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Ahmad Makmom Abdullah

Researcher at Universiti Malaysia Perlis

Publications -  132
Citations -  1814

Ahmad Makmom Abdullah is an academic researcher from Universiti Malaysia Perlis. The author has contributed to research in topics: Air quality index & Agriculture. The author has an hindex of 18, co-authored 130 publications receiving 1423 citations. Previous affiliations of Ahmad Makmom Abdullah include Bangladesh Council of Scientific and Industrial Research & Texas A&M University.

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A comparison of Mesua ferrea L. and Hura crepitans L. for shade creation and radiation modification in improving thermal comfort.

TL;DR: In this article, the authors compared the effectiveness of Mesua ferrea L and Hura crepitans L. in shading creation and radiation modification in improving human thermal comfort in tropical open spaces.
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Impact of regional haze towards air quality in Malaysia: A review

TL;DR: In this paper, the potential origin, chemical compositions, impacts and mitigation strategies of haze in Malaysia were reviewed, where agricultural activities, deforestation and oil palm plantations on peat areas, particularly in Sumatra and Kalimantan, Indonesia were identified as the contributing factors to high intensity combustions that results in transboundary haze.
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Air quality in Malaysia: impacts, management issues and future challenges.

TL;DR: Observations have been made on the long‐term trends of major air pollutants in Malaysia including nitrogen dioxide, carbon monoxide, the ozone and total suspended particulate matter (particularly PM10), and sulfur dioxide, emitted from industrial and urban areas from early 1970s until late 1998.
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Performances of Toluene Removal by Activated Carbon Derived From Durian Shell

TL;DR: Assessment of the effectiveness of activated carbon produced from durian shell in removing toluene vapors showed that the highest BET surface area of the produced DSAC was 1404 m2/g, and there is insignificant difference between removal efficiency of toLUene by DSAC and different toluenes concentrations.
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Ultrafast Transmission Line Fault Detection Using a DWT-Based ANN

TL;DR: Results demonstrate that very accurate classification using one-eighth of a cycle of postevent data is possible and it is shown that the feature vector of the event can be determined from a subset of local currents without using any voltages altogether.