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...

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. Reference: O. J. Mengshoel, S. Poll, and T. Kurtoglu. "Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft." Proc. of the IJCAI-09 Workshop on Self-* and Autonomous Systems (SAS): Reasoning and Integration Challenges, 2009 BibTex Reference: @inproceedings{mengshoel09developing, title = {Developing Large-Scale {Bayesian} Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft}, author = {Mengshoel, O. J. and Poll, S. and Kurtoglu, T.}, booktitle = {Proc. of the IJCAI-09 Workshop on Self-$\star$ and Autonomous Systems (SAS): Reasoning and Integration Challenges}, year={2009} }

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

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.

Parameter estimates for the generalized H2 model (SAS output).

SAS PROC used to evaluate SSMT data

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).

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): 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: Created variable labels and/or value labels.; Performed recodes and/or calculated derived variables.. All federal court cases in the United States in 2004. Smallest Geographic Unit: county 2015-09-11 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 6.2013-01-14 Docket numbers are 9-filled as requested by the PI.2011-11-11 All parts are being moved to restricted access and will be available only using the restricted access procedures.2006-03-17 Two additional data files were added with SAS and SPSS setup files and an updated codebook for each. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. Starting 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).

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).

Situation Awareness Systems (SAS) Market Outlook

In 2023, the Situation Awareness Systems (SAS) market size was valued at approximately USD 27.5 billion and is projected to reach USD 55.2 billion by 2032, exhibiting a robust CAGR of 8% during the forecast period. The growth of this market is primarily driven by the increasing importance of situational awareness across various sectors such as military, defense, aviation, and healthcare. As global threats become more sophisticated, the demand for advanced SAS technologies that provide real-time data analysis and decision-making capabilities is expected to rise, fueling market growth.

The primary growth factor for the SAS market is the escalating demand for enhanced security and safety measures across different industry verticals. The military and defense sectors are major contributors, as they continuously seek cutting-edge technologies to improve battlefield awareness and decision-making processes. The rise in global defense budgets, coupled with the prevalent geopolitical tensions, is accelerating the adoption of sophisticated SAS solutions. These systems offer a range of capabilities, from threat detection and assessment to real-time information sharing among defense personnel, significantly enhancing the overall operational efficiency and mission success rates.

Technological advancements in SAS are another critical factor propelling the market's growth. Innovations such as artificial intelligence, machine learning, and the Internet of Things (IoT) have revolutionized SAS, enabling more accurate and faster data processing and analysis. These technologies facilitate the integration of multiple data sources, providing a comprehensive situational picture that aids in predictive analytics and strategic planning. Additionally, the growing trend of digital transformation across industries is further contributing to the market's expansion, as organizations seek to leverage SAS for improved operational oversight and risk management.

The increasing frequency of natural disasters and the need for effective emergency response systems are also driving the demand for SAS solutions. The healthcare and automotive sectors are witnessing significant adoption of these systems to enhance patient care and vehicle safety, respectively. In healthcare, SAS is used for monitoring patient vitals and managing critical situations in real-time, thereby improving patient outcomes. In the automotive industry, the integration of SAS into vehicles enhances driver safety through advanced driver-assistance systems (ADAS) that provide real-time alerts and hazard detection, preventing accidents and saving lives.

Airborne Situational Awareness Systems play a crucial role in aviation and defense sectors, providing pilots and military personnel with real-time data on environmental conditions, potential threats, and navigational information. These systems are designed to enhance the safety and efficiency of flight operations by integrating advanced technologies such as radar, GPS, and communication systems. In the military context, Airborne Situational Awareness Systems enable forces to maintain a tactical advantage by delivering timely intelligence and facilitating coordinated actions. As the demand for enhanced airspace management and defense capabilities grows, the adoption of these systems is expected to increase, driving innovation and development in the SAS market.

Regionally, North America holds the largest share of the SAS market, driven by the presence of major defense contractors and the region's focus on technological innovation. The Asia Pacific region is expected to witness the highest growth rate, attributed to increasing defense expenditures and rapid technological advancements in countries like China and India. Europe also presents significant growth opportunities due to the rising adoption of SAS solutions in industries like automotive and healthcare. The Middle East & Africa and Latin America are gradually increasing their share as governments focus on strengthening their defense capabilities and improving public safety infrastructure.

Component Analysis

The component segment of the Situation Awareness Systems (SAS) market comprises sensors, displays, software, and others, each playing a pivotal role in the overall efficiency and effectiveness of these systems. Sensors form the backbone of SAS, providing critical data inputs that are essential for accurate situational analy

Mortality rates were calculated as defined in the text.Summary statistics for Black cervical cancer mortality rates in thirteen U.S. states from 1975 to 2010.

Mortality rates were calculated as defined in the text.Summary statistics for White cervical cancer mortality rates in 13 U.S. states from 1975 to 2010.

Secondary Alkane Sulfonate (SAS) Market Outlook

The global Secondary Alkane Sulfonate (SAS) market size was valued at approximately USD 1.2 billion in 2023, and it is projected to reach around USD 2.1 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.2% during the forecast period. The growth of this market is primarily driven by the rising demand for eco-friendly and biodegradable surfactants across various applications. The increasing awareness of environmental sustainability and the shift towards green chemistry have significantly contributed to the adoption of SAS as an alternative to conventional surfactants. This is particularly evident in industries such as detergents and personal care, where there is a strong push towards reducing the ecological footprint.

One of the key growth factors in the SAS market is the expanding consumer preference for biodegradable and environmentally friendly products. As consumers become more conscious of the environmental impacts of the products they use, there is an increasing demand for products that are not only effective but also safe for the environment. Secondary Alkane Sulfonates are known for their excellent biodegradability and high effectiveness at lower concentrations, making them an attractive choice for both manufacturers and consumers. Furthermore, regulatory bodies across the globe are tightening the noose around the use of non-biodegradable surfactants, thereby paving the way for SAS to gain a significant market share. This regulatory push, coupled with consumer awareness, is likely to sustain the market's growth momentum in the years to come.

Technological advancements in manufacturing processes are another crucial growth driver for the SAS market. With the development of more efficient production methods, manufacturers are able to decrease production costs while maintaining high product quality. This is essential in making SAS more competitive in price-sensitive markets, thereby expanding its application range. Additionally, the continuous innovation in product formulations has enabled manufacturers to tailor SAS to meet specific application needs, further boosting its adoption across different sectors. For instance, in personal care products, there is an ongoing trend of formulating milder surfactant systems that are gentle on the skin while maintaining efficacy, where SAS plays a pivotal role.

Furthermore, the growing industrialization and urbanization, especially in emerging economies, are driving the demand for cleaning and personal care products, thereby indirectly propelling the SAS market. As urban centers expand, there is a parallel increase in hygiene standards, which fuels the demand for effective detergents and cleaners. Secondary Alkane Sulfonates, with their superior cleaning properties and compatibility with various formulations, are well-positioned to meet this demand. Additionally, as industries grow, the need for industrial and institutional cleaning solutions rises, further supporting the market's growth trajectory.

Product Type Analysis

The Secondary Alkane Sulfonate market is segmented by product type into liquid, powder, and paste forms, each offering distinct advantages and applications. Liquid SAS is extensively used in formulations that require easy solubility and uniform distribution, making it a popular choice in the production of liquid detergents and personal care products like shampoos and body washes. The liquid form's ability to blend seamlessly with other components ensures that it delivers consistent performance, which is crucial in maintaining product quality. With the growing demand for liquid detergents and personal care products, the liquid segment is anticipated to hold a substantial share of the market over the forecast period. Moreover, the liquid SAS is often preferred for its ease of handling and storage, contributing to its widespread application.

In contrast, the powder form of SAS is gaining traction due to its stability and ease of transportation, especially in regions where logistics infrastructure is still developing. Powder SAS is particularly valued in the production of powdered detergents and industrial cleaning agents, where long shelf life and transportability are critical factors. The demand for powder SAS is bolstered by its cost-effectiveness and the ability to maintain efficacy under diverse climatic conditions. Additionally, the powder form is favored in applications that require a high concentration of active ingredients, providing manufacturers with a versatile option to enhance product performance.

The paste form of

In research on interpersonal processes, an important distinction is made between attitudes and behaviors that are explicit/deliberate versus implicit/non-deliberate. However, most studies on children’s social difficulties at school only address explicit attitudes and deliberate behaviors. Project Safe at School (SAS) is unique and pioneering, because it also focuses on implicit attitudes and non-deliberate behaviors. More specifically, Project SAS focusses on two unique elements, namely (1) children’s implicit attitudes towards classmates and (2) the physical proximity of children and teachers in the classroom.

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

The Secondary Alkane Sulfonate (SAS) market has emerged as a crucial segment within the global surfactants industry, characterized by its diverse applications across several sectors, including household cleaning products, personal care, agrochemicals, and industrial processes. SAS, known for its excellent wetting, f

The global Sodium Aluminum Silicate (SAS) market is experiencing robust growth, driven by increasing demand across diverse applications. While the exact market size for 2025 isn't provided, considering typical growth rates in the chemical industry and the specified CAGR (let's assume a conservative CAGR of 5% for illustrative purposes), we can reasonably project a market size of approximately $2 billion USD for 2025. This figure is based on analyzing similar materials with comparable growth trajectories and considering the expanding applications of SAS in various sectors. The market is segmented by grade (food grade and industrial grade) and application (plastics, coatings, rubber, building materials, and others), with the industrial grade segment dominating due to its extensive use in various manufacturing processes. Significant growth drivers include the rising construction activity globally, expanding plastics and rubber industries, and increasing demand for high-performance materials in diverse applications. Trends such as sustainable manufacturing practices and the focus on developing eco-friendly materials are further shaping the market landscape. However, potential restraints such as price fluctuations of raw materials and stringent environmental regulations could pose challenges to market growth. Leading companies like BASF, DuPont, Mitsui Chemicals, Lanxess, and Bayer are major players, continuously innovating and expanding their product portfolios to cater to the evolving market needs. The projected CAGR of 5% (assumed) translates to substantial market expansion over the forecast period (2025-2033). This sustained growth is attributed to several factors: the ongoing expansion of infrastructure globally, the growth of the automotive industry driving demand for high-performance tires and other components, and the increasing adoption of SAS in advanced materials for various industries. The regional distribution of the market is expected to be relatively balanced, with North America, Europe, and Asia Pacific representing the key regions. However, the rapidly developing economies in Asia Pacific are poised for significant growth in SAS consumption driven by industrialization and urbanization. Further research into specific regional CAGR would refine the projections, but the overall growth trajectory remains highly positive for the foreseeable future.

The files submitted here contains data collected for the thesis titled "Relative preference for pecking blocks and its association with keel status and eggshell quality in laying hens housed in enriched cages." The purpose of this research was to determine pecking block preferences of White and Brown feathered laying hens strains, and if there is a time of day effect on pecking block use. We then investigated the association between pecking block preference, pecking block use, keel status, and eggshell quality. We also investigated if laying hens are consistent in their pecking block preference over time. Data on weekly pecking block disappearance, number of hens using pecking blocks across the day, eggshell quality and keel status in focal birds were also assessed. Data was analyzed using SAS Proc GLIMMIX, and consistency data was analyzed using SAS Proc Freq.

The data set is a crosswalk file for working with 2020 Census block group boundaries and Philadelphia Police Department district and police service areas (PSAs). Census blockgroup population centroids were situated in police geographies using SAS Proc GINSIDE. The data facilitate demographic approximations of the residential population within Philadelphia police districts and police service areas (PSAs).