991 resultados para Computer input-outpus equipment.
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Underbody plows can be very useful tools in winter maintenance, especially when compacted snow or hard ice must be removed from the roadway. By the application of significant down-force, and the use of an appropriate cutting edge angle, compacted snow and ice can be removed very effectively by such plows, with much greater efficiency than any other tool under those circumstances. However, the successful operation of an underbody plow requires considerable skill. If too little down pressure is applied to the plow, then it will not cut the ice or compacted snow. However, if too much force is applied, then either the cutting edge may gouge the road surface, causing significant damage often to both the road surface and the plow, or the plow may ride up on the cutting edge so that it is no longer controllable by the operator. Spinning of the truck in such situations is easily accomplished. Further, excessive down force will result in rapid wear of the cutting edge. Given this need for a high level of operator skill, the operation of an underbody plow is a candidate for automation. In order to successfully automate the operation of an underbody plow, a control system must be developed that follows a set of rules that represent appropriate operation of such a plow. These rules have been developed, based upon earlier work in which operational underbody plows were instrumented to determine the loading upon them (both vertical and horizontal) and the angle at which the blade was operating.These rules have been successfully coded into two different computer programs, both using the MatLab® software. In the first program, various load and angle inputs are analyzed to determine when, whether, and how they violate the rules of operation. This program is essentially deterministic in nature. In the second program, the Simulink® package in the MatLab® software system was used to implement these rules using fuzzy logic. Fuzzy logic essentially replaces a fixed and constant rule with one that varies in such a way as to improve operational control. The development of the fuzzy logic in this simulation was achieved simply by using appropriate routines in the computer software, rather than being developed directly. The results of the computer testing and simulation indicate that a fully automated, computer controlled underbody plow is indeed possible. The issue of whether the next steps toward full automation should be taken (and by whom) has also been considered, and the possibility of some sort of joint venture between a Department of Transportation and a vendor has been suggested.
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The Equipment and Vehicle Revolving Fund report covers all equipment and vehicle purchases through the highway materials and equipment revolving fund during FY 2012.
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Introduction: Therapeutic drug monitoring (TDM) aims at optimizing treatment by individualizing dosage regimen based on measurement of blood concentrations. Maintaining concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculation represents a gold standard in TDM approach but requires computing assistance. In the last decades computer programs have been developed to assist clinicians in this assignment. The aim of this benchmarking was to assess and compare computer tools designed to support TDM clinical activities.¦Method: Literature and Internet search was performed to identify software. All programs were tested on common personal computer. Each program was scored against a standardized grid covering pharmacokinetic relevance, user-friendliness, computing aspects, interfacing, and storage. A weighting factor was applied to each criterion of the grid to consider its relative importance. To assess the robustness of the software, six representative clinical vignettes were also processed through all of them.¦Results: 12 software tools were identified, tested and ranked. It represents a comprehensive review of the available software's characteristics. Numbers of drugs handled vary widely and 8 programs offer the ability to the user to add its own drug model. 10 computer programs are able to compute Bayesian dosage adaptation based on a blood concentration (a posteriori adjustment) while 9 are also able to suggest a priori dosage regimen (prior to any blood concentration measurement), based on individual patient covariates, such as age, gender, weight. Among those applying Bayesian analysis, one uses the non-parametric approach. The top 2 software emerging from this benchmark are MwPharm and TCIWorks. Other programs evaluated have also a good potential but are less sophisticated (e.g. in terms of storage or report generation) or less user-friendly.¦Conclusion: Whereas 2 integrated programs are at the top of the ranked listed, such complex tools would possibly not fit all institutions, and each software tool must be regarded with respect to individual needs of hospitals or clinicians. Interest in computing tool to support therapeutic monitoring is still growing. Although developers put efforts into it the last years, there is still room for improvement, especially in terms of institutional information system interfacing, user-friendliness, capacity of data storage and report generation.
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Objectives: Therapeutic drug monitoring (TDM) aims at optimizing treatment by individualizing dosage regimen based on blood concentrations measurement. Maintaining concentrations within a target range requires pharmacokinetic (PK) and clinical capabilities. Bayesian calculation represents a gold standard in TDM approach but requires computing assistance. The aim of this benchmarking was to assess and compare computer tools designed to support TDM clinical activities.¦Methods: Literature and Internet were searched to identify software. Each program was scored against a standardized grid covering pharmacokinetic relevance, user-friendliness, computing aspects, interfacing, and storage. A weighting factor was applied to each criterion of the grid to consider its relative importance. To assess the robustness of the software, six representative clinical vignettes were also processed through all of them.¦Results: 12 software tools were identified, tested and ranked. It represents a comprehensive review of the available software characteristics. Numbers of drugs handled vary from 2 to more than 180, and integration of different population types is available for some programs. Nevertheless, 8 programs offer the ability to add new drug models based on population PK data. 10 computer tools incorporate Bayesian computation to predict dosage regimen (individual parameters are calculated based on population PK models). All of them are able to compute Bayesian a posteriori dosage adaptation based on a blood concentration while 9 are also able to suggest a priori dosage regimen, only based on individual patient covariates. Among those applying Bayesian analysis, MM-USC*PACK uses a non-parametric approach. The top 2 programs emerging from this benchmark are MwPharm and TCIWorks. Others programs evaluated have also a good potential but are less sophisticated or less user-friendly.¦Conclusions: Whereas 2 software packages are ranked at the top of the list, such complex tools would possibly not fit all institutions, and each program must be regarded with respect to individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Although interest in TDM tools is growing and efforts were put into it in the last years, there is still room for improvement, especially in terms of institutional information system interfacing, user-friendliness, capability of data storage and automated report generation.
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This User’s Guide serves as a reference for field personnel using the sign inventory data collection software tool. This tool was developed to simplify and standardize the collection and updating of sign inventory information. The software and collection methodology was developed by the Iowa DOT Sign Management Task Force and the Center for Transportation Research and Education at Iowa State University. Required Equipment -The data collection process requires both a portable computer and a global positioning system (GPS) device (connected via USB cable). Since computer battery performance varies, a DC power converter is recommended. A check-in/out process has also been established which allows updates to sign information from the central database.
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The resilient modulus (MR) input parameters in the Mechanistic-Empirical Pavement Design Guide (MEPDG) program have a significant effect on the projected pavement performance. The MEPDG program uses three different levels of inputs depending on the desired level of accuracy. The primary objective of this research was to develop a laboratory testing program utilizing the Iowa DOT servo-hydraulic machine system for evaluating typical Iowa unbound materials and to establish a database of input values for MEPDG analysis. This was achieved by carrying out a detailed laboratory testing program designed in accordance with the AASHTO T307 resilient modulus test protocol using common Iowa unbound materials. The program included laboratory tests to characterize basic physical properties of the unbound materials, specimen preparation and repeated load triaxial tests to determine the resilient modulus. The MEPDG resilient modulus input parameter library for Iowa typical unbound pavement materials was established from the repeated load triaxial MR test results. This library includes the non-linear, stress-dependent resilient modulus model coefficients values for level 1 analysis, the unbound material properties values correlated to resilient modulus for level 2 analysis, and the typical resilient modulus values for level 3 analysis. The resilient modulus input parameters library can be utilized when designing low volume roads in the absence of any basic soil testing. Based on the results of this study, the use of level 2 analysis for MEPDG resilient modulus input is recommended since the repeated load triaxial test for level 1 analysis is complicated, time consuming, expensive, and requires sophisticated equipment and skilled operators.
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The objective of this study is to systematically evaluate the Iowa Department of Transportation’s (DOT’s) existing Pavement Management Information System (PMIS) with respect to the input information required for Mechanistic-Empirical Pavement Design Guide (MEPDG) rehabilitation analysis and design. To accomplish this objective, all of available PMIS data for interstate and primary roads in Iowa were retrieved from the Iowa DOT PMIS. The retrieved data were evaluated with respect to the input requirements and outputs for the latest version of the MEPDG software (version 1.0). The input parameters that are required for MEPDG HMA rehabilitation design, but currently unavailable in the Iowa DOT PMIS were identified. The differences in the specific measurement metrics used and their units for some of the pavement performance measures between the Iowa DOT PMIS and MEPDG were identified and discussed. Based on the results of this study, it is recommended that the Iowa DOT PMIS should be updated, if possible, to include the identified parameters that are currently unavailable, but are required for MEPDG rehabilitation design. Similarly, the measurement units of distress survey results in the Iowa DOT PMIS should be revised to correspond to those of MEPDG performance predictions. *******************Large File**************************
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Highway construction is among the most dangerous industries in the US. Internal traffic control design, along with how construction equipment and vehicles interact with the traveling public, have a significant effect on how safe a highway construction work zone can be. An integrated approach was taken to research work-zone safety issues and mobility, including input from many personnel, ranging from roadway designers to construction laborers and equipment operators. The research team analyzed crash data from Iowa work-zone incident reports and Occupational Safety and Health Administration data for the industry in conjunction with the results of personal interviews, a targeted work-zone ingress and egress survey, and a work-zone pilot project.
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The Iowa Department of Transportation spent $344,521 of the Fiscal Year 2013 RUTF/PRF budget reversion in FY 2013 for desktop and server software, information technology professional services and computer equipment.
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FRAX(®) is a fracture risk assessment algorithm developed by the World Health Organization in cooperation with other medical organizations and societies. Using easily available clinical information and femoral neck bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA), when available, FRAX(®) is used to predict the 10-year probability of hip fracture and major osteoporotic fracture. These values may be included in country specific guidelines to aid clinicians in determining when fracture risk is sufficiently high that the patient is likely to benefit from pharmacological therapy to reduce that risk. Since the introduction of FRAX(®) into clinical practice, many practical clinical questions have arisen regarding its use. To address such questions, the International Society for Clinical Densitometry (ISCD) and International Osteoporosis Foundations (IOF) assigned task forces to review the best available medical evidence and make recommendations for optimal use of FRAX(®) in clinical practice. Questions were identified and divided into three general categories. A task force was assigned to investigating the medical evidence in each category and developing clinically useful recommendations. The BMD Task Force addressed issues that included the potential use of skeletal sites other than the femoral neck, the use of technologies other than DXA, and the deletion or addition of clinical data for FRAX(®) input. The evidence and recommendations were presented to a panel of experts at the ISCD-IOF FRAX(®) Position Development Conference, resulting in the development of ISCD-IOF Official Positions addressing FRAX(®)-related issues.