Summary data for the studies used in the meta-analysis of local adaptation (Table 1 from the publication)This table contains the data used in this published meta-analysis. The data were originally extracted from the publications listed in the table. The file corresponds to Table 1 in the original publication.tb1.xlsSAS script used to perform meta-analysesThis file contains the essential elements of the SAS script used to perform meta-analyses published in Hoeksema & Forde 2008. Multi-factor models were fit to the data using weighted maximum likelihood estimation of parameters in a mixed model framework, using SAS PROC MIXED, in which the species traits and experimental design factors were considered fixed effects, and a random between-studies variance component was estimated. Significance (at alpha = 0.05) of individual factors in these models was determined using randomization procedures with 10,000 iterations (performed with a combination of macros in SAS), in which effect sizes a...

no abstract provided

It is commonly believed that if a two-way analysis of variance (ANOVA) is carried out in R, then reported p-values are correct. This article shows that this is not always the case. Results can vary from non-significant to highly significant, depending on the choice of options. The user must know exactly which options result in correct p-values, and which options do not. Furthermore, it is commonly supposed that analyses in SAS and R of simple balanced experiments using mixed-effects models result in correct p-values. However, the simulation study of the current article indicates that frequency of Type I error deviates from the nominal value. The objective of this article is to compare SAS and R with respect to correctness of results when analyzing small experiments. It is concluded that modern functions and procedures for analysis of mixed-effects models are sometimes not as reliable as traditional ANOVA based on simple computations of sums of squares.

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.

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Example of the code used to account for statistical significances for phenotype and other variables.

The simulated synthetic aperture sonar (SAS) data presented here was generated using PoSSM [Johnson and Brown 2018]. The data is suitable for bistatic, coherent signal processing and will form acoustic seafloor imagery. Included in this data package is simulated sonar data in Generic Data Format (GDF) files, a description of the GDF file contents, example SAS imagery, and supporting information about the simulated scenes. In total, there are eleven 60 m x 90 m scenes, labeled scene00 through scene10, with scene00 provided with the scatterers in isolation, i.e. no seafloor texture. This is provided for beamformer testing purposes and should result in an image similar to the one labeled "PoSSM-scene00-scene00-starboard-0.tif" in the Related Data Sets tab. The ten other scenes have varying degrees of model variation as described in "Description_of_Simulated_SAS_Data_Package.pdf". A description of the data and the model is found in the associated document called "Description_of_Simulated_SAS_Data_Package.pdf" and a description of the format in which the raw binary data is stored is found in the related document "PSU_GDF_Format_20240612.pdf". The format description also includes MATLAB code that will effectively parse the data to aid in signal processing and image reconstruction. It is left to the researcher to develop a beamforming algorithm suitable for coherent signal and image processing. Each 60 m x 90 m scene is represented by 4 raw (not beamformed) GDF files, labeled sceneXX-STARBOARD-000000 through 000003. It is possible to beamform smaller scenes from any one of these 4 files, i.e. the four files are combined sequentially to form a 60 m x 90 m image. Also included are comma separated value spreadsheets describing the locations of scatterers and objects of interest within each scene. In addition to the binary GDF data, a beamformed GeoTIFF image and a single-look complex (SLC, science file) data of each scene is provided. The SLC data (science) is stored in the Hierarchical Data Format 5 (https://www.hdfgroup.org/), and appended with ".hdf5" to indicate the HDF5 format. The data are stored as 32-bit real and 32-bit complex values. A viewer is available that provides basic graphing, image display, and directory navigation functions (https://www.hdfgroup.org/downloads/hdfview/). The HDF file contains all the information necessary to reconstruct a synthetic aperture sonar image. All major and contemporary programming languages have library support for encoding/decoding the HDF5 format. Supporting documentation that outlines positions of the seafloor scatterers is included in "Scatterer_Locations_Scene00.csv", while the locations of the objects of interest for scene01-scene10 are included in "Object_Locations_All_Scenes.csv". Portable Network Graphic (PNG) images that plot the location of objects of all the objects of interest in each scene in Along-Track and Cross-Track notation are provided.

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

Abstract (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 district and 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. 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 in the United States in 2002. Smallest Geographic Unit: county 2015-09-18 Six data files were created with docket numbers blanked for Parts 1, 3, and 5, and with docket numbers containing original values for Parts 2, 4, and 6.2012-06-26 All parts are being moved to restricted access and will be available only using the restricted access procedures.2005-04-29 Data files for Part 3, Criminal Data, 2002, Part 4, Civil Pending Data, 2002, and a Civil Pending Restricted Data, 2002 file have been added to the data collection along with corresponding SAS and SPSS setup files and codebooks in PDF formats.2005-01-07 A restricted data file for Part 1, Civil Terminations, 2002, has been added to the data collection. The public use data file for Part 1 and its corresponding SAS and SPSS setup files have been updated. The codebook has been modified to reflect these changes. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. record abstractsStarting with the year 2001, each year of data for Federal Court Cases is released by ICPSR as a separate study number. Federal Court Cases data for the years 1970-2000 can be found in FEDERAL COURT CASES: INTEGRATED DATA BASE, 1970-2000 (ICPSR 8429).

Sequential designs and competing risks methodology are both well established. Their combined use has recently received some attention from a theoretical perspective, but their joint application in practice has been discussed less. The aim of this paper is to provide the applied statistician with a basic understanding of both sequential design theory and competing risks methodology and how to combine them in practice. Relevant references to more detailed theoretical discussions are provided and all discussions are illustrated using a real case study. Extensive R and SAS code is provided in the online supplementary material.

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

Abstract (en): This study is part of a time-series collection of national surveys fielded continuously since 1952. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. A Black supplement of 263 respondents, who were asked the same questions that were administered to the national cross-section sample, is included with the national cross-section of 1,571 respondents. In addition to the usual content, the study contains data on opinions about the Supreme Court, political knowledge, and further information concerning racial issues. Voter validation data have been included as an integral part of the election study, providing objective information from registration and voting records or from respondents' past voting behavior. 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.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. United States citizens of voting age living in private households in the continental United States. A representative cross-section sample, consisting of 1,571 respondents, plus a Black supplement sample of 263 respondents. 2015-11-10 The study metadata was updated.1999-12-14 The data for this study are now available in SAS transport and SPSS export formats, in addition to the ASCII data file. Variables in the dataset have been renumbered to the following format: 2-digit (or 2-character) year prefix + 4 digits + [optional] 1-character suffix. Dataset ID and version variables have also been added. In addition, SAS and SPSS data definition statements have been created for this collection, and the data collection instruments are now available as a PDF file. face-to-face interview, telephone interviewThe SAS transport file was created using the SAS CPORT procedure.

Users are able to access data related discharge information on all emergency department visits. Data is focused on but not limited to emergency room diagnoses, procedures, demographics, and payment source. Background The State Emergency Department Databases (SEDD) is focused on capturing discharge information on all emergency department visits that do not result in an admission, (Information on patients initially seen in the emergency room and then admitted to the hospital is included in the State Inpatient Databases (SID)). The SEDD contains emergency department information from 27 states. The SEDD contain more than 100 clinical and non-clinical variables included in a hospital dis charge abstract, such as: diagnoses, procedures, patient demographics, expected payment source and total charges. User functionality Users must pay to access the SEDD database. SEDD files from 1999-2009 are available through the HCUP Central Distributor. The SEDD data set can be run on desktop computers with a CD-ROM reader, and comes in ASCII format. The data on the CD set require a statistical software package such as SAS or SPSS to use for analytic purposes. The data set comes with full documentation. SAS and SPSS users are provided programs for converting ASCII files. Data Notes Data is available from 1999-2009. The website does not indicate when new data will be updated. Twenty-seven States now currently participate in the SEDD including Arizona, California, Connecticut, Florida, Georgia, Hawaii, Indiana, Iowa, Kansas, Maine, Maryland, Massachusetts, Minnesota, Missouri, Nebraska, New Hampshire, New Jersey, New York, North Carolina, Ohio, Rhode Island, South Carolina, South Dakota, Tennessee, Utah, Vermont, and Wisconsin.

The simulated synthetic aperture sonar (SAS) data presented here was generated using PoSSM [Johnson and Brown 2018]. The data is suitable for bistatic, coherent signal processing and will form acoustic seafloor imagery. Included in this data package is simulated sonar data in Generic Data Format (GDF) files, a description of the GDF file contents, example SAS imagery, and supporting information about the simulated scenes. In total, there are eleven 60 m x 90 m scenes, labeled scene00 through scene10, with scene00 provided with the scatterers in isolation, i.e. no seafloor texture. This is provided for beamformer testing purposes and should result in an image similar to the one labeled "PoSSM-scene00-scene00-starboard-0.tif" in the Related Data Sets tab. The ten other scenes have varying degrees of model variation as described in "Description_of_Simulated_SAS_Data_Package.pdf". A description of the data and the model is found in the associated document called "Description_of_Simulated_SAS_Data_Package.pdf" and a description of the format in which the raw binary data is stored is found in the related document "PSU_GDF_Format_20240612.pdf". The format description also includes MATLAB code that will effectively parse the data to aid in signal processing and image reconstruction. It is left to the researcher to develop a beamforming algorithm suitable for coherent signal and image processing. Each 60 m x 90 m scene is represented by 4 raw (not beamformed) GDF files, labeled sceneXX-STARBOARD-000000 through 000003. It is possible to beamform smaller scenes from any one of these 4 files, i.e. the four files are combined sequentially to form a 60 m x 90 m image. Also included are comma separated value spreadsheets describing the locations of scatterers and objects of interest within each scene. In addition to the binary GDF data, a beamformed GeoTIFF image and a single-look complex (SLC, science file) data of each scene is provided. The SLC data (science) is stored in the Hierarchical Data Format 5 (https://www.hdfgroup.org/), and appended with ".hdf5" to indicate the HDF5 format. The data are stored as 32-bit real and 32-bit complex values. A viewer is available that provides basic graphing, image display, and directory navigation functions (https://www.hdfgroup.org/downloads/hdfview/). The HDF file contains all the information necessary to reconstruct a synthetic aperture sonar image. All major and contemporary programming languages have library support for encoding/decoding the HDF5 format. Supporting documentation that outlines positions of the seafloor scatterers is included in "Scatterer_Locations_Scene00.csv", while the locations of the objects of interest for scene01-scene10 are included in "Object_Locations_All_Scenes.csv". Portable Network Graphic (PNG) images that plot the location of objects of all the objects of interest in each scene in Along-Track and Cross-Track notation are provided.

Abstract

The main objective of the new agricultural statistics program is to provide timely, accurate, credible and comprehensive agricultural statistics to describe the structure of agriculture in Rwanda in terms of land use, crop production and livestock; which can be used for food and agriculture policy formulation and planning, and for the compilation of national accounts statistics.

In this regard, the National Institute of Statistics of Rwanda (NISR) conducted the Seasonal Agriculture Survey (SAS) from November 2015 to October 2016 to gather up-to-date information for monitoring progress on agriculture programs and policies in Rwanda, including the Second Economic Development and Poverty Reduction Strategy (EDPRS II) and Vision 2020. This 2016 RSAS covered three agricultural seasons (A, B and C) and provides data on background characteristics of the agricultural operators, farm characteristics (area, yield and production), agricultural practices, agricultural equipments, use of crop production by agricultural operators and by large scale farmers.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Universe

The 2016 RSAS targeted agricultural operators and large scale farmers operating in Rwanda.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Seasonal Agriculture Survey (SAS) sample is composed of two categories of respondents: agricultural operators1 and large-scale farmers (LSF).

For the 2016 SAS, NISR used as the sampling method a dual frame sampling design combining selected area frame sample3 segments and a list of large-scale farmers.

NISR used also imagery from RNRA with a very high resolution of 25 centimeters to divide the total land of the country into twelve strata. A total number of 540 segments were spread throughout the country as coverage of the survey with 25,346 and 23,286 agricultural operators in Season A and Season B respectively. From these numbers of agricultural operators, sub-samples were selected during the second phases of Seasons A and B.

It is important to note that in each of agricultural season A and B, data collection was undertaken in two phases. Phase I was mainly used to collect data on demographic and social characteristics of interviewees, area under crops, crops planted, rainfall, livestock, etc. Phase II was mainly devoted to the collection of data on yield and production of crops.

Phase I serves at collecting data on area under different types of crops in the screening process, whereas the Phase II is mainly devoted to the collection of data on demographic, social characteristics of interviewees, together with yields of the different crops produced. Enumerated large-scale farmers (LSF) were 558 in both 2015 Season A and B. The LSF were engaged in either crop farming activities only, livestock farming activities only, or both crop and livestock farming activities.

Agricultural operators are the small scale farmers within the sample segments. Every selected segment was firstly screened using the appropriate materials such as the segment maps, GIS devices and the screening form. Using these devices, the enumerators accounted for every plot inside the sample segments. All Tracts6 were classified as either agricultural (cultivated land, pasture, and fallow land) or non-agricultural land (water, forests, roads, rocky and bare soils, and buildings).

During Phase I, a complete enumeration of all farmers having agricultural land and operating within the 540 selected segments was undertaken and a total of 25,495 and 24,911 agricultural operators were enumerated respectively in Seasons A and B. Season C considered only 152 segments, involving 3,445 agricultural operators.

In phase II, 50% of the large-scale farmers were undertaking crop farming activities only and 50% of the large-scale farmers were undertaking both crop and livestock farming and were selected for interview. A sample of 199 and 194 large-scale farmers were interviewed in Seasons A and B, respectively, using a farm questionnaire.

From the agricultural operators enumerated in the sample segments during Phase I, a sample of the agricultural operators was designed for Phase II as follows: 5,502 for Season A, 5,337 for Season B and 644 for Season C. The method of probability proportional to size (PPS) sampling at the national level was used. Furthermore, the total number of enumerated large-scale farmers was 774 in 2016 Season A and 622 in Season B.

The Season C considered 152 segments counting 8,987 agricultural operators from which 963 agricultural operators were selected for survey interviews.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

There were two types of questionnaires used for this survey namely Screening questionnaire and farm questionnaires.

A Screening Questionnaire was used to collect information that enabled identification of an Agricultural Operator or Large Scale Farmer and his or her land use.
Farm questionnaires were of two types: a) Phase I Farm Questionnaire was used to collect data on characteristics of Agricultural Operators, crop identification and area, inputs (seeds, fertilizers, labor, …) for Agricultural Operators and large scale farmers. b) Phase 2 Farm questionnaire was used in the collection of data on crop production and use of production.

It is important to mention that all these Farm Questionnaires were subjected to two/three rounds of data quality checking. The first round was conducted by the enumerator and the second round was conducted by the team leader to check if questionnaires had been well completed by enumerators. For season C, after screening, an interview was conducted for each selected tract/Agricultural Operator using one consolidated Farm questionnaire. All the surveys questionnaires used were published in both English and Kinyarwanda languages.

Cleaning operations

Data editing took place at different stage. Firstly, the filled questionnaires were repatriated at NISR for office editing and coding before data entry started. Data entry of the completed and checked questionnaires was undertaken at the NISR office by 20 staff trained in using the CSPro software. To ensure appropriate matching of data in the completed questionnaires and plot area measurements from the GIS unit, a LOOKUP file was integrated in the CSPro data entry program to confirm the identification of each agricultural operator or LSF before starting data entry. Thereafter, data were entered in computers, edited and summarized in tables using SPSS and Excel.

Response rate

The response rate for Seasonal Agriculture Survey is 98%.

Data appraisal

All Farm questionnaires were subjected to two/three rounds of data quality checking. The first round was conducted by the enumerator and the second round was conducted by the team leader to check if questionnaires had been well completed by enumerators. And in most cases, questionnaires completed by one enumerator were peer-reviewed by another enumerator before being checked by the Team leader.

Andalusian Public Healthcare System (SSPA)

In Spain, the competence for healthcare is the responsibility of the regions. The Andalusian Public Healthcare System (SSPA) is an ecosystem of public and universal healthcare provision, which is made up of a series of public agencies, managed by the Government of Andalusia.

The SSPA has a total of 32 hospitals in the region of Andalusia.

Within this ecosystem, the main healthcare provider agency is the Andalusian Health Service.

Andalusian Health Service (SAS)

The Andalusian Health Service, created in 1986 by Law 8/1986, of May 6, 1986, on the Andalusian Health Service, is attached to the Regional Ministry of Health and Consumer Affairs and performs the functions attributed to it under the supervision and control of the same.

Its mission is to provide healthcare to the citizens of Andalusia, offering quality public health services, ensuring accessibility, equity and user satisfaction, seeking efficiency and optimal use of resources.

The SAS guarantees free public health care to more than 8 million inhabitants, which represents about 17% of the Spanish population. The Andalusian Health Service has 28 hospitals, distributed throughout Andalusia. It is also functionally responsible for the centers belonging to the Public Health Business Agencies and the Aljarafe Public Health Consortium. In addition, there are 14 Health Management Areas.

The Andalusian Health Service is an administrative agency of those provided for in Article 65 of Law 9/2007, of October 22, is attached to the Ministry of Health and Consumer Affairs, depending specifically on the Vice-Ministry, according to Decree 156/2022, of August 9, which establishes the organisational structure of the Ministry of Health and Consumer Affairs. The Andalusian Health Service exercises the functions specified in this Decree, subject to the guidelines and general criteria of health policy in Andalusia and, in particular, the following:

- The management of the set of health services in the field of health promotion and protection, disease prevention, healthcare and rehabilitation that corresponds to it in the territory of the Autonomous Community of Andalusia.

- The administration and management of the health institutions, centers and services that act under its organic and functional dependence.

- The management of the human, material and financial resources assigned to it for the development of its functions.

Diraya: system used in the Andalusian Health Service to support the Electronic Health Record in Andalusia

Diraya is the system used in the Andalusian Health Service to support electronic health records. It integrates all the health information of each person treated in the healthcare centers in Andalusia, so that it is available wherever and whenever it is necessary to treat them, and it is also used for the management of the healthcare system.

Diraya's conceptual model and technological architecture have aroused significant interest in other healthcare administrations thanks to, among others, cutting-edge services such as the electronic prescription or the centralised appointment system.

A description of the Diraya system (objectives, basic components, modules, functional and technological architecture, impact assessment of its implementation, etc.) can be found in the following document: Health Care Information and Management Integrated System.

HD Mini SAS Cable Market Outlook

The global HD Mini SAS Cable market size is expected to witness significant growth, with projections estimating a rise from USD 1.2 billion in 2023 to USD 2.5 billion by 2032, reflecting a CAGR of 8.1%. The growth of this market is driven by the increasing demand for high-speed data transfer solutions in various industries.

The surge in data consumption and the exponential growth of data centers worldwide are primary growth factors for the HD Mini SAS Cable market. As businesses increasingly rely on big data analytics, cloud computing, and IoT, the need for efficient and reliable data transfer becomes paramount. HD Mini SAS Cables, known for their high-speed data transfer capabilities and robust performance, are essential components in modern data infrastructure. Consequently, the proliferation of data centers and the continuous advancement of data-driven technologies significantly bolster market expansion.

Another crucial growth driver is the escalating demand for high-performance computing and real-time data processing in telecommunications and consumer electronics. Telecommunications companies are rapidly upgrading their network infrastructure to support the growing demand for bandwidth-intensive applications like video streaming and online gaming. Similarly, the consumer electronics sector is witnessing an upsurge in the adoption of devices that require high-speed data transfer, such as gaming consoles, high-definition televisions, and storage devices. These trends are expected to propel the demand for HD Mini SAS Cables in the coming years.

The automotive industry's shift towards advanced driver-assistance systems (ADAS) and autonomous vehicles also contributes to market growth. Modern vehicles are increasingly equipped with sophisticated electronics systems that necessitate high-speed data communication for functions such as navigation, infotainment, and safety features. HD Mini SAS Cables play a critical role in ensuring reliable and swift data transmission within these systems, thereby supporting the automotive segment's expansion within the market.

From a regional perspective, North America is anticipated to dominate the HD Mini SAS Cable market, owing to its early adoption of advanced technologies and the presence of major data centers and IT companies. Asia Pacific is expected to exhibit the highest growth rate, driven by rapid technological advancements, increasing investment in data infrastructure, and the booming consumer electronics market in countries like China, India, and Japan. Europe, Latin America, and the Middle East & Africa are also poised to contribute to the market's growth, although their impact may be relatively lower compared to North America and Asia Pacific.

Product Type Analysis

The HD Mini SAS Cable market is segmented by product type into Internal HD Mini SAS Cables and External HD Mini SAS Cables. Internal HD Mini SAS Cables are designed for use within a computer or server, providing connectivity between internal components such as hard drives, SSDs, and motherboards. These cables are crucial in data centers and high-performance computing environments where efficient and reliable internal data transfer is essential. The demand for Internal HD Mini SAS Cables is expected to grow steadily, driven by the increasing deployment of high-density servers and storage solutions.

External HD Mini SAS Cables, on the other hand, are used for connections between external devices and systems. They are commonly employed in scenarios where data needs to be transferred between different hardware units, such as connecting external storage devices to a network or linking multiple servers. External HD Mini SAS Cables are particularly favored in data centers and enterprise environments due to their ability to support high-speed data transfer over longer distances. The growth of cloud computing and the increasing complexity of IT infrastructure are expected to fuel the demand for these cables.

Both internal and external HD Mini SAS Cables are designed to handle large volumes of data with minimal latency and high reliability. However, the choice between the two depends on the specific application requirements and the physical layout of the system. The market for both types of cables is expected to witness robust growth, driven by the ongoing expansion of data centers and the rising demand for high-speed data transfer solutions in various industries.

Manufacturers of HD Mini SAS Cables are continuously innovating to enha

SAS code to reproduce the simulation study and the analysis of the urine osmolarity example. (ZIP)

This data collection contains Supplemental Nutrition Assistance Program (SNAP) SAS proc contents (metadata only) files for Arizona (AZ), Hawaii (HI), Illinois (IL), Kentucky (KY), New Jersey (NJ), New York (NY), Oregon (OR), Tennessee (TN), and Virginia (VA).

File List Code_and_Data_Supplement.zip (md5: dea8636b921f39c9d3fd269e44b6228c) Description The supplementary material provided includes all code and data files necessary to replicate the simulation models other demographic analyses presented in the paper. MATLAB code is provided for the simulations, and SAS code is provided to show how model parameters (vital rates) were estimated.

  The principal programs are Figure_3_4_5_Elasticity_Contours.m and Figure_6_Contours_Stochastic_Lambda.m which perform the elasticity analyses and run the stochastic simulation, respectively.


  The files are presented in a zipped folder called Code_and_Data_Supplement. When uncompressed, users may run the MATLAB programs by opening them from within this directory. Subdirectories contain the data files and supporting MATLAB functions necessary to complete execution. The programs are written to find the necessary supporting functions in the Code_and_Data_Supplement directory. If users copy these MATLAB files to a different directory, they must add the Code_and_Data_Supplement directory and its subdirectories to their search path to make the supporting files available.


  More details are provided in the README.txt file included in the supplement.


  The file and directory structure of entire zipped supplement is shown below.

  Folder PATH listing
Code_and_Data_Supplement
|  Figure_3_4_5_Elasticity_Contours.m
|  Figure_6_Contours_Stochastic_Lambda.m
|  Figure_A1_RefitG2.m
|  Figure_A2_PlotFecundityRegression.m
|  README.txt
|  
+---FinalDataFiles
+---Make Tables
|    README.txt
|    Table_lamANNUAL.csv
|    Table_mgtProbPredicted.csv
|    
+---ParameterEstimation
|  |  Categorical Model output.xls
|  |  
|  +---Fecundity
|  |    Appendix_A3_Fecundity_Breakpoint.sas
|  |    fec_Cat_Indiv.sas
|  |    Mean_Fec_Previous_Study.m
|  |    
|  +---G1
|  |    G1_Cat.sas
|  |    
|  +---G2
|  |    G2_Cat.sas
|  |    
|  +---Model Ranking
|  |    Categorical Model Ranking.xls
|  |    
|  +---Seedlings
|  |    sdl_Cat.sas
|  |    
|  +---SS
|  |    SS_Cat.sas
|  |    
|  +---SumSrv
|  |    sum_Cat.sas
|  |    
|  \---WinSrv
|      modavg.m
|      winCatModAvgfitted.m
|      winCatModAvgLinP.m
|      winCatModAvgMu.m
|      win_Cat.sas
|      
+---ProcessedDatafiles
|    fecdat_gm_param_est_paper.mat
|    hierarchical_parameters.mat
|    refitG2_param_estimation.mat
|    
\---Required_Functions
  |  hline.m
  |  hmstoc.m
  |  Jeffs_Figure_Settings.m
  |  Jeffs_startup.m
  |  newbootci.m
  |  sem.m
  |  senstuff.m
  |  vline.m
  |  
  +---export_fig
  |    change_value.m
  |    eps2pdf.m
  |    export_fig.m
  |    fix_lines.m
  |    ghostscript.m
  |    license.txt
  |    pdf2eps.m
  |    pdftops.m
  |    print2array.m
  |    print2eps.m
  |    
  +---lowess
  |    license.txt
  |    lowess.m
  |    
  +---Multiprod_2009
  |  |  Appendix A - Algorithm.pdf
  |  |  Appendix B - Testing speed and memory usage.pdf
  |  |  Appendix C - Syntaxes.pdf
  |  |  license.txt
  |  |  loc2loc.m
  |  |  MULTIPROD Toolbox Manual.pdf
  |  |  multiprod.m
  |  |  multitransp.m
  |  |  
  |  \---Testing
  |    |  arraylab13.m
  |    |  arraylab131.m
  |    |  arraylab132.m
  |    |  arraylab133.m
  |    |  genop.m
  |    |  multiprod13.m
  |    |  readme.txt
  |    |  sysrequirements_for_testing.m
  |    |  testing_memory_usage.m
  |    |  testMULTIPROD.m
  |    |  timing_arraylab_engines.m
  |    |  timing_matlab_commands.m
  |    |  timing_MX.m
  |    |  
  |    \---Data
  |        Memory used by MATLAB statements.xls
  |        Timing results.xlsx
  |        timing_MX.txt
  |        
  +---province
  |    PROVINCE.DBF
  |    province.prj
  |    PROVINCE.SHP
  |    PROVINCE.SHX
  |    README.txt
  |    
  +---SubAxis
  |    parseArgs.m
  |    subaxis.m
  |    
  +---suplabel
  |    license.txt
  |    suplabel.m
  |    suplabel_test.m
  |    
  \---tight_subplot
      license.txt
      tight_subplot.m