Human–machine interactions using deep-learning methods are important in the research of virtual reality,augmented reality,and metaverse.Such research remains challenging as current interactive sensing interfaces for ...Human–machine interactions using deep-learning methods are important in the research of virtual reality,augmented reality,and metaverse.Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes,signal crosstalk,propagation delay,and demanding configuration requirements.Here,an all-inone multipoint touch sensor(AIOM touch sensor)with only two electrodes is reported.The AIOM touch sensor is efficiently constructed by gradient resistance elements,which can highly adapt to diverse application-dependent configurations.Combined with deep learning method,the AIOM touch sensor can be utilized to recognize,learn,and memorize human–machine interactions.A biometric verification system is built based on the AIOM touch sensor,which achieves a high identification accuracy of over 98%and offers a promising hybrid cyber security against password leaking.Diversiform human–machine interactions,including freely playing piano music and programmatically controlling a drone,demonstrate the high stability,rapid response time,and excellent spatiotemporally dynamic resolution of the AIOM touch sensor,which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.展开更多
古地理重建是研究地质历史时期地表构造过程、海陆格局和地貌环境特征的一项综合研究,并通过绘制表达海洋和大陆的古代轮廓以及重要的地形和地表环境的地图来呈现,是还原地球演化历史、预测能源矿产分布、认识生命和气候演变的基础性工...古地理重建是研究地质历史时期地表构造过程、海陆格局和地貌环境特征的一项综合研究,并通过绘制表达海洋和大陆的古代轮廓以及重要的地形和地表环境的地图来呈现,是还原地球演化历史、预测能源矿产分布、认识生命和气候演变的基础性工作。随着大数据时代的到来,数字化方法的应用为古地理图快速更新和友好呈现提供了方便。目前,全球有多个团队发布了数字化的全球古地理重建模型以及相关的数据和方法,如EarthByte、PaleoMap、UNIL、Deep Time Maps等团队。笔者研究团队基于“深时数字地球(Deep-time Digital Earth,DDE)”国际大科学计划提出的数据-知识-模型驱动的古地理重建思想,提出基于数字化方法驱动的升级更新全球古地理图的新流程,并通过不断尝试地球科学与信息科学的交叉融合,从知识图谱、大数据分析和机器学习技术等方面开发了多项古地理重建应用技术。以东特提斯域中二叠世—中三叠世的古地理重建为例,首先在GPlates软件平台上重建了板块构造框架,再利用岩相古地理图自动生成地形地貌图并结合人工校正,最后在GPlates软件通过图层叠加实现了中二叠世—中三叠世东特提斯域的动态数字综合古地理重建。本用例与广泛使用的Scotese(2021)的古地理图对比,在成图效率、数据丰富性和可追溯性、模型准确性等方面都有明显提升,并为该时期板块运动、冰期消亡、大洋缺氧和生物灭绝等重大地质事件的研究提供新的约束和启示。展开更多
Background Venovenous extracorporeal membrane oxygenation(VV-ECMO)has been demonstrated to be effective in treating patients with virus-induced acute respiratory distress syndrome(ARDS).However,whether the management ...Background Venovenous extracorporeal membrane oxygenation(VV-ECMO)has been demonstrated to be effective in treating patients with virus-induced acute respiratory distress syndrome(ARDS).However,whether the management of ECMO is different in treating H1N1 influenza and coronavirus disease 2019(COVID-19)-associated ARDS patients remains unknown.Methods This is a retrospective cohort study.We included 12 VV-ECMO-supported COVID-19 patients admitted to The First Affiliated Hospital of Guangzhou Medical University,Guangzhou Eighth People's Hospital,and Wuhan Union Hospital West Campus between January 23 and March 31,2020.We retrospectively included VV-ECMO-supported patients with COVID-19 and H1N1 influenza-associated ARDS.Clinical characteristics,respiratory mechanics including plateau pressure,driving pressure,mechanical power,ventilatory ratio(VR)and lung compliance,and outcomes were compared.Results Data from 25 patients with COVID-19(n=12)and H1N1(n=13)associated ARDS who had received ECMO support were analyzed.COVID-19 patients were older than H1N1 influenza patients(P=0.004).The partial pressure of arterial carbon dioxide(PaCO_(2))and VR before ECMO initiation were significantly higher in COVID-19 patients than in H1N1 influenza patients(P<0.001 and P=0.004,respectively).COVID-19 patients showed increased plateau and driving pressure compared with H1N1 subjects(P=0.013 and P=0.018,respectively).Patients with COVID-19 remained longer on ECMO support than did H1N1 influenza patients(P=0.015).COVID-19 patients who required ECMO support also had fewer intensive care unit and ventilator-free days than H1N1.Conclusions Compared with H1N1 influenza patients,COVID-19 patients were older and presented with increased PaCO_(2) and VR values before ECMO initiation.The differences between ARDS patients with COVID-19 and influenza on VV-ECMO detailed herein could be helpful for obtaining a better understanding of COVID-19 and for better clinical management.展开更多
为了提高平整轧制力的预报精度,采用有限元法(finite element method,FEM)与人工神经网络(artificial neural network,ANN)相结合的方法,对DP980和CP1180超高强冷轧带钢在平整轧制过程中的轧制力进行预测。通过建立平整轧制过程的数学模...为了提高平整轧制力的预报精度,采用有限元法(finite element method,FEM)与人工神经网络(artificial neural network,ANN)相结合的方法,对DP980和CP1180超高强冷轧带钢在平整轧制过程中的轧制力进行预测。通过建立平整轧制过程的数学模型,利用有限元法设计了不同工况下的数值模拟试验,为神经网络模型生成训练数据。将摩擦因数与轧制力关联进行迭代优化后作为神经网络模型的输入参数。该轧制力预测方法计算迅速,预测误差在10%以内。展开更多
基金supported by National Natural Science Foundation of China under Grants (U1805261 and 22161142024)A~*STAR SERC AME Programmatic Fund (A18A7b0058)
文摘Human–machine interactions using deep-learning methods are important in the research of virtual reality,augmented reality,and metaverse.Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes,signal crosstalk,propagation delay,and demanding configuration requirements.Here,an all-inone multipoint touch sensor(AIOM touch sensor)with only two electrodes is reported.The AIOM touch sensor is efficiently constructed by gradient resistance elements,which can highly adapt to diverse application-dependent configurations.Combined with deep learning method,the AIOM touch sensor can be utilized to recognize,learn,and memorize human–machine interactions.A biometric verification system is built based on the AIOM touch sensor,which achieves a high identification accuracy of over 98%and offers a promising hybrid cyber security against password leaking.Diversiform human–machine interactions,including freely playing piano music and programmatically controlling a drone,demonstrate the high stability,rapid response time,and excellent spatiotemporally dynamic resolution of the AIOM touch sensor,which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.
文摘古地理重建是研究地质历史时期地表构造过程、海陆格局和地貌环境特征的一项综合研究,并通过绘制表达海洋和大陆的古代轮廓以及重要的地形和地表环境的地图来呈现,是还原地球演化历史、预测能源矿产分布、认识生命和气候演变的基础性工作。随着大数据时代的到来,数字化方法的应用为古地理图快速更新和友好呈现提供了方便。目前,全球有多个团队发布了数字化的全球古地理重建模型以及相关的数据和方法,如EarthByte、PaleoMap、UNIL、Deep Time Maps等团队。笔者研究团队基于“深时数字地球(Deep-time Digital Earth,DDE)”国际大科学计划提出的数据-知识-模型驱动的古地理重建思想,提出基于数字化方法驱动的升级更新全球古地理图的新流程,并通过不断尝试地球科学与信息科学的交叉融合,从知识图谱、大数据分析和机器学习技术等方面开发了多项古地理重建应用技术。以东特提斯域中二叠世—中三叠世的古地理重建为例,首先在GPlates软件平台上重建了板块构造框架,再利用岩相古地理图自动生成地形地貌图并结合人工校正,最后在GPlates软件通过图层叠加实现了中二叠世—中三叠世东特提斯域的动态数字综合古地理重建。本用例与广泛使用的Scotese(2021)的古地理图对比,在成图效率、数据丰富性和可追溯性、模型准确性等方面都有明显提升,并为该时期板块运动、冰期消亡、大洋缺氧和生物灭绝等重大地质事件的研究提供新的约束和启示。
基金support from the Special Project of the Guangdong Science and Technology Department (grant number:2020B1111340013)Mergency Key Program of Guangzhou Laboratory (grant number:EKPG21-29)+1 种基金Guangzhou City School (Institute)Joint Funding Project (grant number:202201020414)the National Natural Science Foundation of China (grant number:81970071).
文摘Background Venovenous extracorporeal membrane oxygenation(VV-ECMO)has been demonstrated to be effective in treating patients with virus-induced acute respiratory distress syndrome(ARDS).However,whether the management of ECMO is different in treating H1N1 influenza and coronavirus disease 2019(COVID-19)-associated ARDS patients remains unknown.Methods This is a retrospective cohort study.We included 12 VV-ECMO-supported COVID-19 patients admitted to The First Affiliated Hospital of Guangzhou Medical University,Guangzhou Eighth People's Hospital,and Wuhan Union Hospital West Campus between January 23 and March 31,2020.We retrospectively included VV-ECMO-supported patients with COVID-19 and H1N1 influenza-associated ARDS.Clinical characteristics,respiratory mechanics including plateau pressure,driving pressure,mechanical power,ventilatory ratio(VR)and lung compliance,and outcomes were compared.Results Data from 25 patients with COVID-19(n=12)and H1N1(n=13)associated ARDS who had received ECMO support were analyzed.COVID-19 patients were older than H1N1 influenza patients(P=0.004).The partial pressure of arterial carbon dioxide(PaCO_(2))and VR before ECMO initiation were significantly higher in COVID-19 patients than in H1N1 influenza patients(P<0.001 and P=0.004,respectively).COVID-19 patients showed increased plateau and driving pressure compared with H1N1 subjects(P=0.013 and P=0.018,respectively).Patients with COVID-19 remained longer on ECMO support than did H1N1 influenza patients(P=0.015).COVID-19 patients who required ECMO support also had fewer intensive care unit and ventilator-free days than H1N1.Conclusions Compared with H1N1 influenza patients,COVID-19 patients were older and presented with increased PaCO_(2) and VR values before ECMO initiation.The differences between ARDS patients with COVID-19 and influenza on VV-ECMO detailed herein could be helpful for obtaining a better understanding of COVID-19 and for better clinical management.
文摘为了提高平整轧制力的预报精度,采用有限元法(finite element method,FEM)与人工神经网络(artificial neural network,ANN)相结合的方法,对DP980和CP1180超高强冷轧带钢在平整轧制过程中的轧制力进行预测。通过建立平整轧制过程的数学模型,利用有限元法设计了不同工况下的数值模拟试验,为神经网络模型生成训练数据。将摩擦因数与轧制力关联进行迭代优化后作为神经网络模型的输入参数。该轧制力预测方法计算迅速,预测误差在10%以内。
