利用飞秒宽带荧光光谱仪揭示光合细菌反应中心原初过程

本文为深入了解光合细菌反应中心内的高效能量转换机制,采用飞秒宽带荧光光谱技术,研究了室温下光合细菌反应中心中光诱导能量转移和电荷分离的动力学过程.得益于此技术的宽带光谱测量能力,直接确认了与细菌叶绿素B和P相关的两种不同的瞬态荧光组分,其Stokes位移分别确定为约197和450cm^(-1).通过对荧光发射动力学的拟合,揭示了从细菌脱镁叶绿素H到细菌叶绿素B(98 fs)和从细菌叶绿素B到细菌叶绿素P(170 fs)的超快能量转移过程.值得注意的是,预期亚200 fs的细菌叶绿素B荧光寿命被显著延长至约400 fs,表明B的电子激发态与P的电子振动态之间可能存在一-定的耦合,并对能量转移过程有潜在的促进作用.上述发现将有助于人们进一步理解电子振动耦合动力学对光合反应中心内光诱导原初过程的影响机制.

Determination of perchlorate and its distribution in unhusked rice in China

Perchlorate concentrations in 387 unhusked rice samples from 15 main producing provinces/municipality in China were investigated by high-performance liquid chromatography-tanden mass spectrometry.The results indicated that perchlorate displays a mean level of 17.17μg/kg in un-husked rice samples.Intriguingly,we also found that perchlorate is mainly observed in rice husk among these collected unhusked rice samples,while less observed in rice bran and milled rice.Specifically,the perchlorate levels in rice were found in the husks(73.61%),bran(10.09%),and milled rice(19.52%),respectively.Our results indicated that there is no significantly perchlorate exposure risk in edible milled rice.

Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions

Purpose–An individual’s driving style significantly affects overall traffic safety.However,driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data.As such,the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies,improving traffic safety and reducing fuel consumption.This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions(DOCs)using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system(ADAS).Design/methodology/approach–Specifically,a driving style recognition framework based on longitudinal DOCs was established.To train the model,a real-world driving experiment was conducted.First,the driving styles of 44 drivers were preliminarily identified through natural driving data and video data;drivers were categorized through a subjective evaluation as conservative,moderate or aggressive.Then,based on the ADAS driving data,a criterion for extracting longitudinal DOCs was developed.Third,taking the ADAS data from 47 Kms of the two test expressways as the research object,six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed.Finally,four machine learning classification(MLC)models were used to classify and predict driving style based on the natural driving data.Findings–The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion.Cautious drivers undertook the largest proportion of the free cruise condition(FCC),while aggressive drivers primarily undertook the FCC,following steady condition and relative approximation condition.Compared with cautious and moderate drivers,aggressive drivers adopted a smaller time headway(THW)and distance headway(DHW).THW,time-to-collision(TTC)and DHW showed highly significant differences in driving style identification,while longitudinal acceleration(LA)showed no significant difference in driving style identification.Speed and TTC showed no significant difference between moderate and aggressive drivers.In consideration of the cross-validation results and model prediction results,the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting>multi-layer perceptron>logistic regression>support vector machine.Originality/value–The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models.This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment,such as ADAS.