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In this study, we examined the potential errors in plasma-density measurements using the cutoff probe method under various structural conditions, such as tip distance and length. Our studies indicate that under conditions of thin sheath thickness, the length or distance of the metal tips on the cutoff probe has a slight effect on the plasma transmission spectrum or cutoff frequency. However, under conditions with a notably thick sheath, the structure of the probe tip can cause an error of up to 2% between the measured cutoff frequency and actual plasma frequency. Consequently, for precise measurements of plasma density using the cutoff probe method, it is imperative to maintain a probe tip distance exceeding five times the sheath width and utilize a sufficiently long probe tip length. This finding is anticipated to provide essential guidelines for the design and fabrication of effective cutoff probes and enhance the accuracy of plasma-density measurements using a cutoff probe. [ABSTRACT FROM AUTHOR]

Context: The incidence and recurrence rate of hamstring strain injuries remain persistently high, with recurrent injuries leading to increased time lost during play and extended recovery periods compared with initial injury. Ultrasound imaging assesses important factors such as hamstring fascicle length (FL), pennation angle (PA), cross-sectional area (CSA), muscle thickness (MT), echo intensity (EI), and shear wave elastography (SWE), all impacting athletic performance. However, its reliability must be established before employing any measurement tool in research or clinical settings. Objectives: To determine the reliability and measurement error of ultrasound for assessing hamstring FL, PA, CSA, MT, EI, and SWE among healthy adults and athletes; to synthesize the information regarding the operationalization of ultrasound. Evidence Acquisition: A systematic literature search was done from January 1990 to February 5, 2023, to identify reliability and validity studies of hamstring ultrasound assessment published in peer-reviewed journals with identifiable methodology of outcome measures. Evidence Synthesis: Intraclass correlation coefficient measurement of 14 included studies reported moderate to excellent intrarater, interrater, and test–retest reliabilities of FL, PA, and MT regardless of the site of muscle testing, probe size, and setting, state of muscle, and use of different techniques in the extrapolation of FL. Good to excellent test–retest reliability rates for all hamstring anatomic CSA along midmuscle and different percentages of thigh length using panoramic imaging. Good intrarater reliability of EI regardless of gender and orientation of the probe but with excellent intrarater reliability in transverse scan using maximum region of interest. Good intrarater, interrater, and interday repeatability on SWE with the muscle in a stretched position. Conclusion: Evidence from studies with a predominantly low risk of bias shows that ultrasound is a reliable tool to measure hamstring FL, PA, CSA, MT, EI, and SWE in healthy adults and athletes under various experimental conditions. [ABSTRACT FROM AUTHOR]

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]

Multi-tier sustainable supply chain relationships (MSSCR) are complex with involvement of multiple organisations, practices, objectives, and interconnectivities. These complexities make it difficult to effectively study MSSCR using traditional empirical statistical correlative research. In MSSCR the relationship between the focal firm and sub-supplier may be affected by the relationship between the focal firm and direct-supplier, and the relationship between the direct-supplier and the sub-supplier. This paper introduces a research methodology, based on K-Means clustering, rough set theory, and cluster membership to investigate complex relationships across multi-tier sustainable supply chain triads. Collaborative advantage variables including knowledge-sharing routines, relation-specific investments, complementary capabilities, effective governance mechanisms, and long-term commitment are conceptually presented to evaluate relationships among MSSCR practices and sustainability performance. Insights for practitioners and researchers are provided. Research insights can be gained by analytically providing alternative explanatory and equifinality relationships amongst variables and multi-pair organisations. The methodology can mitigate the endogeneity problem, such as omitted factors (variables), measurement error, even in small sample data situations. Directions for future research are discussed. [ABSTRACT FROM AUTHOR]

Geomagnetic navigation has become a hot spot in current research because of its characteristics of passiveness and good concealment. However, the magnetic interference from various ferromagnetic substances, electronic equipment, etc., of the carrier will be superimposed on the geomagnetic field, causing magnetometer measurement errors, thus affecting navigation accuracy. In practice, due to the limited maneuverability of the carrier, sufficient geomagnetic observation data cannot be obtained, resulting in the observation equation used for carrier magnetic interference compensation to be seriously pathological. To achieve the compensation of carrier magnetic interference, this paper proposes the total least squares method based on the ridge regression using the L curve to solve ridge parameters. This method can effectively suppress the measurement noise that exists on both sides of the observation equation, and is suitable for alleviating the pathological effects of carrier magnetic interference compensation. Experimental results show that the compensated magnetometer measurement error is reduced to 3% of the carrier magnetic interference by using the method proposed in this paper, which obtains more stable and accurate parameter estimates. [ABSTRACT FROM AUTHOR]

Previously, the multisolute osmotic virial equation with the combining rules of Elliott et al. has been shown to make accurate predictions for multisolute solutions with only single-solute osmotic virial coefficients as inputs. The original combining rules take the form of an arithmetic average for the second-order mixed coefficients and a geometric average for the third-order mixed coefficients. Recently, we derived generalized combining rules from a first principles solution theory, where all mixed coefficients could be expressed as arithmetic averages of suitable binary coefficients. In this work, we empirically extended the new model to account for electrolyte effects, including solute dissociation, and demonstrated its usefulness for calculating the properties of multielectrolyte solutions. First, the osmotic virial coefficients of 31 common salts in water were tabulated based on the available freezing point depression (FPD) data. This was achieved by polynomial fitting, where the degree of the polynomial was determined using a special criterion that accounts for the confidence intervals of the coefficients. Then, the multisolute model was used to predict the FPD of 11 ternary electrolyte solutions. Furthermore, models with the new combining rules and the original combining rules of Elliott et al. were compared using both mole fraction and molality as concentration units. We find that the mole-fraction-based model with the new combining rules performs the best and that the results agree well with independent experimental measurements with an all-system root-mean-square error of 0.24 osmoles/kg (0.45 °C) and close to zero mean bias for the entire dataset (371 data points). [ABSTRACT FROM AUTHOR]

The Jiangmen Underground Neutrino Observatory (JUNO) experiment is the next-generation neutrino experiment that aims at exploring the neutrino mass hierarchy problem. Located 700 m underground in Jiangmen, China, JUNO's central detector is an acrylic sphere filled with 20 kt of liquid scintillator with linear alkylbenzene (LAB) as solvent. To achieve the unprecedented energy resolution of σ E / E ⩽ 3 % , the LAB used in JUNO is required for excellent transparency at the wavelength around 430 nm. In cooperation with Jinling Petrochemical (Nanjing), the newly developed LAB shows an improved attenuation length. The resulting transparency of the LAB requires higher sensitivity in measurement. We have, therefore, upgraded the apparatus and the analysis method for higher precision. In this article, we present the upgraded apparatus and the analysis on improving the measurement error. Among the many new samples, the type NJ66 yields an attenuation length of approximately 30 m. The analysis has applied statistical methods with Monte Carlo simulations. A new model is proposed to account for the deviations between data and the Beer–Lambert model. The long attenuation length of LAB observed in this study suggests feasibility to reach the goal on energy resolution required by JUNO. [ABSTRACT FROM AUTHOR]

Context: To assess the reliability of a remote 2D markerless motion tracking method (Kinovea) to quantify knee and hip angles during dynamic tasks. Methods: Fourteen healthy adults performed body weight squats and lateral lunges while video recording themselves at home. Knee and hip angles were quantified in the sagittal plane for the squats and in the frontal plane for the lateral lunges. Two students each performed the video analysis procedure twice, 2 weeks apart. Intraclass correlation coefficients were used to calculate the intrarater and interrater reliability for angles at maximum depth. The intrarater and interrater agreement over the joint angle-time signals were quantified using a validation metric; an acceptable agreement threshold was set at a validation metric of 0.803 or higher. Standard error of measurement (SEM) was also calculated. Results: Reliability was good to excellent (intraclass correlation coefficients = .80-.98) for all angle comparisons at maximum depth. The agreement over the entire joint angle-time signal was acceptable for all squat variables except for the interrater hip angle comparison (validation metric = 0.797). None of the lateral lunge variables met the threshold of acceptable agreement. The mean SEM across participants for all joint angle-time signal and for maximum depth was acceptable (<5°) for all measurements (SEM = 1.2°-4.9°). Conclusions: Overall, the reliability, agreement, and SEM quantified in this study support the integration of remote methods to quantify lower-extremity kinematics into research and clinical practice. [ABSTRACT FROM AUTHOR]

Polyelectrolytes can electrophoretically be driven through nanopores in order to be detected. The respective translocation events are often very fast and the process needs to be controlled to promote efficient detection. To this end, we attempt to control the translocation dynamics by coating the inner surface of a nanopore. For this, different charge distributions are chosen that result in substantial variations of the pore–polymer interactions. In addition and in view of the existing detection modalities, experimental settings, and nanopore materials, different types of sensors inside the nanopore have been considered to probe the translocation process and its temporal spread. The respective transport of polyelectrolytes through the coated nanopores is modeled through a multi-physics computational scheme that incorporates a mesoscopic/electrokinetic description for the solvent and particle-based scheme for the polymer. This investigation could underline the interplay between sensing modality, nanopore material, and detection accuracy. The electro-osmotic flow and electrophoretic motion in a pore are analyzed together with the polymeric temporal and spatial fluctuations unraveling their correlations and pathways to optimize the translocation speed and dynamics. Accordingly, this work sketches pathways in order to tune the pore–polymer interactions in order to control the translocation dynamics and, in the long run, errors in their measurements. [ABSTRACT FROM AUTHOR]

This study aims to perform the cross-cultural adaptation to European Portuguese of the Composite Physical Function Scale and to assess its validity and reliability in a sample of community-dwelling older adults. The scale was translated into European Portuguese, back translated, and piloted in a sample of 16 representative individuals. Its validity and reliability were tested in an independent sample of 114 community-dwelling older adults (52 were tested twice to assess test–retest reliability). The results showed that the scale had good internal consistency (α =.90), construct validity (ρ =.71) and measurement error (78.8% agreement), and excellent test–retest reliability (κ =.98). However, a ceiling effect was found as 28% of the participants achieved the highest possible score. Although the scale has good measurement properties, the presence of ceiling effects is indicative that this tool is not able to distinguish higher levels of intrinsic capacity within community-dwelling older adults. [ABSTRACT FROM AUTHOR]

This paper investigates the spillover effects of middle school students taking up academic tutoring on students' cognitive and noncognitive skills in the same class in China. The identification relies on variations across classes within schools, which are formed roughly randomly. Correcting for measurement error, we find a significantly positive spillover effect of peers' academic tutoring on students' English test scores, and no spillover effect exists on students' ranks at the school level. For noncognitive performance, we find a positive spillover effect of academic tutoring on improving confidence. Our results show no positive external welfare exists for academic tutoring for students' grades or ranks, consistent with the newly released 'Double Reduction' policy that the Chinese government prohibits extra academic tutoring. [ABSTRACT FROM AUTHOR]

Wearable devices such as the ActiGraph are now commonly used in research to monitor or track physical activity. This trend corresponds with the growing need to assess the relationships between physical activity and health outcomes, such as obesity, accurately. Device‐based physical activity measures are best treated as functions when assessing their associations with scalar‐valued outcomes such as body mass index. Scalar‐on‐function regression (SoFR) is a suitable regression model in this setting. Most estimation approaches in SoFR assume that the measurement error in functional covariates is white noise. Violating this assumption can lead to underestimating model parameters. There are limited approaches to correcting measurement errors for frequentist methods and none for Bayesian methods in this area. We present a non‐parametric Bayesian measurement error‐corrected SoFR model that relaxes all the constraining assumptions often involved with these models. Our estimation relies on an instrumental variable allowing a time‐varying biasing factor, a significant departure from the current generalized method of moment (GMM) approach. Our proposed method also permits model‐based grouping of the functional covariate following measurement error correction. This grouping of the measurement error‐corrected functional covariate allows additional ease of interpretation of how the different groups differ. Our method is easy to implement, and we demonstrate its finite sample properties in extensive simulations. Finally, we applied our method to data from the National Health and Examination Survey to assess the relationship between wearable device‐based measures of physical activity and body mass index in adults in the United States. [ABSTRACT FROM AUTHOR]

Model-based clustering analysis of functional data often has normality assumption. This article considers clustering non Gaussian functional data. We propose a novel non Gaussian functional mixed-effects model without the prior information and clustering number. We use transformation functions to accommodate non Gaussian functional data. Smoothing spline ANOVA and cubic B-spline approximate unknown fixed effects and random effects, respectively. A penalized likelihood is used to estimate unknown parameters, and the consistency and asymptotic normality is provided after that. We take simulations for different measurement error distribution assumptions and adopt the air quality of Italian city data. Both simulation and actual data analysis show that the proposed method performs well and has a better clustering effect. [ABSTRACT FROM AUTHOR]

To study the symmetry and asymmetry of the model error under multiplicative distortion measurement errors setting, we propose a correlation coefficient-based measure between the distribution function and the root of density function. The unknown distribution function and density function are estimated from four kinds of residuals: the conditional mean calibration-based residuals, the conditional absolute mean calibration-based residuals, the conditional variance calibration-based residuals, and the conditional absolute logarithmic calibration-based residuals. We study the asymptotic results of the estimators of correlation coefficient-based measure under four calibrations. Next, we consider statistical inference of the correlation coefficient-based measure by using the empirical likelihood method. The empirical likelihood statistics are shown to be an asymptotically standard chi-squared distribution. Simulation studies demonstrate the performance of the proposed estimators and test statistics. A real example is analyzed to illustrate its practical usage. [ABSTRACT FROM AUTHOR]

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra‐parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non‐constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi‐binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace‐approximated quasi‐likelihood of our model, we further develop a specialized two‐stage expectation‐maximization (EM) algorithm, where a plug‐in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non‐zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti‐citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA‐related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called "SOMNiBUS." [ABSTRACT FROM AUTHOR]

AbstractPurposeMaterials and methodsResultsConclusion\nIMPLICATIONS FOR REHABILITATIONTo investigate the psychometric properties of the MOSE-Benin, a French-language version of the Measure of Stroke Environment (MOSE) for Sub-Saharan Africa.The original English version of the MOSE has been translated into French following the guidelines for cross-cultural adaptation. The resulting questionnaire (MOSE-Benin) was administered to a convenience sample of participants recruited in Benin, a French-speaking country.Eighty-two stroke survivors (41 females; mean ± SD: 54.94 ± 11.6 years old) participated in the study. Internal consistency of each domain of the MOSE-Benin and the overall questionnaire was high (Cronbach’s α: 0.78 to 0.92). Test-retest reliability was excellent (n = 31; ICC: 0.977 to 0.998). Overall, the standard error of measurement (SEM) and the minimum detectable change (MDC) showed very low values (SEM = 0.85; MDC = 2.35). Convergent validity demonstrated moderate correlations for the three domains in separate comparison respectively with the ACTIVLIM-Stroke questionnaire, the Participation Measurement Scale, and the communication domain of the Stroke Impact Scale (r or ρ: 0.42 to 0.54; p < 0.0001).MOSE-Benin has good evidence regarding psychometric properties (i.e., content validity, convergent validity, internal consistency, and test-retest reliability) that can support its use for the assessment of perceived environmental barriers after stroke in a French-speaking Sub-Saharan African country, such as Benin.Stroke survivors in French-speaking Sub-Saharan Africa may face environmental challenges related to acceptability, physical environment, and communication.Stroke survivors should be assessed in their communities to identify environmental barriers, using valid and robust tools.A French version of the MOSE (MOSE-Benin) is now available and can be used to assess the environmental concerns faced by stroke survivors in Sub-Saharan Africa.Stroke survivors in French-speaking Sub-Saharan Africa may face environmental challenges related to acceptability, physical environment, and communication.Stroke survivors should be assessed in their communities to identify environmental barriers, using valid and robust tools.A French version of the MOSE (MOSE-Benin) is now available and can be used to assess the environmental concerns faced by stroke survivors in Sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

Background: Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction modelling that can introduce bias in myopia prediction. Methods: We explore four imputation methods to address missing data and ME: single imputation (SI), multiple imputation under missing at random (MI-MAR), multiple imputation with calibration procedure (MI-ME), and multiple imputation under missing not at random (MI-MNAR). We compare four machine-learning models (Decision Tree, Naive Bayes, Random Forest, and Xgboost) and three statistical models (logistic regression, stepwise logistic regression, and least absolute shrinkage and selection operator logistic regression) in myopia risk prediction. We apply these models to the Shanghai Jinshan Myopia Cohort Study and also conduct a simulation study to investigate the impact of missing mechanisms, the degree of ME, and the importance of predictors on model performance. Model performance is evaluated using the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Results: Our findings indicate that in scenarios with missing data and ME, using MI-ME in combination with logistic regression yields the best prediction results. In scenarios without ME, employing MI-MAR to handle missing data outperforms SI regardless of the missing mechanisms. When ME has a greater impact on prediction than missing data, the relative advantage of MI-MAR diminishes, and MI-ME becomes more superior. Furthermore, our results demonstrate that statistical models exhibit better prediction performance than machine-learning models. Conclusion: MI-ME emerges as a reliable method for handling missing data and ME in important predictors for early-onset myopia risk prediction. [ABSTRACT FROM AUTHOR]

Clustering is widely used in bioinformatics and many other fields, with applications from exploratory analysis to prediction. Many types of data have associated uncertainty or measurement error, but this is rarely used to inform the clustering. We present Dirichlet Process Mixtures with Uncertainty (DPMUnc), an extension of a Bayesian nonparametric clustering algorithm which makes use of the uncertainty associated with data points. We show that DPMUnc out-performs existing methods on simulated data. We cluster immune-mediated diseases (IMD) using GWAS summary statistics, which have uncertainty linked with the sample size of the study. DPMUnc separates autoimmune from autoinflammatory diseases and isolates other subgroups such as adult-onset arthritis. We additionally consider how DPMUnc can be used to cluster gene expression datasets that have been summarised using gene signatures. We first introduce a novel procedure for generating a summary of a gene signature on a dataset different to the one where it was discovered, which incorporates a measure of the variability in expression across signature genes within each individual. We summarise three public gene expression datasets containing patients with a range of IMD, using three relevant gene signatures. We find association between disease and the clusters returned by DPMUnc, with clustering structure replicated across the datasets. The significance of this work is two-fold. Firstly, we demonstrate that when data has associated uncertainty, this uncertainty should be used to inform clustering and we present a method which does this, DPMUnc. Secondly, we present a procedure for using gene signatures in datasets other than where they were originally defined. We show the value of this procedure by summarising gene expression data from patients with immune-mediated diseases using relevant gene signatures, and clustering these patients using DPMUnc. Author summary: Identifying groups of items that are similar to each other, a process called clustering, has a range of applications. For example, if patients split into two distinct groups this suggests that a disease may have subtypes which should be treated differently. Real data often has measurement error associated with it, but this error is frequently discarded by clustering methods. We propose a clustering method which makes use of the measurement error and use it to cluster diseases linked to the immune system. Gene expression datasets measure the activity level of all ∼20,000 genes in the human genome. We propose a procedure for summarising gene expression data using gene signatures, lists of genes produced by highly focused studies. For example, a study might list the genes which increase activity after exposure to a particular virus. The genes in the gene signature may not be as tightly correlated in a new dataset, and so our procedure measures the strength of the gene signature in the new dataset, effectively defining measurement error for the summary. We summarise gene expression datasets related to the immune system using relevant gene signatures and find that our method groups patients with the same disease. [ABSTRACT FROM AUTHOR]

Anthropometric prevalence indicators such as stunting, wasting, and underweight are widely-used population-level tools used to track trends in childhood nutrition. Threats to the validity of these data can lead to erroneous decision making and improper allocation of finite resources intended to support some of the world's most vulnerable populations. It has been demonstrated previously that aggregated prevalence rates for these indicators can be highly sensitive to biases in the presence of non-directional measurement errors, but the quantitative relationship between the contributing factors and the scale of this bias has not been fully described. In this work, a Monte Carlo simulation exercise was performed to generate high-statistics z-score distributions with a wide range of mean and standard deviation parameters relevant to the populations in low- and middle-income countries (LMIC). With the important assumption that the distribution's standard deviation should be close to 1.0 in the absence of non-directional measurement errors, the shift in prevalence rate due to this common challenge is calculated and explored. Assuming access to a given z-score distribution's mean and standard deviation values, this relationship can be used to evaluate the potential scale of prevalence bias for both historical and modern anthropometric indicator results. As a demonstration of the efficacy of this exercise, the bias scale for a set of 21 child anthropometry datasets collected in LMIC contexts is presented. [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]

Background: Recent technological advances have resulted in low-cost GPS loggers that are small enough to be used on a range of seabirds, producing accurate location estimates (± 5 m) at sampling intervals as low as 1 s. However, tradeoffs between battery life and sampling frequency result in studies using GPS loggers on flying seabirds yielding locational data at a wide range of sampling intervals. Metrics derived from these data are known to be scale-sensitive, but quantification of these errors is rarely available. Very frequent sampling, coupled with limited movement, can result in measurement error, overestimating movement, but a much more pervasive problem results from sampling at long intervals, which grossly underestimates path lengths. Methods: We use fine-scale (1 Hz) GPS data from a range of albatrosses and petrels to study the effect of sampling interval on metrics derived from the data. The GPS paths were sub-sampled at increasing intervals to show the effect on path length (i.e. ground speed), turning angles, total distance travelled, as well as inferred behavioural states. Results: We show that distances (and per implication ground speeds) are overestimated (4% on average, but up to 20%) at the shortest sampling intervals (1–5 s) and underestimated at longer intervals. The latter bias is greater for more sinuous flights (underestimated by on average 40% when sampling > 1-min intervals) as opposed to straight flight (11%). Although sample sizes were modest, the effect of the bias seemingly varied with species, where species with more sinuous flight modes had larger bias. Sampling intervals also played a large role when inferring behavioural states from path length and turning angles. Conclusions: Location estimates from low-cost GPS loggers are appropriate to study the large-scale movements of seabirds when using coarse sampling intervals, but actual flight distances are underestimated. When inferring behavioural states from path lengths and turning angles, moderate sampling intervals (10–30 min) may provide more stable models, but the accuracy of the inferred behavioural states will depend on the time period associated with specific behaviours. Sampling rates have to be considered when comparing behaviours derived using varying sampling intervals and the use of bias-informed analyses are encouraged. [ABSTRACT FROM AUTHOR]

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Early diagnosis of inborn errors of metabolism (IEM) is crucial to ensure early detection of conditions which are treatable. This study reports on targeted metabolomic procedures for the diagnosis of IEM of amino acids, acylcarnitines, creatine/guanidinoacetate, purines/pyrimidines and oligosaccharides, and describes its validation through external quality assessment schemes (EQA). Analysis was performed on a Waters ACQUITY UPLC H-class system coupled to a Waters Xevo triple-quadrupole (TQD) mass spectrometer, operating in both positive and negative electrospray ionization mode. Chromatographic separation was performed on a CORTECS C18 column (2.1 × 150, 1.6 µm). Data were collected by multiple reaction monitoring. The internal and EQA results were generally adequate, with a few exceptions. We calculated the relative measurement error (RME) and only a few metabolites displayed a RME higher than 30 % (asparagine and some acylcarnitine species). For oligosaccharides, semi-quantitative analysis of an educational panel clearly identified the 8 different diseases included. Overall, we have validated our analytical system through an external quality control assessment. This validation will contribute to harmonization between laboratories, thus improving identification and management of patients with IEM. [ABSTRACT FROM AUTHOR]

This article addresses the problems with the traditional reinterview approach to estimating the reliability of survey measures. Using data from three reinterview (or panel) studies conducted by the General Social Survey, we investigate the differences between the two-wave correlational approach embodied by the traditional reinterview strategy, compared to estimates of reliability that take the stability of traits into account based on a three-wave model. Our results indicate that the problems identified with the two-wave correlational approach reflect a kind of "Catch-22" in the sense that the only solution to the problem is denied by the approach itself. Specifically, we show that the correctly specified two-wave model, which includes the potential for true change in the latent variable, is underidentified, and thus, unless one is willing to make some potentially risky assumptions, reliability parameters are not estimable. This article compares the two-wave correlational approach to an alternative model for estimating reliability, Heise's estimates based on the three-wave simplex model. Using three waves of data from the GSS panels, which were separated by 2-year intervals between waves, this article examines the conditions under which the wave-1, wave-2 correlations which do not take stability into account approximate the reliability estimate obtained from three-wave simplex models that do take stability into account. The results lead to the conclusion that the differences between estimates depend on the stability and/or fixed nature of the underlying processes involved. Few if any differences are identified when traits are fixed or highly stable, but for traits involving changes in the underlying traits the differences can be quite large, and thus, we argue for the superiority of reinterview designs that involve more than 2 waves in the estimation of reliability parameters. [ABSTRACT FROM AUTHOR]

Researchers are often unsure about the quality of the data collected by third-party actors, such as survey firms. This may be because of the inability to measure data quality effectively at scale and the difficulty with communicating which observations may be the source of measurement error. Researchers rely on survey firms to provide them with estimates of data quality and to identify observations that are problematic, potentially because they have been falsified or poorly collected. To address these issues, I propose the QualMix model, a mixture modeling approach to deriving estimates of survey data quality in situations in which two sets of responses exist for all or certain subsets of respondents. I apply this model to the context of survey reinterviews, a common form of data quality assessment used to detect falsification and data collection problems during enumeration. Through simulation based on real-world data, I demonstrate that the model successfully identifies incorrect observations and recovers latent enumerator and survey data quality. I further demonstrate the model's utility by applying it to reinterview data from a large survey fielded in Malawi, using it to identify significant variation in data quality across observations generated by different enumerators. [ABSTRACT FROM AUTHOR]

Biosocial surveys increasingly use interviewers to collect objective physical health measures (or "biomeasures") in respondents' homes. While interviewers play an important role, their high involvement can lead to unintended interviewer effects on the collected measurements. Such interviewer effects add uncertainty to population estimates and have the potential to lead to erroneous inferences. This study examines interviewer effects on the measurement of physical performance in a cross-national and longitudinal setting using data from the Survey of Health, Ageing and Retirement in Europe. The analyzed biomeasures exhibited moderate-to-large interviewer effects on the measurements, which varied across biomeasure types and across countries. Our findings demonstrate the necessity to better understand the origin of interviewer-related measurement errors in biomeasure collection and account for these errors in statistical analyses of biomeasure data. [ABSTRACT FROM AUTHOR]

Part II of this two‐part article investigates the impact of thermocouple insulation failure on temperature measurement data in forensic fire‐death scenarios. Two different models of glass fiber‐insulated thermocouple wires (GG‐K‐24‐SLE and HH‐K‐24 from Omega Engineering) were passed through a ceramic kiln at temperatures up to 1093°C to measure an ice bath at a constant 0°C. In a separate experiment, the same two models of thermocouple wire plus a BLMI‐XL‐K‐18U‐120 mineral‐insulated metal‐sheathed thermocouple probe were passed through a wood pallet fire to measure an ice bath. In the ceramic kiln, the effect on measurement errors was determined for short vs. long exposure lengths and clean insulation vs. insulation contaminated with pork fat. Glass fiber‐insulated thermocouple wires showed severe failure in both experiments, with errors ranging from −270°C to almost 2200°C. The metal‐sheathed probe showed no evidence of insulation failure and continued to accurately measure the ice bath temperature within expected margins of error around 0°C. This study highlights how exposure of inadequate thermocouples to fire‐level temperatures produces severe errors in temperature data. Consequently, it will not be possible to use this data to draw any accurate conclusions about the effects of fire exposure to human donors or animal proxies. [ABSTRACT FROM AUTHOR]

Thermocouples are utilized to monitor a wide range of temperatures in industrial applications. They are also used in both fire and forensic science research to measure temperatures of fires and of materials exposed to fire. Taking accurate temperature measurements during forensic fire‐death scenarios is very difficult due to direct fire exposure to thermocouples, shrinkage and destruction of tissues, and movements from pyre collapse and pugilistic posturing of human donors. This two‐part study investigates the impacts on the accuracy of temperature data if the selected thermocouples are unable to withstand fire exposure. Part I (this article) provides an overview of thermocouple theory along with evidence of the physical deterioration that occurs when glass fiber‐insulated thermocouple wires are overheated by exposure to fire‐level temperatures in a muffle furnace. This study verified that insulation overheating causes embrittlement and disintegration, which can cause the indicated temperature to reflect a new location of measurement located far away from the original measuring junction at the thermocouple tip. Part II will discuss the measurement errors that occurred due to low electrical resistance of insulation when three different thermocouple models were passed through fire‐level temperatures to measure an ice bath at a constant temperature of 0°C. [ABSTRACT FROM AUTHOR]

Mother‐infant bonding is influenced by several risk and protective factors, and the literature has investigated the relationships between these factors independently. This study aimed to verify the interrelationships of some of these factors and how they influence mother‐infant bonding in Brazil. In this study, 361 mothers participated, and the outcome variable of mother‐infant bonding was assessed using the Postpartum Bonding Questionnaire (PBQ). Multivariate regression analysis was performed using a hierarchical model with three blocks structured according to the influence exerted on mother‐infant bonding. The PBQ's factor scores were estimated and used in the subsequent analyses to decrease measurement error. The variable "violence experienced by mothers" was statistically significant for explaining the second block model but not significant for the third block. Network analysis was performed after multiple regression, showing that the violence experienced by mothers does not directly influence mother‐infant bonding but rather is mediated by postpartum depression. This explains why violence is not significant in the hierarchical multiple regression when maternal depression is added to the model. This study's strengths lie in its utilization of PBQ factor scores and network analysis, enabling the estimation of conditional relationships among variables. This approach provides deeper insights into factors affecting mother‐infant bonding. [ABSTRACT FROM AUTHOR]

Introduction: Happé's Strange Stories task was developed in 1994 to assess theory of mind, the ability to infer mental states in oneself and others. Since then, it has undergone revisions, translations, and adaptations. A modified version of the task, the Strange Stories-Revised (SS-R), previously showed satisfactory qualities in a study aiming at identifying psychometrically acceptable social cognitive measures. Objective: The current study expands upon the psychometric evaluation study by examining the qualities of a short version of the SS-R in a sample of healthy adult subjects. Methods: One hundred and eighteen healthy adults completed the task along with neurocognitive measures. Mean scores of the long and short versions were compared. Associations between ToM as measured by performance on this abbreviated version of the SS-R, and potential confounders were explored. Internal consistency, dimensionality of the short version, and performance comparisons across three stages of aging (18–34; 35–59; 60–85 years old) were investigated, and standard measurement error was calculated to improve precision and data interpretation. Results: Reliability coefficients were comparable in the short and long versions. Principal component analysis showed that a one-factor structure best fits the data. Significant differences were observed in ToM performance across the three age groups, indicating a decline with time that was also captured by the long version, starting during midlife and increasing in significance with age. Conclusion: The short version of the SS-R is a promising measure that can be profitably used in time-limited settings to assess theory of mind. [ABSTRACT FROM AUTHOR]

The presence of measurement errors in data and multicollinearity among the explanatory variables have negative effects on the estimation of regression coefficients. Within this respect, the motivation of this article is to examine measurement errors and multicollinearity problems simultaneously. In this paper, by utilizing three different forms of corrected score functions three consistent ridge regression estimators are proposed. Theoretical comparisons of these new estimators are examined by implementing the mean squared error criterion. Large sample properties of these estimators are investigated without assuming any distributional assumption. Two numerical examples are presented using real data sets and also a simulation study is performed. The findings indicate that the newly proposed three estimators outperform the existing estimators by the criterion of mean squared error. [ABSTRACT FROM AUTHOR]

Partially linear models are useful tools to analyze data from economic, genetic, and other fields. Similar to other data analyses, the identification of influential observations that may be potential outliers is an important step beyond estimation in such models. The objective of this article is to develop some diagnostic measures for identifying influential observations in partially linear models when some of the covariates are measured with errors. Deletion measures are developed based on case deletion, mean shift outlier models, and the corrected likelihood of Nakamura (1990). The performance of the methods is illustrated by an artificial example and a real example. [ABSTRACT FROM AUTHOR]

Objectives: Government officials use criminal records as proxies for past conduct to decide who and how to investigate, arrest, charge, and punish. But those records may be racially biased measures of individual behavior. This paper develops a theoretical definition of bias in criminal records in terms of measurement error. It then seeks to provide empirical estimates of racial bias in official arrest records for a broad swath of offenses. Method: I use official arrest and self-reported crime data from the Pathways to Desistance study to estimate Black-to-white and Hispanic-to-white crime ratios conditional on arrest. I also develop a novel, theory-based empirical test of differential reporting across racial and ethnic groups. Results: Compared to white subjects with the same number of arrests, I estimate that Black subjects committed 53, 30, 23, and 56% fewer property, violent, drug, and DUI offenses, respectively, and that Hispanic subjects committed 19 and 46% fewer drug and DUI offenses. The analysis finds relatively little evidence of differential reporting that would bias my estimates upwards, with the possible exception of drug trafficking offenses. Conclusion: The results provide evidence that Pathways subjects' arrest records are racially biased measures of their past criminal behavior, which could bias decisions of criminal justice officials and risk assessment algorithms that are based on arrest records. [ABSTRACT FROM AUTHOR]

This paper introduces a vision-based displacement measurement method for wind turbine blades in biaxial fatigue testing. Instead of relying on existing strain data, this method collects displacement data to control the loading system. The main idea of this method is to update the pixel radius of the target point. The ratio of the pixel radius of the target point to the actual radius is used as a reference to update the displacement conversion coefficient λ of the next frame image in real-time. Through both static and dynamic experiments, the accuracy and superiority of this method have been verified, and the feasibility of using displacement instead of strain to control fatigue loading has been validated. The data demonstrates that the measurement error between the proposed method and the electronic total station remains within 10%. Compared to the results obtained by the traditional methods, the proposed method has shown significant improvement. The vision-based displacement measurement method not only ensures accuracy but also reduces the complexity of testing, providing more possibilities for fatigue testing of wind turbine blades. [ABSTRACT FROM AUTHOR]

Intraoperative stem anteversion, which is the angle between the lower leg axis and the trial-stem axis with hip flexion and adduction, is generally evaluated by the surgeon's visual estimation during total hip arthroplasty (THA). However, the conventional approach can be influenced by knee osteoarthritis or uncertain surgeon's observation point. Therefore, we developed a new method for measuring the stem anteversion angle in the neutral hip position using an original rod attached to the trial-stem perpendicular to the long axis and parallel to the stem neck. This study aimed to assess the accuracy of our method in comparison with the conventional method of measuring intraoperative stem anteversion angle. We measured the intraoperative stem anteversion angle in consecutive 106 hips of 106 patients who underwent cementless primary THA with a tapered wedge stem. Absolute error in the stem anteversion angle was expressed as the difference between intraoperative (common vs. neutral hip positions) and postoperative computed tomography measurements, i.e., true stem anteversion. Additionally, we investigated the factors affecting these errors. The absolute error of measurement was significantly smaller in the neutral hip position than in the common position (3.0° ± 2.5° vs. 8.0° ± 3.9°; p < 0.0001). The factor associated with the error was advanced knee osteoarthritis in the common position, whereas it was not statistically significant in the neutral hip position. This study suggests that the error in the intraoperative measurement of stem anteversion is decreased by measuring in the neutral hip position during THA. [ABSTRACT FROM AUTHOR]

The global Pavement-mechanistic-empirical (ME) transfer functions have been calibrated using the long-term pavement performance (LTPP) pavement sections at the national level. These functions may need calibration for local conditions for reliable performance predictions. Several calibration studies have been conducted for local conditions, but limited research is available on the impact of calibration on pavement design. This study calibrates and validates the Pavement-ME transfer functions using version 2.6 for Michigan. A total of 256 flexible and 113 rigid sections have been selected based on the performance and availability of Pavement-ME inputs. The authors obtained the Pavement-ME inputs using construction records, plans, job mix formula (JMF), and material testing results. In contrast, the performance data was obtained from the Michigan Department of Transportation (MDOT) pavement management system (PMS) database. The calibrated models were then used for pavement design to estimate the impact of calibration and for comparison with AASHTO93 designs. The paper identifies the controlling distresses for pavement design. A total of 44 new flexible and rigid sections were designed based on the newly calibrated coefficients. The results show a significant improvement in performance prediction after local calibration. A comparison between AASHTO93 and Pavement-ME designs showed a reduction in hot-mix asphalt (HMA) and plain cement concrete (PCC) slab thicknesses for the latter approach. On average, the surface thicknesses using locally calibrated coefficients were thinner by 0.22 in. (0.56 cm) and 0.44 in. (1.12 cm) for flexible and rigid pavements, respectively. Critical design distresses for flexible pavements include bottom-up and thermal cracking. Conversely, transverse cracking and international roughness index (IRI) control the designs for rigid sections. Evaluating the impact of locally calibrated models on pavement designs is essential to obtaining practical and cost-effective design thickness. Practical Applications: This study provides a framework for the local calibration. Several factors affect the local calibration process, including the field performance data. The recorded performance data may have irregularities due to measurement errors, limitations in distress identification, and conversion to the Pavement-ME units. Therefore, the authors recommend analyzing the raw performance data and filtering it (if required) for practicality. Pavement design is one of the most crucial calibration process steps and is often not considered in practice. It is worth mentioning that pavements were designed based on calibrated models, and based on MDOT feedback, the calibration improved the designs. The authors recommend that the calibration results should not be based only on statistical parameters (SEE, bias, etc.) but also on practical engineering judgments. By understanding which distress types are most relevant to their region, agencies can develop mitigation and maintenance strategies leading to longer pavement service lives. For example, MDOT can mitigate the occurrence of thermal cracking, a critical distress, by using modified and improved binders. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring is a promising method for surveying wildlife populations that are easier to detect acoustically than visually. When animal vocalisations can be uniquely identified on an array of sensors, the potential exists to estimate population density through acoustic spatial capture–recapture (ASCR). However, sound classification is imperfect, and in some situations, a high proportion of sounds detected on just a single sensor ('singletons') are not from the target species. We present a case study of bowhead whale calls (Baleana mysticetus) collected in the Beaufort Sea in 2010 containing such false positives. We propose a novel extension of ASCR that is robust to false positives by truncating singletons and conditioning on calls being detected by at least two sensors. We allow for individual-level detection heterogeneity through modelling a variable sound source level, model inhomogeneous call spatial density, and include bearings with varying measurement error. We show via simulation that the method produces near-unbiased estimates when correctly specified. Ignoring source-level variation resulted in a strong negative bias, while ignoring inhomogeneous density resulted in severe positive bias. The case study analysis indicated a band of higher call density approximately 30 km from shore; 59.8% of singletons were estimated to have been false positives. [ABSTRACT FROM AUTHOR]

Purpose: Measuring trunk extensor muscle endurance is important for identifying non-specific low back pain (LBP) risk factors and prognostic indicators, planning treatment goals, and measuring patient progress. This randomized cross-over study evaluated the reliability and validity of a newly instrumented device for measuring lumbar spine extensor muscle endurance. Methods: Thirty healthy men participated in this study. The Biering-Sørensen test (for the endurance time) and the newly invented device (for the number of repetitions) were applied to measure lumbar spine extensor muscle endurance in two separate weeks at a similar time of a day and with the same rater. Test–retest reliability and validity of the devices were examined via an intra-class correlation coefficient [ICC (2,1)], 95% confidence intervals (95% CI), standard errors of the measurement (SEM), minimal detectable change (MDC), and Pearson's correlation coefficient, respectively. Results: Test–retest reliability of the newly instrumented device demonstrated an excellent level of reliability [ICC (2,1): 0.969; 95% CI: 0.934–0.985; SEM: 2.65 repetition; MDC: 3.75 repetition]; while a moderate to good degree of test–retest reliability was found between the Biering-Sørensen test measurements [ICC (2,1): 0.884; 95% CI: 0.758–0.944; SEM: 13.31 s; MDC: 18.82 s]. Compared to the Biering-Sørensen test, the newly instrumented device had a moderately positive correlation (r = 0.283). Conclusion: The newly instrumented device demonstrated adequate reliability and validity compared with the Biering-Sørensen test. Future studies should assess its clinometric properties in patients with musculoskeletal pain also including female participants. [ABSTRACT FROM AUTHOR]

Background: In DIC studies, positional parameters and speckle size are commonly used to characterise speckle images. The influence of edge parameters is ignored. This leads to a great difference between the DIC calculation results of simulated and real images. And some contradictory results are also produced. Objective: The main objective of this paper is to investigate the effect of edge parameters. As well as to give more reasonable parameters to describe the speckle characteristics. Methods: Firstly, this paper proposes a series of more reasonable parameters to describe the speckle features based on the mathematical expression of the speckle image. Subsequently, the effect of different edge functions on the computational error of DIC is investigated. The effect of different edge functions on pre-filtering is also investigated. Finally, real speckle images are produced using Gaussian and step functions to study the difference between the simulated and real speckle images. Results: Generally, it is believed that prefiltering can reduce the computational error of DIC, but for Gaussian edges, prefiltering hardly reduces the error, whereas hybrid edges correctly exhibit this phenomenon. Although the Gaussian edge perform well in the simulation, the actual speckle images taken show that the DIC error corresponding to the camera-acquired Gaussian speckle is much larger than that of the step speckle. Conclusions: The introduction of edge parameters to describe speckle images is necessary for DIC studies. Pre-filtering always reduces the DIC error, but for Gaussian edges this property cannot be demonstrated. The most suitable edges in reality are step edges, not Gaussian edges. [ABSTRACT FROM AUTHOR]

This article discusses nonparametric estimation of a survival function in the presence of measurement errors on the observation of the failure time of interest. One situation where such issues arise would be clinical studies of chronic diseases where the observation on the time to the failure event of interest such as the onset of the disease relies on patient recall or chart review of electronic medical records. It is easy to see that both situations can be subject to measurement errors. To resolve this problem, we propose a simulation extrapolation approach to correct the bias induced by the measurement error. To overcome potential computational difficulties, we use spline regression to approximate the unspecified extrapolated coefficient function of time, and establish the asymptotic properties of our proposed estimator. The proposed method is applied to nonparametric estimation based on interval‐censored data. Extensive numerical experiments involving both simulated and actual study datasets demonstrate the feasibility of this proposed estimation procedure. [ABSTRACT FROM AUTHOR]

A reduced‐rank mixed‐effects model is developed for robust modelling of sparsely observed paired functional data. In this model, the curves for each functional variable are summarized using a few functional principal components, and the association of the two functional variables is modelled through the association of the principal component scores. A multivariate‐scale mixture of normal distributions is used to model the principal component scores and the measurement errors in order to handle outlying observations and achieve robust inference. The mean functions and principal component functions are modelled using splines, and roughness penalties are applied to avoid overfitting. An EM algorithm is developed for computation of model fitting and prediction. A simulation study shows that the proposed method outperforms an existing method, which is not designed for robust estimation. The effectiveness of the proposed method is illustrated through an application of fitting multiband light curves of Type Ia supernovae. [ABSTRACT FROM AUTHOR]

The increasing availability of unoccupied aircraft systems (UAS, also referred to as drones) has led to their use in taking vertical aerial photographs at relatively small spatial scales. These photographs can be used to measure the distances between objects appearing in the photographs. However, relief displacement can cause an object above or below ground level to appear at a point in a vertical aerial photograph that is not directly in-line with the object's actual location, causing a measurement error. A UAS was used in this study as a photographed airborne object because its location and altitude could be controlled. We were interested in predicting the horizontal distance of the UAS's appearance from the centre of a vertical aerial photograph. Predictions of the location of the photographed UAS's appearance in vertical aerial photographs over both level and sloped surfaces matched measured appearance distances within 0.06–0.48 m. This study shows that the relief displacement formulas typically used to compute the height of a vertical structure appearing in a vertical aerial photograph can additionally be used to compute the actual location of an airborne object (e.g., a flying UAS, bird, bat) if the object's altitude is known or can be estimated. [ABSTRACT FROM AUTHOR]

Item response theory (IRT) analyses are often used to evaluate measurement error in educational and psychological test instruments. In such contexts, the latent traits/proficiencies are typically assumed normally distributed and a cumulative normal/logistic measurement link function is applied. Such choices are consistent with constructs that are viewed as bipolar in nature and play a critical role in defining the latent proficiency metric against which the measurement error in the test is evaluated. Recently, alternative models that portray the construct as unipolar have been highlighted as being more appropriate for certain psychopathology and personality constructs. In this paper we extend consideration of unipolar IRT models for a recognition task measure, using several example datasets from various versions of the Author Recognition Test (ART), a measure of print exposure. We show how the decision between unipolar versus bipolar IRT modeling has substantial implications for the quantification and interpretation of measurement error in the ART. In sharp contrast to prior bipolar IRT analyses of the ART, under unipolar IRT measurement error in the ART is minimized at low levels of latent print exposure, and increases as latent print exposure increases. Implications for consideration of unipolar IRT with other constructs and measures (e.g., vocabulary, specialized forms of knowledge) that reflect a similar type of response process are considered in the discussion. [ABSTRACT FROM AUTHOR]

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Purpose: We aimed to establish criteria for determining the deformed lateral torso type to identify individuals requiring measurement methods different from standard methods before extracting dimensions from three-dimensional (3D) scan data. Design/methodology/approach: We collected the 3D body scan data of 119 women aged 70–85 years collected in the 6th Size Korea. Three axes were defined to determine the deformation of the lateral shape, and the angle of each reference axis was used for the analysis. Additionally, to classify the lateral torso shape, 14 experts made visual judgments on the side-view images of the participants. Findings: To identify the axis that best represented the lateral torso shape, we used each angle value of the three reference axes as an independent variable and the expert's visual classification as a dependent variable. Each discriminant function was obtained and accuracy calculated. The whole torso axis exhibited the highest accuracy. Next, an assessment scale was developed to determine the shape of the lateral torso using the angular value of the whole torso axis. Originality/value: The scale developed in this study has the potential to reduce measurement errors arising from elderly deformed torso shapes, thereby enhancing data reliability. [ABSTRACT FROM AUTHOR]

Research suggests that the standard model used to detect opportunistic shifting of core expenses to special items is potentially biased. Such bias has been attributed to the use of accruals, including special item related accruals, as a control for the impact of performance on core earnings in this model. This paper provides an improved classification shifting model which both tests for such accruals-related bias and controls for other sources of error in the measurement of shifting. The paper also modifies conventional market rationality tests in accounting research to examine new dimensions of rationality in relation to measurement and valuation of shifting. The main empirical findings are as follows. First, the improved classification shifting model provides strong evidence of shifting and rejects the hypothesis that inclusion of accruals in the model causes bias. Second, estimates of shifted core expenses generated by the improved model exhibit forecasting properties of shifted earnings. Third, rationality test results are broadly consistent with rationality in relation to shifted core expenses but indicate possible partial (ir)rationality in relation to adjusted special items (i.e., special items excluding shifted core expenses). Further analysis of the latter findings, however, suggests they are more likely related to risk than irrationality. Overall, the paper contributes to improved measurement of shifting and highlights the importance of considering rational expectations when examining stock returns associated with shifting. [ABSTRACT FROM AUTHOR]

In order to meet the requirements of antenna for high bearing capacity, high positioning accuracy and speed stability of the test turntable, a test turntable with simple operation is designed. First of all, the structure of the turntable is introduced. The turntable adopts the form of gear transmission and multi-turn absolute encoder angle measurement. The mechanical simulation analysis of the turntable table is carried out, and the static performance and modal characteristics of the turntable are analyzed. Then, the servo control system of the turntable is introduced. The multi-turn absolute encoder is used as the detection element of the actual position of the turntable. The target position is sent to the controller by the upper computer through the serial port. The controller uses the field programmable logic gate array (FPGA) as the core, and the encoder, controller and control object constitute the position loop; In the software design, the fuzzy PID algorithm is used to replace the traditional PID algorithm. Finally, the turntable accuracy test platform is built, and the autocollimator and polyhedral prism are used to obtain the angle measurement error. The test results show that the control accuracy is better than ±0.01°, the system position accuracy is better than 2.5', and the turntable positioning accuracy and rotational speed stability have been effectively improved. [ABSTRACT FROM AUTHOR]

Cognitive decline represents a severe non‐motor symptom of Parkinson's disease (PD) that can significantly reduce the benefits of subthalamic deep brain stimulation (STN DBS). Here, we aimed to describe post‐surgery cognitive decline and identify pre‐surgery cognitive profile associated with faster decline in STN DBS‐treated PD patients. A retrospective observational study of 126 PD patients treated by STN DBS combined with oral dopaminergic therapy followed for 3.54 years on average (SD = 2.32) with repeated assessments of cognition was conducted. Pre‐surgery cognitive profile was obtained via a comprehensive neuropsychological examination and data analysed using exploratory factor analysis and Bayesian generalized linear mixed models. On the whole, we observed a mild annual cognitive decline of 0.90 points from a total of 144 points in the Mattis Dementia Rating Scale (95% posterior probability interval [−1.19, −0.62]) with high inter‐individual variability. However, true score changes did not reach previously reported reliable change cut‐offs. Executive deficit was the only pre‐surgery cognitive variable to reliably predict the rate of post‐surgery cognitive decline. On the other hand, exploratory analysis of electrode localization did not yield any statistically clear results. Overall, our data and models imply mild gradual average annual post‐surgery cognitive decline with high inter‐individual variability in STN DBS‐treated PD patients. Nonetheless, patients with worse long‐term cognitive prognosis can be reliably identified via pre‐surgery examination of executive functions. To further increase the utility of our results, we demonstrate how our models can help with disentangling true score changes from measurement error in future studies of post‐surgery cognitive changes. [ABSTRACT FROM AUTHOR]