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Error detection through modified phase II process monitoring under different classical estimators
(National Science Foundation: Colombo, 2023-10-12) Jabeen, R.; Zaka, A.
In real life, the distribution of the errors during any life testing of products or process does not meet the assumption of normality. Statistical process control (SPC) is defined as the use of statistical techniques to control a process or production method. SPC tools and procedures can help to monitor process behavior, discover problems in internal systems, and find solutions for production issues. To identify and remove the variation in different reliability processes and to monitor the reliability of machines where the number of errors follows skewed distributions, we develop control charts to keep the process in control. For such situations, we have modified the existing control charts such as Shewhart control chart, exponentially weighted moving average (EWMA), hybrid exponentially weighted moving average (HEWMA) and extended exponentially weighted moving average (EEWMA) control charts. The current study introduced classical estimator based modified control charts for phase-II monitoring by assuming that the errors occur during the process follow skewed distribution called Beta Lehmann 2 Power function distribution (BL2PFD). The proposal for these control charts is based on the percentile estimator. We have compared all these control charts using Monte Carlo simulation studies and real-life applications to compare the proposed control charts. This study shows that an EEWMA control chart based on PE performs better than Shewhart, EWMA and HEWMA control charts, when the underlying distribution of the errors in process monitoring follows BL2PFD. These findings can be useful for researchers and practitioners in dealing with production errors and optimizing the output.
Beyond aesthetics: Integration of textural groups of tropical ornamental shrubs into urban planting designs
(National Science Foundation: Colombo, 2023-10-12) Yakandawala, K.; Bandara, A.; Yakandawala, D.; Abeynayake, R.
Shrubs are popularly incorporated to establish green infrastructure in urban spaces. We argue that the functions provided by shrubs could be further enhanced by giving due consideration to their leaf morphological characters. Therefore, our objective was to recognise how the different morphological characters of leaves, listed as contributing to determining the plant texture in literature, would collectively contribute to recognizing textural groups of plants, and further, to define each of these groups into either coarse, medium, or fine textural categories using ornamental shrubs. We investigated the quantitative and qualitative leaf morphology of 30 tropical ornamental shrubs in the Peradeniya area. According to our analysis, leaf area, petiole length, and internodal distance have significantly contributed to the separation of shrubs into three textural groups; fine, medium and coarse, and were considered as preliminary characters that determine the texture. Leaf hair related characters viz., hair densities on upper and lower surfaces, and the length of hairs on both surfaces, together with qualitative morphological characters, viz., leaf margins, leaf arrangement, and prominent venation were identified as secondary characters that contributed to defining textural groups. Shrubs with coarse texture possess significantly larger leaves, longer petioles and internodal distances compared to fine textured group. Our recommendation is to consider plant textural groups as a criterion in the selection of plants for planting designs during the establishment of green infrastructure in urban spaces, enabling the obtaining of benefits beyond aesthetics, which include other functional, health and environmental benefits, to improve the quality of life of city dwellers under the context of limited urban green spaces.
Chronological attribution of Sinhalese inscriptions using deep learning approaches
(National Science Foundation: Colombo, 2023-10-12) Heenkenda, H.M.S.C.R.; Fernando, T.G.I.
A study of this caliber can be identified as a profound source for a wealth of knowledge as the aim of this study is to present chronological attribution of Sinhalese inscriptions based on deep learning approaches. Inscriptions shed light on a multitude of information such as chronicled civilizational thought, economic status, language evolution, cultural boundaries, details of royal officers, local rules, ethnic groups, land tenure, religious activities, beliefs, and trade and industries. Inscriptions are major assets to showcase inclusive of listed above, multitude information; hence, the benefits served by a study of high caliber, especially to the historical heritage research and to the heritage tourism. Several computer-aided solutions have been proposed to resolve the recognition of inscriptions in the Sri Lankan context. But this paper proposes an optimized classification. A dataset of five hundred images of original Sinhalese inscriptions dating from the 3rd century BC to the present was used to train and test the models. This study adopts four deep learning models to classify Sinhalese inscriptions: a newly proposed convolutional neural network model, and the pre-trained models Inception-v3, VGG-19, and ResNet-50. Palaeographical and morphological rules were adopted in the manual classification of Sinhalese inscriptions into a number of eras, namely, the Early Brahmi (3rd century BC to 1st century AD), Late Brahmi (2nd century AD to 4th century AD), Transitional Brahmi (5th century AD to 7th century AD), Medieval Sinhala (8th century AD to 14th century AD), and Modern Sinhala (15th century AD to the present). The results of the study indicate promising outcomes with accuracies of 70.66%, 85.94%, 57.44%, and 58.77% respectively for used four models. Further, the study revealed that the Inception-v3 model outperformed in classifying the Sinhalese inscriptions in respective eras.
pH-dependent release properties of curcumin encapsulated alginate nanoparticles in skin and artificial sweat
(National Science Foundation: Colombo, 2023-10-12) Shakoor, I.F.; Pamunuwa, G.K.; Karunaratne, D.N.
Topical skin application of curcumin is challenging due to the low solubility and poor stability, including fast photodegradation, of this bioactive compound. Therefore, curcumin encapsulated alginate (CU-Al) nanoparticles were prepared by the ionic gelation method followed by freeze drying to determine the efficacy of alginate in facilitating curcumin release. Evaluation of the release of curcumin from the encapsulate in the presence of artificial sweat (pH 4.7) and skin (pH 5.5), about which the literature is meagre, was carried out after particle size characterization. CU-Al nanoparticles were in the nano-range (186.8 nm), assimilated a negative zeta-potential value (-15.4 ± 8.13 mV), and displayed a high encapsulation efficiency (94.55 ± 0.53%). The release of encapsulated curcumin at pH 5.5 (max. 64%) and at pH 4.7 (max. 27%) were significantly different. In pH 5.5 and pH 4.7, the release profiles of encapsulated curcumin fitted best with the Weibull (followed an anomalous transport mechanism) and Gompertz (followed a super case II transport mechanism) models respectively, displaying sigmoidal release patterns. Diffusion and polymer relaxation/swelling based release at pH 5.5 and rapid polymer relaxation/erosion based release at pH 4.7 have governed the encapsulated curcumin release. The results indicated that CU-Al nanoparticles may be utilized to facilitate controlled and prolonged release of curcumin in both skin and artificial sweat, thereby functioning as a promising novel delivery vehicle for curcumin. However, skin deposition or penetration may be required for yielding a satisfactory topical administration of curcumin during sweating.
Spatial distribution of heavy metals in surface sediments of the Kalametiya Lagoon in southern Sri Lanka: Insights into the pollution status and socio-economic interactions
(National Science Foundation: Colombo, 2023-10-12) Kodikara, K.A.S.; Hoessein, T.; De Silva, P.M.C.S.; Ranasinghe, P.; Somasiri, H.P.P.S.; Madarasinghe, S.K.; Gunathilake, D.U.V.; Ranawaka, D.; Danaee, M.; Andrieu, J.; Dahdouh-Guebas, F.
Heavy metal pollution has become a serious threat to coastal aquatic ecosystems. Therefore, this study, aimed to assess the spatial distribution of five selected heavy metals/metalloids, arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), and mercury (Hg), in surface sediment samples collected from the Kalametiya Lagoon in southern Sri Lanka. Sixteen (16) areas of the lagoon were sampled. The sediment samples were analysed for heavy metal content by using ICP-MS while the water samples were measured for salinity and pH. A questionnaire survey was conducted to investigate the possible sources of heavy metal pollution in the Lagoon. Water pH and salinity showed significant variations across the lagoon. The overall mean value of pH and salinity were 6.68 ± 0.17 and 2.9 ± 2.2 PSU, respectively. The spatial distribution of the heavy metals was not monotonous and showed a high spatial variation. The kernel density maps of the measured heavy metals demarcated several spatially different patches in the lagoon. The mean levels of As, Cd, Cr, Hg, and Pb were lower than the threshold effect level (TEL) although it was higher for Hg in the North inlet. Nevertheless, it was still lower than the potential effect level (PEL). Industrial sewage, river suspended sediments, and agrochemicals such as fertilizers and pesticides were identified as the possible sources for heavy metal loads. Accumulation of toxic heavy metals can be minimized by by-passing the freshwater inflow to the lagoon.