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The feasibility of assessing prognosis over 3 years in persons with a previous stroke/transient ischemic attack in general practice.
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School of Medicine, University of Limerick, Limerick, Ireland.
Background: Stroke has devastating consequences for survivors. Hypertension is the most important modifiable risk factor, and its management largely takes place in primary care. However, most stroke-based research does not occur in this setting.
View Article and Find Full Text PDFMulti-objective design of multi-material truss lattices utilizing graph neural networks.
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Advanced Manufacturing Lab, ETH Zürich, Leonhardstrasse 21, 8092, Zurich, Switzerland.
The rapid advancements in additive manufacturing (AM) across different scales and material classes have enabled the creation of architected materials with highly tailored properties. Beyond geometric flexibility, multi-material AM further expands design possibilities by combining materials with distinct characteristics. While machine learning has recently shown great potential for the fast inverse design of lattice structures, its application has largely been limited to single-material systems.
View Article and Find Full Text PDFFeS@BC prepared from natural pyrite and biomass as peroxydisulfate activator for sulfadiazine degradation.
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School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei, 230601, China.
The efficient degradation of SAs is a significant challenge for the treatment of wastewater. To address this, the FeS@BC was prepared by calcining a mixture of pyrite and biomass, and used to activate peroxydisulfate (PDS) to degrade sulfadiazine (SDZ). The effect of carbon sources (wheat straw, rice husk, and corn cob) on catalytic activity of FeS@BC were investigated by Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), total Fe dissolution and free radical quantification.
View Article and Find Full Text PDFTwo algorithms for improving model-based diagnosis using multiple observations and deep learning.
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College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, 130012, China. Electronic address:
Model-based diagnosis (MBD) is a critical problem in artificial intelligence. Recent advancements have made it possible to address this challenge using methods like deep learning. However, current approaches that use deep learning for MBD often struggle with accuracy and computation time due to the limited diagnostic information provided by a single observation.
View Article and Find Full Text PDFRMKD: Relaxed matching knowledge distillation for short-length SSVEP-based brain-computer interfaces.
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Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), 138632, Singapore. Electronic address:
Accurate decoding of electroencephalogram (EEG) signals in the shortest possible time is essential for the realization of a high-performance brain-computer interface (BCI) system based on the steady-state visual evoked potential (SSVEP). However, the degradation of decoding performance of short-length EEG signals is often unavoidable due to the reduced information, which hinders the development of BCI systems in real-world applications. In this paper, we propose a relaxed matching knowledge distillation (RMKD) method to transfer both feature-level and logit-level knowledge in a relaxed manner to improve the decoding performance of short-length EEG signals.
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