Search

Your search keyword '"Zhou, Zhifeng"' showing total 195 results
Start Over Author "Zhou, Zhifeng" Publication Year Range Last 3 years
195 results on '"Zhou, Zhifeng"'

24. Effect of elastomeric nanoparticles as co-reinforcing agent on rubber composites

Author

Wang, Xiang, Zhou, Zhifeng, Qi, Guicun, Li, Binghai, Song, Zhihai, and Zhang, Xiaohong

Subjects
Energy conservation, Nanoparticles, Rubber, Engineering and manufacturing industries, Science and technology
Abstract

Abstract Elastomeric nanoparticles (ENPs) are rubber nanoparticles that have different effects than carbon black (CB) when added to rubber composites. To further understand the effects of ENPs on rubber composites, [...]

Published
2023
Full Text
View/download PDF

39. Prediction of Vertical Ground Reaction Forces Under Different Running Speeds: Integration of Wearable IMU with CNN-xLSTM.

Author

Chen, Tianxiao, Xu, Datao, Zhou, Zhifeng, Zhou, Huiyu, Shao, Shirui, and Gu, Yaodong

Abstract

Traditional methods for collecting ground reaction forces (GRFs) mainly use lab force plates. Previous research broke this pattern by predicting GRFs with deep learning and data from IMUs like joint acceleration. Joint angle, as a geometric, is easier to collect than acceleration outdoors with cameras. LSTM is one of the deep learning models that have shown good performance in biomechanical studies. xLSTM, as an optimized version of LSTM, has not been used in biomechanical studies and no research has predicted GRFs during running solely using lower limb joint angles. This study collected lower-limb joint angle and vertical ground reaction force data at five speeds from 12 healthy male runners with Xsens sensors. Datasets including three joints and three planes were set as the inputs of four deep learning models for vertical-GRF prediction. CNN-xLSTM consistently performed best in the four deep learning models when different datasets were input (R2 = 0.909 ± 0.064, MAPE = 2.18 ± 0.09, rMSE = 0.061 ± 0.008), and the performance was at a relatively high level at the five speeds. The current findings may contribute to a new GRF measurement and provide a reference for future real-time motion detection and sport injury prediction. [ABSTRACT FROM AUTHOR]

Published
2025
Full Text
View/download PDF

45. Assessment of Muscle Synergies in Chronic Ankle Instability Patients During Unanticipated and Anticipated Landing.

Author

Zhou, Zhifeng, Xu, Datao, Wang, Meizi, Jie, Tianle, Baker, Julien S., Zhou, Huiyu, and Gu, Yaodong

Subjects
*CHRONIC ankle instability, *VASTUS lateralis, *MATRIX decomposition, *GLUTEAL muscles, *NONNEGATIVE matrices, *ANKLE
Abstract

Ankle sprains are a common injury among athletes and the general population, with chronic ankle instability (CAI) being a frequent complication. CAI patients often display altered neuromuscular control adaptations. This study analyzed muscle synergy patterns in 20 CAI patients during anticipated and unanticipated landing tasks to understand their neuromuscular adaptation strategies. Using Nesterov non-negative matrix factorization and K-means clustering, the study identified distinct muscle activation patterns. Results indicated that during unanticipated landings, the gluteus maximus and vastus lateralis showed increased activation weight, while the medial gastrocnemius was more active in anticipated landings. This study highlights that CAI patients display unique muscle synergy patterns during unanticipated landings, relying more on proximal muscles such as the gluteus maximus and vastus lateralis. This adaptation reflects the proximal muscle strategy to enhance stability and compensate for impaired ankle function in unpredictable situations. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

47. Aberrant Inter-hemispheric Connectivity in Patients With Recurrent Major Depressive Disorder: A Multimodal MRI Study

Author

Guo Zheng, Zhang Yingli, Chen Shengli, Zhou Zhifeng, Peng Bo, Hou Gangqiang, and Qiu Yingwei

Subjects
depression, multimodal MRI, interhemispheric connectivity MDD, major depressive disorder, functional connectivity, voxel-mirrored homotopic connectivity, Neurology. Diseases of the nervous system, RC346-429
Abstract

ObjectiveInter-hemispheric network dysconnectivity has been well-documented in patients with recurrent major depressive disorder (MDD). However, it has remained unclear how structural networks between bilateral hemispheres relate to inter-hemispheric functional dysconnectivity and depression severity in MDD. Our study attempted to investigate the alterations in corpus callosum macrostructural and microstructural as well as inter-hemispheric homotopic functional connectivity (FC) in patients with recurrent MDD and to determine how these alterations are related with depressive severity.Materials and MethodsResting-state functional MRI (fMRI), T1WI anatomical images and diffusion tensor MRI of the whole brain were performed in 140 MDD patients and 44 normal controls matched for age, sex, years of education. We analyzed the macrostructural and microstructural integrity as well as voxel-mirrored homotopic functional connectivity (VMHC) of corpus callosum (CC) and its five subregion. Two-sample t-test was used to investigate the differences between the two groups. Significant subregional metrics were correlated with depression severity by spearman's correlation analysis, respectively.ResultsCompared with control subjects, MDD patients had significantly attenuated inter-hemispheric homotopic FC in the bilateral medial prefrontal cortex, and impaired anterior CC microstructural integrity (each comparison had a corrected P < 0.05), whereas CC macrostructural measurements remained stable. In addition, disruption of anterior CC microstructural integrity correlated with a reduction in FC in the bilateral medial prefrontal cortex, which correlated with depression severity in MDD patients. Furthermore, disruption of anterior CC integrity exerted an indirect influence on depression severity in MDD patients through an impairment of inter-hemispheric homotopic FC.ConclusionThese findings may help to advance our understanding of the neurobiological basis of depression by identifying region-specific interhemispheric dysconnectivity.

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results