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Development of an artificial neural network based virtual sensing platform for the simultaneous prediction of emission-performance-stability parameters of a diesel engine operating in dual fuel mode with port injected methanol (2019)
Journal Article
Kakati, D., Roy, S., & Banerjee, R. (2019). Development of an artificial neural network based virtual sensing platform for the simultaneous prediction of emission-performance-stability parameters of a diesel engine operating in dual fuel mode with port injected methanol. Energy Conversion and Management, 184, https://doi.org/10.1016/j.enconman.2019.01.087

Carotid artery wave intensity in mid- to late-life predicts cognitive decline: the Whitehall II study (2019)
Journal Article
Chiesa, S. T., Masi, S., Shipley, M. J., Ellins, E. A., Fraser, A. G., Hughes, A. D., Patel, R. S., Khir, A. W., Halcox, J. P., Singh-Manoux, A., Kivimaki, M., Celermajer, D. S., & Deanfield, J. E. (2019). Carotid artery wave intensity in mid- to late-life predicts cognitive decline: the Whitehall II study. European Heart Journal, 40(28), 2300-2309. https://doi.org/10.1093/eurheartj/ehz189

Accelerated Aortic Stiffening Relative to Carotid Stiffness is Associated With Cognitive Decline: The Whitehall II Study (2019)
Journal Article
Chiesa, S. T., Masi, S., Shipley, M. J., Ellins, E., Halcox, J. P., Fraser, A., Hughes, A. D., Patel, R. S., Khir, A. W., Brunner, E. J., Singh-Manoux, A., Kivimaki, M., Celermajer, D., & Deanfield, J. E. (2019). Accelerated Aortic Stiffening Relative to Carotid Stiffness is Associated With Cognitive Decline: The Whitehall II Study. Circulation, 140(1),

Application of Miller cycle with turbocharger and ethanol to reduce NOx and particulates emissions from diesel engine – A numerical approach with model validations (2019)
Journal Article
Li, C., Wang, Y., Jia, B., & Roskilly, A. (2019). Application of Miller cycle with turbocharger and ethanol to reduce NOx and particulates emissions from diesel engine – A numerical approach with model validations. Applied Thermal Engineering, 150, 904-911. https://doi.org/10.1016/j.applthermaleng.2019.01.056

A review of the current automotive manufacturing practice from an energy perspective (2019)
Journal Article
Giampieri, A., Ling-Chin, J., Ma, Z., Smallbone, A., & Roskilly, A. (2020). A review of the current automotive manufacturing practice from an energy perspective. Applied Energy, 261, Article 114074. https://doi.org/10.1016/j.apenergy.2019.114074

The automotive industry is facing on-going challenges to improve the sustainability of its manufacturing processes and vehicle emissions due to economic, environmental, marketability and policy concerns. This review aims to evaluate steps that could... Read More about A review of the current automotive manufacturing practice from an energy perspective.

Modelling of Domain Wall Associated Eddy Current in Silicon Steels (2019)
Presentation / Conference Contribution
Guan, W., Gao, Y., Muramatsu, K., & Hamzehbahmani, H. (2020, December). Modelling of Domain Wall Associated Eddy Current in Silicon Steels. Presented at IEEE Conference on Electromagnetic Field computation (IEEE CEFC), Pisa, Italy

Solar septic tank: next generation sequencing reveals effluent microbial community composition as a useful index of system performance (2019)
Journal Article
Connelly, S., Pussayanavin, T., J. Randle-Boggis, R., Wicheansan, A., Jampathong, S., Keating, C., …Koottatep, T. (2019). Solar septic tank: next generation sequencing reveals effluent microbial community composition as a useful index of system performance. Water, 11(12), https://doi.org/10.3390/w11122660

Septic tanks are widely deployed for off-grid sewage management but are typified by poor treatment performance, discharge of polluting effluents and the requirement for frequent de-sludging. The Solar Septic Tank (SST) is a novel septic tank design t... Read More about Solar septic tank: next generation sequencing reveals effluent microbial community composition as a useful index of system performance.

Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection (2019)
Presentation / Conference Contribution
Samarth, G., Bhowmik, N., & Breckon, T. (2019, December). Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection. Presented at 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019), Boca Raton, Florida, USA

In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery. We consider the performance of experimentally defined, reduced co... Read More about Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection.