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The Net Promotor Score (NPS) is ubiquitous, relying on a single-item question to capture consumers' word-of-mouth (WOM). The question asks consumers for their likelihood of recommending a brand to friends and colleagues. Despite its popularity and advantages over longer satisfaction surveys, NPS has potential weaknesses. Among them are that the NPS question (1) is double-barreled by asking in a single question for likelihood to recommend to friends and likelihood to recommend to colleagues, (2) focuses on recommendations, and thus, ignores consumers' likelihood to spread negative WOM, and (3) ignores online WOM, which often involves recommendations to strangers rather than friends or colleagues. This paper empirically tests these three potential weaknesses of the NPS measure on the WOM conclusions derived from NPS. Specifically, three experiments vary whether NPS assesses likelihood to recommend to a friend and colleague in a single question (how NPS is currently measured) or in two separate questions. In addition, NPS is compared to responses to an explicit negative WOM question (intent to warn others about the brand). Moreover, across studies, the NPS is reported for a recent positive experience and either a recent negative experience or a recent mixed experience. NPS is also compared to likelihood to engage in online WOM in terms of posting an online review and the intended online rating. By examining these issues, this research sheds light on consumers' interpretations of NPS, the factors that influence these interpretations, and how these factors affect NPS' ability to predict negative WOM, online WOM, as well as satisfaction, loyal behavior, and WOM in general. [ABSTRACT FROM AUTHOR]

BACKGROUND: Social desirability can negatively affect the validity of self-reported measures, including underreporting of stigmatized behaviors like alcohol consumption. The Marlowe-Crowne Social Desirability Scale (SDS) is widely implemented and comprised of Denial and Attribution Domains (i.e., tendencies to deny undesirable traits or attribute socially desirable traits to oneself, respectively). Yet, limited psychometric research has been conducted in sub-Saharan Africa, where the prevalence of unhealthy alcohol consumption is high as well as religiosity and hierarchical social norms. To address this gap, we (a) conducted an exploratory study assessing certain psychometric properties of the 28-item SDS (Runyankole-translated) among persons with HIV (PWH) in Uganda, and (b) examined the relationship between social desirability and self-reported alcohol use. METHODS: We pooled baseline data (N = 1153) from three studies of PWH engaged in alcohol use from 2017 to 2021. We assessed the translated scales construct validity (via confirmatory factor analysis), internal consistency, item performance, differential item functioning by gender, concurrent validity with the DUREL religiosity index domains, and the association between social desirability and self-reported alcohol use. RESULTS: Participants had a mean age of 40.42 years, 63% were men, and 91% had an undetectable HIV viral load. The 28-item SDS had satisfactory construct validity (Model fit indices: RMSEA = 0.07, CFI = 0.84, TLI = 0.82) and internal consistency (Denial Domain ΩTotal = 0.82, Attribution Domain ΩTotal = 0.69). We excluded Item 14 (I never hesitate to help someone in trouble) from the Attribution Domain, which mitigated differential measurement error by gender and slightly improved the construct validity (Model fit indices: RMSEA = 0.06, CFI = 0.86, TLI = 0.85) and reliability (Attribution Domain ΩTotal = 0.72) of the 27-item modified SDS. Using the 27-item SDS, we found that social desirability was weakly correlated with religiosity and inversely associated with self-reported alcohol use after adjusting for biomarker-measured alcohol use and other confounders (β = -0.05, 95% confidence interval: -0.09 to -0.01, p-value = 0.03). CONCLUSIONS: We detected and mitigated measurement error in the 28-item Runyankole-translated SDS, and found that the modified 27-item scale had satisfactory construct validity and internal consistency in our sample. Future studies should continue to evaluate the psychometric properties of the Runyankole-translated SDS, including retranslating Item 14 and reevaluating its performance.

Health agencies rely upon survey-based physical measures to estimate the prevalence of key global health indicators such as hypertension. Such measures are usually collected by nonhealthcare worker personnel and are potentially subject to measurement error due to variations in interviewer technique and setting, termed interviewer effects. In the context of physical measurements, particularly in low- and middle-income countries, interviewer-induced biases have not yet been examined. Using blood pressure as a case study, we aimed to determine the relative contribution of interviewer effects on the total variance of blood pressure measurements in three large nationally representative health surveys from the Global South. We utilized 169,681 observations between 2008 and 2019 from three health surveys (Indonesia Family Life Survey, National Income Dynamics Study of South Africa, and Longitudinal Aging Study in India). In a linear mixed model, we modeled systolic blood pressure as a continuous dependent variable and interviewer effects as random effects alongside individual factors as covariates. To quantify the interviewer effect-induced uncertainty in hypertension prevalence, we utilized a bootstrap approach comparing subsamples of observed blood pressure measurements to their adjusted counterparts. Our analysis revealed that the proportion of variation contributed by interviewers to blood pressure measurements was statistically significant but small: ∼0.24--2.2% depending on the cohort. Thus, hypertension prevalence estimates were not substantially impacted at national scales. However, individual extreme interviewers could account for measurement divergences as high as 12%. Thus, highly biased interviewers could have important impacts on hypertension estimates at the subdistrict level.

This study focuses on estimating a nonparametric regression model with right-censored data when the covariate is subject to measurement error. To achieve this goal, it is necessary to solve the problems of censorship and measurement error ignored by many researchers. Note that the presence of measurement errors causes biased and inconsistent parameter estimates. Moreover, non-parametric regression techniques cannot be applied directly to right-censored observations. In this context, we consider an updated response variable using the Buckley–James method (BJM), which is essentially based on the Kaplan–Meier estimator, to solve the censorship problem. Then the measurement error problem is handled using the kernel deconvolution method, which is a specialized tool to solve this problem. Accordingly, three denconvoluted estimators based on BJM are introduced using kernel smoothing, local polynomial smoothing, and B-spline techniques that incorporate both the updated response variable and kernel deconvolution.The performances of these estimators are compared in a detailed simulation study. In addition, a real-world data example is presented using the Covid-19 dataset. [ABSTRACT FROM AUTHOR]

Online surveys are a widely used mode of data collection. However, as no interviewer is present, respondents face any difficulties they encounter alone, which may lead to measurement error and biased or (at worst) invalid conclusions. Detecting response difficulty is therefore vital. Previous research has predominantly focused on response times to detect general response difficulty. However, response difficulty may stem from different sources, such as overly complex wording or similarity between response options. So far, the question of whether indicators can discriminate between these sources has not been addressed. The goal of the present study, therefore, was to evaluate whether specific characteristics of participants' cursor movements are related to specific properties of survey questions that increase response difficulty. In a preregistered online experiment, we manipulated the length of the question text, the complexity of the question wording, and the difficulty of the response options orthogonally between questions. We hypothesized that these changes would lead to increased response times, hovers (movement pauses), and y-flips (changes in vertical movement direction), respectively. As expected, each manipulation led to an increase in the corresponding measure, although the other dependent variables were affected as well. However, the strengths of the effects did differ as expected between the mouse-tracking indices: Hovers were more sensitive to complex wording than to question difficulty, while the opposite was true for y-flips. These results indicate that differentiating sources of response difficulty might indeed be feasible using mouse-tracking. [ABSTRACT FROM AUTHOR]

Over its 50 years of existence, the National Crime Victimization Survey (NCVS) has changed to address the informational needs of its users, as well as adapt to an evolving survey climate. Perhaps more than any other victim survey in the world, data quality has been an emphasis. In this article, the evolution of the methodology of the survey is described up to the point of the 2024 redesign. The first section provides an abbreviated description of the innovations and methods instituted up through the redesign in 1992. This section highlights key milestones and refers the reader to more detailed descriptions of the origins of the NCVS and the 1992 redesign. The second section provides more detail on methodological issues and changes since the 1992 redesign. This article closes with the challenges the survey faces moving forward into the mid-21st century. [ABSTRACT FROM AUTHOR]

Oral reading fluency (ORF) assessments are commonly used to screen at‐risk readers and evaluate interventions' effectiveness as curriculum‐based measurements. Similar to the standard practice in item response theory (IRT), calibrated passage parameter estimates are currently used as if they were population values in model‐based ORF scoring. However, calibration errors that are unaccounted for may bias ORF score estimates and, in particular, lead to underestimated standard errors (SEs) of ORF scores. Therefore, we consider an approach that incorporates the calibration errors in latent variable scores. We further derive the SEs of ORF scores based on the delta method to incorporate the calibration uncertainty. We conduct a simulation study to evaluate the recovery of point estimates and SEs of latent variable scores and ORF scores in various simulated conditions. Results suggest that ignoring calibration errors leads to underestimated latent variable score SEs and ORF score SEs, especially when the calibration sample is small. [ABSTRACT FROM AUTHOR]

In recent years, the EnodePro® device has been one of the most frequently used velocity sensors to track the bar velocity in resistance training, with the aim of providing load–velocity profiles. However, recent articles highlight a lack of reliability and validity in the estimated maximal strength, which can cause a serious health risk due to the overestimation of the bar velocity. With this study, we aimed to investigate whether imprecision in the measurement could explain the variance in this measurement error. Methods: The research question was evaluated by comparing the integrated velocities from the EnodePro® with the velocities from a high-resolution displacement sensor for the squat and bench press. The velocity was measured with loads corresponding to 30%, 50%, and 70% of the one-repetition maximum (1RM) in moderately trained participants (n = 53, f = 16, m = 37). Intraclass correlation coefficients (ICC) for agreement were supplemented by an exploration of the systematic bias and the random error (mean absolute error (MAE), mean absolute percentage error (MAPE)). Results: The results indicated movement specificity, with the ICC values for the squat ranging from 0.204 to 0.991 and with ICC = 0.678–0.991 for the bench press. Systematically higher velocities were reported by the EnodePro® sensor (p < 0.001–0.176), with an MAE = 0.036–0.198 m/s, which corresponds to an MAPE of 4.09–42.15%. Discussion: The EnodePro® seems to provide overly high velocities, which could result in the previously reported overestimation of the 1RM. Despite the validity problems of force/load–velocity profiles, we suggest evaluating the bar velocity with accurate measurement devices, which is, contrary to previous reports, not the case with the EnodePro®. [ABSTRACT FROM AUTHOR]

AbstractIn financial econometrics, the Sharpe ratio function serves as a gold standard to measure the return-to-risk ratio for comparing different assets or trading strategies. In the recent literature, several methods have been developed to directly or indirectly estimate the Sharpe ratio function, yet none of them apply to the scenario where the covariates are measured with error. To handle this problem, we propose a new method by incorporating the local polynomial smoothing and SIMEX to simultaneously estimate the Sharpe ratio function and the negative log-volatility function in the presence of measurement error. The asymptotic bias and variance of the proposed estimators are also derived under some regularity conditions. We further conduct Monte Carlo simulations to evaluate the finite sample performance, and apply two real data examples to illustrate the usefulness of our new method. [ABSTRACT FROM AUTHOR]

The current study employs the ratio-cum-product estimator to estimate the population mean of a sensitive study variable, aiming to overcome issues related to non-response and measurement error in the context of double sampling. The characteristics of the proposed class of estimators are computed up to the first order of approximation. A comparative analysis is conducted to assess the performance of the suggested estimator amongst the class of estimator and the estimator proposed by Kumar & Zhang (2023). Additionally, theoretical findings are supported by conducting two model based simulation study by using an hypothetical population. The simulation results demonstrates that the proposed ratio-cum-product estimator under double sampling exhibits the lowest mean squared error among Kumar & Zhang (2023) estimator and all classes of suggested estimator which indicates their superior performance in estimating the population mean of a sensitive variable. As a result, the suggested estimator offers a valuable tool for estimating the population mean in surveys conducted across various agriculture, environmental studies, market research and health surveys. [ABSTRACT FROM AUTHOR]

Adequate reference procedures for obtaining the reference zero-angle position are important for precise and accurate posture measurements, but few studies have systematically investigated these. A limited number of previous studies suggest differences in accuracy between procedures, with some causing an underestimation of the true arm elevation angle when sensors are taped to the skin. The reliability of commonly used reference procedures for the measurement of the trunk posture is also not well explored, and alternative procedures may improve precision. Based on this identified gap, this study evaluated the test–retest reliability of the N-position (I-pose), i.e., the standard procedure for recording trunk postures, and compared it with two new alternative procedures. Additionally, the accuracy of the N-position for measuring arm elevation angles was compared with one alternative procedure. A total of 40 participants (22 women and 18 men) aged 26–70 years performed the reference procedures in a laboratory setting. Postures were recorded using a smart workwear system equipped with two inertial measurement units (IMUs) embedded in pockets within the workwear. For the trunk posture, the N-position showed a slight lack of test–retest reliability, while one of the alternative procedures demonstrated better test–retest reliability. For the arm posture, the N-position, which does not include lateral trunk inclination, resulted in a substantial underestimation of the arm elevation angle of approximately 15°, which is a novel finding. In contrast, the posture involving trunk inclination closely matched the targeted reference, with a difference of less than 2°. This study underscores the importance of selecting appropriate reference procedures to ensure precise and accurate posture measurements. [ABSTRACT FROM AUTHOR]

Robot calibration and modelling measurements are commonly performed using a laser tracker. To capture three-dimensional positions, a SMR is attached to the robot. While some researchers employ adhesive bonds for this purpose, such methods often result in inaccurate, unstable and non-repeatable SMR positioning, adversely affecting measurement precision and the traceability of research outcomes. To address these challenges, we investigated alternative methods for attaching an SMR to a robot's flange to achieve both accuracy and repeatability. Additionally, we analysed measurement errors introduced when using a tool to attach the SMR to the flange. As a solution, we developed a 3D-printed mount designed for attachment to the flange. The mount's accuracy was evaluated by assessing its eccentricity and the repeatability of the SMR placement. Experimental results demonstrated that the mount achieved an eccentricity radius of 0.35 mm and repeatability inaccuracies of X = 0.075 mm , Y = 0.328 mm , and Z = 0.485 mm . These values indicate that the mount provides sufficient accuracy to support calibration processes, ensures research traceability, and serves as a viable replacement for adhesive bonds. [ABSTRACT FROM AUTHOR]

Measurement error frequently distorts outcomes in real-world applications, affecting process results. This paper explores how measurement error affects the detection capabilities of a new adaptive control chart with variable sample size in identifying out-ofcontrol conditions. The analysis is based on a model incorporating linear covariates, focusing specifically on the variable sample size with the EWMA chart's ability to detect shifts in the mean. Our findings demonstrate that measurement error considerably influences the chart's performance concerning mean detection. Additionally, we show that the proposed variable sample size strategy outperforms traditional methods under these conditions. [ABSTRACT FROM AUTHOR]

This study investigates the performance of mediation analyses, including manipulation check variables, in experimental studies where manipulated psychological attributes are independent variables. We simulated the level of manipulation intensities and measurement errors of the manipulation check variable to test the validity of the analytic practice. Our results showed that when manipulation is successful and measurement error is low, mediation analyses with the manipulation check variable revealed an unstable path coefficient and standard error. Moreover, many of the detected indirect effects were inconsistent mediation situations. However, when individual differences in psychological attributes remained within the condition (low manipulation intensity) and the manipulation check variable contained low measurement error, the indirect effect indicated the validity of the manipulation. We discuss the implications of our findings for the use of manipulation checks in experimental research. [ABSTRACT FROM AUTHOR]

Certificates of medical evidence are often used to aid the court in assessing the cause and severity of a victim's injuries. In cases with significant blood loss, the question whether the bleeding itself was life-threatening sometimes arises. To answer this, the volume classification of hypovolemic shock described in ATLS® is commonly used as an aid, where a relative blood loss > 30% is considered life-threatening. In a recent study of deaths due to internal haemorrhage, many cases had a relative blood loss < 30%. However, many included cases had injuries which could presumably cause deaths via other mechanisms, making the interpretation uncertain. To resolve remaining ambiguity, we studied whether deaths due to isolated liver lacerations had a relative blood loss < 30%, a cause of death where the mechanism of death is presumably exsanguination only. Using the National Board of Forensic Medicine autopsy database, we identified all adult decedents, who had undergone a medico-legal autopsy 2001–2021 (n = 105 952), where liver laceration was registered as the underlying cause of death (n = 102). Cases where death resulted from a combination of also other injuries (n = 79), and cases that had received hospital care, were excluded (n = 4), leaving 19 cases. The proportion of internal haemorrhage to calculated total blood volume in these fatal pure exsanguinations ranged from 12 to 52%, with 63% of cases having a proportion < 30%. Our results lend further support to the claim that the volume classification of hypovolemic shock described in ATLS® is inappropriate for assessing the degree of life-threatening haemorrhage in medico-legal cases. [ABSTRACT FROM AUTHOR]

Researchers often study dynamic processes of latent variables in everyday life, such as the interplay of positive and negative affect over time. An intuitive approach is to first estimate the measurement model of the latent variables, then compute factor scores, and finally use these factor scores as observed scores in vector autoregressive modeling. However, this approach neglects the uncertainty in the factor scores, leading to biased parameter estimates and threatening the validity of conclusions about the dynamic process. We propose Three-Step Latent Vector Autoregression that adheres to this stepwise procedure while correcting for the factor scores' uncertainty. Stepwise approaches offer various advantages, for example the ability to visualize and inspect the factor scores. A simulation study demonstrates that the method performs well in obtaining correct parameter estimates of a dynamic process. We also provide an empirical example and scripts for implementation in the open-source software R using the lavaan package. [ABSTRACT FROM AUTHOR]

Current sensors based on the tunneling magnetoresistive effect (TMR) are widely used for current measurement due to their high sensitivity, small size, and low power consumption. This paper proposes an effective error correction model to rectify the eccentricity of the transmission line, which can cause a significant measurement error in the ring‐array single‐axis TMR sensor. The model employs a convolutional neural network (CNN) to identify the relationship between the conductor eccentricity and the output of three sensors. The resulting correction factor is then fed back to eliminate the error associated with wire eccentricity. Concurrently, the Sparrow search algorithm (SSA) is employed to optimize the hyperparameters of the convolutional neural network (CNN) in order to enhance the model's performance. The experimental results demonstrate that the maximum error of the ring‐array single‐axis TMR current sensor, corrected by SSA‐CNN, is less than 0.42%, which markedly enhances the precision of the measurement. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In longitudinal studies, the devices used to measure exposures can change from visit to visit. Calibration studies, wherein a subset of participants is measured using both devices at follow-up, may be used to assess between-device differences (ie, errors). Then, statistical methods are needed to adjust for between-device differences and the missing measurement data that often appear in calibration studies. Regression calibration and multiple imputation are two possible methods. We compared both methods in linear regression with a simulation study, considering various real-world scenarios for a longitudinal study of pulse wave velocity. Regression calibration and multiple imputation were both essentially unbiased, but correctly estimating the standard errors posed challenges. Multiple imputation with predicted mean matching produced close agreement with the empirical standard error. Fully stochastic multiple imputation underestimated the standard error by up to 50%, and regression calibration with bootstrapped standard errors performed slightly better than fully stochastic multiple imputation. Regression calibration was slightly more efficient than either multiple imputation method. The results suggest use of multiple imputation with predictive mean matching over fully stochastic imputation or regression calibration in longitudinal studies where a new device at follow-up might be error-prone compared to the device used at baseline. [ABSTRACT FROM AUTHOR]

Accelerated failure time models are appealing due to their intuitive interpretation. However, when covariates are subject to measurement errors, naive estimation becomes severely biased. To address this issue, the regression calibration (RC) approach is a widely applicable and effective method. Traditionally, the RC method requires a good predictor for the true covariate, which can be obtained through parametric distribution assumptions or validation datasets. Consequently, the performance of the estimator depends on the plausibility of these assumptions. In this work, we propose a novel method that utilizes error augmentation to duplicate covariates, facilitating nonparametric estimation. Our approach does not require a validation set or parametric distribution assumptions for the true covariate. Through simulation studies, we demonstrate that our approach is more robust and less impacted by heavy censoring rates compared to conventional analyses. Additionally, an analysis of a subset of a real dataset suggests that the conventional RC method may have a tendency to overcorrect the attenuation effect of measurement error. [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]

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]

AbstractThe convergence rate of an estimator can vary when applied to datasets from different populations. As the population is unknown in practice, so is the corresponding convergence rate. In this article, we introduce a method to conduct inference on estimators whose convergence rates are unknown. Specifically, we extend the subsampling approach of Bertail, Politis, and Romano (1999) to situations where the convergence rate may include logarithmic components. This extension proves to be particularly relevant in certain statistical inference problems. To illustrate the practical relevance and implementation of our results, we discuss two main examples: (i) non parametric regression with measurement error; and (ii) intercept estimation in binary choice models. In each case, our approach provides robust inference in settings where convergence rates are unknown; simulation results validate our findings. [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]

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]

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]

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]

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]