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Welcome to Durham Research Online (DRO)

Durham Research Online (DRO) is the University’s Open Access repository for publications. The primary purpose of DRO is to provide open access to publications authored by staff and students affiliated with Durham University.

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Latest Additions

Persistence of the pattern in the interior of 5d moduli spaces (2024)
Journal Article
Rudelius, T. (2024). Persistence of the pattern in the interior of 5d moduli spaces. Physics Letters B, 853, Article 138640. https://doi.org/10.1016/j.physletb.2024.138640

Castellano, Ruiz, and Valenzuela recently observed a remarkable “pattern” in infinite-distance limits of moduli spaces in quantum gravity, which relates the field space variation of the mass of the lightest tower of particles to the field space varia... Read More about Persistence of the pattern in the interior of 5d moduli spaces.

Experimental parametric evaluation of adsorption characteristics for silica gel - water based open-bed system for seasonal thermal energy storage (2024)
Journal Article
Koley, S., Bao, H., Roskilly, A. P., & Ma, Z. (2024). Experimental parametric evaluation of adsorption characteristics for silica gel - water based open-bed system for seasonal thermal energy storage. Journal of Energy Storage, 89, Article 111812. https://doi.org/10.1016/j.est.2024.111812

This study aims to investigate the potential of using commercial silica gel as an energy storage material in a bulk-scale open bed adsorption-based system to achieve efficient domestic heating using renewable energy sources. Designing an efficient th... Read More about Experimental parametric evaluation of adsorption characteristics for silica gel - water based open-bed system for seasonal thermal energy storage.

Timescales of glacial isostatic adjustment in Greenland: is transient rheology required? (2024)
Journal Article
Pan, L., Mitrovica, J. X., Milne, G. A., Hoggard, M. J., & Woodroffe, S. A. (2024). Timescales of glacial isostatic adjustment in Greenland: is transient rheology required?. Geophysical Journal International, 237(2), 989-995. https://doi.org/10.1093/gji/ggae095

The possibility of a transient rheological response to ice age loading, first discussed in the literature of the 1980s, has received renewed attention. Transient behaviour across centennial to millennial timescales has been invoked to reconcile appar... Read More about Timescales of glacial isostatic adjustment in Greenland: is transient rheology required?.

The metallicity gradients of star-forming regions store information of the assembly history of galaxies (2024)
Journal Article
Jara-Ferreira, F., Tissera, P. B., Sillero, E., Rosas-Guevara, Y., Pedrosa, S. E., De Rossi, M. E., …Bignone, L. (2024). The metallicity gradients of star-forming regions store information of the assembly history of galaxies. Monthly Notices of the Royal Astronomical Society, 530(2), 1369-1385. https://doi.org/10.1093/mnras/stae708

The variations in metallicity and spatial patterns within star-forming regions of galaxies result from diverse physical processes unfolding throughout their evolutionary history, with a particular emphasis on recent events. Analysing MaNGA and EAGLE... Read More about The metallicity gradients of star-forming regions store information of the assembly history of galaxies.

Diagnosis of multiple sclerosis by detecting asymmetry within the retina using a similarity-based neural network (2024)
Journal Article
Bolton, R. C., Kafieh, R., Ashtari, F., & Atapour-Abarghouei, A. (2024). Diagnosis of multiple sclerosis by detecting asymmetry within the retina using a similarity-based neural network. IEEE Access, 12, 62975-62985. https://doi.org/10.1109/access.2024.3395995

Multiple sclerosis (MS) is a chronic neurological disorder that targets the central nervous system, causing demyelination and neural disruption, which can include retinal nerve damage leading to visual disturbances. The purpose of this study is to de... Read More about Diagnosis of multiple sclerosis by detecting asymmetry within the retina using a similarity-based neural network.