Impact Of Experimental Error Research Articles

Study on the Test Error of Silt Dynamic Characteristic and Its Influence on the Peak Ground Acceleration

In order to grasp the nonlinear experimental errors of dynamic shear modulus ratio (DSMR) and damping ratio (DR), as well as the current level of resonant column testing, the GZ-1 resonant column instrument was used to study the probability statistical indicators, basic laws, and the impact of experimental errors on peak acceleration of DSMR and DR of silt under 8 typical shear strains. The results show that, firstly, the DSMR and DR of silt under different shear strains obey normal distributions. Secondly, there is no significant difference between the reference range of the standard deviation of the DSMR and DR of silt and the outer envelope line. This result indicates that the dispersion of experimental errors for the same person is very small. Thirdly, in medium to hard sites, the influence of experimental errors in DSMR and DR on peak acceleration can be ignored. And the impact of DR test errors on peak acceleration should not be ignored on soft ground with a probability range exceeding 95%. Overall, the testing accuracy of the testing personnel proved to generally meet the requirements, while in other special cases, it is necessary to increase the number of parallel tests and improve the testing technology. Otherwise, it will cause certain risks to the estimation of seismic input for engineering structures. Doi: 10.28991/HIJ-2023-04-01-05 Full Text: PDF

Invariant Procedure for Error Sensitivity Analysis Applied to Cyclic Creep Modelling

Cyclic creep, also known as ratcheting, is a progressive accumulation of inelastic strain under cyclic stress-controlled loading. This mechanical effect is of great importance for numerous applications. In engineering practice, phenomenological models of cyclic creep are calibrated against a limited set of macroscopic test data. Since the testing results are prone to systematic and non-systematic experimental errors, the impact of experimental errors on the quality of simulation has to be analysed. A simple inspection procedure is demonstrated and tested. Based on the Monte Carlo computations, it allows for analysis of error propagation through the simulation cycle. The focus of the paper is on the independence of the procedure from the chosen model parametrization. For demonstration purposes, cyclic creep of VT6 alloy is simulated. The corresponding macroscopic constitutive equations are based on the second Ohno-Wang model, combined with refined rule of isotropic hardening. Two different parametrizations are introduced to show that the procedure predicts the same results for both of them.

Comparison of Ordinary, Weighted, and Generalized Least-Squares Straight-Line Calibrations for LC-MS-MS, GC-MS, HPLC, GC, and Enzymatic Assay

The impact of experimental errors in one or both variables on the use of linear least-squares was investigated for method calibrations (response = intercept plus slope times concentration, or equivalently, Y = a(1) + a(2)X ) frequently used in analytical toxicology. In principle, the most reliable calibrations should consider errors from all sources, but consideration of concentration (X) uncertainties has not been common due to complex fitting algorithm requirements. Data were obtained for liquid chromatography-tandem mass spectrometry, gas chromatography-mass spectrometry, high-performance liquid chromatography, gas chromatography, and enzymatic assay. The required experimental uncertainties in response were obtained from replicate measurements. The required experimental uncertainties in concentration were determined from manufacturers' furnished uncertainties in stock solutions coupled with uncertainties imparted by dilution techniques. The mathematical fitting techniques used in the investigation were ordinary least-squares, weighted least-squares (WOLS), and generalized least-squares (GLS). GLS best-fit results, obtained with an efficient iteration algorithm implemented in a spreadsheet format, are used with a modified WOLS-based formula to derive reliable uncertainties in calculated concentrations. It was found that while the values of the intercepts and slopes were not markedly different for the different techniques, the derived uncertainties in parameters were different. Such differences can significantly affect the predicted uncertainties in concentrations derived from the use of the different linear least-squares equations.

Simple method to analyze SNP‐based association studies using DNA pools

Association studies using DNA pools are in principle powerful and efficient to detect association between a marker allele and disease status, e.g., in a case-control design. A common observation with the use of DNA pools is that the two alleles at a polymorphic SNP locus are not amplified in equal amounts in heterozygous individuals. In addition, there are pool-specific experimental errors so that there is variation in the estimates of allele frequencies from different pools that are from the same individuals. As a result of these additional sources of variation, the outcome of an experiment is an estimated count of alleles rather than the usual outcome in terms of observed counts. In this study, we show analytically and by computer simulation that unequal amplification should be taken into account when testing for differences in allele frequencies between pools, and suggest a simple modification of the standard chi(2) test to control the type I error rate in the presence of experimental error variation. The impact of experimental errors on the power of association studies is shown.

Statistical tolerancing using designed experiments in a noisy environment

We consider the method of designed experiments for statistical tolerance analysis, and study the impact of experimental error on its results. It is observed that presence of random error in the experiment environment (e.g. laboratory) could introduce bias in the moment estimators and increases their respective variances. We propose adjustments to the method that would reduce the bias as well as the variance of these estimators. A numerical example is presented.

Finite element based Kerr electro-optic reconstruction of space charge

Recently we used the onion peeling method to reconstruct the axisymmetric electric field distribution of point/plane electrodes from Kerr electro-optic measurements. The method accurately reconstructed the electric field from numerically generated data. However in the presence of experimental noise the performance was less satisfactory. The measurements were especially noisy and unstable near the needle tip which is also the interesting region since most charge injection initiates here. We develop a new algorithm for Kerr electro-optic reconstruction of space charge in axisymmetric point/plane electrode geometries. The algorithm is built on the finite element method (FEM) for Poisson's equation and will be called finite element based Kerr electro-optic reconstruction (FEBKER) hereafter. FEBKER calculates the space charge density directly to avoid the numerical problems associated with taking the divergence of the electric field, uses single parameter light intensity measurements to enable transient analysis, which otherwise is difficult since multiple parameter intensity measurements are slow due to the rotation of polarizers, and is capable of reconstruction even when the number and/or position of measurements are limited by the electrodes and/or the experimental setup. The performance of the algorithm is tested on synthetic Kerr electro-optic data obtained for an axisymmetric point/plane electrode geometry in transformer oil with specified space charge density distributions. The impact of experimental error is analyzed by incorporating random error to the synthetic data. Regularization techniques that decrease the impact of experimental error are applied. In principle FEBKER is applicable to arbitrary three-dimensional geometries as well.

Use and Limitations of Modular Metabolic Control Analysis in Medicine and Biotechnology

By Metabolic Control Analysis (MCA), it has been shown that control on flux is in most cases shared by several enzymes rather than concentrated on one “rate-limiting step.” This analysis also allows the quantification of the control exerted by groups (modules) of enzymes. The modules may correspond to spatial compartments or to functional units. A brief outline of the modular approach to MCA is given. The criteria by which the system can be modularized and the concept of monofunctional unit are explained. Various studies in which control analysis was applied to biotechnological and medical issues are reviewed. In particular, MCA has turned out to be helpful in the assessment of the severity of enzyme deficiencies. Another application is the search for target enzymes or enzyme groups where pharmaceuticals can suppress the metabolism of pathogenic microorganisms most. In biotechnology, modular and “traditional” control analyses are valuable tools for choosing the most promising targets for genetic manipulation so as to increase a biosynthetic flux. As control coefficients are linear approximations, the effect of enhancing the activities of enzymes to a larger extent is often overestimated. Further limitations such as the restriction to stationary states, uncertainties due to spatial heterogeneities and the impact of experimental error are discussed.

Deconvolution of combinatorial libraries for drug discovery: a model system.

Iterative synthesis and screening strategies have recently been used to identify unique active molecules from complex synthetic combinatorial libraries. These techniques have many advantages over traditional screening methods, including the potential to screen large numbers of compounds to identify an active molecule while avoiding analytical separations and structural determination of unknown compounds. It is not clear, however, whether these techniques identify the most active molecular species in the mixtures and, if so, how often. Two key factors which may affect success of the selection process are the presence of many active compounds in the library with a range of activities and the chosen order of unrandomization. The importance of these factors has not been previously studied. Moreover, the impact of experimental errors in determination of subset activities or in randomization during library synthesis is not known. We describe here a model system based on oligonucleotide hybridization that addresses these questions using computer simulations. The results suggested that, within achievable experimental and library synthesis error, iterative deconvolution methods generally find either the best molecule or one with activity very close to the best. The presence of many active compounds in a library influenced the profile of subset activities, but did not preclude selection of a molecule with near optimal activity.