Objective:This study aims to explore the expression patterns of cysteine string protein alpha(CSPα)and cysteine string protein beta(CSPβ)in the mammalian inner ear,with an emphasis on their temporal dynamics during ...Objective:This study aims to explore the expression patterns of cysteine string protein alpha(CSPα)and cysteine string protein beta(CSPβ)in the mammalian inner ear,with an emphasis on their temporal dynamics during the developmental stages of C57BL/6 mice.Methods:We utilized immunofluorescence staining to assess the localization and distribution of CSPαand CSPβwithin the inner ears of C57BL/6 mice and miniature pigs.Additionally,this method facilitated the investigation of their temporal expression profiles.Results:In adult C57BL/6 mice and miniature pigs,CSPαand CSPβwere identified in the cytoplasm of inner hair cells and spiral ganglion cells,yet were absent in outer hair cells.Both proteins were found to colocalize with Ctbp2 on the basal side of the cytoplasm in inner hair cells’basilar membrane.Expression of CSPαwas observed at the nerve fiber termini at the basilar membrane’s base of inner and outer hair cells 10 days postnatally in C57BL/6 mice.Notably,expression of both CSPαand CSPβin the cytoplasm of inner hair cells emerged on the 12th day post-birth,aligning with the timeline for registering cochlear potentials.The expression levels of both proteins increased with age,but were consistently absent in outer hair cells.Contrastingly,expression of CSPαand CSPβwas present in the cytoplasm of inner hair cells in miniature pigs as early as one day post-birth,yet remained absent in the three rows of outer hair cells.Conclusion:CSPαand CSPβexhibit predominant and specific expression in inner hair cells and spiral ganglion cells.A unique expression pattern was observed for CSPα,which was also present at the nerve fiber endings of both inner and outer hair cells.The developmental expression trajectory of CSPαand CSPβin mouse inner hair cells is characterized by an initial absence,followed by a gradual increase.Moreover,the timing of expression onset between mice and miniature pigs indicates distinct temporal dynamics,suggesting a potential role in auditory development.展开更多
单站点传送带给料加工站(Conveyor-serviced production station,CSPS)系统中,可运用强化学习对状态–行动空间进行有效探索,以搜索近似最优的前视距离控制策略.但是多站点CSPS系统的协同控制问题中,系统状态空间的大小会随着站点个数...单站点传送带给料加工站(Conveyor-serviced production station,CSPS)系统中,可运用强化学习对状态–行动空间进行有效探索,以搜索近似最优的前视距离控制策略.但是多站点CSPS系统的协同控制问题中,系统状态空间的大小会随着站点个数的增加和缓存库容量的增加而成指数形式(或几何级数)增长,从而导致维数灾,影响学习算法的收敛速度和优化效果.为此,本文在站点局域信息交互机制的基础上引入状态聚类的方法,以减小每个站点学习空间的大小和复杂性.首先,将多个站点看作相对独立的学习主体,且各自仅考虑邻近下游站点的缓存库的状态并纳入其性能值学习过程;其次,将原状态空间划分成多个不相交的子集,每个子集用一个抽象状态表示,然后,建立基于状态聚类的多站点反馈式Q学习算法.通过该方法,可在抽象状态空间上对各站点的前视距离策略进行优化学习,以寻求整个系统的生产率最大.仿真实验结果说明,与一般的多站点反馈式Q学习方法相比,基于状态聚类的多站点反馈式Q学习方法不仅具有收敛速度快的优点,而且还在一定程度上提高了系统生产率.展开更多
【目的】光伏-聚光太阳能发电(photovoltaic-concentrating solar power,PV-CSP)复合系统结合了PV低成本和CSP高可调度性的优势,但同时也面临弃电和储能利用率低的普遍问题。为实现PV弃电热转化储能利用,提出了一种配置电加热(electric ...【目的】光伏-聚光太阳能发电(photovoltaic-concentrating solar power,PV-CSP)复合系统结合了PV低成本和CSP高可调度性的优势,但同时也面临弃电和储能利用率低的普遍问题。为实现PV弃电热转化储能利用,提出了一种配置电加热(electric heater,EH)装置的PV-CSP复合系统(PV-CSP-EH)。【方法】通过构建PV-CSP-EH复合系统准稳态模型,以1 h为时间间隔分析了系统全年运行特性。通过参数分析和帕累托寻优模型,得到了不同系统配置下的性能变化规律和平准化度电成本(levelized cost of electricity,LCOE)最优参数。【结果】PV-CSP-EH复合系统全年发电量和渗透率比传统PV-CSP复合系统分别提高了8.2%和16.2%;同时,其全年弃电量仅有2 GW·h,弃电回收率、转化率分别达到94.1%、35.2%;在最优配置下,其LCOE可低至0.138美元/(kW·h),比传统PV-CSP复合系统降低了6.8%。【结论】PV-CSP-EH复合系统能够提高发电量和渗透能力,显著降低弃电量,以更经济的方式优化弃风弃光问题,为构建新型电力系统作出贡献。展开更多
基金supported by the Science and Technology Development aid Project of Xuzhou Science and Technology Bureau(KC21249)supported by Hainan Provincial Natural Science Foundation of China(824MS052)Scientific Research Startup Foundation of Hainan University.
文摘Objective:This study aims to explore the expression patterns of cysteine string protein alpha(CSPα)and cysteine string protein beta(CSPβ)in the mammalian inner ear,with an emphasis on their temporal dynamics during the developmental stages of C57BL/6 mice.Methods:We utilized immunofluorescence staining to assess the localization and distribution of CSPαand CSPβwithin the inner ears of C57BL/6 mice and miniature pigs.Additionally,this method facilitated the investigation of their temporal expression profiles.Results:In adult C57BL/6 mice and miniature pigs,CSPαand CSPβwere identified in the cytoplasm of inner hair cells and spiral ganglion cells,yet were absent in outer hair cells.Both proteins were found to colocalize with Ctbp2 on the basal side of the cytoplasm in inner hair cells’basilar membrane.Expression of CSPαwas observed at the nerve fiber termini at the basilar membrane’s base of inner and outer hair cells 10 days postnatally in C57BL/6 mice.Notably,expression of both CSPαand CSPβin the cytoplasm of inner hair cells emerged on the 12th day post-birth,aligning with the timeline for registering cochlear potentials.The expression levels of both proteins increased with age,but were consistently absent in outer hair cells.Contrastingly,expression of CSPαand CSPβwas present in the cytoplasm of inner hair cells in miniature pigs as early as one day post-birth,yet remained absent in the three rows of outer hair cells.Conclusion:CSPαand CSPβexhibit predominant and specific expression in inner hair cells and spiral ganglion cells.A unique expression pattern was observed for CSPα,which was also present at the nerve fiber endings of both inner and outer hair cells.The developmental expression trajectory of CSPαand CSPβin mouse inner hair cells is characterized by an initial absence,followed by a gradual increase.Moreover,the timing of expression onset between mice and miniature pigs indicates distinct temporal dynamics,suggesting a potential role in auditory development.
文摘单站点传送带给料加工站(Conveyor-serviced production station,CSPS)系统中,可运用强化学习对状态–行动空间进行有效探索,以搜索近似最优的前视距离控制策略.但是多站点CSPS系统的协同控制问题中,系统状态空间的大小会随着站点个数的增加和缓存库容量的增加而成指数形式(或几何级数)增长,从而导致维数灾,影响学习算法的收敛速度和优化效果.为此,本文在站点局域信息交互机制的基础上引入状态聚类的方法,以减小每个站点学习空间的大小和复杂性.首先,将多个站点看作相对独立的学习主体,且各自仅考虑邻近下游站点的缓存库的状态并纳入其性能值学习过程;其次,将原状态空间划分成多个不相交的子集,每个子集用一个抽象状态表示,然后,建立基于状态聚类的多站点反馈式Q学习算法.通过该方法,可在抽象状态空间上对各站点的前视距离策略进行优化学习,以寻求整个系统的生产率最大.仿真实验结果说明,与一般的多站点反馈式Q学习方法相比,基于状态聚类的多站点反馈式Q学习方法不仅具有收敛速度快的优点,而且还在一定程度上提高了系统生产率.
文摘【目的】光伏-聚光太阳能发电(photovoltaic-concentrating solar power,PV-CSP)复合系统结合了PV低成本和CSP高可调度性的优势,但同时也面临弃电和储能利用率低的普遍问题。为实现PV弃电热转化储能利用,提出了一种配置电加热(electric heater,EH)装置的PV-CSP复合系统(PV-CSP-EH)。【方法】通过构建PV-CSP-EH复合系统准稳态模型,以1 h为时间间隔分析了系统全年运行特性。通过参数分析和帕累托寻优模型,得到了不同系统配置下的性能变化规律和平准化度电成本(levelized cost of electricity,LCOE)最优参数。【结果】PV-CSP-EH复合系统全年发电量和渗透率比传统PV-CSP复合系统分别提高了8.2%和16.2%;同时,其全年弃电量仅有2 GW·h,弃电回收率、转化率分别达到94.1%、35.2%;在最优配置下,其LCOE可低至0.138美元/(kW·h),比传统PV-CSP复合系统降低了6.8%。【结论】PV-CSP-EH复合系统能够提高发电量和渗透能力,显著降低弃电量,以更经济的方式优化弃风弃光问题,为构建新型电力系统作出贡献。
