Browsing by Author "Dissanayake, D. M. P. V."

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A morphometric analysis of craniofacial features of the Coastal Indigenous people in Sri Lanka
(Sociedad Chilena de Anatomia, 2024-12) Liyanage, S.; Devasingha, D. M. L. C. D.; Dissanayake, D. M. P. V.; Ranaweera, L.
The existence of an indigenous community within a country is a source of pride and warrants significant attention. Sri Lanka is no exception, and, as a country with the fossil remains of anatomically modern Homo sapiens, it is hypothesized that Sri Lankan Indigenous people might harbor ancient genetic signatures. This study aims to establish baseline data of certain craniofacial anthropometric measurements in the Coastal Indigenous people and classify their head, face, and nose types. This study involved 126 (70 Male and 56 Female) unrelated individuals from six villages, representing the Coastal Indigenous population. Sixteen craniofacial measurements were obtained, providing calculations of three craniofacial indices: the Cephalic index, Facial index, and Nasal index. It was apparent that all craniofacial measurements, except nose protrusion of males, had significantly higher dimensions than those of the female participants. In addition to baseline quantitative raw data, the calculated indices are as follows: The mean cephalic, facial, and nasal indices of females were 78.50± 4.84, 88.37±13.06, and 93.93±12.23, respectively, whereas those of males were 78.85±5.76, 91.74±13.70, and 94.58±14.06, respectively. This is the first craniofacial study on Coastal Indigenous people in Sri Lanka. The most common head shape observed among both genders was mesocephalic. Males predominantly exhibited a hyperleptoprosopic facial type, while females mostly showed a leptoprosopic facial type. The most dominant nasal type recorded for both genders was the platyrrhine nasal phenotype. Interestingly, such platyrrhine nose is rarely present in other world populations, except in African populations.
The best-fitting models for weather data in Katunayaka, Sri Lanka
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Sumathipala, N. S.; Hewaarchchi, A. P.; Dissanayake, D. M. P. V.
Climate series patterns are affected by abrupt shifts induced due to the changes in observers, station relocation, and gauge replacements. Identifying these changes is vital in finding the best-fitted models to find the predictions. In this study, we analyze the weather data in Katunayaka, as Katunayaka is an important urban hub, where the main airport is located. This study explores the detection of structural changes in Temperature, Wind Speed, and Humidity for Katunayaka data through statistical analysis to find the best-fitted models for forecasting. Since the weather patterns are changed due to artificial factors, changepoint analysis is applied to identify abrupt shifts. A changepoint is a distributional shift or abrupt change in a time series data structure. Over the years, numerous changepoint detection methods have been proposed by researchers. The Pruned Exact Linear Time (PELT) method is one of these methods standing out for its speed and accuracy in identifying multiple changepoints in mean and variance. Due to the complexity of temporal variations in climate data, simple models often struggle to identify these shifts accurately. Climate time series also exhibit autocorrelation, and failing to account for this can lead to false detections. The objective of this paper is to fit the best models by considering changepoints to forecast weather patterns in Katunayaka, Sri Lanka, a major industrial area. For this study, temperature (°C), wind speed (km/h) data from 2007 to 2022, and humidity (%) data from 2007 to 2010 in Katunayake were used. The Pruned Exact Linear Time method was applied to detect changepoints in the mean and variance of the data. In this study there were no any changepoints detected for the temperature and humidity data but a mean changepoint was detected in wind data. A changepoint was found in March 2012 for the average wind speed for the given period. The fitted model without considering changepoints was ARIMA (2,0,1)(0,1,1) while the fitted model with considering changepoints structure was ARIMA (1,0,1)(2,1,1). The best model for forecasting average wind speed was ARIMA (1,0,1)(2,1,1) which is given under changepoint structure with Root Mean Square Error (RMSE) of 1.24 and Mean Absolute Percentage Error(MAPE) of 8.65. Then ARIMA (2,0,1)(0,1,1) model and ARIMA(0,0,0)(0,1,0) models were fitted to the temperature and humidity data respectively for the forecasting. The detected changepoint confirms a significant shift of the average wind speed. Considering the time of this shift occurs, the best-fitted model is built to predict the wind speed accurately.
Factors influencing milk powder preferences: a comparative study of local and imported brands in Gampaha district
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Maleesha, M. A. N.; Prabhashwara, M.; Wickramage, R.; Abeygoonewardena, V.; Rathnayake, N.; Madhushani, T.; Liyanage, U. P.; Dissanayake, D. M. P. V.
Milk powder is a common choice in Sri Lanka because it's convenient, easy to store, and lasts longer than fresh milk. It is made by removing the moisture from milk, which makes it practical for storage and transport, especially in households where fresh milk isn’t always accessible. However, due to the limited supply of locally produced milk powder, many people rely on imported brands. This study looks at why people in the Gampaha district, the most densely populated district in Sri Lanka, choose between local and imported milk powder. Gampaha has both urban and rural areas, making it a good place to study consumer habits. The research involved a survey given to people in three Divisional Secretary’s Divisions (DSDs) out of 13 in the district. These areas were selected using cluster sampling, and convenience sampling was used to gather the data. The sample size was set at 303, based on a pilot survey, to ensure the findings would be reliable. The results showed that price, quality, and taste were the most important factors influencing people’s choices when it came to milk powder. We used chisquare and G-square tests to confirm that these factors had a significant effect. Interestingly, household income and brand preference have not seemed to play much of a role in people’s decisions. Some respondents also pointed out that herbal drinks like Ranawara, Belimal, and Kolakenda were viewed as alternative beverages to milk powder, especially when milk was not affordable or available. While many people believe that locally produced milk powder is of better quality, they often end up choosing imported brands because they are cheaper and more widely available in stores. There’s also a strong sense among consumers that they want to support local products, but the higher cost of local milk powder makes it hard for many to buy. The study suggests that making local milk powder more affordable and available in more places could reduce the dependency on imports, benefiting not just the consumers but also the national economy by supporting local dairy production. If local milk powder were priced more competitively, it would encourage more people to choose it over imported brands. In conclusion, improving both the affordability and accessibility of local milk powder could help boost its consumption, strengthen the local dairy industry, and reduce reliance on imports, bringing economic benefits to Sri Lanka in the long run.
A Multivariate Analysis of Socioeconomic and Environmental Global Indicators
(4th International Research Symposium on Pure and Applied Sciences, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Samarakoon, H. H. T. P.; Dissanayake, D. M. P. V.
One of the main objectives of every country, whether developed or developing is to achieve sustainable development. Development status is one of the key measurements in identifying the performance of a country. Developed, developing and least developed are the levels of development status measured under different indicators. Therefore, it is highly important to know the related variables or indicators that can describe the development status of a country appropriately. Most of the studies are based on economical, industrial and technological indicators to identify the development status. However, there can be many other indicators that can directly or indirectly impact on the development stages. The aim of this research is to cluster the countries considering socioeconomic and environmental factors. That is, this research attempts to find how several global indicators related to the sustainable development status of a country. This study is considering several socioeconomic and environmental factors namely Gross National Income (GNI) per capita, Literacy Rate (LR), Life Expectancy (LE), Global Peace Indicator (GPI), Pollution Indicator (PI) and Suicide Index (SI). Because of the availability of data, this study is considering data for 90 countries for the year 2018. Due to the presence of correlation between above mentioned indicators, principal component analysis technique was used to construct uncorrelated factor variables. Countries were classified into three clusters based on the above factors using k-means clustering technique. First three components were used in clustering where they account for a proportion 0.94 of the population variance. The results showed that the average GNI, LE and LR values of the first cluster are highest and the average GPI and PI values of the first cluster are lowest with compared to the other two clusters. The average GNI, LE, LR and SI values of the second cluster are lowest and the average PI value of the second cluster is highest with compared to the other two clusters. The above results depict the countries having high gross national income, life expectancy at birth and literacy rate are peaceful where their pollution and suicide rates are high. Hence, this study conclude that it is vital to consider socioeconomic as well as environmental indicators to identify the sustainable status of a country