Analyzing physical activities through wearable devices is a promising research area for improving health assessment.This research focuses on the development of an affordable and real-time Human Activity Recognition(HA...Analyzing physical activities through wearable devices is a promising research area for improving health assessment.This research focuses on the development of an affordable and real-time Human Activity Recognition(HAR)system designed to operate on low-performance microcontrollers.The system utilizes data from a bodyworn accelerometer to recognize and classify human activities,providing a cost-effective,easy-to-use,and highly accurate solution.A key challenge addressed in this study is the execution of efficient motion recognition within a resource-constrained environment.The system employs a Random Forest(RF)classifier,which outperforms Gradient Boosting Decision Trees(GBDT),Support Vector Machines(SVM),and K-Nearest Neighbors(KNN)in terms of accuracy and computational efficiency.The proposed features Average absolute deviation(AAD),Standard deviation(STD),Interquartile range(IQR),Range,and Root mean square(RMS).The research has conducted numerous experiments and comparisons to establish optimal parameters for ensuring system effectiveness,including setting a sampling frequency of 50 Hz and selecting an 8-s window size with a 40%overlap between windows.Validation was conducted on both the WISDM public dataset and a self-collected dataset,focusing on five fundamental daily activities:Standing,Sitting,Jogging,Walking,and Walking the stairs.The results demonstrated high recognition accuracy,with the system achieving 96.7%on the WISDM dataset and 97.13%on the collected dataset.This research confirms the feasibility of deploying HAR systems on low-performance microcontrollers and highlights the system’s potential applications in patient support,rehabilitation,and elderly care.展开更多
Autophagy,a conserved cellular degradation process,is crucial for various cellular processes such as immune responses,inflammation,metabolic and oxidative stress adaptation,cell proliferation,development,and tissue re...Autophagy,a conserved cellular degradation process,is crucial for various cellular processes such as immune responses,inflammation,metabolic and oxidative stress adaptation,cell proliferation,development,and tissue repair and remodeling.Dysregulation of autophagy is suspected in numerous diseases,including cancer,neurodegenerative diseases,digestive disorders,metabolic syndromes,and infectious and inflammatory diseases.If autophagy is disrupted,for example,this can have serious consequences and lead to chronic inflammation and tissue damage,as occurs in diseases such as Chron's disease and ulcerative colitis.On the other hand,the influence of autophagy on the development and progression of cancer is not clear.Autophagy can both suppress and promote the progression and metastasis of cancer at various stages.From inflammatory bowel diseases to gastrointestinal cancer,researchers are discovering the intricate role of autophagy in maintaining gut health and its potential as a therapeutic target.Researchers should carefully consider the nature and progression of diseases such as cancer when trying to determine whether inhibiting or stimulating autophagy is likely to be beneficial.Multidisciplinary approaches that combine cutting-edge research with clinical expertise are key to unlocking the full therapeutic potential of autophagy in digestive diseases.展开更多
文章选取上证综指5分钟收盘价序列高频数据,采用ACF拟合、多种损失函数、SPA检验和VaR回测检验对不同误差分布下的包含时变波动、异方差结构和加权已实现极差的Realized HAR GARCH模型进行研究。实证结果表明,新模型相比于以往模型更能...文章选取上证综指5分钟收盘价序列高频数据,采用ACF拟合、多种损失函数、SPA检验和VaR回测检验对不同误差分布下的包含时变波动、异方差结构和加权已实现极差的Realized HAR GARCH模型进行研究。实证结果表明,新模型相比于以往模型更能够捕捉上证综指的波动特征,具有更好的波动率拟合和预测效果,且VaR度量效果更优。研究丰富了时变长记忆高频波动率模型,从时变波动和噪声异方差结构视角为投资者和监管机构进行风险管控提供参考。展开更多
Har tolgoi铅银多金属矿床位于蒙古南戈壁省省会Dalanzadgad市南约170千米的诺木冈县境内,与我国直线距离56千米,大地构造位置在巴音毛道-雅干-Baruun Tsohio微陆块内。该矿床是南蒙古一个重要的大型铅银多金属矿床,其周围Pb-Zn-Ag-Au矿...Har tolgoi铅银多金属矿床位于蒙古南戈壁省省会Dalanzadgad市南约170千米的诺木冈县境内,与我国直线距离56千米,大地构造位置在巴音毛道-雅干-Baruun Tsohio微陆块内。该矿床是南蒙古一个重要的大型铅银多金属矿床,其周围Pb-Zn-Ag-Au矿床(点)密布,通过该矿床的深入研究。展开更多
基金Human activity data for the experiments were sourced from the Ethics Council for Grassroots Biomedical Research at Phenikaa University.The data collection adhered to Decision No.476/QD-DHP-HÐÐÐthe Ethics Council for Grassroots Biomedical Research at Phenikaa University(No.023.07.01/DHP-HÐÐÐ,2023 Dec).
文摘Analyzing physical activities through wearable devices is a promising research area for improving health assessment.This research focuses on the development of an affordable and real-time Human Activity Recognition(HAR)system designed to operate on low-performance microcontrollers.The system utilizes data from a bodyworn accelerometer to recognize and classify human activities,providing a cost-effective,easy-to-use,and highly accurate solution.A key challenge addressed in this study is the execution of efficient motion recognition within a resource-constrained environment.The system employs a Random Forest(RF)classifier,which outperforms Gradient Boosting Decision Trees(GBDT),Support Vector Machines(SVM),and K-Nearest Neighbors(KNN)in terms of accuracy and computational efficiency.The proposed features Average absolute deviation(AAD),Standard deviation(STD),Interquartile range(IQR),Range,and Root mean square(RMS).The research has conducted numerous experiments and comparisons to establish optimal parameters for ensuring system effectiveness,including setting a sampling frequency of 50 Hz and selecting an 8-s window size with a 40%overlap between windows.Validation was conducted on both the WISDM public dataset and a self-collected dataset,focusing on five fundamental daily activities:Standing,Sitting,Jogging,Walking,and Walking the stairs.The results demonstrated high recognition accuracy,with the system achieving 96.7%on the WISDM dataset and 97.13%on the collected dataset.This research confirms the feasibility of deploying HAR systems on low-performance microcontrollers and highlights the system’s potential applications in patient support,rehabilitation,and elderly care.
文摘Autophagy,a conserved cellular degradation process,is crucial for various cellular processes such as immune responses,inflammation,metabolic and oxidative stress adaptation,cell proliferation,development,and tissue repair and remodeling.Dysregulation of autophagy is suspected in numerous diseases,including cancer,neurodegenerative diseases,digestive disorders,metabolic syndromes,and infectious and inflammatory diseases.If autophagy is disrupted,for example,this can have serious consequences and lead to chronic inflammation and tissue damage,as occurs in diseases such as Chron's disease and ulcerative colitis.On the other hand,the influence of autophagy on the development and progression of cancer is not clear.Autophagy can both suppress and promote the progression and metastasis of cancer at various stages.From inflammatory bowel diseases to gastrointestinal cancer,researchers are discovering the intricate role of autophagy in maintaining gut health and its potential as a therapeutic target.Researchers should carefully consider the nature and progression of diseases such as cancer when trying to determine whether inhibiting or stimulating autophagy is likely to be beneficial.Multidisciplinary approaches that combine cutting-edge research with clinical expertise are key to unlocking the full therapeutic potential of autophagy in digestive diseases.
文摘文章选取上证综指5分钟收盘价序列高频数据,采用ACF拟合、多种损失函数、SPA检验和VaR回测检验对不同误差分布下的包含时变波动、异方差结构和加权已实现极差的Realized HAR GARCH模型进行研究。实证结果表明,新模型相比于以往模型更能够捕捉上证综指的波动特征,具有更好的波动率拟合和预测效果,且VaR度量效果更优。研究丰富了时变长记忆高频波动率模型,从时变波动和噪声异方差结构视角为投资者和监管机构进行风险管控提供参考。
