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Welcome to Repository@Nottingham

The Repository@Nottingham 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. See our Policies for further information.



Latest Additions

Vaccine Protective Efficacy of a Bivalent Equine Influenza H3N8 Virus-like Particle Vaccine in Horses (2025)
Journal Article
O’Kennedy, M. M., Reedy, S. E., Abolnik, C., Khan, A., Smith, T., du Preez, I., Olajide, E., Daly, J., Cullinane, A., & Chambers, T. M. (in press). Vaccine Protective Efficacy of a Bivalent Equine Influenza H3N8 Virus-like Particle Vaccine in Horses. Vaccine,

Equine influenza (EI) is a highly contagious acute respiratory disease of wild and domesticated horses, donkeys, mules, and other Equidae. EI is caused by the Equine Influenza virus (EIV), is endemic in many countries and outbreaks still have a sever... Read More about Vaccine Protective Efficacy of a Bivalent Equine Influenza H3N8 Virus-like Particle Vaccine in Horses.

Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction (2024)
Journal Article
Farooq, A., Raykov, Y., Raykov, P., & Little, M. (2024). Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction. Journal of Machine Learning Research, 25, Article 135

Linear Gaussian exploratory tools such as principal component analysis (PCA) and factor analysis (FA) are widely used for exploratory analysis, pre-processing, data visualization, and related tasks. Because the linear-Gaussian assumption is restricti... Read More about Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction.

Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach (2025)
Journal Article
Evers, L. J. W., Raykov, Y. P., Heskes, T. M., Krijthe, J. H., Bloem, B. R., & Little, M. A. (2025). Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach. Sensors, 25(2), Article 366. https://doi.org/10.3390/s25020366

Objective and continuous monitoring of Parkinson’s disease (PD) tremor in free-living conditions could benefit both individual patient care and clinical trials, by overcoming the snapshot nature of clinical assessments. To enable robust detection of... Read More about Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach.

Giant Light Emission Enhancement in Strain-Engineered InSe/MS2 (M = Mo or W) van der Waals Heterostructures (2025)
Journal Article
Blundo, E., Tuzi, F., Cuccu, M., Fiorentin, M. R., Pettinari, G., Patra, A., Cianci, S., Kudrynskyi, Z. R., Felici, M., Taniguchi, T., Watanabe, K., Patanè, A., Palummo, M., & Polimeni, A. (in press). Giant Light Emission Enhancement in Strain-Engineered InSe/MS2 (M = Mo or W) van der Waals Heterostructures. Nano Letters, https://doi.org/10.1021/acs.nanolett.4c04252

Two-dimensional (2D) heterostructures (HSs) offer unlimited possibilities for playing with layer number, order, and twist angle. The realization of high-performance optoelectronic devices, however, requires the achievement of specific band alignments... Read More about Giant Light Emission Enhancement in Strain-Engineered InSe/MS2 (M = Mo or W) van der Waals Heterostructures.

Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach (2025)
Journal Article
Aguilar-Bejarano, E., Deorukhkar, V., & Woodward, S. (2025). Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach. Molecules, 30(2), Article 355. https://doi.org/10.3390/molecules30020355

The range of chemical databases available has dramatically increased in recent years, but the reliability and quality of their data are often negatively affected by human-error fidelity. The size of chemical databases can make manual data curation/ch... Read More about Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach.