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http://dx.doi.org/10.1016/j.jaad.2020.08.033 | DOI Listing |
Sci Rep
January 2025
Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, 110870, China.
The current research introduces a model-free ultra-local model (MFULM) controller that utilizes the multi-agent on-policy reinforcement learning (MAOPRL) technique for remotely regulating blood pressure through precise drug dosing in a closed-loop system. Within the closed-loop system, there exists a MFULM controller, an observer, and an intelligent MAOPRL algorithm. Initially, a flexible MFULM controller is created to make adjustments to blood pressure and medication dosages.
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January 2025
Department of Geomorphology and Quaternary Geology, Faculty of Oceanography and Geography, University of Gdańsk, Bażyńskiego 4, 80-952, Gdańsk, Poland.
This study introduces a novel methodology for estimating and analysing coastal cliff degradation, using machine learning and remote sensing data. Degradation refers to both natural abrasive processes and damage to coastal reinforcement structures caused by natural events. We utilized orthophotos and LiDAR data in green and near-infrared wavelengths to identify zones impacted by storms and extreme weather events that initiated mass movement processes.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health San Antonio, San Antonio, TX, USA.
Background: APP duplications are a rare form of familial Alzheimer's disease (AD). Research has shown variability in clinical presentation with full duplications. There is limited information on those with partial duplications, especially in underrepresented minorities.
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December 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Background: Individuals with preclinical Alzheimer's disease (AD) show reduced practice effects on annually repeated neuropsychological testing, suggesting a decreased ability to learn over repeated exposures. Remote, digital testing enables the assessment of learning over more frequent time intervals, thereby facilitating a more rapid detection of those early learning deficits. We previously showed that multi-day learning on the Boston Remote Assessment for Neurocognitive Health (BRANCH) was indeed diminished in Αβ+ cognitively unimpaired (CU) older adults.
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December 2024
Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia.
Background: Recent studies suggest learning deficits are the earliest cognitive abnormality to emerge in Alzheimer's disease (AD). Using the Online Repeatable Cognitive Assessment-Language Learning Test (ORCA-LLT), cognitively unimpaired (CU) Aβ+ older adults showed a substantial (d∼2) learning deficit compared to Aβ- controls. This deficit was six times greater than that observed from reduced practice effects after 9+ years of assessment with conventional neuropsychological memory tests (d∼0.
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