of organics. The applied (pre)treatment method should enhance the biotreatability of the pharmaceutical fermentation broth in terms of reduced initial toxicity and higher biodegradability. Preliminary experiments in the pilot biological treatment plant were successful only at low organic loads, whereas the system collapses at higher ones. The characterization of the fermentation broth was started by common physicochemical analysis, whereas several bioassays were used to determine its impact on biological treatment plants and the environment. Toxicity prior to and after WAO was determined using the acute Vibrio fischeri test, measurement of inhibition of O2 consumption, and the Daphnia magna acute test. Ready biodegradability of the treated and untreated broth has also been assessed. WAO experiments were accomplished in the 2?L batch reactor at different temperatures (240/280°C) and operating pressures. WAO experiments confirmed reduction of the toxicity toward microorganisms, whereas oxidized wastewater was more toxic to daphnids. Biodegradability of the oxidized broth has also been enhanced. Further work has been focused on designing appropriate combination of WAO and biological processes.