Modeling and optimization of renewable hydrogen systems: A systematic methodological review and machine learning integration by Mulako Mukelabai, Edward Barbour, Richard Blanchard

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Supplementary information files for "Modeling and optimization of renewable hydrogen systems: A systematic methodological review and machine learning and integration" by Richard Blanchard, Mulako Mukelabai, Edward Barbour

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A real-time machine learning-based methodology for short-term frequency nadir prediction in low-inertia power systems by MJ Jiménez-Navarro, JM Riquelme-Dominguez, M Carranza-García, Francisco Gonzalez-Longatt

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A fault diagnosis methodology for an external gear pump with the use of Machine Learning classification algorithms: Support Vector Machine and Multilayer Perceptron by K. Lakshmanan, A. J. Gil, F. Auricchio, F. Tessicini

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Supplementary files for "Modeling and optimization of renewable hydrogen systems: A systematic methodological review and machine learning integration" - Load Calculation Tool by Richard Blanchard, Edward Barbour, Mulako Mukelabai

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Adaptive frameworks for robust myoelectric hand gesture prediction using machine learning and deep learning by Carl Robinson

“…The research conducted herein places a focus on supplying reliable operational performance and movement dexterity via myoelectric control using machine learning (ML) and deep learning (DL) strategies. …”
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Supplementary information files for Predicting surgical outcomes for chronic exertional compartment syndrome using a machine learning framework with embedded trust by interrogation... by Andrew Houston, Georgina Cosma, Phillipa Turner, Alexander Bennett

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Advancing healthcare AI: Methods to improve robustness, transparency and user-centric integration by Andrew Houston

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An exploration of methodologies to improve semi-supervised hierarchical clustering with knowledge-based constraints by Abeer Aljohani

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A video processing and machine learning based method for evaluating safety-critical operator engagement in a motorway control room by Linyi Jin, Qingyu Ren, Val Mitchell, Andrew May

“…Practitioner summary: This study demonstrates an automatic, real-time, objective, and relatively unobtrusive method for measuring dynamic operator engagement states. Computer vision technologies were used to estimate body posture, then machine learning (ML) was utilised to build the engagement evaluation model. …”
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Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C₆₀ by Heikki Muhli, Xi Chen, Albert P Bartók, Patricia Hernández-León, Gábor Csányi, Tapio Ala-Nissila, Miguel A Caro

“…We present a comprehensive methodology to enable the addition of van der Waals (vdW) corrections to machine learning (ML) atomistic force fields. …”
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Management respond to negative feedback: AI-powered insights for effective engagement by Aytac Gokce, Mina Tajvidi, Nick Hajli

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EEG based arm movement intention recognition towards enhanced safety in symbiotic Human-Robot Collaboration by Achim Buerkle, Will Eaton, Niels Lohse, Tom Bamber, Pedro Ferreira

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Supplementary information files for: Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C₆₀ by Heikki Muhli, Xi Chen, Albert P Bartók, Patricia Hernández-León, Gábor Csányi, Tapio Ala-Nissila, Miguel A Caro

“…Supplementary files for article: Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60.We present a comprehensive methodology to enable the addition of van der Waals (vdW) corrections to machine learning (ML) atomistic force fields. …”
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Chemical, biological, radiological and nuclear event detection and classification using ontology interrogation and Social Media data by Alrefaie Mohamed, Tom Jackson, Ejovwoke Onojeharho, Elayan Suzanne

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Nonsmooth low-rank matrix recovery: methodology, theory and algorithm by Wei Tu, Peng Liu, Yi Liu, Guodong Li, Bei Jiang, Linglong Kong, Hengshuai Yao, Shangling Jui

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A new integrated collision risk assessment methodology for autonomous vehicles by Christos Katrakazas, Mohammed Quddus, Wen-Hua Chen

“…Following the formulation and explanation of the required functions, machine learning classifiers were utilized for the real-time network-level collision prediction and the results were then incorporated into the integrated DBN model for predicting collision probabilities in real-time. …”
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D3-ImgNet: a framework for molecular properties prediction based on data-driven electron density images by Junfeng Zhao, Lixin Tang, Jiyin Liu, Jian Wu, Xiangman Song

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Enhancing automated bug report analysis through advanced neural language models by Guoming Long

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Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring by Ricardo Aguilar Grajeda

“…Afterwards, a novel methodology for measuring daily life sedentary behaviour using plantar pressure data and a machine learning predictive model is developed. …”
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Text-detection and -recognition from natural images by Hanaa Mahmood

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Building accurate exchange-correlation functional for density functional theory through data analytics and optimization by Junfeng Zhao, Lixin Tang, Jiyin Liu, Jian Wu, Xiangman Song

“…The recent advancements in data analytics techniques, such as machine learning, which excel in pattern recognition, offer new opportunities for more effective approximations of the XC functional. …”
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Retrosynthesis report for the 10 best-ranked routes for IM-204 by Rodolfo Teixeira, Brahim Benyahia

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DECODE tool by Georgina Cosma

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Newton-X platform: new software developments for surface hopping and nuclear ensembles by Mario Barbatti, Mattia Bondanza, Rachel Crespo-Otero, Baptiste Demoulin, Pavlo O Dral, Giovanni Granucci, Fábris Kossoski, Hans Lischka, Benedetta Mennucci, Saikat Mukherjee, Marek Pederzoli, Maurizio Persico, Max Pinheiro Jr, Jirí̌ Pittner, Felix Plasser, Eduarda Sangiogo Gil, Ljiljana Stojanovic

“…Both are among the most common methodologies in computational chemistry for photophysical and photochemical investigations. …”
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A multiscale framework for capturing oscillation dynamics of autonomous vehicles in data-driven car-following models by Rowan Davies, Haitao He, Hui Fang

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Developing transferable real-time crash prediction models for highly imbalanced data by Cheuk Ki Man

“…Due to their simplicity in application, various statistical and machine learning models have utilised this methodology in real-time crash prediction which attained satisfactory predictability. …”
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Application of response surface-corrected finite element model and Bayesian neural networks to predict the dynamic response of forth road bridges under strong winds by Yan Liu, Liangliang Hu, Xiaolin Meng, Yan Bao, Craig Hancock

“…This work proposes a dual-driven approach, integrating machine learning and FEM with GNSS and Earth Observation for Structural Health Monitoring (GeoSHM), to bridge the gap in the limited application of dual-driven methods primarily applied for small- and medium-sized bridges to large-span bridge structures. …”
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Schools in control? Questions about the development of the teaching of cybernetics by Martin Owen

“…Alternatives to current practice drawn from modern technologies which embody their own design methodologies are considered. These methods include object oriented methods, machine learning and neural computing. …”
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MBSE Co‐Pilot: a research roadmap by Wenheng Zhang, Callum Cockburn, Michael Henshaw, Peter Douglas, Paul Palmer, Joshua Olivier‐Myall, Siyuan Ji

“…The MBSE Co‐Pilot leverages advances in Artificial Intelligence (AI) technologies, including but not limited to Natural Language Processing, Machine Learning, and Computer Vision, to support model development, model management, and model comprehension.…”
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Artificial intelligence and art history: looking at images in an algorithmic culture by Kathryn Brown

“…Contributors explore recent developments in machine learning and computer vision and debate whether algorithmic analyses of art open new possibilities for human seeing. …”
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Data-driven analysis of decision-making in football by Chaoyi Gu

“…We contribute to the field by solving these challenges and introducing novel Machine Learning techniques, which can be used to evaluate on-pitch decisions more accurately with contextualised modelling.…”
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Probabilistic reasoning for automotive systems by Rhys Comissiong

“…This can potentially lead to the reduction of physical sensors and actuators used on internal combustion engines and other applications.With the increase in computational efficiency, Artificial Intelligence and Machine Learning techniques have gained popularity. …”
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