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    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 Ireland
    Purpose: 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.
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    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, Ireland
    Canine 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/).
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    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, Ireland
    Ratings 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
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    Exploring unknown plant configurations under a multiple model adaptive control framework
    (Elsevier, 2023-11-22) Jesús Ares-Milián, Marlon; Provan, Gregory; Sohège, Yves; Quinones-Grueiro, Marcos; Science Foundation Ireland
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    A GPU implementation of parallel constraint-based local search
    (Institute of Electrical and Electronics Engineers (IEEE), 2014-04-14) Arbelaez, Alejandro; Codognet, Philippe; Seventh Framework Programme; Japan Society for the Promotion of Science
    In this paper we study the performance of constraint-based local search solvers on a GPU. The massively parallel architecture of the GPU makes it possible to explore parallelism at two different levels inside the local search algorithm. First, by executing multiple copies of the algorithm in a multi-walk manner and, second, by evaluating large neighborhoods in parallel in a single-walk manner. Experiments on three well-known problem benchmarks indicate that the current GPU implementation is up to 17 times faster than a well-tuned sequential algorithm implemented on a desktop computer.