Output from programming code written to summarize data describing 2018 MCSP Trial monitoring sites acquired using a SOP 1 (see ServCat reference 103364) of FWS Legacy Regions 2 and 3. Monitoring sites were selected using a custom GRTS draw conducted by USGS in 2017, within monitoring areas associated with select NWRS stations. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and private lands in northern Missouri.

File List Code_and_Data_Supplement.zip (md5: dea8636b921f39c9d3fd269e44b6228c) Description The supplementary material provided includes all code and data files necessary to replicate the simulation models other demographic analyses presented in the paper. MATLAB code is provided for the simulations, and SAS code is provided to show how model parameters (vital rates) were estimated.

  The principal programs are Figure_3_4_5_Elasticity_Contours.m and Figure_6_Contours_Stochastic_Lambda.m which perform the elasticity analyses and run the stochastic simulation, respectively.


  The files are presented in a zipped folder called Code_and_Data_Supplement. When uncompressed, users may run the MATLAB programs by opening them from within this directory. Subdirectories contain the data files and supporting MATLAB functions necessary to complete execution. The programs are written to find the necessary supporting functions in the Code_and_Data_Supplement directory. If users copy these MATLAB files to a different directory, they must add the Code_and_Data_Supplement directory and its subdirectories to their search path to make the supporting files available.


  More details are provided in the README.txt file included in the supplement.


  The file and directory structure of entire zipped supplement is shown below.

  Folder PATH listing
Code_and_Data_Supplement
|  Figure_3_4_5_Elasticity_Contours.m
|  Figure_6_Contours_Stochastic_Lambda.m
|  Figure_A1_RefitG2.m
|  Figure_A2_PlotFecundityRegression.m
|  README.txt
|  
+---FinalDataFiles
+---Make Tables
|    README.txt
|    Table_lamANNUAL.csv
|    Table_mgtProbPredicted.csv
|    
+---ParameterEstimation
|  |  Categorical Model output.xls
|  |  
|  +---Fecundity
|  |    Appendix_A3_Fecundity_Breakpoint.sas
|  |    fec_Cat_Indiv.sas
|  |    Mean_Fec_Previous_Study.m
|  |    
|  +---G1
|  |    G1_Cat.sas
|  |    
|  +---G2
|  |    G2_Cat.sas
|  |    
|  +---Model Ranking
|  |    Categorical Model Ranking.xls
|  |    
|  +---Seedlings
|  |    sdl_Cat.sas
|  |    
|  +---SS
|  |    SS_Cat.sas
|  |    
|  +---SumSrv
|  |    sum_Cat.sas
|  |    
|  \---WinSrv
|      modavg.m
|      winCatModAvgfitted.m
|      winCatModAvgLinP.m
|      winCatModAvgMu.m
|      win_Cat.sas
|      
+---ProcessedDatafiles
|    fecdat_gm_param_est_paper.mat
|    hierarchical_parameters.mat
|    refitG2_param_estimation.mat
|    
\---Required_Functions
  |  hline.m
  |  hmstoc.m
  |  Jeffs_Figure_Settings.m
  |  Jeffs_startup.m
  |  newbootci.m
  |  sem.m
  |  senstuff.m
  |  vline.m
  |  
  +---export_fig
  |    change_value.m
  |    eps2pdf.m
  |    export_fig.m
  |    fix_lines.m
  |    ghostscript.m
  |    license.txt
  |    pdf2eps.m
  |    pdftops.m
  |    print2array.m
  |    print2eps.m
  |    
  +---lowess
  |    license.txt
  |    lowess.m
  |    
  +---Multiprod_2009
  |  |  Appendix A - Algorithm.pdf
  |  |  Appendix B - Testing speed and memory usage.pdf
  |  |  Appendix C - Syntaxes.pdf
  |  |  license.txt
  |  |  loc2loc.m
  |  |  MULTIPROD Toolbox Manual.pdf
  |  |  multiprod.m
  |  |  multitransp.m
  |  |  
  |  \---Testing
  |    |  arraylab13.m
  |    |  arraylab131.m
  |    |  arraylab132.m
  |    |  arraylab133.m
  |    |  genop.m
  |    |  multiprod13.m
  |    |  readme.txt
  |    |  sysrequirements_for_testing.m
  |    |  testing_memory_usage.m
  |    |  testMULTIPROD.m
  |    |  timing_arraylab_engines.m
  |    |  timing_matlab_commands.m
  |    |  timing_MX.m
  |    |  
  |    \---Data
  |        Memory used by MATLAB statements.xls
  |        Timing results.xlsx
  |        timing_MX.txt
  |        
  +---province
  |    PROVINCE.DBF
  |    province.prj
  |    PROVINCE.SHP
  |    PROVINCE.SHX
  |    README.txt
  |    
  +---SubAxis
  |    parseArgs.m
  |    subaxis.m
  |    
  +---suplabel
  |    license.txt
  |    suplabel.m
  |    suplabel_test.m
  |    
  \---tight_subplot
      license.txt
      tight_subplot.m

Output (results) from programming code written to summarize red-imported fire ant (RIFA) abundance from monitoring along transects at custom 2017 GRTS draw sites within select monitoring areas (see SOP 6 in ServCat reference 103385 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX) and Hagerman (TX) NWRs. The spreadsheet labeled as SOP 6 Metrics displays the different estimates in different worksheets. Each worksheet can be used for additional analysis.

Output from programming code written to summarize data describing 2017 MCSP Trial monitoring sites acquired using a SOP 1 (see ServCat reference 103364) of FWS Legacy Regions 2 and 3. 2017 monitoring sites were selected using a custom GRTS draw conducted by USGS, within monitoring areas associated with select NWRS stations. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA), Necedah (WI) NWRs and several locations near the town of Lamoni, Iowa and private lands in northern Missouri.

Credit report of Print Broker Sas contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Output from programming code written to summarize immature monarch butterfly, milkweed and nectar plant abundance from monitoring data acquired using a grid of 1 square-meter quadrats at custom 2017 GRTS draw sites within select monitoring areas (see SOP 3 in ServCat reference 103368 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and northern Missouri. Input data file is named 'FWS_2018_MonMonSOP3DS1_forSAS.csv' and is stored in ServCat reference 137698. See SM 5 (ServCat reference 103388) for dictionary of data fields in the input data file.

Credit report of Alfa Print Sas contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Output from programming code written to summarize 2018 monarch butterfly abundance from monitoring data acquired using a modified Pollard walk at custom 2017 GRTS draw sites within select monitoring areas (see SOP 2 in ServCat reference 103367 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and northern Missouri. Input data file is named 'FWS_2018_MM_SOP2_for_SAS.csv' and is stored in ServCat reference 136485. See SM 5 (ServCat reference 103388) for dictionary of data fields in the input data file.

Parameter estimates for the generalized H2 model (SAS output).

This directory contains analytic code used to build cohorts, dependent variables, and covariates, and run all statistical analyses for the study, "Changes in care associated with integrating Medicare and Medicaid for dual eligible individuals: Examination of a Fully Integrated Special Needs Plan."The code files enclosed in this directory are:SAS_Cohorts_Outcomes 23-9-30.sas. This SAS code file builds study cohorts, dependent variables, and covariates. This code produced a person-by-month level database of outcomes and covariates for individuals in the integration and comparison cohorts.STATA_Models_23-6-5_weight_jama.do. This Stata program reads in the person-by-month level database (output from SAS) and conducts all statistical analyses used to produce the main and supplementary analyses reported in the manuscript.We have provided this code and documentation to disclose our study methods. Our Data Use Agreements prohibit publishing of row-level data for this study. Therefore, researchers would need to obtain Data Use Agreements with data providers to implement these analyses. We also note that some measures reference macros with proprietary code (e.g., Medispan® files) which require a separate user license to run. Interested readers should contact the study PI, Eric T. Roberts (eric.roberts@pennmedicine.upenn.edu) for further information.

Full report, commentary, additional materials, and example SAS output

This package contains two files designed to help read individual level DHS data into Stata. The first file addresses the problem that versions of Stata before Version 7/SE will read in only up to 2047 variables and most of the individual files have more variables than that. The file will read in the .do, .dct and .dat file and output new .do and .dct files with only a subset of the variables specified by the user. The second file deals with earlier DHS surveys in which .do and .dct file do not exist and only .sps and .sas files are provided. The file will read in the .sas and .sps files and output a .dct and .do file. If necessary the first file can then be run again to select a subset of variables.

The resaerch article is on effects of seed rate and interow spacing on the growth, phenological charactersitics , yiled and lodging seve

The matte coated paper market, valued at $709 million in 2025, is projected to experience steady growth, driven by increasing demand from the publishing and packaging sectors. The 5.1% CAGR (Compound Annual Growth Rate) indicates a consistent upward trajectory through 2033. Growth is fueled by the rising popularity of high-quality print materials for books, magazines, and luxury packaging, particularly in regions with strong economies and established print industries. Furthermore, advancements in coating technology are leading to improved paper quality, enhanced printability, and environmentally friendly options, further boosting market appeal. While economic fluctuations and the increasing adoption of digital alternatives pose some challenges, the continued demand for tangible marketing materials and high-quality print products mitigates these restraints. The competitive landscape is comprised of major players like Nippon Paper Industries, Arjowiggins SAS, Oji Holdings, and Stora Enso, among others, who are continually innovating to meet evolving market needs and cater to diverse customer segments, including commercial printers, publishers, and packaging companies. The market segmentation likely includes variations based on paper weight, finish, and application-specific properties. The forecast period (2025-2033) anticipates significant market expansion, with a projected market size exceeding $1 billion by 2033, based on the 5.1% CAGR. Regional variations are expected, with North America and Europe likely retaining significant market shares due to established print industries and consumer preferences. However, emerging economies in Asia and Latin America are also likely to demonstrate notable growth in demand, driven by increasing disposable incomes and rising literacy rates. Strategic partnerships, mergers and acquisitions, and the development of sustainable and innovative product offerings are key strategies that companies within the matte coated paper industry are employing to maintain competitive edge and capture market share.

The Paint, Coatings and Printing Ink Manufacturing industry is adapting amid stagnating downstream markets. Printer ink sales are witnessing a depression, largely in response to the digitalisation phenomenon sweeping across both the public and private sectors. With fewer physical documents in circulation, demand for traditional printer inks has naturally dwindled. Meanwhile, the construction sector, a significant consumer of paints, is navigating a sluggish phase. Rising inflation and high interest rates have constrained new construction projects, stymieing demand for paints and coatings. Overall, industry revenue is projected to contract at a compound annual rate of 0.4%, including a 3.7% slump in 2025, to reach €5.4 billion. Volatile prices caused by the Russian invasion of Ukraine drove up production costs, pressuring bottom lines. However, these price rises enabled industry revenue to expand in the short term, even if output volume shrunk. The ECB survey of professional forecasters has projected a drop of interest rates in 2025 to 3%, which will support renewal in the construction sector, boosting sales of decorative paints. However, this could be offset by prices dropping. As EU climate policies tighten, manufacturers will need to overhaul production methods to adhere to decreasing CO2 emissions allowances. This regulatory environment and rising consumer demand for sustainable products suggest a strategic shift is on the horizon. Competition from abroad, particularly China, could exert downward pressure on domestic vehicle manufacturing and, by extension, the paint sector. The focus on developing bio-based and environmentally friendly paints will likely gain momentum, aligning with the automotive industry's efforts to lower their carbon footprints with innovative, sustainable solutions. Over the five years through 2030, industry revenue is expected to expand at a compound annual rate of 0.3%, to reach €5.5 billion.

Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system:

brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);

functional alterations of the motor division (CAG-NAM).

The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the nervous system (here).

The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.

Exposure estimates were obtained with SAS® software using a 2-dimensional Monte Carlo simulation, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).

Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.

The input and output data for the exposure assessment are reported in the following annexes:

Annex A.1 – Input data for the exposure assessment of CAG-NAN

Annex A.2 – Input data for the exposure assessment of CAG-NAM

Annex B.1 – Output data from the Tier I exposure assessment of CAG-NAN

Annex B.2 – Output data from the Tier I exposure assessment of CAG-NAM

Annex C.1 – Output data from the Tier II exposure assessment of CAG-NAN

Annex C.2 – Output data from the Tier II exposure assessment of CAG-NAM

Further information on the data, methodologies and interpretation of the results are provided in the scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using SAS® software (here).

The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the nervous system (here). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Output from programming code written to summarize monarch butterfly abundance from monitoring data acquired using a modified Pollard walk at custom 2017 GRTS draw sites within select monitoring areas (see SOP 2 in ServCat reference 103367 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA), Necedah (WI) NWRs and several locations near the town of Lamoni, Iowa and private lands in northern Missouri.

This SAS code extracts data from EU-SILC User Database (UDB) longitudinal files and edits it such that a file is produced that can be further used for differential mortality analyses. Information from the original D, R, H and P files is merged per person and possibly pooled over several longitudinal data releases. Vital status information is extracted from target variables DB110 and RB110, and time at risk between the first interview and either death or censoring is estimated based on quarterly date information. Apart from path specifications, the SAS code consists of several SAS macros. Two of them require parameter specification from the user. The other ones are just executed. The code was written in Base SAS, Version 9.4. By default, the output file contains several variables which are necessary for differential mortality analyses, such as sex, age, country, year of first interview, and vital status information. In addition, the user may specify the analytical variables by which mortality risk should be compared later, for example educational level or occupational class. These analytical variables may be measured either at the first interview (the baseline) or at the last interview of a respondent. The output file is available in SAS format and by default also in csv format.