The Semantic Artist Similarity dataset consists of two datasets of artists entities with their corresponding biography texts, and the list of top-10 most similar artists within the datasets used as ground truth. The dataset is composed by a corpus of 268 artists and a slightly larger one of 2,336 artists, both gathered from Last.fm in March 2015. The former is mapped to the MIREX Audio and Music Similarity evaluation dataset, so that its similarity judgments can be used as ground truth. For the latter corpus we use the similarity between artists as provided by the Last.fm API. For every artist there is a list with the top-10 most related artists. In the MIREX dataset there are 188 artists with at least 10 similar artists, the other 80 artists have less than 10 similar artists. In the Last.fm API dataset all artists have a list of 10 similar artists. There are 4 files in the dataset.mirex_gold_top10.txt and lastfmapi_gold_top10.txt have the top-10 lists of artists for every artist of both datasets. Artists are identified by MusicBrainz ID. The format of the file is one line per artist, with the artist mbid separated by a tab with the list of top-10 related artists identified by their mbid separated by spaces.artist_mbid \t artist_mbid_top10_list_separated_by_spaces mb2uri_mirex and mb2uri_lastfmapi.txt have the list of artists. In each line there are three fields separated by tabs. First field is the MusicBrainz ID, second field is the last.fm name of the artist, and third field is the DBpedia uri.artist_mbid \t lastfm_name \t dbpedia_uri There are also 2 folders in the dataset with the biography texts of each dataset. Each .txt file in the biography folders is named with the MusicBrainz ID of the biographied artist. Biographies were gathered from the Last.fm wiki page of every artist.Using this datasetWe would highly appreciate if scientific publications of works partly based on the Semantic Artist Similarity dataset quote the following publication:Oramas, S., Sordo M., Espinosa-Anke L., & Serra X. (In Press). A Semantic-based Approach for Artist Similarity. 16th International Society for Music Information Retrieval Conference.We are interested in knowing if you find our datasets useful! If you use our dataset please email us at mtg-info@upf.edu and tell us about your research. https://www.upf.edu/web/mtg/semantic-similarity

In the 2024 financial year, the airline SAS Scandinavian Airlines generated an operating loss of **** billion Swedish kronor. This was less loss than last year's figure of *** billion Swedish kroner.

The SAS2RAW database is a log of the 28 SAS-2 observation intervals and contains target names, sky coordinates start times and other information for all 13056 photons detected by SAS-2. The original data came from 2 sources. The photon information was obtained from the Event Encyclopedia, and the exposures were derived from the original "Orbit Attitude Live Time" (OALT) tapes stored at NASA/GSFC. These data sets were combined into FITS format images at HEASARC. The images were formed by making the center pixel of a 512 x 512 pixel image correspond to the RA and DEC given in the event file. Each photon's RA and DEC was converted to a relative pixel in the image. This was done by using Aitoff projections. All the raw data from the original SAS-2 binary data files are now stored in 28 FITS files. These images can be accessed and plotted using XIMAGE and other columns of the FITS file extensions can be plotted with the FTOOL FPLOT. This is a service provided by NASA HEASARC .

analyze the survey of consumer finances (scf) with r the survey of consumer finances (scf) tracks the wealth of american families. every three years, more than five thousand households answer a battery of questions about income, net worth, credit card debt, pensions, mortgages, even the lease on their cars. plenty of surveys collect annual income, only the survey of consumer finances captures such detailed asset data. responses are at the primary economic unit-level (peu) - the economically dominant, financially interdependent family members within a sampled household. norc at the university of chicago administers the data collection, but the board of governors of the federal reserve pay the bills and therefore call the shots. if you were so brazen as to open up the microdata and run a simple weighted median, you'd get the wrong answer. the five to six thousand respondents actually gobble up twenty-five to thirty thousand records in the final pub lic use files. why oh why? well, those tables contain not one, not two, but five records for each peu. wherever missing, these data are multiply-imputed, meaning answers to the same question for the same household might vary across implicates. each analysis must account for all that, lest your confidence intervals be too tight. to calculate the correct statistics, you'll need to break the single file into five, necessarily complicating your life. this can be accomplished with the meanit sas macro buried in the 2004 scf codebook (search for meanit - you'll need the sas iml add-on). or you might blow the dust off this website referred to in the 2010 codebook as the home of an alternative multiple imputation technique, but all i found were broken links. perhaps it's time for plan c, and by c, i mean free. read the imputation section of the latest codebook (search for imputation), then give these scripts a whirl. they've got that new r smell. the lion's share of the respondents in the survey of consumer finances get drawn from a pretty standard sample of american dwellings - no nursing homes, no active-duty military. then there's this secondary sample of richer households to even out the statistical noise at the higher end of the i ncome and assets spectrum. you can read more if you like, but at the end of the day the weights just generalize to civilian, non-institutional american households. one last thing before you start your engine: read everything you always wanted to know about the scf. my favorite part of that title is the word always. this new github repository contains t hree scripts: 1989-2010 download all microdata.R initiate a function to download and import any survey of consumer finances zipped stata file (.dta) loop through each year specified by the user (starting at the 1989 re-vamp) to download the main, extract, and replicate weight files, then import each into r break the main file into five implicates (each containing one record per peu) and merge the appropriate extract data onto each implicate save the five implicates and replicate weights to an r data file (.rda) for rapid future loading 2010 analysis examples.R prepare two survey of consumer finances-flavored multiply-imputed survey analysis functions load the r data files (.rda) necessary to create a multiply-imputed, replicate-weighted survey design demonstrate how to access the properties of a multiply-imput ed survey design object cook up some descriptive statistics and export examples, calculated with scf-centric variance quirks run a quick t-test and regression, but only because you asked nicely replicate FRB SAS output.R reproduce each and every statistic pr ovided by the friendly folks at the federal reserve create a multiply-imputed, replicate-weighted survey design object re-reproduce (and yes, i said/meant what i meant/said) each of those statistics, now using the multiply-imputed survey design object to highlight the statistically-theoretically-irrelevant differences click here to view these three scripts for more detail about the survey of consumer finances (scf), visit: the federal reserve board of governors' survey of consumer finances homepage the latest scf chartbook, to browse what's possible. (spoiler alert: everything.) the survey of consumer finances wikipedia entry the official frequently asked questions notes: nationally-representative statistics on the financial health, wealth, and assets of american hous eholds might not be monopolized by the survey of consumer finances, but there isn't much competition aside from the assets topical module of the survey of income and program participation (sipp). on one hand, the scf interview questions contain more detail than sipp. on the other hand, scf's smaller sample precludes analyses of acute subpopulations. and for any three-handed martians in the audience, ther e's also a few biases between these two data sources that you ought to consider. the survey methodologists at the federal reserve take their job...

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.

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 .

Credit report of Last Mile 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.

This macro performs propensity score (PS) adjusted analysis using stratification for cohort studies from an analytic file containing information on patient identifiers, exposure, confounding variables or pre-computed PS, and binary outcomes/censoring time. In the first step, patients from non-overlapping regions of PS distributions are trimmed. Next, PS strata are created using either the distribution of PS in the exposed group only or the entire cohort as specified by the user. Next, this macro calculates weights targeting the ATT (Average Treatment effect among the Treated) or the ATE (Average Treatment Effect in the whole population) as specified by the user. Finally, weighted generalized linear models or weighted Cox-proportional hazards model provides adjusted effect estimates along with confidence intervals calculated using robust variance estimates to account for weighting.

This dataset contains the scrubbed chat logs from the Southeast Atmosphere Study (SAS) project, including NOMADSS (Nitrogen, Oxidants, Mercury and Aerosol Distributions, Sources and Sinks), from May 30 - July 17, 2013. The chat logs contain conversations between scientists and other field project participants regarding data collection within the SAS-NOMADSS project.

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

These SAS files are sample code used for the Monte Carlo studies in a manuscript on statistical properties of four effect size measures for the mediated effect.Citation:Miočević, M., O’Rourke, H. P., MacKinnon, D. P., & Brown, H. C. (2016). The bias and efficiency of five effect size measures for mediation models. Under review at Behavior Research Methods.

SAS script and input files for calculations of sensitivity and specificity based on different model settings and weather data in the weather data file supplied here.

Pregnancy is a condition of broad interest across many medical and health services research domains, but one not easily identified in healthcare claims data. Our objective was to establish an algorithm to identify pregnant women and their pregnancies in claims data. We identified pregnancy-related diagnosis, procedure, and diagnosis-related group codes, accounting for the transition to International Statistical Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis and procedure codes, in health encounter reporting on 10/1/2015. We selected women in Merative MarketScan commercial databases aged 15–49 years with pregnancy-related claims, and their infants, during 2008–2019. Pregnancies, pregnancy outcomes, and gestational ages were assigned using the constellation of service dates, code types, pregnancy outcomes, and linkage to infant records. We describe pregnancy outcomes and gestational ages, as well as maternal age, census region, and health plan type. In a sensitivity analysis, we compared our algorithm-assigned date of last menstrual period (LMP) to fertility procedure-based LMP (date of procedure + 14 days) among women with embryo transfer or insemination procedures. Among 5,812,699 identified pregnancies, most (77.9%) were livebirths, followed by spontaneous abortions (16.2%); 3,274,353 (72.2%) livebirths could be linked to infants. Most pregnancies were among women 25–34 years (59.1%), living in the South (39.1%) and Midwest (22.4%), with large employer-sponsored insurance (52.0%). Outcome distributions were similar across ICD-9 and ICD-10 eras, with some variation in gestational age distribution observed. Sensitivity analyses supported our algorithm’s framework; algorithm- and fertility procedure-derived LMP estimates were within a week of each other (mean difference: -4 days [IQR: -13 to 6 days]; n = 107,870). We have developed an algorithm to identify pregnancies, their gestational age, and outcomes, across ICD-9 and ICD-10 eras using administrative data. This algorithm may be useful to reproductive health researchers investigating a broad range of pregnancy and infant outcomes.

SAS Switch Market Outlook

According to our latest research, the global SAS Switch market size reached USD 1.32 billion in 2024, demonstrating a robust industry presence. The market is projected to expand at a CAGR of 6.8% during the forecast period, reaching an estimated USD 2.35 billion by 2033. This remarkable growth is primarily driven by the escalating demand for high-performance storage solutions and the rapid proliferation of data-centric applications across various industries. As per our latest findings, the SAS Switch market is experiencing heightened adoption due to its ability to facilitate seamless data transfer and storage scalability, making it a critical component in modern IT infrastructures.

One of the most significant growth factors for the SAS Switch market is the exponential surge in data generation globally. Enterprises are increasingly leveraging big data analytics, artificial intelligence, and the Internet of Things (IoT), all of which demand high-speed, reliable, and scalable storage networks. SAS Switches, known for their superior throughput and low latency, are becoming the backbone of enterprise storage architectures. The shift towards digital transformation, coupled with the need for real-time data processing, is further propelling the demand for these switches. Moreover, the growing adoption of cloud computing and virtualization technologies necessitates robust and flexible storage networking, where SAS Switches play a pivotal role in ensuring data integrity and accessibility.

Another crucial factor fueling the growth of the SAS Switch market is the increasing deployment of advanced data centers worldwide. Organizations are investing heavily in upgrading their IT infrastructure to accommodate the rising complexity and volume of data traffic. SAS Switches offer enhanced connectivity, reliability, and scalability, making them ideal for data center environments that require high availability and efficient data management. The trend towards hyper-converged infrastructure and software-defined storage is also driving the integration of SAS Switches, as they support seamless expansion and efficient resource utilization. Additionally, the emergence of edge computing and 5G networks is opening new avenues for SAS Switch deployment in distributed and remote locations, further boosting market growth.

Furthermore, the SAS Switch market is benefiting from continuous technological advancements and innovations in storage networking solutions. Leading manufacturers are focusing on developing high-density, multi-port SAS Switches that cater to the evolving needs of enterprises and service providers. The integration of advanced management features, enhanced security protocols, and energy-efficient designs is making SAS Switches more attractive to a broader range of end-users. The growing emphasis on data security and regulatory compliance is also driving organizations to invest in reliable and robust storage networking solutions, thereby augmenting the demand for SAS Switches. Strategic partnerships, mergers, and acquisitions among key players are further intensifying competition and fostering innovation in the market.

Regionally, North America continues to dominate the SAS Switch market, accounting for the largest market share in 2024. The region's leadership can be attributed to the presence of major technology companies, advanced data center infrastructure, and a high level of digital maturity. Asia Pacific is emerging as the fastest-growing region, driven by rapid industrialization, increasing IT investments, and the expansion of cloud services. Europe is also witnessing significant growth, supported by the adoption of advanced storage solutions in sectors such as BFSI, healthcare, and government. The Middle East & Africa and Latin America are gradually catching up, fueled by digital transformation initiatives and rising demand for efficient storage networking solutions.

Product Type Analysis

The Product Type segment of the SAS Switch market is primarily categorized into Single Port SAS Switch, Dual Port SAS Switch, and Multi-Port SAS Switch. Single Port SAS Switches are typically used in environments where dedicated, point-to-point connectivity is required. These switches are favored in small-scale deployments or specialized applications where simplicity and cost-effectiveness are paramount. While their adoption is relatively modest compared to other types, they remain an essential

SAS Controller Market Outlook

According to our latest research, the global SAS Controller market size in 2024 is valued at USD 2.68 billion, driven by the escalating demand for high-performance data storage solutions across diverse sectors. The market is set to witness robust expansion at a CAGR of 6.7% from 2025 to 2033. By the end of 2033, the SAS Controller market is forecasted to reach a valuation of USD 4.88 billion. This growth trajectory is primarily attributed to the increasing adoption of cloud computing, big data analytics, and the proliferation of enterprise applications that require reliable and scalable storage infrastructures.

The growth of the SAS Controller market is significantly influenced by the rising demand for advanced data storage technologies in enterprise environments. As organizations continue to generate and process massive volumes of data, the need for robust storage management solutions becomes paramount. SAS controllers, with their ability to offer high-speed data transfer, enhanced scalability, and superior reliability, are becoming the preferred choice over traditional storage interfaces. The rapid adoption of virtualization and cloud-based services further amplifies the need for efficient storage architectures, thereby fueling the demand for SAS controllers across various industry verticals. Moreover, the evolution of data center infrastructure and the shift towards hyper-converged systems are expected to drive sustained investments in SAS controller solutions over the coming years.

Another key growth factor for the SAS Controller market is the increasing deployment of servers and storage systems in sectors such as BFSI, healthcare, and manufacturing. These industries require seamless data access, secure storage, and high availability to support mission-critical applications. SAS controllers play a vital role in ensuring data integrity and optimizing storage performance, especially in environments where downtime can result in significant financial losses or compromise sensitive information. The growing digital transformation initiatives across both public and private sectors are creating new opportunities for SAS controller vendors to offer innovative products that cater to evolving storage requirements, including support for higher data rates and integration with hybrid storage architectures.

Technological advancements in SAS controller design, such as the integration of RAID functionalities, enhanced error correction capabilities, and support for next-generation SAS protocols, are also contributing to market growth. Vendors are focusing on developing controllers that can handle increasing data workloads while maintaining energy efficiency and minimizing latency. The emergence of NVMe and SSD-based storage solutions is prompting SAS controller manufacturers to innovate and offer products that provide seamless interoperability and future-proofing for enterprise storage environments. Additionally, the trend towards distributed and edge computing is expected to create further demand for SAS controllers that can deliver high performance in decentralized storage architectures.

From a regional perspective, North America remains the dominant market for SAS controllers, owing to the presence of major technology companies, advanced IT infrastructure, and the early adoption of innovative storage solutions. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid industrialization, increasing investments in data centers, and the expansion of cloud services. Europe and Latin America are also showing steady growth, supported by digitalization initiatives in various industries. The Middle East & Africa region, although still emerging, presents significant potential as enterprises in the region ramp up their investments in IT modernization and storage infrastructure.

In the context of technological advancements, the integration of RAID-on-Chip technology within SAS controllers is gaining traction. This innovation allows for the consolidation of RAID functionalities directly onto the controller chip, enhancing performance and reducing latency. RAID-on-Chip solutions offer improved data protection and reliability, which are critical in environments that demand high availability and fault tolerance. As enterprises continue to seek ways

SAS Expander Market Outlook

According to our latest research, the global SAS Expander market size reached USD 1.35 billion in 2024, reflecting robust demand from data-intensive sectors. The market is projected to expand at a CAGR of 7.2% during the forecast period, reaching approximately USD 2.52 billion by 2033. This growth is primarily driven by the exponential rise in data generation, increasing adoption of high-performance storage solutions, and the expanding footprint of cloud computing and enterprise storage infrastructure worldwide.

One of the key growth factors propelling the SAS Expander market is the surging demand for scalable storage architectures in data centers and cloud service environments. As organizations continue to digitize operations and accumulate vast amounts of structured and unstructured data, there is a critical need for storage systems that offer high throughput, reliability, and flexibility. SAS Expanders, which enable multiple devices to connect to a single host, have become essential in building large-scale, cost-effective storage networks. The ongoing transformation of data centers from traditional on-premises models to hybrid and cloud-based architectures further amplifies the need for advanced SAS Expander solutions that can support dynamic workloads and rapid scaling.

Another significant factor fueling market expansion is the proliferation of enterprise applications and emerging technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT). These technologies generate unprecedented data volumes, necessitating robust backend storage frameworks. SAS Expanders play a pivotal role in facilitating seamless data transfer and storage management, ensuring optimal performance and minimal latency. Additionally, the increasing adoption of virtualization and containerization in enterprise IT environments is placing additional emphasis on storage scalability and flexibility, further accelerating the uptake of SAS Expander products across various industries.

Technological advancements and continuous innovation in SAS Expander design are also contributing to market growth. Vendors are focusing on enhancing product features such as higher port densities, improved data transfer rates, and advanced error correction mechanisms to meet the evolving needs of enterprise customers. The integration of SAS Expanders with NVMe and SSD technologies is creating new opportunities for performance optimization, particularly in mission-critical applications. Furthermore, the trend towards software-defined storage and hyper-converged infrastructure is driving the demand for SAS Expanders that can seamlessly integrate with next-generation storage platforms.

The role of SAS HBA (Host Bus Adapter) in the SAS Expander ecosystem is crucial as it serves as the interface between the server and the SAS Expander. SAS HBAs are designed to manage data flow between the connected storage devices and the host system, ensuring efficient data transfer and communication. With the increasing complexity and scale of data storage environments, the demand for high-performance SAS HBAs is growing. These adapters are essential for optimizing the performance of SAS Expanders by providing the necessary bandwidth and connectivity options to support multiple storage devices. As organizations continue to expand their storage infrastructure, the integration of advanced SAS HBAs becomes vital in maintaining system reliability and performance.

From a regional perspective, North America remains the dominant market for SAS Expander solutions, driven by the presence of leading technology firms, expansive data center infrastructure, and early adoption of advanced storage technologies. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digital transformation, significant investments in data center construction, and the proliferation of cloud services in countries such as China, India, and Japan. Europe also presents substantial growth opportunities, supported by stringent data regulations and increasing adoption of enterprise storage solutions across various sectors. Collectively, these regional dynamics underscore the global relevance and growth trajectory of the SAS Expander market.

Charlie839242/SAS dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

SAS Switch Market Outlook

According to our latest research, the global SAS Switch market size reached USD 1.42 billion in 2024, reflecting a robust industry presence. The market is projected to expand at a CAGR of 7.1% from 2025 to 2033, reaching a forecasted value of USD 2.66 billion by 2033. This growth is primarily driven by the escalating demand for high-performance storage solutions across data-intensive sectors such as cloud computing, enterprise storage, and industrial automation. As organizations continue to transition toward digital transformation and data-centric operations, the adoption of SAS Switches is witnessing significant momentum worldwide.

The primary growth factor for the SAS Switch market is the exponential surge in data generation and storage requirements across enterprises. With the proliferation of big data analytics, artificial intelligence, and machine learning applications, businesses are increasingly relying on robust storage area networks (SANs) to ensure fast, reliable, and secure data access. SAS Switches, known for their high-speed connectivity and scalability, are becoming indispensable in modern data center architectures. Moreover, the growing adoption of hybrid and multi-cloud environments is compelling organizations to invest in advanced storage solutions that can seamlessly integrate with diverse IT infrastructures, further propelling the demand for SAS Switches.

Another critical driver is the increasing focus on business continuity and disaster recovery strategies. Enterprises are prioritizing data protection, backup, and recovery capabilities to safeguard against potential cyber threats, hardware failures, and natural disasters. SAS Switches play a pivotal role in enabling efficient data replication, redundancy, and failover mechanisms, which are essential for ensuring uninterrupted business operations. Additionally, the rise of IoT-enabled industrial automation and smart manufacturing is creating new avenues for SAS Switch deployment, as these environments require high-speed, low-latency data transfer between connected devices and storage systems.

Technological advancements in SAS Switch design, including the integration of intelligent management features and enhanced interoperability, are further contributing to market expansion. Vendors are focusing on developing multi-protocol switches that support both SAS and SATA devices, offering greater flexibility and investment protection for end-users. The emergence of NVMe over Fabrics and the shift towards all-flash storage arrays are also influencing the evolution of SAS Switches, as enterprises seek to maximize performance and minimize latency in their storage networks. These innovations are expected to drive sustained growth and competitive differentiation in the global SAS Switch market over the forecast period.

From a regional perspective, North America continues to dominate the SAS Switch market, accounting for the largest revenue share in 2024, followed closely by Asia Pacific and Europe. The presence of major technology vendors, advanced IT infrastructure, and a strong focus on digital transformation initiatives are key factors supporting market growth in these regions. Meanwhile, emerging economies in Asia Pacific and Latin America are witnessing accelerated adoption of SAS Switches, driven by rapid industrialization, expanding data center investments, and increasing enterprise IT spending. As organizations across all regions prioritize data accessibility, security, and scalability, the global outlook for the SAS Switch market remains highly promising.

Product Type Analysis

The SAS Switch market by product type is primarily segmented into Single Port SAS Switches and Multi Port SAS Switches. Single Port SAS Switches are predominantly used in applications requiring dedicated, point-to-point connectivity, such as small-scale storage networks and direct-attached storage (DAS) environments. These switches are valued for their simplicity, cost-effectiveness, and ease of deployment, especi

Since the outbreak of SARS-CoV-2, antigenicity concerns continue to linger with emerging mutants. As recent variants have shown decreased reactivity to previously determined monoclonal antibodies (mAbs) or sera, monitoring the antigenicity change of circulating mutants is urgently needed for vaccine effectiveness. Currently, antigenic comparison is mainly carried out by immuno-binding assays. Yet, an online predicting system is highly desirable to complement the targeted experimental tests from the perspective of time and cost. Here, we provided a platform of SAS (Spike protein Antigenicity for SARS-CoV-2), enabling predicting the resistant effect of emerging variants and the dynamic coverage of SARS-CoV-2 antibodies among circulating strains. When being compared to experimental results, SAS prediction obtained the consistency of 100% on 8 mAb-binding tests with detailed epitope covering mutational sites, and 80.3% on 223 anti-serum tests. Moreover, on the latest South Africa escaping strain (B.1.351), SAS predicted a significant resistance to reference strain at multiple mutated epitopes, agreeing well with the vaccine evaluation results. SAS enables auto-updating from GISAID, and the current version collects 867K GISAID strains, 15.4K unique spike (S) variants, and 28 validated and predicted epitope regions that include 339 antigenic sites. Together with the targeted immune-binding experiments, SAS may be helpful to reduce the experimental searching space, indicate the emergence and expansion of antigenic variants, and suggest the dynamic coverage of representative mAbs/vaccines among the latest circulating strains. SAS can be accessed at https://www.biosino.org/sas.