在相机和旋转模块组成的旋转扫描系统中,相机与转轴的位姿关系对于后续数据融合至关重要。现有方法通常需借助辅助传感器来建立转轴与相机的位姿关系。为降低标定成本,本文提出一种基于ChArUco(Chess Augmented Reality University of C...在相机和旋转模块组成的旋转扫描系统中,相机与转轴的位姿关系对于后续数据融合至关重要。现有方法通常需借助辅助传感器来建立转轴与相机的位姿关系。为降低标定成本,本文提出一种基于ChArUco(Chess Augmented Reality University of Córdoba)标定板的转轴位姿标定方法。利用ChArUco标定板图像解算不同转角下的相机光心坐标并拟合光心轨迹,根据轨迹中心可确定转轴所在位置。建立了旋转系统的数学模型,推导了在世界坐标系下转轴位姿的数学表达式,并基于旋转系统的空间约束构建联合优化,计算相机和转轴间最佳位姿。利用所提出方法进行旋转相机系统标定实验,实验结果表明:标定后的转轴与相机光轴夹角的平均绝对误差为0.036°,标定结果能有效消除全景拼接时的重影现象,验证了所提出方法的准确性和可行性。展开更多
Dear Editor,Systemic sclerosis(SSc)is an autoimmune connective tissue disease in which there are vascular abnormalities,inflammation,and fibrosis[1].These three characteristics primarily affect the skin and lungs.Of a...Dear Editor,Systemic sclerosis(SSc)is an autoimmune connective tissue disease in which there are vascular abnormalities,inflammation,and fibrosis[1].These three characteristics primarily affect the skin and lungs.Of all the autoimmune rheumatic diseases,SSc has the highest all-cause mortality rate,and the underlying pathogenic processes that mediate disease are still obscure,with wide diff erences in presentation and progression[2,3].展开更多
The Rukwa Rift section of the East Africa Rift System presents a type setting for radiogenic helium accumulation in a petroleum free basin.As a prerequisite for accumulation,a considerable high heat flow anomaly is re...The Rukwa Rift section of the East Africa Rift System presents a type setting for radiogenic helium accumulation in a petroleum free basin.As a prerequisite for accumulation,a considerable high heat flow anomaly is required from tectonothermal events to drive the release and circulation of radiogenic helium in the continental crust.Here we apply statistical analysis on geochemical data observed in thermal springs and recorded heat flow to account for crustal helium mass balance for each tectonothermal event in the region.Our results demonstrate anomalously high heat flow~64-99 mW/m^(2) with a consistent trend of helium isotopic ratio and fluid chemistry in the Rukwa Rift.Mass balance calculation show that the whole crustal volume underlying the East Africa Helium Pool(EAHP)has a capability of producing radiogenic helium of about 9.9×10^(6) mol/yr(22×10^(-6) mol 4He/m^(2) yr)while the total radiogenic helium flux ranges between~2.39×10^(6) mol/yr and~2.68×10^(9) mol/yr.The Tanzania Craton contributes largely to radiogenic helium releasing up to 50% of the total capacity in the region.The total ^(4)He emission in the Rukwa Rift Basin is about 4.45×10^(5)-5.01×10^(8) mol/yr which is thus equivalent to 19%-21% of the total production capacity in the region.These results imply that the helium accumulation in the EAHP would have started as early as Paleoproterozoic(2.349 Ga).These results provide a qualitative and quantitative insight to assess both helium and geothermal potentiality in the region.展开更多
The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and...The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.展开更多
Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of mul...Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.展开更多
The Internet ofThings(IoT)is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications.Anomaly detection haswidely attracted researchers’attentio...The Internet ofThings(IoT)is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications.Anomaly detection haswidely attracted researchers’attention in the last few years,and its effects on diverse applications.This review article covers the various methods and tools developed to perform the task efficiently and automatically in a smart city.In this work,we present a comprehensive literature review(2011 onwards)of three major types of anomalies:network anomalies,sensor anomalies,and videobased anomalies,along with their methods and software tools.Furthermore,anomaly detection methods such as machine learning and deep learning are presented in this work,highlighting their detection strategy techniques,features,applications,issues,and challenges.Moreover,a generic algorithmis also developed to ease the user achieve the taskmore specifically by targeting a specific domain aswell as approach.Comparative studies of three anomalymethods and their analysis identify research discovery areas with their applications.As a result,researchers and practitioners can familiarize themselves with the existing methods for solving real problems,improving methods,and developing new optimum methods for anomaly detection in diverse applications.展开更多
分布式点对点(peer to peer,P2P)电能交易能促进能源就近达到供需平衡,在保证公平性的同时提高电网利用效率。该文提出P2P电能交易模型,并基于非对称纳什谈判(asymmetric nash bargaining,ANB)理论研究合作收益分配机制。首先,建立P2P...分布式点对点(peer to peer,P2P)电能交易能促进能源就近达到供需平衡,在保证公平性的同时提高电网利用效率。该文提出P2P电能交易模型,并基于非对称纳什谈判(asymmetric nash bargaining,ANB)理论研究合作收益分配机制。首先,建立P2P电能交易双方合作模型,二者通过合作产生合作剩余,然后基于非对称纳什谈判理论构建交易双方的收益分配模型,使得合作收益能在买卖双方间得到合理分配,最后通过算例验证所提合作博弈模型的有效性。仿真结果表明,交易双方通过合作,可以较大幅度提高各主体的运行效益以及合作联盟的整体效益,也能体现P2P电能交易卖方和买方在联盟中贡献大小的差异,可以更合理地分配合作收益。展开更多
文摘在相机和旋转模块组成的旋转扫描系统中,相机与转轴的位姿关系对于后续数据融合至关重要。现有方法通常需借助辅助传感器来建立转轴与相机的位姿关系。为降低标定成本,本文提出一种基于ChArUco(Chess Augmented Reality University of Córdoba)标定板的转轴位姿标定方法。利用ChArUco标定板图像解算不同转角下的相机光心坐标并拟合光心轨迹,根据轨迹中心可确定转轴所在位置。建立了旋转系统的数学模型,推导了在世界坐标系下转轴位姿的数学表达式,并基于旋转系统的空间约束构建联合优化,计算相机和转轴间最佳位姿。利用所提出方法进行旋转相机系统标定实验,实验结果表明:标定后的转轴与相机光轴夹角的平均绝对误差为0.036°,标定结果能有效消除全景拼接时的重影现象,验证了所提出方法的准确性和可行性。
文摘Dear Editor,Systemic sclerosis(SSc)is an autoimmune connective tissue disease in which there are vascular abnormalities,inflammation,and fibrosis[1].These three characteristics primarily affect the skin and lungs.Of all the autoimmune rheumatic diseases,SSc has the highest all-cause mortality rate,and the underlying pathogenic processes that mediate disease are still obscure,with wide diff erences in presentation and progression[2,3].
基金funded by United Kingdom Commonwealth Scholarship Commission。
文摘The Rukwa Rift section of the East Africa Rift System presents a type setting for radiogenic helium accumulation in a petroleum free basin.As a prerequisite for accumulation,a considerable high heat flow anomaly is required from tectonothermal events to drive the release and circulation of radiogenic helium in the continental crust.Here we apply statistical analysis on geochemical data observed in thermal springs and recorded heat flow to account for crustal helium mass balance for each tectonothermal event in the region.Our results demonstrate anomalously high heat flow~64-99 mW/m^(2) with a consistent trend of helium isotopic ratio and fluid chemistry in the Rukwa Rift.Mass balance calculation show that the whole crustal volume underlying the East Africa Helium Pool(EAHP)has a capability of producing radiogenic helium of about 9.9×10^(6) mol/yr(22×10^(-6) mol 4He/m^(2) yr)while the total radiogenic helium flux ranges between~2.39×10^(6) mol/yr and~2.68×10^(9) mol/yr.The Tanzania Craton contributes largely to radiogenic helium releasing up to 50% of the total capacity in the region.The total ^(4)He emission in the Rukwa Rift Basin is about 4.45×10^(5)-5.01×10^(8) mol/yr which is thus equivalent to 19%-21% of the total production capacity in the region.These results imply that the helium accumulation in the EAHP would have started as early as Paleoproterozoic(2.349 Ga).These results provide a qualitative and quantitative insight to assess both helium and geothermal potentiality in the region.
文摘The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.
基金funded by the Research,Development,and Innovation Authority(RDIA)—Kingdom of Saudi Arabia(Grant No.13292-psu-2023-PSNU-R-3-1-EF-).
文摘Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.
文摘The Internet ofThings(IoT)is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications.Anomaly detection haswidely attracted researchers’attention in the last few years,and its effects on diverse applications.This review article covers the various methods and tools developed to perform the task efficiently and automatically in a smart city.In this work,we present a comprehensive literature review(2011 onwards)of three major types of anomalies:network anomalies,sensor anomalies,and videobased anomalies,along with their methods and software tools.Furthermore,anomaly detection methods such as machine learning and deep learning are presented in this work,highlighting their detection strategy techniques,features,applications,issues,and challenges.Moreover,a generic algorithmis also developed to ease the user achieve the taskmore specifically by targeting a specific domain aswell as approach.Comparative studies of three anomalymethods and their analysis identify research discovery areas with their applications.As a result,researchers and practitioners can familiarize themselves with the existing methods for solving real problems,improving methods,and developing new optimum methods for anomaly detection in diverse applications.
文摘分布式点对点(peer to peer,P2P)电能交易能促进能源就近达到供需平衡,在保证公平性的同时提高电网利用效率。该文提出P2P电能交易模型,并基于非对称纳什谈判(asymmetric nash bargaining,ANB)理论研究合作收益分配机制。首先,建立P2P电能交易双方合作模型,二者通过合作产生合作剩余,然后基于非对称纳什谈判理论构建交易双方的收益分配模型,使得合作收益能在买卖双方间得到合理分配,最后通过算例验证所提合作博弈模型的有效性。仿真结果表明,交易双方通过合作,可以较大幅度提高各主体的运行效益以及合作联盟的整体效益,也能体现P2P电能交易卖方和买方在联盟中贡献大小的差异,可以更合理地分配合作收益。
