Chromo-Natural Model in Anisotropic Background

Autor: Maleknejad, A., Erfani, E.

Publikováno v: JCAP03(2014)016

In this work we study the chromo-natural inflation model in the anisotropic setup. Initiating inflation from Bianchi type-I cosmology, we analyze the system thoroughly during the slow-roll inflation, from both analytical and numerical points of view.

Externí odkaz: http://arxiv.org/abs/1311.3361

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Enhancing Water Safety: Exploring Recent Technological Approaches for Drowning Detection.

Autor: Jalalifar S; School of Engineering, Macquarie University, Sydney, NSW 2109, Australia., Belford A; School of Engineering, Macquarie University, Sydney, NSW 2109, Australia., Erfani E; School of Information Systems and Technology Management, University of New South Wales, Sydney, NSW 1466, Australia., Razmjou A; School of Engineering, Edith Cowan University, Perth, WA 6027, Australia., Abbassi R; School of Engineering, Macquarie University, Sydney, NSW 2109, Australia., Mohseni-Dargah M; School of Engineering, Macquarie University, Sydney, NSW 2109, Australia., Asadnia M; School of Engineering, Macquarie University, Sydney, NSW 2109, Australia.

Publikováno v: Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Jan 05; Vol. 24 (2). Date of Electronic Publication: 2024 Jan 05.

Artificial intelligence and sustainability: Solutions to social and environmental challenges

Autor: Taghikhah, F, Erfani, E, Bakhshayeshi, I, Tayari, S, Karatopouzis, A, Hanna, B

Human-induced climate change and deterioration of our natural environment are critical problems. Advanced technologies, such as artificial intelligence (AI), offer potential for the development of solutions. Machines that learn by acquiring knowledge

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::6281ed92d1532b7245cb30893f8aa48f
https://hdl.handle.net/10453/171025

Application of Machine Learning Algorithms to Estimate Enzyme Loading, Immobilization Yield, Activity Retention, and Reusability of Enzyme-Metal-Organic Framework Biocatalysts

Autor: Chai, M, Moradi, S, Erfani, E, Asadnia, M, Chen, V, Razmjou, A

The ability to predict enzyme-metal-organic framework (MOF) properties such as enzyme loading, immobilization yield, activity retention, and reusability can maximize product yield and extend the operational life of enzyme-MOF biocatalysts. However, t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::68c4cde430d96f9b8789a66f4d81ca28
https://hdl.handle.net/10453/155183