293 research outputs found
IDENTIFYING GAIT ASYMMETRY USING DIGITAL SENSORS
The purpose of this study was to determine which phases and kinematics were easier to identify gait asymmetry by using digital sensors. Sixteen participants were recruited in this study. The participants were requested to walk naturally under two conditions (with or without asymmetrical load). Four digital sensor sets were attached on 4 limbs to collect kinematics data. The results showed that only the AS1 of Medial-Later acceleration of upper limb on the stance phase significantly different between unloading and loading conditions; on the lower limb were AS1 of Superior-Inferior acceleration and Flex/Extension angular velocity on the swing phase. The digital sensors that attach on upper and lower limbs both can detect gait asymmetry, but the asymmetrical phase and kinematics are different on upper and lower limbs
Theoretical Study of High Performance Germanium Nanowire Quantum Dot
In this report, we demonstrate that Ge-NWQD (nanowire quantum dots) at low
temperatures exhibit apparent Coulomb oscillations than that in Si-NWQD. These
oscillations gradually disappear as the temperature increases, indicating the
influence of phonon scattering. The increase in Coulomb oscillations enables
the device to exhibit multi-level characteristics at low voltage in quantum
flash, and the lower barrier high and high mobility of Ge make it advantageous
for increasing the storage capacity of quantum flash devices. This research
provides design guidelines for optimization of high-performance quantum flash
devices.Comment: 2pages,5figures,Silicon Nanoelectronics Workshop 2023(SNW
Bis[2-(2H-benzotriazol-2-yl)-4-methylphenolato]palladium(II)
In the title complex, [Pd(C13H10N3O)2], the PdII atom is tetracoordinated by two N atoms and two O atoms from two bidentate 2-(2H-benzotriazol-2-yl)-4-methylphenolate ligands, forming a square-planar environment. The asymmetric unit contains one half molecule in which the Pd atom lies on a centre of symmetry
Evaluation of structural transformation in 2D metal–organic frameworks based on a 4,4′-sulfonyldibenzoate linker: microwave-assisted solvothermal synthesis, characterization and applications
Glutamine Supplementation Attenuates Expressions of Adhesion Molecules and Chemokine Receptors on T Cells in a Murine Model of Acute Colitis
A model for predicting physical function upon discharge of hospitalized older adults in Taiwan—a machine learning approach based on both electronic health records and comprehensive geriatric assessment
BackgroundPredicting physical function upon discharge among hospitalized older adults is important. This study has aimed to develop a prediction model of physical function upon discharge through use of a machine learning algorithm using electronic health records (EHRs) and comprehensive geriatrics assessments (CGAs) among hospitalized older adults in Taiwan.MethodsData was retrieved from the clinical database of a tertiary medical center in central Taiwan. Older adults admitted to the acute geriatric unit during the period from January 2012 to December 2018 were included for analysis, while those with missing data were excluded. From data of the EHRs and CGAs, a total of 52 clinical features were input for model building. We used 3 different machine learning algorithms, XGBoost, random forest and logistic regression.ResultsIn total, 1,755 older adults were included in final analysis, with a mean age of 80.68 years. For linear models on physical function upon discharge, the accuracy of prediction was 87% for XGBoost, 85% for random forest, and 32% for logistic regression. For classification models on physical function upon discharge, the accuracy for random forest, logistic regression and XGBoost were 94, 92 and 92%, respectively. The auROC reached 98% for XGBoost and random forest, while logistic regression had an auROC of 97%. The top 3 features of importance were activity of daily living (ADL) at baseline, ADL during admission, and mini nutritional status (MNA) during admission.ConclusionThe results showed that physical function upon discharge among hospitalized older adults can be predicted accurately during admission through use of a machine learning model with data taken from EHRs and CGAs
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