We compiled macroinvertebrate assemblage data collected from 1995 to 2014 from the St. Louis River Area of Concern (AOC) of western Lake Superior. Our objective was to define depth-adjusted cutoff values for benthos condition classes (poor, fair, reference) to provide tool useful for assessing progress toward achieving removal targets for the degraded benthos beneficial use impairment in the AOC. The relationship between depth and benthos metrics was wedge-shaped. We therefore used quantile regression to model the limiting effect of depth on selected benthos metrics, including taxa richness, percent non-oligochaete individuals, combined percent Ephemeroptera, Trichoptera, and Odonata individuals, and density of ephemerid mayfly nymphs (Hexagenia). We created a scaled trimetric index from the first three metrics. Metric values at or above the 90th percentile quantile regression model prediction were defined as reference condition for that depth. We set the cutoff between poor and fair condition as the 50th percentile model prediction. We examined sampler type, exposure, geographic zone of the AOC, and substrate type for confounding effects. Based on these analyses we combined data across sampler type and exposure classes and created separate models for each geographic zone. We used the resulting condition class cutoff values to assess the relative benthic condition for three habitat restoration project areas. The depth-limited pattern of ephemerid abundance we observed in the St. Louis River AOC also occurred elsewhere in the Great Lakes. We provide tabulated model predictions for application of our depth-adjusted condition class cutoff values to new sample data. This dataset is associated with the following publication: Angradi, T., W. Bartsch, A. Trebitz, V. Brady, and J. Launspach. A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 108-120, (2017).

ABSTRACT. Genotype-by-environment interaction refers to the differential response of different genotypes across different environments. This is a general phenomenon in all living organisms and always has been one of the main challenges for biologists and plant breeders. The nonparametric methods based on the rank of original data have been suggested as the alternative methods after parametric methods to analyze data without perquisite assumptions needed for common analysis of variance. But, the lack of statistical software or package, especially for analysis of two-way data, is one of the main reasons that plant breeders have not greatly used the nonparametric methods. Here, we have explained the nonparametric methods and presented a comprehensive two-parts SAS program for calculation of four nonparametric statistical tests (Bredenkamp, Hildebrand, Kubinger and van der Laan-de Kroon) and all of the valid stability statistics including Hühn's parameters (Si(1), Si(2), Si(3), Si(6)), Thennarasu's parameters (NPi(1), NPi(2), NPi(3), NPi(4)), Fox's ranking technique and Kang's rank-sum.

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

This research work studied the effect of timing constraint and overloading of Spectrum Access System (SAS) on the SAS-CBSD protocol. Specifically, it studies how Heartbeat and Grant Request fail as number of CBSDs served by a SAS becomes large for a given service rate. It also looks at the time taken by CBSDs to vacate a channel (on which an incumbent has appeared) at different Heartbeat Interval. These study results are captured in the following files. (1) Number of CBSD vs number of Heartbeat timeout when SAS service rate is 40 requests/sec (2) Number of CBSD vs number of Heartbeat timeout when SAS service rate is 60 requests/sec (3) Number of CBSD vs number of failed grants when SAS service rate is 40 requests/sec (4) Number of CBSD vs number of failed grants when SAS service rate is 60 requests/sec (5) CDF of duration of CBSDs vacating a channel when number of CBSD=700, mean heartbeat interval = 90 s (6) CDF of duration of CBSDs vacating a channel when number of CBSD=1200, mean heartbeat interval = 150 s (7) CDF of duration of CBSDs vacating a channel when number of CBSD=1500, mean heartbeat interval = 220 s

The table presents the percentage of problems where SAS-Pro performed better than, or at par with CE, SSM, and STSA. In addition, the table presents the average improvement in the RMSD, SI, SAS scores for these problems when SAS-Pro is used instead of other solvers.

In a shared spectrum environment, as is the case in the 3.5 GHz Citizens Broadband Radio Service (CBRS), the secondary users with lower priority are managed by independent spectrum access systems (SASs) in order to protect the incumbents with higher priority from interference. The interference protection is guaranteed in terms of a percentile of the aggregate interference power. The current practice requires each SAS to obtain a global snapshot of interference and use a common algorithm to manage it. We present a simplified method to permit each SAS to independently manage its users while still meeting overall aggregate interference protection requirements. The data include statistical upper bound on aggregate interference for some known distributions, which is the core idea of the proposed method. The data also include numerical results of using the proposed interference protection criterion in terms of two metrics, the total number of users moved from the channel in order to protect the incumbent (i.e., the size of the move list) and the realized aggregate interference of all co-channel users at the incumbent. The data is associated with the letter, "Independent Calculation of Move Lists for Incumbent Protection in a Multi-SAS Shared Spectrum Environment," M. R. Souryal and T. T. Nguyen, in IEEE Wireless Communication Letters, Jan. 2021.

The 2008 National Survey of Drinking and Driving Attitudes and Behaviors was composed of a single questionnaire administered to a sample of randomly selected individuals 16 and older, with ages 16 through 24 over-sampled. The respondents were asked about their drinking behavior, their drinking and driving behavior, use of designated drivers, their hosting events in which drinking occurred, risks they perceive associated with drinking and driving, experience with anti-DWI enforcement activity, and their attitudes concerning major intervention strategies.The survey was administered from September 10, 2008 to December 22, 2008. A total of 6,999 respondents completed the survey, including 5,392 landline interviews and 1,607 cell phone interviews. The total number of completed interviews for each of the four Census regions (Northeast, Midwest, South, and West) was 1,409, 1,654, 2,390, and 1,546, respectively.

Multienvironment trials (METs) enable the evaluation of the same genotypes under a v ariety of environments and management conditions. We present META (Multi Environment Trial Analysis), a suite of 31 SAS programs that analyze METs with complete or incomplete block designs, with or without adjustment by a covariate. The entire program is run through a graphical user interface. The program can produce boxplots or histograms for all traits, as well as univariate statistics. It also calculates best linear unbiased estimators (BLUEs) and best linear unbiased predictors for the main response variable and BLUEs for all other traits. For all traits, it calculates variance components by restricted maximum likelihood, least significant difference, coefficient of variation, and broad-sense heritability using PROC MIXED. The program can analyze each location separately, combine the analysis by management conditions, or combine all locations. The flexibility and simplicity of use of this program makes it a valuable tool for analyzing METs in breeding and agronomy. The META program can be used by any researcher who knows only a few fundamental principles of SAS.

The 1990 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population data from the U.S. Census Bureau's 1990 Summary tape File (STF3A) database. The data are available by state and county, county subdivision/mcd, blockgroup, and places, as well as Indian reservations, tribal districts and congressional districts. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

This database is the Third Small Astronomy Satellite (SAS-3) Y-Axis Pointed Observation Log. It identifies possible pointed observations of celestial X-ray sources which were performed with the y-axis detectors of the SAS-3 X-Ray Observatory. This log was compiled (by R. Kelley, P. Goetz and L. Petro) from notes made at the time of the observations and it is expected that it is neither complete nor fully accurate. Possible errors in the log are (i) the misclassification of an observation as a pointed observation when it was either a spinning or dither observation and (ii) inaccuracy of the dates and times of the start and end of an observation. In addition, as described in the HEASARC_Updates section, the HEASARC added some additional information when creating this database. Further information about the SAS-3 detectors and their fields of view can be found at: http://heasarc.gsfc.nasa.gov/docs/sas3/sas3_about.html Disclaimer: The HEASARC is aware of certain inconsistencies between the Start_date, End_date, and Duration fields for a number of rows in this database table. They appear to be errors present in the original table. Except for one entry where the HEASARC corrected an error where there was a near-certainty which parameter was incorrect (as noted in the 'HEASARC_Updates' section of this documentation), these inconsistencies have been left as they were in the original table. This database table was released by the HEASARC in June 2000, based on the SAS-3 Y-Axis pointed Observation Log (available from the NSSDC as dataset ID 75-037A-02B), together with some additional information provided by the HEASARC itself. This is a service provided by NASA HEASARC .

The 1980 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population demographics from the 1980 Summary Tape File (STF3A) database and are organized by state. The population data includes education levels, ethnicity, income distribution, nativity, labor force status, means of transportation and family structure while the housing data embodies size, state and structure of housing Unit, value of the Unit, tenure and occupancy status in housing Unit, source of water, sewage disposal, availability of telephone, heating and air conditioning, kitchen facilities, rent, mortgage status and monthly owner costs. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

In 2023, the number of pilots employed by Scandinavian Airlines was ***, representing a decrease of ** full-time equivalent pilots compared to the previous years. Since 2020, Scandinavian airlines have had one of the lowest numbers of pilots in their 14 years history.

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Scandinavian Airlines’ passenger numbers dropped by nearly ************** between 2019 and 2021 to around *** million passengers due to the coronavirus pandemic. The number of passengers on Scandinavian Airlines flights began rising again in the financial year 2022, totaling **** million scheduled passengers that year. The positive trend persisted in the subsequent year, 2024, with an approximately **** million passengers.

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Risk prevention plans (RPPs) were established by the Act of 2 February 1995 on the strengthening of environmental protection. They are the essential instrument of the State in terms of risk prevention. Their objective is the control of development in areas exposed to a major risk. The PPRs are approved by the prefects and generally carried out by the departmental directorates of the territories (DDT). These plans regulate the occupation of land or its use by prohibitions of construction or requirements on existing or future buildings (constructive provisions, work to reduce vulnerability, restrictions on use or agricultural practices ...). These plans may be under development (prescribed), pre-implemented or approved. The RPP file contains a cover note, a regulatory zoning plan and a by-law. Other graphic documents useful for understanding the approach (hazards, issues, etc.) may be attached. Each PPR is identified by a polygon that corresponds to the set of municipalities concerned within the scope of prescription when it is in the prescribed state; and the restricted zone envelope when in the approved state. This geographical table makes it possible to map the PPRN or PPRT existing on the department.

After the number of flights decreased by ** percent in 2020 due to the impact of the coronavirus pandemic and fell further in 2021, flight numbers began to recover in 2022. In 2022, SAS operated ******* scheduled flights. The positive trend persisted in the subsequent year, 2023, with a total of ******* flights.

This SAS macro generates childhood mortality estimates (neonatal, post-neonatal, infant (1q0), child (4q1) and under-five (5q0) mortality) and standard errors based on birth histories reported by women during a household survey. We have made the SAS macro flexible enough to accommodate a range of calculation specifications including multi-stage sampling frames, and simple random samples or censuses. Childhood mortality rates are the component death probabilities of dying before a specific age. This SAS macro is based on a macro built by Keith Purvis at MeasureDHS. His method is described in Estimating Sampling Errors of Means, Total Fertility, and Childhood Mortality Rates Using SAS (www.measuredhs.com/pubs/pdf/OD17/OD17.pdf, section 4). More information about Childhood Mortality Estimation can also be found in the Guide to DHS Statistics (www.measuredhs.com/pubs/pdf/DHSG1/Guide_DHS_Statistics.pdf, page 93). We allow the user to specify whether childhood mortality calculations should be based on 5 or 10 years of birth histories, when the birth history window ends, and how to handle age of death with it is reported in whole months (rather than days). The user can also calculate mortality rates within sub-populations, and take account of a complex survey design (unequal probability and cluster samples). Finally, this SAS program is designed to read data in a number of different formats.