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RISS 인기검색어
- 검색키워드
- 저자 : M. Tauhidul Islam (검색결과 4 건)
- 무료
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Md Luthfar Rahman Liman,M. Tauhidul Islam,Md. Milon Hossain,Priti Sarker 한국섬유공학회 2020 Fibers and polymers Vol.21 No.10
The practice of natural colorants for polyester fabric dyeing has been rampantly rising in our consumer fashion. Inthis study, two separate natural colorants were extracted from watermelon (Citrullus lanatus) rind (WRS) and flesh (WFS)for polyester coloration. The optical and colorimetric properties of saps and dyed fabrics, dye fiber bonding phenomena,chromophores diffusion behaviors, color-fastness, ecological and economic aspects were examined through various analysesand the corresponding mechanisms were proposed. A wide range of chromophores diffusion was noticed for severalparameter variations and the resulting diffusions were ranging from 42.67 to 83.13 %. Interestingly, WRS and WFS robustthe UV shielding properties into the dyed fabric and the recorded UPF rating was found to be above 50. All color-fastnessproperties including sublimations were very good to excellent (4 to 4/5) except lightfastness. Finally, the ecological andeconomic aspects of WRS/WFS dyeing were also compared with commercial disperse dyes.
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Md. Reazuddin Repon,M. Tauhidul Islam,Md. Abdullah Al Mamun 한국의류학회 2017 Fashion and Textiles Vol.4 No.1
Variety and durability of color are presumed as key constrains of natural dyes. So, this study attempts to investigate the effect of metallic mordants on the color fastness properties of ecologically dyed cotton fabric using banana floral stem sap. Color difference was measured in terms of hue (ΔH*), chroma (ΔL*) and value (ΔC*) difference. Metal ions in residual mordanting bath, dyeing wastewater and level of trace metals in the finished fabric surface were accessed to justify the environmental safety and speculate the health risk respectively. Pre-mordanted specimens were dyed at 100 °C for 60 min. Optical properties of extracted sap were observed by UV visible spectroscopy. Dye fixation with fiber was determined by FTIR-ATR spectra. Atomic absorption spectroscopy was employed to determine the trace metals in finished fabric. Effect of metallic mordants were calculated in terms of color fastness to wash, water, perspiration, rubbing and light for estimating the color durability. Except light fastness property almost all color fastness values were 4/5, i.e. very good. Light fastness properties were improved for mordanting action with metallic salts. The level of trace metals in finished fabric were within the safe zone.
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임채균(Chae-Gyun Lim),에이케이엠 토히둘 이슬람(A.K.M. Tauhidul Islam),정병수(Byeong-Soo Jeong) 한국정보과학회 2014 정보과학회논문지 : 데이타베이스 Vol.41 No.3
마이크로어레이(microarray) 데이터 분석은 특정 세포에서 발현(expression)되는 수천 가지 유전자로부터 중요한 생물학적 정보를 추출하기 위해 널리 사용되고 있다. 그러나 발현된 유전자들 중 대부분은 실제 임상 진단 및 질병 분류와 무관하거나 중요하지 않은 경우가 많기 때문에 소수의 중요한 유전자들을 선별하는 방법이 요구되고 있다. 본 논문에서는 맵리듀스(MapReduce) 프로그래밍 모델을 이용하여 확장 가능한(scalable) 병렬 유전자 선택 기법을 제안한다. 제안하는 기법은 맵리듀스 기반으로 유전자 선별 작업을 병렬적으로 수행하고, 분류의 정확도를 평가하기 위해 kNN 분류 알고리즘을 활용한다. 또한 실험 결과를 통하여 제안 기법이 데이터 크기의 증가와 노드 개수의 차이에 따라 좋은 확장성(scalability)을 제공하고 있고, 선별한 유전자들을 사용한 분류 결과가 전체 유전자 집합을 사용한 분류보다 더 높은 분류 정확도를 가지고 있음을 보인다. Microarray data analysis has been widely used for extracting relevant biological information from thousands of genes simultaneously expressed in a specific cell. Although thousands of genes are expressed in a sample tissue, most of them are irrelevant or insignificant to clinical diagnosis or disease classification because of missing values and some noise. Thus, finding a closely related small set of genes for accurately classifying disease cells is an important research problem. In this paper, we propose a scalable parallel gene selection method using the MapReduce programming model. The proposed method utilizes kNN classifier algorithm for evaluating classification accuracy and uses four real datasets and three synthetic datasets for experimentation. Experimental results show that the proposed method can give good scalability along with the increase of a data size and different numbers of nodes and it can also provide higher classification accuracy than using the whole gene set for classification.
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