This SAS program calculates CFI for each patient from analytic data files containing information on patient identifiers, ICD-9-CM diagnosis codes (version 32), ICD-10-CM Diagnosis Codes (version 2020), CPT codes, and HCPCS codes. NOTE: When downloading, store "CFI_ICD9CM_V32.tab", "CFI_ICD10CM_V2020.tab", and "PX_CODES.tab" as csv files (these files are originally stored as csv files, but Dataverse automatically converts them to tab files). Please read "Frailty-Index-SAS-code-Guide" before proceeding. Interpretation, validation data, and annotated references are provided in "Research Background - Claims-Based Frailty Index".

Epidemiologists often use ratio-type indices (rate ratio, risk ratio and odds ratio) to quantify the association between exposure and disease. By comparison, less attention has been paid to effect measures on a difference scale (excess rate or excess risk). The excess relative risk (ERR) used primarily by radiation epidemiologists is of peculiar interest here, in that it involves both difference and ratio operations. The ERR index (but not the difference-type indices) is estimable in case-control studies. Using the theory of sufficient component cause model, the author shows that when there is no mechanistic interaction (no synergism in the sufficient cause sense) between the exposure under study and the stratifying variable, the ERR index (but not the ratio-type indices) in a rare-disease case-control setting should remain constant across strata and can therefore be regarded as a common effect parameter. By exploiting this homogeneity property, the related attributable fraction indices can also be estimated with greater precision. The author demonstrates the methodology (SAS codes provided) using a case-control dataset, and shows that ERR preserves the logical properties of the ratio-type indices. In light of the many desirable properties of the ERR index, the author advocates its use as an effect measure in case-control studies of rare diseases.

The global parallel shaft indexer market, valued at $714 million in 2025, is projected to experience robust growth, driven by increasing automation across various industries, particularly in automotive manufacturing, packaging, and food processing. The 4.4% CAGR from 2025 to 2033 indicates a steady expansion, fueled by the need for high-precision indexing mechanisms in automated production lines. Demand for improved productivity, enhanced accuracy, and reduced downtime are key factors stimulating market growth. Technological advancements, such as the incorporation of advanced control systems and improved materials, further contribute to this positive trajectory. While potential restraints like initial investment costs and the complexity of integration into existing systems exist, the long-term benefits of increased efficiency and output outweigh these challenges. The market is segmented by application (automotive, packaging, etc.), type (mechanical, electromechanical), and geographic region, with North America and Europe currently holding significant market shares due to established industrial automation infrastructure. Companies like Motion Index Drives, Inc., MCPI SAS, and others are key players, competing through innovation and strategic partnerships to capture market share. The competitive landscape is characterized by both established players and emerging companies vying for market dominance. Differentiation strategies often focus on specialized features, such as high-speed indexing capabilities, customized solutions, or superior reliability. Future growth will likely be influenced by the adoption of Industry 4.0 technologies, including integration with IoT platforms and advanced data analytics for predictive maintenance. The market's expansion is also closely tied to the overall health of the global economy and the sustained investment in automation across diverse industrial sectors. The continuous development of more precise, efficient, and cost-effective parallel shaft indexers will be critical in driving further market penetration.

Weighting systems used for the analysis of the data in Table 1.

The global cylindrical cam indexer market is experiencing robust growth, driven by increasing automation across various industries. While precise market size figures for 2025 are unavailable, a reasonable estimation, considering typical market growth within the automation sector and the provided study period (2019-2033), suggests a market value of approximately $500 million USD in 2025. This signifies a substantial market presence, fueled by the consistent demand for precise and reliable indexing mechanisms in high-speed automated production lines. Key drivers include the rising adoption of automation in manufacturing, particularly in industries like automotive, electronics, and packaging, where precise and repeatable movements are crucial. Furthermore, advancements in cam design and manufacturing technologies, enabling higher speeds, increased accuracy, and improved durability, are contributing to market expansion. The trend toward compact and modular designs is also gaining traction, appealing to manufacturers seeking to optimize space and integrate cam indexers seamlessly into existing production lines. However, the market faces certain restraints. The high initial investment cost associated with implementing cylindrical cam indexers can be a barrier to entry for some smaller manufacturers. Furthermore, the need for specialized technical expertise for installation, maintenance, and troubleshooting can limit widespread adoption, especially in regions with limited skilled labor. Despite these challenges, the long-term outlook for the cylindrical cam indexer market remains positive, with a projected Compound Annual Growth Rate (CAGR) of approximately 8% from 2025 to 2033. This growth trajectory is underpinned by continuous technological advancements and the ongoing demand for enhanced automation capabilities across various sectors. The major players in the market, including CDS Cam Driven Systems, Sonzogni Camme, and others, are continually innovating to meet the evolving needs of manufacturers, leading to further market expansion. This report provides a detailed analysis of the global cylindrical cam indexer market, projecting a market value exceeding $2 billion by 2030. It examines key trends, driving forces, challenges, and growth opportunities within this specialized sector of automation technology. The report leverages extensive primary and secondary research, incorporating data from leading manufacturers and industry experts to deliver actionable insights for businesses operating in or considering entry into this dynamic market.

A complete list of live websites using the Sas Web Ads Banner Video technology, compiled through global website indexing conducted by WebTechSurvey.

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.

The SAS code (Supplementary File 1) and R program code (Supplementary File 2). For the analysis to proceed, this code requires an input data file (Supplementary File 3-5) prepared in excel format (CSV). Data can be stored in any format such as xlsx, txt, xls and others. Economic values in the SAS code are entered manually in the code, but in the R code are stored in an Excel file (Supplementary File 6).

Multispectral radiometry resolutely quantifies canopy attributes of similarly managed monocultures over wide and varied temporal arrays. Likewise, liquid phthalocyanine-containing products are commonly applied to turfgrass as a spray pattern indicator, dormancy colorant, and/or product synergist. While perturbed multispectral radiometric characterization of putting greens within 24 h of treatment by synthetic phthalocyanine colorant has been reported, explicit guidance on subsequent use is absent from the literature. Our objective was to assess creeping bentgrass (Agrostis stolonifera L. ‘Penn G2’) putting green reflectance and growth one to 14 d following semi-monthly treatment by synthetic Cu II phthalocyanine colorant (Col) and petroleum-derived spray oil (PDSO) combination product at a 27 L ha–1 rate and/or 7.32 hg ha–1 soluble N treatment by one of two commercial liquid fertilizers. As observed in a bentgrass fairway companion study, mean daily shoot growth and canopy dark green color index (DGCI) increased with Col+PDSO complimented N treatment. Yet contrary to the fairway study results, deflated mean normalized differential red edge (NDRE) or vegetative index (NDVI) resulted from an associated Col+PDSO artifact that severely impeded near infrared (810-nm) putting green canopy reflectance. Regardless of time from Col+PDSO combination product treatment, the authors strongly discourage turfgrass scientists from employing vegetative indices that rely on 760- or 810-nm canopy reflectance when evaluating such putting green systems. Methods The requested information is described ad nauseum in the Materials & Methods section of the ‘Related Works.’

On 2. Nov., the author mistakenly uploaded a raw data file. Within, the first worksheet/tab titled MSR contained all 475 lines of MSR and vegetative index data. However, consideration for abidance of ANOVA assumptions precluded a small number of dependent variable observations, as employ of garden variety transformations were unsuccessful. Specifically, for percent reflectance of 510-, 560-, 610-, 660-, 760-, and 810-nm spectra; 2, 2, 2, 3, 3, and 4 observations were omitted as missing data, respectively. Likewise, since the dark green color index (DGCI) is calculated by 460, 560, and 660-nm reflectance, five (5) DGCI observations were conceded as missing data. Results described in the ‘Related Works’ report 510-, 560-, 610-, 660-, 760-, and 810-nm reflectance means and inference from 473-, 473-, 473-, 472-, 472-, and 471-observation datasets, respectively. No data were replaced and degree of freedom penalties were incurred in analysis reported in ‘Related Works.’ Likewise, the daily clipping yield data, dCY (2nd worksheet/tab) in the original 2 Nov. file upload, contained 150 observations. The statistical model and analysis of dCY data described in the ‘Related Works’ results report means and inference from a 148-observation dataset. The SAS output for each the reduced (n=148) and full (n=150) datasets are now included in data files. Model diagnostics on the reduced datasets, uploaded 11 Dec., 2022 meet all required assumptions. For the dCY data, the model diagnostics issue and resolution are squarely depicted in the two attached SAS outputs. The same is true for the MSR data, but SAS outputs are not attached. Motivated parties are invited to reanalyze the above-noted dependent variables using the 2 Nov. (full) and 11 Dec. (reduced) data freely available to you in ‘Data Files.’ It is strict Dryad policy that voluntarily uploaded data files not be deleted. Thus, the authors were compelled to append the two regrettably-conflicting datasets with the above explanation, today, 11 Dec. 2022. We hope you have found this explanation helpful and encourage you to forward your questions or comments to Max Schlossberg at mjs38@psu.edu.

SAS Code for Spatial Optimization of Supply Chain Network for Nitrogen Based Fertilizer in North America, by type, by mode of transportation, per county, for all major crops, using Proc OptModel. the code specifies set of random values to run the mixed integer stochastic spatial optimization model repeatedly and collect results for each simulation that are then compiled and exported to be projected in GIS (geographic information systems). Certain supply nodes (fertilizer plants) are specified to work at either 70 percent of their capacities or more. Capacities for nodes of supply (fertilizer plants), demand (county centroids), transhipment nodes (transfer points-mode may change), and actual distance travelled are specified over arcs.

Demographic of front-line medical staff (N = 543).

Study Objectives: Aging is a risk factor for sleep apnoea syndrome (SAS), which is associated with lower quality of life and sudden mortality. However, SAS is often overlooked in older adults without suspicions. Therefore, this study aimed to evaluate SAS incidence and 48 other general factors in older adults.Methods: This cross-sectional study included all non-caregiver-certified, healthy individuals (N = 32) who survived during the long-term cohort study and agreed to participate in apnoea-hypopnoea index (AHI) measurement (aged 83–95 years). AHI and 48 other general factors were evaluated, and simple linear regression analysis was used to identify potential AHI-related factors. Stepwise evaluation was further performed using multiple linear regression analyses.Results: Although no individuals were previously diagnosed with SAS, 30 (93.75%) participants had some degree of SAS (AHI > 5/h), and 22 (68.75%) had severe or moderate SAS (AHI > 15/h). Compared with typical single risk factors represented by body mass index, combining daily steps and other factors improved the fit to the multiple linear regression. Combining daily steps and body mass index improved the fit for males and combining daily steps and red blood cell count improved the fit for females.Conclusion: SAS was highly prevalent in unaware healthy Japanese older adults; combinations of daily steps and body mass index, and daily steps and red blood cell count may predict AHI in such individuals without the need for a specific AHI test.

File List Lamb_et_al_SAScode.txt

Linumdata.txt

Description The file Lamb_et_al_SAScode.txt contains SAS scripts and instructions for conducting nonlinear regression analyses of the Linum data set. The contents of the file can be pasted directly into the script editor in SAS. The file includes a script to import the Linum data set contained in the file Linumdata.txt into SAS. The file Linumdata.txt contains 4 columns and 40 rows (39 data points, one row with column headings). The columns in the data set are as follows: -- TABLE: Please see in attached file. --

This is the dataset used in our case study on architectural smells evolution. We tracked smells from 524 versions among 14 open source Java systems.

More information can be found in our ICSME'19 paper titled: "Investigating instability architectural smells evolution: an exploratory case study".

Additionally, you can find the tool on GitHub: https://github.com/darius-sas/astracker

Fit indices of the Smartphone Addiction Scale—Chinese Short Version (SAS-CSV)’s structural equation model (n = 278).

Providing daily updates of global economic developments, with coverage of high income- as well as developing countries. Daily data updates are provided for exchange rates, equity markets, and emerging market bond indices. Monthly data coverage (updated daily and populated upon availability) is provided for consumer prices, high-tech market indicators, industrial production and merchandise trade.

Convergence validity of the Smartphone Addiction Scale—Chinese Short Version (SAS-CSV).

The SAS syntax replicates the analyses presented in the MAIHDA tutorial by Evans et al. 2024. The dataset and R and Stata codes for the tutorial are available at: https://www.sciencedirect.com/science/article/pii/S235282732400065X?via=ihub and https://osf.io/dtvc3/

The objective of this study was to evaluate the effects of replacing dry ground corn with crude glycerol on intake, apparent digestibility, performance, and carcass characteristics of finishing beef bulls. A completely randomized block design experiment with 25 d for adaptation and 100 d for data collection was conducted, in which 3,640 Nellore bulls (367 ± 36.8 kg; 18 ± 3 mo) were blocked by body weight and assigned to 20 pens. Bulls were randomly assigned to one of four treatments: 0, 5, 10, and 15% (dry matter basis) of crude glycerol in the diet. Initially, 20 bulls were slaughtered to serve as a reference to estimate initial empty body weight, which allowed for carcass gain calculation. Bulls were weighed at the beginning, at two-thirds, and at the end of the experiment for performance calculations. Carcass measurements were obtained by ultrasound. Fecal output was estimated using indigestible neutral detergent fiber as an internal marker. Data were analyzed using the mixed procedures in SAS 9.2 (SAS Institute Inc., Cary, NC). Intake of dry matter, organic matter, and neutral detergent fiber decreased linearly (P < 0.05) with crude glycerol inclusion. However, crude glycerol levels did not affect (P > 0.05) intakes of crude protein, non-fiber carbohydrates, and total digestible nutrients. Digestibility of dry matter, organic matter, neutral detergent fiber, and total digestible nutrients increased quadratically (P < 0.05) with the inclusion of crude glycerol in the diet. Crude glycerol inclusion did not change the intake of digestible dry matter, average daily gain, final body weight, carcass gain, carcass dressing, gain-to-feed ratio, Longissimus thoracis muscle area, and back and rump fat thicknesses (P > 0.05). These results suggest that crude glycerol may be included in finishing beef diets at levels up to 15% without impairing performance and carcass characteristics.

Exploratory factor analysis of the Smartphone Addiction Scale—Chinese Short Version (SAS-CSV) (n = 279).