Outputs (121)

Thermomechanical deformation analysis of a tubular solid oxide steam electrolysis cell. (2023)
Conference Proceeding
KURUSHINA, V., RAJENDRAN, V., PRATHURU, A., HOSSAIN, M., FAISAL, N., SOMAN, A., HORRI, B.A. and CAI, Q. 2023. Thermomechanical deformation analysis of a tubular solid oxide steam electrolysis cell. In Proceedings of the 34th Thermal and fluid analysis workshop 2023 (TFAWS 2023), 21-25 August 2023, Maryland, USA. Washington: NASA [online], article number TFAWS23-ID-7. Available from: https://tfaws.nasa.gov/tfaws23/proceedings/

Technologies behind electrolysis cells for hydrogen production are making progress in terms of portability, cost reduction, performance enhancement, prolonged operation, and integration in stacks and with existing power infrastructure. The solid oxid... Read More about Thermomechanical deformation analysis of a tubular solid oxide steam electrolysis cell..

Tensile and impact properties of melt-blended nylon 6/ethylene-octene copolymer/graphene oxide nanocomposites. (2023)
Conference Proceeding
ATTAR, S., CHEN, B., CATALANOTTI, G. and FALZON, B.G. 2023. Tensile and impact properties of melt-blended nylon 6/ethylene-octene copolymer/graphene oxide nanocomposites. In Falzon, B.G. and McCarthy, C. (eds.) Proceedings of the 23rd International conference on composite materials 2023 (ICCM 23), 30 July - 04 August 2023, Belfast, Northern Ireland. Belfast: Queen's University Belfast. Hosted on ICCM-central [online], paper 253. Available from: http://www.iccm-central.org/Proceedings/ICCM23proceedings/index.htm

The addition of stiff nanoparticles to a polymer matrix usually proves beneficial for the enhancement in stiffness and strength, however, the impact strength is usually lowered. Conversely, the use of elastomeric additives can enhance the toughness a... Read More about Tensile and impact properties of melt-blended nylon 6/ethylene-octene copolymer/graphene oxide nanocomposites..

Predicting and identifying antimicrobial resistance in the marine environment using AI and machine learning algorithms. (2023)
Conference Proceeding
FOUGH, F., JANJUA, G., ZHAO, Y. and DON, A.W. 2023. Predicting and identifying antimicrobial resistance in the marine environment using AI and machine learning algorithms. In Proceedings of the 2023 IEEE (Institute of Electrical and Electronics Engineers) International workshop on Metrology for the sea (MetroSea 2023); learning to measure sea health parameters, 4-6 October 2023, La Valletta, Malta. Piscataway: IEEE [online], pages 121-126. Available from: https://doi.org/10.1109/MetroSea58055.2023.10317294

Antimicrobial resistance (AMR) is an increasingly critical public health issue necessitating precise and efficient methodologies to achieve prompt results. The accurate and early detection of AMR is crucial, as its absence can pose life-threatening r... Read More about Predicting and identifying antimicrobial resistance in the marine environment using AI and machine learning algorithms..

An exploratory study on wind speed profiling of high-rise building/monument using EnviMET. (2023)
Journal Article
ZAKARIA, N.H., SALLEH, S.A., ISA, N.A., CHAN, A., OOI, M.C.G. and ASMAT, A. 2023. An exploratory study on wind speed profiling of high-rise building/monument using EnviMET. Malaysian construction research journal [online], 20(3): special issue for 6th International conference on research methodology for built environment and engineering 2023 (ICRMBEE 2023), 28 February - 2 March 2023, pages 123-137. Available from: https://www.cream.my/prod/mcrj-special-issue-volume-20-no-3-2023-

Envi-MET is a useful tool for simulating wind speed at building heights and modelling microclimatic conditions around buildings, including wind speed around buildings and other structures. Envi-MET is used in this study to simulate wind speed toward... Read More about An exploratory study on wind speed profiling of high-rise building/monument using EnviMET..

A comprehensive review on the potential of green hydrogen in empowering the low-carbon economy: development status, ongoing trends and key challenges. (2023)
Journal Article
ATTEYA, A.I., ALI, D., HOSSAIN, M. and SELLAMI, N. 2023. A comprehensive review on the potential of green hydrogen in empowering the low-carbon economy: development status, ongoing trends and key challenges. Green energy and environmental technology [online], 2. Available from: https://doi.org/10.5772/geet.23

Green hydrogen is currently considered a key element for delivering free-carbon energy. This paper provides an extensive assessment of the potential of green hydrogen technology as a pathway to the low-carbon economy while highlighting the major tech... Read More about A comprehensive review on the potential of green hydrogen in empowering the low-carbon economy: development status, ongoing trends and key challenges..

Multiple decomposition-aided long short-term memory network for enhanced short-term wind power forecasting. (2023)
Journal Article
BALCI, M., DOKUR, E., YUZGEC, U. and ERDOGAN, N. [2024]. Multiple decomposition-aided long short-term memory network for enhanced short-term wind power forecasting. IET renewable power generation [online], Early View. Available from: https://doi.org/10.1049/rpg2.12919

With the increasing penetration of grid-scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset utilization. A... Read More about Multiple decomposition-aided long short-term memory network for enhanced short-term wind power forecasting..

Municipal solid waste air gasification using waste marble powder as a catalyst for syngas production. (2023)
Journal Article
AMIN, N., KHAN, Z., RAZZAQ, A., GHAURI, M., KHURRAM, S., INAYAT, A., JAFFERY, M. and HAMEED, Z. 2024. Municipal solid waste air gasification using waste marble powder as a catalyst for syngas production. Journal of the Energy Institute [online], 113, article number 101496. Available from: https://doi.org/10.1016/j.joei.2023.101496

Waste marble powder (WMP), the main pollutant in the marble processing industry, is predominantly composed of calcite. Its rich calcium-based nature, derived from calcite, positions it as an excellent source for a CO2 sorbent, making it highly conduc... Read More about Municipal solid waste air gasification using waste marble powder as a catalyst for syngas production..

Improved model order reduction techniques with error bounds. (2023)
Journal Article
BASHRAT, S., IMRAN, M., AKRAM, S., WAKEEL, A., BAIG, N.A. and UD-DIN, A.Z. 2024. Improved model order reduction techniques with error bounds. International journal of systems science [online], 55(4), pages 687-700. Available from: https://doi.org/10.1080/00207721.2023.2293683

This paper introduces two enhanced model order reduction techniques designed for scenarios involving frequency-weighted and frequency-limited-interval Gramians in the continuous-time domain. The primary objective is to address the instability issue i... Read More about Improved model order reduction techniques with error bounds..

Advancing security in IoT-driven critical infrastructure: a focus on smart transportation system. (2023)
Journal Article
JIMOH, H.O., ABOLLE-OKOYEAGU, C.J., AHMED, M.O. and LAWAL, N.O. 2023. Advancing security in IoT-driven critical infrastructure: a focus on smart transportation system. American journal of engineering research [online], 12(12), pages 33-46. Available from: https://www.ajer.org/papers/Vol-12-issue-12/12123346.pdf

As new technological platforms such as the Internet of Things (IoT), blockchain, Artificial Intelligence (AI) and Machine Learning (ML) are gradually emerging and being integrated into critical infrastructures which are subjected to digital attacks.... Read More about Advancing security in IoT-driven critical infrastructure: a focus on smart transportation system..

Machine learning model of acoustic signatures: towards digitalised thermal spray manufacturing. (2023)
Journal Article
VISWANATHAN, V., MCCLOSKEY, A., MATHUR, R., NGUYEN, D.T., FAISAL, N.H., PRATHURU, A., LLAVORI, I., MURPHY, A., TIWARI, A., MATTHEWS, A., AGRAWAL, A. and GOEL, S. 2024. Machine learning model of acoustic signatures: towards digitalised thermal spray manufacturing. Mechanical systems and signal processing [online], 208, article number 111030. Available from: https://doi.org/10.1016/j.ymssp.2023.111030

Thermal spraying, an important industrial surface manufacturing process in sectors such as aerospace, energy and biomedical, remains a skill intensive process often involving multiple trial runs impacting the yield. The core research challenge in dig... Read More about Machine learning model of acoustic signatures: towards digitalised thermal spray manufacturing..