https://bura.brunel.ac.uk/handle/2438/8622 2026-08-27T00:21:45Z 2026-08-27T00:21:45Z Alshdaifat, Ahmad Ibrahim Balachandran, Wamadeva Hunaiti, Ziad https://bura.brunel.ac.uk/handle/2438/33755 2026-08-25T02:02:25Z 2026-08-13T00:00:00Z

Title: Artificial Intelligence for Coronary Artery Disease Prediction Using ECG and CCTA: A Systematic Review Authors: Alshdaifat, Ahmad Ibrahim; Balachandran, Wamadeva; Hunaiti, Ziad Abstract: Coronary artery disease (CAD) is the leading cause of death worldwide, highlighting the need for more reliable and efficient diagnostic tools beyond conventional methods. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown strong potential for detecting obstructive CAD by learning complex patterns from electrocardiogram (ECG) and coronary computed tomography angiography (CCTA) data. This rapid systematic review assesses and compares the diagnostic performance and methodological quality of AI models built for CAD prediction using ECG and CCTA data. A systematic search following PRISMA-ScR guidelines was conducted for primary studies published between 2021 and 2025. Eleven studies were included, six using ECG data and five using CCTA data. Methodological quality was evaluated using the PROBAST+AI tool. ECG-based models achieved AUCs of 0.72–0.961 and CCTA-based models showed slightly stronger top-end performance, with AUCs of 0.77–0.97. External validation was uncommon in both groups, applied in only 40% of CCTA studies and 33% of ECG studies, so neither modality demonstrated clearly greater validation maturity. Despite these strong results, PROBAST+AI assessment revealed a high risk of bias in 90.9% of the included studies, largely due to weaknesses in the analysis domain, including poor handling of missing data and the absence of model calibration reporting. AI models show strong diagnostic accuracy for CAD across both modalities, although external validation was limited and applied in a minority of studies. However, the widespread methodological bias means these tools should currently support clinical decision-making rather than replace standard diagnostic methods. Future studies should focus on prospective multicentre validation and the use of multimodal data. Description: Data Availability Statement: No new data were created in this study. The data extracted from included studies are available within the article, in Table 1, Table 2, Table 3, Table 4 and Table 5, or from the corresponding author upon reasonable request. This review was prospectively registered on the Open Science Framework, https://doi.org/10.17605/OSF.IO/H793Y.; Supplementary Materials are available online at: https://www.mdpi.com/3042-6707/1/3/22#app1-aimed-01-00022 .

2026-08-13T00:00:00Z Dabashi, Al Hussein Qiu, Dongmeng Zhang, Xin Taylor, Gareth https://bura.brunel.ac.uk/handle/2438/33752 2026-08-25T02:02:25Z 2026-08-19T00:00:00Z

Title: Innovative Testbed Configurations and Interfacing Techniques for Real-Time Digital Simulation of Cyber-Physical Energy and Power Systems Authors: Dabashi, Al Hussein; Qiu, Dongmeng; Zhang, Xin; Taylor, Gareth Abstract: Testbed configurations and interfacing methods for real-time digital simulation of cyber-physical power systems (CPPS) remain complex and fragmented across the literature, limiting the clarity with which researchers can compare tools, reproduce testbeds, and select suitable platforms for communication-aware control and cyber security studies. This paper addresses this issue by first comparing the latest works with particular focus on the method of interfacing and configuration architecture, and second, by showcasing two representative testbeds, used for analysing the impact of cyber events on distribution system and microgrid operation. The first testbed interfaces HYPERSIM (OPAL-RT Technologies) with MATLAB (version R2025a) using Modbus over TCP/IP for data exchange. This testbed configuration is capable of capturing the impacts of communication delays on Volt-VAR control in a modified IEEE 13-node test feeder. The second testbed uses two real-time power simulators, OPAL-RT and Typhoon HIL, both interfaced with the discrete-event simulator (DES) EXata Network Modelling (by Keysight Technologies) through modular Python-based scripts. This interfacing effectively enables the simulation of cyber attacks in microgrids. These testbeds show how recent advances in real-time simulation are enabling more practical cross-domain analysis of communication effects, control performance, and cyber vulnerabilities, while informing the design of more capable and future-ready CPPS simulation environments. Description: Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

2026-08-19T00:00:00Z Qu, Youxi Lou, Xicheng Meng, Hongying Li, Zhangyong Wang, Jianlin Mao, Kunpeng Li, Xinwei https://bura.brunel.ac.uk/handle/2438/33689 2026-08-14T02:01:00Z 2026-07-16T00:00:00Z

Title: EEG-DBNet: a dual-branch framework for temporal-spectral representation learning of motor imagery electroencephalography Authors: Qu, Youxi; Lou, Xicheng; Meng, Hongying; Li, Zhangyong; Wang, Jianlin; Mao, Kunpeng; Li, Xinwei Abstract: Purpose: Motor imagery electroencephalography (MI-EEG) decoding remains challenging due to low signal-to-noise ratio and complex temporal-spectral characteristics. This study aims to develop a robust deep learning framework for effective EEG representation learning. Methods: We propose EEG-DBNet, a dual-branch neural network that jointly models temporal dynamics and spectral representations of EEG signals. The model integrates local and global convolutional modules to enable multi-scale feature extraction, complementing the dual-branch design for multi-dimensional temporal-spectral representation learning. To validate robustness, experiments are conducted on two public datasets as well as a self-collected MI-EEG dataset acquired under controlled laboratory conditions. Results: Experimental results show that EEG-DBNet achieves the best average performance on the two public benchmark datasets, BCI Competition IV-2a and IV-2b. On the self-collected CQUPT dataset, EEG-DBNet obtains competitive performance compared with representative baseline methods, suggesting its potential applicability to laboratory-acquired MI-EEG decoding. These results indicate that the proposed temporal-spectral dual-branch design is effective, while further validation on larger self-collected datasets is still needed. Conclusion: The proposed EEG-DBNet provides an effective solution for MI-EEG decoding with improved robustness. The inclusion of multiple datasets, particularly laboratory-acquired self-collected data, highlights its potential for practical brain-computer interface applications. Description: Data availability: The public BCI Competition IV-2a and IV-2b datasets analyzed in this study are publicly available at https://www.bbci.de/competition/iv/. The self-collected CQUPT dataset used in this study is available from the corresponding author upon reasonable request.

2026-07-16T00:00:00Z Hayrapetyan, A Tumasyan, A Adam, W Andrejkovic, JW Benato, L Bergauer, T Chatterjee, S Damanakis, K Dragicevic, M Hussain, PS Jeitler, M Mora Herrera, C Rebello Teles, P Soeiro, M Vilela Pereira, A Aldá, WL CMS Collaboration Barroso Ferreira Filho, M Brandao Malbouisson, H Carvalho, W Reid, ID Chinellato, J Da Costa, EM Kyberd, P Da Silveira, GG Khan, A Cole, JE Krammer, N Li, A Liko, D Mikulec, I Schieck, J Schöfbeck, R Schwarz, D Sonawane, M Waltenberger, W Wulz, CE Janssen, T Van Laer, T Van Mechelen, P Breugelmans, N D’Hondt, J Dansana, S De Moor, A Delcourt, M Heyen, F Lowette, S Makarenko, I Müller, D Tavernier, S Tytgat, M Van Onsem, GP Van Putte, S Vannerom, D Bilin, B Clerbaux, B Das, AK De Bruyn, I De Lentdecker, G Evard, H Favart, L Gianneios, P Jaramillo, J Khalilzadeh, A Khan, FA Lee, K Mahdavikhorrami, M Malara, A Paredes, S Shahzad, MA Thomas, L Vanden Bemden, M Vander Velde, C Vanlaer, P De Coen, M Dobur, D Gokbulut, G Hong, Y Knolle, J Lambrecht, L Marckx, D Mota Amarilo, K Skovpen, K Van Den Bossche, N van der Linden, J Wezenbeek, L Benecke, A Bethani, A Bruno, G Caputo, C De Favereau De Jeneret, J Delaere, C Donertas, IS Giammanco, A Guzel, AO Jain, S Lemaitre, V Lidrych, J Mastrapasqua, P Tran, TT Alves, GA Coelho, E Correia Silva, G Hensel, C Menezes De Oliveira, T https://bura.brunel.ac.uk/handle/2438/33658 2026-08-15T07:01:47Z 2025-06-16T00:00:00Z

Title: Search for heavy neutral resonances decaying to tau lepton pairs in proton-proton collisions at √𝑠 = 13  TeV Authors: Hayrapetyan, A; Tumasyan, A; Adam, W; Andrejkovic, JW; Benato, L; Bergauer, T; Chatterjee, S; Damanakis, K; Dragicevic, M; Hussain, PS; Jeitler, M; Mora Herrera, C; Rebello Teles, P; Soeiro, M; Vilela Pereira, A; Aldá, WL; CMS Collaboration; Barroso Ferreira Filho, M; Brandao Malbouisson, H; Carvalho, W; Reid, ID; Chinellato, J; Da Costa, EM; Kyberd, P; Da Silveira, GG; Khan, A; Cole, JE; Krammer, N; Li, A; Liko, D; Mikulec, I; Schieck, J; Schöfbeck, R; Schwarz, D; Sonawane, M; Waltenberger, W; Wulz, CE; Janssen, T; Van Laer, T; Van Mechelen, P; Breugelmans, N; D’Hondt, J; Dansana, S; De Moor, A; Delcourt, M; Heyen, F; Lowette, S; Makarenko, I; Müller, D; Tavernier, S; Tytgat, M; Van Onsem, GP; Van Putte, S; Vannerom, D; Bilin, B; Clerbaux, B; Das, AK; De Bruyn, I; De Lentdecker, G; Evard, H; Favart, L; Gianneios, P; Jaramillo, J; Khalilzadeh, A; Khan, FA; Lee, K; Mahdavikhorrami, M; Malara, A; Paredes, S; Shahzad, MA; Thomas, L; Vanden Bemden, M; Vander Velde, C; Vanlaer, P; De Coen, M; Dobur, D; Gokbulut, G; Hong, Y; Knolle, J; Lambrecht, L; Marckx, D; Mota Amarilo, K; Skovpen, K; Van Den Bossche, N; van der Linden, J; Wezenbeek, L; Benecke, A; Bethani, A; Bruno, G; Caputo, C; De Favereau De Jeneret, J; Delaere, C; Donertas, IS; Giammanco, A; Guzel, AO; Jain, S; Lemaitre, V; Lidrych, J; Mastrapasqua, P; Tran, TT; Alves, GA; Coelho, E; Correia Silva, G; Hensel, C; Menezes De Oliveira, T Abstract: A search for heavy neutral gauge bosons (𝑍′) decaying into a pair of tau leptons is performed in proton-proton collisions at √𝑠 = 13  TeV at the CERN LHC. The data were collected with the CMS detector and correspond to an integrated luminosity of 138  fb⁻¹. The observations are found to be in agreement with the expectation from standard model processes. Limits at 95% confidence level are set on the product of the 𝑍′ production cross section and its branching fraction to tau lepton pairs for a range of 𝑍′ boson masses. For a narrow resonance in the sequential standard model scenario, a 𝑍′ boson with a mass below 3.5 TeV is excluded. This is the most stringent limit to date from this type of search. Description: Data availability: Release and preservation of data used by the CMS Collaboration as the basis for publications is guided by the CMS data preservation, re-use and open access policy [77]. CMS data availability statement, 10.7483/OPENDATA.CMS.1BNU.8V1W.; A version of the article is available at arXiv:2412.04357v2 [hep-ex] (https://arxiv.org/abs/2412.04357) under a CC BY license. Comments: Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at https://cms-results.web.cern.ch/cms-results/public-results/publications/EXO-21-016 (CMS Public Pages). Report numbers: CMS-EXO-21-016, CERN-EP-2024-292. Journal reference: Phys. Rev. D 111 (2025) 112004. Submission history: From: The CMS Collaboration: [v1] Thu, 5 Dec 2024 17:16:13 UTC (454 KB); [v2] Wed, 18 Jun 2025 08:14:20 UTC (418 KB).

2025-06-16T00:00:00Z