Development and simulation studies of an unsteady state biofilter model for the treatment of cyclic air emissions of an α‐pinene gas stream

This paper describes the development and simulation of an unsteady state biofilter model used to predict dynamic behaviour of cyclically‐operated biofilters and compares it with experimental results obtained from three, parallel, bench‐scale biofilters treating both periodically fluctuating concentrations and constant concentrations of an α‐pinene‐laden gas stream. The dynamic model, using kinetic parameters estimated from the constant concentration biofilter, was able to predict the performance of cyclic biofilters operating at short cycle periods (ie, in the order of minutes and hours). Steady state kinetic data from a constant concentration biofilter can be used to predict unsteady state biofilter operation. At a 24 h cycle period, the dynamic model compared well with experimental results. For long cycle periods (ie, hours and days), removal efficiency decreased after periods of non‐loading: the longer the period of non‐loading, the poorer the biofilter's performance at the re‐commencement of pollutant loading. At longer time scales the model did not effectively predict transient behaviour, as adsorption and changes in kinetic parameters were not accounted for. Modelling results showed that similar biofiltration performance for the cyclic and constant concentration biofiltration of α‐pinene is expected for biofilters operating solely in the first order kinetics regime. Poorer performance for cyclic biofilters following Monod kinetics spanning the entire kinetics range is expected as the cycle amplitude increases. The most important parameters affecting the performance of a cyclically‐operated biofilter with short cycle periods are: amplitude of cyclic fluctuations, C_{g, max}/C_g, relative value of the half‐saturation constant in the Monod expression, K_s, and effective diffusivity of α‐pinene in the biofilm, D_e. Copyright © 2005 Society of Chemical Industry     

Microcalorimetry for characterization of film formation and cure of coatings and adhesives

A new microcalorimeter with eight parallel channels using robust, low cost sensors for characterization of coatings and adhesives is described and first experiments on coatings and adhesives are presented. The calorimetric sensors are based on thin glass plates (20 mm × 20 mm, thickness 150 μm) with heater and thermocouple sputtered on the surfaces (calorimetric active area of about 9 mm²). The setup allows heating and cooling experiments as well as isothermal measurements in the temperature-modulated mode with up to eight sensors in parallel. The measured quantities are the real (C^′_p) and imaginary part (C^″_p) of the complex heat capacity (), the related absolute value of the heat capacity () and the heat flow . An industrial computer (NI PXI system) with specific software for calibration and data recording controls the electronic components. Sensors can be embedded in a temperature controlled oven (heating and cooling by Peltier elements) or alternatively in a climatic cabinet with controlled temperature and humidity.

The method has been applied successfully to monitoring of film formation of aqueous polymer dispersions (styrene-acrylate copolymer) and curing of coatings.     

Result Merging Technique for Answering XPath Query over XSLT Transformed Data

Caching stores the results of previously answered queries in order to answer succeeding queries faster by reusing these results. We propose two different approaches for using caches of XSLT transformed XML data in order to answer queries. The first approach checks whether or not a current query Q can be directly answered from the result of a previously answered query Qi stored in the cache. The new query is otherwise submitted to the source over the network, the answer of the query is determined, transmitted back to the client, and stored in the cache. The second approach determines only the intersection Q−Qi and integrates the result of Q−-Qi into the previous results in the cache, which requires applying a numbering scheme for the output of the XSLT stylesheet. We show by experimental results that the second approach can significantly speed up the answering time in comparison to the first approach, but is not significantly slower in few worst cases than the second approach.     

An experiment on the role of graphical elements in architecture visualization

The evolution and maintenance of large-scale software systems requires first an understanding of its architecture before delving into lower-level details. Tools facilitating the architecture comprehension tasks by visualization provide different sets of configurable, graphical elements to present information to their users. We conducted a controlled experiment that exemplifies the critical role of such graphical elements when aiming at understanding the architecture. In our setting, a different configuration of graphical elements had significant influence on program comprehension tasks. In particular, a 63% gain in effectiveness in architectural analysis tasks was achieved simply by changing the configuration of the graphical elements of the same tool. Based on the results, we claim that significant effort should be spent on the configuration of architecture visualization tools and that configurability should be a requirement for such tools.

Machine learning for vessel trajectories using compression,alignments and domain knowledge

In this paper we present a machine learning framework to analyze moving object trajectories from maritime vessels. Within this framework we perform the tasks of clustering, classification and outlier detection with vessel trajectory data. First, we apply a piecewise linear segmentation method to the trajectories to compress them. We adapt an existing technique to better retain stop and move information and show the better performance of our method with experimental results. Second, we use a similarity based approach to perform the clustering, classification and outlier detection tasks using kernel methods. We present experiments that investigate different alignment kernels and the effect of piecewise linear segmentation in the three different tasks. The experimental results show that compression does not negatively impact task performance and greatly reduces computation time for the alignment kernels. Finally, the alignment kernels allow for easy integration of geographical domain knowledge. In experiments we show that this added domain knowledge enhances performance in the clustering and classification tasks.     

The Conditional Probability Density Function for a Reflected Brownian Motion

Models in economics and other fields often require a restricted Brownian motion because frequently implicit or explicit barriers restrict the domain. This paper contributes to the literature on reflected Brownian motion by deriving its conditional density function as a closed-form expression that consists of infinite sums of Gaussian densities. This solution is compared with an alternative, trigonometric expression derived earlier. Numerical analyses reveal that convergence properties of the expression derived in this paper are superior to those of the alternative representation for most practically relevant set-ups. Despite the complex appearance of the density formula, its use only requires fractions of a second on simple desktop computers such that, next to the theoretical appeal, also practicability is guaranteed.