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Journal Cover Journal of the National Science Foundation of Sri Lanka
  [SJR: 0.166]   [H-I: 8]   [2 followers]  Follow
  This is an Open Access Journal Open Access journal
   ISSN (Print) 1391-4588
   Published by Sri Lanka Journals Online Homepage  [49 journals]
  • Subject Index Vol 44 - 2016

    • Abstract: Published on 2016-12-27 09:48:10
  • Author Index Vol 44 - 2016

    • Abstract: Published on 2016-12-27 09:48:06
  • List of Referees Vol 44-2016

    • Abstract: Published on 2016-12-27 09:48:02
  • Bioconcentration modelling of alcohol ethoxylates by quantitative
           structure activity relationship approach: a first look

    • Abstract: Alcohol ethoxylates (AEs) are a class of nonionic surfactants. This study overviewed the environmental health effects and quantitative structure activity relationships generated for bioconcentration factors of seventeen alcohol ethoxylates, which are currently in commercial use as household detergents. The X-data matrix consisted of 560 molecular descriptors which was calculated by the DRAGON® molecular modelling environment. The logarithms of bioconcentration factors calculated by EPI® toxicology estimation suite were used as the response factor. Out of two quantitative structure activity relationships generated, one exhibited a model fit of 0.95 and a power of prediction of 0.42. The second was superior in terms of model fit, which was 0.92 and a power of prediction of 0.7. The predicted bioconcentration values exhibited a minimum percentage error of 10 % and a maximum of 37 %. Prediction accuracy became better with increasing bioconcentration factor. A convincing relationship between bioconcentration and calculated molecular descriptors for alcohol ethoxylates was obtained, hence the capability of quantitative structure activity relationship approach for modelling the environmental behaviour of AEs at a fully empirical level was demonstrated. Constructing a quantitative structure activity relationship having a realistic predictive power over a variety of commercial AEs may be challenging, but with the use of finely tuned chemical descriptors and better modelling tools it could be possible to accurately and rapidly predict toxicities as well as the environmental behaviour of AEs. Published on 2016-12-27 08:36:48
  • Un-supervised segmentation and quantisation of malignancy from breast MRI

    • Abstract: In this paper, a magnetic resonance imaging (MRI) based image segmentation technique has been proposed, which uses a magnetic resonance parametric information model for breast tumor segmentation. The methodology has been developed on two dimensional MRI datasets. With the help of the proposed technique, breast tumor tissues can be segmented in 6 - 8 minutes with more precision and reproducibility than manual (supervised) segmentation, which takes more than two hours to segment breast tumor tissues. Thus, the proposed semi-automatic (un-supervised) technique can be applied to analyse MRI images, which improves the procedure for diagnosing breast cancer, and it can also be used to generate two-dimensional view of tumor in case of surgical operations.  Published on 2016-12-27 08:36:38
  • Investigation of branching structure formation by solutions of a
           mathematical model of pattern formation in coral reefs

    • Abstract: A reaction-diffusion type mathematical model for the growth of corals in a tank, describing the spatial time evolution of the biomass of dissolved nutrients (food of polyps) and dissolved solid materials (calcium carbonate) of the tank, is considered. Some properties of the spatial patterns when the model parameters lie in the Turing space are investigated based on dispersion relation and unstable wave numbers of the linearised system. Branching structure formation process in the model is explained analytically. The model is solved numerically in one dimension subject to no-flux boundary conditions and it is shown that the numerical results agree with the analytically derived properties of the solutions.    Published on 2016-12-27 08:36:37
  • Characterisation of B cell epitopes of dengue virus NS1 protein using
           bioinformatics approach

    • Abstract: Linear B cell epitopes of the dengue virus nonstructural protein 1 (NS1) were predicted using three B cell epitope prediction tools, Ellipro, Bepipred and SVMTrip. Fifty sequences from each dengue serotype, representing a wide geographic area and a time span, were aligned using MEGA 6 software. The predictions were evaluated by comparing the results among the three tools and with currently available data on assay positive immunogenic epitopes of dengue NS1. A total of 22 regions on the NS1 protein ranging 6 to 21 amino acids in size were predicted as epitopes with a high probability score by the three prediction tools. Many were found to be predicted as epitopes by more than one tool, showing a good agreement in the predictions by the three tools. Further, many of the epitopes overlapped with, or partially constituted regions on the NS1 protein, which have been previously identified to be immunogenic as measured by various biochemical assays. Each of the 22 epitopes and the whole NS1 protein were then characterised for their percentages of conservation and phylogenetic relationship among and within each dengue serotypes, using IEDB and MEGA 6 analysis tools. Three epitopes were found to be highly conserved within the serotype but highly variable among the 04 dengue serotypes, suggesting that they could be used as possible serotype specific diagnostic markers. Three other epitopes were specific for the dengue group and more than 85 % conserved among the four dengue serotypes, showing a potential use as dengue group specific diagnostic markers. The bioinformatics approach appears a promising method in the identification of B cell epitopes of dengue NS1 with therapeutic potential.  Published on 2016-12-27 08:36:22
  • Wind speed analysis and energy calculation based on mixture distributions
           in Narakkalliya, Sri Lanka

    • Abstract: Wind is an important resource for generating renewable energy. To effectively estimate wind energy potential, Weibull, lognormal and gamma distribution functions have been widely used. However, when the distribution is bi-modal these conventional distribution functions are not suitable. Since Narakkalliya is affected by both monsoon seasons to varying degrees, the wind speed data show a bi-modal behaviour. A study was conducted at Narakkalliya in 2001 to calculate the energy generated from wind using the conventional Weibull distribution. The objective of this study was to show that energy calculation could be carried out more accurately using mixture distribution. Thus in this study, mixture Weibull distributions, mixture lognormal and Weibull distribution, mixture gamma and Weibull distribution were considered to model the wind speed frequency distribution (WSFD). Parameters of the above mentioned distributions were calculated using maximum likelihood method. The goodness-of-fit of the distributions was compared using K-S error, Chi-square test and root mean square error. Mixture Weibull distribution had the lowest error followed by the mixture lognormal and Weibull distribution. The fitted mixture Weibull distribution and the power curve data for the Narakkalliya wind turbine were used for energy calculation. According to the analysis mixture Weibull estimates 83.2 % of the actual energy while Weibull estimates 75.9 % of the actual.  Published on 2016-12-27 08:36:00
  • Biodegradation of agrowastes by lignocellulolytic activity of an oyster
           mushroom, Pleurotus sapidus

    • Abstract: Lignocellulosic biomasses derived from dedicated crops and agro-industrial waste materials are promising renewable resources for the production of fuels and other value added bio-products. The production of ligninolytic and cellulolytic enzymes from different lignocellulosic agricultural wastes by Pleurotus sapidus WC 529 was investigated in the current study. The production pattern of the enzymes was examined during the growth of the organism for a period of 10 days, and the enzyme activities were expressed in units/mL. Cultivation in the solid-state culture of banana stalk (BS) gave rise to higher levels of laccase, manganese peroxidase (MnP) and lignin peroxidase (LiP) activities compared to the cellulolytic enzymes. Response surface methodology (RSM) was adopted to optimise the culturing conditions for maximum enzyme secretion. Optimal conditions yielding the highest enzyme activities were: initial pH, 4; temperature, 35 °C; moisture level, 60 %; inoculum size, 4 mL, and incubation time, 120 hours. The crude lignocellulolytic enzyme extract presented potential efficiency for the delignification of different lignocellulosic substrates within 48 hours. The results suggested the feasibility of lignocellulolytic enzyme production using cost effective agro-industrial residues that can be effectively used for lignin biodegradation. Published on 2016-12-27 08:35:51
  • Online tracking and event clustering for vision systems

    • Abstract: This paper proposes a comprehensive method for online-event clustering in videos. Adaptive Gaussian mixture model was modified to obtain consistent foreground estimates for object tracking by introducing shadow filtering, stillness handling, visual impulse removal and visual distortion filtering. Object-events were defined in terms of feature trajectories of foreground and they were modelled using the time series modelling technique. A cross-substitution based model comparison method was employed to compare the disparity between events. Spectral clustering (SC) was utilised to cluster events, and methods for SC initial parameter selection have been proposed. A method for cluster identity assignment in consecutive clustering iterations is also utilised to handle the evolving nature of the unsupervised learning methodology adopted. The proposed method is capable of producing reliable clustering results online, amidst a number of complications including dynamic backgrounds, object shadows, camera distortions, sudden foreground bursts and inter-object interactions.  Published on 2016-12-27 08:35:44
School of Mathematical and Computer Sciences
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