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亚马逊物流+亚马逊客户卓越运营系统(ACES)-3全球标杆项目(GlobalBenchma。。。

亚马逊物流+亚马逊客户卓越运营系统(ACES)-3全球标杆项目(GlobalBenchma。。。

亚马逊物流+亚马逊客户卓越运营系统(ACES)-3全球标杆项⽬(GlobalBenchma。

3.3.3全球标杆项⽬(Global BenchmarkingProject)亚马逊飞轮是我们快速成长的引擎,在运营中⼼我们有责任去⽀持这种快速的成长。

这种快速的成长意味着,⼯作有更多的机会,公司会更健康的发展。

我们做全球标杆项⽬(GlobalBenchmarking Project)这项⼯作,可以使我们的成本更低,为客户提供更多选择和完美的客户体验。

我们所讲全球标杆项⽬Global Benchmarking Project,是来⾃整个亚马逊全球各个FC的最佳实践,我们要将这些最佳实践标准化。

我们是把亚马逊和亚马逊运营最佳实践提取出来,并把这些领域组合成运营系统来发展⼏千名现有的和未来的领导以⽀持我们未来若⼲年的发展在2014年, WWACES 和 Ops 发起了这项⼯作,进⼀步改善安全、质量和成本,从⽽给客户带来更好的体验。

在北美 ,IND1和PHX6被选为最优的运营中⼼,他们的管理实践和系统配置被研究,⼀些关键的对⽐元素被⼀步步被详细描述,并提供了⼀些技术和功能的⽅法,最后标准化为34个⽂档。

在中国区,在2014年11⽉底开始。

中国是启动global benchmarking project第⼀个⾮英语国家。

由于中国区各个运营在布局,产品结构和⼯艺过程不⼀样,我们不能像北美把IND1 /PHX6直接作为标杆FC。

中国区 ACES 团队在学习北美运营中⼼最佳实践⽅法的基础上将会把中国区内最优的⽅法整合到⼀起,建⽴出⼀套建适合于中国区的⽂档。

全球标杆项⽬(GlobalBenchmarking Project)与持续改进的循环ContinuousImprovementCycle (CIC) 关系这个项⽬可以带来什么?· 识别新的成本节约机会· 超过30个标杆⽐较的流程· 详细的流程⽂件和审核表· 在改善之前保证在最基本状态· 提⾼计划与执⾏· ⽇常的流程审核· 定期的业绩回顾· 跟踪与提升· 对可变成本的正⾯影响和过以往Benchmarking项⽬的不同点:i. Self-Audit Tool (SAT)-我们这个项⽬在完成⽂档翻译和引进,⽂档的回顾,确定,批准,现场培训后,最重要的和以往不同的⼀步将启动盲审(Blindaudit)。

DJI Mavic 3 D-Log FAQ

DJI Mavic 3 D-Log FAQ

Mavic 3中的D-L og2022.05.20内容1. 前言 (3)2.D-Log下的ISO感度系统 (3)3.原生ISO感度和动态范围 (3)4.Mavic 3 D-Log 和 Mavic 2 Pro DLog-M的差异 (6)1.前言Mavic 3发布以来,不少用户表示对于D-Log模式有一些疑问,所以我们提供了这份文档,描述了D-Log中的几个重要的概念以及D-Log相对于之前的DLog-M的差异。

这个文档的目的是解决以下疑问:●Mavic 3上的原生ISO感度(Native ISO)是多少?它是什么含义?●为什么在Mavic 3上的ISO感度是从400开始的?●Mavic 3上的D-Log和Mavic 2 Pro上的DLog-M有什么不同的地方?2.D-Log下的ISO感度系统拍照模式的ISO感度有相对明确的工业标准。

比如CIPA DC-004-2004 Sensitivity of Digital Cameras和ISO12232 Photography — Digital still cameras — Determination of exposure index, ISO speed ratings, standard output sensitivity, and recommended exposure index 等都是广泛采用的标准。

目前常见的拍照模式的ISO感度代表的是在一定的曝光量(被摄亮度、光圈、快门的组合)的情况下获得的照片相对亮度。

虽然也存在基于噪声的ISO感度计算方案,但是并没有被广泛采用。

关于视频的ISO感度并没有通用的标准,数值基本由厂商根据自家标准决定。

DJI Mavic系列无人机相机的视频的普通模式下的ISO感度与拍照模式是大体一致的。

而D-Log模式则需要有不同的考虑。

从上面的描述可以看出,拍照模式和视频的普通模式,ISO感度的出发点主要是画面直出时的亮度;而D-Log模式是为后期制作而生的,所以素材的另外两个要素——动态范围和噪声水平——与亮度同样重要。

单反相机功能参数表中英文对照

单反相机功能参数表中英文对照

S (single frame), CL (continuous low speed), CH (continuous high speed), Q (quietshutter-release), (self-timer), (remote control), MUP (mirror up)S(单张拍摄),CL(低速连拍),CH(高速连拍),Q(安静快门释放),(自拍),(远程控制),MUP(反光板升起), , , , , , : Auto flash with auto pop-up P, S, A, M, : Manual pop-up with button release, , , , , , : 自动闪光灯自动弹出P, S, A, M, : 手动弹出按钮释放quality, image size, and image area settings. •FX (36 x 24) image area*•Includes images taken with non-DX lenses when On is selected for Auto DX crop.•Includes images taken with DX lenses when On is selected for Auto DX crop.•1All figures are approximate. File size varies with scene recorded.•2Maximum number of exposures that can be stored in memory buffer at ISO 100. Drops if optimal quality is selected for JPEG compression, ISO sensitivity is set to Hi0.3 or higher, or long exposure noise reduction or auto distortion control is on.•3Figures assume JPEG compression is set to Size priority. Selecting optimal quality increases the file size of JPEG images; number of images and buffer capacity drop accordingly.The following SD memory cards have been tested and approved for use in the camera. Cards with class 6 or faster write speeds are recommended for movie recording.。

Canon EOS M50 数字单反摄影机说明书

Canon EOS M50 数字单反摄影机说明书

1:1
Approx: 21.5 MP 4640 x 4640
Approx: 10.2 MP 3200 x 3200
Approx: 5.4 MP 2320 x 2320
Approx: 2.6 MP 1600 x 1600
Approx: 32.3 MP 6960 x 4640
File Numbering
Specifications
Type
Type
Digital single-lens non-reflex AF/AE camera with built-in flash
Recording Media
SD, SDHC and SDXC Memory Cards SD speed class compatibility. UHS speed class compatibility. High-speed writing is supported when a UHS-I or UHS-II-compatible SD card is used. Multimedia cards (MMC) cannot be used (card error will be displayed).
Pixel Unit
Square pixel approx. 3.2 µm
Aspect Ratio
3:2
Dust Removal Feature
(1) Self Cleaning Sensor Unit • Removes dust adhering to the low-pass filter. • Self-cleaning can be done automatically when the power is turned on or off. Manual cleaning also possible. (2) Dust Delete Data acquisition and appending • The coordinates of the dust adhering to the low-pass filter are detected by a test shot and appended to subsequent images. • The dust coordinate data appended to the image is used by the EOS software to automatically erase the dust spots.

pip换源——精选推荐

pip换源——精选推荐

pip换源Python 修改 pip 源为国内源1.临时换源:#清华源pip install markdown -i https:///simple# 阿⾥源pip install markdown -i https:///pypi/simple/# 腾讯源pip install markdown -i /pypi/simple# ⾖瓣源pip install markdown -i /simple/2.永久换源:# 清华源pip config set global.index-url https:///simple# 阿⾥源pip config set global.index-url https:///pypi/simple/# 腾讯源pip config set global.index-url /pypi/simple# ⾖瓣源pip config set global.index-url /simple/# 换回默认源pip config unset global.index-urlpip换源的⽅式在使⽤Python安装包⼯具pip时经常会出现下载很慢的情况,这其中有很⼤⼀部分原因和pip的源有关,在我们安装python后,通常python解释器⾃带pip这个⼯具,但是这⾥pip是设置的默认源,也就是官⽅源:PYPI国内源路径阿⾥云⾖瓣(douban)清华⼤学中国科学技术⼤学换源⽅式这⾥我们提供两种换源的⽅式:1. 临时换源2. 永久换源临时换源临时换源只需要在pip安装包时,加上⼀个-i参数后接源的url即可:# 下载python中的Django包,这⾥使⽤的是⾖瓣源pip install django -i /simple显然不是⼀个⼀劳永逸的⽅法,只有下少量包的时候有使⽤的场景,下⾯我要介绍永久换源的⽅法,通过这个⽅式换源,那么以后我们下载的包就可以全部从这个url中下载了,这样⼤⼤减轻了我们的⼯作量,明显⽐临时换源的⽅法更好。

CHIRPS和CHIRTS API客户端说明说明书

CHIRPS和CHIRTS API客户端说明说明书

Package‘chirps’October12,2022Type PackageTitle API Client for CHIRPS and CHIRTSVersion0.1.4URL https:///chirps/BugReports https:///ropensci/chirps/issuesDescription API Client for the Climate Hazards Center'CHIRPS'and'CHIRTS'.The'CHIRPS'data is a quasi-global(50°S–50°N)high-resolution(0.05arc-degrees)rainfall data set,which incorporates satellite imageryand in-situ station data to create gridded rainfall time series for trendanalysis and seasonal drought monitoring.'CHIRTS'is a quasi-global(60°S–70°N),high-resolution data set of daily maximum and minimumtemperatures.For more details on'CHIRPS'and'CHIRTS'data please visitits official home page<https:///data>.License MIT+file LICENSEEncoding UTF-8LazyData trueDepends R(>=3.5.0),methodsImports httr,jsonlite,sf,stats,terra(>=1.2-10)Suggests climatrends,knitr,markdown,rmarkdown,testthat(>=2.1.0), vcr(>=0.5)Language en-GBRoxygenNote7.1.2VignetteBuilder knitrNeedsCompilation noAuthor Kauêde Sousa[aut,cre](<https:///0000-0002-7571-7845>), Adam H.Sparks[aut](<https:///0000-0002-0061-8359>),Aniruddha Ghosh[aut](<https:///0000-0003-3667-8019>),Pete Peterson[ctb](API Client implementation),William Ashmall[ctb](API Client implementation),Jacob van Etten[ths](<https:///0000-0001-7554-2558>),SveinØ.Solberg[ths](<https:///0000-0002-4491-4483>)12as.geojsonMaintainer Kauêde Sousa<*******************>Repository CRANDate/Publication2022-01-1312:52:52UTCR topics documented:as.geojson (2)chirps (3)get_chirps (4)get_chirts (6)get_esi (8)get_imerg (10)precip_indices (11)tapajos (13)Index14 as.geojson Methods to coerce geographical coordinates into a geojson polygonDescriptionTake single points from geographical coordinates and coerce into a geojson of geometry’Polygon’Usageas.geojson(lonlat,dist=1e-05,nQuadSegs=2L,...)##Default S3method:as.geojson(lonlat,dist=1e-05,nQuadSegs=2L,...)##S3method for class sfas.geojson(lonlat,dist=1e-05,nQuadSegs=2L,...)Argumentslonlat a data.frame or matrix with geographical coordinates lonlat,in that order,or an object of class’sf’with geometry type’POINT’or’POLYGON’dist numeric,buffer distance for all lonlatnQuadSegs integer,number of segments per quadrant...further arguments passed to sf methodsValueAn object of class’geosjon’for each row in lonlatchirps3 Examples#Default S3Method#random geographic points within bbox(10,12,45,47)library("sf")set.seed(123)lonlat<-data.frame(lon=runif(1,10,12),lat=runif(1,45,47))gjson<-as.geojson(lonlat)##################S3Method for objects of class sf#random geographic points within bbox(10,12,45,47)library("sf")set.seed(123)lonlat<-data.frame(lon=runif(5,10,12),lat=runif(5,45,47))lonlat<-st_as_sf(lonlat,coords=c("lon","lat"))gjson<-as.geojson(lonlat)chirps API Client for CHIRPS and CHIRTSDescriptionAPI Client for the Climate Hazards Center’CHIRPS’and’CHIRTS’.The’CHIRPS’data is a quasi-global(50°S–50°N)high-resolution(0.05arc-degrees)rainfall data set,which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.’CHIRTS’is a quasi-global(60°S–70°N),high-resolution data set of daily maximum and minimum temperatures.For more details on’CHIRPS’and’CHIRTS’data please visit its official home page<https:///data>.NoteWhile chirps does not redistribute the data or provide it in any way,we encourage users to cite Funk et al.(2015)when using CHIRPS and Funk et al.(2019)when using CHIRTS.Funk et al.(2015).Scientific Data,2,150066.doi:10.1038/sdata.2015.66Funk et al.(2019).Journal of Climate,32(17),5639–5658.doi:10.1175/JCLID180698.1Author(s)Kauêde Sousa and Adam H.Sparks and Aniruddha GhoshSee AlsoUseful links:•JOSS paper:doi:10.21105/joss.02419•Development repository:https:///ropensci/chirps•Static documentation:https:///chirps/•Report bugs:https:///ropensci/chirps/issues•CHC website:https://get_chirps Get CHIRPS precipitation dataDescriptionGet daily precipitation data from the"Climate Hazards Group".Two server sources are available.Thefirst,"CHC"(default)is recommended for multiple data-points,while"ClimateSERV"is rec-ommended when few data-points are required(~50).Usageget_chirps(object,dates,server,...)##Default S3method:get_chirps(object,dates,server,as.matrix=FALSE,...)##S3method for class SpatVectorget_chirps(object,dates,server="CHC",as.raster=TRUE,...)##S3method for class SpatRasterget_chirps(object,dates,server="CHC",as.matrix=TRUE,as.raster=FALSE,...)##S3method for class sfget_chirps(object,dates,server,as.sf=FALSE,...)##S3method for class geojsonget_chirps(object,dates,server,as.geojson=FALSE,...)Argumentsobject input,an object of class data.frame(or any other object that can be coerced to data.frame),SpatVector,SpatRaster,sf or geojsondates a character of start and end dates in that order in the format"YYYY-MM-DD"server a character that represent the server source"CHC"or"ClimateSERV"...additional arguments passed to terra or sf methods See detailsas.matrix logical,returns an object of class matrixas.raster logical,returns an object of class SpatRasteras.sf logical,returns an object of class sfas.geojson logical,returns an object of class geojsonDetailsData description at https:///products/CHIRPS-2.0/README-CHIRPS.txt Additional arguments when using server="CHC"resolution:numeric,resolution of CHIRPS tiles either0.05(default)or0.25degreesAdditional arguments when using server="ClimateSERV"dist:numeric,buffer distance for each object coordinatenQuadSegs:integer,number of segments per buffer quadrantoperation:supported operations for ClimateSERV are:operation valuemax=0min=1median=2sum=4average=5(default value)ValueA matrix,raster or a data frame of CHIRPS data:id the index for the rows in objectdates the dates from which CHIRPS was requestedlon the longitude as provided in objectlat the latitude as provided in objectchirps the CHIRPS value in mmNoteget_chirps may return some warning messages given by sf,please look sf documentation for pos-sible issues.ReferencesFunk C.et al.(2015).Scientific Data,2,150066.doi:10.1038/sdata.2015.66Exampleslibrary("chirps")library("terra")#Case1:return as a data.framedates<-c("2017-12-15","2017-12-31")lonlat<-data.frame(lon=c(-55.0281,-54.9857),lat=c(-2.8094,-2.8756))r1<-get_chirps(lonlat,dates,server="CHC")#Case2:return a matrixr2<-get_chirps(lonlat,dates,server="CHC",as.matrix=TRUE)#Case3:input SpatVector and return rasterf<-system.file("ex/lux.shp",package="terra")v<-vect(f)r3<-get_chirps(v,dates,server="CHC",as.raster=TRUE)#Case4:using the server"ClimateSERV"r4<-get_chirps(lonlat,dates,server="ClimateSERV")#Case5:from"ClimateSERV"and return as a matrixr5<-get_chirps(lonlat,dates,server="ClimateSERV",as.matrix=TRUE)get_chirts Get CHIRTS temperature data dataDescriptionGet daily maximum and minimum temperature data from the"Climate Hazards Group".CHIRTS-daily is a global2-m temperature product that combines the monthly CHIRTSmax data set with the ERA5reanalysis to produce routinely updated data to support the monitoring of temperature extreme.Data is currently available from1983to2016.Soon available to near-present.Usageget_chirts(object,dates,var,...)##Default S3method:get_chirts(object,dates,var,as.matrix=FALSE,...)##S3method for class SpatVectorget_chirts(object,dates,var,as.raster=TRUE,...)##S3method for class SpatRasterget_chirts(object,dates,var,as.raster=TRUE,...)Argumentsobject an object of class data.frame(or any other object that can be coerced to a data.frame),SpatVector,or SpatRasterdates a character of start and end dates in that order in the format"YYYY-MM-DD"var character,A valid variable from the options:“Tmax”,“Tmin”,“RHum”and “HeatIndex”...additional arguments passed to terraas.matrix logical,returns an object of class matrixas.raster logical,returns an object of class SpatRasterDetailsVariable description from https:///products/CHIRTSdaily/aaa.Readme.txtTmax Daily average maximum air temperature at2m above groundTmin Daily average minimum air temperature at2m above groundRHum Daily average relative humidityHeatIndex Daily average heat indexValueA SpatRaster object if as.raster=TRUE,else matrix,list,or data.frameAdditional argumentsinterval:supported intervals are“daily”,“pentad”,“dekad”,“monthly”,“2-monthly”,“3-monthly”, and“annual”.Currently hard coded to“daily”.Exampleslibrary("chirps")library("terra")#Case1:input a data frame return a data frame in the long formatdates<-c("2010-12-15","2010-12-31")lonlat<-data.frame(lon=c(-55.0281,-54.9857),lat=c(-2.8094,-2.8756))temp1<-get_chirts(lonlat,dates,var="Tmax")#Case2:input a data frame return a matrixtemp2<-get_chirts(lonlat,dates,"Tmax",as.matrix=TRUE)#Case3:input a raster and return rasterf<-system.file("ex/lux.shp",package="terra")v<-vect(f)temp3<-get_chirts(v,dates,var="Tmax",as.raster=TRUE)#Case4:input a raster and return rastertemp4<-get_chirts(v,dates,var="Tmax",as.matrix=TRUE)get_esi Get evaporative stress index(ESI)dataDescriptionGet evaporative stress index(ESI)from SERVIR Global via ClimateSERV API Client.ESI is avail-able every four(or twelve)weeks from2001to present.The dataset may contain cloudy data which is returned as NA s.ClimateSERV works with geojson of type’Polygon’.The input object is then transformed into polygons with a small buffer area around the point.Usageget_esi(object,dates,operation=5,period=1,...)##Default S3method:get_esi(object,dates,operation=5,period=1,...)##S3method for class sfget_esi(object,dates,operation=5,period=1,as.sf=FALSE,...)##S3method for class geojsonget_esi(object,dates,operation=5,period=1,as.geojson=FALSE,...) Argumentsobject input,an object of class data.frame(or any other object that can be coerced to data.frame),SpatVector,SpatRaster,sf or geojsondates a character of start and end dates in that order in the format"YYYY-MM-DD"operation optional,an integer that represents which type of statistical operation to perform on the datasetperiod an integer value for the period of ESI data,four weeks period=1,twelve weeks =2...additional arguments passed to terra or sf methods See detailsas.sf logical,returns an object of class sfas.geojson logical,returns an object of class geojsonDetailsoperation:supported operations are:operation valuemax=0min=1median=2sum=4average=5(default value)dist:numeric,buffer distance for each object coordinatenQuadSegs:integer,number of segments per buffer quadrantValueA data frame of ESI data:id the index for the rows in objectdates the dates from which ESI was requestedlon the longitude as provided in objectlat the latitude as provided in objectesi the ESI valueNoteget_esi may return some warning messages given by sf,please look sf documentation for possible issues.Exampleslonlat<-data.frame(lon=c(-55.0281,-54.9857),lat=c(-2.8094,-2.8756))dates<-c("2017-12-15","2018-06-20")#by default the function set a very small buffer around the points#which can return NAs due to cloudiness in ESI datadt<-get_esi(lonlat,dates=dates)#the argument dist passed through sf increase the buffer areadt<-get_esi(lonlat,dates=dates,dist=0.1)10get_imerg get_imerg Get Integrated Multisatellite Retrievals for GPM(IMERG)dataDescriptionThe IMERG dataset provides near-real time global observations of rainfall at10km resolution, which can be used to estimate total rainfall accumulation from storm systems and quantify the intensity of rainfall andflood impacts from tropical cyclones and other storm systems.IMERG is a daily precipitation dataset available from2015to present within the latitudes70and-70.Usageget_imerg(object,dates,operation=5,...)##Default S3method:get_imerg(object,dates,operation=5,...)##S3method for class sfget_imerg(object,dates,operation=5,as.sf=FALSE,...)##S3method for class geojsonget_imerg(object,dates,operation=5,as.geojson=FALSE,...)Argumentsobject input,an object of class data.frame(or any other object that can be coerced to data.frame),SpatVector,SpatRaster,sf or geojsondates a character of start and end dates in that order in the format"YYYY-MM-DD"operation optional,an integer that represents which type of statistical operation to perform on the dataset...additional arguments passed to terra or sf methods See detailsas.sf logical,returns an object of class sfas.geojson logical,returns an object of class geojsonDetailsoperation:supported operations are:operation valuemax=0min=1median=2sum=4average=5(default value)dist:numeric,buffer distance for each object coordinatenQuadSegs:integer,number of segments per buffer quadrantValueA data frame of IMERG data:id the index for the rows in objectdates the dates from which imerg was requestedlon the longitude as provided in objectlat the latitude as provided in objectimerg the IMERG valueExampleslonlat<-data.frame(lon=c(-55.0281,-54.9857),lat=c(-2.8094,-2.8756))dates<-c("2017-12-15","2017-12-31")dt<-get_imerg(lonlat,dates)dtprecip_indices Compute precipitation indices over a time series.DescriptionCompute precipitation indices over a time series.Usageprecip_indices(object,timeseries=FALSE,intervals=NULL)Argumentsobject an object of class chirps as provided by get_chirpstimeseries logical,FALSE for a single point time series observation or TRUE for a time series based on intervalsintervals integer no lower than5,for the days intervals when timeseries=TRUEValueA dataframe with precipitation indices:MLDS maximum length of consecutive dry day,rain<1mm(days)MLWS maximum length of consecutive wet days,rain>=1mm(days)R10mm number of heavy precipitation days10>=rain<20mm(days)R20mm number of very heavy precipitation days rain>=20(days)Rx1day maximum1-day precipitation(mm)Rx5day maximum5-day precipitation(mm)R95p total precipitation when rain>95th percentile(mm)R99p total precipitation when rain>99th percentile(mm)Rtotal total precipitation(mm)in wet days,rain>=1(mm)SDII simple daily intensity index,total precipitation divided by the number of wet days(mm/days)ReferencesAguilar E.,et al.(2005).Journal of Geophysical Research,110(D23),D23107.Kehel Z.,et al.(2016).In:Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits(eds Bari A.,Damania A.B.,Mackay M.,Dayanandan S.),pp.151–174.CRC Press.Exampleslonlat<-data.frame(lon=c(-55.0281,-54.9857),lat=c(-2.8094,-2.8756))dates<-c("2017-12-15","2017-12-31")dt<-get_chirps(lonlat,dates,server="ClimateSERV")#take the indices for the entire periodprecip_indices(dt,timeseries=FALSE)#take the indices for periods of7daysprecip_indices(dt,timeseries=TRUE,intervals=7)tapajos13 tapajos Tapajos National ForestDescriptionGeometries for the Tapajos National Forest,a protected area in the Brazilian AmazonUsagetapajosFormatAn object of class’sfc_POLYGON’within the bounding box xmin:-55.41127ymin:-4.114584 xmax:-54.7973ymax:-2.751706SourceThe data was provided by the Chico Mendes Institute via https:///enIndex∗datasetstapajos,13∗utility functionsas.geojson,2as.geojson,2chirps,3chirps-package(chirps),3data.frame,5,7,8,10get_chirps,4,11get_chirts,6get_esi,8get_imerg,10precip_indices,11sf,2,5,8–10SpatRaster,5,7,8,10SpatVector,5,7,8,10tapajos,13terra,5,7,8,1014。

食品营养学名词解释

●食品:指各种供人食用或者饮用的成品和原料以及按照传统既是食品又是药品的物品,但是不包括以治疗为目的的物品。

●食品加工:将食品原料经过不同的加工、处理、调配制成各种加工食品的过程可统称为食品加工。

●转基因食品:又称为基因改良食品和转基因食品,通常是指一种经基因修饰的生物体产生的,或由该物质本身构成的食品。

●食品营养学:是研究人体营养规律的一门学科.主要研究食物,营养与人体生长发育和健康的关系以及提高食品营养价值的措施●营养:原意指“谋求养生”。

是指人摄取食物后,在体内消化和吸收和代谢、利用其中的营养素以维持生长发育、组织更新和处于健康状态的总过程。

●营养素:是指具有营养功能的物质,包括蛋白质、脂类、碳水化合物、维生素、矿物质、水、膳食纤维七大类。

●产能营养素:在人体摄取的营养中,只有碳水化合物、脂肪和蛋白质在体内能产生能量,营养学上称这三种营养素为“产能营养素”或“热原质”。

●好的营养:人体从食物中获得保持人体正常生理功能和最佳健康状况所需数量的必需营养素。

●营养密度:食品中以单位热量为基础所含重要营养素的浓度。

●食品的营养价值:食物中营养素及能量满足人体需要的程度。

包括营养素是否种类齐全,数量是否充足和相互比例是否适宜,并且是否被人体消化、吸收和利用。

●营养不良:由于一种或一种以上营养素缺乏或过剩所造成的机体健康异常或疾病状态。

●营养质量指数/食物营养指数(index ofnutritional quality ,INQ):指食物中某种营养素满足一日所需程度与能量满足一日所需程度的比值。

●膳食营养素参考摄入量(dietary referenceintakes ,DRI):DRIs是一组每日平均膳食营养素摄入量的参考值,其中包括4项内容:平均需要(EAR)、推荐摄入量(RNI)、适宜摄入量(AI)和可耐受最高摄入量(UL)。

●膳食结构:指膳食中各类食物的数量及其在膳食中所占的比例。

●平均需要量(estimated average requirement ,EAR):根据某些指标判断可以满足某一指定性别年龄及生理状况群体中50%个体需要的摄入水平。

有线数字互动电视系统技术规范测试用例(临时版)V1.0.docx

有线数字互动电视系统技术规范测试用例(临时版)V1.02011-6-7目录1.测试环境 (7)1.1测试环境71.1.1测试地点71.1.2测试接口说明71.2测试人员81.3测试时间安排82.接口测试 (10)2.1媒资类接口102.1.1A1接口102.1.2A2接口232.1.3A3接口2.1.4A4接口362.1.5A5接口412.1.6A6接口442.1.7A7接口462.2内容分发类接口 462.2.1B1接口462.2.2B2接口482.3终端服务类接口 502.3.1F1接口502.3.2D1接口512.3.3D2接口2.3.4C1接口712.3.5S1接口752.4会话管理类接口 802.4.1S2接口802.4.2S3接口822.4.3S4接口842.4.4S5接口852.5资源管理类接口 902.5.1R1接口902.5.2R2接口922.6计费认证授权类接口1052.6.1E1接口1051.测试环境1.1测试环境1.1.1测试地点国家广播电影电视总局NGB业务研究实验室(北京市西城区真武庙二条真武家园4号楼地下一层NGB业务研究实验室)。

1.1.2测试接口说明1.2测试人员1.3测试时间安排2.接口测试2.1媒资类接口2.1.1A1接口2.1.1.1 内容注入_注入请求2.1.1.2 内容注入_注入2.1.1.3 内容注入_取消注入2.1.1.4 内容注入_注入状态查询2.1.1.4 内容注入_注入状态上报2.1.2A2接口2.1.2.1 元数据交付_节目元数据交付请求2.1.2.2 元数据交付_元数据交付结果回馈2.1.3A3接口2.1.3.1 元数据发布_节目元数据发布2.1.3.2 元数据发布_编排信息发布2.1.3.3 元数据发布_EPG模板发布_请求2.1.3.4 元数据发布_EPG模板发布_门户系统获取模板文件并生效2.1.3.5 元数据发布_EPG模板发布_门户系统模板生效结果反馈2.1.4A4接口2.1.4.1 FTP方式内容发布2.1.4.2 HTTP方式内容发布2.1.4.3 取消发布2.1.4.4 内容删除2.1.5A5接口2.1.5.1 实时内容注入2.1.5.2 等待注入过程中取消注入2.1.5.3 注入过程中取消操作2.1.6A6接口2.1.6.1 运营数据同步_业务包信息同步2.1.6.2运营数据同步_同步结果反馈2.1.7A7接口2.2内容分发类接口2.2.1B1接口2.2.1.1 Index、Trickfile生成2.2.1.2 CDN和推流系统之间的内容分发2.2.2B2接口2.2.2.1 Index、Trickfile生成2.2.2.2 CDN和推流系统之间的内容分发2.3终端服务类接口2.3.1F1接口。

健康饮食ppt英语课件


02 03
Experience Sharing
Britons introduce their dietary culture and habits, emphasizing the importance of a balanced diet and regular meals, as well as the measures of eating and drinking in social cases
Dietary guidelines
Recommendations for a healthy diet typically include increasing intake of fruits, vegetables, whole grains, and lean protein sources while limiting processed foods, saturated fat, and added sugar Equal hydration and mindful eating practices are also emphasized
cautious of driving on an empty stomach
03
Balanced die
Incorporate a variety of foods, including whole grains, fruits,
vegetables, lean proteins, and health fits, to maintain a healthy
Recommended meal frequency
3-5 meals per day, with consistent timing
Quantitative control

太原西山煤田煤系锂镓赋存特征及工业前景——刘汉斌

Abstract:In order to study occurrence characteristics of lithium and gallium and cut off grade in Taiyuan Xishan coalfield,the distribution characteristics and determinant of lithium and gallium in the coal-bearing strata of the Xishan coalfield were discussed. The relationship between lithium and gallium content of coal and gangue and Al2 O3 ,kaolinite and granularity was analysed,and intrinsic storage capacity and industrial prospects in the coal-bearing strata of the Xishan coalfield were discussed. Research show that the lithium content in coal from Xishan coalfield is higher than the average value of lithium in coal from the country,and the content of gallium in coal is higher than the average value of Shanxi coal. The contents of lithium and gallium in coal gangue are significantly higher than those in coal seams,but they are lower than the recommended recovery index for lithium and gallium in coal. Vertically,the lithium content of coal in the Shanxi group is higher than that in the Taiyuan group,but this feature is not evident in the clips. Besides,the content of lithium and gallium shows a good positive correlation with Al2 O3 content in coal,and mainly exsited in the coarse-grained material,while it is less in the fine-grained material. The intrinsic resources of lithium and gangue in the coal-bearing strata of the Xishan coalfield are 23 900 tons and 71 000 tons respectively. However,under current economic and technological conditions,lithium does not have industrial development value. The coal seam in the lower Shanxi Formation in the ancient Jiaojia Xingjiashe mining area and the No. 8 coal field in the Malan mining area has a
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PM 7.2: Audit Procedures FoundationEY Global Workpaper IndexOverview of the Recommended Global IndexWorkpaper index reference CommentsA section Engagement deliverables,control and review - see 1.2for detailsAA Consolidation working papersAA01onwards Workpapers in relation to the audit of the consolidation N/ABSCOTs, walkthroughs and tests of controlsB0.SCOT.1System notes and flowcharts Where SCOT is the abbreviated letters for that SCOT, for example REV for revenue, PUR for purchases etcB0.SCOT.2Walkthrough templates B0.SCOT.3Tests of control documentationBalance sheet accounts C Cash, including bank balancesD Marketable securities/short- term investments The indexes C to VE are in line with current GAMx indexes and relate to both significant and not significant accounts.First sub-sections (e.g., C0, D0, VE0) should be used for indexing lead schedules, with subsequent references (e.g., C01, C02 etc, up to VE01, VE02,VE03 etc) being used for PSPs in numerical order, followed by OSPs.Where more than one significant account exists under a GAMx index, an extra number should be used to distinguish them - for example inProvisions, teams could have P1 for provisions,P2 for accruals, and so on. Indexing would then be:P1_0 leadsheet for provisions, P2_0 leadsheet for accrualsExample indexing/file naming would then be: P1_Provisions workbook P2_Accruals workbookE Accounts receivable, tradeF InventoriesG Prepaid expenses, deferred charges and other assets HInvestments, including investments in affiliatesIIntercompany balances and related party transactionsJ Long-term receivables, non-current deposits and other long-term financial assets KProperty, plant and equipment and related income statement accountsPM 7.2: Audit Procedures FoundationWorkpaper index reference CommentsL Intangibles, including goodwillM Notes payableN Accounts payable, tradeO Income taxes, deferred taxes and related income statement accountsOOT Indirect taxesP Provisions, accrued and other liabilities, deferred incomePWA Warrantee accrualsQ Long-term debt, leases and related income statement accountsR not usedS Derivatives/hedging/ commitments/ contingenciesT EquityIncome statement–revenueaccountsUA Revenue/salesUB Other incomeUC Finance incomeIncome statement–expenseaccountsVA Costs of salesVC Selling and distribution expensesVD Administrative and other expensesVE Finance expensesOtherW Journal entry work X Not usedY Not usedZ Other accountsZ01 and onwards OSPs in relation to other accounts not covered by above indexesPERM Permanent filePERM_I_xx.1Permanent file documents innumerical orderThis is for any documents that need to be retainedone year to the next, filed in numerical order, withI denoting the index (e.g., E Accounts receivable)and xx denoting the year it was put onto the file.PERM_I_xx.2and onwardsNext documentPM 7.2: Audit Procedures FoundationEngagement deliverables, control and reviewWorkpaper index reference Comments A Section -engagement deliverables, control and review A1-Financial statements workpapersA1.1Signed/copy of signed audited financial statements and audit report (if separate) plus other workpapers relating to overall checking of the financialstatements.This will include work in respect of the overall checking of thefinancial statements (including different versions of cashflow statements, footnotes etc). Other workpapers will include the audit report, disclosure checklists, audit work on primary statements and disclosures, etc.A1.2Overall analytical review of financial statements A1.3Segmental reporting A1.4Other public information Including other filings A1.5Trial balanceA2-Quality control/sign offsA2.1Review and approval summary (RAS)If not incorporated into GAMxscreensA2.2GAMx archiving form Refer to local guidance todetermine appropriate use andretention of archive checklists A2.3onwar dsOther checklists/forms that are country specific Local country/Area to determine specifics hereA3-Summary review memorandumA3.1Summary review memorandum A3.1 needed only if notincorporated into GAMx screen A3.2Summary of audit differencesA3.2 needed only if not Incorporated into GAMxA3.2SAD –client communication copy A4-External reporting to clientA4.1Board report/other reports to those charged with governanceA4.2Current year management letter A5-General audit proceduresA5.0Program/form for general audit procedures (PGAP)Indexing here is in line with the Global PGAP template EYG 350,this can all be documented in the PGAP screen.A5.1Preliminary engagement activities:client acceptance and continuance -Engagement agreement -independenceA5.2Minutes and contractsA5.3Inquiry regarding litigation and claimsA5.4Consideration of laws and regulations in audit of financial statements A5.5Related parties A5.6Going concernA5.7Management representationsA5.8Cross-border capital market filingsPM 7.2: Audit Procedures FoundationA5.9Financial institutionsA5.10Environmental mattersA5.11onwardsOther local requirements, as necessaryA6-Internal reportingA6.1Documents received from component teams (we are primary team)A6.2Documents sent to primary team (we are component team)A7-Planning and audit administrationA7.1ASM If not incorporated into GAMx screensA7.2Timetable, budgets and schedulingA7.3Understand the business (UBT)If not incorporated into GAMx screensA7.4Determine needs for specialist skills If not incorporated into GAMx screensA7.5Entity-level control form A7.6Fraud considerations formA7.7PM/TE If not incorporated into GAMx screensA7.8Group instructions sent by us (we are primary team)A7.9Group instructions received by us (we are component team)A7.10Other planning documents as necessaryA8-Matters relating to the next year’s audit A8.0onwardsCarry-forward notes。

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