Pub Date : 2016-04-01DOI: 10.1109/MERCON.2016.7480165
K. D. H. N. Kahawita, A. Samarasekara
In the current period of time synthetic polymers are rapidly spreading among people due to the advantages of advanced mechanical properties with low cost and weight. However, due to the low biodegradability, synthetic materials produce environmental pollution. Therefore the productions of biodegradable materials are playing a major role to save the environment. All natural materials are biologically degraded by the actions of micro-organisms in the environment. The biomass is mainly consists of cellulose, hemicellulose and lignin and they are the most abundant biopolymers present on the earth. Out of the three biopolymers cellulose and hemicellulose have comparatively high biodegradability due to the polysaccharide structure. Sawmill waste is one of the major waste with lack of attention in Sri Lanka. Therefore, the research is focused to produce low cost, biodegradable composite material for engineering applications by extracting cellulosic materials present in sawmill waste. For the study, separately collected five common timber samples and the mixture of each (equal mass of five most common wood wastes) were used. Finally, it concludes that the maximum percentage of cellulose and hemicellulose can be extracted from the waste wood mix sample (68. 7%) and the extracted natural polymers were characterized using FTIR analysis.
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Pub Date : 2016-04-01DOI: 10.1109/MERCON.2016.7480132
M. A. Abeyrathna, D. A. Abeygunawrdane, R. A. A. V. Wijesundara, V. B. Mudalige, M. Bandara, Shehan Perera, Danaja Maldeniya, Kaushalya Madhawa, Sriganesh Locknathan
This paper discusses about predicting Dengue out-breaks in Sri Lanka using heterogeneous data sets: Mobile Network Big data and epidemiological data. Up to now, Dengue epidemiological prediction was largely done using the past Dengue cases and weather data. However, very recently it was discovered that infection can propagate through humans, where an infected human travels to a vulnerable area and mosquitoes of that area will bite him, and contract and spread the virus. Hence this research explores the potential of incorporating human mobility, derived through mobile network data in predicting Dengue propagation. This paper presents the various data sources, how the data fusion was conducted and how the fused data was fed into the model and the results obtained and a discussion thereof, including the potential of extending the research.
{"title":"Dengue propagation prediction using human mobility","authors":"M. A. Abeyrathna, D. A. Abeygunawrdane, R. A. A. V. Wijesundara, V. B. Mudalige, M. Bandara, Shehan Perera, Danaja Maldeniya, Kaushalya Madhawa, Sriganesh Locknathan","doi":"10.1109/MERCON.2016.7480132","DOIUrl":"https://doi.org/10.1109/MERCON.2016.7480132","url":null,"abstract":"This paper discusses about predicting Dengue out-breaks in Sri Lanka using heterogeneous data sets: Mobile Network Big data and epidemiological data. Up to now, Dengue epidemiological prediction was largely done using the past Dengue cases and weather data. However, very recently it was discovered that infection can propagate through humans, where an infected human travels to a vulnerable area and mosquitoes of that area will bite him, and contract and spread the virus. Hence this research explores the potential of incorporating human mobility, derived through mobile network data in predicting Dengue propagation. This paper presents the various data sources, how the data fusion was conducted and how the fused data was fed into the model and the results obtained and a discussion thereof, including the potential of extending the research.","PeriodicalId":184790,"journal":{"name":"2016 Moratuwa Engineering Research Conference (MERCon)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126897283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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