Scientific formula for ideal body weight calculation

What is rent indexation? Every year, on the date on which the contract entered into force, the rent for your accommodation may be adjusted to the cost of living. Until December 1993, this indexation was always based on the fluctuations of the consumer price index. From January 1994 onward, the health index became the mandatory basis. Which lease agreements are eligible? Signed Written lease agreement Oral lease agreement Before 28th February 1991 Indexation if both parties provided this Between 28th February 1991 and 31st May 1997 allowed After 31st May 1997 allowed except when excluded in agreement indexation not allowed How is the rent indexation calculated? basic rent (2) X new index (3) The indexed rent = ———————————————— initial index (1) The lease agreement entered into force before 1st January 1984 The lease agreement entered into force on or after 1st January 1984 The lease agreement entered into force on or after 1st January 2019 for a main residence in the Flemish Region agreement signed before 1/01/1981 agreement signed between 1/01/1981 and 31/12/1983 agreement signed before 1/02/1994 agreement signed on or after 1/02/1994 agreement signed on or after 01/01/2019 (1) initial index= December 82 (82,54) (1) initial index= index of the month preceding the adjustment or entry into force of the agreement in 1983 (1) initial index= index of the month proceding the signature signature of the agreement (1) initial index= health index of the month preceding the signature of agreemeent (1) initial index= health index of the month preceding entry into force of the contract or the rent review (2) basic rent = rent: - set by court order - failing such court order, basic rent used in the calculation of the indexed rent in 1990 - failing this, last rent paid in 1983. (2) basic rent = agreed rent (3) new index = the (health) index of the month preceding the anniversary of the entry into force of the agreement Source: FPS Justice, Brussels Housing Code, Walloon Decree on Housing Leases and Flemish Housing Rental Decree Calculate your rent yourself More information Following the 6th state reform, rent indexation has become a regional competence. Since 1st January 2018, 15th March 2018 and 1st January 2019, respectively, new regulations on rent have come into force in the Brussels-Capital Region, in the Walloon Region and in the Flemish Region (Brussels Housing Code, Walloon Decree on Housing Leases and Flemish Housing Rental Decree). If these texts do not regulate a particular aspect of tenancy, the federal legislation on leases is applicable (see the brochure on the law on rents published by the FPS Justice). In concrete terms, only the Flemish Region has modified the indexation calculation. For contracts concluded after the 1st January 2019, the initial index is now the health index of the month preceding the entry into force of the contract, while previously, it was the health index of the month preceding the signature of the lease. For questions on the indexation For questions on legal aspects Statbel (FPS Economy) North Gate - Koning Albert II-laan 16 1000 Brussels Tel. : +32 [0]800 120 33 (9:00 - 17:00) e-mail : ind@economie.fgov.be Flanders https://www.wonenvlaanderen.be/een-woning-huren https://www.woninghuur.vlaanderen/huurdecreet-vanaf-01012019 Wallonia http://lampspw.wallonie.be/dgo4/site_logement/contacts#dept Brussels https://www.belgium.be/en/housing/renting_a_home https://www.baliebrussel.be/nl/kosteloze-rechtshulp/juridische-tweedelijnsbijstand Justitiehuizen Wallonië en Brussel Vlaams Gewest

The Program Access Index (PAI) is one of the measures FNS uses to reward states for high performance in the administration of the Supplemental Nutrition Assistance Program (SNAP). Performance awards were authorized by the Farm Security and Rural Investment Act of 2002 (also known as the 2002 Farm Bill). The PAI is designed to indicate the degree to which low-income people have access to SNAP benefits. The purpose of this step-by-step guide is to describe the calculation of the Program Access Index (PAI) in detail. It includes all of the data, adjustments, and calculations used in determining the PAI for every state.

Dataset

This dataset was created by Michael Nowell

Released under Community Data License Agreement - Sharing - Version 1.0

Contents

Measuring the usage of informatics resources such as software tools and databases is essential to quantifying their impact, value and return on investment. We have developed a publicly available dataset of informatics resource publications and their citation network, along with an associated metric (u-Index) to measure informatics resources’ impact over time. Our dataset differentiates the context in which citations occur to distinguish between ‘awareness’ and ‘usage’, and uses a citing universe of open access publications to derive citation counts for quantifying impact. Resources with a high ratio of usage citations to awareness citations are likely to be widely used by others and have a high u-Index score. We have pre-calculated the u-Index for nearly 100,000 informatics resources. We demonstrate how the u-Index can be used to track informatics resource impact over time. The method of calculating the u-Index metric, the pre-computed u-Index values, and the dataset we compiled to calculate the u-Index are publicly available.

Excel files using output from the LandSHIFT model to calculate changes in BII in India for the four scenarios and the base year 2010.

This dataset contains the compilation of the reference concavity analysis calculated for the manuscript "Impact of changing concavity indices on channel steepness and divide migration metrics" - JGR:Earth Surface

Boris Gailleton - boris.gailleton@gfz-potsdam.de
Simon M. Mudd
Fiona J. Clubb
Stuart W.D. Grieve
and Martin D. Hurst

The files are organised by folders, each representing one field site. They contain a csv file with the different information used for table 1 in the main manuscript as well as few useful figures. The summary CSVs have the following collumns:

raster_name: a unique ID
best_fit: the best fit concavity index
err_neg: the lower bound
err_pos: the higher bound
best_fit_norm_by_range: the best fit concavity index (calculated with the range method)
err_neg_norm_by_range: the lower bound (calculated with the range method)
err_pos_norm_by_range: the higher bound (calculated with the range method)
D*_XXX: disorder for each concavity index tested
D*_r_XXX: ranged disorder for each concavity index tested
X_median: the median X coordinate of the basin in local WGS84 - UTM coordinates
X_firstQ: the median X coordinate of the basin in local WGS84 - UTM coordinates
X_thirdtQ: the median X coordinate of the basin in local WGS84 - UTM coordinates
Y_median: the median X coordinate of the basin in local WGS84 - UTM coordinates
Y_firstQ: the median X coordinate of the basin in local WGS84 - UTM coordinates
Y_thirdtQ: the median X coordinate of the basin in local WGS84 - UTM coordinates

The local UTM zones are the following (N: North, S: South):

Andes_Chile: 19S
Arkansas: 15N
Bureinsky_range_russia: 52N
Carpathians: 35N
Caucasus: 38N
Central_sierra_madre: 13N
Corsica: 31N
Ethiopia: 37N
Lesotho: 35S
Luzon_Phillippines: 51S
North_of_Beijing: 50N
Nujang: 46N
Oregon_Coast_Ranges: 10N
San_Gabriel_Mts: 11N
Southern_Altai: 47N
Southern_Brazil: 23S
West_Zoid_Afrika: 33S
Wisconsin: 15N
Yemen: 38N
atlas: 29N
dolomites: 33N
hida: 54N
himalayas: 45N
kentucky_and_west_virginia: 17N
northern_appalachians: 17N
olympic: 10N
pyrenees: 31N
southern_appalachians: 10N
taiwan: 51N
tien_shan: 44N
zagros: 38N

There is also a summary csv file compiling all the information in the root folder.

Most of the field sites also have a number of figures:

_CDF_IQR: Cumulative distributed function of the inter-quartile range of concavity indices' uncertainties for all the basins in the area
_histogram_all_fits: Histogram of all the best fits
_MAP_best_fits: Map of the best fits
_D_star_range_theta_X: Map of D_star_r for the median best fit of all the basins (i.e. how good the median best fit is for each basins)
_min_Dstar_for_each_basins: Map of minimum D_star for each basin, representing the quality of the best fit for each basins

Note that few field sites only have the csv file, as they are themselves compilation of multiple analysis.

All the calculations have been done usign lsdtopytools (10.5281/zenodo.4774992)

Bangladesh BD: Net Barter Terms of Trade Index data was reported at 68.332 2000=100 in 2020. This records an increase from the previous number of 65.803 2000=100 for 2019. Bangladesh BD: Net Barter Terms of Trade Index data is updated yearly, averaging 103.596 2000=100 from Dec 1980 (Median) to 2020, with 41 observations. The data reached an all-time high of 162.264 2000=100 in 1985 and a record low of 57.575 2000=100 in 2011. Bangladesh BD: Net Barter Terms of Trade Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Trade Index. Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year's trade values as weights.;United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.;;

This data release provides tabulated liquefaction potential index (LPI) values calculated for a standard set of magnitudes (M), peak ground accelerations (PGA), and groundwater depths (GWD), as described in detail in Engler and others (2025). We use these data to rapidly interpolate LPI values for any M-PGA-GWD combination. The LPI results are computed at cone penetration test (CPT) sites in the San Francisco Bay Area (Holzer and others, 2010). Additionally, the CPT sites are classified using surface geology maps (Wentworth and others, 2023; Wills and others, 2015; Witter and others, 2006).

Yearly citation counts for the publication titled "GPU-based fast gamma index calculation".

Dataset Description:

• This dataset has population data of each Indian district from 2001 and 2011 censuses.
• The special thing about this data is that it has centroids for each district and state.
• Centroids for a district are calculated by mapping border of each district as a polygon of latitude/longitude points in a 2D plane and then calculating their mean center.
• Centroids for a state are calculated by calculating the weighted mean center of all districts that constitutes a state. The population count is the weight assigned to each district.

Example Analysis:

• The complete code for calculating the centroids and web scraping for the data is shared on GitHub.

• The purpose of this project was to map population density center for each state.

• You can also read about the complete project here: https://medium.com/@sumit.arora/plotting-weighted-mean-population-centroids-on-a-country-map-22da408c1397

Output Screenshots: Indian districts mapped as polygons https://i.imgur.com/UK1DCGW.png" alt="Indian districts mapped as polygons">

Mapping centroids for each district https://i.imgur.com/KCAh7Jj.png" alt="Mapping centroids for each district">

Mean centers of population by state, 2001 vs. 2011 https://i.imgur.com/TLHPHjB.png" alt="Mean centers of population by state, 2001 vs. 2011">

National center of population https://i.imgur.com/yYxE4Hc.png" alt="National center of population">

Index figures on production prices of dwellings and other buildings reflect the relation between the output value and the output volume and can be used to convert the value of construction output from current prices to fixed prices. The output price index is derived from the series "New dwellings; output indices 2000=100". From the 2nd quarter 2009 on, the figures of the series 2005 = 100 are used and linked to the series 2000 = 100. Statistics Netherlands publishes data on the value of construction output. The volume of construction output, however, cannot be deduced from the value, which is subject to price changes. The price index on the building costs of new dwellings eliminates the effect of price changes. The price index on construction output is calculated by distributing the value of the output (current prices) over the quarters essential to the price setting of the building project. Subsequently, the quarterly output is calculated in fixed prices by using the price index on the building costs of new dwellings. The index figure of the output price is the sum of the current prices divided by the sum of the fixed prices (*100).

Possibilities for selection: - Total construction - Total construction of new dwellings/buildings - New dwellings - New buildings in the private sector - New buildings in the non-commercial sector - Total other buildings - Other dwellings - Other buildings in the private sector - Other buildings in the non-commercial sector

Data available from 1st quarter 2000 till 4th quarter 2016 Frequency: discontinued

Status of the figures: The figures of 2016 are provisional. Since this table has been discontinued, the data will not become definitive.

Changes as of January 29 2018 None, this table is discontinued.

When will new figures become available? This table is succeeded by Production on buildings; price index 2015 = 100. See paragraph 3.

Linking recommendation If you want to compile long-term series with linked price indices on production of buildings, you can link the figures on price level 1995 with the figures on price level 2000. For that, the percentage change from the 2nd quarter 2005 with the 1st quarter 2005 must be calculated, as the price index for the 1st quarter 2005 is the last figure published on price level 1995. This change must then be adjusted to the figures for the 1st quarter 2005 of the series 1995. The 2nd quarter index of the linked series is calculated by calculating the difference between the 1st quarter 2005 and the 2nd quarter 2005 according to the series on price level 2000 and multiplying this by the index for the 1st quarter 2005 according to the series on price level 1995.

In the example: (119/120) x 148=147 (rounded). For the 3rd quarter 2005 the index is calculated analogously, where because of rounding problems the first quarter figures must be used for the link.

Dataset consists of high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA’s Toxicity and Exposure Forecaster (ToxCast and ExpoCast) project. This dataset is associated with the following publication: Wegner, S., C. Pinto, C. Ring, and J. Wambaugh. High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 137: 105470, (2020).

This dataset contains all the intermediate parameters and calculation results of the directional expansion index in the Wuhan Metropolitan Area from 1995 to 2020. Each data is vector data, and the intermediate parameters are in the attribute table of the vector data.

Calculation of Goodness of Fit (GOF) index.

An equation to properly analyse the MXI proposed by Hoek (2008).

Calculation of Deb's Index of photoperiod sensitivity from dates of sowing and anthesis of 81 rice landraces cultivated in short-day season and 13 landraces cultivated in long-day season in different years.

Open-file report; contains unpublished data that has not yet been peer-reviewed.

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants. More information and details about the data provided can be found at http://www.bls.gov/cpi

(1) The Human Development Index (HDI) is compiled by the United Nations Development Programme (UNDP) to measure a country's comprehensive development in the areas of health, education, and economy according to the UNDP's calculation formula.(2) Explanation: (1) The HDI value ranges from 0 to 1, with higher values being better. (2) Due to our country's non-membership in the United Nations and its special international situation, the index is calculated by our department according to the UNDP formula using our country's data. The calculation of the comprehensive index for each year is mainly based on the data of various indicators adopted by the UNDP. (3) In order to have the same baseline for international comparison, the comprehensive index and rankings are not retroactively adjusted after being published.(3) Notes: (1) The old indicators included life expectancy at birth, adult literacy rate, gross enrollment ratio, and average annual income per person calculated by purchasing power parity. (2) The indicators were updated to include life expectancy at birth, mean years of schooling, expected years of schooling, and nominal gross national income (GNI) calculated by purchasing power parity. Starting in 2011, the GNI per capita was adjusted from nominal value to real value to exclude the impact of price changes. Additionally, the HDI calculation method has changed from arithmetic mean to geometric mean. (3) The calculation method for indicators in the education domain changed from geometric mean to simple average due to retrospective adjustments in the 2014 Human Development Report for the years 2005, 2008, and 2010-2012. Since 2016, the education domain has adopted data compiled by the Ministry of Education according to definitions from the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organization for Economic Co-operation and Development (OECD).