Obtaining maps and data from the U.S. Geological Survey1

The U.S. Geological Survey produces a variety of resource information for the United States. This includes many data bases of particular interest to planners such as land use and terrain information prepared by the National Mapping Division, water quantity and quality data collected by Water Resources Division, and coal resource information gathered by the Geologic Division. These data are stored in various forms, and information on their availability can be obtained from appropriate offices in the U.S. Geological Survey as well as from USGS Circular 777. These data have been used for the management, development, and monitoring of our Nation's resources by Federal, State, and local agencies.     

Survey of Electrical Engineering and Computer Science Departments in the U. S.

As a part of a major curriculum study undertaken by the IEEE Computer Society Education Committee in the fall of 1974, a subcommittee surveyed existing curriculum and equipment in both computer science and electrical engineering departments to assess the current situation. The survey was also intended to update the COSINE survey of electrical engineering departments conducted in the fall of 1972.1     

The national coal-resources data system of the U.S. geological survey

The National Coal Resources Data System (NCRDS) was designed by the U.S. Geological Survey (USGS) to meet the increasing demands for rapid retrieval of information on coal location, quantity, quality, and accessibility. An interactive conversational query system devised by the USGS retrieves information from the data bank through a standard computer terminal. The system is being developed in two phases.Phase I, which currently is available on a limited basis, contains published areal resource and chemical data. The primary objective of this phase is to retrieve, calculate, and tabulate coal-resource data by area on a local, regional, or national scale. Factors available for retrieval include: state, county, quadrangle, township, coal field, coal bed, formation, geologic age, source and reliability of data, and coal-bed rank, thickness, overburden, and tonnage, or any combinations of variables. In addition, the chemical data items include individual values for proximate and ultimate analyses, BTU value, and several other physical and chemical tests. Information will be validated and deleted or updated as needed.Phase II is being developed to store, retrieve, and manipulate basic point source coal data (e.g., field observations, drill-hole logs), including geodetic location; bed thickness; depth of burial; moisture; ash; sulfur; major-, minor-, and trace-element content; heat value; and characteristics of overburden, roof rocks, and floor rocks. The computer system may be used to generate interactively structure-contour or isoline maps of the physical and chemical characteristics of a coal bed or to calculate coal resources.     

Understanding U.S. regional linguistic variation with Twitter data analysis

We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S.     

Development and validation of AVHRR-derived regional forest cover data for the north-eastern U.S.

As part of developing the geographic information system (GIS) to support a north-eastern U.S. regional forest change modelling effort, we investigated the utility of several sources of AVHRR data in regional forest cover mapping. Single-date classified Advanced Very High Resolution Radiometer (AVHRR) imagery in combination with existing USGS Land Use/Land Cover data was used to create a forest cover database that encompassed eastern New York state and all of New England. The USGS EROS Data Center Conterminous U.S. Land Cover Characteristics database was also evaluated for comparison. Statistical analysis showed that the AVHRR-derived regional land cover datasets provided estimates of total forest area that were comparable to U.S. Forest Service county level estimates. The AVHRR imagery recorded after leaf fall appeared to enhance the discrimination of coniferous vs. deciduous forests.     

数据起源安全研究综述*

数据起源是描述数据来源及其所经历处理过程的元数据。数据起源常用于数据错误溯源、数据重建和数据可信性验证。起源安全是制约起源技术大规模应用的关键瓶颈。为推动起源安全研究,本文首先从完整性、机密性和可用性三方面分析了起源安全的内涵;然后介绍了目前广受关注的起源安全机制：起源过滤和起源感知的访问控制,分别分析了这两类起源安全机制的主要特征,并对比了现有工作的优点和不足;最后指出了起源安全研究的发展方向。     

Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers

We combined remote sensing and in-situ measurements to estimate evapotranspiration (ET) from riparian vegetation over large reaches of western U.S. rivers and ET by individual plant types. ET measured from nine flux towers (eddy covariance and Bowen ratio) established in plant communities dominated by five major plant types on the Middle Rio Grande, Upper San Pedro River, and Lower Colorado River was strongly correlated with Enhanced Vegetation Index (EVI) values from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the NASA Terra satellite. The inclusion of maximum daily air temperatures (T_a) measured at the tower sites further improved this relationship. Sixteen-day composite values of EVI and T_a were combined to predict ET across species and tower sites (r² = 0.74); the regression equation was used to scale ET for 2000-2004 over large river reaches with T_a from meteorological stations. Measured and estimated ET values for these river segments were moderate when compared to historical, and often indirect, estimates and ranged from 851-874 mm yr^− 1. ET of individual plant communities ranged more widely. Cottonwood (Populus spp.) and willow (Salix spp.) stands generally had the highest annual ET rates (1100-1300 mm yr^− 1), while mesquite (Prosopis velutina) (400-1100 mm yr^− 1) and saltcedar (Tamarix ramosissima) (300-1300 mm yr^− 1) were intermediate, and giant sacaton (Sporobolus wrightii) (500-800 mm yr^− 1) and arrowweed (Pluchea sericea) (300-700 mm yr^− 1) were the lowest. ET rates estimated from the flux towers and by remote sensing in this study were much lower than values estimated for riparian water budgets using crop coefficient methods for the Middle Rio Grande and Lower Colorado River.     

Monitoring the 1988 severe drought in Indiana,U.S.A. using AVHRR data

Data from the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA–10 satellite was collected over Indiana for the 1987 and 1988 growing seasons. A Normalized Difference (NDVI) transformation was applied to the data. Over 45 fields representing 8 soil associations were sampled to assess the effects of the 1988 drought on the development of natural and cultivated vegetation. The results show the effect of the lack of available moisture to the plants and its effect on the response measured by the AVHRR sensor.     

Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information

A spatially explicit dataset of aboveground live forest biomass was made from ground measured inventory plots for the conterminous U.S., Alaska and Puerto Rico. The plot data are from the USDA Forest Service Forest Inventory and Analysis (FIA) program. To scale these plot data to maps, we developed models relating field-measured response variables to plot attributes serving as the predictor variables. The plot attributes came from intersecting plot coordinates with geospatial datasets. Consequently, these models serve as mapping models. The geospatial predictor variables included Moderate Resolution Imaging Spectrometer (MODIS)-derived image composites and percent tree cover; land cover proportions and other data from the National Land Cover Dataset (NLCD); topographic variables; monthly and annual climate parameters; and other ancillary variables. We segmented the mapping models for the U.S. into 65 ecologically similar mapping zones, plus Alaska and Puerto Rico. First, we developed a forest mask by modeling the forest vs. nonforest assignment of field plots as functions of the predictor layers using classification trees in See5©. Secondly, forest biomass models were built within the predicted forest areas using tree-based algorithms in Cubist©. To validate the models, we compared field-measured with model-predicted forest/nonforest classification and biomass from an independent test set, randomly selected from available plot data for each mapping zone. The estimated proportion of correctly classified pixels for the forest mask ranged from 0.79 in Puerto Rico to 0.94 in Alaska. For biomass, model correlation coefficients ranged from a high of 0.73 in the Pacific Northwest, to a low of 0.31 in the Southern region. There was a tendency in all regions for these models to over-predict areas of small biomass and under-predict areas of large biomass, not capturing the full range in variability. Map-based estimates of forest area and forest biomass compared well with traditional plot-based estimates for individual states and for four scales of spatial aggregation. Variable importance analyses revealed that MODIS-derived information could contribute more predictive power than other classes of information when used in isolation. However, the true contribution of each variable is confounded by high correlations. Consequently, excluding any one class of variables resulted in only small effects on overall map accuracy. An estimate of total C pools in live forest biomass of U.S. forests, derived from the nationwide biomass map, also compared well with previously published estimates.     

A method for drawing slope maps from contour maps by automatic data acquisition and processing

A method for drawing slope (angle) maps is described. After the contour lines of a topographic map have been expressed in digital form by a digitizer, the ground surface is considered as a set of partly overlapping trend surfaces. Slope values then are calculated at the mesh points of a grid superimposed on each of these mathematically expressed surfaces. The final map consists of a set of isolines (isoclines) obtained by slope values interpolation. The computer is used to determine the slope values, whereas the final map is obtained by a plotter.     

从美国速记员资格认证看速记的重要性

美国速记员资格认证分为各个地方州认证和联邦政府认证,要求严格,规定详细,突出了速记工作的重要性和速记员职责的特别性。美国速记的历史源远流长,对中国的影响也很深远。了解和学习美国的速记工作有助于中国速记的发展,更好为现代化建设服务。     

The U.S. geological survey rass-statpac system for management and statistical reduction of geochemical data

RASS is an acronym for Rock Analysis Storage System and STATPAC, for Statistical Package. The RASS and STATPAC computer programs are integrated into the RASS—STATPAC system for the management and statistical reduction of geochemical data. The system, in its present form, has been in use for more than 9 yr by scores of U.S. Geological Survey geologists, geochemists, and other scientists engaged in a broad range of geologic and geochemical investigations. The principal advantage of the system is the flexibility afforded the user both in data searches and retrievals and in the manner of statistical treatment of data. The statistical programs provide for most types of statistical reduction normally used in geochemistry and petrology, but also contain bridges to other program systems for statistical processing and automatic plotting.     

Phase measurements in MAESTRO polarimetric data from the U.K. test sites

Tests using MAESTRO data from the Reedham and Feltwell test sites suggest that the assumptions of a multi-variate complex zero-mean Gaussian distribution for scattering am litudes and a linear distortion model for polarimetric data lead to a viable model for C-band observations, though with some deviations between theory and measurements. P·band observations arc in general not consistent with this data model. When viable, the Gaussian model provides a basis for defining phase information, and has implications for the statistical measures which should be used to do so. The usual mean and standard deviation sta istics arc shown to have undesirable characteristics when applied to phase measurements. The effects of calibration and noise on phase measurements from distributed targets arc discussed, and other system effects on phase arc noted. Analysis of phase data from fields at the Feltwell test site shows that at C band the copolarized phase difference can discriminate between different crop types, but not at P band.     

浅谈地勘地质资料整理的重要性

地勘地质资料整理在现代矿产资源开采、地质工程建设过程中起到了非常重要的作用,直接关系着矿产资源开采质量、效率以及地质工程的安全可靠性,因此,企业应当加强重视。对地勘地质资料整理的重要性进行了分析,并在此基础上,就其整理方法及具体应用提出了一些观点和建议,以期为我国地勘地质行业的发展提供参考。     

VLSI research in the U.S.A.

Co-operative R&D in Advanced Information Technologies is not a Japanese privilege.     

美国国防部全面部署IPv6的启示

IPv6在全球正受到越来越多的重视。在亚洲,由于缺少地址等原因,人们对待IPv6的态度要比欧美积极,特别是日本和韩国。日本政府把IPv6技术的确立、普及与国际贡献作为政府的基本政策,把现有网络过渡到IPv6网络作为“超高速网络建设和竞争政策”的具体目标,明确设定在2005年完成互联网向IPv6的过渡。目前日本在IPv6的研发与应用方面走在世界的前列。日本NTT公司已经在亚太地区(包括日本、韩国、马来西亚、台湾、香港与澳大利亚)和欧洲(包括英国、荷兰、芬兰、德国与西班牙)提供IPv6商用业务。韩国在战略、政策、立法、项目资助、国际合…