Optical characteristics of waste stabilization ponds: recommendations for monitoring.

The optical character of waste stabilization ponds (WSPs) is of concern for several reasons. Algal photosynthesis, which produces oxygen for waste oxidation in WSPs, is influenced by attenuation of sunlight in ponds. Disinfection in WSPs is influenced by optical characteristics because solar UV exposure usually dominates inactivation. The optical nature of WSPs effluent also affects assimilation by receiving waters. Despite the importance of light behaviour in WSPs, few studies have been made of their optical characteristics. We discuss simple optical measures suitable for routine monitoring of WSPs (including at sites remote from laboratories): optical density of filtrates - an index of dissolved coloured organic (humic) matter, visual clarity - to provide an estimate of the beam attenuation coefficient (a fundamental quantity needed for optical modelling) colour (hue) - as an indicator of general WSP 'condition' and irradiance attenuation quantifying depth of light penetration. The value of optical characterisation of WSPs is illustrated with reference to optical data for WSPs in NZ (including high-rate algal ponds) treating dairy cattle wastewater versus domestic sewage. We encourage increased research on optical characteristics of WSPs and the incorporation of optical measures in monitoring and modelling of WSP performance.     

Spectrum efficient region-based resource allocation with fractional loading for FH-OFDMA cellular systems

A practical resource management method that can significantly reduce cochannel interference (CCI) and improve spectrum utilisation in FH-OFDMA packet-based cellular networks is presented. The proposed method seeks an effective combination of dynamic resource allocation with fractional coding and bit loading to respectively minimise CCI and maximise system throughput for a desired performance.     

Effects of surfactant/silica and silica/cerium ratios on the characteristics of mesoporous Ce-MCM-41

Using the surfactant CTMABr (cetyltrimethyl ammonium bromide) and cerium(IV) sulfate, mesoporous Ce-MCM-41 molecular sieves were produced under a hydrothermal condition with various surfactant/silica (surfactant/Si) and silica/cerium (Si/Ce) ratios. Changes to the structural traits caused by changing the molar ratios of both surfactant/Si and Si/Ce were investigated. XRD (X-ray diffraction), FT-IR (fourier transform infrared spectroscopy), and SEM (scanning electro microscopy) were used for the characterization of prepared mesoporous samples. Among the tested molar ratios, surfactant/Si ratio of 0.5 and 0.2 showed highest values of d_1 0 0 and intensity, respectively, for the Si-MCM-41. XRD analysis also identified a quintessential hexagonal structure of Ce-MCM-41 for the Si/Ce molar ratio higher than 40 (maintaining the surfactant/Si ratio at 0.2). When cerium content was increased to have the Si/Ce molar ratio of 20, the hexagonal structure of Ce-MCM-41 was collapsed due to the structural stress of substituted cerium. FT-IR results confirmed calcination of Ce-MCM-41 and the incorporation of Ce⁴⁺ ions of cerium sulfate into the silica surface with proper removal of the surfactant. Rod-like shape with rounded edges of the prepared Ce-MCM-41 samples was identified by SEM. These results suggest surfactant/Si ratio of 0.2 and Si/Ce ratio of 40 for the production of Ce-MCM-41 with the highest level of crystallinity.     

Mode Matching Analysis of a Tapered Coaxial Tip in a Parallel-Plate Waveguide

A tapered coaxial tip in a parallel plate waveguide is investigated. A boundary-value problem of electromagnetic scattering from a tapered coaxial monopole is solved rigorously based on the Fourier transform, eigenfunction expansion, and mode matching. Scattering parameters are represented in a series and computed to understand the behavior of the scattering in terms of the coaxial tip geometry.     

A robust algorithm for text region detection in natural scene images

In this paper, a new method for detecting text regions in natural scene images is presented. The proposed algorithm is based on the segmentation of objects in a scene, followed by the identification of text objects by a support vector machine (SVM). First, to segment objects in the scene, the input image is separated into chromatic and achromatic regions according to the distribution of red, green and blue (RGB) elements, and different clustering algorithms are applied. Second, each object is transformed into the wavelet domain for multi-resolution analysis, and moment features of the wavelet coefficients are used in the SVM for the classification of text objects. The proposed approach provides robustness to non-uniform illumination by using different clustering algorithms according to the characteristics of the colour components in the segmentation. Also, moment features, used for classification, are invariant to the size, direction, shape and other properties of texts. Experimental results demonstrate the effectiveness of this approach.     

Recrystallization of phenylbutazone using supercritical fluid antisolvent process

Phenylbutazone was recrystallized from its solutions by using a supercritical fluid antisolvent process. It was dissolved in acetone and supercritical carbon dioxide was injected into the solution, thereby inducing supersaturation and particle formation. Variation in the physical properties of the recrystallized phenylbutazone was investigated as a function of the crystallizing temperature and the carbon dioxide injection rate. The recrystallized particles showed cleaner surfaces and more ordered morphology compared to the particles obtained by other methods such as solvent evaporation. X-ray diffraction patterns indicated that the crystallinity of the particles had been modified upon the recrystallization. Differential scanning calorimetry measurement revealed that the crystallizing temperature influenced the thermal stability of the resulting crystals. Larger crystals were produced when the carbon dioxide injection rate was reduced.     

Learning user preferences of route choice behaviour for adaptive route guidance

As the use of navigation systems becomes more widespread, the demand for advanced functions of navigation systems also increases. In the light of user satisfaction, personalisation of route guidance by incorporating user preferences is one of the most desired features. A user model applied to personalised route guidance is presented. The user model adaptively updates route selection rules when it discovers the predicted choice differs from the actual choice of the driver. This study employs a decision tree learning algorithm, the C4.5 algorithm, which has advantages over other data mining methods in terms of its comprehensible model structure. Simulation experiments with a real-world network were conducted to analyse the applicability of the model to adaptive route guidance and the accuracy of its prediction     

The evolving patterns of inter-industrial knowledge structure: Case of Korean manufacturing in the 1980s

The notion of knowledge-based economy premises that technological knowledge be created, accumulated and disseminated through the interactive learning among principal actors in the national system. This paper analyzes, from a dynamic perspective, the structure of inter-industrial technological knowledge. Both human-driven disembodied channel and capital-driven embodied channel are investigated based on network analysis. The set of empirical data covers the Korean manufacturing sector during the 1980s. Overall, density of network tends to be increasing over time, implying that knowledge network becomes expanded and intensified. A number of distinctive features are identified between knowledge types and industrial categories. The findings in turn render important policy implications that should be addressed when developing technology policy. Clearly, the policy framework needs to be industry-specific and country-specific in accordance with the development stage and industrial structure of reference time. This revised version was published online in August 2006 with corrections to the Cover Date.     

Using classification techniques for informal requirements in the requirements analysis-supporting system

In order to efficiently develop large-scale and complicated software, it is important for system engineers to correctly understand users’ requirements. Most requirements in large-scale projects are collected from various stakeholders located in various regions, and they are generally written in natural language. Therefore, the initial collected requirements must be classified into various topics prior to analysis phases in order to be usable as input in several requirements analysis methods. If this classification process is manually done by analysts, it becomes a time-consuming task. To solve this problem, we propose a new bootstrapping method which can automatically classify requirements sentences into each topic category using only topic words as the representative of the analysts’ views. The proposed method is verified through experiments using two requirements data sets: one written in English and the other in Korean. The significant performances were achieved in the experiments: the 84.28 and 87.91 F1 scores for the English and Korean data sets, respectively. As a result, the proposed method can provide an effective function for an Internet-based requirements analysis-supporting system so as to efficiently gather and analyze requirements from various and distributed stakeholders by using the Internet.