Measurement Uncertainty Research Articles

Separation of measurement uncertainty into quantum and classical parts based on Kirkwood–Dirac quasiprobability and generalized entropy

Abstract Measurement in quantum mechanics is notoriously unpredictable. The uncertainty in quantum measurement can arise from the noncommutativity between the state and the measurement basis which is intrinsically quantum, but it may also be of classical origin due to the agent’s ignorance. It is of fundamental as well as practical importance to cleanly separate the two contributions which can be directly accessed using laboratory operations. Here, we propose two ways of decomposition of the total measurement uncertainty additively into quantum and classical parts. In the two decompositions, the total uncertainty of a measurement described by a positive-operator-valued measure (POVM) over a quantum state is quantified respectively by two generalized nonadditive entropies of the measurement outcomes; the quantum parts are identified, respectively, by the nonreality and the nonclassicality—which captures simultaneously both the nonreality and negativity—of the associated generalized Kirkwood–Dirac quasiprobability relative to the POVM of interest and a projection-valued measure and maximized over all possible choices of the latter; and, the remaining uncertainties are identified as the classical parts. Both decompositions are shown to satisfy a few plausible requirements. The minimum of the total measurement uncertainties in the two decompositions over all POVM measurements are given by the impurity of the quantum state quantified by certain generalized quantum entropies, and are entirely classical. We argue that nonvanishing genuine quantum uncertainty in the two decompositions are sufficient and necessary to prove quantum contextuality via weak measurement with postselection. Finally, we suggest that the genuine quantum uncertainty is a manifestation of a specific measurement disturbance.

Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

Due to its flexibility, cloud computing has become essential in modern operational schemes. However, the effective management of cloud resources to ensure cost-effectiveness and maintain high performance presents significant challenges. The pay-as-you-go pricing model, while convenient, can lead to escalated expenses and hinder long-term planning. Consequently, FinOps advocates proactive management strategies, with resource usage prediction emerging as a crucial optimization category. In this research, we introduce the multi-time series forecasting system (MSFS), a novel approach for data-driven resource optimization alongside the hybrid ensemble anomaly detection algorithm (HEADA). Our method prioritizes the concept-centric approach, focusing on factors such as prediction uncertainty, interpretability and domain-specific measures. Furthermore, we introduce the similarity-based time-series grouping (STG) method as a core component of MSFS for optimizing multi-time series forecasting, ensuring its scalability with the rapid growth of the cloud environment. The experiments performed demonstrate that our group-specific forecasting model (GSFM) approach enabled MSFS to achieve a significant cost reduction of up to 44%.

Ethanol stability from nine years of a blind quality control program in blood alcohol analysis.

A blind quality control (BQC) program in blood alcohol analysis was implemented at the Houston Forensic Science Center (HFSC) in September 2015. By mimicking authentic toxicology blood evidence, the laboratory can perform a concurrent evaluation of their technical and administrative casework procedures and test the accuracy and reliability of their volatile analysis method in a format that is blinded to the analyst. From September 2015 to November 2023, HFSC's Quality Division submitted 1228 antemortem whole blood samples: 292 ethanol-negative samples and 936 ethanol-positive samples at sixteen target concentrations (0.051, 0.080, 0.100, 0.110, 0.120, 0.130, 0.150, 0.160, 0.170, 0.180, 0.190, 0.200, 0.230, 0.240, 0.250, and 0.260 g/dL). A second, unopened blood tube in 168 of the 1228 BQCs was also analyzed after 721-1140 days: 24 ethanol-negative samples and 144 ethanol-positive samples at five target concentrations (0.080, 0.100, 0.130, 0.180, and 0.240 g/dL). All 316 ethanol-negative samples remained negative. After 42-758 days, the average (median, range) change in ethanol concentration of the 936 positive samples was -1.4% (-1.3%, -12.0% to +8.4%) with a statistically significant difference (P < 0.001) observed for the gradual decline in blood alcohol concentration (BAC) over time. The average BAC percentage differences per target concentration, ranged from -6.4% (-0.008 g/dL) to +5.7% (+0.011 g/dL), were within HFSC's current measurement uncertainty (9.4% at k=3), showing no apparent correlation between the change in ethanol and the theoretical target concentration. As the analysis time between the two blood specimens from the same evidence kit extended, the loss in ethanol significantly increased (P < 0.001).

Efficient ensemble uncertainty estimation in Gaussian processes regression

Abstract Reliable uncertainty measures are required when using data-based machine learning interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse Gaussian process regression (GPR) type MLIPs a stochastic uncertainty measure akin to the query-by-committee approach often used in conjunction with neural network based MLIPs. The uncertainty measure is coined ‘label noise’ ensemble uncertainty as it emerges from adding noise to the energy labels in the training data. We find that this method of calculating an ensemble uncertainty is as well calibrated as the one obtained from the closed-form expression for the posterior variance when the sparse GPR is treated as a projected process. Comparing the two methods, our proposed ensemble uncertainty is, however, faster to evaluate than the closed-form expression. Finally, we demonstrate that the proposed uncertainty measure acts better to support a Bayesian search for optimal structure of Au20 clusters.

USING RETROSPECTIVE DATA TO ASSESS THE UNCERTAINTY OF INDICATORS OF BIOLOGICAL SAFETY OF FOOD AND FEED RAW MATERIALS

The results of the analysis of the advantages and disadvantages of the use of retrospective data for the implementation of the evaluation of uncertainty in the measurement of indicators of biological safety of food and feed raw materials are given. The model measurement procedure used quantitative determination of genetically modified organisms by means of polymerase chain reaction in real time 250 measurement results of certified reference material with a content of 0.1% genetically modified soybean line obtained under conditions of intra-laboratory reproducibility and 10 results of relevant inter-laboratory comparisons with the participation accredited provider. The suitability of retrospective data was assessed by the Shapiro-Wilk criterion and statistical controllability analysis using standardized score charts and modified Shewhart charts, uncertainty by 4 methods described in general and branch international guidelines and regulations. The results of using a combination of modified Shewhart maps demonstrate the acceptability of this approach for establishing the suitability of retrospective measurement results as input data for uncertainty assessment. By integrating additional "fitness limits" to a set of standardized control limits, this analysis acquires the ability to simultaneously establish not only stability, through the interpretation of geometric patterns, but also compliance with specific criteria. Using this approach, objective evidence of the suitability of retrospective data for estimating measurement uncertainty was obtained. The distribution of retrospective data obtained under conditions of intralaboratory reproducibility is assumed to be normal. As a result of the assessment of statistical controllability and compliance with the established criteria, objective evidence of the suitability of the data for uncertainty assessment was obtained. Analysis of uncertainty intervals of subgroups with the volume of 25 measurements, using 4 assessment methods, demonstrated significant variability in the results of measuring biological safety indicators. The uncertainty interval could narrow or widen by almost 100% of the value within fifty measurements. Median values obtained using different uncertainty estimation methods were 2.69%, 22.69%, 39.23, and 43.08%. This variability is due to the limited coverage of sources of uncertainty, which is inherent in each of the analyzed methods. The most optimal method is the evaluation based on the routine measurement of the certified reference material. Retrospective results of routine measurement of certified reference material and interlaboratory comparisons are an effective source of input data for assessing the uncertainty of the results of measuring biological safety indicators of agricultural raw materials. There is a need for further research to establish an assessment model that will provide an opportunity to take into account the optimal number of influential sources of uncertainty that are inherent in biological systems and cannot be simultaneously taken into account using existing approaches offered by standard assessment methods.

Temperature effect on seawater fCO2 revisited: theoretical basis, uncertainty analysis and implications for parameterising carbonic acid equilibrium constants

Abstract. The sensitivity of the fugacity of carbon dioxide in seawater (fCO2) to temperature (denoted υ, reported in % °C−1) is critical for the accurate fCO2 measurements needed to build global carbon budgets and for understanding the drivers of air–sea CO2 flux variability across the ocean. However, understanding and computing υ have been restricted to either using empirical functions fitted to experimental data or determining it as an emergent property of a fully resolved marine carbonate system, and these two approaches are not consistent with each other. The lack of a theoretical basis and an uncertainty estimate for υ has hindered resolving this discrepancy. Here, we develop a new approach for calculating the temperature sensitivity of fCO2 based on the equations governing the marine carbonate system and the van 't Hoff equation. This shows that, to first order, ln (fCO2) should be proportional to 1/tK (where tK is temperature in kelvin), rather than to temperature, as has previously been assumed. This new approach is, to first order, consistent with calculations from a fully resolved marine carbonate system, which we have incorporated into the PyCO2SYS software. Agreement with experimental data is less convincing but remains inconclusive due to the scarcity of direct measurements of υ, particularly above 25 °C. However, the new approach is consistent with field data, performing better than any other approach for adjusting fCO2 by up to 10 °C if spatiotemporal variability in its single fitted coefficient is accounted for. The uncertainty in υ arising from only measurement uncertainty in the main experimental dataset where υ has been directly measured is in the order of 0.04 % °C−1, which corresponds to a 0.04 % uncertainty in fCO2 adjusted by +1 °C. However, spatiotemporal variability in υ is several times greater than this, so the true uncertainty due to the temperature adjustment in fCO2 adjusted by +1 °C using the most widely used constant υ value is around 0.24 %. This can be reduced to around 0.06 % using the new approach proposed here, and this could be further reduced by more measurements. The spatiotemporal variability in υ arises mostly from the equilibrium constants for CO2 solubility and carbonic acid dissociation (K1∗ and K2∗), and its magnitude varies significantly depending on which parameterisation is used for K1∗ and K2∗. Seawater fCO2 can be measured accurately enough that additional experiments should be able to detect spatiotemporal variability in υ and distinguish between different parameterisations for K1∗ and K2∗. Because the most widely used constant υ was coincidentally measured from seawater with roughly global average υ, our results are unlikely to significantly affect global air–sea CO2 flux budgets, but they may have more important implications for regional budgets and studies that adjust by larger temperature differences.

QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories.

Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices. QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively. Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size andoffering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results. In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

COADREADx: A comprehensive algorithmic dissection of colorectal cancer unravels salient biomarkers and actionable insights into its discrete progression.

Colorectal cancer is a common condition with an uncommon burden of disease, heterogeneity in manifestation, and no definitive treatment in the advanced stages. Renewed efforts to unravel the genetic drivers of colorectal cancer progression are paramount. Early-stage detection contributes to the success of cancer therapy and increases the likelihood of a favorable prognosis. Here, we have executed a comprehensive computational workflow aimed at uncovering the discrete stagewise genomic drivers of colorectal cancer progression. Using the TCGA COADREAD expression data and clinical metadata, we constructed stage-specific linear models as well as contrast models to identify stage-salient differentially expressed genes. Stage-salient differentially expressed genes with a significant monotone trend of expression across the stages were identified as progression-significant biomarkers. The stage-salient genes were benchmarked using normals-augmented dataset, and cross-referenced with existing knowledge. The candidate biomarkers were used to construct the feature space for learning an optimal model for the digital screening of early-stage colorectal cancers. The candidate biomarkers were also examined for constructing a prognostic model based on survival analysis. Among the biomarkers identified are: CRLF1, CALB2, STAC2, UCHL1, KCNG1 (stage-I salient), KLHL34, LPHN3, GREM2, ADCY5, PLAC2, DMRT3 (stage-II salient), PIGR, HABP2, SLC26A9 (stage-III salient), GABRD, DKK1, DLX3, CST6, HOTAIR (stage-IV salient), and CDH3, KRT80, AADACL2, OTOP2, FAM135B, HSP90AB1 (top linear model genes). In particular the study yielded 31 genes that are progression-significant such as ESM1, DKK1, SPDYC, IGFBP1, BIRC7, NKD1, CXCL13, VGLL1, PLAC1, SPERT, UPK2, and interestingly three members of the LY6G6 family. Significant monotonic linear model genes included HIGD1A, ACADS, PEX26, and SPIB. A feature space of just seven biomarkers, namely ESM1, DHRS7C, OTOP3, AADACL2, LPHN3, GABRD, and LPAR1, was sufficient to optimize a RandomForest model that achieved > 98% balanced accuracy (and performant recall) of cancer vs. normal on external validation. Design of an optimal multivariate model based on survival analysis yielded a prognostic panel of three stage-IV salient genes, namely HOTAIR, GABRD, and DKK1. Based on the above sparse signatures, we have developed COADREADx, a web-server for potentially assisting colorectal cancer screening and patient risk stratification. COADREADx provides uncertainty measures for its predictions and needs clinical validation. It has been deployed for experimental non-commercial use at: https://apalanialab.shinyapps.io/coadreadx/.

Ecophysiological variables retrieval and early stress detection: insights from a synthetic spatial scaling exercise

ABSTRACT The ability to access physiologically driven signals, such as surface temperature, photochemical reflectance index (PRI), and sun-induced chlorophyll fluorescence (SIF), through remote sensing (RS) are exciting developments for vegetation studies. Accessing this ecophysiological information requires considering processes operating at scales from the top-of-the-canopy to the photosystems, adding complexity compared to reflectance index-based approaches. To investigate the maturity and knowledge of the growing RS community in this area, COST Action CA17134 SENSECO organized a Spatial Scaling Challenge (SSC). Challenge participants were asked to retrieve four key ecophysiological variables for a field each of maize and wheat from a simulated field campaign: leaf area index (LAI), leaf chlorophyll content (C ab), maximum carboxylation rate (V cmax,25), and non-photochemical quenching (NPQ). The simulated campaign data included hyperspectral optical, thermal and SIF imagery, together with ground sampling of the four variables. Non-parametric methods that combined multiple spectral domains and field measurements were used most often, thereby indirectly performing the top-of-the-canopy to photosystem scaling. LAI and C ab were reliably retrieved in most cases, whereas V cmax,25 and NPQ were less accurately estimated and demanded information ancillary to RS imagery. The factors considered least by participants were the biophysical and physiological canopy vertical profiles, the spatial mismatch between RS sensors, the temporal mismatch between field sampling and RS acquisition, and measurement uncertainty. Furthermore, few participants developed NPQ maps into stress maps or provided a deeper analysis of their parameter retrievals. The SSC shows that, despite advances in statistical and physically based models, the vegetation RS community should improve how field and RS data are integrated and scaled in space and time. We expect this work will guide newcomers and support robust advances in this research field.

No evidence for interstellar fireballs in the CNEOS database

Context. The detection of interstellar meteors, especially meteorite-dropping meteoroids, would be transformative, as this would enable direct sampling of material from other stellar systems on Earth. One candidate is the fireball observed by U.S. government sensors on January 8, 2014. It has been claimed that fragments of this meteoroid have been recovered from the ocean floor near Papua New Guinea and that they support an extrasolar origin. Based on its parameters reported in the Center for Near Earth Object Studies (CNEOS) catalog, the fireball exhibits a hyperbolic excess velocity that indicates an interstellar origin; however, the catalog does not report parameter uncertainties. Aims. To achieve a clear confirmation of the fireball’s interstellar origin, we assessed the underlying error distributions of the catalog data. Our aim was also to confirm whether the fragments of this meteoroid survived passage through the atmosphere and assess all conditions needed to unambiguously determine the fragments’ origin. Methods. We approached the investigation of the entire catalog using statistical analyses and modeling, and we provide a comprehensive analysis of the individual hyperbolic CNEOS cases. Results. We have developed several independent arguments indicating substantial uncertainties in the velocity and radiant position of the CNEOS events. We determined that all the hyperbolic fireballs exhibit significant deviations from the majority of the events in one of their velocity components, and we show that such mismeasurements can produce spurious parameters. According to our estimation of the speed measurement uncertainty for the catalog, we found that it is highly probable that such a catalog containing only Sun-bound meteors would show at least one event that appears highly unlikely to be Sun-bound. We also establish that it is unlikely that any fragments from a fireball traveling at the high inferred velocities could survive passage through the atmosphere. When assuming a much lower velocity, some fragments of this meteoroid could survive; however, they would be of a common Solar System origin and thus highly probable to be indistinguishable from the quantity of other local micrometeorites that have gradually accumulated on the sea floor. Conclusions. We conclude that there is no evidence in the CNEOS data to confirm or reject the interstellar origin of any of the nominally hyperbolic fireballs in the CNEOS catalog. Therefore, the claim of an interstellar origin for the fireball recorded over Papua New Guinea in 2014 remains unsubstantiated. We have also gathered arguments that refute the claim that the collected spherules from the sea floor originated in the body of this fireball.

Novel analysis of alignment error on spherical Fizeau interferometer and uncertainty evaluation of sphericity calibration system based on random ball test

The National Metrology Institute of Japan (NMIJ) has developed a high-precision sphericity calibration system utilizing a Fizeau interferometer and conducted a thorough evaluation of the system's measurement uncertainty. Generally, there are two principal sources of uncertainty in the measurement outcomes when utilizing a spherical Fizeau interferometer. First, the accuracy of the system is significantly influenced by the prior knowledge of the reference sphere surface's absolute profile. To address this, the study established a straightforward yet effective calibration system for the reference sphere surface using a random ball test method. Second, the system's precision is affected by misalignment aberration, which occurs when there is any lateral or longitudinal displacement of the test sphere from the confocal position relative to the reference sphere. This misalignment can introduce both high-order shape errors (misalignment aberrations) and low-order shape errors (alignment errors). Through analytical consideration of an observation coordinate system on the camera plane, this study delves into misalignment aberrations, suggesting that the impact of misalignment should be determined experimentally for each reference sphere unit due to potential imperfections that may be revealed by misalignment. Furthermore, the study proposes that uncertainties related to misalignment aberrations are theoretically confirmed to be smaller than previous studies by conducting an in-depth uncertainty analysis of the misalignment, with a focus on the observation coordinate system on the camera plane. Notably, the research demonstrates that a measurement uncertainty level of λ/100 is achievable, maintaining a broader tolerance for misalignment than previously reported studies. The uncertainty of calibration of the reference sphere unit's absolute profile was 4.2 nm and the uncertainty of sample measurement was determined to be 4.6 nm with misalignment tolerance of ±λ/10. This advancement marks a stride toward improving the accuracy and reliability of sphericity measurements, offering potential for widespread application in precision engineering and metrology.

Measurement Uncertainty and Validation for Quantification of Marijuana Metabolite: (−)-11-nor-9-Carboxy-Δ9-THC in Human Urine by GC-MS/MS

Background: Since the International Olympic Committee (IOC) was established, sports doping control analyses have revealed a high rate of positive cases for cannabinoids. Cannabinoids were banned in all sports where they were used in competition as per the Prohibited List published annually. Further, it was also included in the threshold drug category. Consequently, developing a reliable method for urine Cannabinoids metabolite quantification plays a pivotal role in sports dope testing. Objective: This work aimed to develop and validate a reliable, cost-effective, robust gas chromatography-tandem mass spectrometry method for detecting (−)-11-nor-9-Carboxy-Δ9- THC component in human urine samples, in compliance with ICH and WADA guidelines. Method: The sample preparation was done by enzymatic hydrolysis for deconjugation, further proceeded with solid phase extraction (SPE), liquid-liquid extraction (LLE), and using an XAD2 column, and N-methyl trimethylsilyl trifluoroacetamide (MSTFA) for derivatization. Results: The linearity was obtained in a range of 50–300 ng/mL, and the correlation coefficient was found to be higher than 0.99. Throughout the entire validation study, the difference in Retention Time (RT) for the analyte, including the Internal Standard (IS), was shown to be less than 1.0%. The quantification limit (LOQ) was calculated at a level of 50 ng/mL in human urine samples for the 3 most abundant ion transitions. The detection limit (LOD) was established at 4 ng/mL. Conclusion: The accuracy, precision, linearity, recovery, quantification limit, and selectivity by GC-MS/MS technique were found acceptable and well satisfactory while following the ICH guidelines. The developed method has been proven to be fit for purpose in accordance with the enforced Guidelines of WADA.

Entropy Engineering of 2D Materials.

Entropy, a measure of disorder or uncertainty in the thermodynamics system, has been widely used to confer desirable functions to alloys and ceramics. The incorporation of three or more principal elements into a single sublattice increases the entropy to medium and high levels, imparting these materials a mélange of advanced mechanical and catalytic properties. In particular, when scaling down the dimensionality of crystals from bulk to the 2D space, the interplay between entropy stabilization and quantum confinement offers enticing opportunities for exploring new fundamental science and applications, since the structural ordering, phase stability, and local electronic states of these distorted 2D materials get significantly reshaped. During the last few years, the large family of high-entropy 2D materials is rapidly expanding to host MXenes, hydrotalcites, chalcogenides, metal-organic frameworks (MOFs), and many other uncharted members. Here, the recent advances in this dynamic field are reviewed, elucidating the influence of entropy on the fundamental properties and underlying elementary mechanisms of 2D materials. In particular, their structure-property relationships resulting from theoretical predictions and experimental findings are discussed. Furthermore, an outlook on the key challenges and opportunities of such an emerging field of 2D materials is also provided.

Heat Capacity of Fish Oil at High Temperatures and Pressures

This paper reports new results from studies of the isobaric heat capacities of OMEGA-3 “950” fish oil provided by SOLGAR INC (USA) at high temperatures and high pressures. The measurements were carried out on a differential scanning calorimeter (ITs-400) with an automatic data acquisition system in the temperature range from 298.15 to 473.15 K and at pressures from 0.098 to 39.2 MPa. The coverage factor for the 95% confidence level for a two-way coverage interval is assumed to be 2. The expanded measurement uncertainty of heat capacity is estimated to be 2.4%, pressure is estimated to be 0.05% and temperature is estimated to be 15 mK. Experimental data on the fish oil isobaric heat capacity as a function of temperature and pressure are approximated by the correlation equation proposed in the work. At atmospheric pressure, the deviations between those calculated by the correlation equation and the current measured data on isobaric heat capacity are within the range of average absolute deviations (AAD) = 0.22% (standard deviation St. Dev = 0.28% and maximum deviation Max. Dev = 0.39%). Additionally, a comparison was made of the state parameters obtained during the experiment and the literature data at the studied parameters.

Sustainable additive manufacturing supply chains with a plithogenic stakeholder analysis: Waste reduction through digital transformation

The Additive Manufacturing (AM) industry is of paramount importance as means of personalized design capabilities by rapid prototyping, using exact amount of required materials avoiding waste, and applying Industry 4.0 technologies with digital transformation ability. The characteristics of AM are regarded as the prerequisites for sustainability in the manufacturing industry. However, there are limited papers discussing the stakeholders and sustainability objectives in a single comprehensive analysis. Hence, the purpose of this research is to identify the degree of each stakeholder to achieve the objective of sustainability in AM supply chain by proposing a novel Plithogenic Fuzzy MACTOR approach. Results reveal that the customers are in triggering position to shape entire AM supply chain as the demand generators. Workforce is a significant player in the AM supply chain by creating the design, manufacturing, marketing, and communicating to provide the collaborations in this business segment. Academia positions here a supportive role to enable the whole supply chain members in terms of delivering technological advancements, training people, and providing the workforce. The AM manufacturers and material / software / printer suppliers are the key players leading to the supply chain by producing the main business products. Thus, a practitioner can interpret its position in the AM supply chain and understand the requirements of sustainability points in detail. Besides, this study provides a theoretical contribution to the literature by extending the MACTOR analysis with Plithogenic sets via including a different uncertainty measure.

Compensation of isotope ratio mismatch in double isotope dilution inductively coupled plasma mass spectrometry (ID-ICP/MS)

Abstract Exact-matching double isotope dilution inductively coupled plasma mass spectrometry (ID-ICP/MS) is widely used to characterize reference materials (RMs) in elemental analysis. In this technique, achieving exactly matching isotope ratios for the sample and calibration blends is regarded as an important prerequisite for obtaining accurate measurement results. However, meeting this condition requires multiple time-consuming iterative measurements. In the current study, an alternative approach that can avoid lengthy iterative procedures while maintaining the accuracy of the ID-ICP/MS results was successfully investigated. We examined the effects of an extensively wide range of inexact-matching isotope ratios (approximately 75 %–130 %) in double ID-ICP/MS for elements with a wide mass range. Our experimental study, using gravimetrically prepared samples of Ca, Cd, Cu, Fe, Hg, Mg, Ni, Pb, and Zn, demonstrated that, despite deliberately introducing mismatching of isotope ratios, the accuracy of the ID-ICP/MS results remained consistent with relative measurement bias of typically less than 0.5 %. The absence of a systematic bias due to deviations in the sample blend isotope ratios from the target ratios of the calibration blends revealed that the variability due to an isotope ratio mismatch was sufficiently compensated for. Furthermore, the expanded measurement uncertainties were sufficiently small with negligible variations observed across the different matching ratios. Typically, they were less than 1 %, except for Fe, Hg, Pb, and Zn which were less than 2 %. This assertion is also supported by theoretically calculated error magnification factors. Consequently, it is feasible to directly utilize the marginally estimated mass fraction of the analyte of interest without extensive iterative measurements. The findings of this study provide robust data for ID-ICP/MS, allowing to circumvent lengthy iterative procedures while maintaining the accuracy and precision of the measurement results, particularly in the characterization of reference materials for elemental analysis.

Construction Quality Evaluation of Concrete Structures in Hydraulic Tunnels Based on CWM-UM Modeling

The construction time of concrete structures in hydraulic tunnels is long, the construction environment is complex, and there are many influencing factors. The requirements for construction quality are high not only to meet the strength requirements but also to meet the design requirements of erosion resistance, crack resistance, and seepage resistance according to its specific operating environment. Therefore, evaluating the construction quality of concrete structures in hydraulic tunnels is of great significance. Considering the randomness and fuzziness of factors affecting the construction quality of concrete structures in hydraulic tunnels, this paper proposes a comprehensive evaluation model based on combined weighting (CWM) and uncertainty measurement theory (UM). The improved analytic hierarchy process (IAHP) and the CRITIC method are used to determine the subjective and objective weights of evaluation indicators. Combined weighting is based on the principle of minimum entropy, and the UM method is used to evaluate the construction quality level. Finally, taking a hydraulic tunnel as an example, its construction quality grade is calculated to be III, according to the evaluation model proposed in this paper, which matches the engineering reality, and a comparative study is made with the mixture element topology theory at the same time. It is verified that the evaluation model can scientifically and reasonably evaluate the construction quality level of concrete structures in hydraulic tunnels.

Active disturbance rejection control for systems with uncertainties and noise using super twisting differentiator-based event-triggered ESO

This paper focuses on the noise suppression issue of high-gain extended state observer (ESO) for systems with uncertainty and unknown measurement noise in the active disturbance rejection control (ADRC) paradigm. A special combination of super twisting differentiator (STD) and event-triggered ESO is devised with a designed event-triggered mechanism. Specially, the proposed STD-ETESO incorporates a finite-time switching strategy, which improves the poor transient performance of the STD acting as a pre-filter to prepare relatively smooth signals for the ESO. The locally asymptotic boundedness of the estimation error can be guaranteed by the Input-state-stability (ISS) and existing research for the STD. The effectiveness of the proposed method is validated by a general example and a practical motion control system. Different integral criteria is introduced to evaluate the overall tracking accuracy and energy usage for comparison with existing different ESOs, which reveals the superiority of the proposed STD-ETESO in noise attenuation and disturbance rejection.

Precise Reflectance/Transmittance Measurements of Highly Reflective Optics with Saturated Cavity Ring-Down Signals

In this paper, a data processing approach was developed to accurately extract the ring-down time and amplitude of the saturated cavity ring-down (CRD) signal; both were utilized to determine simultaneously the high reflectance and residual transmittance of highly reflective (HR) mirrors with a dual-channel CRD configuration. The influence of saturation was eliminated by deleting the beginning saturated data points of the saturated CRD signal and fitting the remaining non-saturated CRD signal to a single-exponential function. By comparing the reflectance/transmittance measurement results of HR samples obtained via data processing of saturated CRD signals and via single-exponentially fitting non-saturated CRD signals with utilization of neutral density filter(s) to eliminate saturation, it was found that the reflectances obtained with both methods were in excellent agreement, while the residual transmittance obtained with the saturated CRD signal was more accurate than that obtained with the neutral-density-filter-attenuated non-saturated CRD signal. The proposed data processing method eliminated the need to use the neutral density filters, therefore avoiding the adding of the optical density error to the uncertainty of residual transmittance measurement and improving the measurement accuracy. The proposed data processing method also extended the dynamic range of the dual-channel CRD scheme for simultaneous measurement of reflectance, transmittance and optical loss.

Impact of gut permeability on estimation of oral bioavailability for chemicals in commerce and the environment.

Performance of pharmacokinetic models developed using in vitro-to-in vivo extrapolation (IVIVE) methods may be improved by refining assumptions regarding fraction absorbed (Fabs) through the intestine, a component of oral bioavailability (Fbio). Although in vivo measures of Fabs are often unavailable for non-pharmaceuticals, in vitro measures of apparent permeability (Papp) using the Caco-2 cell line have been highly correlated with Fabs. We measured bidirectional Papp for over 400 non-pharmaceutical chemicals using the Caco-2 assay. A random forest quantitative structure-property relationship (QSPR) model was developed using these and peer-reviewed pharmaceutical data. Both Caco-2 data (R²=0.37) and the QSPR model (R²=0.29) were better at predicting human bioavailability compared to in vivo rat data (R²=0.23). After incorporation into a high throughput toxicokinetics (HTTK) framework for IVIVE, the Caco-2 data were used to estimate in vivo administered equivalent dose (AED) for bioactivity assessed in vitro, The HTTK-predicted plasma steady state concentrations (Css) for IVIVE were revised, with modest changes predicted for poorly absorbed chemicals. Experimental data were evaluated for sources of measurement uncertainty, which were then accounted for using the Monte Carlo method. Revised AEDs were subsequently compared with exposure estimates to evaluate effects on bioactivity:exposure ratios, a surrogate for risk. Only minor changes in the margin between chemical exposure and predicted bioactive doses were observed due to the preponderance of highly absorbed chemicals.