Edit Machinery And Tools Sas Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Between 2020 and 2024, the data protection supervisory authorities in Cyprus had the highest change in budget among the European Union countries, as their authority's budget grew by 130 percent during the measured period. The second-highest increase in budget was recorded at the Austria's data protection authority.

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.

Alter Oak Sas Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The raw data for each of the analyses are presented. Baseline severity difference (probands only) (Figure A in S1 Dataset), Repeated measures analysis of change in lesion severity (Figure B in S1 Dataset). Logistic regression of survivorship (Figure C in S1 Dataset). Time to cure (Figure D in S1 Dataset). Each data set is given as a SAS code for the data itself, and the equivalent analysis to that performed in JMP (and reported in the text). Data are presented in SAS format as this is a simple text format. The data and code were generated as direct exports from JMP, and additional SAS code added as needed (for instance, JMP does not export code for post-hoc tests). Note, however, that SAS rounds to less precision than JMP, and can give slightly different results, especially for REML methods. (DOCX)

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Data from World Development Indicators and Climate Change Knowledge Portal on climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use.

Live release rate for companion animals

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. --

Sas Fashion Editor Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

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.

Demand for professional photographic activities in France is driven by consumer preferences for personalised and bespoke experiences, as well as corporate and advertising demands for high-quality visual content. Weddings, in particular, provide lucrative revenue opportunities for photographers, with a steady marriage rate in France supporting demand. Advertising agencies command a large share of demand as they seek professionally captured photos and video content for their marketing campaigns. Revenue is expected to climb at a compound annual rate of 4.2% over the five years through 2025, including a 0.5% hike in 2025 to €1.4 billion. Revenue plummeted in 2020 due to COVID-19 restrictions across France, which squeezed advertising budgets and led to the postponement of many weddings, corporate events, conferences and sports and cultural events. Professional photographers in France witnessed a rebound in revenue in 2021 and 2022 with the resumption of corporate events and personal photoshoots. However, inflationary pressures and geopolitical tensions have constrained consumer and business spending on non-essential services, including professional photography. This has led to restrained industry revenue growth from late 2022 onwards. The proliferation of smartphones with high-quality cameras and user-friendly photo-enhancing apps has intensified competition from amateur photographers, creating pricing pressures and squeezing demand for traditional professional photography services. However, the digital shift also brings opportunities, as sectors like advertising and e-commerce seek professional photography to enhance their online presence. Revenue is projected to climb at a compound annual rate of 1.1% over the five years through 2030 to €1.4 billion. Easing inflation and lower interest rates in France will drive business and consumer expenditure on photography services. Positive business sentiment will encourage businesses to hike spending on services like product photography and corporate event photography. Although a stagnant marriage rate in France may temper growth in wedding photography, expanding disposable incomes are likely to fuel private spending on personal event photoshoots. The competition from smartphones with advanced camera capabilities, which allow individuals to take and edit high-quality photos themselves anytime, anywhere, will encourage French photographic companies to target niche markets like aerial drones and industrial photography. Integrating advanced technologies like AI and VR can offer photographers new revenue streams and competitive advantages. Companies integrating advanced technologies will benefit from enhanced operational efficiency, attracting more clients and improving profitability, while those that fail to invest in technology tools will fall behind.

To analyze these data as presented, you must have the SAS system software (e.g.SAS 2016) installed. Once you have unpacked the ZIP file, change the path within the SAS files to point to the directory where you have unpacked the data, and run the programs, which have .SAS extensions. Some data are in .csv files, but most are in SAS data sets. If you do not have SAS, you can still use conversion utilities in other software, such as R, to read that data.

SAS Institute, Inc. 2016.The SAS System for Windows, Release 9.4.SAS Institute, Cary, NC.

PurposeChronic Respiratory Disease Questionnaire Self-Administered Standardized (CRQ-SAS) is a valid and reliable tool that evaluates the health-related quality of life among the adult population affected with chronic respiratory disorders (CRDs) and has been translated into many languages as per need. The main objective of this study was to translate the CRQ-SAS into the Urdu language and evaluate its psychometric properties.MethodologyIt was a two-staged study that consisted of translating the original version into Urdu language and then psychometric testing of the translated version. The reliability of the translated questionnaire was assessed by measuring its internal consistency, test-retest reliability, standard error of mean (SEM) & minimal detectable change (MDC). Validity was determined by evaluating its content for content validity, construct (convergent and discriminative) validity, and exploratory factor analysis. Data was analyzed using SPSS v 28 with an alpha level < 0.05 considered to be significant.ResultsCRQ-SAS U had an excellent internal consistency (Cronbach’s Alpha α = 0.89), test-retest reliability (ICC2,1) = 0.91 of all items, and low SEM = 0.11 and MDC = 0.65. S-CVI was 0.9, with statistically significant difference across the response of COPD patients and healthy subjects, and a high degree of correlation with St Georges Respiratory Questionnaire (r = 0.7–0.9) proving CRQ-SAS U content, discriminant and convergent valid respectively. Exploratory factor analysis identified two factors responsible for 80% of the variance.ConclusionCRQ-SAS U demonstrated optimal psychometric properties which renders it to be used in Urdu speaking populations with COPD.

This dataset includes profile discrete measurements of dissolved inorganic carbon, total alkalinity, pH on total scale, water temperature, salinity, dissolved oxygen and other parameters measured during the R/V Oden SAS-Oden 2021 (SO21) cruise (EXPOCODE 77DN20210725) in the Arctic Ocean from 2021-07-25 to 2021-09-20. The SAS-Oden 2021 expedition (SO21) with icebreaker Oden1 (IB Oden) is the Swedish contribution to the international scientist-driven initiative †Synoptic Arctic Survey†(SAS)2. SAS will collect primary ecosystem data in the Arctic Ocean in 2020-2022 from both ice-breaking and non-ice-breaking research vessels. The goal of SAS is to generate a comprehensive dataset that allows for an improved characterization of the Arctic Ocean with respect to its (1) physical oceanography, (2) marine ecosystems and (3) carbon cycle. The complete SAS dataset will provide a unique baseline that will allow for tracking climate change and its impacts as they unfold in the Arctic region over the coming years, decades and centuries.

PLOSsyphThis is an ASCII file that is space delimited that was created in SAS. It has the variables that were used in the published paper. The readme.sas file is a .sas file that reads the data. You will need to change the infile statement to reflect the path to where you put the data.

Abstract (en): The purpose of this data collection is to provide an official public record of the business of the federal courts. The data originate from 94 district and 12 appellate court offices throughout the United States. Information was obtained at two points in the life of a case: filing and termination. The termination data contain information on both filing and terminations, while the pending data contain only filing information. For the appellate and civil data, the unit of analysis is a single case. The unit of analysis for the criminal data is a single defendant. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. All federal court cases, 1970-2000. 2012-05-22 All parts are being moved to restricted access and will be available only using the restricted access procedures.2005-04-29 The codebook files in Parts 57, 94, and 95 have undergone minor edits and been incorporated with their respective datasets. The SAS files in Parts 90, 91, 227, and 229-231 have undergone minor edits and been incorporated with their respective datasets. The SPSS files in Parts 92, 93, 226, and 228 have undergone minor edits and been incorporated with their respective datasets. Parts 15-28, 34-56, 61-66, 70-75, 82-89, 96-105, 107, 108, and 115-121 have had identifying information removed from the public use file and restricted data files that still include that information have been created. These parts have had their SPSS, SAS, and PDF codebook files updated to reflect the change. The data, SPSS, and SAS files for Parts 34-37 have been updated from OSIRIS to LRECL format. The codebook files for Parts 109-113 have been updated. The case counts for Parts 61-66 and 71-75 have been corrected in the study description. The LRECL for Parts 82, 100-102, and 105 have been corrected in the study description.2003-04-03 A codebook was created for Part 105, Civil Pending, 1997. Parts 232-233, SAS and SPSS setup files for Civil Data, 1996-1997, were removed from the collection since the civil data files for those years have corresponding SAS and SPSS setup files.2002-04-25 Criminal data files for Parts 109-113 have all been replaced with updated files. The updated files contain Criminal Terminations and Criminal Pending data in one file for the years 1996-2000. Part 114, originally Criminal Pending 2000, has been removed from the study and the 2000 pending data are now included in Part 113.2001-08-13 The following data files were revised to include plaintiff and defendant information: Appellate Terminations, 2000 (Part 107), Appellate Pending, 2000 (Part 108), Civil Terminations, 1996-2000 (Parts 103, 104, 115-117), and Civil Pending, 2000 (Part 118). The corresponding SAS and SPSS setup files and PDF codebooks have also been edited.2001-04-12 Criminal Terminations (Parts 109-113) data for 1996-2000 and Criminal Pending (Part 114) data for 2000 have been added to the data collection, along with corresponding SAS and SPSS setup files and PDF codebooks.2001-03-26 Appellate Terminations (Part 107) and Appellate Pending (Part 108) data for 2000 have been added to the data collection, along with corresponding SAS and SPSS setup files and PDF codebooks.1997-07-16 The data for 18 of the Criminal Data files were matched to the wrong part numbers and names, and now have been corrected. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. (1) Several, but not all, of these record counts include a final blank record. Researchers may want to detect this occurrence and eliminate this record before analysis. (2) In July 1984, a major change in the recording and disposition of an appeal occurred, and several data fields dealing with disposition were restructured or replaced. The new structure more clearly delineates mutually exclusive dispositions. Researchers must exercise care in using these fields for comparisons. (3) In 1992, the Administrative Office of the United States Courts changed the reporting period for statistical data. Up to 1992, the reporting period...

Urine osmolarity UOSM, the exposure of main interest, is included in all models. The initial set of adjustment variables for these models was selected by the disjunctive cause criterion. Hazard ratios (HR), confidence limits (CI) and p-values are given. Model stability was evaluated by bootstrap inclusion frequencies (based on bootstrap resamples). UOSM, creatinine clearance, and proteinuria were log2-transformed and therefore, corresponding hazard ratios are per doubling of each variable.Abbreviations and symbols: , significance threshold; ABE, augmented backward elimination; ACEI/ARBs, use of angiotensin-converting enzyme inhibitors and Angiotensin II type 1 receptor blockers; BE, backward elimination; CI, confidence interval; HR, hazard ratio; , change-in-estimate threshold; Uosm, urine osmolarity (mosm/L).Urine osmolarity example: final models selected by backward elimination (BE) with a significance threshold , augmented backward elimination (ABE) with and a change-in-estimate threshold , and unselected model (No selection).

BackgroundThe incidence of sleep apnea syndrome (SAS) is reported to be markedly high in patients with chronic kidney disease (CKD). Therefore, it is extremely important to know whether SAS affects prognosis in patients with CKD. Further, it is imperative to understand the prognostic impact of home continuous positive airway pressure (CPAP) therapy, which is one of the most common treatments for SAS.Materials and MethodsWe used a clinical database to identify patients with CKD using diagnosis codes. We included patients with CKD aged 20 years or more, not on renal replacement therapy, with a known change in renal function for at least 1 year. The propensity score was used to compare event rates for patients with SAS and those without SAS. In addition, the prognostic impact of CPAP therapy was investigated. The primary outcome is a composite of death, initiation of renal replacement therapy, hospitalization for heart failure, ischemic heart disease, and cerebrovascular disease.ResultsFrom the database, 31,294 patients with CKD without SAS and 1,026 with SAS were found to be eligible. Of these, 419 (41%) patients with SAS and 10,713 (34%) patients without SAS (P < 0.01) reached the primary outcome. After adjustment with the propensity score, the SAS group was found to have a similarly poor prognosis (P < 0.01): the hazard ratio for the primary outcome was 1.26 (95% CI, 1.08–1.45, P < 0.01) in the group with SAS compared with the group without SAS. Conversely, in patients with SAS and using CPAP, the hazard ratio was lower and did not differ significantly (HR 0.96, 95% CI: 0.76–1.22, P = 0.76).ConclusionIn patients with CKD and SAS, the risk of death and cardiovascular disease is high. In addition, patients treated with CPAP may have improved life expectancy.

The advent of powerful smartphones and the ubiquity of social media platforms like Instagram and TikTok have fundamentally disrupted traditional market structures, leading to a contraction in demand for standard consumer photo services. This disruption has heightened competitive intensity, squeezed profit and forced established photographic businesses to adapt their business models. While these trends have challenged volume-driven companies, the resilience of premium, event-based segments, particularly wedding photography and the rise in commercial imaging have helped cushion the industry's overall decline. Revenue in the Photographic Activities industry in Europe is anticipated to contract at a compound annual rate of 1.9% over the five years through 2025, including a tumble of 0.7% in 2025, to reach €11.3 billion. Photographers have faced a perfect storm of challenges. Smartphone penetration across Europe has eroded everyday demand for professional photographic and photo-processing services. Consumer photo product segments, including prints and basic editing, have been hardest hit, as user-friendly apps and DIY culture fuelled price competition and declining order volumes. At the same time, macroeconomic pressures, particularly the surge in energy and raw material costs, have squeezed profitability and prompted restructuring across the industry. Price-sensitive corporate clients shifted spend towards cost-effective digital content and, increasingly, AI-generated imagery, further dampening the fortunes of independent studios. However, the premium end of the market, notably in weddings and niche commercial assignments, proved more resilient, supporting higher transaction values and encouraging specialisation among surviving studios. Revenue is expected to climb at a compound annual rate of 1.6% over the five years through 2030 to €12.3 billion. An expected rebound in economic growth and easing inflation, with eurozone headline inflation projected to fall to 1.7% by 2026, should buoy both consumer and business confidence. This will likely trigger renewed investment in marketing shoots, corporate events and specialist services, including drone imagery and cinematic video. The marriage rate across Europe is anticipated to continue its long-term decline. However, photographic businesses can capitalise on a higher spend per wedding, as the popularity of destination events should sustain the premium wedding photography segment. At the same time, the accelerated adoption of artificial intelligence (AI) promises gains in operational efficiency and service innovation, but is set to intensify competition and lower barriers to entry for new market participants. Photographic businesses that embrace technology, diversify their service portfolios and cultivate strong client relationships will be best placed to thrive in an increasingly specialised, value-driven market.