BackgroundWhile different ways of presenting treatment effects can affect health care decisions, little is known about which presentations best help people make decisions consistent with their own values. We compared six summary statistics for communicating coronary heart disease (CHD) risk reduction with statins: relative risk reduction and five absolute summary measures—absolute risk reduction, number needed to treat, event rates, tablets needed to take, and natural frequencies.Methods and FindingsWe conducted a randomized trial to determine which presentation resulted in choices most consistent with participants' values. We recruited adult volunteers who participated through an interactive Web site. Participants rated the relative importance of outcomes using visual analogue scales (VAS). We then randomized participants to one of the six summary statistics and asked them to choose whether to take statins based on this information. We calculated a relative importance score (RIS) by subtracting the VAS scores for the downsides of taking statins from the VAS score for CHD. We used logistic regression to determine the association between participants' RIS and their choice. 2,978 participants completed the study. Relative risk reduction resulted in a 21% higher probability of choosing to take statins over all values of RIS compared to the absolute summary statistics. This corresponds to a number needed to treat (NNT) of 5; i.e., for every five participants shown the relative risk reduction one additional participant chose to take statins, compared to the other summary statistics. There were no significant differences among the absolute summary statistics in the association between RIS and participants' decisions whether to take statins. Natural frequencies were best understood (86% reported they understood them well or very well), and participants were most satisfied with this information.ConclusionsPresenting the benefits of taking statins as a relative risk reduction increases the likelihood of people accepting treatment compared to presenting absolute summary statistics, independent of the relative importance they attach to the consequences. Natural frequencies may be the most suitable summary statistic for presenting treatment effects, based on self-reported preference, understanding of and satisfaction with the information, and confidence in the decision.Clinical Trials RegistrationISRCTN85194921Please see later in the article for the Editors' Summary

Key Table Information.Table Title.Construction: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2223BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesConstruction workers annual wages($1,000)Construction workers for pay period including March 12Construction workers for pay period including June 12Construction workers for pay period including September 12Construction workers for pay period including December 12Construction, production and/or development and exploration workers annual hours (1,000)Other employees annual wages ($1,000)Other employees for pay period including March 12Other employees for pay period including June 12Other employees for pay period including September 12Other employees for pay period including December 12Total fringe benefits ($1,000)Employers cost for legally required fringe benefits ($1,000)Employers cost for voluntarily provided fringe benefits ($1,000)Total selected costs ($1,000) Cost of materials, components, packaging and/or supplies used, minerals received, or purchased machinery installed ($1,000)Cost of construction work subcontracted out to others ($1,000)Cost of purchased land ($1,000)Total cost of selected power, fuels, and lubricants ($1,000)Cost of gasoline and diesel fuel ($1,000)Cost of natural gas and manufactured gas ($1,000)Cost of on-highway use of gasoline and diesel fuel ($1,000)Cost of off-highway use of gasoline and diesel fuel ($1,000)Cost of all other fuels and lubricants ($1,000)Cost of purchased electricity ($1,000)Value of construction work ($1,000)Value of construction work on government owned projects ($1,000)Value of construction work on federally owned projects ($1,000)Value of construction work on state and locally owned projects ($1,000)Value of construction work on privately owned projects ($1,000)Value of other business done ($1,000)Value of construction work subcontracted in from others ($1,000)Net value of construction work ($1,000)Value added ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, beginning of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, end of year ($1,000)Gross value of depreciable assets (acquisition costs), beginning of year ($1,000)Total capital expenditures for buildings, structures, machinery, and equipment (new and used) ($1,000)Total retirements ($1,000)Gross value of depreciable assets (acquisition costs), end of year ($1,000)Total depreciation during year ($1,000)Total rental payments or lease payments ($1,000)Rental payments or lease payments for buildings and other structures ($1,000)Rental payments or lease payments for machinery and equipment ($1,000)Total other operating expenses ($1,000)Temporary staff and leased employee expenses ($1,000)Expensed computer hardware and other equipment ($1,000)Expensed purchases of software ($1,000)Data processing and other purchased computer services ($1,000)Communication services ($1,000)Repair and maintenance services of buildings and/or machinery ($1,000) Refuse removal (including hazardous waste) services ($1,000)Advertising and promotional services ($1,000)Purchased professional and technical services ($1,000) Taxes and license fees ($1,000)All other operating expenses ($1,000)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical locati...

Attendance absences have a substantial impact on student’s future physical and mental health as well as academic progress. Numerous personal, familial, and social issues are among the causes of student absences. Any kind of absence from school should be minimized. Extremely high rates of student absences may indicate the abrupt commencement of a serious school health crisis or public health crisis, such as the spread of tuberculosis or COVID-19, which provides school health professionals with an early warning. We take the extreme values in absence data as the object and attempt to apply the extreme value theory (EVT) to describe the distribution of extreme values. This study aims to predict extreme instances of student absences. School health professionals can take preventative measures to reduce future excessive absences, according to the predicted results. Five statistical distributions were applied to individually characterize the extreme values. Our findings suggest that EVT is a useful tool for predicting extreme student absences, thereby aiding preventative measures in public health.

Key Table Information.Table Title.Manufacturing: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2231BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesProduction and/or development and exploration workers annual wages ($1,000)Production workers first-quarter payroll ($1,000)Production and/or development and exploration workers for pay period including March 12Construction, production and/or development and exploration workers annual hours (1,000)Other employees annual wages ($1,000)Other employees first-quarter payroll ($1,000)Other employees for pay period including March 12Total fringe benefits ($1,000)Employer's cost for health insurance ($1,000)Employer's cost for defined benefit pension plans ($1,000)Employer's cost for defined contribution plans ($1,000)Employer's cost for other fringe benefits ($1,000)Total cost of supplies and/or materials ($1,000)Cost of materials, components, packaging and/or supplies used, minerals received, or purchased machinery installed ($1,000)Cost of resales ($1,000)Cost of contract work ($1,000)Cost of purchased fuels consumed ($1,000)Cost of purchased electricity ($1,000)Quantity of electricity purchased for heat and power ($1,000)Quantity of generated electricity ($1,000)Quantity of electricity sold or transferred ($1,000)Value added ($1,000)Total inventories, beginning of year ($1,000)Finished goods or minerals products, crude petroleum, and natural gas liquids inventories, beginning of year ($1,000)Work-in-process inventories, beginning of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, beginning of year ($1,000)Total inventories, end of year ($1,000)Finished goods or minerals products, crude petroleum, and natural gas liquids inventories, end of year ($1,000)Work-in-process inventories, end of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, end of year ($1,000)Gross value of depreciable assets (acquisition costs), beginning of year ($1,000)Gross value of depreciable assets (acquisition costs) for buildings and other structures, beginning of year ($1,000)Gross value of depreciable assets (acquisition costs) for machinery and equipment, beginning of year ($1,000)Total capital expenditures for buildings, structures, machinery, and equipment (new and used) ($1,000)Capital expenditures for buildings and other structures ($1,000)Capital expenditures for machinery and equipment ($1,000)Capital expenditures for automobiles, trucks, etc. for highway use ($1,000) Capital expenditures for computers and peripheral data processing equipment ($1,000)Capital expenditures for all other machinery and equipment ($1,000)Total retirements ($1,000)Retirements for buildings and other structures ($1,000)Retirements for machinery and equipment ($1,000)Gross value of depreciable assets (acquisition costs, end of year) ($1,000)Gross value of depreciable assets (acquisition costs) for buildings and other structures, end of year ($1,000)Gross value of depreciable assets (acquisition costs) for machinery and equipment, end of year ($1,000)Total depreciation during year ($1,000)Total rental payments or lease payments ($1,000)Rental payments or lease payments for buildings and other structures ($1,000)Rental payments or lease payments for machinery and equipment ($1,000)Total other operating expenses ($1,000)Temporary staff and leased employee expenses ($1,000)Expensed computer hardware and other equipment ($1,000)Expensed purchases of software ($1,000)Data processing and other purchased computer services ($1,000)Communication services ($1,000)Repair and maintenance services of buildings and/or machinery ($1,000) Refuse removal (including hazardous ...

θw is a measure of genetic diversity, Tajima's D and Fu's Fs are two summaries of allele frequencies.Mmc - Mycoplasma mycoides subsp. capri, M. c. - Mycoplasma capricolum (both subsp.), Mmm - Mycoplasma mycoides subsp. mycoides, M. sp. - unassigned Mycoplasma species,*Significant values p<0.05.

Key Table Information.Table Title.Arts, Entertainment, and Recreation: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2271BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesOperating expenses ($1,000)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S., State, Combined Statistical Area, Metropolitan and Micropolitan Statistical Area, Metropolitan Division, Consolidated City, County (and equivalent), and Economic Place (and equivalent; incorporated and unincorporated) levels that vary by industry. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels. For information about NAICS, see Economic Census Code Lists..Business Characteristics.For selected Services sectors, data are published by Tax Status (All establishments, Establishments subject to federal income tax, and Establishments exempt from federal income tax)..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher le...

This series is composed of five select physical marine parameters (water salinity and water temperature for surface and near bottom waters and sea ice) for two climate scenarios (RCP 45 and RCP 8.5) and three statistics (minimum, median and maximum) from an ensemble of five downscaled global climate models. The source data for this data series is global climate model outcomes from the Coupled Model Intercomparison Project 5 (CMIP5) published by the Intergovernmental Panel on Climate Change (Stocker et al 2013).

The source data were provided in NetCDF format for each of the downsampled climate models based on the five CMIP5 global climate models: MPI: MPI-ESM-LR, HAD: HadGEM2-ES, ECE: EC-EARTH, GFD: GFDL-ESM2M, IPS: IPSL-CM5A-MR. The data included monthly mean, maximum, minimum and standard deviation calculations and the physical variables provided with the climate scenario models included sea ice cover, water temperature, water salinity, sea level and current strength (as two vectors) as well as a range of derived biogeochemical variables (O2, PO4, NO3, NH4, Secci Depth and Phytoplankton).

These global atmospheric climate model data were subsequently downscaled from global to regional scale and incorporated into the high-resolution ocean–sea ice–atmosphere model RCA4–NEMO by the Swedish Meteorological and Hydrological Institute (Gröger et al 2019) thus providing a wide range of marine specific parameters. The Swedish Geological Survey used these data in the form of monthly mean averages to calculate change in multi-annual (30-year) climate averages from the beginning and end of the 21st century for the five select parameters as proxies for climate change pressures.

Each dataset uses only source data models based on an assumption of atmospheric climate gas concentrations in line with either the IPCCs representative concentration pathway RCP 4.5 or RCP 8.5. Changes were calculated as the difference between two multiannual (30 year) mean averages; one for a historical reference climate period (1976-2005) and one for an end of century projection (2070-2099). These data were extracted for each of the five downscaled CMIP5 models individually and then combined into ensemble summary statistics (ensemble minimum, median and maximum). In the Ensemble_Maximum/Median/Minimum_Rasters datasets, changes in mean (May-Sept) surface temperature and bottom temperature are given in Degrees Celsia (°C); changes in mean annual surface salinity and bottom salinity are given in Practical Salinity Units (PSU); changes in mean (October-April) sea ice are given in Percentage Points (pp).

In the Normalized_Rasters datasets, the changes are normalized using a linear stretch so that a cell value of zero represents no projected and a cell value of 100 represents a value equal to or above the mean change in Swedish national waters. The values representing 100 are: 4 °C for surface temperature; 3 °C for bottom temperature; -1.5 PSU for surface salinity; -2.0 PSU for bottom salinity; and -40 pp for sea ice. These were also the chosen reference values for determining, via expert review, the sensitivity of ecosystem components to changes in these parameters (for further information refer to the Symphony method).

Notes on interpretation. This dataset does not highlight inter-annual or inter-decadal climate variability (e.g. extreme events) or changes in biochemical parameters (e.g. O2, chlorophyll, secchi depth etc) resulting from change in surface temperature. Areas of no-data inshore were filled using extrapolating from nearby cells (using similar depths for benthic data) so data near the coast and particularly within archipelagos, bays and estuaries is not robust. Users should refer to the associated climemarine uncertainty map for this parameter. The uncertainty map shows the interquatile range from the climate ensemble and the area of no-data as 'interpolated values'. For any application which requires more temporally or spatially explicit information (e.g. at sub/national decision making) it is highly recommended that the user contact SMHI for access to the latest climate model source data (in NetCDF format) which contains much more detail and a far wider selection of parameters. For regional applications (e.g. at the scale of the Baltic Sea) - it should be noted that these data will likely require normalisation to regional rather than national values and that sensitivity scores used may differ.

ClimeMarine was selective in its choice of pressure parameters. SMHI have additional data available for other parameters such as O2, secchi depth and nutrients which could be included in future. This is complicated because many parameters are influenced by riverine discharge and therefore by decisions related to watershed management - disentanglement of impacts from climate vs river basin management becomes a complication. In a similar way, data on sealevel rise is also available which could be used to estimate impacts on the coast but likewise complicating factors such as isostatic uplift and coastal defence and management policies would need to be considered.

For simplicity and to reduce the amount of datasets to a manageable level for this assessment the source data were further limited and summarised in several ways:

Only the monthly mean averages of seawater temperature, salinity and sea ice (i.e. key physical parameters) were utilized.
For seawater salinity and temperature, the depth dimension (i.e. the water column) was summarised from 56 depth levels to just two: the surface and the deepest (bottom) waters.
Only two of the three climate periods were selected: a historical reference period: 1976-2005 (to represent the current status) and the projected end of century period: 2070-2099. Only two of the three available emission scenarios were selected detailing the consequence of intermediate and very high climate gas emissions : Representative Concentration Pathway (RCP) 4.5 and 8.5 (see SEDAC 2021).

Each dataset included in the series comes with extensive metadata.

The data processing followed the following steps:

Extraction of data for each parameter from NetCDF to TIFF Rasters for each model, emission scenario, depth level (using scripts in NCO, CDO and R). Calculation of climate ensemble statistics - Minimum, Mean, Median and Maximum (using Arcpy and Numpy)
Reprojection and resampling from the 2nm NEMO-RCO from Lat/Long WGS84 grid to the 250m ETRS89 LAEA Symphony grid (using Arcpy)
Extrapolation to fill no-data cells based on proximity and similar depths (using Arcpy script and the ArcGIS spatial analyst extension) Calculation of change for each parameter as the end of century multi-annual mean minus the reference multi-annual mean (using an Arcpy script)
Inversion of if negative (i.e. decreases) to positive (i.e. magnitude of change)
Normalisation as a linear stretch from 0 to 100 where zero equates to no change and 100 equates to the maximum pixel value in Swedish waters from the RCP 8.5 ensemble mean dataset with any values over this pixel value also set to 100 (Arcpy script)

NetCDF source data used in this analysis can be requested from the Swedish Meteorological and Hydrological Institute - kundtjanst@smhi.se

Processing scripts (R and arcpy) and interim raster data can be requested from the Geological Survey of Sweden - kundtjanst@sgu.se

The objective behind attempting this dataset was to understand the predictors that contribute to the life expectancy around the world. I have used Linear Regression, Decision Tree and Random Forest for this purpose. Steps Involved: - Read the csv file - Data Cleaning: - Variables Country and Status were showing as having character data types. These had to be converted to factor - 2563 missing values were encountered with Population variable having the most of the missing values i.e 652 - Missing rows were dropped before we could run the analysis. 3) Run Linear Regression - Before running linear regression, 3 variables were dropped as they were not found to be having that much of an effect on the dependent variable i.e Life Expectancy. These 3 variables were Country, Year & Status. This meant we are now working with 19 variables (1 dependent and 18 independent variables) - We run the linear regression. Multiple R squared is 83% which means that independent variables can explain 83% change or variance in the dependent variable. - OULTLIER DETECTION. We check for outliers using IQR and find 54 outliers. These outliers are then removed before we run the regression analysis once again. Multiple R squared increased from 83% to 86%. - MULTICOLLINEARITY. We check for multicollinearity using the VIF model(Variance Inflation Factor). This is being done in case when two or more independent variables showing high correlation. The thumb rule is that absolute VIF values above 5 should be removed. We find 6 variables that have a VIF value higher than 5 namely Infant.deaths, percentage.expenditure,Under.five.deaths,GDP,thinness1.19,thinness5.9. Infant deaths and Under Five deaths have strong collinearity so we drop infant deaths(which has the higher VIF value). - When we run the linear regression model again, VIF value of Under.Five.Deaths goes down from 211.46 to 2.74 while the other variable's VIF values reduce very less. Variable thinness1.19 is now dropped and we run the regression once more. - Variable thinness5.9 whose absolute VIF value was 7.61 has now dropped to 1.95. GDP and Population are still having VIF value more than 5 but I decided against dropping these as I consider them to be important independent variables. - SET THE SEED AND SPLIT THE DATA INTO TRAIN AND TEST DATA. We run the train data and get multiple R squared of 86% and p value less than that of alpha which states that it is statistically significant. We use the train data to predict the test data to find out the RMSE and MAPE. We run the library(Metrics) for this purpose. - In Linear Regression, RMSE (Root Mean Squared Error) is 3.2. This indicates that on an average, the predicted values have an error of 3.2 years as compared to the actual life expectancy values. - MAPE (Mean Absolute Percentage Error) is 0.037. This indicates an accuracy prediction of 96.20% (1-0.037). - MAE (Mean Absolute Error) is 2.55. This indicates that on an average, the predicted values deviate by approximately 2.83 years from the actual values.

We use DECISION TREE MODEL for the analysis.

• Run the required libraries (rpart, rpart.plot, RColorBrewer, rattle).
• We run the decision tree analysis using rpart and plot the tree. We use fancyRpartPlot.
• We use 5 fold cross validation method with CP (complexity parameter) being 0.01.
• In Decision Tree , RMSE (Root Mean Squared Error) is 3.06. This indicates that on an average, the predicted values have an error of 3.06 years as compared to the actual life expectancy values.
• MAPE (Mean Absolute Percentage Error) is 0.035. This indicates an accuracy prediction of 96.45% (1-0.035).
• MAE (Mean Absolute Error) is 2.35. This indicates that on an average, the predicted values deviate by approximately 2.35 years from the actual values.

We use RANDOM FOREST for the analysis.

• Run library(randomForest)
• We use varImpPlot to find out which variables are most significant and least significant. Income composition is the most important followed by adult mortality and the least relevant independent variable is Population.
• Predict Life expectancy through random forest model.
• In Random Forest , RMSE (Root Mean Squared Error) is 1.73. This indicates that on an average, the predicted values have an error of 1.73 years as compared to the actual life expectancy values.
• MAPE (Mean Absolute Percentage Error) is 0.01. This indicates an accuracy prediction of 98.27% (1-0.01).
• MAE (Mean Absolute Error) is 1.14. This indicates that on an average, the predicted values deviate by approximately 1.14 years from the actual values.

Conclusion: Random Forest is the best model for predicting the life expectancy values as it has the lowest RMSE, MAPE and MAE.

Statistical values of the multiple regression analysis with mean gray matter rest-CBF as the dependent variable and five other variables [RAPM score, the total S-A creativity test score, age, sex (male = 0, female = 1), and the POMS score] as independent variables.

The χ2 values are depicted in the lower left triangle; smaller values indicate a greater similarity between the subsamples. Overlapping coefficients (OVLs) are depicted in the upper right triangle; higher values indicate a greater similarity between the subsamples. Particip. = participants; Val. = Valence (seven point scale from −3 up to +3); Arsl. = Arousal (five point scale from 1 up to 5).ab) Means within a column with different superscripts are significantly different.* p < .05;** p < .01;*** p < .001 (Bonferroni-corrected for n = 28 tests).Descriptive statistics and comparison of the subsamples of Study 2.

Dataset Description: The dataset contains information about properties. Each property has a unique property ID and is associated with a location ID based on the subcategory of the city. The dataset includes the following attributes:

Property ID: Unique identifier for each property. Location ID: Unique identifier for each location within a city. Page URL: The URL of the webpage where the property was published. Property Type: Categorization of the property into six types: House, FarmHouse, Upper Portion, Lower Portion, Flat, or Room. Price: The price of the property, which is the dependent feature in this dataset. City: The city where the property is located. The dataset includes five cities: Lahore, Karachi, Faisalabad, Rawalpindi, and Islamabad. Province: The state or province where the city is located. Location: Different types of locations within each city. Latitude and Longitude: Geographic coordinates of the cities. Steps Involved in the Analysis:

Statistical Analysis:

Data Types: Determine the data types of the attributes. Level of Measurement: Identify the level of measurement for each attribute. Summary Statistics: Calculate mean, standard deviation, minimum, and maximum values for numerical attributes. Data Cleaning:

Filling Null Values: Handle missing values in the dataset. Duplicate Values: Remove duplicate records, if any. Correcting Data Types: Ensure the correct data types for each attribute. Outliers Detection: Identify and handle outliers in the data. Exploratory Data Analysis (EDA):

Visualization: Use libraries such as Seaborn, Matplotlib, and Plotly to visualize the data and gain insights. Model Building:

Libraries: Utilize libraries like Sklearn and pickle. List of Models: Build models using Linear Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), XG Boost, Gradient Boost, and Ada Boost. Model Saving: Save the selected model into a pickle file for future use. I hope this captures the essence of the provided information. Let me know if you need any further assistance!

Attendance absences have a substantial impact on student’s future physical and mental health as well as academic progress. Numerous personal, familial, and social issues are among the causes of student absences. Any kind of absence from school should be minimized. Extremely high rates of student absences may indicate the abrupt commencement of a serious school health crisis or public health crisis, such as the spread of tuberculosis or COVID-19, which provides school health professionals with an early warning. We take the extreme values in absence data as the object and attempt to apply the extreme value theory (EVT) to describe the distribution of extreme values. This study aims to predict extreme instances of student absences. School health professionals can take preventative measures to reduce future excessive absences, according to the predicted results. Five statistical distributions were applied to individually characterize the extreme values. Our findings suggest that EVT is a useful tool for predicting extreme student absences, thereby aiding preventative measures in public health.

Descriptive statistics for all continuous spatial metrics including minimum, maximum and mean values as well as standard deviation; sample size n = 167 desks (five survey responses contained missing information on seat type and were excluded from the analysis).

PurposeHurricane Helene damaged forests in the Southeastern United States last September. The combination of abundant rainfall and high winds resulted in a range of impacts from defoliation and partial crown damage to snapped boles and uprooted trees. Forests with moderate and high severity disturbance are a concern because of altered fire behavior and suppression difficulty (see safety alert). Fire behavior may be more extreme in hurricane disturbed stands from the increased surface fuels and the reduced canopy cover. The addition of down trees and large branches in hurricane disturbed stands can also impede firefighter mobility, increase the difficulty of line construction, and expose firefighters to additional hazards. This strategic wildfire rapid risk assessment seeks to identify areas where hurricane disturbance may compound existing wildfire risks to inform post-hurricane fuels management priorities and wildfire response strategies.MethodsHurricane SeverityThe extent and severity of hurricane disturbance was mapped from two sources based on regional fire and fuels specialist feedback:HiForm four class disturbance map for the Southern Appalachians from the USDA Forest Service Southern Research Station, andDeltaViewer five class disturbance map for the full hurricane path from the USDA Forest Service Geospatial Technology and Applications Center (GTAC).Both disturbance maps were created using remote sensing change detection based on Sentinel 10-m resolution satellite imagery.Local feedback indicated that the HiForm map provided the most accurate depiction of high severity disturbance in the Southern Appalachians, but the map does not cover the full extent of the hurricane path. Therefore, we combined the maps as follows:Reprojected both data sources to match the LANDFIRE projection, cell size (30-m), and cell alignment using the majority resampling technique;In the Southern Appalachians:Classified the original HiForm values as indicated in Table 1;Classified the original DeltaViewer values as indicated in the “Severity for Southern Appalachians” column of Table 2;Assigned the final severity as the maximum of the reclassed HiForm and DeltaViewer values.For the remainder of the hurricane path:Classified the original DeltaViewer values as indicated in the “Severity for Rest of Path” column of Table 2;Combined reclassified severity maps for Southern Appalachians and rest of hurricane path; andFiltered out any non-forest from the hurricane severity map using the criteria of greater than zero canopy cover as mapped by LANDFIRE 2023 (v2.4.0).Table 1. HiForm hurricane disturbance values and assigned severity level.ValueDescriptionSeverity1Large gap blowdownsHigh (3)2Heterogenous areas with severe or mixed damageModerate (2)3Scattered low severity or broad light impacts that are non-structuralLow (1)4No/minor impactsNone (0)Table 2. DeltaViewer hurricane disturbance values and assigned severity level.ValueDescriptionSeverity for Southern AppalachiansSeverity for Rest of Path0No data/cloudsNone (0)None (0)1No damageNone (0)None (0)2Slight damageNone (0)None (0)3Moderate damageLow (1)Low (1)4Severe damageModerate (2)Moderate (2)5Catastrophic damageModerate (2)High (3)Wildfire RiskWildfire risk data came from the Southern Wildfire Risk Assessment (https://www.southernwildfirerisk.com/), also known as SouthWRAP. The current public data distribution for SouthWRAP focuses on wildfire hazard. The latest update also includes a pilot complete wildfire risk assessment accounting for wildfire likelihood, wildfire intensity, highly valued resource and asset (HVRA) presence, HVRA response to fire of different intensity levels, and HVRA relative importance (RMRS-GTR-315, Scott et al. 2013). We used the expected Net Value Change (eNVC) layers for people and property, infrastructure, and drinking water to focus our analysis on the most important values for communities. The eNVC data is intended for pre-fire planning work because it accounts for spatial differences in the probability of fire.The SouthWRAP wildfire risk values for people and property, infrastructure, and drinking water were summed into a total risk raster. The total risk raster was classified into four levels for our analysis by calculating the 40th, 70th, and 90th percentiles of non-zero pixel values within an analysis area defined using a 30-mi buffer around the approximate hurricane path (Table 4). The 30-mi buffer was chosen to capture a representative area of the Southeast within the coverage of SouthWRAP, which ends close to the northern edge of the hurricane path.Table 4. Definition of wildfire risk classes derived from the total SouthWRAP risk layer. Note that more negative eNVC (risk) values indicate higher potential for loss.Wildfire RiskArea (ac)Area (%)PercentilesLowHighVery low (0)51,904,84075.50-40-0.00270.0000Low (1)8,414,18212.240-70-0.0106-0.0027Moderate (2)5,609,4538.270-90-0.0399-0.0106High (3)2,804,7274.190-100-200.0000-0.0399Combined RiskTo combine the wildfire risk and hurricane severity information into a single rating, we defined a risk matrix (Table 5) to depict increasing concern with both increasing wildfire risk and hurricane severity. Table 5. Combined risk matrix used to combine wildfire risk and hurricane severity information. Hurricane SeverityWildfire RiskNoLowModerateHighNo0123Low1234Moderate2345High3455Table 6. Area mapped by combined risk values.Combined riskArea (ac)Area (%)None (0)27,275,44866.7Very low (1)6,592,76616.1Low (2)4,404,41310.8Moderate (3)2,293,7065.6High (4)242,8930.6Extreme (5)83,6240.2Hexel SummariesWe generated 5-km2 hexels across the analysis area to support regional-scale planning. Zonal statistics were used to calculate relevant summary statistics from the original raster products including the mean values for wildfire risk, hurricane severity, and combined risk, as well as the percent of hexel area in the more extreme classes of wildfire risk, hurricane severity, and combined risk (Table 7). Additionally, we assigned hexels a percentile (rank) based on the mean combined risk for two analysis sets: 1) all hexels in the hurricane path and 2) only hexels with mapped hurricane disturbance. The recommendation is to rank and visualize the hexels based on either the “Combined risk (mean)”, “Combined risk percentile (whole path)”, or “Combined risk percentile (hurricane disturbed)” values. The additional “percent of area” attributes are provided to allow end users to experiment with alternative thresholds for rating risk and to loosen the spatial overlap criterion that is inherent in the raster application of the combined risk matrix.Table 7. Attributes calculated for each hexel.NameAliasDescriptionUIDUIDUnique identifier for hexelRisk_mWildfire risk (mean)Mean of wildfire risk assessment classesHurr_mHurricane severity (mean)Mean of hurricane severityCTM_mCombined risk (mean)Mean of combined risk matrixRisk_mhperWildfire risk moderate or high (percent area)Percent of hexel with moderate or high wildfire riskRisk_hperWildfire risk high (percent area)Percent of hexel with high wildfire riskHurr_mhperHurricane severity moderate or high (percent area)Percent of hexel with moderate or high hurricane severityHurr_hperHurricane severity high (percent area)Percent of hexel with high hurricane severityCRM_mhperCombined risk high or extreme (percent area)Percent of hexel with high or extreme combined risk matrixCRM_mperCombined risk extreme (percent area)Percent of hexel with extreme combined risk matrixCRM_m_pCombined risk percentile (whole path)Hexel percentile (rank) based on combined risk matrix mean across whole pathCRM_m_p_ihCombined risk percentile (hurricane disturbed)Hexel percentile (rank) based on combined risk matrix mean for hurricane disturbed hexelsAcknowledgementsThank you to Curt Stripling from the Texas A&M Forest Service for sharing the SouthWRAP wildfire risk assessment data. For more information on SouthWRAP, see https://www.southernwildfirerisk.com/.

The biological inventory assemblages, along with summary statistics and trait valuesThe biological inventory assemblages, along with summary statistics such as their area, richness and trait values [mean(variance)]. All inventories were originally based on Meese (2005). For both birds and mammals we use five similar trait categories: diet, body size, activity time and two measures of foraging niche. Assemblage mean mass is based on log transformed data. Diet include estimates of the proportional use of each of seven dietary categories for mammals (seeds, fleshy fruits, nectar and pollen, other plant material, invertebrates, fish, vertebrates) and eight dietary categories for birds (seeds, fleshy fruits, nectar and pollen, other plant material, invertebrates, fish, carrion, other vertebrates. For birds the first foraging niche trait reflects proportional use of each of seven foraging categories (in water below surface of water, in water on surface, terrestrial ground-level, understory,...

Raw and processed data of decimal colour values for media incubated on sterilized Sylgard 184, Sylgard 527, and plastic over a 5-day period, including descriptive statistics. RGB data was obtained using ImageJ and converted to decimal format by calculating the product of the R, G, and B values (R × G × B).

SAM prevalence and concentration index value for ICDS coverage of SAM children, by state, NFHS 4 & 5.

Descriptive statistics for the number of under five children death per 100 mothers.

Descriptive statistics of obtained NMI values for the five football datasets.

Top five significant GO term enrichment analysis of the potential pleiotropic genes.