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Open Access research that is tackling the climate emergency...

Addressing the energy challenge confronting society is a strategic research theme for Strathclyde. Researchers from across the institution, spanning multiple disciplines, are therefore working together to understand ways of reducing the environmental impacts of energy use, improving energy efficiency, coping with declining fossil fuel supplies, managing an ageing energy infrastructure, and devising policy or economic levers to achieve higher penetration of renewable energy systems and technologies. Strathprints makes this scholarly research content available Open Access thereby ensuring results are available to everyone in order meet the global climate challenge.

Explore some of this Open Access research from the departments of Mechanical & Aerospace Engineering, Electronic & Electrical Engineering, Civil & Environmental Engineering, Naval Architecture, Ocean & Marine Engineering, Economics, Entrepreneurship and the School of Government & Public Policy.

Or explore all of Strathclyde's Open Access research...

Strathprints

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Number of items: 4.

2022

Hall, Iain and Powell, Cheyenne and Riccardi, Annalisa (2022) Constraint programming for scheduling the operations of STRATHcube : a nanosatellite for detecting space debris. In: 73rd International Astronautical Congress (IAC), 2022-09-18 - 2022-10-22, Paris Convention Centre.

Powell, Cheyenne and Riccardi, Annalisa (2022) On-board re-planning of an earth observation satellite for maximisation of observation campaign goals. In: 73rd International Astronautical Congress (IAC), 2022-09-18 - 2022-10-22, Paris Convention Centre.

2021

Powell, Cheyenne and Riccardi, Annalisa (2021) Towards explainability of on-board satellite scheduling for end user interactions. In: 72nd International Astronautical Congress, 2021-10-25 - 2021-10-29, Dubai World Trade Centre.

Marchetti, Francesco and Wilson, Callum and Powell, Cheyenne and Minisci, Edmondo and Riccardi, Annalisa; Romero, Juan and Martins, Tiago and Rodríguez-Fernández, Nereida, eds. (2021) Convolutional Generative Adversarial Network, via Transfer Learning, for traditional Scottish music generation. In: Artificial Intelligence in Music, Sound, Art and Design. Lecture Notes in Computer Science, 12693 . Springer, ESP. ISBN 978-3-030-72914-1

This list was generated on Thu Feb 9 06:56:12 2023 GMT.