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Institution

University of Portsmouth

EducationPortsmouth, Portsmouth, United Kingdom
About: University of Portsmouth is a education organization based out in Portsmouth, Portsmouth, United Kingdom. It is known for research contribution in the topics: Population & Galaxy. The organization has 5452 authors who have published 14256 publications receiving 424346 citations. The organization is also known as: Portsmouth and Gosport School of Science and Art & Portsmouth and Gosport School of Science and the Arts.


Papers
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Journal ArticleDOI
TL;DR: It was concluded that exercise must be at a high intensity to affect performance on the flanker task and both the SAS and HPAA appear to play a role in the exercise-cognition interaction.

99 citations

Journal ArticleDOI
TL;DR: It is concluded that both partial phase-resetting and partial additive power contribute dynamically to the generation of ERPs.

99 citations

Journal ArticleDOI
TL;DR: A historical perspective about the evaluation of AI in healthcare is provided and key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance are examined.
Abstract: Objectives: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance. Method: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. Results: There is a rich history and tradition of evaluating AI in healthcare. While evaluators can learn from past efforts, and build on best practice evaluation frameworks and methodologies, questions remain about how to evaluate the safety and effectiveness of AI that dynamically harness vast amounts of genomic, biomarker, phenotype, electronic record, and care delivery data from across health systems. This paper first provides a historical perspective about the evaluation of AI in healthcare. It then examines key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance. Practical aspects of evaluating AI in healthcare, including approaches to evaluation and indicators to monitor AI are also discussed. Conclusion: Commitment to rigorous initial and ongoing evaluation will be critical to ensuring the safe and effective integration of AI in complex sociotechnical settings. Specific enhancements that are required for the new generation of AI-enabled clinical decision support will emerge through practical application.

99 citations

Journal ArticleDOI
01 Aug 2009-Geology
TL;DR: In this paper, pore-fluid values of pore fluids from two distinct sedimentary settings (e.g., a riverine-dominated site on the northern California margin (Eel River shelf; 120 m water depth) and a biogenic opal-rich volcaniclastic deep-sea sediments from the Southern Ocean (north and south of the Crozet Plateau; 3000-4000m water depth).
Abstract: Pore-fluid Fe isotopes may be a unique tracer of sediment respiration by dissimilatory Fe-reducing bacteria, but to date, pore-fluid Fe isotope measurements have been restricted to continental shelf settings Here, we present δ56Fe values of pore fluids from two distinct sedimentary settings: (1) a riverine-dominated site on the northern California margin (Eel River shelf; 120 m water depth) and (2) biogenic opal-rich volcaniclastic deep-sea sediments from the Southern Ocean (north and south of the Crozet Plateau; 3000–4000 m water depth) The Fe isotope compositions of Crozet region pore fluids are significantly less fractionated (δ56Fe = +012‰ to −001‰) than the Eel River shelf (δ56Fe = −065‰ to −340‰) and previous studies of pore-fluid Fe isotopes, relative to average igneous rocks Our data represent the first measurements of Fe isotope compositions in pore fluids from deep-sea sediments A comparison of pore-fluid δ56Fe with the relative abundance of highly labile Fe in the reactive sedimentary Fe pool demonstrates that the composition of Fe isotopes in the pore fluids reflects the different extent of sedimentary Fe redox recycling between these sites

99 citations

Journal ArticleDOI
TL;DR: An experiment to classify four hand motions using different features is used to prove that new features have better classification performance, and proves that the AMR can improve sEMG pattern recognition accuracy rate.
Abstract: Feature extraction is one of most important steps in the control of multifunctional prosthesis based on surface electromyography (sEMG) pattern recognition. In this paper, a new sEMG feature extraction method based on muscle active region is proposed. This paper designs an experiment to classify four hand motions using different features. This experiment is used to prove that new features have better classification performance. The experimental results show that the new feature, active muscle regions (AMR), has better classification performance than other traditional features, mean absolute value (MAV), waveform length (WL), zero crossing (ZC) and slope sign changes (SSC). The average classification errors of AMR, MAV, WL, ZC and SSC are 13%, 19%, 26%, 24% and 22%, respectively. The new EMG features are based on the mapping relationship between hand movements and forearm active muscle regions. This mapping relationship has been confirmed in medicine. We obtain the active muscle regions data from the original EMG signal by the new feature extraction algorithm. The results obtained from this algorithm can well represent hand motions. On the other hand, the new feature vector size is much smaller than other features. The new feature can narrow the computational cost. This proves that the AMR can improve sEMG pattern recognition accuracy rate.

99 citations


Authors

Showing all 5624 results

NameH-indexPapersCitations
Robert C. Nichol187851162994
Gavin Davies1592036149835
Daniel Thomas13484684224
Will J. Percival12947387752
Claudia Maraston10336259178
I. W. Harry9831265338
Timothy Clark95113753665
Kevin Schawinski9537630207
Ashley J. Ross9024846395
Josep Call9045134196
David A. Wake8921446124
L. K. Nuttall8925354834
Stephen Neidle8945732417
Andrew Lundgren8824957347
Rita Tojeiro8722943140
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202363
2022282
2021961
2020976
2019905
2018850