Institution
Universiti Teknologi Petronas
Education•Ipoh, Malaysia•
About: Universiti Teknologi Petronas is a education organization based out in Ipoh, Malaysia. It is known for research contribution in the topics: Adsorption & Ionic liquid. The organization has 6127 authors who have published 11284 publications receiving 119400 citations.
Topics: Adsorption, Ionic liquid, Catalysis, Membrane, Biomass
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a simple, cheap and ecofriendly method is reported to synthesize stabilized gold nanoparticles of size 35-75nm at room temperature using aqueous Elaeis guineensis (oil palm) leaves extract without addition of any external agent.
59 citations
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TL;DR: In this paper, the dual functional (thermodynamic and kinetic) behavior of quaternary ammonium salt namely tetramethyl ammonium chloride (TMACl) for both methane (CH4) and carbon dioxide (CO2) hydrates was evaluated in the presence and absence of aqueous TMACl solutions (1, 5 and 10wt%) through T-cycle method at different temperature and pressure conditions.
59 citations
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TL;DR: In this paper, a finite element model for predicting the mechanical behavior of polypropylene (PP) composites reinforced with carbon nanotubes (CNTs) at large deformation scale is presented.
59 citations
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TL;DR: The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT–FC environment and results indicated the better performance of the proposed approach compared with existing methods.
Abstract: Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT-FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.
59 citations
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TL;DR: This review provides a summary of enzymatic systems involved in enhancing the hydrolysis stage and consequently improve biogas production and shows that the use of enzymes improves the biog as well as the operating conditions of pretreatment and the types of substrates.
59 citations
Authors
Showing all 6203 results
Name | H-index | Papers | Citations |
---|---|---|---|
Muhammad Imran | 94 | 3053 | 51728 |
Muhammad Shahbaz | 92 | 1001 | 34170 |
Muhammad Farooq | 92 | 1341 | 37533 |
Markus P. Schlaich | 74 | 472 | 25674 |
Abdul Basit | 74 | 570 | 20078 |
Keat Teong Lee | 71 | 276 | 16745 |
Abdul Latif Ahmad | 68 | 490 | 22012 |
Cor J. Peters | 52 | 262 | 9472 |
Suzana Yusup | 52 | 437 | 8997 |
Muhammad Nadeem | 52 | 409 | 9649 |
Umer Rashid | 51 | 381 | 10081 |
Hamidi Abdul Aziz | 49 | 345 | 9083 |
Serge Palacin | 45 | 201 | 8376 |
Muhammad Awais | 43 | 272 | 6704 |
Zakaria Man | 43 | 245 | 5301 |