Three-dimensional interpolation of soil data: fertility and pedomorphological features in southern Brazil

The graphical representation of spatial soil properties in a digital environment is complex because it requires a conversion of data collected in a discrete form onto a continuous surface. The objective of this study was to apply three-dimension techniques of interpolation and visualization on soil texture and fertility properties and establish relationships with pedogenetic factors and processes in a slope area. The GRASS Geographic Information System was used to generate three-dimensional models and ParaView software to visualize soil volumes. Samples of the A, AB, BA, and B horizons were collected in a regular 122-point grid in an area of 13 ha, in Pinhais, PR, in southern Brazil. Geoprocessing and graphic computing techniques were effective in identifying and delimiting soil volumes of distinct ranges of fertility properties confined within the soil matrix. Both three-dimensional interpolation and the visualization tool facilitated interpretation in a continuous space (volumes) of the cause-effect relationships between soil texture and fertility properties and pedological factors and processes, such as higher clay contents following the drainage lines of the area. The flattest part with more weathered soils (Oxisols) had the highest pH values and lower Al3+ concentrations. These techniques of data interpolation and visualization have great potential for use in diverse areas of soil science, such as identification of soil volumes occurring side-by-side but that exhibit different physical, chemical, and mineralogical conditions for plant root growth, and monitoring of plumes of organic and inorganic pollutants in soils and sediments, among other applications. The methodological details for interpolation and a three-dimensional view of soil data are presented here.

Annual Report Of the State Geologist, March 2012

What we do: Since 1892, the Iowa Geological and Water Survey (IGWS) has provided earth, water, and mapping science to all Iowans. We collect and interpret information on subsurface geologic conditions, groundwater and surface water quantity and quality, and the natural and built features of our landscape. This information is critical for: Predicting the future availability of economic water supplies and mineral resources. Assuring proper function of waste disposal facilities. Delineation of geologic hazards that may jeopardize property and public safety. Assessing trends and providing protection of water quality and soil resources. Applied technical assistance for economic development and environmental stewardship. Our goal: Providing the tools for good decision making to assure the long-term vitality of Iowa’s communities, businesses, and quality of life. Information and technical assistance are provided through web-based databases, comprehensive Geographic Information System (GIS) tools, predictive groundwater models, and watershed assessments and improvement grants. The key service we provide is direct assistance from our technical staff, working with Iowans to overcome real-world challenges. This report describes the basic functions of IGWS program areas and highlights major activities and accomplishments during calendar year 2011. More information on IGWS is available at http://www.igsb.uiowa.edu/.

The Annual Condition of Education Report, 2010

The Annual Condition of Education Report includes an expanded Background Demographics section. This section contains information on population, economic, and social data, providing a comparison between Iowa, other states, and the Nation. Geographic Information System (GIS) maps are presented to allow the reader to easily compare between two or more geographies. In some instances, trends are also reported. Information displayed in this section was obtained from a variety of sources and is noted on each table or figure. In all areas, the most recent data available were used.

The Annual Condition of Education Report, 2011

The Annual Condition of Education Report includes an expanded Background Demographics section. This section contains information on population, economic, and social data, providing a comparison between Iowa, other states, and the Nation. Geographic Information System (GIS) maps are presented to allow the reader to easily compare between two or more geographies. In some instances, trends are also reported. Information displayed in this section was obtained from a variety of sources and is noted on each table or figure. In all areas, the most recent data available were used.

Influence of compliance to respiratory weaning consensus conference criteria on respiratory outcome within 48h after planned extubation

INTRODUCTION. A two-step assessment (readiness to wean (RW) followed by spontaneousbreathing trial (SBT)) of predefined criteria is recommended before planned extubation(PE)1.OBJECTIVES. We aimed to evaluate if compliance to all guideline criteria was associatedwith better respiratory outcome within 48 h after PE.METHODS. The data (extracted from our clinical information system) of 458 consecutivepatients who underwent PE after C48 h of invasive ventilation in our medico-surgical ICUwere analyzed. We evaluated compliance with guidelines [1] regarding respiratory rate, tidalvolume, PaO2, FiO2, PEEP, PaCO2, pH, heart rate, systolic arterial pressure and arrhythmiaduringRWand SBT assessment (RW and SBT within 2 h). A patient was classified as RW+ ifallRWcriteria were fulfilled andRW-if at least 1 criterion was violated. The same approachwas used to define SBT+ and SBT- patients. During the 48 h following PE, we assessed theoccurrence of post-PE respiratory failure (PRF) (defined as the presence of at least 1 consensuscriterion of respiratory failure [1]), reintubation (after NIV failure or because of immediateintubation criteria) and cumulative duration of post-PE ventilation (PPEV = Post-PE invasive+ non-invasive ventilation). ICU mortality was recorded. Comparisons for variousoutcomes were performed by Chi-square and t tests.RESULTS. All consensus criteria were fulfilled in 77.3% of the patients during RW and in68.1% of the patients during SBT.[Compliance to weaning criteria and outcome]N = 458 PRF (%) Reintubation (%) PPEV (min) ICU mortality (%)All patients 53.5 10.0 542 ± 664 6.1RW+ 50.0 9.3 490 ± 626 5.4RW- 65.4* 12.5 718 ± 757** 8.7SBT+ 52.6 8.0 498 ± 594 6.7SBT- 55.5 14.4*** 637 ± 788**** 4.8Occurrence of PRF only was not associated with increased ICU mortality: 4.2 versus 7.8%,p = 0.11. By contrast, ICU mortality was significantly increased in patients requiring reintubation:21.7 versus 4.4%. p\0.001; * p = 0.006 RW+ versus RW-; ** p = 0.003RW+ versus RW-; *** p = 0.035 SBT+ versus SBT-; **** p = 0.030 SBT+ versusSBTCONCLUSIONS.In our ICU, compliance to all criteria of the two-step published approach ofrespiratory weaning was not optimal but reintubation rate was comparable to published data.Compliance with consensus conference guidelines was associated with lower reintubation rateand shorter PPEV but not with ICU mortality. As mortality was increased by reintubation,more sensitive and specific criteria to predict the risk of reintubation are probably needed.REFERENCE. Boles JM, et al. Eur Respir J 2007;29:1033-56.

Artificial-Intelligence-Based Optimization of the Management of Snow Removal Assets and Resources Final Report, October 2002

Geographic information systems (GIS) and artificial intelligence (AI) techniques were used to develop an intelligent snow removal asset management system (SRAMS). The system has been evaluated through a case study examining snow removal from the roads in Black Hawk County, Iowa, for which the Iowa Department of Transportation (Iowa DOT) is responsible. The SRAMS is comprised of an expert system that contains the logical rules and expertise of the Iowa DOT’s snow removal experts in Black Hawk County, and a geographic information system to access and manage road data. The system is implemented on a mid-range PC by integrating MapObjects 2.1 (a GIS package), Visual Rule Studio 2.2 (an AI shell), and Visual Basic 6.0 (a programming tool). The system could efficiently be used to generate prioritized snowplowing routes in visual format, to optimize the allocation of assets for plowing, and to track materials (e.g., salt and sand). A test of the system reveals an improvement in snowplowing time by 1.9 percent for moderate snowfall and 9.7 percent for snowstorm conditions over the current manual system.

Decision Support Model for Assessing Archaeological Survey Needs for Bridge Replacement Projects in Iowa, 2006

The Bridges Decision Support Model is a geographic information system (GIS) that assembles existing data on archaeological sites, surveys, and their geologic contexts to assess the risk of bridge replacement projects encountering 13,000- to 150-year-old Native American sites. This project identifies critical variables for assessing prehistoric sites potential, examines the quality of available data about the variables, and applies the data to creating a decision support framework for use by the Iowa Department of Transportation (Iowa DOT) and others. An analysis of previous archaeological surveys indicates that subsurface testing to discover buried sites became increasingly common after 1980, but did not become routine until after the adoption of guidelines recommending such testing, in 1993. Even then, the average depth of testing has been relatively shallow. Alluvial deposits of sufficient age, deposited in depositional environments conducive to human habitation, are considerably thicker than archaeologists have routinely tested.

Joint Enrollment Report, 2011

The Iowa Department of Education collects information on joint enrollment in Iowa’s 15 community colleges. Jointly enrolled students are high school students enrolled in community college credit coursework. Most jointly enrolled students enroll through Senior Year Plus programs such as PSEO (Postsecondary Enrollment Options) and concurrent enrollment. Others enroll independently by paying tuition or enrolling in courses delivered through contractual agreements that do not meet the definition of concurrent enrollment. For more information about Senior Year Plus programs, please refer to the department’s website. This report consists of fiscal year and trend data on joint enrollment including headcount enrollment, credit hours, student demographics, and enrollment by program type and offering arrangement. All data included in this report is taken from the Community College Management Information System (MIS) and confirmed by each college, unless otherwise noted.

Iowa Community Colleges Student Enrollment Report, December 2000

The Iowa community colleges transmit the Fall 2000 enrollments to the Department of Education after the 14th day of the fall term start date. All data in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college transmittal sheet. The Fall 2000 unduplicated credit headcount enrollment of 65,473 demonstrates an increase of 3 percent over Fall 1999 enrollment of 63,809.

Iowa Community Colleges Student Enrollment Report, December 2007

Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.

Iowa Community Colleges Student Fall Enrollment Report, December 2009

Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.

Iowa Community Colleges Student Fall Enrollment Report, December 2001

Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.

Iowa Community Colleges Student Fall Enrollment Report, December 2003

Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.

Iowa Community Colleges Student Fall Enrollment Report, November 2003

Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.

Iowa Community Colleges Student Fall Enrollment Report, November 2003

Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.