Improper Calibration Research Articles

Machine Learning Prediction Techniques in the Optimization of Diagnostic Laboratories’ Network Operations

The article presents an outline of the concept of a prototype system allowing for the optimization of inventory management in a diagnostic laboratory on the basis of patients results. The effectiveness of laboratory diagnostics depends largely on the appropriate management of resources and the quality of tests. A functional quality management system is an integral element of every diagnostic laboratory, ensuring reliability and appropriate work standards. This system includes maintaining correct and reliable analytical test results as well as the optimal use of the laboratory equipment’s processing capacity and the appropriate organization of the supply chain—both analytical material and reagents. It is extremely important to avoid situations in which tests cannot be performed due to a lack of reagents, the overloading of analyzers, or improper calibration. Therefore, the accurate prediction of the number of orders is crucial to optimize the laboratory’s operations, both in the short term—for the next few hours and minutes—and in the longer term, even monthly, which will allow for the appropriate planning of reagent stock. As part of the research presented in this article, machine learning methods were used to implement the above functionalities, which allowed for the development of a prototype of a laboratory optimization system using patient test results as a basis.

Application of traceable calibration for gaseous oxidized mercury in air

BackgroundThe current speciation methods for mercury (Hg) measurements are fraught with considerable uncertainty, from sample collection to calibration. High reactivity of gaseous oxidized Hg (GOM) species and their ultra-trace level presence makes them difficult to sample and calibrate. Given that improper calibration may lead to measurement biases, reliable and metrologically traceable calibration methods are required for accurately quantifying GOM in air. In the present study, we applied the recently developed calibration method based on non-thermal plasma oxidation of elemental Hg, to a commercially available Hg air speciation system for actual environmental measurements of GOM for the first time. ResultsHg species such as HgO, HgCl2, and HgBr2 were produced with trace amounts of reactant gases (oxygen and electrolytically produced chlorine and bromine) and the production was driven by plasma-assisted oxidation. The plasma oxidation efficiency of elemental Hg with oxygen was 98.5 ± 7.5 % (k = 2), while that for chlorine and bromine was 96.8 ± 6.9 % (k = 2) and 97.4 ± 9.6 % (k = 2), respectively. The calibration method was tested against the internal permeation (Hg0) source of the Tekran 2537B Hg analyzer on-field by loading HgO to different KCl-coated denuders using the plasma. GOM concentrations were measured using the Tekran speciation system. With internal calibration, concentrations were up to 9.1 % lower than those in plasma calibration, thereby emphasizing the importance of the calibration strategy. Measurement uncertainty (k = 2) further emphasizes this distinction. Internal calibration measurement uncertainty was 36.8 %, while plasma calibration boasted lower uncertainty at 13.8 %. SignificanceThe non-thermal plasma calibration strategy, as a unique and discrete calibration method traceable to the NIST SRM 3133 for ambient air GOM measurements, provide a higher level of confidence in the accuracy of GOM measurements with several advantages over other methods. Calibrations at extreme low concentrations (<100 pg) are possible with this method relevant to ambient air GOM concentrations.

Systematical calibration and validation of discrete element models for fiber reinforced cement treated aggregates

Cracking of pavement materials affects the service performance of pavement structures. It is widely investigated by discrete element method (DEM) because the DEM can present the cracking behavior and mechanism from the mecroscopic perspective. However, improper calibration method is time-consuming and the simulation results are not applicable for different loading conditions. To address this issue, a systematical calibration method is proposed to establish a more reasonable DEM model, and it is used to explore the cracking behavior and mechanism for pavement materials. The fiber reinforced cement treated aggregates (F-CTA) used to construct the pavement base are selected as an example to elaborate the process. First, contacts within the F-CTA were divided into five groups according to physical components. Then, laboratory tests were performed to obtain macro mechanical properties including strength and modulus. Next, the corresponding virtual tests were established and the contact model parameters were calibrated from mono-component to mixture. This calibration method was validated by the three-point bending test incorporating all contact parameters obtained from the above calibrations and the process of cracking evolution was analyzed. Results show that the simulated mechanical responses and crack distribution based on the systematical calibration are in good agreement with laboratory measurements and observations, exhibiting well reliability for calibrating contact parameters of composite material. Young's modulus of the F-CTA is sensitive to the mecro effective modulus in the virtual indirect tensile test while Young's modulus and tensile strength are vulnerable to the mecro cohesion in the virtual uniaxial compression test. The mortar-aggregate and mortar-aggregate interface is relatively weak where cracks are prone to appear. The fibers can optimize the distribution of the crack network. The cracking evolution contains three stages and the second stage has a significant influence on the cracking behavior of the F-CTA.

Honey–Water Content Analysis by Mixing Models Using a Self-Calibrating Microwave Method

Microwave techniques, as an indirect approach, can be applied for analyzing water content in honey by way of permittivity measurements. However, these techniques require proper calibration to accurately perform such indirect evaluation. Improper calibration standards used in this calibration process could naturally result in a reduction in the accuracy and thus the performance of dielectric characterization using microwaves. Self-calibrating microwave techniques can reduce the effects of imprecise standards and thus improve the performance of microwave measurements by bypassing the requirement of calibration standards. In this study, we develop a self-calibrating microwave measurement technique to determine the relative permittivity of honey samples and implement binary mixing models to predict adulteration levels of water-adulterated honey. From this implementation, it is observed that the parallel-capacitance mixing model could efficiently be applied to determine the concentration of water adulteration by examining the differences between absolute values of the real parts of the measured and predicted complex permittivities of adulterated honey.

On the Heuristic Procedure to Determine Processing Parameters in Additive Manufacturing Based on Materials Extrusion.

We present a heuristic procedure for determining key processing parameters (PPs) in materials-extrusion-based additive manufacturing processes. The concept relies on a design-of-experiment approach and consists of eleven “test objects” to determine the optimal combinations of key PPs values, starting with the PPs for printing the first layer and progressing to more complex geometric features, e.g., “bridges”. In each of the test objects, several combinations of the known PPs’ values are used, and only the values resulting in the best printed-part quality are selected for the following tests. The concept is intrinsically insensitive to different artefacts of the additive manufacturing machine (e.g., discrepancies between the nominal and actual nozzle diameters, and improper calibration of the feeding screws) and the optimal values of key PPs for manufacturing defect-free parts under the actual processing conditions can be determined. We validated the proposed procedure for two common commercial polymer feedstock materials, and we show that, by using the proposed procedure, it is possible to reduce the optimization time down to several hours, as well as to reduce the amount of consumed feedstock material. Tensile tests revealed a strong effect of amorphous and semi-crystalline nature of the polymer on the results of optimization. To the best of our knowledge, this is the first attempt to describe a systematic approach for optimizing PPs for materials extrusion-based additive manufacturing processes without relying on statistical data analysis or virtual simulations. The concept was implemented as a web-tool 3DOptimizer®.

Generational differences in trust in digital assistants

Human trust in automation has been studied extensively within safety critical domains (military, aviation, process control, etc.) because harmful consequences are associated with the improper calibration of trust in automated systems in these domains (Parasuraman &amp; Riley, 1997). As such, researchers have worked to identify important factors which help humans build trust in such systems (Hoff &amp; Bashir, 2015). With the explosion of AI in consumer technologies, it is becoming equally critical to understand how humans interact with everyday devices. This study investigated how factors that have been identified to impact trust in automation in safety critical domains influence the trust and use of popular digital assistants (Siri, Cortana, Bixby or Google Now). We conducted an online survey with 278 regular users of digital assistants across three generations (GenX, GenY, and GenZ). The results demonstrate that, even after controlling for dispositional factors (i.e., individual characteristics such as age, culture, gender), GenZ exhibited higher trust in digital assistants than GenX. More interestingly, linear regression analyses revealed a different set of predictors of trust for each generation. Results from this survey have implications for the design of digital assistants.

Spring constant and sensitivity calibration of FluidFM micropipette cantilevers for force spectroscopy measurements

The fluidic force microscope (FluidFM) can be considered as the nanofluidic extension of the atomic force microscope (AFM). This novel instrument facilitates the experimental procedure and data acquisition of force spectroscopy (FS) and is also used for the determination of single-cell adhesion forces (SCFS) and elasticity. FluidFM uses special probes with an integrated nanochannel inside the cantilevers supported by parallel rows of pillars. However, little is known about how the properties of these hollow cantilevers affect the most important parameters which directly scale the obtained spectroscopic data: the inverse optical lever sensitivity (InvOLS) and the spring constant (k). The precise determination of these parameters during calibration is essential in order to gain reliable, comparable and consistent results with SCFS. Demonstrated by our literature survey, the standard error of previously published SCFS results obtained with FluidFM ranges from 11.8% to 50%. The question arises whether this can be accounted for biological diversity or may be the consequence of improper calibration. Thus the aim of our work was to investigate the calibration accuracy of these parameters and their dependence on: (1) the aperture size (2, 4 and 8 µm) of the hollow micropipette type cantilever; (2) the position of the laser spot on the back of the cantilever; (3) the substrate used for calibration (silicon or polystyrene). It was found that both the obtained InvOLS and spring constant values depend significantly on the position of the laser spot. Apart from the theoretically expectable monotonous increase in InvOLS (from the tip to the base of the cantilever, as functions of the laser spot’s position), we discerned a well-defined and reproducible fluctuation, which can be as high as ±30%, regardless of the used aperture size or substrate. The calibration of spring constant also showed an error in the range of −13/+20%, measured at the first 40 µm of the cantilever. Based on our results a calibration strategy is proposed and the optimal laser position which yields the most reliable spring constant values was determined and found to be on the first pair of pillars. Our proposed method helps in reducing the error introduced via improper calibration and thus increases the reliability of subsequent cell adhesion force or elasticity measurements with FluidFM.

Comparing resistivity models from 2D and 1D inversion of frequency domain HEM data over rough terrains: cases study from Iran and Norway

ABSTRACTFrequency domain helicopter-borne electromagnetic (FHEM) surveys have been used as an effective tool for the exploration of underground resources for as long as airborne electromagnetics (AEM) has existed. Large FHEM data sets are commonly interpreted with very fast 1D inversion algorithms that often numerically adequately fit the data sets, even though they yield incorrect results near 2D/3D geological structures. The present study aims to compare 1D and 2D inversion algorithms when applied to the reconstruction of geologically complex regions. We have developed a 2D inversion algorithm incorporating the Levenberg–Marquardt least-squares approach regularised through spatial constraints to retrieve 2D electrical resistivity models associated with arbitrary surface topography. The approach uses a 2D finite element frequency domain solution and a tailored triangular meshing algorithm based on the Ruppert’s Delaunay refinement for the forward modelling. We illustrate how rough topographic effects obscure the FHEM response and affect recovered resistivity models through numerical experiments. We also demonstrate the influence of acquisition frequency and resistivity structure on the topographic effect. In the Appendix, we discuss the FHEM footprint concept from a 2D perspective to assess how 2D effects affect and bias 1D inversion results. Complex 2D synthetic scenarios are presented to compare 1D and 2D inversion in various settings. Two field cases from Norway and Iran are presented to show the model improvements with 2D inversion. For the Norwegian case, the 2D FHEM inversion aligns well with a model retrieved from ground-based electrical resistivity tomography. We show the bias imposed on the 2D inversion of the data set from Iran by improper system calibration.

Short communication: Improving repeatability of cows’ body weight recorded by an automated milking system

We showed a systematic approach to tackle problems present in body weight (BW) data collected in automated milking systems (AMS) at each milking and further data edits, leading to an improved repeatability. The AMS units provide data on numerous characteristics of cows, including body weight (BW), which are potentially useful both for genetic studies and many other purposes (e.g. management). Online collection of BW is prone to errors due to sensor(s) malfunction, improper calibration, sensors’ drift, etc. The cumulative influence of the following adjustments on BW repeatability were studied: no adjustments - the full initial data set; Adjustment I: removing data from one unstable AMS unit; Adjustment II: removing data based on the raw data distribution; Adjustment III: removing data based on the distribution of residuals; Adjustment IV: predicting the removed data points. Additionally, the number of BW records required over the short term to estimate a cow's BW at that time was established. In total, 676 113 BW measurements for 558 cows collected in 6 AMS units were analyzed. The repeatability of the BW obtained using the initial unedited data set was 0.72. After removing one AMS unit the repeatability increased to 0.78. Removing potentially incorrect data based on the raw BW measurements distribution further increased the repeatability to 0.81. Removing data based on the residuals did not increase the repeatability any further; however, substituting missing data by predicted values increased repeatability to 0.82. The proposed procedure may be successfully applied to remove erroneous data and to increase both BW repeatability and overall usefulness of BW data recorded at each milking by AMS. The steps employed to correct the BW records may be potentially useful for other traits collected by AMS during milking.

Using data analytics to capture revenue management benefits in turbulent periods

Revenue management (RM) is adversely affected and its benefits sharply reduced when the system and processes are not adjusted to changing market conditions. A history-based model will take time to adjust – the months it may take to adjust will potentially cost an airline significantly in lost revenue opportunities. This article explores a data analytics-based approach to capture RM benefits in periods of changing market conditions and when the system has lost accuracy because of improper calibration. It describes a real case study with an analytical framework that provided the airline an ability to continue to capture RM benefits while it made its transition to a more comprehensive solution.

Sentiment Lost: The Effect of Projecting the Empirical Pricing Kernel Into a Smaller Filtration Set

Supported by empirical examples, this paper provides a theoretical analysis to show what is the impact of an improper calibration of the physical measure on the estimation of the empirical pricing kernel. While extracting the risk-neutral measure from option data provides a naturally forward looking measure, extracting the real world probability from a stream of historical returns is only partially informative, thus suboptimal with respect to investors’ future beliefs. In virtue of this disalignment, most of papers present in literature are then affected by a non-homogeneity bias.From a probabilistic viewpoints, the missing beliefs are totally unaccessible stopping times on the coarser filtration set. As a consequence, an absolutely continuous local or strict local martingale, once projected on it, becomes continuous with jumps.As a result of a non fully informative physical measure, the proposed empirical pricing kernel is no longer a true martingale, as required by the classical theory, but a strict local martingale with consequences on the probabilistic nature of the relative risk neutral measure.Finally we show how the implied options’ moments help in reducing the degree of inaccessibility and shorten the distance between what is theoretically required and empirically accessible.

Correction on the distortion of Scheimpflug imaging for dynamic central corneal thickness.

The measurement of central corneal thickness (CCT) is important in ophthalmology. Most studies concerned the value at normal status, while rare ones focused on its dynamic changing. The commercial Corvis ST is the only commercial device currently available to visualize the two-dimensional image of dynamic corneal profiles during an air puff indentation. However, the directly observed CCT involves the Scheimpflug distortion, thus misleading the clinical diagnosis. This study aimed to correct the distortion for better measuring the dynamic CCTs. The optical path was first derived to consider the influence of factors on the use of Covis ST. A correction method was then proposed to estimate the CCT at any time during air puff indentation. Simulation results demonstrated the feasibility of the intuitive-feasible calibration for measuring the stationary CCT and indicated the necessity of correction when air puffed. Experiments on three contact lenses and four human corneas verified the prediction that the CCT would be underestimated when the improper calibration was conducted for air and overestimated when it was conducted on contact lenses made of polymethylmethacrylate. Using the proposed method, the CCT was finally observed to increase by 66 ± 34 μm at highest concavity in 48 normal human corneas.

Optimal Color Estimation by Combining Multiple Spectrodensitometer Measurements

Cultural heritage digitization centers worldwide rely on quality management systems to insure the accuracy and consistency of their digitization efforts. Universal to all digitization quality management is the accurate measurement of color test targets. Testing at the Library of Congress has shown that significant variability exists between manufacturers and models of spectrodensitometers reading standard test targets. This variability has been measured commonly as &#x394;E2000 3 and as large as &#x394;E2000 4, leading to improper calibration of imaging systems and less than ideal color accuracy. In this paper we present two robust statistics-based algorithms that estimate the true color from multiple spectrodensitometer measurements, effectively reducing measurement error. The estimated colors can then be regarded as the ground truth for imaging device calibration and performance evaluation. Besides the true color estimation, the second algorithm in particular characterizes the performance of the devices by estimating their bias and variances.

STRIPING NOISE REMOVAL OF IMAGES ACQUIRED BY CBERS 2 CCD CAMERA SENSOR

Abstract. CCD Camera is a multi-spectral sensor that is carried by CBERS 2 satellite. Imaging technique in this sensor is push broom. In images acquired by the CCD Camera, some vertical striping noise can be seen. This is due to the detectors mismatch, inter detector variability, improper calibration of detectors and low signal-to-noise ratio. These noises are more profound in images acquired from the homogeneous surfaces, which are processed at level 2. However, the existence of these noises render the interpretation of the data and extracting information from these images difficult. In this work, spatial moment matching method is proposed to modify these images. In this method, the statistical moments such as mean and standard deviation of columns in each band are used to balance the statistical specifications of the detector array to those of reference values. After the removal of the noise, some periodic diagonal stripes remain in the image where their removal by using the aforementioned method seems impossible. Therefore, to omit them, frequency domain Butterworth notch filter was applied. Finally to evaluate the results, the image statistical moments such as the mean and standard deviation were deployed. The study proves the effectiveness of the method in noise removal.

Predictions of frame compliance and apex morphology in sharp nanoindentations

If all components in a nanoindentation system are well calibrated and a reference material has unique hardness, H and reduced modulus, Er independent of the indentation depths, the load, L and the penetration depth, h in the indentation loading curve of the reference material can be correlated by L=Kh2. Here the constant K is expressed by H, Er and indenter geometry constants. By using H and Er of a fused silica and the Berkovich geometry, an analytical expression for the indentation loading curve could be derived. To compare with this analytical loading curve, experimental indentation data were measured with two commercial nanoindenters. The experimental loading curves shifted leftward or rightward from the analytical loading curve and this depth deviation was attributed to improper calibration of the nanoindenters. Quantitative calibrations of frame compliance and indenter bluntness were tried for the raw nanoindentation data and this resulted in consistent nanoindentation data regardless of the used nanoindenters.

Problems Involved in Using Improper Calibration CRMs in Geochemical Analyses: A Case Study on Mafic Rocks of Boggulakonda Pluton, East of Cuddapah Basin, India

The performance and response of two commonly used certified reference materials (CRMs) for mafic rocks MRG-1 (CANMET, Canada) and JGb-1 (Geological Survey of Japan), both gabbroic in composition, are evaluated for a set of mafic plutonic rocks (gabbros) from Boggulakonda (BK) occurring to the east of Proterozoic Cuddapah Basin, South India. In this study, quadrupole-inductively coupled plasma mass spectrometry was used to generate trace and rare earth element (REE) data using MRG-1 and JGb-1 as calibration standards separately for a comparative study. It is found that the two CRMs show highly variable major, minor and trace element compositions except RREE. When used for BK gabbros, JGb-1 has yielded better results than MRG-1 with respect to trace elements (HFS elements Nb and Ta in specific), whereas a good agreement was observed between both CRMs for REE concentrations when compared with certified data. The Nb- Ta element distribution patterns for BK gabbros in the spidergrams plotted using JGb-1 as CRM show relatively prominent and pronounced negatively spiking anomalies with reference to Th, whereas using MRG-1 as calibration CRM yielded unresolved and unclear Nb-Ta anomalies in the present study. The geochemistry (major, minor and trace) of MRG-1 is different when compared to that of JGb-1. Probably this is reason for the disparity in the results obtained by inductively coupled plasma mass spectrometry on mafic plutonic rocks. The study conducted on the BK gabbros, reveals better and reliable results using JGb-1 as CRM. This study also reveals the importance of using proper CRM for calibration having closely matching trace and REE concentrations in addition to major and minor elements, for obtaining reliable data in geological samples.

PERBANDINGAN INTERPOLASI SPASIAL DENGAN METODE ORDINARY DAN ROBUST KRIGING PADA DATA SPASIAL BERPENCILAN (Studi Kasus: Curah Hujan di Kabupaten Karangasem)

Kriging as optimal spatial interpolation can produce less precise predictive value if there are outliers among the data. Outliers defined as extreme observation value of the other observation values that may be caused by faulty record keeping, improper calibration equipment or other posibbilities. Development of Ordinary Kriging method is Robust Kriging which transforms weight of clasic variogram thus become variogram that robust to outlier. The spatial data that used in this research is the spatial data that contains outliers and meet the assumptions of Ordinary Kriging. The analysis showed that the estimation value of Ordinary Kriging and Robust Kriging method is not much different in terms of Mean Absolute Deviation values which generated by both methods. An increase value of Mean Absolute Deviation on Robust Kriging estimation does not indicate that the Ordinary Kriging method is more precise than Robust Kriging method in the rainfall estimates of Amlapura control point remind that Robust Kriging does not eliminate the data of observation such as the Ordinary Kriging method. In general, Ordinary Kriging and Robust Kriging method can estimate the rainfall value of Amlapura control point quite well although it is not able to cover the changes in rainfall value that occurs due to the behavior geographic data.

Worldwide proficiency test for routine analysis of δ2H and δ18O in water by isotope‐ratio mass spectrometry and laser absorption spectroscopy

The interpretation of stable hydrogen and oxygen isotope data in isotope hydrology relies on accurate, high-precision analytical measurements of the (2)H:(1)H and (18)O:(16)O ratios in liquid H(2)O samples. A synthesis of the International Atomic Energy Agency (IAEA) worldwide proficiency test for analytical laboratories conducting routine analysis of δ(2)H and δ(18)O values in water (WICO2011) by isotope-ratio mass spectrometry (IRMS) and laser absorption spectroscopy (LAS) technology has been carried out. This test revealed that >96% of the 160 laboratory submissions provided acceptable results within ±2‰ for δ(2) H values and ±0.2‰ for δ(18)O values of the established reference values for four test waters, and no difference in outcomes based on IRMS vs. LAS technology was found for good performing laboratories. The leading cause of outliers appeared to be improper calibration or compromised storage of laboratory standard and primary reference waters; hence the importance of judicious storage of lab standards cannot be understated. The deprecated practice of single standard normalization was identified as a problem for some laboratories. We further recommend that laboratories strive to report parsimonious long-term precisions based upon control standards, and to improve quantification and correction for LAS instrumental drift and inter-sample carryover effects.

Improved Methods for PCA-Based Reconstructions: Case Study Using the Steig et al. (2009) Antarctic Temperature Reconstruction

Abstract A detailed analysis is presented of a recently published Antarctic temperature reconstruction that combines satellite and ground information using a regularized expectation–maximization algorithm. Though the general reconstruction concept has merit, it is susceptible to spurious results for both temperature trends and patterns. The deficiencies include the following: (i) improper calibration of satellite data; (ii) improper determination of spatial structure during infilling; and (iii) suboptimal determination of regularization parameters, particularly with respect to satellite principal component retention. This study proposes two methods to resolve these issues. One utilizes temporal relationships between the satellite and ground data; the other combines ground data with only the spatial component of the satellite data. Both improved methods yield similar results that disagree with the previous method in several aspects. Rather than finding warming concentrated in West Antarctica, the authors find warming over the period of 1957–2006 to be concentrated in the peninsula (≈0.35°C decade−1). This study also shows average trends for the continent, East Antarctica, and West Antarctica that are half or less than that found using the unimproved method. Notably, though the authors find warming in West Antarctica to be smaller in magnitude and find that statistically significant warming extends at least as far as Marie Byrd Land. This study also finds differences in the seasonal patterns of temperature change, with winter and fall showing the largest differences and spring and summer showing negligible differences outside of the peninsula.

Cross-Validating FRAP and FCS to Quantify the Impact of Photobleaching on In Vivo Binding Estimates

Binding can now be quantified in live cells, but the accuracy of such measurements remains uncertain. To address this uncertainty, we compare fluorescence recovery after photobleaching (FRAP) and fluorescence correlation spectroscopy (FCS) measurements of the binding kinetics of a transcription factor, the glucocorticoid receptor, in the nuclei of live cells. We find that the binding residence time measured by FRAP is 15 times longer than that obtained by FCS. We show that this discrepancy is not likely due to the significant differences in concentrations typically used for FRAP and FCS, nor is it likely due to spatial heterogeneity of the nucleus, improper calibration of the FCS focal volume, or the intentional FRAP photobleach. Instead, our data indicate that photobleaching of bound molecules in FCS is mainly responsible. When this effect is minimized, FRAP and FCS measurements nearly agree, although cross-validation by other approaches is now required to rule out mutual errors. Our results demonstrate the necessity of a photobleach correction for FCS measurements of GFP-tagged molecules that are bound for >0.25 s, and represent an important step forward in establishing a gold standard for in vivo binding measurements.