Gesture classification based on surface electromyography(sEMG)have been extensively investigated towards the control of smart prosthetic hands.However,most studies did not consider the effects of arm position and move...Gesture classification based on surface electromyography(sEMG)have been extensively investigated towards the control of smart prosthetic hands.However,most studies did not consider the effects of arm position and movements that frequently occur in daily activities.This study aims to address the gesture classification challenge under arm movements.We collected sEMG and acceleration(ACC)data from fourteen participants,including two individuals with radial artery amputations,while performing gestures in both static and dynamic arm states.Using the collected data,the performance of three machine learning methods was evaluated for gesture classification under both arm movements and static arm conditions.The results revealed a 17.48%decrease in average classification accuracy in the dynamic state compared to the static state when using sEMG signals.Subsequently,the improvement in classification accuracy under arm movements was validated using both sEMG and ACC,with deep learning achieving the highest average accuracy of 84.35%across healthy subjects.Additionally,the study assessed the impact of gesture similarity on classification performance and evaluated the practical efficacy of classifiers for amputees.To further enhance gesture classification accuracy under arm movements,a two-stage gesture classification model training method based on ResNet18 was proposed.This method first learns a generalized motion prototype from population data and then adapts it to individual subjects via fine-tuning,resulting in a 2.49%improvement in average recognition accuracy across all 14 subjects.展开更多
Understanding the complex interactions among armed conflict events is important for the simulation and prediction of conflict risks.Many studies have suggested that conflict events affect neighbouring regions.Here we ...Understanding the complex interactions among armed conflict events is important for the simulation and prediction of conflict risks.Many studies have suggested that conflict events affect neighbouring regions.Here we find that conflicts exhibit not only localized effects but also significant teleconnections.By applying complex network analysis,the spatiotemporal scale of conflict teleconnections is quantified,revealing a characteristic spatial distance of approximately 1500 km and a rapid propagation delay of about 10 days.Moreover,distinct teleconnection patterns are identified through network coefficients.Eight major hubs,located in South Asia,the Middle East,and Sub-Saharan Africa,emerge and exhibit pronounced spatial heterogeneity in their interactions.Furthermore,analysis of the underlying driving mechanisms indicates that differences in teleconnection patterns are likely associated with the flows of energy,materials,and information,with information playing the predominant role in shaping these interactions.These findings provide insights into long-distance interactions in armed conflict,offering a better understanding of conflict risk.展开更多
Stroke patients experience varying degrees of upper limb functional impairment.Although bilateral arm training can help stroke patients recover movement after stroke,little is known about the way in which the brain an...Stroke patients experience varying degrees of upper limb functional impairment.Although bilateral arm training can help stroke patients recover movement after stroke,little is known about the way in which the brain and muscles work together during this type of training.To address this,we conducted a cross-sectional study at The Seventh Affiliated Hospital,Sun Yat-sen University in China,where we observed the connection between brain and muscle activity during bilateral upper limb training in 21 stroke patients and 17 healthy controls.We used functional near-infrared spectroscopy and surface electromyography to measure changes in cerebral cortex oxygenation and upper limb muscle contraction signals,respectively.The results showed that,compared with the healthy control group,stroke patients had reduced functional connectivity and more irregular muscle activity in the affected flexor muscle during bilateral upper limb training.Moreover,we found a significant correlation between the surface electromyographic signal characteristics of upper limb muscles and cerebral oxygenation indicators of multiple brain regions in stroke patients.These findings indicate that bilateral upper limb training is an effective rehabilitation method that improves upper limb motor function in stroke patients by promoting brain functional connectivity and improving muscle activity patterns.展开更多
The convective boundary layer(CBL),also known as the mixing layer,constitutes the critical lower segment of the atmosphere that significantly influences daily human activities.The growing demand for precise weather fo...The convective boundary layer(CBL),also known as the mixing layer,constitutes the critical lower segment of the atmosphere that significantly influences daily human activities.The growing demand for precise weather forecasts is driven by the requirements of agriculture,transportation,and routine societal functions.To enhance understanding of the CBL,this study investigates the spatiotemporal variability in the CBL and its controlling factors using four-year Doppler lidar,surface flux,and profiling measurements at five ARM Southern Great Plains sites within a 100 km radius.This investigation utilizes data collected exclusively under clear-sky conditions or scattered low-cloud conditions.Results reveal significant spatial differences in CBL evolutions.Daily mixing layer heights(MLHs)vary up to 1 km(30%of the mean)in late afternoon.There is a clear east–west contrast:western sites(C1,E32,E37)exhibit higher summer MLH(1.9–2.1 km)and vertical velocity variances(1.0–1.2 m2s−2)than eastern sites(1.6–1.8 km),reversing in winter.Temporally,the MLH peaks at 70%of the sunrise–sunset interval,the lagging heat flux(HF)peaks at 50%;and the seasonal MLH maxima lag the HF by approximately one month,influenced by nighttime PBL(planetary boundary layer)properties.The HF and lower tropospheric stability are the main factors of influence for the CBL,but site-specific dependencies highlight the critical roles of local factors,underscoring the need for including them in CBL modeling.展开更多
基金supported in part by the National Key R&D Program of China(No.2024YFB4707900)in part by the National Natural Science Foundation of China(No.52021003).
摘要Gesture classification based on surface electromyography(sEMG)have been extensively investigated towards the control of smart prosthetic hands.However,most studies did not consider the effects of arm position and movements that frequently occur in daily activities.This study aims to address the gesture classification challenge under arm movements.We collected sEMG and acceleration(ACC)data from fourteen participants,including two individuals with radial artery amputations,while performing gestures in both static and dynamic arm states.Using the collected data,the performance of three machine learning methods was evaluated for gesture classification under both arm movements and static arm conditions.The results revealed a 17.48%decrease in average classification accuracy in the dynamic state compared to the static state when using sEMG signals.Subsequently,the improvement in classification accuracy under arm movements was validated using both sEMG and ACC,with deep learning achieving the highest average accuracy of 84.35%across healthy subjects.Additionally,the study assessed the impact of gesture similarity on classification performance and evaluated the practical efficacy of classifiers for amputees.To further enhance gesture classification accuracy under arm movements,a two-stage gesture classification model training method based on ResNet18 was proposed.This method first learns a generalized motion prototype from population data and then adapts it to individual subjects via fine-tuning,resulting in a 2.49%improvement in average recognition accuracy across all 14 subjects.
摘要Understanding the complex interactions among armed conflict events is important for the simulation and prediction of conflict risks.Many studies have suggested that conflict events affect neighbouring regions.Here we find that conflicts exhibit not only localized effects but also significant teleconnections.By applying complex network analysis,the spatiotemporal scale of conflict teleconnections is quantified,revealing a characteristic spatial distance of approximately 1500 km and a rapid propagation delay of about 10 days.Moreover,distinct teleconnection patterns are identified through network coefficients.Eight major hubs,located in South Asia,the Middle East,and Sub-Saharan Africa,emerge and exhibit pronounced spatial heterogeneity in their interactions.Furthermore,analysis of the underlying driving mechanisms indicates that differences in teleconnection patterns are likely associated with the flows of energy,materials,and information,with information playing the predominant role in shaping these interactions.These findings provide insights into long-distance interactions in armed conflict,offering a better understanding of conflict risk.
摘要Stroke patients experience varying degrees of upper limb functional impairment.Although bilateral arm training can help stroke patients recover movement after stroke,little is known about the way in which the brain and muscles work together during this type of training.To address this,we conducted a cross-sectional study at The Seventh Affiliated Hospital,Sun Yat-sen University in China,where we observed the connection between brain and muscle activity during bilateral upper limb training in 21 stroke patients and 17 healthy controls.We used functional near-infrared spectroscopy and surface electromyography to measure changes in cerebral cortex oxygenation and upper limb muscle contraction signals,respectively.The results showed that,compared with the healthy control group,stroke patients had reduced functional connectivity and more irregular muscle activity in the affected flexor muscle during bilateral upper limb training.Moreover,we found a significant correlation between the surface electromyographic signal characteristics of upper limb muscles and cerebral oxygenation indicators of multiple brain regions in stroke patients.These findings indicate that bilateral upper limb training is an effective rehabilitation method that improves upper limb motor function in stroke patients by promoting brain functional connectivity and improving muscle activity patterns.
基金funded by DOE-ASR(Grant No.DESC0020171)the National Science Foundation(NSF)(Grant Nos.AGS 1917693 and 2431365).
摘要The convective boundary layer(CBL),also known as the mixing layer,constitutes the critical lower segment of the atmosphere that significantly influences daily human activities.The growing demand for precise weather forecasts is driven by the requirements of agriculture,transportation,and routine societal functions.To enhance understanding of the CBL,this study investigates the spatiotemporal variability in the CBL and its controlling factors using four-year Doppler lidar,surface flux,and profiling measurements at five ARM Southern Great Plains sites within a 100 km radius.This investigation utilizes data collected exclusively under clear-sky conditions or scattered low-cloud conditions.Results reveal significant spatial differences in CBL evolutions.Daily mixing layer heights(MLHs)vary up to 1 km(30%of the mean)in late afternoon.There is a clear east–west contrast:western sites(C1,E32,E37)exhibit higher summer MLH(1.9–2.1 km)and vertical velocity variances(1.0–1.2 m2s−2)than eastern sites(1.6–1.8 km),reversing in winter.Temporally,the MLH peaks at 70%of the sunrise–sunset interval,the lagging heat flux(HF)peaks at 50%;and the seasonal MLH maxima lag the HF by approximately one month,influenced by nighttime PBL(planetary boundary layer)properties.The HF and lower tropospheric stability are the main factors of influence for the CBL,but site-specific dependencies highlight the critical roles of local factors,underscoring the need for including them in CBL modeling.
摘要【目的】早发性肌无力综合征(early onset muscle weakness syndrome,MW)是新近发现的一种荷斯坦牛遗传缺陷,患病犊牛表现为出生后趴卧不起、后肢肌肉萎缩等,其遗传机制与L型钙通道蛋白α1S亚基编码基因CACNA1S的单碱基突变相关。本研究旨在建立该遗传缺陷的分子检测方法,并探究其在国内荷斯坦牛群体中的扩散情况。【方法】基于MW致病位点特异性DNA序列,设计扩增阻滞突变系统-聚合酶链式反应(ARMS-PCR)检测引物,对317份荷斯坦牛冻精和毛囊样本进行MW遗传缺陷基因筛查,通过Sanger测序对ARMS-PCR检测结果进行验证,并利用系谱数据追溯MW突变源头。【结果】ARMS-PCR检出MW携带者21头,携带率为6.62%(21/317),未发现缺陷基因纯合子,提示该突变为隐性纯合致死。Sanger测序与ARMS-PCR所得基因型完全一致,证明该技术具有高度准确性。系谱追溯表明,MW携带者的遗传来源可追溯至1984年出生的荷斯坦公牛Southwind Bell of Bar-Lee。【结论】MW遗传缺陷已在中国荷斯坦牛群体中扩散且携带率较高,建议牧场应尽早开展MW遗传缺陷基因检测和风险评估,采取科学的选种选配措施以减少遗传缺陷导致的经济损失。