This paper investigates the data collection in an unmanned aerial vehicle(UAV)-aided Internet of Things(IoT) network, where a UAV is dispatched to collect data from ground sensors in a practical and accurate probabili...This paper investigates the data collection in an unmanned aerial vehicle(UAV)-aided Internet of Things(IoT) network, where a UAV is dispatched to collect data from ground sensors in a practical and accurate probabilistic line-of-sight(LoS) channel. Especially, access points(APs) are introduced to collect data from some sensors in the unlicensed band to improve data collection efficiency. We formulate a mixed-integer non-convex optimization problem to minimize the UAV flight time by jointly designing the UAV 3D trajectory and sensors’ scheduling, while ensuring the required amount of data can be collected under the limited UAV energy. To solve this nonconvex problem, we recast the objective problem into a tractable form. Then, the problem is further divided into several sub-problems to solve iteratively, and the successive convex approximation(SCA) scheme is applied to solve each non-convex subproblem. Finally,the bisection search is adopted to speed up the searching for the minimum UAV flight time. Simulation results verify that the UAV flight time can be shortened by the proposed method effectively.展开更多
目的:研究比较两种基质辅助激光解吸电离飞行时间质谱仪(Autof ms 1000与MALDI Biotyper)对临床常见丝状病原真菌鉴定的准确性和效率。方法:共纳入32株丝状病原真菌标准菌株和120株临床分离菌株,按照标准操作方法分别用Autof ms 1000与M...目的:研究比较两种基质辅助激光解吸电离飞行时间质谱仪(Autof ms 1000与MALDI Biotyper)对临床常见丝状病原真菌鉴定的准确性和效率。方法:共纳入32株丝状病原真菌标准菌株和120株临床分离菌株,按照标准操作方法分别用Autof ms 1000与MALDI Biotyper对相关菌株进行鉴定,对相关结果及测序分子鉴定结果进行对比分析。结果:在种水平上,Autof ms 1000质谱仪共检出140株丝状病原真菌,检出正确率为92.1%(140/152),其中126株分值在9.0以上,2株出现错误鉴定,误鉴定比例为1.3%(2/152);MALDI Biotyper质谱仪共正确检出98株,检出正确率为64.4%,2.0分以上的菌株47株,仅3株鉴定分值大于2.3分,7株出现误鉴定。结论:Autof ms 1000与MALDI Biotyper这两种质谱鉴定系统均能用于临床常见丝状病原真菌的快速鉴定,而Autof ms 1000质谱仪更具优势。展开更多
采用顶空固相微萃取-全二维气相色谱-飞行时间质谱(headspace solid phase microextraction in combination with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry,HS-SPME-GC×GC-...采用顶空固相微萃取-全二维气相色谱-飞行时间质谱(headspace solid phase microextraction in combination with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry,HS-SPME-GC×GC-TOFMS)技术对全脂牛乳(whole milk,WM)、低脂牛乳(low-fat milk,LFM)和脱脂牛乳(non-fat milk,NFM)3种牛乳样品进行挥发性化合物分析,结果表明:共检测到49种挥发性化合物,其中2-壬酮、2-十一酮等奇数碳链的甲基酮构成WM的主要风味化合物;偏最小二乘法判别分析表明,其模型可以很好地区分3种牛乳样品,并且有较好的方差和交叉验证预测能力;通过变量投影重要性>1、P≤0.05且含量≥1%筛选出9种化合物,被认定为关键香气差异化合物,这些化合物可能是导致3种牛乳风味不同的主要因素;聚类热图结果表明,NFM因异味化合物(如十六醛)的存在可能导致不良感官表现,而WM和LFM存在更多的香气化合物,令其在感官方面具有饱满丰富的香气。本研究建立了HS-SPME-GC×GC-TOFMS分析牛乳的研究方法,为乳制品风味改进和乳制香精调配提供了理论指导。展开更多
基金supported by the National Key Research and Development Program under Grant 2022YFB3303702the Key Program of National Natural Science Foundation of China under Grant 61931001+1 种基金supported by the National Natural Science Foundation of China under Grant No.62203368the Natural Science Foundation of Sichuan Province under Grant No.2023NSFSC1440。
文摘This paper investigates the data collection in an unmanned aerial vehicle(UAV)-aided Internet of Things(IoT) network, where a UAV is dispatched to collect data from ground sensors in a practical and accurate probabilistic line-of-sight(LoS) channel. Especially, access points(APs) are introduced to collect data from some sensors in the unlicensed band to improve data collection efficiency. We formulate a mixed-integer non-convex optimization problem to minimize the UAV flight time by jointly designing the UAV 3D trajectory and sensors’ scheduling, while ensuring the required amount of data can be collected under the limited UAV energy. To solve this nonconvex problem, we recast the objective problem into a tractable form. Then, the problem is further divided into several sub-problems to solve iteratively, and the successive convex approximation(SCA) scheme is applied to solve each non-convex subproblem. Finally,the bisection search is adopted to speed up the searching for the minimum UAV flight time. Simulation results verify that the UAV flight time can be shortened by the proposed method effectively.
文摘目的:研究比较两种基质辅助激光解吸电离飞行时间质谱仪(Autof ms 1000与MALDI Biotyper)对临床常见丝状病原真菌鉴定的准确性和效率。方法:共纳入32株丝状病原真菌标准菌株和120株临床分离菌株,按照标准操作方法分别用Autof ms 1000与MALDI Biotyper对相关菌株进行鉴定,对相关结果及测序分子鉴定结果进行对比分析。结果:在种水平上,Autof ms 1000质谱仪共检出140株丝状病原真菌,检出正确率为92.1%(140/152),其中126株分值在9.0以上,2株出现错误鉴定,误鉴定比例为1.3%(2/152);MALDI Biotyper质谱仪共正确检出98株,检出正确率为64.4%,2.0分以上的菌株47株,仅3株鉴定分值大于2.3分,7株出现误鉴定。结论:Autof ms 1000与MALDI Biotyper这两种质谱鉴定系统均能用于临床常见丝状病原真菌的快速鉴定,而Autof ms 1000质谱仪更具优势。
文摘采用顶空固相微萃取-全二维气相色谱-飞行时间质谱(headspace solid phase microextraction in combination with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry,HS-SPME-GC×GC-TOFMS)技术对全脂牛乳(whole milk,WM)、低脂牛乳(low-fat milk,LFM)和脱脂牛乳(non-fat milk,NFM)3种牛乳样品进行挥发性化合物分析,结果表明:共检测到49种挥发性化合物,其中2-壬酮、2-十一酮等奇数碳链的甲基酮构成WM的主要风味化合物;偏最小二乘法判别分析表明,其模型可以很好地区分3种牛乳样品,并且有较好的方差和交叉验证预测能力;通过变量投影重要性>1、P≤0.05且含量≥1%筛选出9种化合物,被认定为关键香气差异化合物,这些化合物可能是导致3种牛乳风味不同的主要因素;聚类热图结果表明,NFM因异味化合物(如十六醛)的存在可能导致不良感官表现,而WM和LFM存在更多的香气化合物,令其在感官方面具有饱满丰富的香气。本研究建立了HS-SPME-GC×GC-TOFMS分析牛乳的研究方法,为乳制品风味改进和乳制香精调配提供了理论指导。
