Water and Solute Movement in Air Pollution Control Residues Measured by Time-Domain Reflectometry

The mobility of pollutants from air pollution control (APC) residues is strongly influenced by its contact with water. This study aims to verify the applicability of time-domain reflectometry (TDR) to infer water and solute movement in situ in an APC residue–compost mixture in a laboratory column. The calibrated dielectric permittivity–volumetric water content relationship (K?θv) for the material shows a distinct plateau at midrange volumetric water contents, which are underestimated by Topp’s K?θv model throughout the studied range. The coating of probes enables a clear TDR signal collection and analysis up to electrical conductivity values of 280?mS?cm?1. Heterogeneities in packing and channel flow in the column are revealed by TDR measurements. Water contents inferred by TDR signal analysis require careful interpretation when used in materials such as APC residues, due to the high amount of water bound in the hydration shells of ions released into the pore water.     

Simple and accurate four-node axisymmetric element

Based on the assumed strain method, a simple four-node axisymmetric solid element is introduced. The assumed strain field is carefully selected to preserve the correct rank of the element stiffness matrix and to achieve high accuracy. The strain field is developed in conjunction with orthogonal projections and no matrix inversions are needed. The coarse mesh accuracy in bending and in typical axisymmetric load cases is excellent even for nearly incompressible materials. The strain-driven format obtained is well suited for materials with non-linear stress–strain relations. Several numerical examples are presented where the excellent performance of the proposed simple element is verified. Copyright © 2006 John Wiley & Sons, Ltd.     

Strategies to obtain pattern fidelity in nanowire growth from large-area surfaces patterned using nanoimprint lithography

Position controlled nanowire growth is important for nanowire-based optoelectronic components which rely on light emission or light absorption. For solar energy harvesting applications, dense arrays of nanowires are needed; however, a major obstacle to obtaining dense nanowire arrays is seed particle displacement and coalescing during the annealing stage prior to nanowire growth. Here, we explore three different strategies to improve pattern preservation of large-area catalyst particle arrays defined by nanoimprint lithography for nanowire growth. First, we see that heat treating the growth substrate prior to nanoimprint lithography improves pattern preservation. Second, we explore the possibility of improving pattern preservation by fixing the seed particles in place prior to annealing by modifying the growth procedure. And third, we show that a SiN _x growth mask can fully prevent seed particle displacement. We show how these strategies allow us to greatly improve the pattern fidelity of grown InP nanowire arrays with dimensions suitable for solar cell applications, ultimately achieving 100% pattern preservation over the sampled area. The generic nature of these strategies is supported through the synthesis of GaAs and GaP nanowires.

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The robust vehicle routing problem with time windows

This paper addresses the robust vehicle routing problem with time windows. We are motivated by a problem that arises in maritime transportation where delays are frequent and should be taken into account. Our model only allows routes that are feasible for all values of the travel times in a predetermined uncertainty polytope, which yields a robust optimization problem. We propose two new formulations for the robust problem, each based on a different robust approach. The first formulation extends the well-known resource inequalities formulation by employing adjustable robust optimization. We propose two techniques, which, using the structure of the problem, allow to reduce significantly the number of extreme points of the uncertainty polytope. The second formulation generalizes a path inequalities formulation to the uncertain context. The uncertainty appears implicitly in this formulation, so that we develop a new cutting plane technique for robust combinatorial optimization problems with complicated constraints. In particular, efficient separation procedures are discussed. We compare the two formulations on a test bed composed of maritime transportation instances. These results show that the solution times are similar for both formulations while being significantly faster than the solutions times of a layered formulation recently proposed for the problem.     

Trajectory planning in the infinity norm for linear control systems

When planning trajectories, a reasonable demand on the generated curves is that they interpolate through given points, or given intervals, at given specified times, or that they go through these points or intervals at times lying in specified time windows. This problem is addressed here as an optimal control problem, minimizing the infinity norm of the inputs of a discretized linear control system since the infinity norm minimization problem arises naturally when doing trajectory planning for both controlled switching systems and non-holonomic robotic systems.     

Evaluation of data sources for mapping glacial meltwater features

The meltwater system of disintegrating ice sheets provides an important source of information for the reconstruction of ice-retreat patterns during deglaciation. Recent method development in glacial geomorphology, using satellite imagery and digital elevation models (DEMs) for glacial landform mapping, has predominantly been focused on the identification of lineation and other large-scale accumulation features. Landforms created by meltwater have often been neglected in these efforts. Meltwater features such as channels, deltas and fossil shorelines were traditionally mapped using stereo interpretation of aerial photographs. However, during the transition into the digital era, driven by a wish to cover large areas more economically, meltwater features were lost in most mapping surveys. We have evaluated different sets of satellite images and DEMs for their suitability to map glacial meltwater features (lateral meltwater channels, eskers, deltas, ice-dammed lake drainage channels and fossil shorelines) in comparison with the traditional mapping from aerial photographs. Several sets of satellite images and DEMs were employed to map the landform record of three reference areas, located in northwestern Scotland, northeastern Finland and western Sweden. The employed satellite imagery consisted of Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Satellite Pour l'Observation de la Terre (SPOT) 5 and Indian Remote Sensing (IRS) 1C, and the DEMs used were from NEXTMap Britain, Panorama, National elevation data set of Sweden and National Land Survey of Finland. ASTER images yielded better results than the panchromatic band of Landsat 7 ETM+?in all three regions, despite the same spatial resolution of the data. In agreement with previous studies, this study shows that DEMs display accumulation features such as eskers suitably well. Satellite images are shown to be insufficiently detailed for the interpretation of smaller features such as meltwater channels. Hence, satellite imagery and DEMs of intermediate resolution contain meltwater system information only at a general level that allows for the identification of landforms of medium to large sizes. It is therefore pertinent that data with an appropriate spatial and spectral resolution are accessed to fulfil the need of a particular mapping effort. Stereo interpretation of aerial photographs continues to be an advisable method for local meltwater system reconstructions; alternatively, it can be replaced by mapping from high-resolution DEMs such as NEXTMap Britain. For regional to sub-continental reconstructions, the use of ASTER satellite imagery is recommended, because it provides both spectral and spatial resolutions suitable for the identification of meltwater features on a medium to large scale.     

MIMO system identification: state-space and subspace approximationsversus transfer function and instrumental variables

The identification of multi-input multi-output (MIMO) linear systems has previously received a new impetus with the introduction of the state-space (SS) approach based on subspace approximations. This approach has immediately gained popularity, owing to the fact that it avoids the use of canonical forms, requires the determination of only one structural parameter, and has been empirically shown to yield MIMO models with good accuracy in many cases, However, the SS approach suffers from several drawbacks: there is no well-established rule tied to this approach for determining the structural parameter, and, perhaps more important the SS parameter estimates depend on the data in a rather complicated way, which renders almost futile any attempt to analyze and optimize the performance of the estimator. In this paper, we consider a transfer function (TF) approach based on instrumental variables (IV), as an alternative to the SS approach. We use the simplest canonical TF parameterization in which the denominator is equal to a scalar polynomial times the identity matrix. The analysis and optimization of the statistical accuracy of the TF approach is straightforward. Additionally, a simple test tailored to this approach is devised for estimating the single structural parameter needed. A simulation study, in which we compare the performances of the SS and the TF approaches, shows that the latter can provide more accurate models than the former at a lower computational cost     

Array interpolation and DOA MSE reduction

Interpolation or mapping of data from a given real array to data from a virtual array of more suitable geometry is well known in array signal processing. This operation allows arrays of any geometry to be used with fast direction-of-arrival (DOA) estimators designed for linear arrays. In an earlier companion paper , a first-order condition for zero DOA bias under such mapping was derived and was also used to construct a design algorithm for the mapping matrix that minimized the DOA estimate bias. This bias-minimizing theory is now extended to minimize not only bias, but also to consider finite sample effects due to noise and reduce the DOA mean-square error (MSE). An analytical first-order expression for mapped DOA MSE is derived, and a design algorithm for the transformation matrix that minimizes this MSE is proposed. Generally, DOA MSE is not reduced by minimizing the size of the mapping errors but instead by rotating these errors and the associated noise subspace into optimal directions relative to a certain gradient of the DOA estimator criterion function. The analytical MSE expression and the design algorithm are supported by simulations that show not only conspicuous MSE improvements in relevant scenarios, but also a more robust preprocessing for low signal-to-noise ratios (SNRs) as compared with the pure bias-minimizing design developed in the previous paper.     

A GC–MS-based metabonomic investigation of blood serum from irritable bowel syndrome patients undergoing intervention with acidified milk products

In the present study, the use of gas chromatography mass spectrometry (GC–MS)-based metabonomics to characterize blood serum in an intervention study of patients suffering from the common gastrointestinal disorder irritable bowel syndrome (IBS) was investigated. The patients included in the study consumed an acidified milk product with (n = 30) or without probiotics (n = 31) (Lactobacillus paracasei F19, Lactobacillus acidophilus LA-5 and Bifidobacterium lactis BB-12) for an 8-week period, and blood serum samples were collected before and after the intervention. Acidified milk is commonly used as a delivering vector for probiotics in commercial consumer settings. The serum samples were extracted and derivatized using N-Methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA), and GC–MS analysis was carried out. Multivariate data analysis including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and S-plot was applied on the obtained GC–MS data, which revealed higher serum lactate, glutamine, proline creatinine/creatine, and aspartic acid levels and lower serum glucose levels after the intervention period for both treatment groups. Consequently, the present study indicated an effect of acidified milk consumption on the plasma metabolite profile, which was independent of a concomitant intake of probiotics. In addition, the present study demonstrates that GC–MS is a useful analytical technique for metabonomics studies of blood serum.