S
Sara Hawi
Researcher at Cranfield University
Publications - 6
Citations - 130
Sara Hawi is an academic researcher from Cranfield University. The author has contributed to research in topics: Polymer & Composite number. The author has an hindex of 2, co-authored 2 publications receiving 37 citations.
Papers
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Journal ArticleDOI
Resilient and agile engineering solutions to address societal challenges such as coronavirus pandemic
Saurav Goel,Saurav Goel,Saurav Goel,Sara Hawi,Gaurav Goel,Vijay Kumar Thakur,Vijay Kumar Thakur,Anupam Agrawal,Clare Hoskins,Oliver Pearce,Tanvir Hussain,Hari M. Upadhyaya,Graham L. W. Cross,Asa H. Barber +13 more
TL;DR: Overall, this paper attempts to identify key areas where manufacturing can be employed to address societal challenges such as COVID-19.
Journal ArticleDOI
Nature inspired materials: Emerging trends and prospects
Nirmal Kumar Katiyar,Nirmal Kumar Katiyar,Gaurav Goel,Gaurav Goel,Sara Hawi,Saurav Goel,Saurav Goel,Saurav Goel +7 more
TL;DR: Goel et al. as discussed by the authors reviewed examples from the laboratory to industrial scale to highlight emerging opportunities in nature-inspired materials and found that they possess specific functionality that rely on our ability to harness particular electrical, mechanical, biological, chemical, sustainable, or combined gains.
Peer ReviewDOI
Critical Review of Nanopillar-Based Mechanobactericidal Systems
Journal ArticleDOI
Magnetic field assisted 3D printing of short carbon fibre-reinforced polymer composites
TL;DR: In this article , the results from using a high-resolution optical microscopy and scanning electron microscopy showed that while carbon fibres within the Onyx had aligned in response to the magnetic field, the PLA samples were visibly unchanged by the magnetic fields.
Journal ArticleDOI
Automatic Parkinson's disease detection based on the combination of long-term acoustic features and Mel frequency cepstral coefficients (MFCC)
TL;DR: In this paper , the authors investigated the effect of a dataset incorporating long-term and short-term features known as Mel frequency cepstral coefficients (MFCC) on the performance of random forest model for Parkinson's disease detection.