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Shafiq-ur-Rehman Massan
Researcher at Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology
Publications - 13
Citations - 161
Shafiq-ur-Rehman Massan is an academic researcher from Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology. The author has contributed to research in topics: Turbine & Metaheuristic. The author has an hindex of 5, co-authored 13 publications receiving 109 citations. Previous affiliations of Shafiq-ur-Rehman Massan include Mohammad Ali Jinnah University.
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A new explicit approximation to Colebrook’s friction factor in rough pipes under highly turbulent cases
TL;DR: In this paper, a new explicit formula to approximate friction in pipes of considerable roughness under highly turbulent regimes is formulated by the help of Colebrook's equation, and the performance of proposed method over recent approximations in literature is tested.
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Wind turbine micrositing by using the firefly algorithm
TL;DR: It is demonstrated that the recently discovered firefly algorithm is a better technique for the optimization of large wind farms than the genetic algorithms and finite difference method by a wide margin.
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A new metaheuristic optimization algorithm inspired by human dynasties with an application to the wind turbine micrositing problem
TL;DR: The proposed dynastic optimization algorithm (DOA) has been successfully applied to the traditional WTM and encouraging results have been obtained and it is demonstrated that the proposed approach is equally viable as other existing algorithms, like the Genetic algorithm and Differential evolution algorithm.
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Applications of unmanned aerial vehicles: a review
TL;DR: The numerous applications of UAVs are studied and summarized in tabular form according to the current need of the autonomous technology in various areas of agriculture, military and civilian usage of the modern age.
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An overview of ai enabled m-iot wearable technology and its effects on the conduct of medical professionals in public healthcare in pakistan
TL;DR: The results show that clinicians are reluctant to use technology though the results also reveal that same clinicians have positive influence of performance and effort expectations on their intention to usetechnology that leads the actual behavior of using the technology.