The Mine Backfill Services Market Size Was Worth USD 5.37 Billion in 2023 and Is Expected To Reach USD 11.66 Billion by 2032, CAGR of 9.00%.

Figure 3's data

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Watershed Data Management (WDM) database file WBDR19.WDM is updated with the quality-assured and quality-controlled meteorological and hydrologic data for the period October 1, 2019, through September 30, 2020, following the guidelines documented in Bera (2017) and is renamed as WBDR20.WDM. Meteorological data other than precipitation (wind speed, solar radiation, air temperature, dewpoint temperature, and potential evapotranspiration) were copied from ARGN20.WDM and stored in this WDM file. Errors have been found in each of ARGNXX.WDM prior to Water Year (WY) 2023. XX represents last two digits of a WY. A WY is the 12-month period, October 1 through September 30, in which it ends. WBDR20.WDM contains erroneous meteorological data and related flag values thereby. WBDR20.WDM is removed. User is advised to download WBDR22.WDM from https://doi.org/10.5066/P1LDIASU. WBDR22.WDM contains corrected meteorological data from ARGN23.WDM (Bera, 2024a) for the period from January 1, 1997, through September 30, 2022. This database file also contains the quality-assured andquality-controlled hydrologic data for the period January 1, 1997, through September 30, 2022, processed following the guidelines documented in Bera (2017). While WBDR20.WDM is available from the author, all the records in WBDR20.WDM can be found in WBDR22.WDM as well. Data in dataset number (DSN) 107 and 801–811 are used in comparisons of precipitation data. DSN 107 contains hourly precipitation data from tipping bucket raingages collected at Argonne National Laboratory at Argonne, Illinois. DSN 801-811 contains the processed Next Generation Weather Radar (NEXRAD)-Multisensor Precipitation Estimates (MPE) data from 11 NEXRAD–MPE subbasins in the West Branch DuPage River watershed as described in Bera and Ortel (2018). The data are downloaded and uploaded daily into a WDM database that is used for the real-time streamflow simulation system. Data from DSN 107 and 801-811 are copied from this WDM and stored in WBDR22.WDM. DSN 107 and 801-811 are updated with the data through September 30, 2022. Data in DSN 4031 (water-surface elevation from West Branch DuPage River at Fawell Dam) is updated through September 30, 2022, similarly (Bera, 2017). During October 1, 2019, through September 30, 2020, the daily total rainfall (in the water year summary for water year 2020) from the rain gage using the HOBO® logger did not equal the sum of the instantaneous values pulled from the USGS National Water Information System database (NWIS) for several days. This is due to multiple tips occurring within the same minute. NWIS only counts the first tip and ignores any other tips that occur within the same minute. HOBO® loggers only record the time of a tip and the data is post processed to apply midnight time stamps and backfill 5- or 15-minute instantaneous values into the data log. The multiple tips occurring in the same minute are accurate, thus so is the daily total in the water year summary table. Table 1 shows the list of station(s) using the HOBO® logger that had different daily total rainfall in the Water Data Report than those computed from the data pulled from NWIS. The days with the difference of 0.03 inches or more are filled with the nearby stations as listed in Table 1. The rain gages using DCP loggers provide a value or data point every 5 or 15 minutes and do not show any difference from the instantaneous values pulled from NWIS. The complete list of missing precipitation data period and the nearby stations used to fill in those missing periods from October 1, 2019, through September 30, 2020, is given in the Table 2. Both Table 1 and Table 2 are in comma separated values (CSV) file format. The list of snow affected days of precipitation data and the missing and estimated period of the stage and flow data in WBDR22.WDM database during the period October 1, 2019, through September 30, 2020, are given in the USGS annual Water Data Report at https://wdr.water.usgs.gov. To open WBDR22.WDM file user needs to install Sara Timeseries utility described in the section "Related External Resources". First posted - March 21, 2022 (available from author) References Cited: Bera, M., 2024a, Meteorological Database, Argonne National Laboratory, Illinois: U.S. Geological Survey data release, https://doi.org/10.5066/P146RBHK. _ 2024b, Watershed Data Management (WDM) Database (WBDR22.WDM) for West Branch DuPage River Streamflow Simulation, DuPage County, Illinois, January 1, 2007, through September 30, 2022: U.S. Geological Survey data release, https://doi.org/10.5066/P1LDIASU. Bera, M., and Ortel, T.W., 2018, Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system: U.S. Geological Survey Open-File Report 2017–1159, 16 p., https://doi.org/10.3133/ofr20171159. Bera, M., 2017, Watershed Data Management (WDM) database for West Branch DuPage River streamflow simulation, DuPage County, Illinois, January 1, 2007, through September 30, 2013: U.S. Geological Survey Open-File Report 2017–1099, 39 p., https://doi.org/10.3133/ofr20171099.

Environmental Impact Statement: Kemps Creek reclamation project, raw materials extraction and backfill operation, Elizabeth Drive : Assessment of traffic implications

Figure 1's data

The watershed data management (WDM) database SC16.WDM is updated with the processed data for the period October 1, 2016, through September 30, 2017, and renamed as SC17.WDM. The precipitation data are collected from a tipping-bucket rain-gage network and the hydrologic data (stage and discharge) are collected at USGS streamflow-gaging stations in and around DuPage County, Illinois. Hourly precipitation and hydrologic data for the period October 1, 2016, through September 30, 2017, are processed following the guidelines described in Bera (2014) and appended to SC16.WDM and renamed as SC17.WDM. Meteorological data (wind speed, solar radiation, air temperature, dewpoint temperature, and potential evapotranspiration) from October 1, 2016, through September 30, 2017, are copied from ARGN17.WDM and appended to SC17.WDM. Data in dataset number (DSN) 107 and 801–810 are used in comparisons of precipitation data. DSN 107 contains hourly precipitation data collected at Argonne National Laboratory at Argonne, Illinois. DSN 801-810 contains the processed Next Generation Weather Radar (NEXRAD)-Multisensor Precipitation Estimates (MPE) data from 10 NEXRAD–MPE subbasins in the Salt Creek watershed as described in Bera and Ortel (2018). Data in these DSNs are not quality-assured and quality-controlled. The data are downloaded and uploaded daily into a WDM database that is used for the real-time streamflow simulation system. Data from DSN 107 and 801-810 are copied from this WDM and stored in SC17.WDM. DSN 107 and 801-810 are updated with the data through September 30, 2017. Data in DSN 5400 (water-surface elevation at the quarry) and 5700 (water surface elevation at Thorndale) are updated through September 30, 2017, similarly (Murphy and Ishii, 2006). Each rain gage station uses one of two different data loggers: the HOBO® or the WaterLOG® H-522+ XL™ Data Collection Platform (DCP). During the period October 1, 2016, through September 30, 2017, the daily total value (in the water year summary for water year 2017) from the rain gage using the HOBO® logger did not match with the sum of the instantaneous values pulled from NWIS-WEB for several days. This is due to multiple tips occurring within the same minute. NWIS-WEB only counts the first tip and ignores any other tips that occur within the same minute. HOBO® loggers only record the time of a tip, and the data is post processed to apply midnight time stamps and backfill 5- or 15- minute instantaneous values into the data log. The multiple tips occurring in the same minute are accurate, thus so is the daily total in the water year summary table. Table 1 shows the list of station(s) using the HOBO® logger that had different daily total rainfall in the Water Data Report than those computed from the data pulled from NWIS-WEB. The days with the difference of 0.03 inches or more are filled with the nearby stations as listed in Table 1. The DCP loggers on the other hand, provide a value or data point every 5- or 15- minutes. The rain gage using a DCP logger does not show any such difference from the instantaneous values pulled from NWIS-WEB. Errors have been found in each of ARGNXX.WDM prior to WY23. XX represents last two digits of a water year (WY). A WY is the 12-month period, October 1 through September 30, in which it ends. SC17.wdm contains erroneous meteorological data and related flag values thereby. SC17.WDM is removed. User is advised to download SC22.WDM from a link at https://doi.org/10.5066/P14D6FRA. SC22.WDM contains corrected meteorological data from ARGN23.WDM (Bera, 2024a) for the period from January 1, 1997, through September 30, 2022. This database file also contains the quality-assured and quality-controlled hydrologic data for the period January 1, 1997, through September 30, 2022, processed following the guidelines documented in Bera (2014). While SC17.WDM is available from the author, all the records in SC17.WDM can be found in SC22.WDM as well. The complete list of missing precipitation data periods and the nearby stations used to fill in those missing periods from October 1, 2016, through September 30, 2017, is given in Table 2. The list of snow affected days of precipitation data and the missing and estimated period of the stage and flow data in SC22.WDM database during the period October 1, 2016, through September 30, 2017, are given in the USGS annual Water Data Report at https://waterdata.usgs.gov/nwis. To open the WDM database SC22.WDM user needs to install Sara Timeseries utility described in the section "Related External Resources". First posted - July 24, 2019 (available from author) References Cited: Bera, M., 2024a, Meteorological Database, Argonne National Laboratory, Illinois: U.S. Geological Survey data release, https://doi.org/10.5066/P146RBHK. _ 2024b, Watershed Data Management (WDM) Database (SC22.WDM) for Salt Creek Streamflow Simulation, DuPage County, Illinois, January 1, 1997, through September 30, 2022: U.S. Geological Survey, https://doi.org/10.5066/P14D6FRA. Bera, M., and Over, T.M., 2018, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2017: U.S. Geological Survey data release, https://doi.org/10.5066/F7H1318R. Bera, M., 2014, Watershed Data Management (WDM) database for Salt Creek streamflow simulation, DuPage County, Illinois, water years 2005–11: U.S. Geological Survey Data Series 870, 18 p., http://dx.doi.org/10.3133/ds870. Murphy, E.A., and Ishii, A.L., 2006, Watershed Data Management (WDM) Database for Salt Creek Streamflow Simulation, DuPage County, Illinois: U.S. Geological Survey Open-File Report 2006-1248, 20 p., http://pubs.usgs.gov/of/2006/1248/.

This is the dataset used for the paper "Microbial hydrogen sinks in the sand-bentonite backfill material for the deep geological disposal of radioactive waste", submitted for publication in December 2023.

An Open Context "documents" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This record is part of the "Murlo" data publication.

The Continuous Mining Backfill Equipment market plays a crucial role in the mining industry by facilitating the efficient and sustainable extraction of mineral resources. This specialized equipment is designed to support the backfilling of mined-out areas, which not only enhances the stability of the mine but also m

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The ability to produce timely and accurate flu forecasts in the United States can significantly impact public health. Augmenting forecasts with internet data has shown promise for improving forecast accuracy and timeliness in controlled settings, but results in practice are less convincing, as models augmented with internet data have not consistently outperformed models without internet data. In this paper, we perform a controlled experiment, taking into account data backfill, to improve clarity on the benefits and limitations of augmenting an already good flu forecasting model with internet-based nowcasts. Our results show that a good flu forecasting model can benefit from the augmentation of internet-based nowcasts in practice for all considered public health-relevant forecasting targets. The degree of forecast improvement due to nowcasting, however, is uneven across forecasting targets, with short-term forecasting targets seeing the largest improvements and seasonal targets such as the peak timing and intensity seeing relatively marginal improvements. The uneven forecasting improvements across targets hold even when “perfect” nowcasts are used. These findings suggest that further improvements to flu forecasting, particularly seasonal targets, will need to derive from other, non-nowcasting approaches.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The Air Backfill Tamper market has become an essential component within the construction and civil engineering industries, providing a reliable solution for compacting soil and backfill materials to enhance stability and support structures. Characterized by its pneumatic operation, the air backfill tamper offers eff

Backfill materials are used in underground engineering to fill voids and buried excavated parts. In this study, solid waste was utilised as a raw material mixed with different amounts of polypropylene fibres to determine the optimal sodium hydroxide content, water—solid ratio, and fibre content. The uniaxial compressive strength (UCS) of the produced backfill materials was measured, and the interfacial structures were analysed via scanning electron microscopy. The results revealed that the mechanical properties of the backfill materials were influenced in the order sodium hydroxide doping > water—solid ratio > fibre doping. The optimal material composition corresponded to a sodium hydroxide content of 3%, water—solid ratio of 0.28, and fibre content of 5 ‰. The slag produced a C—S–H gel. Meanwhile, the fly ash and gangue contained large amounts of aluminium, which formed hydrated aluminosilicates. The addition of polypropylene fibres reduced the number of internal defects in the backfill structure and increased the UCS.

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The dataset includes the original mining surface profile data collected by the terrestrial laser scanning (TLS) and radar information on backfill quality outside the segmental lining which was obtained by the ground-penetration radar (GPR) detection, which supports the numerical analysis outlined in the article titled “Numerical evaluation of segmental tunnel lining with voids in outside backfill.”

Historical ILI: Uncertainty quantification of ARLR method’s nowcast (one-week ahead forecast) using historical (without backfill) ILI data for 3 different forecast weeks.

Brussels-Capital Region : spatial extents of the stratigraphic units (SU/BCR), SU/BCR_011 Backfill and SU/BCR_12 Silts cover excepted. This data is limited to the Brussels-Capital Region’s border extended with a 500 m buffer zone. The hydrogeological unit (HU/BCR) of each SU/BRC is indicated in the attribute data. These spatial extents were extracted from the BruStrati3D v1.1 geological model, except for the extensions of the quaternary units which were extracted from the Databank Ondergrond Vlaanderen database. This data is generated from models and may contain errors, inaccuracies and gaps. It has informative value and may under no circumstances replace a study carried out by an expert. Bruxelles Environnement cannot be held liable for the consequences of the use of this information.