Light interaction model References
自动化超市的定义英文作文
Title: Definition of Automated SupermarketsAutomated supermarkets represent a pioneering concept in retail, integrating cutting-edge technology to revolutionize the shopping experience. These stores are designed to minimize human intervention while maximizing efficiency and convenience for customers.1.Introductiono Introduce the concept of automated supermarkets in the context of modern retail trends.o Highlight the significance of automation in enhancing operational efficiency and customer satisfaction.1.Core Componentso Describe the essential components that define automated supermarkets:⏹Robotic Systems: Autonomous robots that manage inventory, restock shelves, and assistin customer service.⏹Smart Shelves: RFID-enabled shelves that track inventory levels in real-time, ensuringaccurate stock management.⏹Self-Checkout Stations: Automated payment systems that allow customers to scan andpay for items independently.⏹AI-Powered Analytics: Data-driven insights used to optimize store layout, inventorymanagement, and customer flow.1.Technological Integrationo Discuss the advanced technologies employed in automated supermarkets:⏹Internet of Things (IoT): Connecting devices and systems to streamline operations andenhance responsiveness.⏹Artificial Intelligence (AI): Algorithms that analyze data to improve decision-makingprocesses and personalize customer interactions.⏹Automation Software: Platforms that orchestrate tasks such as inventory management,logistics, and customer service.1.Customer Experienceo Explore the impact of automation on the shopping experience:⏹Efficiency: Reduced wait times and seamless checkout processes enhance conveniencefor customers.⏹Personalization: AI-driven recommendations based on past purchases and preferencesimprove customer satisfaction.⏹Accessibility: Enhanced accessibility features cater to diverse customer needs, includingthose with disabilities.1.Operational Benefitso Highlight the advantages for retailers adopting automated supermarket models:⏹Cost Efficiency: Reduced labor costs and optimized inventory management lead toimproved profitability.⏹Scalability: Easily replicable model that can be adapted to various store sizes andlocations.⏹Real-Time Insights: Data analytics provide actionable insights for inventory forecasting,marketing strategies, and operational improvements.1.Challenges and Considerationso Address the challenges associated with automated supermarkets:⏹Technology Integration: Ensuring seamless integration of diverse systems andtechnologies.⏹Security: Safeguarding customer data and preventing cybersecurity threats.⏹Human Interaction: Balancing automation with the need for human oversight andcustomer assistance.1.Future Outlooko Discuss the future trends and developments in automated supermarkets:⏹Expansion: Increasing adoption globally with advancements in technology andinfrastructure.⏹Innovation: Continued development of AI, robotics, and IoT to further enhance storeoperations.⏹Market Influence: Impact on traditional retail models and consumer expectations.1.Conclusiono Summarize the transformative impact of automated supermarkets on retail industry dynamics.o Emphasize the role of innovation and technology in shaping the future of retail shopping experiences.1.Referenceso Cite relevant sources and case studies that illustrate the evolution and benefits of automated supermarket concepts.This comprehensive overview illustrates how automated supermarkets redefine traditional retail paradigms through advanced technology, promising enhanced efficiency, customer satisfaction, and operational excellence in the retail sector.。
光合作用综述
• Thermodynamics of photosynthesis
• Chlorophyll fluorescence
• Regulation of photosynthesis
• The C4 and CAM photosynthesis
• Evolution of photosynthesis
Photosynthesis Lecture 1
PICB
Contents
• The role of photosynthesis in solving the food, energy and environmental problems
• Can we improve photosynthesis • The Calvin cycle
• Nobel prizes in photosynthesis research
Schematic diagram of plant primary metabolism
WIMOVAC Model Structure
Source references:
Plants, Genes & Biotechnology. (2002) (Eds. Chrispeels, MJ & Sadava, DE). American Society for Plant Biology/Jones & Bartlett Publishers, Boston. Ort, D.B. & Long, S.P. Converting solar energy into crop production. Pp. 240269.
• The Farquhar model and gas exchange measurement, practical exercises
动态模拟autoregressive关系的软件包说明说明书
Package‘dynsim’October13,2022Title Dynamic Simulations of Autoregressive RelationshipsVersion1.2.3Date2021-06-20URL https:///package=dynsimBugReports https:///christophergandrud/dynsim/issuesDescription Dynamic simulations and graphical depictions of autoregressiverelationships.License GPL-3Depends R(>=3.0.0)Imports ggplot2(>=1.0.1.9003),grid,gridExtra(>=2.0.0),MASSSuggests DataCombineEncoding UTF-8BuildVignettes trueLazyData trueRoxygenNote7.1.1NeedsCompilation noAuthor Christopher Gandrud[aut,cre],Laron K.Williams[aut],Guy D.Whitten[aut]Maintainer Christopher Gandrud<*****************************>Repository CRANDate/Publication2021-06-2016:10:01UTCR topics documented:dynsim (2)dynsimGG (4)grunfeld (7)Index81dynsim Dynamic simulations of autoregressive relationshipsDescriptiondynsim dynamic simulations of autoregressive relationshipsUsagedynsim(obj,ldv,scen,n=10,sig=0.95,num=1000,shocks=NULL,...) Argumentsobj the output object the estimation model.ldv s the lagged dependent variablescen data frame or list of data frames.Specifies the values of the variables used to generate the predicted values when t=0.If only one scenario is desired thenscen should be a data frame.If more than one scenario is desired then the t=0values should be in data frames contained in a list.n numeric.Specifies the number of iterations(or time period)over which the program will generate the predicted value of the dependent variable.The defaultis10.sig numeric.Specifies the level of statistical significance of the confidence intervals.Any value allowed be greater than0and cannot be greater than1.num numeric.Specifies the number of simulations to compute for each value of n.The default is1000.shocks data frame.Allows the user to choose independent variables,their values,and times to introduce these values.Thefirst column of the data frame must be calledtimes this will contain the times in n to use the shock values.The followingcolumns’names must match the names of the variables whose values you wishto alter.You do not need to specify values for variables that you want to remainthe same as in scen.In times n where shock values are not specified,non-ldvvariable values will revert to those in scen.If*is used to create interactions,interaction terms will befitted appropriately....arguments to pass to methods.DetailsA post-estimation technique for producing dynamic simulations of autoregressive models.ValueThe command returns a data.frame and dynsim class object.This can contain up to columns elements:•scenNumber:The scenario number.•time:The time points.•shock.:Columns containing the values of the shock variables at each point in time.•ldvMean:Mean of the simulation distribution.•ldvLower:Lower bound of the simulation distribution’s central interval set with sig.•ldvUpper:Upper bound of the simulation distribution’s central interval set with sig.•ldvLower50:Lower bound of the simulation distribution’s central50percent interval.•ldvUpper50:Upper bound of the simulation distribution’s central50percent interval.The output object is a data frame class object.Do with it as you like.ReferencesWilliams,L.K.,&Whitten,G.D.(2011).Dynamic Simulations of Autoregressive Relationships.The Stata Journal,11(4),577-588.Williams,L.K.,&Whitten,G.D.(2012).But Wait,There’s More!Maximizing Substantive Inferences from TSCS Models.Journal of Politics,74(03),685-693.Examples#Load packagelibrary(DataCombine)#Load Grunfeld datadata(grunfeld,package="dynsim")#Create lag invest variablegrunfeld<-slide(grunfeld,Var="invest",GroupVar="company",NewVar="InvestLag")#Convert company to factor for fixed-effects specificationgrunfeld$company<-as.factor(grunfeld$company)#Estimate basic modelM1<-lm(invest~InvestLag+mvalue+kstock+company,data=grunfeld)#Estimate model with interaction between mvalue and kstockM2<-lm(invest~InvestLag+mvalue*kstock+company,data=grunfeld)#Set up scenarios for company4##List version##attach(grunfeld)Scen1<-data.frame(InvestLag=mean(InvestLag,na.rm=TRUE),mvalue=quantile(mvalue,0.05),kstock=quantile(kstock,0.05),company4=1)Scen2<-data.frame(InvestLag=mean(InvestLag,na.rm=TRUE),mvalue=mean(mvalue),kstock=mean(kstock),company4=1)Scen3<-data.frame(InvestLag=mean(InvestLag,na.rm=TRUE),mvalue=quantile(mvalue,0.95),kstock=quantile(kstock,0.95),company4=1)detach(grunfeld)##Not run:##Alternative data frame version of the scenario builder##attach(grunfeld)ScenComb<-data.frame(InvestLag=rep(mean(InvestLag,na.rm=TRUE),3),mvalue=c(quantile(mvalue,0.95),mean(mvalue),quantile(mvalue,0.05)),kstock=c(quantile(kstock,0.95),mean(kstock),quantile(kstock,0.05)),company4=rep(1,3))detach(grunfeld)##End(Not run)#Combine into a single listScenComb<-list(Scen1,Scen2,Scen3)##Run dynamic simulations without shocks and no interactionsSim1<-dynsim(obj=M1,ldv="InvestLag",scen=ScenComb,n=20)##Run dynamic simulations without shocks and interactionsSim2<-dynsim(obj=M2,ldv="InvestLag",scen=ScenComb,n=20)##Run dynamic simulations with shocks#Create data frame of shock valuesmShocks<-data.frame(times=c(5,10),kstock=c(100,1000),mvalue=c(58,5000))#Run simulations without interactionsSim3<-dynsim(obj=M1,ldv="InvestLag",scen=ScenComb,n=20,shocks=mShocks)#Run simulations with interactionsSim4<-dynsim(obj=M2,ldv="InvestLag",scen=ScenComb,n=20,shocks=mShocks)dynsimGG Plot dynamic simulation results from dynsimDescriptiondynsimGG uses ggplot2to plot dynamic simulation results created by dynsim.UsagedynsimGG(obj,lsize=1,color,alpha=0.5,xlab="\nTime",ylab="Predicted Value\n",title="",="Scenario",bels,legend="legend",shockplot.var,shockplot.ylab,shockplot.heights=c(12,4),shockplot.heights.units=c("cm","cm"))Argumentsobj a dynsim class object.lsize size of the smoothing line.Default is1.See ggplot2.color character string.Specifies the color of the lines and ribbons.If only one scenariois to be plotted then it can either be a single color value using any color valueallowed by ggplot2.The default is the hexadecimal color"#2B8CBE".If morethan one scenario is to be plotted then a color brewer palette is set.The defaultis"Set1".See scale_colour_brewer.alpha numeric.Alpha(e.g.transparency)for the ribbons.Default is alpha=0.1.Seeggplot2.xlab a label for the plot’s x-axis.ylab a label of the plot’s y-axis.title the plot’s main title. name of the legend(if applicable).bels character vector specifying the labels for each scenario in the legend.legend specifies what type of legend to include(if applicable).The default is legend="legend".To hide the legend use legend=FALSE.See discrete_scale formore details.shockplot.var character string naming the one shock variable to plotfitted values of over timespecified underneath the main plot.shockplot.ylab character string for the shockplot’s y-axis label.shockplot.heightsnumeric vector with of length2with units of the main and shockplot heightplots.shockplot.heights.unitsa character vector of length2with the unit types for the values in shockplot.heights.See unit for details.DetailsPlots dynamic simulations of autoregressive relationships from dynsim.The central line is the mean of the simulation distributions.The outer ribbon is the furthest extent of the simulation distributions’central intervals found in dynsim with the sig argument.The middle ribbons plot the limits of the simulation distributions’central50Examples#Load packagelibrary(DataCombine)#Load Grunfeld datadata(grunfeld,package="dynsim")#Create lag invest variablegrunfeld<-slide(grunfeld,Var="invest",GroupVar="company",NewVar="InvestLag")#Convert company to factor for fixed-effects specificationgrunfeld$company<-as.factor(grunfeld$company)#Estimate basic modelM1<-lm(invest~InvestLag+mvalue+kstock+company,data=grunfeld)#Set up scenarios for company4attach(grunfeld)Scen1<-data.frame(InvestLag=mean(InvestLag,na.rm=TRUE),mvalue=quantile(mvalue,0.05),kstock=quantile(kstock,0.05),company4=1)Scen2<-data.frame(InvestLag=mean(InvestLag,na.rm=TRUE),mvalue=mean(mvalue),kstock=mean(kstock),company4=1)Scen3<-data.frame(InvestLag=mean(InvestLag,na.rm=TRUE),mvalue=quantile(mvalue,0.95),kstock=quantile(kstock,0.95),company4=1)detach(grunfeld)#Combine into a single listScenComb<-list(Scen1,Scen2,Scen3)##Run dynamic simulations without shocksSim1<-dynsim(obj=M1,ldv="InvestLag",scen=ScenComb,n=20)#Create plot legend labelLabels<-c("5th Percentile","Mean","95th Percentile")#PlotdynsimGG(Sim1,bels=Labels)grunfeld7 ##Run dynamic simulations with shocks#Create data frame of shock valuesmShocks<-data.frame(times=c(5,10),kstock=c(100,1000))#Run simulationsSim2<-dynsim(obj=M1,ldv="InvestLag",scen=ScenComb,n=20,shocks=mShocks)#PlotdynsimGG(Sim2,bels=Labels)#Plot with accompanying shock plotdynsimGG(Sim2,bels=Labels,shockplot.var="kstock")grunfeld A data set from Grunfeld(1958)DescriptionA data set from Grunfeld(1958)FormatA data set with200observations and6variablesSourceGrunfeld,Yehuda.1958.The Determinants of Corporate Investment.PhD thesis.University of Chicago.Index∗datasetsgrunfeld,7discrete_scale,5dynsim,2,4,6dynsimGG,4grunfeld,7scale_colour_brewer,5unit,58。
利用MRI三维男性人体模型对航天员所受空间辐射的估算重点
利用MRI三维男性人体模型对航天员所受空间辐射的估算【摘要】目的建立空间辐射环境下，航天员器官所受辐射剂量及对其健康危险的计算方法。
方法利用符合航天员体征的人体MRI图像，建立三维男性人体模型及辐射数据库，并结合蒙特卡罗粒子输运程序GEANT4用于剂量计算。
结果我们得到了模拟各向同性抽样情况下，10 MeV到500 MeV单个质子对人体辐射敏感器官的吸收剂量及有效剂量。
结论在航天员体征三维人体模型及辐射数据库的基础上,利用空间舱内测量质子谱，得到了舱内累计剂量。
计算的皮肤剂量为148.6 μGy/d,该值与美国和俄罗斯发表的数据100～300 μGy/d比较接近。
【关键词】人体模型磁共振成像剂量质子蒙特卡罗粒子运输程序GEANT4The space radiation environment consists of geomagnetically trapped proton and electrons, galactic cosmic radiation, and at times, high energy solar particles that can penetrate spacecraft and spacesuits to produce a significant radiation exposure to crewmembers. The body organs can be injured by the cosmic particles. But the body organ dose cannot be measured directly，we can only predict the dose by calculation.The radiation protection quantity indicating the degree ofrisk of an exposed individual is the effective dose E, which is the sum of organ absorbed dose weighted by the tissue weighting factorsfor each organ［12］ . To assessCorresponding author: JIA Xianghong jiajerry@*Foundation item: Supported by the advanced space medico engineering research project of China (01100305)the absorbed dose distribution at specific locations of organs in the body, careful specification of the human body and theirradiation conditions is needed. The radiation transport and energy deposition in the body are taken care of by a Monte Carlo code.The first mathematical human phantom model, known as medical internal radiation dose(MIRD) phantom, was designed at Oak Ridge National Laboratory for the adult human male［3］ . This model described the shapes of human body and organs by combinations of mathematical equations describing planes, cylindrical, conical, elliptical, and spherical surfaces［47］ .However, it is clearthat the human anatomy is too complex to be realistically modeledwith a limited set of equations. With the development of the computer and the medical technology, new types of body model the voxel model has been used, which can very precisely describe both the shape ofthe body and of the internal organs. The voxel models were first introduced by Gibbs et al，followed by Williams et al, and othervoxel phantoms were constructed during the last decade［8］. These voxel models are based on tomographic images taken from Caucasian subjects. Since the dose distribution in a body is affected by the physical and anatomical characteristics of the body, it is worthwhile to have a male model for the Chinese astronaut.In this paper, the voxel model for a male Chinese astronautwas constructed from magnetic resonance (MR) date. The protonradiation exposures to the critical body organs identified by the National Council on Radiation Protection and Measurements (NCRP) were calculated using the Monte Carlo code GEANT4. We obtained thecritical body organs absorbed doses and effective doses of the modelin isotropic proton fields with energies ranges from 10 MeV to 500 MeV .MethodsOriginal imagesThe quality of original image data for constructing auct the voxelhealthy adult Chinese male. The selected subject was a healthy maleof about 170 cm in height and about 69 kg in weight, which are well within the range of reference height and weight of male adult Chinese: 170 cm and 60.5 kg, respectively. Because the encephalic anatomic conformation is complex, the body above the neck (including the neck) was scanned with a 2 mm slice width. The head scanned 80 slices andthe neck scanned 85 slic es, the size of these images is 240×240 pixels. The body below the neck was scanned with 4 mm slice width, total 400 slices, the size of the breast and abdomen images is256×196 pixels, and the size of the other images is 256×206 pixels. We get 706 data files, which are 80 Mb in all. The original MR images data in DICOM (Digital Communications in Medicine) format were converted into JPEG (Joint Photographic Coding Experts Group) format image files for segmentation and indexing process.Identify the organs When getting the JPEG format image files, we can segment the organs. Two kinds of segmentation methods, manual and automated segmentation, were used depending on the clearness of boundary between organs and tissues. Because most of the organs in these images are overlapped, only contours of the skin, lungs, brain, and leg bones were automatically segmented. A majority of organs should be segmented with manual drawing in the cooperation of medicinal technicians and computerized. Skin was described as one voxel layer outside the surface of the body, which resulted in a skin thickness of 2 mm, slightly thicker than the 1.3 mm reported for the thickness of epidermis and dermis in the Caucasian reference data ［8］.We analyses the data of the gray images, identify the boundaryof the organs in every image. Because of the difference of individual, we should amend the data of the images according to the Reference Man by ICRP 23 and Chinese Reference Man［］ .All skeletal components were assumed to be homogeneously distributed in the bone because the bone marrow was not identified in MR images. The bone surface could not be described with the voxel resolution of thisstudy because the thickness of bone surface was estimated at 0.01 mm.Voxel database The resolutions of the original images were different: 1 mm×1 mm×2 mm in head and neck, 2 mm×2 mm×4 mm in the others. To reduce the computational errors, we should unify the sizeof the voxel into 2 mm×2 mm×2 mm. We united the four adjacentvoxels into one in head and neck slice. To other slices, along the z axis, we segmented one voxel into two. In total, we constructed data arrays consisting of about 100 million voxels of a size of 2 mm×2midentified one voxel, including the the coordinate and the message of the organ which a certain voxel belonged to. We considered that inone voxel the density was invariability and one voxel only belongedto one organ.Element components for organs The tissues could be described with the average tissue compositions and densities, according to ICRU 44, ICRP Reference Man and Chinese Reference Man.The followingdifferent types ofTable 1 Mass ratio of element component for some organs and tissueselemental tissue compositions were considered for the calculation: lung (0.3 g/cm3), skin (1.1 g/ cm3), bone (1.4 g/ cm3), soft tissue and other organs (about 1.05 g/ cm3). Table 1 gives mass ratio of element components for some organs and tissues［１１］.Monte Carlo calculationThe widespread acceptance of computational models in radiation dosimetry had been made possible with the availability ofodes and very fast personal computers since the late 1980’s.Among all the Monte Carlo codes, there are six general purpose codes that have been widely used.1) Electron gamma shower(EGS)4, originally developed at Stanford Linear Accelerator Center, is well known for its detailed physicsCarlo neutron and photo transport code(MCNP), originated from Los Alamos National Laboratory, has the capability to transport photons, neutrons and in the version 4B, also the electrons (Hendricks). 3)Los alamos high energy transport code(LAHET) is a code for the transport and interaction of nucleons, pions, muons, light ions, andCarlo neutron and photo transport code extended(MCNPX), released in 1999, is a merged code that combines the theoretical models of the LAHET Code System with the general features of the MCNP to provide al). 5) FLUKA was used to simulate high energy proton current originally in 1962. Subsequently with the development of the computer technology, the scope of its application has extended. Now, it can simulate the physics process of electron, neutron, and ion. The most advantage of the program is the precision of its physics process. (Fasso,Ranft and Sala) 6) GEANT4,developed by CERN (European laboratory for particle physics) and KEK, is a program package making use of thewell user interface. The user can customize his/her own physical module, geometric module and particle message as well as can record it［8］. In this paper, GEANT4 was chosen to stimulate the transport and interaction of protons.Because the number of the voxel model records is about 8 051 274, the “useable” RAM seemingly less than the size of the model containing a total of over eight million voxels and additional coding.A significant amount of effort was required to reduce the memory burden by using aused one integer to replace one organ. Then, we numbered the voxel in the same order in every slice, so the planar matrix was transformed into a linearity table. The coordinate of the matrix was the serial number of the voxel, and the content of the table was the integer of the organ. At last, we incorporated the consecutive cells which had the same numerical value of the organ. Figure 1 showed the flow ofons of organ equivalent doses and effective doses were gotten using the MonteMeV, selecting one energy point every 5 MeV, in all 99 energy points. The model was placed in the center of a sphere whose radius is 0.984 m, on the spherical surface we sampled the proton’s position and direction. In every energy point, we sampled million times, and2 Some organ’s mass of this MRI mode l,ICRP reference man, standard Japanese and KORMAN(g)organsMRI modelICRP reference manstandard JapaneseKORMANbrain 1 884 1 450 (1)790.5heart 910 840 800.9 360*lung 978 1 200 1100 986.2bladder 58 50 40 64.8esophagus 31 40 40 32.3stomach 96.7 150 140ResultsAdult male voxel model and database The adult male voxeliof the database, and fig.3 showed the human model reconstructed. We could calculate the mass of the organs with the densities and the number of the voxel. Table 2 showed some organ mass of the voxel model,ICRP Reference Man, standard Japanese and KORMAN［］.Large discrepancies in organmasses were observed mainly due to the difference of individual and the different resolution of the original images.Dose calculation In space, the particle is isotropy, so we sampled the particle source in a sphere, and their directions were sampled in random. Because of the large calculation quantity, three servers equipped with two 2.3 GHz Intel processor and 1 G RAM operated by Linux were used for computation，computing about one month. Fig.4 showed the absorbed dose in some organs aroused bysingle energy proton.ConclusionMaking use of the spectrum measured in space, we can predict the cumulate dose. The calculated skin dose is about 148.6 μGy/d, which is similar to the value measured by LiF that is 152 μGy/d, and also located in the range of 100～300 μGy/d, calculated and measured data by USA and Russia.Fig.4 Absorbed dose in some organs aroused by single energy protonBecause of the low resolution of original MR images, the organ contours such as small intestine, hypothyroid and red bone marrow are not clearly distinguished in MR images. So error of the absorbed dose in these organs is a little big. A more precise voxel model with smaller voxel size will be constructed if higher resolution tomographic data are available. Then we can amend the model, and predict the dose more accurately.【参考文献】［1］ Mountford PJ,Temperton.Recommendations of the international commission on radiological protection 1990［J］.European Journal of［2］ ICRP.Conversion coefficients for use in radiological protection against external radiation［J］.Ann［3］ Snyder WS,Ford MR,Warner GG,et al. Estimates of absorbed fractions for monoenergetic photon source uniformly distributed in various organs of a heterogeneous phantom［R］.Medical Internal Radiation Dose Committee (MIRD) Society of Nuclear Medicine, 1969, Pamphlet No. 5, Supplement No. 3. J. Nuclear Med. 10.［4］ Hwang JL,Poston JW,Shoup RL,et al.Maternal, fetal and pediatric phantoms［R］.Oak Ridge National Laboratory, Oak Ridge, TN,［5］ Cristy M.Mathematical phantoms representing children of various ages for use in estimates of internal dose［R］.Oak Ridge［6］ Cristy M,Eckerman KF.Specific absorbed fractions of energy at various ages from internal photon sources［R］.Oak Ridge［7］ Stabin MG. Internal Dosimetry in Pediatric Nuclear Medicine［M］.In:Treves ST,eds.Pediatric Nuclear Medicine NewYork:Springer Verlog,199［8］calculations［J］［9］ ICRP Publication 23, Report of the Task Group on Reference Man［M］. International Commission on Radiological Protection.Pergamon, Oxford, 1975.［10］ Atwell W. Space radiation assessment of radiosensitive body organs in the international space station. In Fundamentals for the Assessment of Risks from Environmental Radiation［C］.Inc. Baum stark-Khan, et al(eds.). Netherlands: Kluwer AcademicBublishers,1999:513-518.［11］ Lee C,Lee J,Lee C.Korean adult male voxel model KORMAN ［J］ 2.［12］ Nagaoka T,Watanabe S,Sakurai K,et al, Development andmodels［J］。
调节模式图
Calculator for simple slopes and points to plot for a standard 2-way moderated regression interaction with a co This assumes that both both variables are centered around 0. variances and covariances are obtained by requ regression coefficients. Bs refer to unstandardized regression coefficients (see Aiken & West, 1991; and Cohe for a detailed discussion of the procedures used here). Make sure that you calculate the interaction term by m The equation for this regression takes the following form: y = B0 + Bx + Bz Bx*z + eIf you found this utility helpful then you could reference it as follows:Sibley, C. G. (2008). Utilities for examining interactions in multiple regression [computer software]. University Chris Sibley, Auckland University, 2008. Comments (hopefully positive ones) and questions casimple slopestandard errort-value p-valueLow value ofCalculator for simple slopes and points to plot for a standard 2-way linear regression interaction with a continu This assumes that the continuous predictor variable is centered around 0 and the categorical moderator is dum are obtained by requesting the covariance matrix for the regression coefficients. Bs refer to unstandardized re and Cohen, Cohen, West, & Aiken, 2003, for a detailed discussion of the procedures used here)The equation for this regression takes the following form: y = B0 + Bx + Bz Bx*z + eIf you found this utility helpful then you could reference it as follows:Sibley, C. G. (2008). Utilities for examining interactions in multiple regression [computer software]. University Chris Sibley, Auckland University, 2008. Comments (hopefully positive ones) and questions caModerator dummy code Moderator dummy codesimple slope standard errort-value p-valueLow valuThis page calculates the points to plot for the effect of a categorical predictor (or IV) on a continuous outcome when, for example, you want to examine whether the effect of an experimental IV with 2 levels is moderated b original equations for testing simple slopes so that they are solved for 0 and 1 values of the IV, at +/-1 SD of t as it is more common in moderated regression when one has a categorical and continuous variable interaction as categorical. This procedure makes the same basic assumptions as the other calculators on this page, i.e., tdummy coded 0,1. Variances and covariances are obtained by requesting the covariance matrix for the regres Note that the points to plot are identical to those where one treats the continuous variable as the predictor and switched around on the graph. The simple slopes and standard errors will differ however. I suggest using a ba The simple slopes in this context can be interpreted as a test of the difference between levels of the IV at +1 S The equation for this regression takes the following form: y = B0 + Bx + Bz Bx*z + eIf you found this utility helpful then you could reference it as follows:Sibley, C. G. (2008). Utilities for examining interactions in multiple regression[computer software]. University Chris Sibley, Auckland University, 2008. Comments (hopefully positive ones) and questions casimple slope standard errort-valuep-valueLow value ofUSER SPECIFIED VALUESPredictor Low Value 0Predictor High Value 1Moderator Low Value -0.8Moderator High Value0.8sample size 200number of control variables 0simple slope standard errorB for constant 13.446t-value B for predictor (x) 7.309p-value B for moderator (z)1.030B for interaction term (x*z) -5.549simple slope se of constant 0.010*********standard errorse of predictor (x) 0.010*********t-value se of moderator (z)0.010*********p-valuese of interaction term (x*z)0.010*********0.000cov of predictor and interaction term 1.00E-05cov of moderator and interaction term1.00E-02Optional Extrascov of intercept and moderator1.00E-07intercept standard errort-value p-valueSlope at Low Valu Difference in points Intercept at Low Va0.0001.0000.0005.00010.00015.00020.00025.00030.000PredictorGet the covariances from the variance-covariance matrix of the regression coefficients.Note.To get the cov of the intercept and moderator you need to 'trick' SPSS by deleting the intercept from the model and then entering a new 'constant' variable into the equation where all values = 1. Then take the estimated covariance of this new variable with the interaction term. This value is only necessary to enter if you want to test the significance of the intercepts.Do this by unticking the following box:References for the procedures implemented on this page.Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. London, United K Cohen, J., & Cohen, P., & West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis fregression interaction with a continuous predictor and continuous moderator. variances are obtained by requesting the covariance matrix for theAiken & West, 1991; and Cohen, Cohen, West, & Aiken, 2003,ulate the interaction term by multiplying centered scores of x and z.computer software]. University of Auckland.ns can be sent to c.sibley@Predictor -1 SD Predictor +1 SDModerator -1 SD 6.7738.598Moderator +1 SD 5.702 4.328ssion interaction with a continuous predictor and categorical moderator.he categorical moderator is dummy coded 0,1. variances and covariances s. Bs refer to unstandardized regression coefficients (see Aiken & West, 1991; dures used here)computer software]. University of Auckland.ns can be sent to c.sibley@Predictor -1 SD Predictor +1 SD Moderator dummy coded 0 5.109 5.017Moderator dummy coded 1 4.999 5.391-0.051simple slope 0.216 0.054standard error 0.093 -0.939t-value 2.321 0.349p-value 0.021w value of moderator (0)High value of moderator (1) 0or IV) on a continuous outcome moderated by a continuous variable. This may be a useful approach IV with 2 levels is moderated by a continuous variable. To do the, this page simply rearranges the values of the IV, at +/-1 SD of the moderator. This approach is not often seen in the literature,continuous variable interaction to treat the continuous variable as the predictor and the moderatorcalculators on this page, i.e., that the continuous moderator is centered, and the categorical IV is covariance matrix for the regression coefficients. Bs refer to unstandardized regression coefficients . us variable as the predictor and categorical variable as the moderator, except that they are nowhowever. I suggest using a bar graph to represent this type of interaction (just personal opinion). between levels of the IV at +1 SD of the moderator, and levels of the IV at -1 SD of the moderator. computer software]. University of Auckland.ns can be sent to c.sibley@IV coded 0IV coded 1Moderator -1 SD 4.637 4.614Moderator +1 SD 3.843 4.620-0.023simple slope 0.7770.298standard error0.332-0.077t-value 2.3390.938p-value 0.020ue of moderator (-1 SD)High value of moderator (+1 SD)Predictor LowPredictor HighModerator Low 12.62224.370Moderator High14.27017.14011.748simple slope 2.8700.012standard error 0.013965.696t-value 213.9020.000p-value 0.0001.030simple slope-4.5190.010standard error 0.142103.000t-value -31.7960.000p-value 0.00012.622intercept 14.2700.013standard error 0.013986.094t-value 1113.7570.000p-value 0.000w Value of ModeratorSlope at High Value of Moderator Difference in points at High Predictorpoints at Low Predictorow Value of ModeratorIntercept at High Value of Moderatorinteractions. London, United Kingdom: SAGE Publications.egression/correlation analysis for the behavioral sciences. London: Lawrence Erlbaum.。
Creo_View_MCAD_Feature_Comparison
Construction Geometry
Import XML file containing CG Export Construction Geometry into an external XML file View existing Construction geometry
Lighting
Simple light source positioning View Layers
Creo View Lite
Creo View MCAD
ProductView Standard
Smart Explode
Explode parts, assemblies Define and control the explosion
Send Creo Elements/View viewable via email as an attachment Send image via email as an attachment Insert a new Product Structure into the existing Product Structure Insert branch link (reference another PVS / ED file) Re-order Product Structure
Sectioning
Quarter cut Capped sections Export section to CGM format Section selected parts Geometry-driven section placement Export into 2D image formats(.bmp, jpg, etc.) Export to IGES
量子力学中的Jaynes-Cummings模型态演化分析(胡丽红)
量子力学中的Jaynes-Cummings模型态演化分析专业：光信息科学与技术学号：20080810090205学生姓名：胡丽红指导老师：熊狂炜摘要Jaynes—Cummings(J-C)模型是由单个二能级原子（或分子）与一个单模量子化光场组成的相互作用系统，反映的是单原子和单模辐射场之间的相互作用的两能级量子力学模型。
它基于偶极近似和旋转波近似，着重处理电磁场与原子的近共振作用。
J-C模型形式简单，是个精确可解的量子系统，并蕴含了丰富的物理内涵，能广泛的应用到许多领域中去，是量子光学、激光物理、核磁共振等问题中常用的一种模型。
本文主要通过两种不同的方法：待定系数法和矩阵法对Jaynes—Cummings 模型的态演化进行理论计算。
主要考虑在共振情况下，我们求得态函数系数的变化图。
在光场初始态处于真空态或相干态等不同情况下，系统会呈现出不同的量子特征如：真空拉比震荡、崩塌与复原现象。
为了进一步完善光场与原子相互作用的量子理论，本文还介绍了几种推广的Jaynes—Cummings模型。
关键词：Jaynes—Cummings模型；态演化；共振The state evolution analysis of Jaynes-Cummings model inquantum mechanicsAbstractJaynes-Cummings (J-C) model is up to the individual two-level atoms (or molecular) and a single-mode optical field of quantization interaction system.It reflects a two-level quantum mechanical model of the interaction between a single atom and the Single-mode radiation field. It is based on dipole approximation and the rotating wave approximation, mainly deal with the near resonance effect between the electromagnetic field and the atomic. J-C model is simple in form and it is a quantum systems that can be solved precisely.It contains rich connotation of physical, which can widely used to many fields.It’s a model commonly used in the study of quantum optics, laser physics, nuclear magnetic resonance (NMR) and many other problems. This paper mainly uses two different methods: the method of undetermined coefficients and matrix method to perform the theoretical calculation of the state evolution of the Jaynes-Cummings mode. Mainly considering in the condition of resonance, We can obtain the variation diagrams of the coefficients to the corresponding normal function. In the different initial states of light field like in vacuum state or coherent states and so on the different cases, the system will be present different quantum characteristics such as vacuum rabbi shocks, collapse and restoration phenomenon. In order to further perfect the quantum theory of the interaction between the light field and the atoms , this paper introduces several kinds of promotion Jaynes-Cummings models.Keywords: Jaynes-Cummings model; State evolution; resonanc目录引言 (1)第一章JAYNES-CUMMINGS模型 (2)1.1J-C模型的相关介绍 (2)1.1.1 标准J-C模型的物理内涵，重要性和局限性 (2)1.1.2 标准J-C模型的线性与非线性推广 (3)1.2J-C模型的基本原理 (4)第二章用待定系数法计算J-C模型的态函数随时间演化规律 (8)第三章用矩阵法计算J-C模型的态函数随时间演化规律 (17)3.1光与原子的相互作用-缀饰原子态 (17)3.1.1 光场中原子的波函数 (17)3.1.2 互作用哈密顿量的对角化 (19)3.1.3 缀饰原子态 (22)3.2光与原子的相互作用J-C模型 (25)3.2.1 量子拉比振荡 (25)3.2.2 单模自发发射 (28)3.2.3崩塌和复原 (29)第四章几种推广的J-C模型 (33)4.1双光子J-C模型 (33)4.2 型三能级原子与光场R AMAN相互作用 (33)第五章总结 (34)致谢 (35)参考文献 (36)附录 (37)华东交通大学毕业设计（论文）引言Jaynes-Cummings(J-C)模型是由E．T．Jaynes和F．W．Cummings于1963年提出来的，是由单个二能级原子（或分子）与一个单模量子化光场组成的相互作用系统，反映的是单原子和单模辐射场之间的相互作用的两能级量子力学模型。
气相色谱仪单词
积分型检测器integrating detector激光光热检测器laser and light heat detector激光解吸质谱法laser desorption MS，LDMS激光裂解器laser pyrolyzer激光色谱laser chromatography激光诱导光热光偏转测量detection of laser-induced light heat…激光诱导光束干涉检测detection of laser-induced light beam I…激光诱导毛细管振动测量laser-reduced capillary vibration det…激光诱导荧光检测器laser-induced fluorescence detector记忆峰memory peak记忆效应memory effect夹层槽sandwich chamber假峰ghost peak间断洗脱色谱法interrupted-elution chromatography间接光度（检测）离子色谱法indirect photometric io n chromato… 间接光度（检测）色谱法indirect photometric chromatography间接检测indirect detection间接荧光检测indirect fluorescence detection间接紫外检测indirect ultraviolet detection检测器detector检测器检测限detector detectability检测器灵敏度detector sensitivity检测器线性范围detector linear range碱火焰电离检测器alkali flame ionization detector，AFID碱洗法alkali wash剪纸称重法cut-paper weighing method减尾剂tailing reducer减压液相色谱vacuum liquid chromatography键合固定相bonded stationary phase键合型离子交换剂bonded ion exchanger焦耳热joule heating胶束薄层色谱法micellar thin layer chromatography胶束液相色谱法micellar liquid chromatography交联度crosslinking degree阶梯梯度stagewise gradient介电常数检测器dielectric constant detector金属配合物离子色谱法metal complex ion chromatography，MCIC 金属氧化物固定相metal oxides stationary phase金属作用色谱metal interaction chromatography进样阀injection valve进样量sample size进样器injector静态顶空分析法static headspace analysis静态涂渍法static coating method径流柱radial flow column径向流动色谱radial flow chromatography径向压缩柱radial compression column径向展开法radial development径向展开色谱radial development chromatography净保留体积net retention volume居里点裂解器Curie point pyrolyzer矩形池rectangle form pool聚苯乙烯PS/DVB聚硅氧烷高温裂解去活high-temperature pyrolysis deactivation… 聚合物基质离子交换剂polymer substrate ion exchanger绝对检测器absolute detector开口分流open split开口管柱open tubular column可见光检测器visible light detector可交换离子exchangable ion空间性谱带加宽band broadening in space空穴色谱法vacancy chromatography孔结构pore structure孔径pore diameter孔径分布pore size distribution控制单元control unit快速色谱法high-speed chromatography离心逆流色谱centrifugal counter-current chromatography离心制备薄层色谱法centric-preparation TLC离子对色谱法ion pair chromatography，IPC离子对试剂ion pair reagent离子对探针检测ion-pairing probes detection离子对形成模型ion pair formation model离子交换电动色谱ion-exchange electrokinetic chromatography 离子交换剂ion exchanger离子交换毛细管电色谱ion exchange capillary electrokinetic离子交换膜ion exchange membrane离子交换色谱法ion exchange chromatography，IEC离子交换树脂ion exchange resin离子交换位置ion exchange site离子交换柱ion exchange column离子排斥色谱法ion exclusion chromatography，ICE离子色谱法ion chromatography，IC离子色谱仪ion chromatograph离子相互作用模型ion interaction model离子相互作用色谱法ion interaction chromatography，IIC离子抑制色谱法ion suppression chromatography，ISC理论塔板高度height equivalent to a theoretical plate（HETP）理论塔板数number of theoretical plates两性电解质ampholytes两性离子zwitter-ion两性离子交换剂zwitterion exchanger裂解气相色谱法pyrolysis gas chromatography PyGC临界胶束浓度critical micelle concentration淋洗剂eluent淋洗离子eluent ion淋洗色谱法elution chromatography馏分收集器fraction collector流动池flow cell电离截面检测器ionization cross section detector电歧视效应the effect of electrical discrimination电迁移进样electrophoretic injection电渗流electroendosmotic flow电渗流标记物electroendosmotic flow marker电渗流淌度electroendosmotic mobility电位检测器electricity potential detector电泳淌度electrophoretic mobility电子俘获检测器electron capture detector电子迁移率检测器electron mobility detector调整保留时间adjusted retention time调整保留体积adjusted retention volume叠加内标法added internal standard method顶空气相色谱法headspace gas chromatography，GC-HS顶替法displacement development顶替色谱法displacement chromatography动态包覆dynamic coating动态分离dynamic separatio动态复合离子交换模型dynamic complex ion exchange model动态改性dynamic modification动态离子交换模型dynamic ion exchange model动态涂渍dynamic coating动态涂渍法dynamic coated method动态脱活dynamic de-activity短柱色谱法short column chromatography堆积硅珠stacked silica bead堆积性能bulk property多次反射池multi-reflect pool多分散度polydispersity多功能基离子交换剂multi-functional group ion exchanger多角度激光光散射光度计multi-angle laser light scattering ph…多孔层开口管柱porous layer open tubular column，PLOT多孔高聚物PLOT柱porous polymer beads PLOT column多孔硅胶porous silica gel多孔聚合物气液固色谱柱porous polymer beads GLS column GLS 多孔石墨碳porous graphitic carbon，PGC多孔载体porous support多脉冲实验multiple pulse experiments多维色谱法multi-dimensional chromatography多维色谱仪multidimensional chromatograph多用色谱仪unified chromatograph惰性气体鼓泡吹扫脱气sweeping degas by inert gas二次化学平衡secondary chemical equilibria ，SCE二极管阵列检测器diode-array detector，DAD二维色谱法two-dimensional chromatography二元溶剂体系dual solvent system反冲洗back wash反吹技术back flushing technique反峰negative peak反离子counter ion反气相色谱法inverse gas chromatography （IGC）反相高效液相色谱法reversed phase high performance liquid ch… 反相离子对色谱reversed phase ion pair chromatography反相离子对色谱法reversed phase ion-pair chromatography反相毛细管电色谱reverse capillary electrokinetic chromatogr…反相柱reversed phase column反应气相色谱法reaction gas chromatography反应色谱reaction chromatography反圆心式展开anti-circular development反转电渗流reverse electroendosmotic flow范第姆特方程式van Deemter equation仿生传感器Biomimic electrode放射性电离检测器radio ionization detector放射性检测器radioactivity detector放射自显影autoradiography非极性固定相non-polar stationary phase非极性键合相non-polar bonded phase非金属离子传感器non-metal ion sensor非水系凝胶色谱柱non-aqua-system gel column非水相色谱nonaqueous phase chromatography非吸附性载体non-adsorptive support非线性分流non-linearity split stream非线性色谱non-linear chromatography非线性吸附等温线non-linear adsorption isotherm非抑制型电导检测non-suppressed conductance detection非抑制型离子色谱法non-suppressed ion chromatography，NSIC 费尔盖特效益Fellgett advantage酚醛离子交换树脂phenolic ion exchange resin分离－反应－分离展开SRS development分离数separation number分离因子separation factor分离柱separation column分流split stream分流比split ratio分流进样法split sampling分流器splitter分配等温线distribution isotherm分配色谱partition chromatography分配系数partition coefficient分析型色谱仪analytical type chromatograph分子扩散molecular diffusion分子量分布molecular weight distribution分子量检测器molecular weight detector分子筛molecular sieve分子筛色谱molecular sieve chromatography分子吸附molecular adsorption分子吸收光谱molecular absorption spectroscopy 封尾endcapping峰高peak height。