A low utilization rate of public chargers and unmatched deployment of public charging sta-tions(CSs)are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation.This study...A low utilization rate of public chargers and unmatched deployment of public charging sta-tions(CSs)are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation.This study proposes an optimization methodology for public CS deployment,considering real charging behavior and interactions between battery elec-tric vehicle(BEV)users and CSs.Realistic charging choice behavior is modeled based on surveys,and a dynamic charging decision chain is simulated,allowing interactions between BEV users and CSs through an agent-based modeling(ABM)approach.The charging-related activities are triggered by state of charge(SOC)levels randomly generated from distributions derived from real BEV operating data,including the random SOC levels at the start of a trip,the SOC level that prompts the user to charge the BEV,and the SOC level at which the user stops charging the BEV.A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs,to determine the location and the capacity of new CSs.The objective is to minimize the total time cost per BEV user,including travel time,charging time and waiting time in the queue.An application is conducted,for the deployment of fast CSs in Washington State,USA.The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems.The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs,leading to a more balanced utilization among CSs.The proposed approach is expected to contribute to better planning and deployment of public CSs,satisfaction of the booming charging demand,and increased utilization of pub-lic CSs.展开更多
Since its discovery in the 1980s,the insect cell-baculovirus expression vector system(IC-BEVS)has been widely used in biomedical applications,such as recombinant protein expression,drug screening,vaccine development,g...Since its discovery in the 1980s,the insect cell-baculovirus expression vector system(IC-BEVS)has been widely used in biomedical applications,such as recombinant protein expression,drug screening,vaccine development,gene therapy and so on[1].As a eukaryotic system,IC-BEVS has great development prospects due to its advantages such as high safety,simple operation,simultaneous expression of multi-subunit proteins,and suitability for large-scale cultivation[2].展开更多
为了解我国牛肠道病毒(BEV)流行现状,为其防控提供理论依据,本试验从四川省成都市某牛场的腹泻病牛粪便样本中分离得到1株病毒,将其命名为SC-726并进行后续研究。将SC-726接种牛肾细胞(MDBK)后观察细胞病变效应(CPE),计算病毒含量,使用...为了解我国牛肠道病毒(BEV)流行现状,为其防控提供理论依据,本试验从四川省成都市某牛场的腹泻病牛粪便样本中分离得到1株病毒,将其命名为SC-726并进行后续研究。将SC-726接种牛肾细胞(MDBK)后观察细胞病变效应(CPE),计算病毒含量,使用透射电子显微镜观察该病毒的形态特征,分析其理化特性、核酸型和细胞嗜性,绘制一步生长曲线,最后对该分离株进行5′非翻译区(5′UTR)基因测序以分析其遗传演化。结果显示,SC-726分离株感染MDBK细胞后,细胞发生明显的CPE;病毒最高滴度为1×10^(6.2) TCID_(50)/0.1 m L;电镜下观察到直径约30 nm的无囊膜球形粒子,符合传统小RNA病毒形态学特征;理化特性鉴定结果显示,该分离株几乎不受有机溶剂(乙醚、氯仿)和胰蛋白酶的影响,同时具有一系列与BEV相符的特征,如耐酸、不耐强碱、热敏感;DNA抑制剂阿糖胞苷(Ara-C)对该病毒滴度无影响,判定为RNA病毒;SC-726株能够在MDBK、乳仓鼠肾细胞(BHK-21)、猪肾细胞(PK-15)、非洲绿猴胚胎肾细胞(Marc-145)和犬肾细胞(MDCK)等多种动物细胞上增殖;遗传进化分析结果显示,该分离株为F型牛肠道病毒(BEV-F)。本试验从腹泻牛粪便样本中成功分离出1株BEV-F,进一步丰富了我国BEV资料库,为该病毒病的防治提供了理论依据。展开更多
昆虫杆状病毒作为生物杀虫剂以及在基因治疗方面的应用有着独特的优越性,而昆虫杆状病毒表达系统(BEVS)也是当今基因工程领域四大表达系统之一,得到广泛的应用。在我们对部分昆虫杆状病毒分子生物学已有较深入认知的情况下,研究其病毒...昆虫杆状病毒作为生物杀虫剂以及在基因治疗方面的应用有着独特的优越性,而昆虫杆状病毒表达系统(BEVS)也是当今基因工程领域四大表达系统之一,得到广泛的应用。在我们对部分昆虫杆状病毒分子生物学已有较深入认知的情况下,研究其病毒表达系统及生物过程,将有助于我们有效地利用其优越的性能,开拓新的实用价值。类病毒颗粒(Virus-like particles,VLP)是BEVS的一类重要产物,已经被证实是高度免疫的。有研究证明,在低感染复数(Multiplicity of infection,MOI)状态下,将得到高的VLP收成。在低成本的情况下,得到VLP的大量生成,是进行本文工作的动力之一。本文建立了BEVS的一些变量与感染复数之间关系的模型,并对该模型进行了合理性验证。展开更多
基金supported by the National Natural Science Foundation of China(No.71971162)Key Research Project from Shanxi Transportation Holdings Group(No.20-JKKJ-1).
文摘A low utilization rate of public chargers and unmatched deployment of public charging sta-tions(CSs)are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation.This study proposes an optimization methodology for public CS deployment,considering real charging behavior and interactions between battery elec-tric vehicle(BEV)users and CSs.Realistic charging choice behavior is modeled based on surveys,and a dynamic charging decision chain is simulated,allowing interactions between BEV users and CSs through an agent-based modeling(ABM)approach.The charging-related activities are triggered by state of charge(SOC)levels randomly generated from distributions derived from real BEV operating data,including the random SOC levels at the start of a trip,the SOC level that prompts the user to charge the BEV,and the SOC level at which the user stops charging the BEV.A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs,to determine the location and the capacity of new CSs.The objective is to minimize the total time cost per BEV user,including travel time,charging time and waiting time in the queue.An application is conducted,for the deployment of fast CSs in Washington State,USA.The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems.The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs,leading to a more balanced utilization among CSs.The proposed approach is expected to contribute to better planning and deployment of public CSs,satisfaction of the booming charging demand,and increased utilization of pub-lic CSs.
文摘Since its discovery in the 1980s,the insect cell-baculovirus expression vector system(IC-BEVS)has been widely used in biomedical applications,such as recombinant protein expression,drug screening,vaccine development,gene therapy and so on[1].As a eukaryotic system,IC-BEVS has great development prospects due to its advantages such as high safety,simple operation,simultaneous expression of multi-subunit proteins,and suitability for large-scale cultivation[2].
文摘为了解我国牛肠道病毒(BEV)流行现状,为其防控提供理论依据,本试验从四川省成都市某牛场的腹泻病牛粪便样本中分离得到1株病毒,将其命名为SC-726并进行后续研究。将SC-726接种牛肾细胞(MDBK)后观察细胞病变效应(CPE),计算病毒含量,使用透射电子显微镜观察该病毒的形态特征,分析其理化特性、核酸型和细胞嗜性,绘制一步生长曲线,最后对该分离株进行5′非翻译区(5′UTR)基因测序以分析其遗传演化。结果显示,SC-726分离株感染MDBK细胞后,细胞发生明显的CPE;病毒最高滴度为1×10^(6.2) TCID_(50)/0.1 m L;电镜下观察到直径约30 nm的无囊膜球形粒子,符合传统小RNA病毒形态学特征;理化特性鉴定结果显示,该分离株几乎不受有机溶剂(乙醚、氯仿)和胰蛋白酶的影响,同时具有一系列与BEV相符的特征,如耐酸、不耐强碱、热敏感;DNA抑制剂阿糖胞苷(Ara-C)对该病毒滴度无影响,判定为RNA病毒;SC-726株能够在MDBK、乳仓鼠肾细胞(BHK-21)、猪肾细胞(PK-15)、非洲绿猴胚胎肾细胞(Marc-145)和犬肾细胞(MDCK)等多种动物细胞上增殖;遗传进化分析结果显示,该分离株为F型牛肠道病毒(BEV-F)。本试验从腹泻牛粪便样本中成功分离出1株BEV-F,进一步丰富了我国BEV资料库,为该病毒病的防治提供了理论依据。
文摘昆虫杆状病毒作为生物杀虫剂以及在基因治疗方面的应用有着独特的优越性,而昆虫杆状病毒表达系统(BEVS)也是当今基因工程领域四大表达系统之一,得到广泛的应用。在我们对部分昆虫杆状病毒分子生物学已有较深入认知的情况下,研究其病毒表达系统及生物过程,将有助于我们有效地利用其优越的性能,开拓新的实用价值。类病毒颗粒(Virus-like particles,VLP)是BEVS的一类重要产物,已经被证实是高度免疫的。有研究证明,在低感染复数(Multiplicity of infection,MOI)状态下,将得到高的VLP收成。在低成本的情况下,得到VLP的大量生成,是进行本文工作的动力之一。本文建立了BEVS的一些变量与感染复数之间关系的模型,并对该模型进行了合理性验证。
