GP_cn_issue_28_Dec_2011
Kernel SHAP 0.4.1 说明书
Package‘kernelshap’December3,2023Title Kernel SHAPVersion0.4.1Description Efficient implementation of Kernel SHAP,see Lundberg and Lee(2017),and Covert and Lee(2021)<http://proceedings.mlr.press/v130/covert21a>.Furthermore,for up to14features,exact permutation SHAP values can be calculated.Thepackage plays well together with meta-learning packages like'tidymodels','caret'or'mlr3'.Visualizations can be done using theR package'shapviz'.License GPL(>=2)Depends R(>=3.2.0)Encoding UTF-8RoxygenNote7.2.3Imports foreach,stats,utilsSuggests doFuture,testthat(>=3.0.0)Config/testthat/edition3URL https:///ModelOriented/kernelshapBugReports https:///ModelOriented/kernelshap/issuesNeedsCompilation noAuthor Michael Mayer[aut,cre],David Watson[aut],Przemyslaw Biecek[ctb](<https:///0000-0001-8423-1823>)Maintainer Michael Mayer<************************>Repository CRANDate/Publication2023-12-0314:20:02UTCR topics documented:is.kernelshap (2)is.permshap (3)12is.kernelshapkernelshap (3)permshap (9)print.kernelshap (11)print.permshap (12)summary.kernelshap (13)summary.permshap (14)Index15 is.kernelshap Check for kernelshapDescriptionIs object of class"kernelshap"?Usageis.kernelshap(object)Argumentsobject An R object.ValueTRUE if object is of class"kernelshap",and FALSE otherwise.See Alsokernelshap()Examplesfit<-lm(Sepal.Length~.,data=iris)s<-kernelshap(fit,iris[1:2,-1],bg_X=iris[,-1])is.kernelshap(s)is.kernelshap("a")is.permshap3 is.permshap Check for permshapDescriptionIs object of class"permshap"?Usageis.permshap(object)Argumentsobject An R object.ValueTRUE if object is of class"permshap",and FALSE otherwise.See Alsokernelshap()Examplesfit<-lm(Sepal.Length~.,data=iris)s<-permshap(fit,iris[1:2,-1],bg_X=iris[,-1])is.permshap(s)is.permshap("a")kernelshap Kernel SHAPDescriptionEfficient implementation of Kernel SHAP,see Lundberg and Lee(2017),and Covert and Lee (2021),abbreviated by CL21.For up to p=8features,the resulting Kernel SHAP values are exact regarding the selected background data.For larger p,an almost exact hybrid algorithm involving iterative sampling is used,see Details.Usagekernelshap(object,...)##Default S3method:kernelshap(object,X,bg_X,pred_fun=stats::predict,feature_names=colnames(X),bg_w=NULL,exact=length(feature_names)<=8L,hybrid_degree=1L+length(feature_names)%in%4:16,paired_sampling=TRUE,m=2L*length(feature_names)*(1L+3L*(hybrid_degree==0L)),tol=0.005,max_iter=100L,parallel=FALSE,parallel_args=NULL,verbose=TRUE,...)##S3method for class rangerkernelshap(object,X,bg_X,pred_fun=function(m,X,...)stats::predict(m,X,...)$predictions, feature_names=colnames(X),bg_w=NULL,exact=length(feature_names)<=8L,hybrid_degree=1L+length(feature_names)%in%4:16,paired_sampling=TRUE,m=2L*length(feature_names)*(1L+3L*(hybrid_degree==0L)),tol=0.005,max_iter=100L,parallel=FALSE,parallel_args=NULL,verbose=TRUE,...)##S3method for class Learnerkernelshap(object,X,bg_X,pred_fun =NULL,feature_names =colnames(X),bg_w =NULL,exact =length(feature_names)<=8L,hybrid_degree =1L +length(feature_names)%in%4:16,paired_sampling =TRUE,m =2L *length(feature_names)*(1L +3L *(hybrid_degree ==0L)),tol =0.005,max_iter =100L,parallel =FALSE,parallel_args =NULL,verbose =TRUE,...)Argumentsobject Fitted model object....Additional arguments passed to pred_fun(object,X,...).X(n ×p )matrix or data.frame with rows to be explained.The columns should only represent model features,not the response (but see feature_names on how to overrule this).bg_XBackground data used to integrate out "switched off"features,often a subset of the training data (typically 50to 500rows)It should contain the same columns as X .In cases with a natural "off"value (like MNIST digits),this can also be a single row with all values set to the off value.pred_funPrediction function of the form function(object,X,...),providing K ≥1predictions per row.Its first argument represents the model object ,its second argument a data structure like X .Additional (named)arguments are passed via ....The default,stats::predict(),will work in most cases.feature_names Optional vector of column names in X used to calculate SHAP values.By de-fault,this equals colnames(X).Not supported if X is a matrix.bg_w Optional vector of case weights for each row of bg_X .exactIf TRUE ,the algorithm will produce exact Kernel SHAP values with respect to the background data.In this case,the arguments hybrid_degree ,m ,paired_sampling ,tol ,and max_iter are ignored.The default is TRUE up to eight features,and FALSE otherwise.hybrid_degreeInteger controlling the exactness of the hybrid strategy.For 4≤p ≤16,the default is 2,otherwise it is 1.Ignored if exact =TRUE .•0:Pure sampling strategy not involving any exact part.It is strictly worse than the hybrid strategy and should therefore only be used for studying properties of the Kernel SHAP algorithm.•1:Uses all 2p on-off vectors z withz ∈{1,p −1}for the exact part,which covers at least 75%of the mass of the Kernel weight distribution.The remaining mass is covered by random sampling.•2:Uses all p (p +1)on-off vectors z withz ∈{1,2,p −2,p −1}.This covers at least 92%of the mass of the Kernel weight distribution.The remaining mass is covered by sampling.Convergence usually happens in the minimal possible number of iterations of two.•k>2:Uses all on-off vectors withz ∈{1,...,k,p −k,...,p −1}.paired_samplingLogical flag indicating whether to do the sampling in a paired manner.This means that with every on-off vector z ,also 1−z is considered.CL21shows its superiority compared to standard sampling,therefore the default (TRUE )should usually not be changed except for studying properties of Kernel SHAP algo-rithms.Ignored if exact =TRUE .m Even number of on-off vectors sampled during one iteration.The default is 2p ,except when hybrid_degree ==0.Then it is set to 8p .Ignored if exact =TRUE .tolTolerance determining when to stop.Following CL21,the algorithm keeps iter-ating until max (σn )/(max (βn )−min (βn ))<tol,where the βn are the SHAP values of a given observation,and σn their standard errors.For multidimen-sional predictions,the criterion must be satisfied for each dimension separately.The stopping criterion uses the fact that standard errors and SHAP values are all on the same scale.Ignored if exact =TRUE .max_iter If the stopping criterion (see tol )is not reached after max_iter iterations,the algorithm stops.Ignored if exact =TRUE .parallelIf TRUE ,use parallel foreach::foreach()to loop over rows to be explained.Must register backend beforehand,e.g.,via ’doFuture’package,see README for an example.Parallelization automatically disables the progress bar.parallel_argsNamed list of arguments passed to foreach::foreach().Ideally,this is NULL (default).Only relevant if parallel =TRUE .Example on Windows:if object is a GAM fitted with package ’mgcv’,then one might need to set parallel_args =list(.packages ="mgcv").verbose Set to FALSE to suppress messages and the progress bar.DetailsPure iterative Kernel SHAP sampling as in Covert and Lee (2021)works like this:1.A binary "on-off"vector z is drawn from {0,1}p such that its sum follows the SHAP Kernel weight distribution (normalized to the range {1,...,p −1}).2.For each j with z j =1,the j -th column of the original background data is replaced by the corresponding feature value x j of the observation to be explained.3.The average prediction v z on the data of Step 2is calculated,and the average prediction v 0on the background data is subtracted.4.Steps 1to 3are repeated m times.This produces a binary m ×p matrix Z (each row equals one of the z )and a vector v of shifted predictions.5.v is regressed onto Z under the constraint that the sum of the coefficients equals v 1−v 0,where v 1is the prediction of the observation to be explained.The resulting coefficients are the Kernel SHAP values.This is repeated multiple times until convergence,see CL21for details.A drawback of this strategy is that many (at least 75%)of the z vectors will havez ∈{1,p −1},producing many duplicates.Similarly,at least 92%of the mass will be used for the p (p +1)possible vectors withz ∈{1,2,p −2,p −1}.This inefficiency can be fixed by a hybrid strategy,combining exact calculations with sampling.The hybrid algorithm has two steps:1.Step 1(exact part):There are 2p different on-off vectors z withz ∈{1,p −1},covering a large proportion of the Kernel SHAP distribution.The degree 1hybrid will list those vectors and use them according to their weights in the upcoming calculations.Depending on p ,we can also go a step further to a degree 2hybrid by adding all p (p −1)vectors with z ∈{2,p −2}to the process etc.The necessary predictions are obtained along with other calculations similar to those described in CL21.2.Step 2(sampling part):The remaining weight is filled by sampling vectors z according to Kernel SHAP weights renormalized to the values not yet covered by Step 1.Together with the results from Step 1-correctly weighted -this now forms a complete iteration as in CL21.The difference is that most mass is covered by exact calculations.Afterwards,the algorithm iterates until convergence.The output of Step 1is reused in every iteration,leading to an extremely efficient strategy.If p is sufficiently small,all possible 2p −2on-off vectors z can be evaluated.In this case,no sampling is required and the algorithm returns exact Kernel SHAP values with respect to the given background data.Since kernelshap()calculates predictions on data with MN rows (N is the background data size and M the number of z vectors),p should not be much higher than 10for exact calculations.For similar reasons,degree 2hybrids should not use p much larger than 40.ValueAn object of class "kernelshap"with the following components:•S :(n ×p )matrix with SHAP values or,if the model output has dimension K >1,a list of K such matrices.•X :Same as input argument X .•baseline :Vector of length K representing the average prediction on the background data.•SE :Standard errors corresponding to S (and organized like S ).•n_iter :Integer vector of length n providing the number of iterations per row of X .•converged :Logical vector of length n indicating convergence per row of X .•m :Integer providing the effective number of sampled on-off vectors used per iteration.•m_exact :Integer providing the effective number of exact on-off vectors used per iteration.•prop_exact :Proportion of the Kernel SHAP weight distribution covered by exact calcula-tions.•exact :Logical flag indicating whether calculations are exact or not.•txt :Summary text.•predictions :(n ×K )matrix with predictions of X .Methods(by class)•kernelshap(default):Default Kernel SHAP method.•kernelshap(ranger):Kernel SHAP method for"ranger"models,see Readme for an exam-ple.•kernelshap(Learner):Kernel SHAP method for"mlr3"models,see Readme for an exam-ple.References1.Scott M.Lundberg and Su-In Lee.A unified approach to interpreting model predictions.Proceedings of the31st International Conference on Neural Information Processing Systems, 2017.2.Ian Covert and Su-In Lee.Improving KernelSHAP:Practical Shapley Value Estimation Us-ing Linear Regression.Proceedings of The24th International Conference on Artificial Intel-ligence and Statistics,PMLR130:3457-3465,2021.Examples#MODEL ONE:Linear regressionfit<-lm(Sepal.Length~.,data=iris)#Select rows to explain(only feature columns)X_explain<-iris[1:2,-1]#Select small background dataset(could use all rows here because iris is small)set.seed(1)bg_X<-iris[sample(nrow(iris),100),]#Calculate SHAP valuess<-kernelshap(fit,X_explain,bg_X=bg_X)s#MODEL TWO:Multi-response linear regressionfit<-lm(as.matrix(iris[,1:2])~Petal.Length+Petal.Width+Species,data=iris) s<-kernelshap(fit,iris[1:4,3:5],bg_X=bg_X)summary(s)#Non-feature columns can be dropped via feature_namess<-kernelshap(fit,iris[1:4,],bg_X=bg_X,feature_names=c("Petal.Length","Petal.Width","Species"))spermshap Permutation SHAPDescriptionExact permutation SHAP algorithm with respect to a background dataset,see Strumbelj and Kononenko.The function works for up to14features.Usagepermshap(object,...)##Default S3method:permshap(object,X,bg_X,pred_fun=stats::predict,feature_names=colnames(X),bg_w=NULL,parallel=FALSE,parallel_args=NULL,verbose=TRUE,...)##S3method for class rangerpermshap(object,X,bg_X,pred_fun=function(m,X,...)stats::predict(m,X,...)$predictions,feature_names=colnames(X),bg_w=NULL,parallel=FALSE,parallel_args=NULL,verbose=TRUE,...)##S3method for class Learnerpermshap(object,X,bg_X,pred_fun=NULL,feature_names=colnames(X),bg_w=NULL,parallel=FALSE,parallel_args=NULL,verbose=TRUE,...)Argumentsobject Fitted model object....Additional arguments passed to pred_fun(object,X,...).X(n×p)matrix or data.frame with rows to be explained.The columns should only represent model features,not the response(but see feature_names on howto overrule this).bg_X Background data used to integrate out"switched off"features,often a subset of the training data(typically50to500rows)It should contain the same columnsas X.In cases with a natural"off"value(like MNIST digits),this can also be asingle row with all values set to the off value.pred_fun Prediction function of the form function(object,X,...),providing K≥1 predictions per row.Itsfirst argument represents the model object,its secondargument a data structure like X.Additional(named)arguments are passed via....The default,stats::predict(),will work in most cases.feature_names Optional vector of column names in X used to calculate SHAP values.By de-fault,this equals colnames(X).Not supported if X is a matrix.bg_w Optional vector of case weights for each row of bg_X.parallel If TRUE,use parallel foreach::foreach()to loop over rows to be explained.Must register backend beforehand,e.g.,via’doFuture’package,see READMEfor an example.Parallelization automatically disables the progress bar.parallel_args Named list of arguments passed to foreach::foreach().Ideally,this is NULL (default).Only relevant if parallel=TRUE.Example on Windows:if object isa GAMfitted with package’mgcv’,then one might need to set parallel_args=list(.packages="mgcv").verbose Set to FALSE to suppress messages and the progress bar.ValueAn object of class"permshap"with the following components:•S:(n×p)matrix with SHAP values or,if the model output has dimension K>1,a list of K such matrices.•X:Same as input argument X.•baseline:Vector of length K representing the average prediction on the background data.•m_exact:Integer providing the effective number of exact on-off vectors used.•exact:Logicalflag indicating whether calculations are exact or not(currently TRUE).•txt:Summary text.•predictions:(n×K)matrix with predictions of X.print.kernelshap11Methods(by class)•permshap(default):Default permutation SHAP method.•permshap(ranger):Permutation SHAP method for"ranger"models,see Readme for an ex-ample.•permshap(Learner):Permutation SHAP method for"mlr3"models,see Readme for an ex-ample.References1.Erik Strumbelj and Igor Kononenko.Explaining prediction models and individual predictionswith feature contributions.Knowledge and Information Systems41,2014.Examples#MODEL ONE:Linear regressionfit<-lm(Sepal.Length~.,data=iris)#Select rows to explain(only feature columns)X_explain<-iris[1:2,-1]#Select small background dataset(could use all rows here because iris is small)set.seed(1)bg_X<-iris[sample(nrow(iris),100),]#Calculate SHAP valuess<-permshap(fit,X_explain,bg_X=bg_X)s#MODEL TWO:Multi-response linear regressionfit<-lm(as.matrix(iris[,1:2])~Petal.Length+Petal.Width+Species,data=iris) s<-permshap(fit,iris[1:4,3:5],bg_X=bg_X)s#Non-feature columns can be dropped via feature_namess<-permshap(fit,iris[1:4,],bg_X=bg_X,feature_names=c("Petal.Length","Petal.Width","Species"))sprint.kernelshap Prints"kernelshap"ObjectDescriptionPrints"kernelshap"Object12print.permshapUsage##S3method for class kernelshapprint(x,n=2L,...)Argumentsx An object of class"kernelshap".n Maximum number of rows of SHAP values to print....Further arguments passed from other methods.ValueInvisibly,the input is returned.See Alsokernelshap()Examplesfit<-lm(Sepal.Length~.,data=iris)s<-kernelshap(fit,iris[1:3,-1],bg_X=iris[,-1])sprint.permshap Prints"permshap"ObjectDescriptionPrints"permshap"ObjectUsage##S3method for class permshapprint(x,n=2L,...)Argumentsx An object of class"permshap".n Maximum number of rows of SHAP values to print....Further arguments passed from other methods.ValueInvisibly,the input is returned.summary.kernelshap13See Alsopermshap()Examplesfit<-lm(Sepal.Length~.,data=iris)s<-permshap(fit,iris[1:3,-1],bg_X=iris[,-1])ssummary.kernelshap Summarizes"kernelshap"ObjectDescriptionSummarizes"kernelshap"ObjectUsage##S3method for class kernelshapsummary(object,compact=FALSE,n=2L,...)Argumentsobject An object of class"kernelshap".compact Set to TRUE for a more compact summary.n Maximum number of rows of SHAP values etc.to print....Further arguments passed from other methods.ValueInvisibly,the input is returned.See Alsokernelshap()Examplesfit<-lm(Sepal.Length~.,data=iris)s<-kernelshap(fit,iris[1:3,-1],bg_X=iris[,-1])summary(s)14summary.permshap summary.permshap Summarizes"permshap"ObjectDescriptionSummarizes"permshap"ObjectUsage##S3method for class permshapsummary(object,compact=FALSE,n=2L,...)Argumentsobject An object of class"permshap".compact Set to TRUE for a more compact summary.n Maximum number of rows of SHAP values etc.to print....Further arguments passed from other methods.ValueInvisibly,the input is returned.See Alsopermshap()Examplesfit<-lm(Sepal.Length~.,data=iris)s<-permshap(fit,iris[1:3,-1],bg_X=iris[,-1])summary(s)Indexforeach::foreach(),6,10is.kernelshap,2is.permshap,3kernelshap,3kernelshap(),2,3,7,12,13permshap,9permshap(),13,14print.kernelshap,11print.permshap,12stats::predict(),5,10summary.kernelshap,13summary.permshap,1415。
STM32固件库使用手册的中文翻译版
因为该固件库是通用的，并且包括了所有外设的功能，所以应用程序代码的大小和执行速度可能不是最优 的。对大多数应用程序来说，用户可以直接使用之，对于那些在代码大小和执行速度方面有严格要求的应 用程序，该固件库驱动程序可以作为如何设置外设的一份参考资料，根据实际需求对其进行调整。
1.3.1 变量 ................................................................................................................................................ 28 1.3.2 布尔型 ............................................................................................................................................ 28 1.3.3 标志位状态类型 ........................................................................................................................... 29 1.3.4 功能状态类型 .............................................................................................................
气泡混合轻质土使用规程
目次1总则 (3)2术语和符号 (4)2.1 术语 (4)2.2 符号 (5)3材料及性能 (6)3.1 原材料 (6)3.2 性能 (6)4设计 (8)4.1 一般规定 (8)4.2 性能设计 (8)4.3 结构设计 (9)4.4 附属工程设计 (10)4.5 设计计算 (10)5配合比 (13)5.1 一般规定 (13)5.2 配合比计算 (13)5.3 配合比试配 (14)5.4 配合比调整 (14)6工程施工 (15)6.1 浇筑准备 (15)6.2 浇筑 (15)6.3 附属工程施工 (15)6.4 养护 (16)7质量检验与验收 (17)7.1 一般规定 (17)7.2 质量检验 (17)7.3 质量验收 (18)附录A 发泡剂性能试验 (20)附录B 湿容重试验 (22)附录C 适应性试验 (22)附录D 流动度试验 (24)附录E 干容重、饱水容重试验 (25)附录F 抗压强度、饱水抗压强度试验 (27)附录G 工程质量检验验收用表 (28)本规程用词说明 (35)引用标准名录 (36)条文说明 (37)Contents1.General provisions (3)2.Terms and symbols (4)2.1 Terms (4)2.2 Symbols (5)3. Materials and properties (6)3.1 Materials (6)3.2 properties (6)4. Design (8)4.1 General provisions (8)4.2 Performance design (8)4.3 Structure design (9)4.4 Subsidiary engineering design (9)4.5 Design calculation (10)5. Mix proportion (13)5.1 General provisions (13)5.2 Mix proportion calculation (13)5.3 Mix proportion trial mix (14)5.4 Mix proportion adjustment (14)6. Engineering construction (15)6.1 Construction preparation (15)6.2 Pouring .............................................................. .. (15)6.3 Subsidiary engineering construction (16)6.4 Maintenance (17)7 Quality inspection and acceptance (18)7.1 General provisions (18)7.2 Quality evaluate (18)7.3 Quality acceptance (19)Appendix A Test of foaming agent performance (20)Appendix B Wet density test (22)Appendix C Adaptability test (23)Appendix D Flow value test.................................................................................. .. (24)Appendix E Air-dry density and saturated density test (25)Appendix F Compressive strength and saturated compressive strength test (27)Appendix G Table of evaluate and acceptance for quality (28)Explanation of Wording in this code (35)Normative standard (36)Descriptive provision (37)1总则1.0.1为规范气泡混合轻质土的设计、施工，统一质量检验标准，保证气泡混合轻质土填筑工程安全适用、技术先进、经济合理，制订本规程。
11-11-0316-00-000m-proposed-resolution-of-cid-11001
7
Class 3 frame received from nonassociated STA
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Disassociated because sending STA is leaving (or has left) BSS
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STA requesting (re)association is not authenticated with responding STA
Ensure consistency between .confirm and .response by making the enumerated listof the .confirm contain all the values in the .response.
The Changes
Changes are specified relative to D7.03.
Decree 248 Single Window Operation Manuals and Tut
Voluntary Report –Voluntary - Public Distribution Date:December 22, 2021Report Number:CH2021-0174Report Name:Decree 248 Single Window Operation Manuals and Tutorial Country:China - People's Republic ofPost: BeijingReport Category:Agricultural Situation, Agricultural Trade Office Activities, Policy and Program Announcements, National Plan, Beverages, Agriculture in the News, Citrus, Coffee, Dairy and Products, Market Development Reports, Avocado, Canned Deciduous Fruit, Dried Fruit, Fresh Deciduous Fruit, Fresh Fruit, Kiwifruit, Raisins, Stone Fruit, Strawberries, Fishery Products, Grain and Feed, Livestock and Products, MISC-Commodity, Oilseeds and Products, Potatoes and Potato Products, Poultry and Products, Sugar, Tomatoes and Products, Tree Nuts, Vegetables, WinePrepared By:Alan HallmanApproved By:Adam BransonReport Highlights:The report contains FAS China's understanding of the overseas facility registration process as required by the General Administration of Customs of the People's Republic of China (GACC) in Decree 248. In addition, this report contains attachments of unofficial translations of the Competent Authority and Overseas Enterprise Operation Manuals for the GACC Single Window registration website. In addition, the report contains information from recent training sessions and background information on Decree 248 to facilitate self-registration for U.S. food and agribusiness operations affected by Decree 248.General Information:This report provides information related to FAS China’s understanding of the General Administration of Customs of the People’s Republic of China (G ACC) overseas facility registration process. This report includes translations of GACC operation manuals for 1) Competent Authorities; 2) Overseas Enterprises; and 3) a tutorial to the Single Window website self-registration process developed by a third party. In addition, the report has an Excel table attachment that includes the Harmonized System (HS) tariff codes and commodity descriptions affected by Decree 248. FAS China conveys its thanks and appreciation to staff and officers at the Embassies of Australia, Canada, New Zealand, and other colleagues at the United States Embassy in Beijing, China who jointly translated the two GACC manuals.Decree 248 notified by GACC requires the registration of overseas food production and cold storage facilities that produce or handle specific food products exported to China. The regulation sets out two registration pathways for facilities that produce products within its scope – self-registration, and registration by a competent authority located in the exporting country.Article 7 of Decree 248 lists 18 product categories that GACC initially indicated would require an overseas facility to register before having product be presented for customs clearance in China. GACC indicated that it expects facilities producing products in those categories to register through a competent authority. In addition, Article 9 of Decree 248 requires the self-registration of overseas facilities that are outside of the 18 product categories. Since publishing the list of 18 product categories GACC has provided and continues making “systems improvements” to a table of Chin a’s Harmonized System (HS) tariff codes at the 10-digit level inside the Single Window website that are apparently the actual scope of goods that require registration. The table also provides GACC inspection and quarantine codes and additional descriptions for the products affected by Decree 248. However, there are several food and related products that are not in GACC’s table. Moreover, some commodities can fall in or outside the scope of Decree 248 depending upon their end use.For example, although the HS code 0805100000 for “Oranges, Fresh or Dried” is included on the table, the GACC inspection and quarantine code indicates that only those facilities handling “Oranges, Fresh or Dried” that have also been “candied” or “sweetened” need to self-register. In other words, facilities handling “regular” oranges are not required to register with the GACC under Decree 248.Another example is sorghum, which appears in the table under the Chinese HS code 1007900000 with GACC inspection and quarantine codes 102 and 103 – for other edible sorghum, inshell and shelled, respectively. GACC is requesting that facilities handling inshell or shelled sorghum for human consumption register that facility through a competent authority. Based on this information, there is no expectation that U.S. exporters of sorghum for non-food use register under Decree 248. However, the United States Government already provides a complete list, based on a previous agreement, of U.S. grain exporting companies to GACC for food and non-food sorghum. Numerous attempts to seek clarification from GACC have gone unanswered.GACC recently announced that a facility registration number will have to be presented during customs clearance for imports of commodities covered by Decree 248 starting on January 1, 2022. GACC has said that products produced prior to January 1, 2022, will not require facility registration numbers printed on the inner and outer packaging of the products. GACC officials have also said that either the GACC facility registration number or the facility registration number issued by the competent authority in the exporting country must be printed on the inner and outer packaging of products produced on or after January 1, 2022. Because the destination of most products is not known at the time of production or manufacturing, U.S.-based facilities working with these products will need to assess what facility registration number to use on products that could be destined for China.The Single Window self-registration website requires that facilities attempting to register select a Chinese 10-digit HS code and the 3-digit GACC inspection and quarantine code to identify their product(s). The attached Excel table can be a guide for identifying this information. If a facility cannot identify which HS code may be used, they should consider the description and end-use of their product(s) and/or contact their existing or a potential importer to assess the tariff line that will be used for customs clearance. Companies attempting to self-register should contact GACC staff, identified below, when they have questions about the Single Window system and the applicability of the registration requirements to their establishments and product(s). As China continues making “system improvements” to the Single Window website, facilities attempting to self-register should consult official information and conduct a “Product type Query” in the Single Window website. The attached table and the information in the Single Window website also include a column where GACC indicates if facilities producing that product should self-register or be registered by a competent authority.The self-registration process requires a significant amount of information. Registrants may find it helpful to review the rest of this report and the attached translation of GACC’s operation manual for enterprises before commencing.The translation of GACC’s operation manual for competent authorities is attached as a reference. U.S.-based facilities that GACC is requesting register through a competent authority are encouraged to read the U.S. Food and Drug Administration’s (FDA) Constituent Update dated December 6. It can be found at this link.FAS China has published the following reports on Decrees 248 and 249 to inform and educate U.S. food and agricultural stakeholders.∙Decrees 248 and 249 – January 1 Implementation Date [Public Notice 103] – December 14, 2021∙Decree 248 - FDA Constituent Update – December 8, 2021∙Decree 248 Single Window User Manual and Training - November 30, 2021∙Decree 248 HS Codes Published in GACC Single Window – November 30, 2021∙Informing Industry and Supporting Trade Facilitation as China Implements Decrees 248 and 249 –November 10, 2021∙Decree 248 Information Update – November 19, 2021∙GACC Issues Interpretation of Decree 248 - November 6, 2021∙Decree 248 Foreign Facilities Self-Registration Website Launched - November 3, 2021∙Decree 248 Unofficial Self-Registration Guide for Overseas Food Facilities - November 2, 2021∙Notes from Information Sessions on GACC Decrees 248 and 249 - October 22, 2021∙GACC Issues Explanatory Letter on Decree 248 - October 8, 2021∙Interpretative Guidance on Imported Food Labeling Requirements in Decree 249 - August 13, 2021 ∙Decrees 248 and 249 Status Update on Facilities Registration and Food Safety Measures - May 25, 2021∙Administrative Measures on Import and Export Food Safety - Decree 249 - May 7, 2021∙Overseas Facilities Registration Regulation - Decree 248 - April 20, 2021∙China Notified the Draft Administrative Measures on Import and Export Food Safety - December 11, 2020∙China Notifies Revised Overseas Facilities Registration Regulations as TBT 1522 - December 2, 2020∙China Released Draft Administrative Measures for Registration of Overseas Producers of Imported Foods - December 6, 2019Attachments:211221 GACC 248 SW Overseas Enterprise Operation Manual Eng Translation.docx211221 FAS China Third Party SW Self Registration Guide.docx211215 GACC Decree 248 HS Code List English Translation.xlsx211223 GACC 248 SW Competent Authority Operation Manual Eng Translation.docx。
GLPKAPI R包用户指南说明书
Introduction to glpkAPILouis Luangkesorn∗November10,20221IntroductionThis document introduces the use of the glpkAPI package1for R.The GNU Linear Programming Package(GLPK)is intended for solving linear program-ming(LP)and mixed integer programming(MIP)and other related problems. In addition,it includes facilities for converting problem information between the GNU MathProg language(a subset of the AMPL mathematical programming language),free andfixed MPS,and the CPLEX LP formats.2The GLPK pack-age is an interface into the C Application Programming Interface(API)to the GLPK solver.This document will introduce the use of the GLPK package through the use of the cannery problem from Dantzig3which is used in the GNU MathProg documentation.4The modelfile describing the cannery problem can be found in Appendix A.2Entering the modelTo use glpk,first load the package.>library(glpkAPI)Next read in the model and data.There are several ways of entering the model.glpk can read the model and data in a GNU MathProg Language(GMPL)modelfile.Alternatively,the model and data can be entered using the GLPK API.∗*******************.Thanks to Leo Lopes for his comments and suggestions.1Package glpkAPI maintained by Gabriel Gelius-Dietrich2GNU Linear Programming Kit:Reference Manual,Version4.54Draft,March2014.3The demand data here is from the GLPK documentation,which differs slightly from Dantzig,Linear Programming and Extensions,Princeton University Press,Princeton,NJ, 1963.The documentation demand values are used here for consistancy.4GNU Linear Programming Kit:Modeling Language GNU MathProg,Version4.50Draft, May2013.12.1Reading a GNU MathProg Language modelTo use a GNU MathProg model requires several steps.1.Allocating the workspace using initProbGLPK().The problem can thenbe given an name using setProbNameGLPK().2.Reading model section using mplAllocWkspGLPK()and mplReadModelGLPK().3.Reading data section(s)using mplReadDataGLPK().4.Generating the model using mplGenerateGLPK().5.Building the problem object using result<-mplBuildProbGLPK().6.Solving the problem using solveSimplexGLPK().7.Postsolving the model using mplPostsolveGLPK().8.Freeing the workspace using mplFreeWkspGLPK()and delProbGLPK()>mip<-initProbGLPK()>setProbNameGLPK(mip,"transport")>trans<-mplAllocWkspGLPK()>result<-mplReadModelGLPK(trans,+system.file("extdata","transport.mod",package="glpkAPI"),skip=0) >result<-mplGenerateGLPK(trans)>result<-mplBuildProbGLPK(trans,mip)If the data was in a separatefile,it would need to be read in usingmplReadDataGLPK(trans,"transport.mod")Then examine the problem size within R.The rows represent the objective function as well as the supply and demandconstraints.>numrows<-getNumRowsGLPK(mip)>numrows[1]6>for(i in1:numrows){+print(getRowNameGLPK(mip,i))+}[1]"cost"[1]"supply[Seattle]"[1]"supply[San-Diego]"[1]"demand[New-York]"[1]"demand[Chicago]"[1]"demand[Topeka]"2The columns represent the decision variables,which are the units sent over the cannary-market links.>numcols<-getNumColsGLPK(mip)>numcols[1]6>for(j in1:numcols){+print(getColNameGLPK(mip,j))+}[1]"x[Seattle,New-York]"[1]"x[Seattle,Chicago]"[1]"x[Seattle,Topeka]"[1]"x[San-Diego,New-York]"[1]"x[San-Diego,Chicago]"[1]"x[San-Diego,Topeka]">print(getNumNnzGLPK(mip))[1]18After the model and data are entered,the model can then be solved using any one of many algorithms and the output would go to the specified output file.For the Simplex method,the solveSimplexGLPK()takes the problem name and solves it using the Simplex method.>return<-solveSimplexGLPK(mip)>return<-mplPostsolveGLPK(trans,mip,GLP_MIP);We can then look at the solution in terms of the objective and constraints >for(i in1:numrows){+print(getRowNameGLPK(mip,i))+print(getRowPrimGLPK(mip,i))+}[1]"cost"[1]153.675[1]"supply[Seattle]"[1]350[1]"supply[San-Diego]"[1]550[1]"demand[New-York]"[1]325[1]"demand[Chicago]"[1]300[1]"demand[Topeka]"[1]2753as well as the decision variables.>for(j in1:numcols){+print(getColNameGLPK(mip,j))+print(getColPrimGLPK(mip,j))+}[1]"x[Seattle,New-York]"[1]50[1]"x[Seattle,Chicago]"[1]300[1]"x[Seattle,Topeka]"[1]0[1]"x[San-Diego,New-York]"[1]275[1]"x[San-Diego,Chicago]"[1]0[1]"x[San-Diego,Topeka]"[1]275Finally,clean up the workspace.>mplFreeWkspGLPK(trans)>delProbGLPK(mip)2.2Using the APIIf the problem data already in R,such as pulled from a database or the result of previous analysis,the model and the data can be specified using the API.First create R data objects to hold the various model parameters.>print("USING API")[1]"USING API">canneries<-c("Seattle","San-Diego")>capacity<-c(350,600)>markets<-c("New-York","Chicago","Topeka")>demand<-c(325,300,275)>distance<-c(2.5,2.5,1.7,1.8,1.8,1.4)>dim(distance)<-c(2,3)>freight<-90To use the API,define a problem instance and indicate that the objective is to minimize cost.>lpi<-initProbGLPK()>setProbNameGLPK(lpi,"cannery API")>setObjNameGLPK(lpi,"Total Cost")>setObjDirGLPK(lpi,GLP_MIN)4There are6columns,corresponding to the six potential cannery-market pairswhose transport the model solving for,each of which has a lower bound of zero.>numlinks<-length(distance)>nummarkets<-length(markets)>numcanneries<-length(canneries)>addColsGLPK(lpi,numlinks)[1]1>for(i in1:numcanneries){+cannerystartrow<-(i-1)*nummarkets+for(j in1:nummarkets){+colname<-toString(c(canneries[i],markets[j]))+transcost<-distance[i,j]*freight/1000+setColNameGLPK(lpi,cannerystartrow+j,colname)+setColBndGLPK(lpi,cannerystartrow+j,GLP_LO,0.0,0.0)+setObjCoefsGLPK(lpi,cannerystartrow+j,transcost)+}+}Next,we will add constraints.There are5constraints,two supply con-straints relating to the canneries and three demand constraints relating to themarkets.In addition,we will make thefirst row correspond to the objectivefunction.The objective row will be free,and does not have upper or lowerbounds.>numcanneries<-length(canneries)>nummarkets<-length(markets)>addRowsGLPK(lpi,numcanneries+nummarkets+1)[1]1>setRowsNamesGLPK(lpi,1,getObjNameGLPK(lpi))>for(i in1:numcanneries){+setRowsNamesGLPK(lpi,i+1,toString(c("Supply",canneries[i])))+setRowBndGLPK(lpi,i+1,GLP_UP,0,capacity[i])+}>for(j in1:nummarkets){+setRowsNamesGLPK(lpi,numcanneries+j+1,toString(c("Demand",markets[j]))) +setRowBndGLPK(lpi,numcanneries+j+1,GLP_LO,demand[j],0)+}Now,load the constraint matrix which represents the objective function andthe constraints.The non-zero values of the matrix are entered as three vectors,each with one element for each non-zero value.A vector to indicate the row,avector to indicate the column,and a vector which contains the matrix elementst,we call loadMatrixGLPK(lpi)tofinish.5>#create variables to hold the constraint information>ia<-numeric()>ja<-numeric()>ar<-numeric()>#add in objective coefficients>>for(i in1:numcols){+ia[i]<-1+ja[i]<-i+ar[i]<-getObjCoefGLPK(lpi,i)+}>for(i in1:numcanneries){+#supply constraints+cannerysupplyrow=numcols+(i-1)*nummarkets+for(j in1:nummarkets){+ia[cannerysupplyrow+j]<-(i+1)+ja[cannerysupplyrow+j]<-(i-1)+numcanneries*(j-1)+1 +ar[cannerysupplyrow+j]<-1+}+#demand constraints+marketdemandrow=numcols+numcanneries*nummarkets+for(j in1:nummarkets){+colnum<-(i-1)*nummarkets+j+ia[marketdemandrow+colnum]<-numcanneries+j+1+ja[marketdemandrow+colnum]<-colnum+ar[marketdemandrow+colnum]<-1+}+}>loadMatrixGLPK(lpi,length(ia),ia,ja,ar)Then,examine the problem entered in the API.>numrows<-getNumRowsGLPK(lpi)>numrows[1]6>numcols<-getNumColsGLPK(lpi)>numcols[1]6>for(i in1:numrows){+print(getRowNameGLPK(lpi,i))+}[1]"Total Cost"[1]"Supply,Seattle"6[1]"Supply,San-Diego"[1]"Demand,New-York"[1]"Demand,Chicago"[1]"Demand,Topeka">for(j in1:numcols){+print(getColNameGLPK(lpi,j))+}[1]"Seattle,New-York"[1]"Seattle,Chicago"[1]"Seattle,Topeka"[1]"San-Diego,New-York"[1]"San-Diego,Chicago"[1]"San-Diego,Topeka">print(getNumNnzGLPK(lpi))[1]18Finally solve using the simplex method and look at the solution. >solveSimplexGLPK(lpi)[1]0>for(i in1:numrows){+print(getRowNameGLPK(lpi,i))+print(getRowPrimGLPK(lpi,i))+}[1]"Total Cost"[1]153.675[1]"Supply,Seattle"[1]325[1]"Supply,San-Diego"[1]575[1]"Demand,New-York"[1]325[1]"Demand,Chicago"[1]300[1]"Demand,Topeka"[1]275>for(j in1:numcols){+print(getColNameGLPK(lpi,j))+print(getColPrimGLPK(lpi,j))+}7[1]"Seattle,New-York"[1]325[1]"Seattle,Chicago"[1]300[1]"Seattle,Topeka"[1]0[1]"San-Diego,New-York"[1]0[1]"San-Diego,Chicago"[1]0[1]"San-Diego,Topeka"[1]275And save the results to afile.>printSolGLPK(lpi,"transout.api")[1]02.3Using API to modify the modelNow,we will solve the version of the problem that is found in Dantzig.The demand at New York and Topeka are both300instead of325and275.This next section will use the API to modify the problem as read through the MathProg file.In order to examine an individual row,we need to index the rows and columns.This is done through the use of createIndexGLPK().Then we can use the findRowGLPK()and findColGLPK()>cindex<-createIndexGLPK(lpi)>new_york_row=findRowGLPK(lpi,"Demand,New-York")>topeka_row=findRowGLPK(lpi,"Demand,Topeka")>new_york_row[1]4>topeka_row[1]6>setRowBndGLPK(lpi,new_york_row,GLP_LO,300,0)>setRowBndGLPK(lpi,topeka_row,GLP_LO,300,0)We can solve this modified problem and look at the results.>solveSimplexGLPK(lpi)[1]08>for(i in1:numrows){+print(getRowNameGLPK(lpi,i))+print(getRowPrimGLPK(lpi,i))+print(getRowDualGLPK(lpi,i)) +}[1]"Total Cost"[1]151.2[1]0[1]"Supply,Seattle"[1]300[1]0[1]"Supply,San-Diego"[1]600[1]0[1]"Demand,New-York"[1]300[1]0.225[1]"Demand,Chicago"[1]300[1]0.153[1]"Demand,Topeka"[1]300[1]0.126>for(j in1:numcols){+print(getColNameGLPK(lpi,j))+print(getColPrimGLPK(lpi,j))+print(getColDualGLPK(lpi,j))+print(getObjCoefGLPK(lpi,j)) +}[1]"Seattle,New-York"[1]300[1]0[1]0.225[1]"Seattle,Chicago"[1]300[1]0[1]0.153[1]"Seattle,Topeka"[1]0[1]0.036[1]0.162[1]"San-Diego,New-York"[1]0[1]09[1]0.225[1]"San-Diego,Chicago"[1]0[1]0.009[1]0.162[1]"San-Diego,Topeka"[1]300[1]0[1]0.126Finally,clean up the workspace.>delProbGLPK(lpi)A ModelfileTRANSPORT.MOD#A TRANSPORTATION PROBLEM##This problem finds a least cost shipping schedule that meets#requirements at markets and supplies at factories.##References:#Dantzig,G B.,Linear Programming and Extensions#Princeton University Press,Princeton,New Jersey,1963, #Chapter3-3.set I;/*canning plants*/set J;/*markets*/param a{i in I};/*capacity of plant i in cases*/param b{j in J};/*demand at market j in cases*/param d{i in I,j in J};/*distance in thousands of miles*/param f;/*freight in dollars per case per thousand miles*/10param c{i in I,j in J}:=f*d[i,j]/1000;/*transport cost in thousands of dollars per case*/var x{i in I,j in J}>=0;/*shipment quantities in cases*/minimize cost:sum{i in I,j in J}c[i,j]*x[i,j];/*total transportation costs in thousands of dollars*/s.t.supply{i in I}:sum{j in J}x[i,j]<=a[i];/*observe supply limit at plant i*/s.t.demand{j in J}:sum{i in I}x[i,j]>=b[j];/*satisfy demand at market j*/data;set I:=Seattle San-Diego;set J:=New-York Chicago Topeka;param a:=Seattle350San-Diego600;param b:=New-York325Chicago300Topeka275;param d:New-York Chicago Topeka:=Seattle 2.5 1.7 1.8San-Diego 2.5 1.8 1.4;param f:=90;end;B OutputThe following is the output of the command:printSolGLPK(lpi,"transout.api") Problem:cannery APIRows:6Columns:6Non-zeros:18Status:OPTIMALObjective:Total Cost=153.675(MINimum)11No.Row name St Activity Lower bound Upper bound Marginal------------------------------------------------------------------------1Total Cost B153.6752Supply,SeattleB325350 3Supply,San-DiegoB575600 4Demand,New-YorkNL3253250.225 5Demand,ChicagoNL3003000.153 6Demand,TopekaNL2752750.126 No.Column name St Activity Lower bound Upper bound Marginal------------------------------------------------------------------------1Seattle,New-YorkB32502Seattle,ChicagoB30003Seattle,TopekaNL000.036 4San-Diego,New-YorkNL00<eps 5San-Diego,ChicagoNL000.009 6San-Diego,TopekaB2750Karush-Kuhn-Tucker optimality conditions:KKT.PE:max.abs.err=2.84e-14on row1max.rel.err=9.22e-17on row1High qualityKKT.PB:max.abs.err=0.00e+00on row0max.rel.err=0.00e+00on row0High qualityKKT.DE:max.abs.err=0.00e+00on column0max.rel.err=0.00e+00on column0High qualityKKT.DB:max.abs.err=0.00e+00on row0max.rel.err=0.00e+00on row012High qualityEnd of output13。
MT3336规格书
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MT3336 GPS Host-Based Solution Confidential A
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对外贸易经济合作部关于印发中国－加拿大《纺织品出口许可证数据电脑核查系统备忘录》的通知
对外贸易经济合作部关于印发中国－加拿大《纺织品出口许可证数据电脑核查系统备忘录》的通知文章属性•【制定机关】对外经济贸易部(已更名)•【公布日期】1997.05.12•【文号】[1997]外经贸管纺函字第165号•【施行日期】1997.05.12•【效力等级】部门规范性文件•【时效性】现行有效•【主题分类】进出口贸易正文对外贸易经济合作部关于印发中国－－加拿大《纺织品出口许可证数据电脑核查系统备忘录》的通知（〔１９９７〕外经贸管纺函字第１６５号１９９７年５月１２日）各省、自治区、直辖市及计划单列市外经贸委（厅、局），配额许可证事务局，部国际贸易ＥＤＩ服务中心，各有关部委直属总公司：为有效地执行中国－－加拿大今年２月２６日在渥太华签署的《纺织品出口许可证数据电脑核查系统备忘录》（以下简称《备忘录》），防止伪证、空证和超配额、无配额发证情况的发生，保证我输加拿大纺织品出口的有秩序发展，现将《备忘录》全文及中文译文印发给你们《详见附件》，并就有关问题通知如下：一、各签证机关、各出口企业应严格按照部下发的配额分配计划及《全国纺织品被动配额出口许可证系统工作规范》的规定签发、申领许可证。
各签证机关必须审查合同、商业发票、信用证或提单后签发许可证确保签证内容的准确性。
禁止无配额、超配额发证。
出口企业应尽量于接近出运时间前领证，严禁无合同领证。
二、各签证机关应按照《全国纺织品被动配额出口许可证系统工作规范》及时向部ＥＤＩ中心准确上报签证数据。
签证数据上报后，无正当理由不得撤证。
凡已传数据并已在进口国申领进口许可证的不得再办理撤证，ＥＤＩ中心不再接受该许可证的撤证数据。
三、严禁申领许可证后不出运货物，发空证。
中加联网后，我部（贸管司）将按加方反馈数据定期检查已申领的出口许可证使用情况。
各地外经贸委（厅、局）收到本通知后，请尽快下发各有关出口企业，同时要求签证人员和企业有关人员尽快熟悉、掌握《备忘录》的各项内容，并在工作中对照《备忘录》加以落实。
中华人民共和国商务部产业损害调查局丙烯酸酯反倾销措施期终复审产业损害调查问卷国内生产者调查问卷
保密版□公开版□中华人民共和国商务部产业损害调查局丙烯酸酯反倾销措施期终复审产业损害调查问卷国内生产者调查问卷公司名称：地址：联系人：联系人职务：联系电话：传真：邮政编码：电子邮件地址：答卷递交时间：年月日（收到答卷时由调查机关填写）1调查期：2003年1月1日至2007年12月31日答卷截止日期：2008年6月2日2中华人民共和国商务部产业损害调查局期终复审调查问卷（国内生产者调查问卷）3案由：本案是由上海华谊丙烯酸有限公司、江苏裕廊化工有限公司、北京东方石油化工有限公司东方化工厂、台塑丙烯酸酯（宁波）有限公司、中国石油天然气股份有限公司吉林石化分公司、浙江卫星丙烯酸有限公司、沈阳石蜡化工有限公司和山东齐鲁石化开泰实业股份有限公司等八家企业于2008年2月1日代表中国国内丙烯酸酯产业正式提出反倾销期终复审调查申请，中华人民共和国商务部（以下简称商务部），依照《中华人民共和国反倾销条例》的规定，于2008年4月9日正式公告立案，决定对原产于韩国、马来西亚、新加坡和印度尼西亚的进口丙烯酸酯所适用的反倾销措施进行期终复审调查。
被调查产品1.名称：丙烯酸酯2.税则号：《中华人民共和国海关进口税则》（2007）中的税则号列为：3.产品描述：丙烯酸酯是无色透明液体，是由原料丙烯酸和相应的醇类（甲、乙、丁、辛醇）在离子交换树脂作用下，发生酯化反应生成丙烯酸酯类（甲、乙、丁、辛酯）。
酯化反应产物经过分离、精制后，得到丙烯酸酯产品（甲、乙、丁、辛酯）。
丙烯酸酯是有机化工原料，以丙烯酸酯所制得的高聚合物具有优良的耐候、耐紫外光、耐热和耐水等独特的性能，在各种化学品的改良方面具有巨大的潜力，可应用于涂料、粘合剂、皮革、化纤、造纸、印刷等行业。
与原反倾销调查案件的被调查产品规格一致，本次申请调查产品包括以下四种规格：（1）丙烯酸甲酯（MA ）化学分子式：CH 2=CHCOOCH 3 （2）丙烯酸乙酯（EA ）化学分子式：CH 2=CHCOOC 2H 5 （3）丙烯酸正丁酯(BA) 化学分子式：CH 2=CHCOOC 4H 9（4）丙烯酸2-乙基已酯（2EHA ）（又名丙烯酸异辛酯）化学分子式：C 11H 20O 2你公司是否在调查期内生产过被调查产品的同类产品？（ ）否（请在以下申明书中签字，并立即把这一页答卷及申明书寄回商务部）（ ）是（仔细阅读说明，在以下申明书中签字，完成所有的问卷后寄回商务部）你公司是否支持本次期终复审调查？ （ ）是（ ）否—请简述理由。
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2011年第12期总第28期欢迎阅读第28期全科研究综述。
《英国医学杂志》最近刊发的一项实施良好的大型荟萃分析显示：2型糖尿病患者接受强化降糖治疗不会改善总死亡率及心血管疾病相关死亡。
研究者们建议临床医生在考虑使用强化降糖疗法治疗2型糖尿病患者时需要格外谨慎。
在自然疗法版块，一项来自中国的小规模研究发现：与常规护理相比，音乐治疗与乳腺癌患者抑郁症状减轻相关、并能缩短根治性乳房切除术后住院治疗的时间。
祝广大读者朋友们新年快乐！身体健康！万事顺意！Research Review 中国info@1.强化降糖治疗对2型糖尿病患者总死亡率、心血管疾病相关死亡和微血管并发症的影响作用：基于随机对照临床试验的一项荟萃分析作者：Boussageon R 等摘要：研究纳入13项评估强化降糖治疗对2型糖尿病患者总死亡率、心血管疾病相关死亡和微血管并发症影响的随机对照临床试验，共纳入34,533名成人患者（年龄≥18岁），其中18,315名患者接受了强化降糖治疗而16,218名患者接受标准治疗。
强化降糖治疗对降低总死亡率（危险比1.04）和心血管疾病相关死亡（危险比1.11）的作用有限。
虽然研究显示强化降糖治疗能显著降低非致死性心肌梗死（危险比0.85，p<0.001）和微量白蛋白尿（危险比0.90，p<0.001）的发生率，但将研究局限于高质量临床试验（Jadad评分>3）后，这种有利作用不再具有统计学显著性意义，而充血性心力衰竭发病风险47%的上升具有了显著意义。
值得注意的是，强化治疗与严重低血糖事件风险显著增加两倍相关联（危险比2.33，p<0.001）。
在长达5年的治疗期间，每117至150名患者中有一名患者需要接受预防心肌梗死的治疗、每32至142名患者中有一名患者需要接受预防微量白蛋白尿的治疗、每15至52名患者中就有一名患者需要预防严重低血糖发作。
评论：这篇文章真的会引起轩然大波！降低血糖也许并不是2型糖尿病管理最重要和最终的标准。
这项大型、对照良好的荟萃分析显示，强化降糖治疗不能降低患者总死亡率、心血管疾病相关死亡。
强化降糖组患者发生低血糖的风险比标准治疗组患者高很多，低血糖导致的伤害可能会抵消强化降糖治疗的其他益处。
我们仍需要更多的相关研究，也许接受一种折中的治疗方法会更好。
参考文献：BMJ. 2011;343:d4169./content/343/bmj.d4169.abstract2.罹患慢性阻塞性肺疾病的终生风险作者：Gershon AS等摘要：这项回顾性纵向人群研究监控了加拿大安大略省所有在1996年时年龄不大于14岁、且不患有慢性阻塞性肺疾病（COPD）的儿童，持续随访至受试者80岁，观察人群患COPD的终生风险。
试验期内，共诊断579,466例COPD，人群患COPD的终生风险是27.6%。
此外，男性受试者患病风险高于女性患者（男性29.7%，女性25.6%），社会经济地位较低者的患病风险高于社会经济地位较高者（分别是32.1%和23.0%），乡村居民患病风险高于城市居民（分别是32.4%和27.6%）。
评论：COPD病因包括多种因素，例如吸烟、哮喘和职业暴露。
这项大型连锁记录研究显示加拿大人群中COPD的高患病率令人担忧，远高于充血性心衰和心肌梗死等疾病。
我们需要意识到这个问题的严重性，政策制定者应该考虑提高公众认识、增加COPD相关研究基金等积极措施。
参考文献：Lancet 2011;378(9795):991–6/journals/lancet/article/PIIS0140-6736%2811%2960990-2/fulltext3.全科医疗中，能预测慢性阻塞性肺疾病诊断准确性的相关因子作者：Zwar NA等摘要：研究者们在全科医疗中，比较了慢性阻塞性肺疾病（COPD）临床诊断标准和支气管扩张剂后肺功能检查这两种方法的诊断作用，并研究了医护人员、医疗实践及患者的特征与两种诊断结果一致性之间的相关性。
研究招募悉尼地区44所全科诊所内的56位全科医生，医生在患者中选择出既往诊断COPD且使用呼吸系统处方药物、年龄在40-80岁之间的合适患者。
共有445名患者参与试验，其中257名（占总人数57.8%）经支气管扩张剂后肺功能测定诊断为COPD伴或不伴哮喘，16名（占总人数3.6%）诊断哮喘，82名（占总人数18.4%）肺功能正常，90名（占总人数20.2%）有其他肺部疾病。
肺功能检查并不能帮助预测临床诊断与肺功能检查诊断的结果是否一致。
高龄患者与诊断一致性显著相关；患有合并症的患者两种方法获得诊断结果一致的比例较低。
评论：我们还需要加强针对全科诊所医护人员的肺功能检查技术培训。
对肺功能检查结果的解读（不论是由临床医生进行或由肺功能室技师进行）直接与患者付出努力/技术人员受到鼓励的质量相关。
这项研究也反映了另一系列英国研究的结果-----肺功能测定是诊断COPD的金标准，但如果你的诊所没有肺功能测定仪、不能送患者到肺功能实验室接受检查，那么进行一系列的问卷调查也可以帮助你获得较高的诊断准确性。
参考文献：Med J Aust. 2011;195(4):168-71..au/public/issues/195_04_150811/zwa10562_fm.html4.连续服用两片单药和单片复方药（氨氯地平和阿托伐他汀）的疗效对比：澳大利亚人在2006年---2010年间的经验作者： Simons LA 等摘要：澳大利亚药物福利计划收集自2005年4月至2010年3月的全国药物使用数据，在连续接受药物治疗高血压和血脂异常的患者中，对比单片复方药与两片单药疗法的效果。
部分患者服用一片氨氯地平阿托伐他汀复方药（AA）；另一部分患者服用一片氨氯地平加一片阿托伐他汀，在试验开始前的6个月内患者未接受双药联合治疗。
共4,146名患者服用单片复方药AA，其中11%患者未能完成第1个月的首次重复治疗，12个月内33%患者终止治疗（治疗平均持续时间[MPT]是35个月）。
共6,204名患者服用一片氨氯地平加一片阿托伐他汀，23%患者未能完成第1个月的首次重复治疗、59%患者在12个月内终止治疗（MPT是7个月）。
经过多变量分析，如果没有前期治疗，单药治疗终止率上升至165%，如果前期治疗中不包含钙离子通道拮抗剂或他汀类药物，则终止率仅上升48-55%。
在未接受前期抗高血压治疗的患者中，单药治疗的MPT是8个月，而接受前期治疗的患者单药治疗的MPT为≥37个月。
评论：看起来，哮喘、COPD、高血压和高脂血症等很多无症状疾病的情况都是一样的。
治疗方法使用药物越少、越简单，那么患者的治疗依从性就越好。
其他疾病，例如糖尿病和哮喘的复方治疗药物已获得较多研究证据支持，但说服研究基金会支持复方药物研究尚存阻力。
参考文献：Med J Aust. 2011;195(3):134-7..au/public/issues/195_03_010811/sim11268_fm.html5.在全科医疗中，使用简单检查能否鉴别出良性前列腺增生？-------意在改善全科医疗诊断方法的一项临床试验作者：Carballido J等摘要：文章报道了一项意在改善全科医疗诊断方法的临床试验（D-IMPACT）的研究结果，该试验试图在全科医疗中寻找能帮助全科医生对年龄≥50岁、自然出现下尿路症状（LUTS）的男性患者中鉴别、诊断出良性前列腺增生（BPH）的一类简单检查。
患者出现频率最高的LUTS是夜尿和排尿无力、尿线变细。
共66%患者确诊BPH，其中32%患者BPH病情进展的风险较高（即PSA>1.5ng/ml，且前列腺体积≥30cm3）。
患者年龄、国际前列腺症状评分（IPSS）和前列腺特异性抗原（PSA）分别独立的与BPH诊断显著相关，进行逻辑回归分析发现，上述变量的正向预测值（PPV）是77.1%。
评论：是的，这篇文章有价值---但是，这项研究没有关注良性前列腺增生与前列腺癌之间的区别，这可是一个很难回答的重要问题。
在我看来，这项研究的结果完全可以预测。
实际上，研究本身没能对目前国际上该领域内存在的困惑有所帮助。
参考文献：Int J Clin Pract. 2011;65(9):989-96./doi/10.1111/j.1742-1241.2011.02735.x/abstract6.甲氧苄氨嘧啶-硫酸甲恶唑可能诱发服用螺内酯的中老年患者出现高血钾作者：Antoniou T等摘要：这项为期18年的病例对照研究评估了复方新诺明（甲氧苄氨嘧啶-硫酸甲恶唑）在年龄≥66岁服用螺内酯患者出现高血钾的风险。
研究共纳入248例病例（因高血钾而入院治疗）和783例配对对照（未收入院），均在14天内曾服用复方新诺明、阿莫西林、诺氟沙星或呋喃妥因。
复方新诺明导致的患者因高血钾而入院治疗的风险显著高于阿莫西林（校正后危险比12.4,95%置信区间[7.1,21.6]，人群归因分数59.7%）和呋喃妥因（校正后危险比2.4,95%置信区间[1.3,4.6]），但并不高于诺氟沙星（校正后危险比1.6,95%置信区间[0.8,3.4]）。
研究者总结说，服用螺内酯的中老年患者应该避免服用复方新诺明。
评论：随着服用螺内酯的心衰患者增多，该药物导致高钾血症的患病率也增高。
这项研究强调了同时服用螺内酯和复方新诺明与高钾血症风险之间的关联。
在临床实践中，高钾血症最常见的原因是，肾功能不全的中老年患者同时服用ACEI/ARB2类药物和螺内酯。
参考文献：BMJ 2011;343:d5228/content/343/bmj.d5228.full7.类风湿关节炎患者的贫血：我们是否能够忽略它？作者： Bloxham J等摘要：研究者从一所医院2009年的数据库中收集数据，调查了2，000名类风湿关节炎（RA）患者的贫血原因。
共有199名患者（占总人数10%）确诊贫血（连续两次检查，血红蛋白水平<11g/dL），其中90名为缺铁性贫血（IDA）、78名为慢性病性贫血（ACD）、25名为大细胞性贫血（MCA），6名为手术后贫血。
在90名IDA患者中，53名接受检查，其中23名患者各项检查均正常；另30名患者的贫血原因分别是胃肠道出血（25名患者）、妇科相关失血（3名患者）和泌尿系统出血（2名患者）。
在ACD患者中，45名能接受强化RA治疗，另9名患者需要接受促红细胞生成素治疗。
在25名MCA患者中，贫血原因分别是未发觉的维生素B12缺乏（12名患者）、药物诱发（4名患者）、髓系恶性肿瘤（3名患者）、甲状腺功能低减（2名患者）和酒精中毒（2名患者）。
评论：全科医生在诊断过程中很大一部分是在分析可能概率。