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".

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 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.

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...

SAS Controller Market Outlook

According to our latest research, the global SAS Controller market size reached USD 2.4 billion in 2024, with steady expansion driven by the increasing adoption of high-performance storage solutions across various industries. The market is forecasted to grow at a CAGR of 7.2% from 2025 to 2033, reaching an estimated value of USD 4.6 billion by 2033. The key growth factor propelling this market is the escalating demand for data-intensive applications and the need for robust, scalable storage infrastructures in enterprise environments, particularly as digital transformation initiatives accelerate globally.

The SAS Controller market is experiencing robust growth due to the exponential rise in data generation and storage requirements across industries such as IT & telecommunications, BFSI, healthcare, and manufacturing. As organizations transition towards digitized operations, the volume of structured and unstructured data has surged, necessitating advanced storage solutions that offer high reliability, speed, and scalability. SAS controllers, with their superior performance compared to traditional SATA controllers, are increasingly preferred for mission-critical applications, big data analytics, and cloud computing workloads. The proliferation of data centers and the ongoing migration to hybrid and multi-cloud environments further amplify the demand for SAS controllers, as enterprises seek solutions that can seamlessly manage and protect vast amounts of data with minimal latency.

Another critical growth factor for the SAS Controller market is the rapid evolution of storage technologies and the integration of advanced features such as RAID (Redundant Array of Independent Disks) support, enhanced data security, and improved energy efficiency. Manufacturers are continuously innovating to offer SAS controllers that not only deliver high throughput and reliability but also support complex storage architectures and virtualization environments. The increasing adoption of SSDs (Solid State Drives) in enterprise storage systems has also contributed to the market’s growth, as SAS controllers are designed to optimize the performance of both SSDs and HDDs. Moreover, the emergence of AI and machine learning workloads, which require high-speed data access and processing, is further fueling the demand for advanced SAS controller solutions.

The SAS Controller market is also benefiting from the growing emphasis on data security and regulatory compliance, especially in sectors such as healthcare, BFSI, and government. Organizations in these industries are subject to stringent data protection regulations, making it imperative to deploy storage solutions that ensure data integrity, redundancy, and rapid recovery in case of hardware failures. SAS controllers, particularly those with RAID capabilities, are well-suited to meet these requirements by providing robust fault tolerance and efficient data management. Additionally, the trend towards edge computing and IoT deployments is creating new opportunities for SAS controller vendors, as decentralized environments require reliable and high-performance storage solutions to handle real-time data processing and analytics.

From a regional perspective, North America currently dominates the SAS Controller market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s leadership is attributed to the presence of major data center operators, advanced IT infrastructure, and high adoption rates of cloud computing and enterprise storage solutions. However, Asia Pacific is expected to exhibit the fastest growth during the forecast period, driven by rapid digitalization, increasing investments in data centers, and the expansion of BFSI and healthcare sectors in emerging economies such as China and India. Europe also presents significant growth potential, supported by stringent data protection regulations and the growing demand for high-performance storage solutions in industries such as manufacturing and government.

Product Type Analysis

The SAS Controller market by product type is segmented into RAID SAS Controllers and Non-RAID SAS Controllers, each catering to distinct storage requirements across various applications. RAID SAS Controllers have gained significant traction in recent years, primarily due to their ability to provide enhanced data redundancy, fault tolerance, and performance optimization. These controllers are widely deployed

The global HD-mini SAS Cable market size was valued at USD XX million in 2025 and is projected to reach USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The increasing adoption of HD-mini SAS in data centers and enterprise applications, such as high-performance computing (HPC) and cloud computing, is driving the growth of the market. Moreover, the rising demand for high-speed data transmission and the proliferation of data-intensive applications are further contributing to the market growth. The market is segmented based on application into enterprise storage, data centers, and cloud computing. The enterprise storage segment held the largest share in 2025 and is expected to maintain its dominance throughout the forecast period. This growth is attributed to the increasing adoption of HD-mini SAS in storage systems due to its high-speed data transfer capabilities and reliability. The data centers segment is anticipated to grow at a significant CAGR during the forecast period. This growth is driven by the rising adoption of HD-mini SAS in data centers for high-speed data connectivity between servers and storage devices.

The Mini SAS HD Connector market is booming, projected to reach $1.8B by 2033 with a 15% CAGR. Driven by cloud computing and AI, this report analyzes market size, growth trends, key players (TE Connectivity, Amphenol, Molex), and regional insights. Discover the opportunities and challenges in this high-speed interconnect market.

File List Power trends grouped counts analytical.sas -- SAS code. Power trends grouped counts simulation.sas -- SAS code. Description Power trends grouped counts analytical.sas uses the analytical method described in the main article for approximating standard errors, precision, and power associated with detecting trends across grouped count data. Power trends grouped counts simulation.sas uses simulations and repeated calls to a nonlinear mixed model to estimate power to detect trends across grouped count data. Parameters are defined at the bottom of the macro. Both files must be run using SAS statistical software. Further details are provided in the macros, and in the original paper.

The global SAS & SATA & RAID Controller market is projected to reach an impressive market size of approximately $3,500 million by 2025, exhibiting a robust Compound Annual Growth Rate (CAGR) of around 9.5% throughout the forecast period of 2025-2033. This substantial growth is primarily fueled by the escalating demand for high-speed data transfer and reliable data storage solutions across a multitude of industries. The increasing adoption of cloud computing, big data analytics, and the Internet of Things (IoT) are significant drivers, necessitating advanced controller technologies to manage vast and rapidly growing datasets. Furthermore, the continuous evolution of server technologies and the persistent need for enhanced data protection and performance in enterprise environments are also contributing to market expansion. The market is segmented into hardware and software cards, catering to diverse application needs within the Internet industry, service industry, manufacturing, financial sector, and government. Key trends shaping the SAS & SATA & RAID Controller market include the integration of AI and machine learning capabilities into RAID controllers for predictive maintenance and intelligent data management, as well as the ongoing shift towards NVMe (Non-Volatile Memory Express) technology, which demands more sophisticated controller architectures. The expansion of data centers globally and the increasing complexity of enterprise storage infrastructures further bolster the market's upward trajectory. While the market presents significant opportunities, potential restraints such as the high initial investment costs for advanced controller solutions and the emergence of alternative storage technologies could pose challenges. However, the compelling benefits of improved data integrity, performance, and scalability offered by SAS, SATA, and RAID controllers are expected to outweigh these limitations, ensuring sustained market growth and innovation.

The detailed codes and sample call of the SAS program %n_gssur. (SAS)

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 HBA Market Outlook

According to our latest research, the global SAS HBA (Serial Attached SCSI Host Bus Adapter) market size reached USD 1.47 billion in 2024, and it is poised to grow at a CAGR of 7.1% during the forecast period, reaching an estimated USD 2.74 billion by 2033. This robust growth is driven by increasing demand for high-speed and reliable data transfer solutions across data centers, enterprise storage, and server environments. The proliferation of big data analytics, cloud computing, and the expansion of enterprise IT infrastructure are among the primary factors fueling market expansion, as organizations worldwide seek efficient and scalable storage connectivity solutions.

One of the most significant growth factors for the SAS HBA market is the exponential rise in data generation and storage requirements across various industries. With digital transformation initiatives accelerating globally, organizations are investing heavily in advanced storage systems to manage and process vast volumes of data efficiently. SAS HBAs play a crucial role in enabling high-speed, low-latency connections between servers and storage devices, ensuring seamless data flow and robust performance. The growing adoption of cloud-based services, virtualization, and high-performance computing (HPC) further amplifies the need for scalable and reliable storage connectivity, driving the demand for SAS HBA solutions in both enterprise and hyperscale data center environments.

Another critical driver propelling the SAS HBA market is the ongoing evolution of storage technologies and the increasing complexity of enterprise IT infrastructure. As businesses transition from traditional storage architectures to more sophisticated, hybrid, and software-defined storage environments, the need for versatile and high-capacity connectivity solutions has become paramount. SAS HBAs offer backward compatibility, enhanced error correction, and superior scalability compared to legacy solutions, making them an ideal choice for organizations seeking to future-proof their storage investments. The integration of advanced features such as multi-path I/O, improved power management, and support for higher data transfer rates positions SAS HBAs as essential components in modern IT ecosystems.

Furthermore, the surge in demand for mission-critical applications and real-time data processing across sectors such as BFSI, healthcare, manufacturing, and government is accelerating the adoption of SAS HBA solutions. These applications require uninterrupted access to large datasets and depend on the high reliability and performance provided by SAS HBA technology. The increasing prevalence of AI, machine learning, and IoT-driven workloads is also contributing to the marketÂ’s momentum, as these technologies necessitate robust storage connectivity to handle intensive data processing requirements. As a result, vendors are continuously innovating and expanding their product portfolios to cater to the evolving needs of diverse end-users.

In addition to SAS HBAs, Fibre Channel HBA technology is gaining traction as an alternative storage connectivity solution, particularly in environments where high-speed data transfer and low latency are critical. Fibre Channel HBAs are known for their ability to provide dedicated bandwidth and enhanced reliability, making them a preferred choice for mission-critical applications in sectors such as finance, healthcare, and telecommunications. As organizations continue to seek robust and scalable storage solutions, the integration of Fibre Channel HBAs into existing IT infrastructures offers a pathway to achieving optimal performance and efficiency. The growing adoption of this technology underscores the importance of versatile connectivity options in modern data center environments.

From a regional perspective, North America continues to dominate the global SAS HBA market, accounting for the largest revenue share in 2024, followed by Europe and the Asia Pacific. The strong presence of leading technology companies, early adoption of advanced storage solutions, and significant investments in data center infrastructure are key factors supporting North AmericaÂ’s leadership position. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by rapid digitalization, expanding enterprise IT infrastructure, and increasing investment

Along track temperature, Salinity, backscatter, Chlorophyll Fluoresence, and normalized water leaving radiance (nLw).

On the bow of the R/V Roger Revelle was a Satlantic SeaWiFS Aircraft Simulator (MicroSAS) system, used to estimate water-leaving radiance from the ship, analogous to to the nLw derived by the SeaWiFS and MODIS satellite sensors, but free from atmospheric error (hence, it can provide data below clouds).

The system consisted of a down-looking radiance sensor and a sky-viewing radiance sensor, both mounted on a steerable holder on the bow. A downwelling irradiance sensor was mounted at the top of the ship's meterological mast, on the bow, far from any potentially shading structures. These data were used to estimate normalized water-leaving radiance as a function of wavelength. The radiance detector was set to view the water at 40deg from nadir as recommended by Mueller et al. [2003b]. The water radiance sensor was able to view over an azimuth range of ~180deg across the ship's heading with no viewing of the ship's wake. The direction of the sensor was adjusted to view the water 90-120deg from the sun's azimuth, to minimize sun glint. This was continually adjusted as the time and ship's gyro heading were used to calculate the sun's position using an astronomical solar position subroutine interfaced with a stepping motor which was attached to the radiometer mount (designed and fabricated at Bigelow Laboratory for Ocean Sciences). Protocols for operation and calibration were performed according to Mueller [Mueller et al., 2003a; Mueller et al., 2003b; Mueller et al., 2003c]. Before 1000h and after 1400h, data quality was poorer as the solar zenith angle was too low. Post-cruise, the 10Hz data were filtered to remove as much residual white cap and glint as possible (we accept the lowest 5% of the data). Reflectance plaque measurements were made several times at local apparent noon on sunny days to verify the radiometer calibrations.

Within an hour of local apparent noon each day, a Satlantic OCP sensor was deployed off the stern of the R/V Revelle after the ship oriented so that the sun was off the stern. The ship would secure the starboard Z-drive, and use port Z-drive and bow thruster to move the ship ahead at about 25cm s-1. The OCP was then trailed aft and brought to the surface ~100m aft of the ship, then allowed to sink to 100m as downwelling spectral irradiance and upwelling spectral radiance were recorded continuously along with temperature and salinity. This procedure ensured there were no ship shadow effects in the radiometry.

Instruments include a WETLabs wetstar fluorometer, a WETLabs ECOTriplet and a SeaBird microTSG.
Radiometry was done using a Satlantic 7 channel microSAS system with Es, Lt and Li sensors.

Chl data is based on inter calibrating surface discrete Chlorophyll measure with the temporally closest fluorescence measurement and applying the regression results to all fluorescence data.

Data have been corrected for instrument biofouling and drift based on weekly purewater calibrations of the system. Radiometric data has been processed using standard Satlantic processing software and has been checked with periodic plaque measurements using a 2% spectralon standard.

Lw is calculated from Lt and Lsky and is "what Lt would be if the
sensor were looking straight down". Since our sensors are mounted at
40o, based on various NASA protocols, we need to do that conversion.

Lwn adds Es to the mix. Es is used to normalize Lw. Nlw is related to Rrs, Remote Sensing Reflectance

Techniques used are as described in:
Balch WM, Drapeau DT, Bowler BC, Booth ES, Windecker LA, Ashe A (2008) Space-time variability of carbon standing stocks and fixation rates in the Gulf of Maine, along the GNATS transect between Portland, ME, USA, and Yarmouth, Nova Scotia, Canada. J Plankton Res 30:119-139

simulation data and code(1) The codes and relevant files for simulation studies can also be downloaded at http://www4.stat.ncsu.edu/~tzeng/Data/Genetics2014.GxE.binary/

(2) Code for simulations: • "simulation_code.sas" is the SAS code to perform the simulation, including data generation and analyses • "glmm_em_sim.sas" is the SAS macro to calculate the estimates of the variance component and covariate coefficients • "COSI_geno.zip" is the zipped version of "COSI_geno.csv", which contains the 10K haplotypes of 100 rare loci generated from COSI. From COSI_geno.csv, we randomly drew two haplotypes with replacement to form a subject’s genotype.

(3) One replication of the simulated data (1500 subjects): • "simu.dataset1.Y.E.txt" contains the trait variable and the environmental variable • "simu.dataset1.geno.txt" contains the simulated genotypessascode_4_Dryad.zip

Dr. Kevin Bronson provides a unique nitrogen and water management in cotton agricultural research dataset for compute, including notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, and laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs. This data was collected using a Hamby rig as a high-throughput proximal plant phenotyping platform. The Hamby 6000 rig Ellis W. Chenault, & Allen F. Wiese. (1989). Construction of a High-Clearance Plot Sprayer. Weed Technology, 3(4), 659–662. http://www.jstor.org/stable/3987560 Dr. Bronson modified an old high-clearance Hamby 6000 rig, adding a tank and pump with a rear boom, to perform precision liquid N applications. A Raven control unit with GPS supplied variable rate delivery options. The 12 volt Holland Scientific GeoScoutX data recorder and associated CropCircle ACS-470 sensors with GPS signal, was easy to mount and run on the vehicle as an attached rugged data acquisition module, and allowed the measuring of plants using custom proximal active optical reflectance sensing. The HS data logger was positioned near the operator, and sensors were positioned in front of the rig, on forward protruding armature attached to a hydraulic front boom assembly, facing downward in nadir view 1 m above the average canopy height. A 34-size class AGM battery sat under the operator and provided the data system electrical power supply. Data suffered reduced input from Conley. Although every effort was afforded to capture adequate quality across all metrics, experiment exterior considerations were such that canopy temperature data is absent, and canopy height is weak due to technical underperformance. Thankfully, reflectance data quality was maintained or improved through the implementation of new hardware by Bronson. See included README file for operational details and further description of the measured data signals. Summary: Active optical proximal cotton canopy sensing spatial data and including few additional related metrics and weak low-frequency ultrasonic derived height are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2014 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig. Acquired data conforms to location standard methodologies of the plant phenotyping. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. Additional ultrasonic data signal explanation is offered as annotated time-series charts. The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths. Lint and seed yields, first open boll biomass, and nitrogen uptake were also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).

The Serial Attached SCSI (SAS) Solid-State Drive (SSD) market is experiencing robust growth, driven by increasing demand for high-performance, reliable storage solutions across diverse sectors. The enterprise segment dominates, fueled by the need for faster data processing and reduced latency in data centers and cloud infrastructure. Client applications, while smaller in market share, are witnessing expansion due to the increasing adoption of high-performance computing in workstations and gaming PCs. The market is segmented by drive capacity, with 1.6TB and 4TB drives currently leading, although larger capacity drives are expected to gain traction as prices fall and demand for greater storage capacity increases. Key players like Kingston Technology, Micron, Seagate, Samsung, Toshiba, Dell, and Western Digital are engaged in intense competition, focusing on technological advancements, performance optimization, and cost reduction to maintain market leadership. Geographic growth is strong, particularly in North America and Asia Pacific, driven by burgeoning technology industries and increased infrastructure investments. Restraints include the higher cost of SAS SSDs compared to other storage technologies like SATA SSDs and the potential for slower adoption in price-sensitive markets. However, the long-term outlook remains optimistic given the continued growth of data centers, cloud computing, and the growing need for enhanced data processing capabilities. The forecast period (2025-2033) projects continued growth, influenced by factors such as increasing adoption of virtualization, the growing demand for high-speed data transmission in applications requiring low latency, and the emergence of new data-intensive applications in various industries, including finance, healthcare, and media. While specific CAGR values are not provided, a reasonable estimate, considering market trends and technological advancements, would place the annual growth rate between 15-20% over the forecast period. This growth will be further propelled by improvements in SAS SSD technology leading to increased storage capacity, performance enhancements, and overall cost reductions. The competitive landscape will likely witness strategic partnerships, mergers, and acquisitions as companies strive to expand their market share and diversify their product portfolios. Regional growth will continue to be uneven, with developed economies leading the way due to greater adoption rates and higher investment in technological infrastructure.

The Disk Array Controller Card market is projected to be valued at $2.5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 6.8%, reaching approximately $4.6 billion by 2034.