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- 저자 : Aziz Jamal (검색결과 3 건)
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Cu based electrically programmable fuse characteristics for CMOS technology
Kim Honggyun,Aziz Jamal,Chavan Vijay D.,Kim Deok-kee 한국물리학회 2023 Current Applied Physics Vol.49 No.-
Conventional electrically programmable fuse (eFuse) includes a cathode, anode and a fuse link in between, which can be programmed by an external stimulus. In this study, programming characteristics of M1 and M2 Cu eFuse with or without a programming transistor were investigated in 28-nm CMOS technology. M2 eFuse showed a lower programming current density than M1 eFuse, and wide eFuse (M1or M2) showed a lower programming current density than narrow eFuse. Also, the heat dissipation in M2 eFuse was lower than M1 eFuse due to surrounding ultra-low-k dielectric (ULK) with low thermal conductivity. The area dependence of eFuse was also investigated. The dissipated heat in wide Cu eFuse was higher than narrow Cu eFuse due to more generated power per unit volume in wide eFuse. As compared to M1 eFuse, M2 wide eFuse with ULK surrounding medium proved to be a promising candidate for the future eFuse option with the minimal statistical variations and lowest programming current.
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Moon, Surk-Sik,Rahman, Md. Aziz Abdur,Manir, Md. Maniruzzaman,Ahamed, V.S. Jamal 대한약학회 2010 Archives of Pharmacal Research Vol.33 No.8
Bioassay-directed fractionation of a methanolic extract from the seeds of Draba nemorosa (Brassicaceae) led to isolation of a new flavonol glycoside, drabanemoroside (5, kaempferol 3-O-${\alpha}$-L-rhamnopyranosyl-(1${\rightarrow}$2)-${\alpha}$-L-arabinopyranose) along with four known flavonoid derivatives (1-4), four cardenolide glycosides (6-9). Kaempferol glycosides 2 and 5 showed strong cytotoxicity against human small lung cancer cell line A549 and melanoma SK-Mel-2 with an $IC_{50}$ of 0.5 ${\mu}g/mL$ and 1.9 ${\mu}g/mL$, respectively. Cardenolide glycosides 6-9 showed potent cyto-toxicity (A549) in the range of 0.01-0.032 ${\mu}g/mL$. Their structures were characterized based on spectroscopic data (2D NMR, HRTOFMS, IR, and UV) and comparison of literature values. The carbohydrate units were also confirmed by comparing the hydrolysate of 5 with authentic monosaccharides.
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Classification Model for Water Quality using Machine Learning Techniques
Salisu Yusuf Muhammad,Mokhairi Makhtar,Azilawati Rozaimee,Azwa Abdul Aziz,Azrul Amri Jamal 보안공학연구지원센터 2015 International Journal of Software Engineering and Vol.9 No.6
The problem of water pollution is increasing every day, due to the industries’ waste product disposal, migration of people from rural to urban areas, crowded population, untreated sewage disposal, wastewater and other harmful chemicals’ discharge from the industries. There is a need to resolve this problem for us to get good water that can be used for domestic purposes. This article proposes a suitable classification model for classifying water quality based on the machine learning algorithms. The paper analyzed and compared performance of various classification models and algorithms in order to identify the significant features that contributed in classifying water quality of Kinta River, Perak Malaysia. Five models with respective algorithms were tested and compared with their performance. In assessing the result, the Lazy model using K Star algorithm was the best classification model among the five models had the most outstanding accuracy of 86.67%. Generally, wastewater is harmful to our lives, and bringing scientific models in solving this problem is obligatory.
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