特征线方法(Method of Characteristics,MOC)因其具备强大的几何处理能力,且在计算过程中亦能兼顾计算成本和计算精度,被广泛应用于高保真数值模拟计算中。常见的中子输运计算方法除MOC外,还包括碰撞概率法(Collision Probability metho...特征线方法(Method of Characteristics,MOC)因其具备强大的几何处理能力,且在计算过程中亦能兼顾计算成本和计算精度,被广泛应用于高保真数值模拟计算中。常见的中子输运计算方法除MOC外,还包括碰撞概率法(Collision Probability method,CP)和界面流法(Interface Current method,IC)等。本文从方法理论以及数值计算两方面将MOC、CP和IC进行比较分析,评估其在pin-by-pin计算中的能力。同时在MOC计算中,不同的参数选择会对计算成本和计算精度产生影响,因此有必要进行敏感性分析以寻求最佳参数。本文首先将三种计算方法从原理上进行比较分析,再基于2D C5G7-MOX基准题完成了数值计算及MOC参数敏感性初步分析。计算结果表明:MOC在计算精度、计算效率和内存开销上均优于CP和IC。MOC的计算耗时和内存开销分别为23.9 min和37.5 MB,与参考解的相对误差仅为6.04×10^(-4)。而CP和IC的计算耗时分别为MOC的56.7倍和15.6倍,内存开销分别为MOC的407.7倍和32.8倍。进一步通过参数敏感性分析发现:网格划分对计算内存开销以及计算时间的影响最大,而极角的选择对计算精度的影响最大,并且给出一组综合优化建议参数:网格划分6×6,极角为GAUS且数目为2,方位角个数为30。该组参数的计算耗时为45.4 min,内存开销为264.7 MB,相对误差为5.9×10^(-5),归一化后的栅元均方根误差为0.002 55。展开更多
The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundes...The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor jamming.Recently,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography approach.Initially,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m channels.Later,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper bounds.Finally,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV system.The findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and eavesdroppers.The results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.展开更多
Objectives:The Kirsten rat sarcoma virus(KRAS)G12D oncogenic mutation poses a significant challenge in treating solid tumors due to the lack of specific and effective therapeutic interventions.This study aims to explore...Objectives:The Kirsten rat sarcoma virus(KRAS)G12D oncogenic mutation poses a significant challenge in treating solid tumors due to the lack of specific and effective therapeutic interventions.This study aims to explore innovative approaches in T cell receptor(TCR)engineering and characterization to target the KRAS G12D7-16 mutation,providing potential strategies for overcoming this therapeutic challenge.Methods:In this innovative study,we engineered and characterized two T cell receptors(TCRs),KDA11-01 and KDA11-02 with high affinity for the KRAS G12D7-16 mutation.These TCRs were isolated from tumor-infiltrating lymphocytes(TILs)derived from tumor tissues of patients with the KRAS G12D mutation.We assessed their specificity and anti-tumor activity in vitro using various cancer cell lines.Results:KDA11-01 and KDA11-02 demonstrated exceptional specificity for the HLA-A*11:01-restricted KRAS G12D7-16 epitope,significantly inducing IFN-γrelease and eliminating tumor cells without cross-reactivity or alloreactivity.Conclusions:The successful development of KDA11-01 and KDA11-02 introduces a novel and precise TCR-based therapeutic strategy against KRAS G12D mutation,showing potential for significant advancements in cancer immunotherapy.展开更多
利用多接入边缘计算(multi-access edge computing,MEC)和终端直传通信(Device to Device,D2D)技术,可以提升电力智能巡检中传感数据传输和处理的能力,但需要解决频谱复用和干扰条件下的网络资源优化分配问题。针对D2D辅助的MEC网络,文...利用多接入边缘计算(multi-access edge computing,MEC)和终端直传通信(Device to Device,D2D)技术,可以提升电力智能巡检中传感数据传输和处理的能力,但需要解决频谱复用和干扰条件下的网络资源优化分配问题。针对D2D辅助的MEC网络,文章提出了一种基于深度强化学习的资源联合优化分配算法。首先在频道复用与干扰、功率和计算等资源约束条件下,分析了D2D辅助的MEC网络的终端容量、功耗和时延计算方法;然后综合考虑吞吐量、功耗和时延等指标要求,建立了基于综合效益函数最大化的资源优化分配模型;最后采用深度强化学习算法实现任务卸载和资源分配的联合优化。仿真结果表明,该算法可有效提升系统容量和任务卸载的综合性能。展开更多
文摘特征线方法(Method of Characteristics,MOC)因其具备强大的几何处理能力,且在计算过程中亦能兼顾计算成本和计算精度,被广泛应用于高保真数值模拟计算中。常见的中子输运计算方法除MOC外,还包括碰撞概率法(Collision Probability method,CP)和界面流法(Interface Current method,IC)等。本文从方法理论以及数值计算两方面将MOC、CP和IC进行比较分析,评估其在pin-by-pin计算中的能力。同时在MOC计算中,不同的参数选择会对计算成本和计算精度产生影响,因此有必要进行敏感性分析以寻求最佳参数。本文首先将三种计算方法从原理上进行比较分析,再基于2D C5G7-MOX基准题完成了数值计算及MOC参数敏感性初步分析。计算结果表明:MOC在计算精度、计算效率和内存开销上均优于CP和IC。MOC的计算耗时和内存开销分别为23.9 min和37.5 MB,与参考解的相对误差仅为6.04×10^(-4)。而CP和IC的计算耗时分别为MOC的56.7倍和15.6倍,内存开销分别为MOC的407.7倍和32.8倍。进一步通过参数敏感性分析发现:网格划分对计算内存开销以及计算时间的影响最大,而极角的选择对计算精度的影响最大,并且给出一组综合优化建议参数:网格划分6×6,极角为GAUS且数目为2,方位角个数为30。该组参数的计算耗时为45.4 min,内存开销为264.7 MB,相对误差为5.9×10^(-5),归一化后的栅元均方根误差为0.002 55。
基金funded by Taif University,Taif,Saudi Arabia,Project No.(TUDSPP-2024-139).
文摘The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor jamming.Recently,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography approach.Initially,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m channels.Later,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper bounds.Finally,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV system.The findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and eavesdroppers.The results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.
基金funded by the key R&D Project of Hubei Province(Social Development),China(2022BCA018)the Cooperative Innovation Center of Industrial Fermentation(Ministry of Education&Hubei Province),China(2022KF16)to Kanghong Hu.
文摘Objectives:The Kirsten rat sarcoma virus(KRAS)G12D oncogenic mutation poses a significant challenge in treating solid tumors due to the lack of specific and effective therapeutic interventions.This study aims to explore innovative approaches in T cell receptor(TCR)engineering and characterization to target the KRAS G12D7-16 mutation,providing potential strategies for overcoming this therapeutic challenge.Methods:In this innovative study,we engineered and characterized two T cell receptors(TCRs),KDA11-01 and KDA11-02 with high affinity for the KRAS G12D7-16 mutation.These TCRs were isolated from tumor-infiltrating lymphocytes(TILs)derived from tumor tissues of patients with the KRAS G12D mutation.We assessed their specificity and anti-tumor activity in vitro using various cancer cell lines.Results:KDA11-01 and KDA11-02 demonstrated exceptional specificity for the HLA-A*11:01-restricted KRAS G12D7-16 epitope,significantly inducing IFN-γrelease and eliminating tumor cells without cross-reactivity or alloreactivity.Conclusions:The successful development of KDA11-01 and KDA11-02 introduces a novel and precise TCR-based therapeutic strategy against KRAS G12D mutation,showing potential for significant advancements in cancer immunotherapy.
文摘利用多接入边缘计算(multi-access edge computing,MEC)和终端直传通信(Device to Device,D2D)技术,可以提升电力智能巡检中传感数据传输和处理的能力,但需要解决频谱复用和干扰条件下的网络资源优化分配问题。针对D2D辅助的MEC网络,文章提出了一种基于深度强化学习的资源联合优化分配算法。首先在频道复用与干扰、功率和计算等资源约束条件下,分析了D2D辅助的MEC网络的终端容量、功耗和时延计算方法;然后综合考虑吞吐量、功耗和时延等指标要求,建立了基于综合效益函数最大化的资源优化分配模型;最后采用深度强化学习算法实现任务卸载和资源分配的联合优化。仿真结果表明,该算法可有效提升系统容量和任务卸载的综合性能。
