Data input verification

Platinum resistance thermometers are currently used by the NIST for calibration verification of other thermometer types for the temperature range 13.8 to 904 K. In addition, they are one of the easiest types of thermometers to interface with a computer for data input. On the other hand, platinum resistance thermometers are very expensive, extremely sensitive to physical changes and shock, have a slow response time, and therefore can take a long time to equilibrate to a given temperature. Thus, resistance thermometers are often used only for calibration purposes in many labs. 

Screen input formatting and input verification, data recovery, referential integrity, data transfers, print controls... 

LIMS usually work tightly with SOPs or a method management system. These systems provide the documented rules for exception management in sample data analysis batch evaluation. The concept of user input verification may replace the rule-based calculation functionality in existing LIMS. The required information logistics to solve this task is based on the following assumptions ... 

Subroutine VLDTA2. VLDTA2 loads the binary vapor-liquid equilibrium data to be correlated. If the data are in units other than those used internally, the correct conversions are made here. This subroutine also reads the estimated standard deviations for the measured variables and the initial parameter estimates. All input data are printed for verification. 

The process of field validation and testing of models was presented at the Pellston conference as a systematic analysis of errors (6. In any model calibration, verification or validation effort, the model user is continually faced with the need to analyze and explain differences (i.e., errors, in this discussion) between observed data and model predictions. This requires assessments of the accuracy and validity of observed model input data, parameter values, system representation, and observed output data. Figure 2 schematically compares the model and the natural system with regard to inputs, outputs, and sources of error. Clearly there are possible errors associated with each of the categories noted above, i.e., input, parameters, system representation, output. Differences in each of these categories can have dramatic impacts on the conclusions of the model validation process. 

Frequency domain performance has been analyzed with goodness-of-fit tests such as the Chi-square, Kolmogorov-Smirnov, and Wilcoxon Rank Sum tests. The studies by Young and Alward (14) and Hartigan et. al. (J 3) demonstrate the use of these tests for pesticide runoff and large-scale river basin modeling efforts, respectively, in conjunction with the paired-data tests. James and Burges ( 1 6 ) discuss the use of the above statistics and some additional tests in both the calibration and verification phases of model validation. They also discuss methods of data analysis for detection of errors this last topic needs additional research in order to consider uncertainties in the data which provide both the model input and the output to which model predictions are compared. 

All mathematical models require some assumed data on the source of release for a material. These assumptions form the input data which is then easily placed into a mathematical equation. The assumed data is usually the size or rate of mass released, wind direction, etc. They cannot possibly take into account all the variables that might exist at the time of the incident. Unfortunately most of the mathematical equations are also still based on empirical studies, laboratory results or in some cases TNT explosion equivalents. Therefore they still need considerable verification with tests simulations before they can be fully accepted as valid. 

Model validation requires confirming logic, assumptions, and behavior. These tasks involve comparison with historical input-output data, or data in the literature, comparison with pilot plant performance, and simulation. In general, data used in formulating a model should not be used to validate it if at all possible. Because model evaluation involves multiple criteria, it is helpful to find an expert opinion in the verification of models, that is, what do people think who know about the process being modeled ... 

The TRAACS 800+ is controlled by a personal computer and the features provided include complete interactive control via keyboard or mouse calculation of results as necessary taking into account baseline or sensitivity drift, graphical output of calibration curves for all calibration types—either Hnear or non-hnear, input facility for sample identification data allowing storage on disc and real-time results together with chart traces on a computer printer. The programs allow easy access to input or data files and connection to other computers, and gives system performance verification to CLP standards and built-in QC charts. 

A successor to PESTANS has recently been developed which allows the user to vary transformation rate and with depth l.e.. It can describe nonhomogeneous (layered) systems (39,111). This successor actually consists of two models - one for transient water flow and one for solute transport. Consequently, much more Input data and CPU time are required to run this two-dimensional (vertical section), numerical solution. The model assumes Langmuir or Freundllch sorption and first-order kinetics referenced to liquid and/or solid phases, and has been evaluated with data from an aldlcarb-contamlnated site In Long Island. Additional verification Is In progress. Because of Its complexity, It would be more appropriate to use this model In a hl er level, rather than a screening level, of hazard assessment. 

Overall, this study indicated that generic simulation of pharmacokinetics at the lead optimization stage could be useful to predict differences in pharmacokinetic parameters of threefold or more based upon minimal measured input data. Fine discrimination of pharmacokinetics (less than twofold) should not be expected due to the uncertainty in the input data at the early stages. It is also apparent that verification of simulations with in vivo data for a few compounds of each new compound class was required to allow an assessment of the error in prediction and to identify invalid model assumptions. 

The system should include built-in checks of the correct entry and processing of data. In order to verify the validation data, some computer systems may periodically be submitted to a defined group of inputs for which the result is known and the result must be kept and filed. If the results are acceptable, the computer system is operating well, but if the results do not match the expected ones, the computer system is not working properly and maintenance is required. In this situation, all the results obtained from the referred computer system since the last validation verification are consided questionable and must be reevaluated. 

Relatively high uncertainty of models, which need verification and validation inputs from monitoring data and... 

If you desire to have a 0.5% water cut specification, you may do so by replacing RWBBL = (Q1/.99). 01 with RWBBL = (Q1/.995). 005 in the equations shown later. RWBBL is the remaining water in the treated crude oil, bpd. Q1 is the dry crude oil rate, bpd. You shall also need to input another KF factor of, say, 150 in place of the defaulted 170 Kf factor. I recommend that you also have proven field data and back-calculate more precisely and exactly what KF factor value is required for a 0.5% water cut. Actual oil operating test runs should be used. A 1-KF value per water cut is not feasible for all the varied crude oils. The 170 factor used for a 1% water cut is, however, a proven one for crude oils ranging from 12 to over 30°API and for viscosities 15 cP and below at the operating temperature. Verification of this defaulted KF factor, 170, is recommended for your particular case. 

The second is concerned with the need to have a complete and sensible chemical mechanism, valid over a wide range of temperature. Even a relatively simple combustion system will involve dozens of reactions, so that a well established reaction rate data base is essential. It is equivalently essential that the results be verified by comparison with detailed experimental data--such as that provided by laser probes. For example, in a study of the ozone decomposition flame (20). it was found that certain alternative but wrong choices of key input parameters were not discernible if flame speed were used as the sole predicted result for verification however, these choices did produce considerable differences in the profiles of the transient oxygen atom concentration and the temperature. 

FJ clusters (in FJ units, or as a model for specified rare-gas atom clusters) continue to be used as a benchmark system for verification and tuning in method development. With the work of Romero et al. [52], there are now proposed global minimum structures and energies available on the internet [53], up to n=309. This considerably extends the Cambridge cluster database [54], but the main body of data comes from EA work that used the known FJ lattices (icosahedral, decahedral, and face-centered cubic) as the input. This is obviously dangerous,... 

There is an explicit rehance on operator involvement in the verification of the capmred data, whereby the software presents the operator with uninterpretable input image for manual resolution. Validation needs to take account of all dimensions of the system, testing with a sufficiently varied selection of input image. Specific considerations for the vahdation of OCR systems are ... 

Data verification (i) verification of input data and (ii) three methods—double-blind, double-key, and program-edit methods. 

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