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The article discusses the development of a real-time digital twin for predicting azimuthal thermoacoustic instabilities in hydrogen-based annular combustors. The study demonstrates the successful prediction of thermoacoustic dynamics, accurate estimation of model biases, and optimal inference of system parameters using a bias-regularized ensemble Kalman filter. Researchers have contributed to understanding flame dynamics, acoustic resonance, and instabilities in combustion chambers and gas turbine combustors to improve the understanding of complex combustion processes. [Extracted from the article]

The aim of this study is to investigate the test–retest reliability of the Closed Kinetic Chain Upper Extremity Stability Test (CKCUEST) and modified-CKCUEST in children aged 7–10 years and to compare the two test versions within the same group. The study was completed with fifty-three children. Average, normalized, and power scores were obtained from the maximum number of touches in 15 × 3 s in the tests. Tests were repeated seven days apart for test–retest reliability. Intraclass Correlation Coefficient (ICC3,1), Standard Error of Measurement (SEM), Minimum Detectable Change (MDC) were tested total/by gender. Children’s opinions about the tests were questioned. The test–retest ICC3,1 values for CKCUEST were 0.96 for average score, 0.95 for normalized score, 0.97 for power score. For the modified-CKCUEST, test–retest reliabilities were 0.94, 0.92, 0.96, respectively. CKCUEST and modified-CKCUEST demonstrated excellent test–retest reliability for children aged 7–10 years. Participants stated that the modified-CKCUEST was more suitable as a test position. [ABSTRACT FROM AUTHOR]

The results of measurements of 235U/238U isotope ratios in microparticles of a reference material performed by Cameca IMS-4f and Cameca IMS 1280-HR mass spectrometers are compared. Twenty-eight particles of various shapes and sizes and also one spherical uranium oxide particle approximately 0.6 μ in size were analyzed by both instruments. These particles were selected based on a preliminary SEM–EDX analysis. The results indicate that the Cameca IMS-1280 HR mass spectrometer can measure uranium isotope ratios with precision up to thousandths of percent, while the Cameca IMS-4f mass spectrometer can measure only up to tenths of percent. For measurements of uranium-235 concentrations at the 0.7% level, the error range for mass spectrometers with a "large" magnet is two orders of magnitude smaller than the errors from mass spectrometers with a "small" magnet, with measurement errors more than ten times lower. Additionally, while mass spectrometers with a small magnet cannot measure minor isotope concentrations at the 5 × 10–3% level in submicrometer particles, mass spectrometers with a large magnet can achieve this with an error of less than 10%. [ABSTRACT FROM AUTHOR]

A sample containing microparticles of two different uranium materials with distinct uranium isotopic compositions was analyzed during an international experiment. Mass spectrometric analysis of 25 particles revealed that, within the measurement error, six particles had an isotopic composition consistent with that of the first material, and three particles were consistent with the second material. However, the uranium isotopic composition of the remaining 16 particles did not match either of the known materials. This indicates that the preliminary examination of uranium microparticles by scanning electron microscopy can identify particles carrying information about the isotopic composition of the source materials. [ABSTRACT FROM AUTHOR]

Background: A recent systematic review revealed issues in regard to performing and reporting agreement and reliability studies for ordinal scales, especially in the presence of more than two observers. This paper therefore aims to provide all necessary information in regard to the choice among the most meaningful and most used measures and the planning of agreement and reliability studies for ordinal outcomes. Methods: This paper considers the generalisation of the proportion of (dis)agreement, the mean absolute deviation, the mean squared deviation and weighted kappa coefficients to more than two observers in the presence of an ordinal outcome. Results: After highlighting the difference between the concepts of agreement and reliability, a clear and simple interpretation of the agreement and reliability coefficients is provided. The large sample variance of the various coefficients with the delta method is presented or derived if not available in the literature to construct Wald confidence intervals. Finally, a procedure to determine the minimum number of raters and patients needed to limit the uncertainty associated with the sampling process is provided. All the methods are available in an R package and a Shiny application to circumvent the limitations of current software. Conclusions: The present paper completes existing guidelines, such as the Guidelines for Reporting Reliability and Agreement Studies (GRRAS), to improve the quality of reliability and agreement studies of clinical tests. Furthermore, we provide open source software to researchers with minimum programming skills. [ABSTRACT FROM AUTHOR]

Background: The Headache Disability Questionnaire (HDQ) evaluates pain intensity, daily activities, work/school disruptions, and the impact on recreational activities. It was aimed to translate the HDQ into Turkish and evaluate its reliability and validity. Methods: This study included 130 participants, consisting of 105 females and 25 males. The original HDQ was translated into Turkish language using Beaton guidelines. Reliability was assessed using internal consistency and Intraclass Correlation Coefficient. Exploratory (EFA) and Confirmatory Factor Analysis (CFA) were conducted to evaluate the structural validity. For convergent validity, the Turkish version of the HDQ, along with the Headache Impact Test-6 (HIT-6) and Migraine Disability Assessment Scale (MIDAS), was administered to individuals with headaches. The HDQ was retested one week later to assess its reliability. Results: The Turkish version of the HDQ demonstrated good reliability, with ICC and Cronbach's α values of 0.842 and 0.914, respectively. Standard error measurement (SEM) and Minimal Detectable Change (MDC) values were 5.89 and 16.33 units. Bland-Altman plots confirmed a high level of agreement between initial and retest scores EFA revealed a two-factor structure, clustering items into Factor 1 (items 1, 2, 5, 7, and 9) and Factor 2 (items 3, 4, 6, and 8), which was subsequently confirmed by CFA. Convergent validity was confirmed through good correlations with HIT-6, and MIDAS. No ceiling or floor effects were observed. Conclusions: The study demonstrates that the Turkish version of the HDQ is a valid and reliable instrument for evaluating the effect of headaches on daily living, exhibiting strong internal consistency and test-retest reliability, making it suitable for both clinical practice and research purposes. Trial registration: Trial registration date is January 30, 2021 (NCT04736654). Clinical trials registration number: NCT04736654. [ABSTRACT FROM AUTHOR]

ABSTRACT In the framework of causal inference, average treatment effect (ATE) is one of crucial concerns. To estimate it, the propensity score based estimation method and its variants have been widely adopted. However, most existing methods were developed by assuming that binary treatments are precisely measured. In addition, propensity scores are usually formulated as parametric models with respect to confounders. However, in the presence of measurement error in binary treatments and nonlinear relationship between treatments and confounders, existing methods are no longer valid and may yield biased inference results if these features are ignored. In this paper, we first analytically examine the impact of estimation of ATE and derive biases for the estimator of ATE when treatments are contaminated with measurement error. After that, we develop a valid method to address binary treatments with misclassification. Given the corrected treatments, we adopt the random forest method to estimate the propensity score with nonlinear confounders accommodated and then derive the estimator of ATE. Asymptotic properties of the error‐eliminated estimator are established. Numerical studies are also conducted to assess the finite sample performance of the proposed estimator, and numerical results verify the importance of correcting for measurement error effects. [ABSTRACT FROM AUTHOR]

Estimating production profile from temperature data is of great significance for production increase and cost reduction in well production process. This study aims to provide a cost-effective direct method of interpreting flow profiles from temperature measurements. The concept of representative inspired by randomized distribution regarding heat conduction representative points for each no-influx section is proposed. Three assumptions regarding existence, distribution and independence of representatives are introduced to establish a copula-based statistical method of converting fitted distributions of representative, to distributions of relaxation distances for each no-influx section, and then to estimate the production profile. The method is applied on a horizontal gas well on the east slope of western Sichuan depression, and the simulated results are compared with the production logging data. The error of estimators for each no-influx section is between 6.7% and 11.8%, which is satisfactory. Sensitivity analyses are conducted regarding temperature measurement error, and the results verify the robustness of this method. This paper provides a reliable solution to interpret production profile from relatively inaccurate temperature data when the perforation zones are adjacently close. The findings of this study can help for better understanding of low-cost production allocation and uncertain flow state modeling problems. [ABSTRACT FROM AUTHOR]

The aims of this study were 1) to propose a simple two-dimensional (2D) video-based method to measure segment lengths in the clean and jerk during weightlifting competitions, and 2) to check concurrent validity and test-retest reliability compared to 3D video-based measurements. In the first part of the study, key body positions for the assessment of segment lengths via 2D measurement method were determined. In the second part, concurrent validity was verified, comparing the 2D segment length measurements to 3D measurements. In the third part, the test-retest reliability of the 2D approach was investigated. In the clean and in the jerk, the lift-off/start position and the position of maximum barbell velocity displayed a minimised offset between 'real' 3D segment lengths and 2D projections of the segment lengths. For the concurrent validity assessment, the overall random measurement error was 4.3% with concordance correlation coefficients ranging from 0.64 to 0.88. The standard error of measurement in the test-retest procedure was 2.4% on average with intraclass correlation coefficients ranging from 0.85 to 0.96. The proposed 2D video-based approach to measure body segment lengths during weightlifting exercises is proven to be a valid and reliable procedure. [ABSTRACT FROM AUTHOR]

Blades are the core components of rotating machinery, and the blade vibration status directly impacts the working efficiency and safe operation of the equipment. The blade tip timing (BTT) technique provides a solution for blade vibration monitoring and is currently a prominent topic in research on blade vibration issues. Nevertheless, the non-stationary factors present in actual engineering applications introduce inaccuracies in the BTT technique, resulting in blade vibration measurement errors. The theory of blade vibration difference offers a new perspective for high-precision BTT techniques. This paper optimizes the traditional circumferential Fourier fitting (CFF) algorithm. According to the blade departure time measurement mechanism, four sets of BTT signals are obtained by two probes, six sets of blade vibration differences are established, and, then, a blade vibration difference-based circumferential Fourier fitting (BVD-CFF) algorithm for blade synchronous vibration parameter identification is proposed. Simulation studies demonstrate that the BVD-CFF algorithm exhibits superior anti-noise performance. Moreover, experimental investigations on a high-speed rotation blade vibration test rig and a large-scale centrifugal compressor test rig display that the engine order of blade synchronous vibrations obtained by the BVD-CFF algorithm are essentially the same as those obtained by the strain gauge method. [ABSTRACT FROM AUTHOR]

Fixed-point thickness measurement is commonly used in corrosion detection within petrochemical enterprises, but it suffers from low detection efficiency for localized thinning, limitations regarding measurement locations, and high equipment costs due to insulation and cooling layers. To address these challenges, this paper introduces a wireless passive ultrasonic thickness measurement technique based on a pulse compression algorithm. The research methodology encompassed the development of mathematical and circuit models for single coil and wireless energy transmission, the proposal of a three-terminal wireless energy mutual coupling system, and the establishment of a finite element model simulating the ultrasonic body wave thickness measurement and wireless energy transmission system. An experimental setup was constructed to conduct thickness measurements on metal samples varying in thickness, shape, and material composition. The experimental findings revealed that the wireless ultrasonic echo signal, when processed using the pulse compression algorithm, achieved a thickness measurement accuracy approximately ten times superior to that of the untreated echo signal. This significant improvement in accuracy facilitates the high-density deployment of thickness measurement points in petrochemical applications. [ABSTRACT FROM AUTHOR]

To eliminate the noise interference caused by continuous external environmental disturbances on the rotor signals of a maglev gyroscope, this study proposes a noise reduction method that integrates an adaptive particle swarm optimization variational modal decomposition algorithm with a strategy for error compensation of the trend term in reconstructed signals, significantly improving the azimuth measurement accuracy of the gyroscope torque sensor. The optimal parameters for the variational modal decomposition algorithm were determined using the adaptive particle swarm optimization algorithm, allowing for the accurate decomposition of noisy rotor signals. Additionally, using multi-scale permutation entropy as a criterion for discriminant, the signal components were filtered and summed to obtain the denoised reconstructed signal. Furthermore, an empirical mode decomposition algorithm was employed to extract the trend term of the reconstructed signal, which was then used to compensate for the errors in the reconstructed signal, achieving significant noise reduction. On-site experiments were conducted on the high-precision GNSS baseline of the Xianyang Yuan Tunnel in the second phase of the project to divert water from the Han River to the Wei River, where this method was applied to process and analyze multiple sets of rotor signals. The experimental results show that this method effectively suppresses continuous external environmental interference, reducing the average standard deviation of the compensated signals by 46.10% and the average measurement error of the north azimuth by 45.63%. Its noise reduction performance surpasses that of the other four algorithms. [ABSTRACT FROM AUTHOR]

This study contributes to improving the accuracy of temperature measurements with a platinum resistance temperature detector (RTD) by proposing techniques to mitigate the error due to self-heating by the operating current. An assessment of the measurement errors of the platinum RTD was carried out to study ways to improve their accuracy. High accuracy can be achieved by individual calibration using a voltage divider circuit to measure resistance, the substitution method, and the transitional measure. It was shown that each of these approaches offers potential improvements in the accuracy of temperature measurements using RTDs. However, one of the genuine limitations is the error due to heating the RTD by the operating current. To address this, both linear and nonlinear methods for correcting the error due to heating by the operating current were studied. This paper examines how these methods can be applied to mitigate the influence of self-heating on measurement accuracy. Moreover, the residual errors associated with these methods of correction were estimated. The analysis showed that while these methods can reduce the errors significantly, there remain limitations below which it is not possible to mitigate the error. [ABSTRACT FROM AUTHOR]

Kinematic assessment of ball kicking may require significant human effort (e.g., traditional vision-based tracking systems). Wearables offer a potential solution to reduce processing time. This systematic review collated measurement properties (validity, reliability, and/or accuracy) of wearable kinematic-based technology systems used to evaluate soccer kicking. Seven databases were searched for studies published on or before April 2024. The protocol was previously published and followed the PRISMA 2020 statement. The data items included any validity, reliability, and/or accuracy measurements extracted from the selected articles. Twelve articles (1011 participants) were included in the qualitative synthesis, showing generally (92%) moderate methodological quality. The authors claimed validity (e.g., concurrent) in seven of the eight studies found on the topic, reliability in two of three, and accuracy (event detection) in three of three studies. The synthesis method indicated moderate evidence for the concurrent validity of the MPU-9150/ICM-20649 InvenSense and PlayerMaker™ devices. However, limited to no evidence was identified across studies when considering wearable devices/systems, measurement properties, and specific outcome variables. To conclude, there is a knowledge base that may support the implementation of wearables to assess ball kicking in soccer practice, while future research should further evaluate the measurement properties to attempt to reach a strong evidence level. [ABSTRACT FROM AUTHOR]

Aiming at the problems, in which the traditional radar signal sorting method has high requirements for manual experience and poor adaptability, and considering the differences in received power caused by radar beam scanning under long-term observation, an end-to-end signal sorting method based on the instance segmentation network SOLOv2 and using an antenna scan pattern (ASP) is proposed in this letter. Firstly, the interleaved pulse sequences of multiple radar signals with various inter-pulse modulation types, scan patterns, and gain patterns are simulated, mimetic image mapping is constructed to visualize the interleaved pulse sequences as mimetic point graphs, and the index relationship between pulses and pixel points is recorded. Subsequently, the SOLOv2 instance segmentation network is used to segment the mimetic point graph at the pixel level, thereby clustering the discrete pixel points in the image. Finally, based on the index relationship recorded during the construction of the mimetic image mapping, the clustering results of points in the image are traced back to the clustering of pulses, achieving end-to-end intelligent radar signal sorting. Through simulation experiments, it was verified that, compared with YOLOv8-based, U-Net-based, and traditional signal sorting methods, the sorting accuracy of the proposed method increased by 9.26%, 11.17%, and 24.55% in the scenario of five signals with 30% missing pulse ratio (MPR), and increased by 13.33%, 18.88%, and 23.94% in the scenario of five signals with 30% spurious pulse ratio (SPR), respectively. The results show that by introducing the stable parameter, namely ASP, the proposed method can achieve signal sorting with highly overlapping parameters and adapt to non-ideal conditions with measurement errors, missing pulses, and spurious pulses. [ABSTRACT FROM AUTHOR]

AbstractMost genetic studies recruit high-risk families and the discoveries are based on nonrandomly selected groups. We must consider the consequences of this ascertainment process in order to apply the results of genetic research to the general population. Furthermore, in such studies, problem arises in inferring the disease status correctly since measurements of risk factors that are observed in the study are often noisy or indirect. In this paper, we examine the ascertainment as well as measurement error effects on the parameter estimates. En route, we develop a flexible model that adjusts the standard cumulative logistic regression model for clustered ordinal data by incorporating ascertainment and measurement error corrections. A customized Monte Carlo EM (MCEM) based technique is employed to analyze the model. The novelty of this technique is that it has the flexibility to handle the high dimensional setup. The simulation studies suggest that the ascertainment adjustment is necessary and effective. The need for measurement error adjustment in the model is also justified. The methodology is illustrated using a small scale survey data. [ABSTRACT FROM AUTHOR]

Highlights The present investigation introduced an indirect approach to accurately characterize thermal expansion and chemical shrinkage coefficients of the BA9916 resin, which is a thermoset epoxy resin with thermoplastic additives for enhanced impact performance, throughout the curing process. To ensure modeling accuracy, experimental measurements of thermal decomposition and curing kinetics were also considered. Characterization of the BA9916 resin based on experiments completely was difficult due to its exceptionally high viscosity caused by the thermoplastic additives, imposing major obstacles in degassing during sample preparation. To tackle this problem, an indirect characterization method was utilized, beginning with characterizing the unidirectional (UD) composites with the BA9916 resin. Based on the measured composite properties, the corresponding characteristics of the BA9916 resin were then calculated using the modulus‐modified rule‐of‐mixtures with carbon fiber reinforcement properties provided by the material supplier. Subsequently, the representative volume element (RVE) models for the UD composites with various configurations, including different volume fractions of carbon fibers, degrees of curing and curing temperature profiles, were established for simulation with the theoretically calculated resin properties and supplier‐provided fiber characteristics. Homogenized results of the RVE modeling were validated against the experimental measurements with an error of less than 6%, demonstrating the effectiveness of this modeling‐based method for characterizing the coefficients of thermal expansion (CTE) and chemical shrinkage (CSC) of the highly viscous thermoset resin under the curing condition. The resin is a thermoset epoxy resin toughened using thermoplastic additives. The excessive viscosity of the resin hindered effective degassing. The thermo‐chemical properties of the resin were characterized indirectly. A non‐contact approach was used to determine the CTE and CSC. [ABSTRACT FROM AUTHOR]

A series of Y 2 MoO 6 :0.01Er3+/0.09 Yb3+ upconversion phosphors doped with different concentrations of Li + ions (0.05, 0.10, 0.15, 0.20, 0.25, and 0.30 mol) were prepared by the high temperature solid phase reaction method, and their physical phase structures and upconversion luminescence were analyzed. At the excitation wavelength of 980 nm, the addition of Li+ sharply increased the upconversion luminescence intensity. With the increase of Li+ ion content, the luminescence intensity first increases and then decreases. When the doping amount reaches 0.25 mol, the luminescence intensity reaches the maximum, and the sample emits green light in the two-photon process. Y 2 MoO 6 :0.01Er3+/0.09 Yb3+/0.25Li + exhibits excellent temperature sensing properties. Its absolute sensitivity S a reaches up to 0.012 K-1, the relative sensitivity S r is 0.017 K-1 and the measurement error δ T is less than 0.3 K at room temperature. Its temperature sensing performance is far superior to similar materials. In addition, Judd-Ofelt theory calculations revealed that in comparison with Y 2 MoO 6 :0.01Er3+/0.09 Yb3+, its Ω 2 increased from 0.56 × 10−20 cm2 to 3.68 × 10−20 cm2, and the spectroscopic quality factor Ω 4 /Ω 6 ratio reached 4.09, which is much higher than that of the other fluorescent materials. The main reason is that Li + replaces the Y3+ ion lattice site, causing lattice distortion and reducing the lattice symmetry around the active ion. The increase in asymmetry increases the possibility of spontaneous radiative transitions. It is shown that the prepared upconversion luminescent materials have potential applications in the field of solid-state lasers and optical sensing. [ABSTRACT FROM AUTHOR]

Computer vision strain sensors typically require the camera position to be fixed, limiting measurements to surface deformations of structures at pixel‐level resolution. Also, sensors have a service term significantly shorter than the designed service term of the structures. This paper presents research on a high durable computer vision sensor, microimage strain sensing (MISS)‐Silica, which utilizes a smartphone connected to an endoscope for measurement. It is designed with a range of 0.05 ε, enabling full‐stage strain measurement from loading to failure of structures. The sensor does not require the camera to be fixed during measurements, laying the theoretical foundation for embedded computer vision sensors. Measurement accuracy is improved from pixel level to sub‐pixel level, with pixel‐based measurement errors around 8 µε (standard deviation approximately 7 µε) and sub‐pixel calculation errors around 6 µε (standard deviation approximately 5 µε). Sub‐pixel calculation has approximately 30% enhancement in measurement accuracy and stability. MISS‐Silica features easy data acquisition, high precision, and long service term, offering a promising method for long‐term measurement of both surface and internal structures. [ABSTRACT FROM AUTHOR]

A detailed map of the distribution of dust at high Galactic latitudes is essential for future cosmic microwave background polarization experiments because the dust, while diffuse, remains a significant foreground in these regions. We develop a Bayesian model to identify a region of the Hertzsprung–Russell (HR) diagram suited to constrain the single-star extinction accurately at high Galactic latitudes. Using photometry from Gaia , 2MASS (Two Micron All-Sky Survey), and AllWISE, and parallax from Gaia , we employ nested sampling to fit the model to the data and analyse the posterior over stellar parameters for both synthetic and real data. Charting low variations in extinction is complex due to systematic errors and degeneracies between extinction and other stellar parameters. The systematic errors can be minimized by restricting our data to a region of the HR diagram where the stellar models are most accurate. Moreover, the degeneracies can be reduced by including astrophysical priors and spectroscopic constraints. We show that accounting for the measurement error of the data and the assumed inaccuracies of the stellar models are critical in accurately recovering small variations in extinction. We compare the posterior distribution for individual stars with spectroscopic stellar parameter estimates from the LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope) and Gaia ESO and illustrate the importance of estimating extinction and effective temperature as a joint solution. [ABSTRACT FROM AUTHOR]

The cluster-weighted model (CWM) is a model-based clustering approach that utilizes a mixture of regression models to cluster data points based on both a response variable Y and covariates , where the covariates are assumed to be random. The Gaussian CWM (GCWM) is the most commonly used member of the CWM family, where the Gaussian distribution is adopted for both the covariates and the response given the covariates. In mixture of regression, assignment of data points to the clusters is based on the conditional distribution of the response variable given covariates and is independent of the covariates' distribution. In CWM, to increase clustering performance, the covariates' distribution is also used to assign data points to the clusters. Existing researches on CWMs are limited to the directly observed covariates, which may not reflect real-world scenarios where measurement errors (MEs) occur. The measurement error can lead to inconsistent estimates, consequently, produce spurious or obscure clusters. In this article, we assume that random covariates are latent, observed with an independent ME that has the Gaussian distribution. A new generalized expectation maximization algorithm is defined for estimating model parameters. The performance of the proposal is illustrated and compared with the GCWM using both simulated and real data. [ABSTRACT FROM AUTHOR]

We propose a covariance ratio measure for symmetry or asymmetry of a probability density function. This measure is constructed by the ratio of the covariance connected with the density function and the distribution function. We first propose a non parametric moment-based estimator of the covariance ratio measure and study its asymptotic results. Next, we consider statistical inference of the covariance ratio measure by using the empirical likelihood method. The empirical likelihood statistic is shown to be asymptotically a standard chi-squared distribution. Last, we study the covariance ratio measure when the random variable is unobserved under the multiplicative distortion measurement errors setting. The density function and the distribution function of the unobserved variable are estimated by using four calibrated variables. Appealing to these estimators, the calibrated covariance ratio measures are proposed and further shown to be asymptotically efficient as if there are no multiplicative distortion effects. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimators and test procedures. These methods are applied to analyze two real datasets for illustration. [ABSTRACT FROM AUTHOR]

In this article, we study the non parametric estimation of some regression curves when the data are observed with additive distortions, and these distortions for unobservable response variables and covariates are connected with a common observed confounding variable. We study the estimates of the non parametric mean function and its first derivative, the variance function, the Sharpe ratio function, and the correlation curve function. We obtain asymptotic normality results for the proposed non parametric estimators. Monte Carlo simulation experiments are conducted to examine the performance of the proposed estimators. The proposed estimators are applied to analyze a QSAR fish toxicity dataset for illustration. [ABSTRACT FROM AUTHOR]

The main shaft of a wind turbine is a critical component that ensures the normal operation of the turbine, and its axial displacement directly impacts its efficiency and safety. The inaccurate measurement of axial displacement may lead to severe issues such as shaft fractures, causing turbine shutdowns. Correcting measurement errors related to axial displacement is essential to prevent potential accidents. This study proposes an improved error correction method for measuring the axial displacement of wind turbine main shafts. Using a position-sensitive detector (PSD) and laser triangulation, the axial and radial displacements of the main shaft are measured to address environmental interference and cost constraints. Additionally, a Sparrow Search Algorithm- Backpropagation (SSA-BP) model is constructed based on operational data from the wind turbine's main shaft to correct the system's nonlinear errors. The Sparrow Search Algorithm (SSA) is employed to optimize the weights and thresholds of the Backpropagation (BP) neural network, enhancing prediction accuracy and model stability. Initially, a main shaft displacement measurement system based on a precision displacement stage was developed, and system stability tests and displacement measurement experiments were conducted. The experimental results demonstrate that the system stability error is ±0.025 mm, which is lower than the typical error of 0.05 mm in contact measurement. After model correction, the maximum nonlinear errors of the axial and radial displacement measurements are 0.83% and 1.29%, respectively, both of which are lower than the typical measurement error of 2% in contact measurements. This indicates that the proposed model can reliably and effectively correct the measurement errors. However, further research is still necessary to address potential limitations, such as its applicability in extreme environments and the complexity of implementation. [ABSTRACT FROM AUTHOR]

High-voltage and analog measurements occupy a large amount of automated test equipment (ATE) overhead. A method for realizing trimming automation through mixed digital and analog circuits is proposed. Taking a 40 nm 1 MB Flash chip test as an example, current high-voltage and analog quantity trimming accounts for 6.9% of the total test time. Through adding digital and analog circuits to the Built-In Self-Test (BIST) circuit, the trimming test time is reduced from 2.94 s to 0.23 s, and the ratio of the total test time is reduced to 0.58%. Compared with the original chip probing (CP) total time, the total time is reduced by 6.35%, the ATE overhead accounts for 19.35% of the total test time, and the Flash area is 0.63%. The measurement accuracy reaches 750 μV~6 mV, and the measurement error is 0.15% ± 7 mV, compared with the conventional ATE trimming method. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Protoacoustics has emerged as a promising real‐time range measurement method for proton therapy. Optical hydrophones (OHs) are considered suitable to detect protoacoustic waves owing to their ultracompact size and high sensitivity. In our previous research, we demonstrated that the time‐of‐arrival (TOA) measured by an OH showed good agreement with the simulated ground truth in a homogeneous medium.The purpose of the study was to experimentally evaluate the accuracy of the TOA and compression peak pressures detected by the OH. Protoacoustic waves that undergo the typical distortions occurring in the human body were investigated. In such cases, the use of small detectors such as OHs is desirable to minimize the effects of detector size and directivity.A 100‐MeV proton pencil beam emitted from a fixed‐field alternating gradient accelerator was irradiated onto a homogeneous water phantom and a water phantom with a half‐ or full‐sized silicone plate downstream of the Bragg peak (BP) or a bone plate that covered half of the beam cross‐section in the beam path. The OH was shifted 70 mm laterally across the beam axis downstream of the BP to measure the protoacoustic waves. The k‐WAVE acoustic wave transport simulation was employed as the ground truth. The TOA and the first compression peak pressures were compared between the simulation and experiment.The TOA deviation against the ground truth was primarily attributed to alignment errors of the measurement devices and phantoms, with deviations of < 1 mm. The peak pressure distribution closely resembled the ground truth, with FWHM differences of 0.0%–3.0% for the tested geometries.The OH was able to determine the TOA and peak pressures with sufficient accuracy in heterogeneous phantoms, even without considering the effect of the size of the detector or directivity on the measurements. [ABSTRACT FROM AUTHOR]

AbstractThe appearance of measurement errors and multicollinearity in data are two problematic issues that adversely affect the estimation of regression coefficients. In such circumstances, concerning regression coefficients, it can be practicable to utilize some prior information in the type of stochastic linear restrictions. The main purpose of this article is to overcome the problems of measurement errors and multicollinearity concurrently by utilizing stochastic prior information for the estimation of model coefficients in replicated ultrastructural measurement error (RUME) models. To this end, three classes of stochastic restricted estimators and their feasible forms are proposed. Large sample properties of newly introduced estimators are studied without making any distributional assumption. To evaluate the performance of newly defined estimators a numerical example is done by using real data, Cigar data set, and a Monte Carlo simulation study is also conducted. It is concluded that the newly proposed stochastic restricted estimators outperform the predefined estimators for the RUME models in the existence of multicollinearity. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, a one-sided adaptive exponentially weighted moving average X¯ scheme utilizing the truncation method (called the one-sided TAEWMA X¯ chart) is proposed for monitoring processes involving measurement errors. Leveraging the linear covariate error model, both the Markov chain model and the Monte Carlo simulation are established to evaluate the run length (RL) properties of the proposed chart. In the presence of measurement errors, an optimal search strategy is developed to identify the optimal design parameters of the scheme. Subsequently, the effects of measurement errors on both zero-state and steady-state average run length (ARL) performance of the proposed scheme are studied. Comparative studies with two other existing schemes reveal that, despite the significant effects of measurement errors on ARL performance, the proposed chart remains superior to the existing competing schemes in shift detection, especially when multiple measurement operations are involved. Finally, an illustrative example is provided to demonstrate the application of the suggested scheme. [ABSTRACT FROM AUTHOR]

Background: In the winter of 2022/2023, excess death estimates for Germany indicated a 10% elevation, which has led to questions about the significance of this increase in mortality. Given the inherent errors in demographic forecasting, the reliability of estimating a 10% deviation is questionable. This research addresses this issue by analyzing the error distribution in forecasts of weekly deaths. By deriving empirical prediction intervals, we provide a more accurate probabilistic study of weekly expected and excess deaths compared to the use of conventional parametric intervals. Methods: Using weekly death data from the Short-term Mortality Database (STMF) for 23 countries, we propose empirical prediction intervals based on the distribution of past out-of-sample forecasting errors for the study of weekly expected and excess deaths. Instead of relying on the suitability of parametric assumptions or the magnitude of errors over the fitting period, empirical prediction intervals reflect the intuitive notion that a forecast is only as precise as similar forecasts in the past turned out to be. We compare the probabilistic calibration of empirical skew-normal prediction intervals with conventional parametric prediction intervals from a negative-binomial GAM in an out-of-sample setting. Further, we use the empirical prediction intervals to quantify the probability of detecting 10% excess deaths in a given week, given pre-pandemic mortality trends. Results: The cross-country analysis shows that the empirical skew-normal prediction intervals are overall better calibrated than the conventional parametric prediction intervals. Further, the choice of prediction interval significantly affects the severity of an excess death estimate. The empirical prediction intervals reveal that the likelihood of exceeding a 10% threshold of excess deaths varies by season. Across the 23 countries studied, finding at least 10% weekly excess deaths in a single week during summer or winter is not very unusual under non-pandemic conditions. These results contrast sharply with those derived using a standard negative-binomial GAM. Conclusion: Our results highlight the importance of well-calibrated prediction intervals that account for the naturally occurring seasonal uncertainty in mortality forecasting. Empirical prediction intervals provide a better performing solution for estimating forecast uncertainty in the analyses of excess deaths compared to conventional parametric intervals. [ABSTRACT FROM AUTHOR]

Clustering analysis of functional data, which comprises observations that evolve continuously over time or space, has gained increasing attention across various scientific disciplines. Practical applications often involve functional data that are contaminated with measurement errors arising from imprecise instruments, sampling errors, or other sources. These errors can significantly distort the inherent data structure, resulting in erroneous clustering outcomes. In this article, we propose a simulation‐based approach designed to mitigate the impact of measurement errors. Our proposed method estimates the distribution of functional measurement errors through repeated measurements. Subsequently, the clustering algorithm is applied to simulated data generated from the conditional distribution of the unobserved true functional data given the observed contaminated functional data, accounting for the adjustments made to rectify measurement errors. We illustrate through simulations show that the proposed method has improved numerical performance than the naive methods that neglect such errors. Our proposed method was applied to a childhood obesity study, giving more reliable clustering results. [ABSTRACT FROM AUTHOR]

Introduction: While randomized controlled trials remain the reference standard for evaluating treatment efficacy, there is an increased interest in the use of external control arms (ECA), namely in oncology, using real-world data (RWD). Challenges related to measurement of real-world oncology endpoints, like progression-free survival (PFS), are one factor limiting the use and acceptance of ECAs as comparators to trial populations. Differences in how and when disease assessments occur in the real-world may introduce measurement error and limit the comparability of real-world PFS (rwPFS) to trial progression-free survival. While measurement error is a known challenge when conducting an externally-controlled trial with real-world data, there is limited literature describing key contributing factors, particularly in the context of multiple myeloma (MM). Methods: We distinguish between biases attributed to how endpoints are derived or ascertained (misclassification bias) and when outcomes are observed or assessed (surveillance bias). We further describe how misclassification of progression events (i.e., false positives, false negatives) and irregular assessment frequencies in multiple myeloma RWD can contribute to these biases, respectively. We conduct a simulation study to illustrate how these biases may behave, both individually and together. Results: We observe in simulation that certain types of measurement error may have more substantial impacts on comparability between mismeasured median PFS (mPFS) and true mPFS than others. For instance, when the observed progression events are misclassified as either false positives or false negatives, mismeasured mPFS may be biased towards earlier (mPFS bias = −6.4 months) or later times (mPFS bias = 13 months), respectively. However, when events are correctly classified but assessment frequencies are irregular, mismeasured mPFS is more similar to the true mPFS (mPFS bias = 0.67 months). Discussion: When misclassified progression events and irregular assessment times occur simultaneously, they may generate bias that is greater than the sum of their parts. Improved understanding of endpoint measurement error and how resulting biases manifest in RWD is important to the robust construction of ECAs in oncology and beyond. Simulations that quantify the impact of measurement error can help when planning for ECA studies and can contextualize results in the presence of endpoint measurement differences. [ABSTRACT FROM AUTHOR]

Due to the error between the strain measured by the fiber grating sensor pasted on the surface of the substrate and the real strain of the substrate, the analysis of the relationship between the strain measured by the fiber and the actual strain of the substrate is the focus of this research. Since structures are subjected to various harsh conditions in actual service, such as temperature changes, fatigue, corrosion, aging, cracks and other external factors, when using surface-adhesive fiber Bragg grating (FBG) sensors for structural health monitoring, all external factors may affect the measurement accuracy of the sensors and cause measurement errors, so it is necessary to consider some specific factors in a comprehensive manner. In this paper, we theoretically study the average strain transfer rate of the three-layer strain transfer model of a surface-adhesive fiber grating sensor under the effects of temperature change and fatigue load, derive the formula for the average dynamic strain transfer rate under the combined effects of two external factors using the shear-lag method and analyze the parameters of the fiber-optic sensor to correct the error and optimize the measurement accuracy of the sensor in order to better monitor the structure under temperature change and fatigue load. The shear hysteresis method is also used to analyze the parameters of the fiber-optic sensor to correct the errors and optimize the measurement accuracy of the sensor in order to better monitor the real dynamic strain of the substrate under temperature and fatigue loads and provide theoretical guidance for its measurement. [ABSTRACT FROM AUTHOR]

AbstractPurposeMaterials and methodsResultsConclusionsTo translate the American Shoulder and Elbow Surgeon (ASES) score into Indonesian and to assess its psychometric properties.The ASES score was translated into Indonesian following the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) guidelines. Next, the validity and reliability of the Indonesia-ASES (I-ASES) were investigated. Patients with shoulder problems at a hospital in Indonesia who were willing to participate completed three questionnaires: the I-ASES, the Medical Outcomes Study 12-Item Short-Form Health Survey (SF-12), and the Shoulder Pain and Disability Index (SPADI). Participants were asked to complete the I-ASES a second time after a one-week interval. In adherence to COSMIN guidelines, we assessed construct validity using hypothesis testing, test–retest reliability, internal consistency, floor and ceiling effects, and measurement error. The Bland–Altman method was used to evaluate systematic bias.The I-ASES showed good construct validity. No floor or ceiling effects were found. An ICC value of 0.82 was found, indicating a robust level of test–retest reliability. A Cronbach α of 0.90 implied good internal consistency. Bland and Altman analysis showed no systematic bias. The standard error of measurement (SEM), the minimal detectable change at the individual level (MDCind) and at the group level (MDCgrp) were 2.3, 6.5, and 0.6, respectively.The I-ASES shows good validity and reliability for evaluating shoulder problems in Indonesian-speaking patients and reveals comparable psychometric properties to those of the other languages version of ASES.Implications for rehabilitationThe Indonesian version of the American Shoulder and Elbow Surgeons standardized Shoulder Assessment Form (I-ASES) is now available.I-ASES demonstrated satisfactory reliability and validity.I-ASES can be considered a valid and reliable questionnaire for use in Indonesian patients with shoulder problem.The Indonesian version of the American Shoulder and Elbow Surgeons standardized Shoulder Assessment Form (I-ASES) is now available.I-ASES demonstrated satisfactory reliability and validity.I-ASES can be considered a valid and reliable questionnaire for use in Indonesian patients with shoulder problem. [ABSTRACT FROM AUTHOR]

Background: The Foot Function Index (FFI) is a reliable and widely used standardized questionnaire that measures the impact of foot pathology on function. With 571 million Hindi-speaking people living globally and an increasing incidence of foot-related pathologies, it is imperative to cross-culturally translate and adapt a Hindi version of the FFI (FFI-Hi). We aimed to translate, cross-cultural adapt, and psychometrically test the FFI-Hi for use in Hindi-speaking individuals with foot conditions. Methods: The translation of FFI-Hi was performed according to guidelines given by MAPI Research Trust. A total of 223 Hindi-speaking participants afflicted with foot conditions completed the FFI-Hi alongside the Short Form 36 (SF-36) questionnaire. The study duration spanned between October 2023 and January 2024. The initial phase was the translation and adaptation of FFI to cultural context. Followed by testing of psychometric properties involving of 133 participants for the test-retest reliability of FFI-Hi after a 7-day interval. Results: The mean age of the participants was 47.10 (± 8.1) years. The majority of the participants were male (n = 148, 66.4%) and the most common foot condition was plantar fasciopathy (n = 91, 40.8%). The mean score of FF-Hi was 33.7 ± 11.7. The internal consistency of FFI-Hi was good with the Cronbach's alpha (α) value of 0.891 and excellent reproducibility with the intra-class correlation of 0.90. The 95% minimal detectable change (MCD) and the standard error of measurement of the FFI-Hi was 22.02 and 7.94 respectively. Convergent validity between FFI-Hi subscales and SF-36 domains was moderate. Factor analysis corroborated the multidimensional nature of the FFI-Hi. Conclusion: The FFI-Hindi version was successfully cross-culturally adapted, translated and demonstrated acceptable psychometric properties to be used in clinical practice and research. Further, the context-specific Hindi language version of FFI will enhance the utility of FFI in foot function evaluation and remove language barrier in patients reporting disability and activity limitation related to foot conditions. Registration: Clinical Trials Registry of India (CTRI/2023/07/055734). [ABSTRACT FROM AUTHOR]

AbstractNowadays research into affect frequently employs intensive longitudinal data to assess fluctuations in daily emotional experiences. The resulting data are often analyzed with moderated autoregressive models to capture the influences of contextual events on the emotion dynamics. The presence of noise (e.g., measurement error) in the measures of the contextual events, however, is commonly ignored in these models. Disregarding noise in these covariates when it is present may result in biased parameter estimates and wrong conclusions drawn about the underlying emotion dynamics. In a simulation study we evaluate the estimation accuracy, assessed in terms of bias and variance, of different moderated autoregressive models in the presence of noise in the covariate. We show that estimation accuracy decreases when the amount of noise in the covariate increases. We also show that this bias is magnified by a larger effect of the covariate, a slower switching frequency of the covariate, a discrete rather than a continuous covariate, and constant rather than occasional noise in the covariate. We also show that the bias that results from a noisy covariate does not decrease when the number of observations increases. We end with a few recommendations for applying moderated autoregressive models based on our simulation. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the efficacy of microchips and 3D microsensors in the measurement of orthodontic forces. Methods: Through September 2023, comprehensive searches were conducted on PubMed/MEDLINE, SCOPUS and SCIELO without restrictions. Results: After removing duplicate entries and applying the eligibility criteria, 23 studies were included for analysis. All the studies were conducted in vitro, and slightly more than half of them were centred on evaluating orthodontic forces exerted by aligners. Eight utilized microchips as measurement tools, while the remaining studies made use of 3D microsensors for their assessments. In the context of fixed appliances, key findings included a high level of agreement in 3‐dimensional orthodontic force detection between simulation results and actual applied forces. Incorporating critical force‐moment combinations during smart bracket calibration reduced measurement errors for most components. Translational tooth movement revealed a moment‐to‐force ratio, aligning with the bracket's centre of resistance. The primary findings in relation to aligners revealed several significant factors affecting the forces exerted by them. Notably, the foil thickness and staging were found to have a considerable impact on these forces, with optimal force transmission occurring at a layer height of 150 μm. Furthermore, the type of material used in 3D‐printing aligners influenced the force levels, with attachments proving effective in generating extrusive forces. Deliberate adjustments in aligner thickness were observed to alter the forces and moments generated. Conclusions: Microchips and 3D sensors provide precise and quantitative measurements of orthodontic forces in in vitro studies, enabling accurate monitoring and control of tooth movement. [ABSTRACT FROM AUTHOR]

Motivated by the challenges in analyzing gut microbiome and metagenomic data, this work aims to tackle the issue of measurement errors in high-dimensional regression models that involve compositional covariates. This paper marks a pioneering effort in conducting statistical inference on high-dimensional compositional data affected by mismeasured or contaminated data. We introduce a calibration approach tailored for the linear log-contrast model. Under relatively lenient conditions regarding the sparsity level of the parameter, we have established the asymptotic normality of the estimator for inference. Numerical experiments and an application in microbiome study have demonstrated the efficacy of our high-dimensional calibration strategy in minimizing bias and achieving the expected coverage rates for confidence intervals. Moreover, the potential application of our proposed methodology extends well beyond compositional data, suggesting its adaptability for a wide range of research contexts. [ABSTRACT FROM AUTHOR]

This paper explores the psychometric properties of scores derived from autogenerated test forms by introducing three conceptual frameworks: Alternate Test Forms, Randomly Parallel Forms, and Approximately Parallel Forms. Each framework provides a distinct perspective on score comparability, definitions of true score and standard error of measurement (SEM), and the necessity of equating. Through a simulation study, we illustrate how these frameworks compare in terms of true scores and SEMs, while also assessing the impact of equating on score comparability across varying levels of form variability. Ultimately, this study seeks to lay the groundwork for implementing scoring practices in large‐scale standardized assessments that use autogenerated forms. [ABSTRACT FROM AUTHOR]

To enhance meteorological detection methods, an atmospheric boundary layer detection system based on a rotary-wing unmanned aerial vehicle (UAV) was proposed. Computational fluid dynamics (CFD) was employed to model the surrounding airflow distribution during UAV hovering, thereby determining the optimal positions for sensor installation. A novel radiation shield was designed for the temperature sensor, offering both excellent radiation shielding and superior ventilation. To further improve temperature measurement accuracy, an error correction model based on CFD and neural network algorithms was designed. CFD was used to quantify the temperature measurement errors of the sensor under different environmental conditions. Subsequently, random forest and multilayer perceptron algorithms were employed to train and learn from the simulated temperature errors, resulting in the development of the error correction model. To validate the accuracy of the detection system, comparative experiments were conducted using the measurement values from the 076B temperature observation instrument as a reference. The experimental results indicate that the mean absolute error, root mean square error, and correlation coefficient between the experimental temperature errors and the algorithm-predicted errors are 0.055, 0.066, and 0.971 °C, respectively. The average error of the corrected temperature data is 0.05 °C, which shows substantial agreement with the reference temperature data. During UAV hovering, the average discrepancies between the temperature, humidity, and air pressure data of the detection system and the ground-based reference data are 0.6 °C, 1.6% RH, and 0.77 hPa, respectively. [ABSTRACT FROM AUTHOR]

Underground topographic bases are of particular importance in the design and management of underground mining works. They can be dependent on the geodetic and topographic system on the surface, but they can also be independent in relation to a particular reference system. In the case of dependent topographic bases, it is preferable, from the point of view of the precisions required for the drawing of mining works in safe conditions, that they are related to two points known by their x, y (fixed) coordinates. The quantities measured in such situations are angles and distances. Measurement errors are transmitted to the determined quantities and consequently it is necessary to analyze the factors with which such a process can be evaluated. The necessary scientific approach is presented below. [ABSTRACT FROM AUTHOR]

Monitoring deformations in mining pillars in salt mines is crucial for detecting spalling, cracks, and distortions caused by internal pressures, with the aim of preventing mining accidents and maintaining structural integrity. At Salina Ocna Dej, annual measurements have not utilized drone technology, which was recently introduced in Romania. The measurements conducted with the DJI Phantom 4 Pro Plus drone in 2020 and 2024 provided essential data on changes in the mining pillar. In 2020, difficulties caused by air currents slightly affected the measurements, but by 2024, improved conditions allowed for more accurate results. The horizontal sections taken at various elevations revealed significant spalling and distortions, particularly on the eastern and western sides, with substantial rupture in the northwest corner and displacements on the northern side. Comparing the data from the two measurement sets showed a significant difference in precision, highlighting the need for regular inspections to prevent premature erosion and avoid accidents. [ABSTRACT FROM AUTHOR]

A novel dual-channel ultrasonic flowmeter based on the time-difference method is proposed, aiming at solving the measurement error due to the installation angle of the transducers and improving the measurement accuracy. The angle error is eliminated by optimizing the ultrasonic propagation path so that it is parallel to the fluid flow direction. The pipeline design is optimized to reduce the pressure loss to ensure high-precision measurements at different flow rates. In addition, in order to solve the measurement accuracy problem caused by the transducer position, the measurement results of the two channels are fused by Secretary Bird Optimization Algorithm-Back Propagation Neural network, which reduces the error of the measurements and improves the overall accuracy of the measurements. The results of system error analysis and uncertainty evaluation show that the calibrated flowmeter has a maximum relative error of 0.6% and a maximum repeatability of 0.7%, which proves its reliability and effectiveness in fluid measurement. [ABSTRACT FROM AUTHOR]

After a fracture, patients have reduced willingness to bend and extend their elbow joint due to pain, resulting in muscle atrophy, contracture, and stiffness around the elbow. Moreover, this may lead to progressive atrophy of the muscles around the elbow, resulting in permanent functional loss. Currently, a goniometer is used to measure the range of motion, ROM, to evaluate the recovery of the affected limb. However, the measurement process can cause measurement errors ranging from 4 to 5 degrees due to unskilled operation or inaccurate placement, leading to inaccurate judgments of the recovery of the affected limb. In addition, the current measurement methods do not include an assessment of muscle endurance. In this paper, the proposed device combines image recognition and a myoelectric signal sensor to measure the joint movement angle and muscle endurance. The movement angle of the elbow joint is measured using image recognition. Muscle endurance is measured using the myoelectric signal sensor. The measured data are transmitted to a cloud database via an app we have proposed to help medical staff track a patient's recovery status. The average error value of static image recognition and verification is up to 0.84 degrees. The average error value of dynamic image recognition and verification is less than 1%. The average error of total harmonic distortion (THD) verified by the myoelectric signal sensor is less than ±3%. It was proven that our system could be applied to measuring elbow joint range of motion. Since this is pilot research, most of the measurement subjects are healthy people without dysfunction in arm movement, and it is difficult to observe differences in the measurement results. In the future, experiments will be conducted on patients with elbow fractures through the IRB. This is expected to achieve the effect of encouraging patients to be actively rehabilitated at home through their measurement data and images of their actions being displayed in real time using our cheap and compact device and app. [ABSTRACT FROM AUTHOR]

Background/Objectives: Patients with non-specific chronic neck pain (NSCNP) exhibit sensorimotor disturbances, with proprioception impairment considered an important aspect. The aim of this study was to assess the reliability and validity of a novel inertial sensor-based electrogoniometer (KFORCE Sens®) for cervical spine (CS) proprioception measurement in patients with NSCNP. Methods: The within-day intra-rater reliability of CS proprioception and its association with patient demographics and clinical status were examined in fifty-nine patients with NSCNP, aged between 25–65 years, recruited from primary care. CS proprioception was examined via angle reproduction, in angles set mid-range in the available CS range of motion (ROM) in each motion direction. The clinical status evaluation comprised the maximum and average pain intensity in the last week, disability, fear of movement/re-injury, catastrophizing, neck awareness, and CS-ROM. Reliability was assessed using the intraclass correlation coefficient (ICC2,1), standard error of the measurement (SEM), and minimum detectable change (MDC95%). Pearson's R assessed between-measures associations. Results: CS proprioception reliability was good (ICC2,1 = 0.75–0.89), with low measurement error (SEM = 1.38–3.02° and MDC95% = 3.83–8.38°). Correlations between participants' CS proprioception and their clinical status or demographics were not significant. Conclusions: The reliability of CS proprioception assessment with the KFORCE Sens® was good in a sample of mildly to moderately disabled patients with CNP and thus deemed suitable for further research in this field. [ABSTRACT FROM AUTHOR]

Background: The presence of sensorimotor control deficits in adolescent idiopathic scoliosis compared to typically developed adolescents is supported by the literature but lacks reliability studies for assessment in this population. This study aimed to assess the reliability of eight sensorimotor control tests, in terms of static and dynamic balance, joint position sense (JPS) tests of the extremities and the spine, and a functional upper extremity proprioceptive test in adolescent idiopathic scoliosis subjects. Methods: Sixty adolescent idiopathic scoliosis subjects were divided into four groups. Each group underwent two tests by the same examiner, repeated at 15 min intervals. Reliability was measured using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), and smallest detectable difference (SDD). Results: The results showed high reliability for the upper extremity functional proprioception test, for the dynamic and static balance test, and for the spinal lateral flexion joint position sense test in both directions. On the other hand, the shoulder external rotation, knee extension, elbow flexion, and spinal flexion joint position sense tests demonstrated poor reliability in adolescent idiopathic scoliosis subjects. Conclusions: Therapists are recommended to use the functional upper extremity proprioception test, the Fukuda test, the static balance test using a force footplate, and the spinal lateral flexion joint position sense test for assessing treatment progress in this population. [ABSTRACT FROM AUTHOR]

One of the reactive forms of oxygen is hydrogen peroxide (H2O2), which has been investigated as a key component of growth processes and stress responses. Different methods for the determination of H2O2 production by animal and bacterial cells exist; however, its detection in algal cell cultures is more complicated due to the presence of photosynthetic pigments in the cells and the complex structure of cell walls. Considering these issues, a reliable, quick, and simple method for H2O2 detection is needed in phycological research. The aim of this methodological study was to optimize an Amplex UltraRed method for the fluorometric detection of H2O2 produced by microalgae cells, using a wild‐type strain of Chlamydomonas reinhardtii as a model. The results showed that (i) potassium phosphate is the most suitable reaction buffer for this method, (ii) a 560 nm wavelength variant is the most appropriate as the excitation wavelength for fluorescence spectra measurement, (iii) a 50:50 ratio for the reaction mixture to sample was the most suitable, (iv) the fluorescence signal was significantly influenced by the density of the microalgae biomass, and (v) sample fortification with H2O2 allowed for an increase of the method's reliability and repeatability. The proposed protocol of the Amplex UltraRed method for the fluorometric detection of H2O2 produced by microalgae cells can yield a sensitive and accurate determination of the content of the test compound, minimizing measurement errors, eliminating chlorophyll autofluorescence problem, and compensating for the matrix effect. This method can be applied to the study of other microalgae species. [ABSTRACT FROM AUTHOR]

Aims: This study explored the reliability, validity and perceived comfort of a novel thumb pressure measure and calculation of a toe–thumb index to identify their suitability as an adjunct or alternatives to ankle–brachial and toe–brachial indices. Methods and Results: Repeated manual thumb and toe systolic blood pressures were conducted using two raters, over two time points, on 34 healthy participants. Concurrent automated toe, thumb and brachial systolic blood pressures as well as comfort ratings for these measures (using a 10 mm visual analogue scale) were captured once by a research assistant. Automated thumb and brachial measures showed fair correlation (ρ = 0.36, p = 0.03) and a toe–thumb index and toe–brachial index good correlation (ρ = 0.62, p < 0.01). Intraclass correlation coefficients (ICC) identified moderate intra‐rater reliability for manual thumb pressures for Rater 1 and 2 (ICC 0.57, 95% CI [0.14, 0.79] and ICC 0.74, 95% CI [0.49, 0.87], respectively), while inter‐rater reliability was poor (ICC = 0.16, 95% CI [−0.85, 0.47]). Concurrent validity comparing manual and automated measures for thumb pressure was also poor (ICC −0.05, 95% CI [−1.06, 0.72] and ICC 0.42, 95% CI [−0.16, 0.72] Rater 1 and 2 respectively). Thumb measures were significantly more comfortable than brachial measures (5 mm, p < 0.00). Conclusion: Thumb systolic pressures are correlated with brachial systolic pressures, with reasonable intra‐rater reliability, however, correlation is only fair and measurement error wider than clinically acceptable. Furthermore, manual measures are poorly correlated with automated units. Consequently, caution is required in applying these techniques. As thumb measures were perceived as significantly more comfortable than brachial measures and have an advantage where brachial pressures cannot, or should not, be obtained, further evaluation is warranted. [ABSTRACT FROM AUTHOR]

Adaptive action-related reactions to errors, i.e. (meta-)cognitive processes and behaviours directly aimed at overcoming an error, have been proposed to benefit learning outcomes. However, causally interpretable findings are sparse in the current literature. Addressing this research deficit, the present study aimed at investigating whether action-related error adaptivity during the learning process facilitates knowledge acquisition in an actual learning situation. Data of 177 undergraduate students was analysed in structural equation models. Adaptive action-related reactions to errors were simultaneously assessed via self-reports and a behavioural indicator (voluntary repetitions of previous learning material after errors). Both forms of operationalisation were positively correlated and revealed similar result patterns. In two separate models, self-reported as well as behavioural adaptive action-related reactions to errors indicated a positive effect on knowledge acquisition. These findings further validate the self-report measurement of action-related error adaptivity and point to its direct impact on the learning outcome. [ABSTRACT FROM AUTHOR]

The shear wavefront propagates in a single direction, influenced by the phase deviation of the missing orthogonal direction in the interference pattern. Furthermore, the restriction of phase sampling points in the shear direction has a certain impact on attaining high spatial resolution in wavefront reconstruction. To attain high-precision wavefront reconstruction, it is necessary to acquire additional sampled data from various orthogonal shear directions. During our investigation, a wavefront reconstruction method was proposed for multi-directional orthogonal lateral shearing interferometry. This method establishes a relationship model that corresponds to multi-directional differential wavefront and differential Zernike polynomials. Using the principle of wavefront reconstruction with differential Zernike polynomials, it allows for the reconstruction of wavefronts from any orthogonal-direction lateral shearing interference patterns. To validate the efficacy of the proposed method, the wavefront reconstruction accuracy of various sets of arbitrarily oriented shearing interferograms was simulated and analyzed. Additionally, the results were compared to those obtained from the average differential wavefront of multiple orthogonal shearing interferograms. The results show that by choosing multiple orthogonal shear directions to improve phase sampling data, wavefront reconstruction can be successfully accomplished using any number of orthogonal lateral shearing interferograms. This effectively reduces the impact of both random and systematic errors on the spatial resolution of the wavefront during the reconstruction process. Ultimately, the accuracy of the proposed method was confirmed through experimental validation. After comparing the repeatability measurement with the results obtained from the ZYGO interferometer, it was discovered that the precision of the relative measurement error in RMS was superior to 0.01λ. [ABSTRACT FROM AUTHOR]

Measurement error and information bias are ubiquitous in epidemiology, yet directed acyclic graphs (DAGs) are infrequently used to represent them, in contrast with confounding and selection bias. This represents a missed opportunity to leverage the full utility of DAGs to depict associations between the variables we actually analyse in practice: empirically measured variables, which are necessarily measured with error. In this article, we focus on applying causal diagrams to depict the data-generating mechanisms that give rise to the data we analyse, including measurement error. We begin by considering empirical data considerations using a general example, and then build up to a specific worked example from the clinical epidemiology of hearing health. Throughout, our goal is to highlight both the challenges and the benefits of using DAGs to depict measurement error. In addition to the application of DAGs to conceptual causal questions (which pertain to unmeasured constructs free from measurement error), which is common, we highlight the advantages associated with applying DAGs to also include empirically measured variables and—potentially—information bias. We also highlight the implications implied by this use of DAGs, particularly regarding the unblocked backdoor path causal structure. Ultimately, we seek to help increase the clarity with which epidemiologists can map traditional epidemiological concepts (such as information bias and confounding) onto causal graphical structures. [ABSTRACT FROM AUTHOR]