https://bura.brunel.ac.uk/handle/2438/13035 2026-09-10T12:32:25Z https://bura.brunel.ac.uk/handle/2438/33844 Title: Large language model-based multi-agent systems for financial markets simulation: a survey Authors: Liu, Qinyuan; Yao, Lihang; Wang, Zidong; Yang, Yufan; Tang, Yifei; Cheng, Dawei; Jiang, Changjun Abstract: Traditional financial modeling approaches, such as econometric methods and dynamic stochastic general equilibrium models, exhibit inherent limitations in capturing the highly interactive and strategic nature of modern financial systems. To overcome these limitations, multi-agent systems (MASs) have increasingly been adopted as a computational paradigm well suited for simulating the complex behaviors and interactions of participants in financial environments. Within this paradigm, the emerging class of large language model (LLM)-based MASs has demonstrated unprecedented potential for modeling intricate financial interactions. This study provides a systematic review of LLM-based MAS applications in financial markets. We first present the motivation for adopting LLM-based MASs and highlight their key characteristics, including heterogeneity, autonomy, adaptability, and bounded rationality, which render them particularly effective for representing complex financial ecosystems. Then, we conduct a technical analysis of financial agents, providing the enabling techniques for LLM-based agents, a taxonomy of their foundational models, and the underlying mechanisms that facilitate the emergence of collective intelligence in LLM-based MASs. Furthermore, we survey some representative applications of LLM-based MASs in financial markets, including market dynamics simulation, systemic risk analysis, policy evaluation, algorithmic trading, and sentiment analysis, while critically discussing the associated technical and regulatory challenges. Through this comprehensive synthesis, we aim to provide a state-of-the-art reference for intelligent agent-based approaches in computational finance and to identify key opportunities and limitations that characterize this rapidly evolving research field. 2026-06-24T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33842 Title: Recursive state estimation for multisensor systems over cloud radio access networks: Handling the bit rate allocation issue Authors: Song, Jiahao; Wang, Zidong; Liu, Qinyuan; He, Xiao Abstract: The dramatic evolution of modern communication technologies has led to the emergence of the Cloud Radio Access Network (C-RAN) as a cutting-edge architecture for communication systems deployed in industry. In this paper, the problem of networked state estimation is addressed for a class of multisensor systems with C-RAN serving as the infrastructure for data transmission and processing. A comprehensive mathematical model is established for the underlying C-RAN with a focus on imperfections in data transmission. A state estimator, based on the Kalman filter, is developed using data transmitted through C-RAN. Furthermore, the resource allocation issue, an essential task in C-RAN, is modeled as a bit rate allocation problem, and the relationship between bit rate constraints and estimation performance is thoroughly analyzed. A sufficient condition for the convergence of the recursive state estimation algorithm is proposed. The particle swarm optimization algorithm is employed to allocate the bit rate to enhance the estimation accuracy. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed state estimator and the bit rate allocation method. 2026-07-13T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33790 Title: LogicGate: A Student-Centred Digital Game-Based Learning Experience Authors: Coleman, Thomas; Money, Arthur Abstract: ... Description: ... 2026-01-01T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33777 Title: The Pan Prostate Cancer Group Dataset - Genomic, Transcriptomic and Methylation data for Clinical Discovery Authors: Jakobsdottir, G Maria; Brook, Mark N; Sahli, Atef; Leeman, Gregory; Favero, Francesco; Uhrig, Sebastian; Girma, Etsehiwot G; Gerhauser, Clarissa; Lutsik, Pavlo; Lamy, Philippe; Rausch, Tobias; Dinalankara, Wikum; Dong, Ruining; Dundee, Philip; Esenturk, Emre; Falkenbach, Fabian; Fanelli, Giuseppe N; Fradet, Yves; Fu, Yunfan; Furlano, Kira; Georgeson, Peter; Gheybi, Kazzem; Graefen, Markus; Grima, Corinna; Hall, Grace A; Hamdy, Freddie; Heilmann, Jessica; Hong, Matthew KH; Hutchison, William J; Jiang, Jue; Kerger, Michael; Khani, Francesca; Kiriy, Daria; Kurganovs, Natalie; Lamb, Alastair D; Leung, Wing-Kit; Lim, Beng; Mangiola, Stefano; Massie, Charlie E; McCoy, Patrick; Mensah, Nana; Mills, Ian; Milne-Clark, Toby; Molania, Ramyar; Nguyen, Anne; Norden, Sam; Nuzzo, Pier Vitale; Olsen, Andre; Pakula, Hubert; Pan, Yidan; Park, Daniel J; Pederzoli, Filippo F; Penington, Jocelyn; Peters, Justin S; Phal, Pramit M; Pirhady, Parsa; Plass, Christoph; Raine, Keiran; Robinson, Brian; Gihawi, Abraham; Hamid, Anis; Jung, Chol-hee; Pellegrina, Diogo; Imada, Eddie Luidy; Yamaguchi, Takafumi N; Zanettini, Claudio; Cheng, Kevin CL; Queiroz, Lucio R; Xu, Yaobo; Jaratlerdsiri, Weerachai; Barton, Lucy M; Lach, Radoslaw; Barrowdale, Daniel; Batra, Rajbir N; Burns, Dan; Kerry, Giselle; Sankar, Aravind; Feran, Breon; Feuerbach, Lars; Fernandez-Sanroman, Angel; Hernando, Barbara; Wirth, Christopher; Haberland, Valeriia; Hong, Chen; Sboner, Andrea; He, Xiaotong; Erickson, Andrew; Holmes, Vivien; Mehmood, Arfa; Nykter, Matti; Omar, Mohamed; Al-Muftah, Noora; Baena, Esther; Bedo, Justin; Bergeron, Alain; Berlin, Alejandro; Borre, Michael; Campbell, Bethany K; Carelli, Ryan; Cheung, Melissa; Chow, Ken; Clarkson, Michael J; Cmero, Marek; Collins, Colin; Faini, Angelo Corso; Costello, Tony; Dennis, Nening; Dev, Harveer Abstract: ... Description: ... 2026-01-01T00:00:00Z