Ammonia is a key industry raw material for fertilizers and the electro-reduction of N_(2)(NRR)can be served as a promising method.It is urgently needed to discover advanced catalysts while the lack of design principle...Ammonia is a key industry raw material for fertilizers and the electro-reduction of N_(2)(NRR)can be served as a promising method.It is urgently needed to discover advanced catalysts while the lack of design principles still hinders the high-throughput screen of efficient candidates.Herein,we have provided an up-to-date review of NRR catalysts mainly on theoretical works and highlighted the latest achievements on descriptors,which can be served as valid guidance of optimal catalysts.The descriptors are classified with adsorption energy and the corresponding derived ones,which can screen the NRR catalysts from various aspects.Finally,the challenges and opportunities in the descriptor field are presented.展开更多
In today's fast-paced modern life, whether for fitness training, outdoor adventures, or daily commutes, we all yearn for quick-dry apparel that can rapidly wick away moisture and keep our bodies dry and comfortabl...In today's fast-paced modern life, whether for fitness training, outdoor adventures, or daily commutes, we all yearn for quick-dry apparel that can rapidly wick away moisture and keep our bodies dry and comfortable. As a standout in functional textiles, quick-dry fabrics are becoming the top choice for more and more people, thanks to their exceptional moisture-wicking performance and rapid drying capabilities.展开更多
As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The ...As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.展开更多
目的基于真实世界数据(Real World Data,RWD)对注射泵的主要有效性评价功能进行研究,最大程度反映其临床实践中的真实性能表现,以建立科学有效的评价方法。方法选取本院重症医学科3种品牌(M、F、L)注射泵共计37台次作为研究对象,根据建...目的基于真实世界数据(Real World Data,RWD)对注射泵的主要有效性评价功能进行研究,最大程度反映其临床实践中的真实性能表现,以建立科学有效的评价方法。方法选取本院重症医学科3种品牌(M、F、L)注射泵共计37台次作为研究对象,根据建立的临床使用评价指标,分别在5、25和50 mL/h 3种流速下,获得注射泵出第1滴液的时间和响应时间来测试注射泵快速启动的性能差异,计算流速平均值、相对示值误差和示值重复性来测试注射泵注射精度的性能差异;分别在5、10和15 mL/h 3种流速下,获得注射泵的流速波动下限和间歇时间来测试注射泵联机中继的性能差异。结果在快速启动功能方面,设定流速越大,注射泵出第1滴液的时间和响应时间越短,且在3种流速下,M品牌注射泵出第1滴液的时间和响应时间都最短(P<0.05)。在注射精度功能方面,在3种流速下,M和L品牌的流速控制性能和示值稳定性都显著优于F品牌(P<0.05)。在联机中继功能方面,人工模拟联机中继功能导致的流速波动下限和间歇时间差异显著(P<0.05),且设定流速越大,实时流速从下降到恢复设定值需要的间歇时间就越短。另外,M品牌的自动联机中继功能可有效解决前序泵与后序泵中继过程中产生的药物流量抖动或间断,但有瞬时流速上升,相对能够保证药物流量的稳定性和连续性。结论使用正确的基于RWD的注射泵临床使用评价方法不仅能够规范临床应用行为,还能够为注射泵的评价、准入/淘汰和集中采购/价格谈判提供数据支撑。展开更多
基金supported by the National Natural Science Foundation of China(No.21603109)the Henan Joint Fund of the National Natural Science Foundation of China(No.U1404216)+2 种基金the Special Fund of Tianshui Normal University,China(No.CXJ2020-08)the Scientific Research Program Funded by Shaanxi Provincial Education Department(No.20JK0676)supported by Natural Science Basic Research Program of Shanxi(Nos.2022JQ-108,2022JQ-096).
文摘Ammonia is a key industry raw material for fertilizers and the electro-reduction of N_(2)(NRR)can be served as a promising method.It is urgently needed to discover advanced catalysts while the lack of design principles still hinders the high-throughput screen of efficient candidates.Herein,we have provided an up-to-date review of NRR catalysts mainly on theoretical works and highlighted the latest achievements on descriptors,which can be served as valid guidance of optimal catalysts.The descriptors are classified with adsorption energy and the corresponding derived ones,which can screen the NRR catalysts from various aspects.Finally,the challenges and opportunities in the descriptor field are presented.
文摘In today's fast-paced modern life, whether for fitness training, outdoor adventures, or daily commutes, we all yearn for quick-dry apparel that can rapidly wick away moisture and keep our bodies dry and comfortable. As a standout in functional textiles, quick-dry fabrics are becoming the top choice for more and more people, thanks to their exceptional moisture-wicking performance and rapid drying capabilities.
基金supported by the Meteorological Soft Science Project(Grant No.2023ZZXM29)the Natural Science Fund Project of Tianjin,China(Grant No.21JCYBJC00740)the Key Research and Development-Social Development Program of Jiangsu Province,China(Grant No.BE2021685).
文摘As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.
