Measurement Uncertainty Research Articles

Weak-Lensing Shear-Selected Galaxy Clusters from the Hyper Suprime-Cam Subaru Strategic Program: II. Cosmological Constraints from the Cluster Abundance

We present cosmological constraints using the abundance of weak-lensing shear-selected galaxy clusters in the Hyper Suprime-Cam (HSC) Subaru Strategic Program. The clusters are selected on the aperture-mass maps constructed using the three-year (Y3) weak-lensing data with an area of ≈500deg 2, resulting in a sample size of 129 clusters with high signal-to-noise ratios ν≥4.7. Owing to the deep, wide-field, and uniform imaging of the HSC survey, this is by far the largest sample of shear-selected clusters, for which the selection solely depends on gravity and is free from any assumptions about the dynamical state and complex baryon physics. Informed by the optical counterparts, the shear-selected clusters span a redshift range of z≲0.7 with a median of z≈0.3. The lensing sources are securely selected at z≳0.7 with a median of z≈1.3, leading to nearly zero cluster member contamination. We carefully account for (1) the bias in the photometric redshift of sources, (2) the bias and scatter in the weak-lensing mass using a simulation-based calibration, and (3) the measurement uncertainty that is directly estimated on the aperture-mass maps using an injection-based method developed in a companion paper (Chen et al. submitted). In a blind analysis, the fully marginalized posteriors of the cosmological parameters are obtained as Ωm=0.50−0.24+0.28, σ8=0.685−0.088+0.161, Ŝ8≡σ8(Ωm/0.3)0.25=0.835−0.044+0.041, and σ8Ωm/0.3=0.993−0.126+0.084 in a flat ΛCDM model. We compare our cosmological constraints with other studies, including those based on cluster abundances, galaxy-galaxy lensing and clustering, and Cosmic Microwave Background observed by Planck, and find good agreement at levels of ≲2σ. [abridged]

Effect of metallic materials on magnetic resonance image uniformity: a quantitative experimental study.

To assess quantitatively the effect of metallic materials on MR image uniformity using a standardized method. Six types of 1cm cubic metallic materials (i.e., Au, Ag, Al, Au-Ag-Pd alloy, Ti, and Co-Cr alloy) embedded in a glass phantom filled were examined and compared with no metal condition inserted as a reference. The phantom was scanned five times under each condition using a 1.5-T MR superconducting magnet scanner with an 8-channel phased-array brain coil and head and neck coil. For each examination, the phantom was scanned in three planes: axial, coronal, and sagittal using T1-weighted spin echo (SE) and gradient echo (GRE) sequences in accordance with the American Society for Testing and Materials (ASTM) F2119-07 standard. Image uniformity was assessed using the non-uniformity index (NUI), which was developed by the National Electrical Manufacturers Association (NEMA), as an appropriate standardized measure for investigating magnetic field uniformity. T1-GRE images with Co-Cr typically elicited the lowest uniformity, followed by T1-GRE images with Ti, while all other metallic materials did not affect image uniformity. In particular, T1-GRE images with Co-Cr showed significantly higher NUI values as far as 6.6cm at maximum equivalent to 11 slices centering around it in comparison with the measurement uncertainty from images without metallic materials. We found that MR image uniformity was influenced by the scanning sequence and coil type when Co-Cr and Ti were present. It is assumed that the image non-uniformity in Co-Cr and Ti is caused by their high magnetic susceptibility.

Precision comparison of intensity ratios and area ratios in spectral analysis

The long-debated question in analytical chemistry of which of the area ratio or the intensity ratio is the more precise has yielded no definitive analytical conclusion. To address this issue theoretically, we derived analytical solutions for the lower limits of estimation precision for spectral parameters, including the intensity ratio and area ratio, based on the Cramér–Rao lower bound (CRLB) framework for a Gaussian spectrum. The precisions of spectral parameter estimations from the analytical solutions were consistent with results obtained from Monte Carlo simulations. Our theoretical and simulation results revealed that the precision of estimating the area ratio surpassed that of the intensity ratio by a factor of 2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{2}$$\\end{document}. Additionally, our experimental results aligned well with both theoretical predictions and simulation outcomes, further validating our approach. This increased precision of the area ratio is due to negative covariance between intensity and bandwidth, rather than the area containing more intensity information, as often misinterpreted. Consequently, and quite counter intuitively, prior bandwidth and intensity related information does not improve the area ratio precision: it worsens it. The analytical solution we derived represents the fundamental limits of spectral parameter measurement precision. Thus, it can be used as an alternative method for estimating the minimum error when experimental measurement uncertainty cannot be determined.

Development of PMUT based ultrasonic gas flowmeter with conical transducer cavities for the validation of industrial standards

The recessed installation of transducers in ultrasonic gas flowmeters (USFMs) creates cylindrical transducer cavities that induce local flow distortions and vortex formations, resulting in significant measurement uncertainty. Currently, USFMs with small diameters predominantly rely on conventional bulk-PZT ultrasonic transducers, which are relatively large and hinder the optimization of vortices. Miniaturized piezoelectric micromachined ultrasonic transducers (PMUTs) present an effective solution for mitigating vortex effects; however, their acoustic performance is constrained by the sensor area, resulting in PMUT-based USFMs that fail to meet industrial measurement requirements. This article introduces a small-diameter USFM utilizing a single PMUT element integrated with the specially designed conical transducer cavity. This design effectively reduces the half-power beamwidth (HPBW) of the single PMUT element to one-fifth of its original size and increases the transmit signal amplitude by 7.2 dB. Computational fluid dynamics (CFD) simulations demonstrate that the conical transducer cavity optimizes vortex distribution within the cavity, reducing vortex intensity. The experimental result demonstrates that the proposed USFM achieves an indicated error of less than ±1 % and repeatability of less than 0.5 %, which meets the class 1.5 accuracy level. This design fully complies with the accuracy standards set in GB/T 39841–2021, validating the PMUT-based USFM for industrial flow measurement.

Computational renormalization for thermal conductivity of porous asphalt concrete based on hybrid finite element-neural network method

Thermal conductivity of porous asphalt concrete (PAC) directly affects PAC pavement temperature field, which is closely related to the heat island effect in summer and pavement icing in winter. Due to the large and complex pores and the uneven surface in PAC, there is measurement uncertainty using traditional laboratory tests. In this study, a finite element-neural network (FE-NN) method was proposed to predict the thermal conductivity of PAC based on the three-dimensional (3-D) heterogeneous structure. Additionally, laboratory tests were conducted to measure the thermal conductivity of PAC, fine aggregate mixture (FAM), and basalt aggregate. The results served as input parameters for the FE-NN method and were used to verify its accuracy. Based on the validated method, various factors affecting the thermal conductivity of PAC were analyzed. The analysis results show that, compared to the transient plane source (TPS) method, the backcalculation method can achieve results similar to those obtained with the TPS method for basalt aggregate and FAM, but provides more stable measurements when applied to PAC. The accuracy of the FE-NN method is acceptable, with relative errors of 1.92∼4.34 % compared to the experimental results. Besides, 2-D structural models tend to underestimate the effective thermal conductivity of PAC, with values 83.2∼84.1 % lower than those calculated by 3-D models. When the representative volume element (RVE) sizes are larger than 256×256×256, the variances show little differences and are acceptable. Furthermore, typical aggregate thermal conductivity values were used as input, revealing a nonlinear increase in the effective thermal conductivity of PAC. When the void content increased from 18 % to 24 %, the effective thermal conductivity of PAC decreased by 19.83 %. The effective thermal conductivity of PAC changes more significantly with the thermal conductivity of the aggregate than that with the variation of porosity in an interval. The analysis findings provide insights for better evaluating the pavement temperature field and heat transfer to improve the PAC pavement material designs.

Optimization of ore pillar recovery based on weighting combined with uncertainty measurement theory

Abstract Given the multi-objective nature and inherent uncertainty in evaluating ore pillar mining schemes, the analytic hierarchy process (AHP) and entropy method were applied to determine factor weights. A new combined weighting method was proposed to obtain more objective and accurate results. Seven quantitative and four qualitative indicators were selected based on various mining performance factors and grading standards. A combined weighting–uncertainty measurement theory evaluation model was then constructed, integrating weight values and confidence levels from the recognition criteria. This model was applied to evaluate three proposed schemes, determining their relative merits and ranking. The evaluation demonstrated that the combined weighting–uncertainty measurement model outperformed both the fuzzy theory and the TOPSIS method in delivering comprehensive, objectivity and reliable results for assessing ore pillar mining schemes.

Low-Cost Device for Measuring Wastewater Flow Rate in Open Channels

This research paper describes the development of a low-cost device for measuring wastewater flow rates in open channels, a significant advancement enabled by the evolution of microcomputers and processing techniques. A laboratory setup was constructed to validate the device’s accuracy against a standard flow measurement method, optimizing key parameters to achieve a linear relationship between detected and set flow rates, while considering hardware limitations and energy efficiency. The central focus of the research was developing a method to measure the velocity of contaminated fluid using ultrasonic signals, employing the cross-correlation method for signal delay analysis in a stochastic environment. This was complemented by a procedure to measure fluid levels, also based on ultrasonic signals. The device’s reliability was assessed through repeatability and uncertainty measurements, confirming its accuracy with an extended uncertainty not exceeding an average of 3.47% for flows above 40 L/min. The device has potential to provide valuable data on the operational dynamics of sanitary networks, crucial for developing and calibrating simulation models.

Upconverting thermal history paint for investigations of short thermal events

The main goal of the work was to develop phosphorescent coating for investigations of surface temperature distribution resulted from short duration and high energy thermal events such as small rocket engine testing. For that purpose, we developed thermal history paint with upconverting Gd2O3: Er3+, Yb3+, Mg2+ nanopowder. The developed paint was heated by Joule effect with precise control over temperature and heating time. Photoluminescence signal of cooled samples was excited with 980 nm laser and collected in automatic manner with use of an optical fibre probe connected to photospectrometer. The results show that irreversible changes of paint emission are temperature and heating time specific for calcination temperatures from 700 °C to 1150 °C. Calibration curves were prepared for heating times of 10, 60 and 180 seconds. Finally, 2D surface temperature samples were determined with use of upconverting thermal history paint and compared to IR camera and pyrometric measurements. The results indicate that uncertainty of temperature measurement below 10 °C was achieved for temperatures above 1000 °C. It has been demonstrated that developed thermal history paint allows for measurements of 2D surface temperature distribution with high spatial resolution and good agreement to standard measurements techniques like thermal camera and pyrometer.

Neuronal activity in the ventral tegmental area during goal-directed navigation recorded by low-curvature microelectrode arrays

Navigating toward destinations with rewards is a common behavior among animals. The ventral tegmental area (VTA) has been shown to be responsible for reward coding and reward cue learning, and its response to other variables, such as kinematics, has also been increasingly studied. These findings suggest a potential relationship between animal navigation behavior and VTA activity. However, the deep location and small volume of the VTA pose significant challenges to the precision of electrode implantation, increasing the uncertainty of measurement results during animal navigation and thus limiting research on the role of the VTA in goal-directed navigation. To address this gap, we innovatively designed and fabricated low-curvature microelectrode arrays (MEAs) via a novel backside dry etching technique to release residual stress. Histological verification confirmed that low-curvature MEAs indeed improved electrode implantation precision. These low-curvature MEAs were subsequently implanted into the VTA of the rats to observe their electrophysiological activity in a freely chosen modified T-maze. The results of the behavioral experiments revealed that the rats could quickly learn the reward probability corresponding to the left and right paths and that VTA neurons were deeply involved in goal-directed navigation. Compared with those in no-reward trials, VTA neurons in reward trials presented a significantly greater firing rate and larger local field potential (LFP) amplitude during the reward-consuming period. Notably, we discovered place fields mapped by VTA neurons, which disappeared or were reconstructed with changes in the path–outcome relationship. These results provide new insights into the VTA and its role in goal-directed navigation. Our designed and fabricated low-curvature microelectrode arrays can serve as a new device for precise deep brain implantation in the future.

A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing

The ability and rapid access to execution data and information in manufacturing workshops have been greatly improved with the wide spread of the Internet of Things and artificial intelligence technologies, enabling real-time unmanned integrated control of facilities and production. However, the widespread issue of data quality in the field raises concerns among users about the robustness of automatic decision-making models before their application. This paper addresses three main challenges relative to field data quality issues during automated real-time decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, and sensor fault-tolerant control. To address these problems, this paper proposes a risk assessment framework in the case of continuous production workshops. The framework aims to determine a method for systematically assessing data quality issues in specific scenarios. It specifies the preparation requirements, as well as assumptions such as the preparation of datasets on typical working conditions, and the risk assessment model. Within the framework, the data quality issues in real-time decision-making are transformed into data deviation problems. By employing the Monte Carlo simulation method to measure the impact of these issues on the decision risk, a direct link between sensor quality and risks is established. This framework defines specific steps to address the three challenges. A case study in the steel industry confirms the effectiveness of the framework. This proposed method offers a new approach to assessing safety and reducing the risk of real-time unmanned automatic decision-making in industrial settings.

Integrating Measurement Uncertainty Analysis into Laboratory Education for the Development of Critical Thinking and Practical Skills

Integrating Measurement Uncertainty Analysis into Laboratory Education for the Development of Critical Thinking and Practical Skills

Understanding human activity with uncertainty measure for novelty in graph convolutional networks

Understanding human activity with uncertainty measure for novelty in graph convolutional networks

Prediction of refraction error after toric lens implantation with biometric input data uncertainties and power labelling tolerances.

The purpose of this study was to simulate the impact of biometric measure uncertainties, lens equivalent and toric power labelling tolerances and axis alignment errors on the refractive outcome after cataract surgery with toric lens implantation. In this retrospective non-randomised cross sectional Monte-Carlo simulation study we evaluated a dataset containing 7458 LenStar 900 preoperative biometric measurements. The biometric uncertainties from literature, lens power labelling according to ISO 11979, and axis alignment tolerances of a modern toric lens (Hoya Vivinex) were taken to be normally distributed and used in a Monte-Carlo simulation with 100 000 samples per eye. The target variable was the defocus equivalent (DEQ) derived using the Castrop (DEQC) and the Haigis (DEQH) formulae. Mean/median / 90% quantile DEQC was 0.22/0.21/0.36 D and DEQH was 0.20/0.19/0.32 D. Ignoring the variation in lens power labelling and toric axis alignment the respective DEQC was 0.20/0.19/0.32 D and DEQH was 0.18/0.17/0.29 D. DEQC and DEQH increased with shorter eyes, steeper corneas, equivalent lens power and highly with toric lens power. According to our simulation results, uncertainties in biometric measures, lens power labelling tolerances, and axis alignment errors are responsible for a significant part of the refraction prediction error after cataract surgery with toric lens implantation. Additional labelling of the exact equivalent and toric power on the lens package could be a step to improve postoperative results.

Wear analysis of material measures and stylus probes in repetitive tactile calibration

Tactile industrial measuring instruments are frequently calibrated with material measures defined in ISO 5436–1 to ensure their function, determine their measurement uncertainty and assess the capability of measuring processes. Since these process needs to be repeated on a regular basis, the long-term stability of both material measures and the stylus is essential to enable a repeatable and reliable measurement. Since the tactile sampling of a material measure involves a mechanical interaction, there is the potential for wear when repetitive measurements are performed. We present a comprehensive experimental study on the effects of wear for different stylus instruments – including a reference plane scanning system and multiple skidded scanning systems. Multiple measurement methods are used to determine the state of the stylus and the surface of the material measure, both before and after their mechanical interaction. The impact of the wear on the accuracy of the results can be described, as can the connection between the wear and the hardness of the skidded probe. The study found that significant wear effects on surfaces with even >500 HV become qualitatively visible much earlier than they can be quantitatively detected, with extreme load cases of >1000 repetitive measurements at the same position being necessary to produce major wear influences. Thus, regular visual inspections in case of extensive repetitive measurements can be recommended, just as well as a low measuring force and a reduction of the number of measurements for measurement capability analysis to 12 measurements.

Investigation of insecticide residues in fig and health risk assessment

This study aimed to detect insecticide, acaricide, and nematicide residues in 92 fresh and 50 dried fig samples collected from locations with intensive fig, Ficus carica L. (Rosales: Moraceae) production in Aydın, Türkiye in 2022. The analysis method was validated according to SANTE 11312/2021 guidelines. The recoveries ranged between 70% and 120%, with repeatability (RSDr) and within-laboratory reproducibility (RSDwR) ≤ 20% and expanded measurement uncertainties below 50% for all insecticides indicating satisfactory analytical performance. In total, 114 different insecticides were screened, revealing residues in 27 samples. Bifenazate was detected in 13 samples, etoxazole in 3 samples, spiromesifen in 5 samples, and both bifenazate and spiromesifen in 6 samples. No detectable residues were found in the dried fig samples. All samples in which bifenazate was detected were above the European Union Maximum Residue Limits (EU-MRL). Etoxazole and spiromesifen, unauthorized in figs, showed etoxazole residues above EU-MRLs, while spiromesifen residues were below EU-MRLs. The acute and chronic health risk indices for the insecticides were found to be below 1, indicating low health risks associated with fig consumption. The risk assessment suggests that fig consumption is safe for consumers.

High-sensitivity humidity sensor based on Mach-Zehnder interferometer in seven-core fiber

A Mach-Zehnder Interferometer (MZI) based on seven-core fiber (SCF) for humidity sensing is proposed and experimentally demonstrated. The MZI is formed by sandwiched a section of SCF in between two single mode fibers (SMFs), while a fiber taper and a brief multimode fiber (MMF) act as fiber couplers to excite and couple the intermodal interference in the SCF. The optical field distribution and coupling efficiency of the MZI with different SCF lengths are analysed. The theoretical results show that the MZI is sensitive to SCF lengths, and it is expected to obtain highly sensitive humidity response. Humidity sensing experiments shows a high sensitivity of 0.6603 nm/%RH over a relative humidity (RH) range of 44–83 %RH, and the maximum measurement uncertainty introduced by the long-term stability is 0.0006 %RH. Human respiration measurement exhibits the response time and recovery time of the MZI are 0.44 s and 1.24 s respectively. The temperature effect is also experimentally investigated. Such an all-fiber MZI presents advantages of simple configuration, high sensitivity and good stability, making it has potential in humidity measurement fields.

Pruning convolutional neural networks for inductive conformal prediction

Neural network pruning is a popular approach to reducing model storage size and inference time by removing redundant parameters in the neural network. However, the uncertainty of predictions from pruned models is unexplored. In this paper we study neural network pruning in the context of conformal predictors (CP). The conformal prediction framework built on top of machine learning algorithms supplements their predictions with reliable uncertainty measure in the form of prediction sets, under the independent and identically distributed assumption on the data. Convolutional neural networks (CNNs) have complicated architectures and are widely used in various applications nowadays. Therefore, we focus on pruning CNNs and, in particular, filter-level pruning. We first propose a brute force method that estimates the contribution of a filter to the CP’s predictive efficiency and removes those with the least contribution. Given the computation inefficiency of the brute force method, we also propose the Taylor expansion to approximate the filter’s contribution. Furthermore, we improve the global pruning method by protecting the most important filters within each layer from being pruned. In addition, we explore the ConfTr loss function which is optimized to yield maximal CP efficiency in the context of neural network pruning. We have conducted extensive experimental studies and compared the results regarding the trade-offs between predictive efficiency, computational efficiency, and network sparsity. These results are instructive for deploying pruned neural networks with applications using conformal prediction where reliable predictions and reduced computational cost are relevant, such as in safety-critical applications.

Form Deviation Uncertainty and Conformity Assessment on a Coordinate Measuring Machine

Coordinate measuring machines are widely used in the industrial field due to their ease of automation. However, estimating the measurement uncertainty is a delicate task, especially when controlling for deviation, given the large number of factors that influence the measurement. A precise estimate of the uncertainty is crucial to avoid incorrect conformity assessments. The purpose of this study is to control geometrical-form tolerance specifications, taking into consideration their associated uncertainty. A surface fitting model based on the least squares criterion is proposed, allowing one to obtain the variance–covariance matrix by iterative calculation according to the Levenberg–Marquard optimization method. The form deviation is then evaluated following the Geometrical Product Specifications (GPS) Standard, and its associated uncertainty is estimated using the guide to the expression of uncertainty in measurement (GUM) propagation of the uncertainty law. Finally, the conformity assessment is performed based on the measured deviation and its associated uncertainty. Different results for the measurement of straightness, flatness, circularity, roundness, and cylindricity are presented and detailed. This model is thereafter validated by a Monte Carlo simulation, and interlaboratory comparisons of the obtained results were performed, which showed satisfactory outcome. This contribution is of great use to manufacturing companies and metrology laboratories, allowing them to meet the normative guidelines, which stipulates that each measurement result must be accompanied by its associated uncertainty.

Evidence for validity and reliability of a research-based assessment instrument on measurement uncertainty

The Survey of Physics Reasoning on Uncertainty Concepts in Experiments (SPRUCE) was designed to measure students’ proficiency with measurement uncertainty concepts and practices across ten different assessment objectives to help facilitate the improvement of laboratory instruction focused on this important topic. To ensure the reliability and validity of this assessment, we conducted a comprehensive statistical analysis using classical test theory. This analysis includes an evaluation of the test as a whole, as well as an in-depth examination of individual items and assessment objectives. We make use of a previously reported on scoring scheme involving pairing items with assessment objectives, creating a new unit for statistical analysis referred to as a “couplet.” The findings from our analysis provide evidence for the reliability and validity of SPRUCE as an assessment tool for undergraduate physics labs. This increases both instructors’ and researchers’ confidence in using SPRUCE for measuring students’ proficiency with measurement uncertainty concepts and practices to ultimately improve laboratory instruction. Additionally, our results using couplets and assessment objectives demonstrate how these can be used with traditional classic test theory analysis. Published by the American Physical Society 2024

Force sensitivity calibration of artificial mastoids

The Standards and Calibration Laboratory (SCL) has set up a system for calibration of artificial mastoids to meet the growing demand for calibration of bone-conduction hearing aids and audiometers in the audiology industry of Hong Kong. An artificial mastoid (or mechanical coupler, as specified in IEC 60318-6) is a device that mimics the mechanical characteristics of the human mastoid (one of the bones of the skull behind the ear). It is basically composed of a dome-shaped structure and a force transducer. This paper mainly illustrates the method for measuring the force sensitivity level (in unit of dB re 1 pC/N) of an artificial mastoid in the frequency range of 125 Hz to 8 kHz. The calibration system has been automated to meet the demand for artificial mastoid calibration services in Hong Kong. This paper describes (i) the method of determining the force sensitivity level of an artificial mastoid, (ii) the measurement model and uncertainty evaluation, and (iii) the measurement results and details of the calibration system.