History and evaluation of national-scale geochemical data sets for the United States

Six national-scale,or near national-scale,geochemical data sets for soils or stream sediments exist for the United States.The earliest of these,here termed the ＇Shacklette＇ data set,was generated by a U.S. Geological Survey（USGS） project conducted from 1961 to 1975.This project used soil collected from a depth of about 20 cm as the sampling medium at 1323 sites throughout the conterminous U.S.The National Uranium Resource Evaluation Hydrogeochemical and Stream Sediment Reconnaissance（NUREHSSR） Program of the U.S.Department of Energy was conducted from 1975 to 1984 and collected either stream sediments,lake sediments,or soils at more than 378,000 sites in both the conterminous U.S.and Alaska.The sampled area represented about 65%of the nation.The Natural Resources Conservation Service（NRCS）,from 1978 to 1982,collected samples from multiple soil horizons at sites within the major crop-growing regions of the conterminous U.S.This data set contains analyses of more than 3000 samples.The National Geochemical Survey,a USGS project conducted from 1997 to 2009,used a subset of the NURE-HSSR archival samples as its starting point and then collected primarily stream sediments, with occasional soils,in the parts of the U.S.not covered by the NURE-HSSR Program.This data set contains chemical analyses for more than 70,000 samples.The USGS,in collaboration with the Mexican Geological Survey and the Geological Survey of Canada,initiated soil sampling for the North American Soil Geochemical Landscapes Project in 2007.Sampling of three horizons or depths at more than 4800 sites in the U.S.was completed in 2010,and chemical analyses are currently ongoing.The NRCS initiated a project in the 1990s to analyze the various soil horizons from selected pedons throughout the U.S.This data set currently contains data from more than 1400 sites.This paper（1） discusses each data set in terms of its purpose,sample collection protocols,and analytical methods;and（2） evaluates each data set in terms of its appropriateness as a national-scale geochemical database and its usefulness for nationalscale geochemical mapping.

Mirror Mapping of Laser Interferometer Measurement System on Ultra-precise Movement Stage

The accuracy and repeatability of the laser interferometer measurement system （LIMS） are often limited by the mirror surface error that comes from the mirror surface shape and distortion. This paper describes a new method to calibrate mirror map on ultraprecise movement stage （UPMS） with nanopositioning and to make a real-time compensation for the mirror surface error by using mirror map data tables with the software algorithm. Based on the mirror map test model, the factors affecting mirror map are analyzed through geometric method on the UPMS with six digrees of freedom. Dam processing methods including spline interpolation and spline offsets are used to process the raw sampling data to build mirror map tables. The linear interpolation as compensation method to make a real-time correction on the stage mirror unflatness is adopted and the correction formulas are illuminated. In this way, the measurement accuracy of the system is obviously improved from 40 nm to 5 nm.

利用WGS84-AW3D30 DSM data map表面模型数据作西安80或国家2000大地坐标系统的地形图技术探讨

针对日常野外测绘及地形图绘制过程中存在的各种问题,本文提供了一种快速、简洁的地形图绘制方法,即利用现成的网络上得到的数据通过一系列的坐标系统转换过程得到工作所需的数据,并完成地形图绘制,为地形图绘制提供了一种新的思路。

Highly Available Hypercube Tokenized Sequential Matrix Partitioned Data Sharing in Large P2P Networks

Peer-to-peer (P2P) networking is a distributed architecture that partitions tasks or data between peer nodes. In this paper, an efficient Hypercube Sequential Matrix Partition (HS-MP) for efficient data sharing in P2P Networks using tokenizer method is proposed to resolve the problems of the larger P2P networks. The availability of data is first measured by the tokenizer using Dynamic Hypercube Organization. By applying Dynamic Hypercube Organization, that efficiently coordinates and assists the peers in P2P network ensuring data availability at many locations. Each data in peer is then assigned with valid ID by the tokenizer using Sequential Self-Organizing (SSO) ID generation model. This ensures data sharing with other nodes in large P2P network at minimum time interval which is obtained through proximity of data availability. To validate the framework HS-MP, the performance is evaluated using traffic traces collected from data sharing applications. Simulations conducting using Network simulator-2 show that the proposed framework outperforms the conventional streaming models. The performance of the proposed system is analyzed using energy consumption, average latency and average data availability rate with respect to the number of peer nodes, data size, amount of data shared and execution time. The proposed method reduces the energy consumption 43.35% to transpose traffic, 35.29% to bitrev traffic and 25% to bitcomp traffic patterns.

Soil polygon disaggregation through similarity-based prediction with legacy pedons

Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified.This restricts current applications of the maps in site-specific agricultural management and environmental modelling.We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under-or over-sampled legacy pedon data for the disaggregation.The method consisted of three steps.First,environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors.Second,according to soil types of the pedon sites,the similarities were aggregated to derive similarity distribution for each soil type.Third,a hardening process was performed on the maps to allocate candidate soil types within the polygons.The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba,Canada.Based on 186 independent pedon sites,the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62.The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map,which was commonly used in practice.Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy,indicating that new environmental covariates need to be developed.We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.

Effects of rainfall on the weekday traffic flow in major cities of the Beijing-Tianjin-Hebei region, China, in 2021

Rainfall can bring uncertainties to the traffic flow and influence the normal function of urban transportation systems.The impact of precipitation on the urban traffic flow,especially the different impacts among cities and areas within a city,is worth investigating.Here,we analysed the impact of precipitation on the traffic flow in the urban areas of the Beijing-Tianjin-Hebei region by comparing the traffic flow in non-precipitation and rainy weather with different hourly precipitation intensities in 2021.The increase in the travel time index(TTI)is chosen to represent the influence of precipitation on the transportation system.The results show that the maximum of the average TTI increases on the city scale under various rainfall intensities by 3.3%,6.6%and 10.8%in Beijing,Tianjin and Shijiazhuang,respectively.In general,the increase in the TTI contributed by precipitation is the greatest at morning and afternoon peak hours,and the traffic congestion degree increases with the rainfall intensity.However,in the morning peak,afternoon peak and midday hours in Beijing and Tianjin,the influences of the weak rainfall intensity on the traffic flow are generally great,whereas the traffic congestion degree caused by heavy precipitation is relatively low.Particularly,in morning peak hours,the congestion reduction reaches approximately 2%,which may be related to the spatial difference in the impacts of precipitation on the traffic flow and the changes in people's travel intention under different rainfall intensities.The findings can help better understand the relationship between rainfall and urban traffic flow characteristics and also potentially contribute to the development of impact-oriented climate predictions.