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RISS 인기검색어
- 검색키워드
- 저자 : Ghassan Sulaiman (검색결과 3 건)
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Ghassan Sulaiman,Mohammad K. Younes,Ghassan A. Al-Dulaimi 대한환경공학회 2018 Environmental Engineering Research Vol.23 No.1
Air quality and pollution have recently become a major concern; vehicle emissions significantly pollute the air, especially in large and crowded cities. There are various factors that affect vehicle emissions; this research aims to find the most influential factors affecting CO₂ and NOx emissions using Adaptive Neural Fuzzy Inference System (ANFIS) as well as a systematic approach. The modified ANFIS (MANFIS) was developed to enhance modelling and Root Mean Square Error was used to evaluate the model performance. The results show that percentages of CO₂ from trucks represent the best input combination to model. While for NOx modelling, the best pair combination is the vehicle delay and percentage of heavy trucks. However, the final MANFIS structure involves two inputs, three membership functions and nine rules. For CO₂ modelling the triangular membership function is the best, while for NOx the membership function is two-sided Gaussian.
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Molecular Structure and Anti-proliferative Effect of Galangin in HCT-116 Cells: In vitro Study
Ghassan Mohammad Sulaiman 한국식품과학회 2016 Food Science and Biotechnology Vol.25 No.1
Galangin is a naturally occurring plant flavonoid with potential anticancer activity. In present work, the Becke three-parameter hybrid exchange functional method and the Lee-Yang-Parr correction functional methods were used to investigate the structural properties of galangin. The structure-activity relationship analysis has been performed to determine its antioxidant pharmacophore by using density functional theory method and quantum chemical calculations. The free radical scavenging activities of galangin were analyzed with the use of 2, 2-diphenyl-1-picrylhydrazyl and compared with Vitamin C as a control. Galangin decreased the cell proliferation rate in HCT-116 cells and showed concentration- and time-dependent response. Galangin significantly increase the inhibitory effect on HCT-116 clonogenicity and promotes cell cycle arrest at the G2/M or G1 phase, as confirmed by flow cytometry analysis.
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Integration of Traffic Management and an Artificial Intelligence to Evaluate Urban Air Quality
Mohammad K. Younes,Ghassan Sulaiman,Ali Al-Mashni 한국대기환경학회 2020 Asian Journal of Atmospheric Environment (AJAE) Vol.14 No.3
Emissions from motor vehicles are the primary source of air pollution, especially in congested urban centres. However, through effective traffic management, it has been found that the level of pollution can be significantly reduced, facilitating the mobility of urban arterials. This study aims to quantify the extent of traffic emissions and to identify the influence of traffic management to improve air quality and reducing traffic emissions. An Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed to estimate the extent of traffic emissions(NO2 and PM10) at certain intersections. Then, a traffic management simulation software was also used to simulate traffic and to build a traffic improvement scenario at these intersections. This was followed by measuring the improvement in air quality due to traffic management modification, analysed using the developed ANFIS model. The results showed that reducing the delay at certain intersections may reduce NO2 and PM10 significantly. The proposed hybrid model increased the forecasting accuracy and improved the perception between the relationship between traffic characteristics and pollutant emissions. Additionally, it facilitates the work of city planners and helps decision making regarding urban air quality.
ScienceON
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