Tyndall National Institute - Journal Articles
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Item Investigation of the soft-magnetic properties of CoZrTaB laminated cores by dielectric layer tuning(Institute of Electrical and Electronics Engineers (IEEE), 2023-09-26) Wei, Guannan; Das, Rajasree; Lordan, Daniel; Lorenc, Marek; Clark, Barry; Hurley, David P. F.; Hayes, Mike; O'Mathuna, Cian; Sai, Ranajit; McCloskey, Paul; Tokyo Electron Magnetic Solutions Ltd; Enterprise IrelandSoft magnetic properties of thin films for use as a core material are critical for the realization of future miniaturized electromagnetic devices operating at frequencies of tens or hundreds of megahertz. Laminated stacks consisting of alternate thin layers of magnetic material and dielectric material are widely used to suppress eddy current losses that dominate, especially at a higher frequency of operation. Thus, identifying a suitable dielectric layer, its optimum thickness, and the understanding of its effect on the performance of the laminated core is important. In this letter, six different CoZrTaB (CZTB) laminated cores are reported, featuring a variety of dielectric materials (AlN, SiN, Al 2 O 3 , and oxide CZTB) and/or dielectric thickness (5, 15, and 50 nm). This study shows that stacks with different dielectric materials have a varied residual stress that plays an important role in inducing magnetic anisotropy, thus affecting the permeability. CZTB stacks with oxide CZTB dielectric show the best combination of high permeability, low coercivity, and low losses at high frequency.Item EEG datasets for healthcare: A scoping review(Institute of Electrical and Electronics Engineers (IEEE), 2024-03-11) Peres da Silva, Caroline; Tedesco, Salvatore; O’Flynn, Brendan; Science Foundation Ireland; European Regional Development FundThe rapidly evolving landscape of artificial intelligence (AI) and machine learning has placed data at the forefront of healthcare innovation. Electroencephalography (EEG) has gained significant attention for its potential to revolutionize healthcare applications. However, the effective utilization of EEG data in advancing medical diagnoses and treatment hinges on the availability and quality of relevant datasets. In this context, we conducted a scoping review to explore the wealth of EEG datasets designed for healthcare applications. This review serves as a critical exploration of the current landscape, aiming to identify datasets related to healthcare conditions while assessing their reusability. Our findings highlight both the opportunities and limitations in the wealth of open access EEG datasets. Available. As AI increasingly relies on high-quality, well labelled data, barriers impeding the sharing and utilization of EEG data for healthcare (such as lack of comprehensive documentation or adherence to FAIR principles) must be addressed so as to leverage the potential of advanced deep learning models to unlock new possibilities for diagnosis and analysis of a wide array of medical conditions.Item Utilising dynamic motor control index to identify age-related differences in neuromuscular control(Elsevier, 2024-03-10) Burke, Laura; Khokhlova, Liudmila; O'Flynn, Brendan; Tedesco, Salvatore; Science Foundation Ireland; European Regional Development Fund; Enterprise IrelandPurpose: Considering the relationship between aging and neuromuscular control decline, early detection of age-related changes can ensure that timely interventions are implemented to attenuate or restore neuromuscular deficits. The dynamic motor control index (DMCI), a measure based on variance accounted for (VAF) by one muscle synergy (MS), is a metric used to assess age-related changes in neuromuscular control. The aim of the study was to investigate the use of one-synergy VAF, and consecutively DMCI, in assessing age-related changes in neuromuscular control over a range of exercises with varying difficulty. Methods: Thirty-one subjects walked on a flat and inclined treadmill, as well as performed forward and lateral stepping up tasks. Motion and muscular activity were recorded, and muscle synergy analysis was conducted using one-synergy VAF, DMCI, and number of synergies. Results: Difference between older and younger group was observed for one-synergy VAF, DMCI for forward stepping up task (one-synergy VAF difference of 2.45 (0.22, 4.68) and DMCI of 9.21 (0.81, 17.61), p = 0.033), but not for lateral stepping up or walking. Conclusion: The use of VAF based metrics and specifically DMCI, rather than number of MS, in combination with stepping forward exercise can provide a low-cost and easy to implement approach for assessing neuromuscular control in clinical settings.Item Prediction of working outcomes in trainee dogs using the novel Assistance Dog Test Battery (ADTB)(Elsevier, 2024-02-28) Marcato, Marinara; Tedesco, Salvatore; O'Mahony, Conor; O'Flynn, Brendan; Galvin, Paul; ERDF; Interreg; Science Foundation Ireland; Department of Agriculture, Food and the Marine, IrelandCanine behaviour is commonly assessed using test batteries comprising a test protocol and ethogram scoring system. These are particularly valuable for assistance dog organisations as a tool for evaluating trainee dogs’ proficiency in fundamental skills. The goal of this study was to design and validate a new test battery to assess the suitability of trainee dogs for assistance work at different stages of the training programme. The main objective was to develop a machine-learning tool capable of predicting working outcomes. Accordingly, the novel Assistance Dog Test Battery (ADTB) was developed. Trainee assistance dogs participating in this research performed the test at 3 weeks and 10 weeks after starting formal training. The results from the univariate logistic regression analysis were used to select the variables for the reduced feature sets that were used for modelling. The machine learning models were built using the data collected at 3 and 10 weeks separately and predicted working outcomes with an area under the ROC curve of 0.74 and 0.84, respectively. This research demonstrated the relationship between the novel ADTB ethogram measures and working outcomes in assistance dogs. The machine learning model created using the data collected at 3 weeks achieved comparable performance to the state-of-the-art, while the model built using the data collected at 10 weeks substantially outperformed it. These preliminary results suggest that the ADTB is a reliable tool for the prediction of working outcomes in trainee assistance dogs. Hence, assistance dog organisations can reduce the cost of training by using model predictions as a guide for deciding which dogs to withdraw from training. The data collected and the code developed in this research are publicly available on Mendeley Data (https://doi.org/10.17632/5mzfpt455r.1) and GitHub, respectively (https://github.com/mmarcato/dog_ethogram/).Item Comparison between the Canine Behavioral Assessment and Research Questionnaire and Monash Canine Personality Questionnaire – Revised to predict training outcome in apprentice assistance dogs(Elsevier, 2023-10-18) Marcato, Marinara; Tedesco, Salvatore; O'Mahony, Conor; O'Flynn, Brendan; Galvin, Paul; European Regional Development Fund; Interreg; Science Foundation Ireland; Department of Agriculture, Food and the Marine, IrelandRatings of canine behaviour and personality are a convenient method used by dog training organisations to gather information about prospective and trainee dogs. The objective of this study was to compare the use of two rating questionnaires to predict training outcomes in assistance dogs. It was of interest to investigate the predictive power of a questionnaire answered by the dog trainer in case no puppy raiser questionnaire was available. Two standardised ratings were used, in particular, the Canine Behavioral Assessment and Research Questionnaire (C-BARQ) was completed by puppy raisers around the time the dogs started formal training and the Monash Canine Personality Questionnaire - Revised (MCPQ-R) was completed by dog trainers at ten weeks of training. Rating data were independently analysed to investigate their relationship with training outcomes. The results from the univariate logistic regression analysis were used to select the variables for the reduced feature sets that were used for modelling. The novel machine learning models built with data collected using the C-BARQ and MCPQ-R achieved similar performance in predicting training outcomes, an area under the ROC curve of 0.84 and 0.85, respectively. The novel models developed in this research were the most effective early prediction of suitability for assistance work compared to previously reported studies. The MCPQ-R was demonstrated for the first time to be a reliable canine behavioural assessment method for estimating future outcomes in trainee dogs. The dataset and code used are publicly available on GitHub: https://github.com/mmarcato/dog_questionnaire