Amputation in the transfemoral amputee (TFA) results in loss of sensory feedback of the amputated limb and therefore results in the poor postural stability. To assess the postural stability, the limit of stability (LOS) is a reliable parameter. In this study, we have investigated the effect of vibrotactile feedback (VF) on the LOS during the weight shifting exercise (WSE) for a TFA. The data of centre of pressure (COP) during WSE was collected from five TFA and five healthy individuals using a zebris force plate. The VF was provided on the amputated/healthy limb's anterior and posterior part of the stump/thigh during forward and backward WSE, respectively. A customized foot insole with 24 embedded dielectric sensors was used to drive the vibratory motor. The effect of VF was analyzed by pre and post-test. Results show that with the use of VF, TFA significantly improved (t-test, p < .05) the sound limb's LOS during forward WSE. Also, ANOVA analysis between WSE divisions shows that the prosthetic limb does not follow the path of WSE. We further examine the spectral power using the Welch method to determine the dominant sway frequency of COP. It shows a decreased frequency between 0.5-2 Hz in the healthy and decreased frequency between 0-0.5 Hz and >2 Hz in the amputee with VF. It concluded that VF could improve the LOS of TFA during WSE which ultimately leads to postural stability enhancement.
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http://dx.doi.org/10.1080/08990220.2019.1572602 | DOI Listing |
Sci Rep
December 2024
Department of Nano-Chemical Engineering, Faculty of Advanced Technologies, Shiraz University, Shiraz, Iran.
MXene-based (nano)materials have recently emerged as promising solutions for antibiotic photodegradation from aquatic environments, yet they are limited by scalability, stability, and selectivity challenges in practical settings. We formulated FeO-SiO/MXene ternary nano-photocomposites via coupled wet impregnation and sonochemistry approach for optimised tetracycline (TC) removal (the second most used antibiotic worldwide) from water using response surface methodology-central composite design (RSM-CCD). The photocatalysts containing various loading of FeO/SiO (5-45 wt%) on the MXene with a range of calcination temperatures (300-600 °C) via RSM optimisation were synthesised, characterised regarding crystallinity properties, surface morphology, binding energy, and light absorption capability, and analysed for TC degradation efficiency.
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December 2024
Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550025, China.
Multi-insertion/deletion polymorphisms (Multi-InDels), as the novel genetic markers, show great potential in forensic research. Whereas, forensic researchers mainly focus on the multi-InDels on the autosomes, which can provide relatively limited information in some complex paternity cases. In this study, a novel X chromosomal multi-InDel multiplex amplification system was designed, containing 22 multi-InDels and one STR locus on the X chromosome.
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December 2024
School of Marxism, China University of Political Science and Law (CUPL), Beijing, 100091, China.
To improve students' understanding of physical education teaching concepts and help teachers analyze students' cognitive patterns, the study proposes an association learning-based method for understanding physical education teaching concepts using deep learning algorithms, which extracts image features related to teaching concepts using convolutional neural networks. Moreover, a neurocognitive diagnostic model based on hypergraph convolution is constructed to mine the data of students' long-term learning sequences and identify students' cognitive outcomes. The findings revealed that the highest accuracy of the association graph convolutional neural network was 0.
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December 2024
School of Public Administration, Guangzhou University, Guangzhou, 510006, China.
The randomness and volatility of existing clean energy sources have increased the complexity of grid scheduling. To address this issue, this work proposes an artificial intelligence (AI) empowered method based on the Environmental, Social, and Governance (ESG) big data platform, focusing on multi-objective scheduling optimization for clean energy. This work employs a combination of Particle Swarm Optimization (PSO) and Deep Q-Network (DQN) to enhance grid scheduling efficiency and clean energy utilization.
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December 2024
Advanced Research Institute for Digital-Twin Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Traditional hydraulic structures rely on manual visual inspection for apparent integrity, which is not only time-consuming and labour-intensive but also inefficient. The efficacy of deep learning models is frequently constrained by the size of available data, resulting in limited scalability and flexibility. Furthermore, the paucity of data diversity leads to a singular function of the model that cannot provide comprehensive decision support for improving maintenance measures.
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