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Welcome to Repository@Hull

The Repository@Hull is intended to be an Open Access showcase for the published research output of the university. Whenever possible, refereed documents accepted for publication, or finished artistic compositions presented in public, will be made available here in full digital format, and hyperlinks to standard published versions will be provided.



Latest Additions

Electrochemical sensor for the detection of scaling ions in formation water (2018)
Thesis
Crapnell, R. D. (2018). Electrochemical sensor for the detection of scaling ions in formation water. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4628886

This thesis aims to develop appropriate sensing chemistry for the electrochemical detection of alkaline-earth metal ions in formation water. Accordingly, the first chapter outlines the relevance of this work and gives an overview of the methods curre... Read More about Electrochemical sensor for the detection of scaling ions in formation water.

Ex situ experimentation to determine if introduced artificial habitat can provide alternative refuge to hazardous anthropogenic structures (2024)
Journal Article
Norman, J., Clark, D., Henshaw, A., Wright, R. M., Cattaneo, M. E. G. V., & Bolland, J. D. (2024). Ex situ experimentation to determine if introduced artificial habitat can provide alternative refuge to hazardous anthropogenic structures. Restoration Ecology, Article e14157. https://doi.org/10.1111/rec.14157

Highly degraded lowland river ecosystems are of global concern to restoration practitioners. Hazardous anthropogenic structures, such as those used for water level management (i.e. pumping stations), present a mortality risk to fish and associated ch... Read More about Ex situ experimentation to determine if introduced artificial habitat can provide alternative refuge to hazardous anthropogenic structures.

Tracking aquatic animals for fisheries management in European waters (2024)
Journal Article
Özgül, A., Birnie-Gauvin, K., Abecasis, D., Alós, J., Aarestrup, K., Reubens, J., …Lennox, R. J. (2024). Tracking aquatic animals for fisheries management in European waters. Fisheries Management and Ecology, Article e12706. https://doi.org/10.1111/fme.12706

Acoustic telemetry (AT) has emerged as a valuable tool for monitoring aquatic animals in both European inland and marine waters over the past two decades. The European Tracking Network (ETN) initiative has played a pivotal role in promoting collabora... Read More about Tracking aquatic animals for fisheries management in European waters.

Movements and habitat use of native and invasive piscivorous fishes in a temperate and channelized lowland river (2024)
Journal Article
Nolan, E. T., Hindes, A. M., Bolland, J. D., Davies, P., Gutmann Roberts, C., Tarkan, A. S., & Britton, J. R. (2024). Movements and habitat use of native and invasive piscivorous fishes in a temperate and channelized lowland river. Hydrobiologia, https://doi.org/10.1007/s10750-024-05533-2

Lowland temperate rivers provide important habitats for piscivorous fishes, but with their year-round spatial and temporal habitat use is often poorly understood, including their use of off-channel habitats. Here, the movements and habitat use of the... Read More about Movements and habitat use of native and invasive piscivorous fishes in a temperate and channelized lowland river.

Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks (2024)
Journal Article
Garaev, R., Rasheed, B., & Khan, A. M. (2024). Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks. Algorithms, 17, Article 162. https://doi.org/10.3390/a17040162

Deep neural networks (DNNs) have gained prominence in various applications, but remain vulnerable to adversarial attacks that manipulate data to mislead a DNN. This paper aims to challenge the efficacy and transferability of two contemporary defense... Read More about Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks.