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The article discusses the feasibility, success, and sustainability of critical care echocardiography curricula, focusing on the measurement of left ventricular outflow tract velocity time integral (LVOT VTI) and diameter. The study evaluates how critical care medicine (CCM) fellows perform compared to a gold standard in real clinical settings, highlighting the importance of standardized education and evaluation in echocardiography. While the study shows promising results in training CCM fellows in specific ACCE measurements, it also emphasizes the need for further education and resources to achieve mastery in advanced critical care echocardiography. The authors suggest that most CCM trainees may not require full ACCE mastery but rather competency to recognize danger and seek assistance in real-time decision-making. [Extracted from the article]

The assessment of driver assistance system effectiveness in crash scenarios can be conducted through simulations. This approach allows for system improvements and contributes to overall road safety. However, it is important to recognize that simulations are an approximation of reality. Specifically, simulation models can significantly impact the results. In our work, we focus on sensor models that provide environmental information to driver assistance systems. Real-world sensor data differ from idealized simulation data due to measurement errors and environmental influences. We evaluate various sensor models for their applicability in simulating effectiveness assessments. Our research is implemented using the open-source platform openPASS. We investigate the impact of inaccurate sensor data on the effectiveness of an autonomous emergency braking system based on longitudinal traffic crashes from the German In-Depth Accident Study database. To achieve this, we compare stochastic phenomenological models with ideal sensor models. In the evaluation of the modeled autonomous emergency braking, we observed a major influence on the effectiveness due to the less accurate, more realistic sensor data. This evaluation approach enables early optimization of the considered driver assistance system's robustness against inaccuracies. [ABSTRACT FROM AUTHOR]

Individuals' lifestyles are affected by valgus and varus deformities in the rearfoot, causing pain in the joints and plantar surface due to the misalignment between the tibial and calcaneus. In orthopedics, medical professionals measure this misalignment by using X-ray systems and goniometers. The X-ray emits ionizing radiation that can cause damage through cumulative exposure over a lifetime, whereas the goniometer will produce measurement errors. This patent review conducted a technological search of systems and methods across various databases using inclusion and exclusion criteria. These thirty-five obtained patents provide valuable information about mechanical, electronic, and mechatronic technologies and non-ionizing radiation to evaluate valgus and varus deformities. The patents are classified into stationary mechanisms, stationary electronic devices, dynamic mechanisms, dynamic electronic devices, stationary mechatronic devices, and dynamic mechatronic devices. They are further categorized based on their measurement methods as either visual or automatic. Additionally, the patents are grouped by usage mode into sitting, standing, and walking. This patent review aims to provide medical professionals with little-known techniques for measuring and evaluating the rearfoot alignment. [ABSTRACT FROM AUTHOR]

Simplifying trajectory data can improve the efficiency of trajectory data analysis and query and reduce the communication cost and computational overhead of trajectory data. In this paper, a real-time trajectory simplification algorithm (SSFI) based on the spatio-temporal feature information of implicit trajectory points is proposed. The algorithm constructs the preselected area through the error measurement method based on the feature information of implicit trajectory points (IEDs) proposed in this paper, predicts the falling point of trajectory points, and realizes the one-way error-bounded simplified trajectory algorithm. Experiments show that the simplified algorithm has obvious progress in three aspects: running speed, compression accuracy, and simplification rate. When the trajectory data scale is large, the performance of the algorithm is much better than that of other line segment simplification algorithms. The GPS error cannot be avoided. The Kalman filter smoothing trajectory can effectively eliminate the influence of noise and significantly improve the performance of the simplified algorithm. According to the characteristics of the trajectory data, this paper accurately constructs a mathematical model to describe the motion state of objects, so that the performance of the Kalman filter is better than other filters when smoothing trajectory data. In this paper, the trajectory data smoothing experiment is carried out by adding random Gaussian noise to the trajectory data. The experiment shows that the Kalman filter's performance under the mathematical model is better than other filters. [ABSTRACT FROM AUTHOR]

High-frequency ripple current excitation reduces the lithium precipitation risk of batteries during self-heating at low temperatures. To study the heat generation behavior of batteries under high-frequency ripple current excitation, this paper establishes a thermal model of LIBs, and different types of LIBs with low-temperature self-heating schemes are studied based on the established thermal model. Under the consideration of contact impedance, this paper tests the heat production of the battery under high-frequency ripple current and establishes an accurate thermal model of a lithium-ion battery under the excitation of high-frequency ripple current, and the absolute value of the maximum relative error between the measurement results and the thermal model is reduced from 181.4% to less than 20.6%, which makes the battery thermal model under high-frequency ripple current excitation more accurate. Then, based on the established thermal model, the calculation method of the minimum heating power required for different batteries under the same low-temperature self-heating conditions is proposed for batteries of different sizes, thermal properties, and internal resistances, and the preferred low-temperature self-heating scheme for different types of batteries is proposed by comparing the current multiplicity required for different high-frequency ripple current self-heating schemes. [ABSTRACT FROM AUTHOR]

Background: To establish the automatic soft‐tissue analysis model based on deep learning that performs landmark detection and measurement calculations on orthodontic facial photographs to achieve a more comprehensive quantitative evaluation of soft tissues. Methods: A total of 578 frontal photographs and 450 lateral photographs of orthodontic patients were collected to construct datasets. All images were manually annotated by two orthodontists with 43 frontal‐image landmarks and 17 lateral‐image landmarks. Automatic landmark detection models were established, which consisted of a high‐resolution network, a feature fusion module based on depthwise separable convolution, and a prediction model based on pixel shuffle. Ten measurements for frontal images and eight measurements for lateral images were defined. Test sets were used to evaluate the model performance, respectively. The mean radial error of landmarks and measurement error were calculated and statistically analysed to evaluate their reliability. Results: The mean radial error was 14.44 ± 17.20 pixels for the landmarks in the frontal images and 13.48 ± 17.12 pixels for the landmarks in the lateral images. There was no statistically significant difference between the model prediction and manual annotation measurements except for the mid facial‐lower facial height index. A total of 14 measurements had a high consistency. Conclusion: Based on deep learning, we established automatic soft‐tissue analysis models for orthodontic facial photographs that can automatically detect 43 frontal‐image landmarks and 17 lateral‐image landmarks while performing comprehensive soft‐tissue measurements. The models can assist orthodontists in efficient and accurate quantitative soft‐tissue evaluation for clinical application. [ABSTRACT FROM AUTHOR]

With the rapid development of smart sensor technology and the Internet of things, ensuring data accuracy and system reliability is paramount. As the number of sensors increases with demand for high-resolution, high-quality input to decision-making systems, models and digital twins, manual quality control of sensor data is no longer an option. In this paper, we leverage insights into sensor technology, environmental dynamics and the correlation between data from different sensors for automatic diagnostics of a sensor node. We propose a method for combining results of automatic quality control of individual sensors with tests for detecting simultaneous anomalies across sensors. Building on both sensor and application knowledge, we develop a diagnostic logic that can automatically explain and diagnose instead of just labeling the individual sensor data as "good" or "bad". This approach enables us to provide diagnostics that offer a deeper understanding of the data and their quality and of the health and reliability of the measurement system. Our algorithms are adapted for real time and in situ operation on the sensor node. We demonstrate the diagnostic power of the algorithms on high-resolution measurements of temperature and conductivity from the OBSEA observatory about 50 km south of Barcelona, Spain. [ABSTRACT FROM AUTHOR]

Due to its simplicity and high reliability, the Oscillating Water Column (OWC) has been a prominent focus of research. This study investigates the impact of OWC geometry on hydrodynamic performance by analyzing the effects of draft, chamber width, and Power Take Off (PTO) damping on energy conversion efficiency, reflection coefficient, transmission coefficient, and dissipation coefficient. The motion of the water column under different PTO damping conditions is analyzed. In addition, the hydrodynamic performance of the submerged breakwater-OWC hybrid system is studied and compared with that of a standalone OWC. The results show that a higher opening height improves the energy conversion efficiency of the OWC under medium-to-long waves. There is an optimal draft for the OWC to maximize the energy conversion efficiency for different wave periods. Meanwhile, the optimal draft increases with the wave period. Increasing PTO damping does not change the resonance period of the OWC, while the free surface elevation inside the chamber reduces. Within a range of periods around the maximum inclination, multi-point measurements of the wave elevation in the chamber are necessary to reduce measurement errors caused by the spatial non-uniformity of the water column. The submerged breakwater-OWC hybrid system exhibits a higher energy conversion efficiency under medium-to-long waves compared to a standalone OWC, and there is an optimal breakwater length. [ABSTRACT FROM AUTHOR]

Supplemental Nutrition Assistance Program (SNAP) is the cornerstone food assistance program in the United States and has been shown to reduce the risk of food insecurity. Most research on the causal effect of SNAP on food insecurity relies on the 12‐month food insecurity scale along with usage of SNAP at any point during the year. However, recent social surveys ask about experiences with food insecurity in the 30 days prior to the survey. In this paper, we examine whether similar protective effects of SNAP against food insecurity are obtained whether using the 30‐day or 12‐month food insecurity scale using the December Supplement of the Current Population Survey for 2002–2019. Results indicate comparable average treatment effects of SNAP in mitigating food insecurity across both 30‐day and 12‐month reference periods. [ABSTRACT FROM AUTHOR]

In the context of hierarchical system modeling, ensuring constraints between different hierarchy levels are met, so, for instance, ensuring the aggregation constraints are satisfied, is essential. However, modelling and forecasting each element of the hierarchy independently introduce errors. To mitigate this balance error, it is recommended to employ an optimal data reconciliation technique with an emphasis on measurement and modeling errors. In this study, three different machine learning methods for development were investigated. The first method involves no data reconciliation, relying solely on machine learning models built independently at each hierarchical level. The second approach incorporates measurement errors by adjusting the measured data to satisfy each constraint, and the machine learning model is developed based on this dataset. The third method is based on directly fine-tuning the machine learning predictions based on the prediction errors of each model. The three methods were compared using three case studies with different complexities, namely mineral composition estimation with 9 elements, forecasting of retail sales with 14 elements, and waste deposition forecasting with more than 3000 elements. From the results of this study, the conclusion can be drawn that the third method performs the best, and reliable machine learning models can be developed. [ABSTRACT FROM AUTHOR]

The application of coal technologies for energy generation leads to high pollutant emissions. Thus, governmental and international organizations have created new programs and laws for monitoring emissions. Recently, the government of Kazakhstan has introduced regulations for the measurement of emissions produced by factories and power plants. However, the requirements and Corecommendations for the monitoring methods have not been defined. Therefore, this article addresses the problem and focuses on determining the measurement errors made by optical SGK510 and electrochemical POLAR devices used for coal power plants. The hypothesis is based on the fact that there are currently no systems for monitoring probe drying, and its implementation is expensive. The main methods are analyzed, namely their operation, taking into account the presence of water particles in samples, and the possibility of using adjustment coefficients is considered. The main pollutants chosen for analysis are CO, NO, NO2, NOx, SO2, and O2. Using the Broich–Pagan test, homoscedasticity was determined, and the Fisher test showed the possibility of using tuning coefficients. The data for the optical method were compared to measurements taken using Inspector 500. The error for SO2 determination was 7.19% for NO, 44.0985% for NO2, 733.26% for NOx, 7.39% for O2, 2.75% for CO, 60.81%. The comparison between SGK510 and POLAR demonstrated the following errors: for CO—1.5%, for NOx—82.4405%, for SO2—41.17%, for O2—11.61%. According to the Fisher criteria analysis of the optical method, only SO2 and CO values measured by SGK510 in comparison to Inspector 500 had close similarity, while others demonstrated high deviations. The significance tests were carried out by Fisher's, t-test, and ANOVA methods. For the electrochemical measurement, only CO values had close similarity. In the future, methods will be proposed to improve the accuracy of the system while reducing maintenance costs, as well as cleaning sampling systems. The multicomponent analysis application for accuracy improvement with the exhaust gas humidity, temperature, and flow consideration was recommended as a possible solution. [ABSTRACT FROM AUTHOR]

Understory terrain plays a multi-faceted role in ecosystems, biodiversity, and productivity in forests by influencing different major factors, such as hydrological processes, soils, climate, and light conditions. Strong illuminants (e.g., sunlight) from ground surfaces and atmosphere can introduce additional photons into the ATLAS system. These photons can, consequently, be mistakenly identified as laser photons reflected from ground surfaces. The presence of such ambient light, particularly under low-photon-count conditions, can significantly increase elevation measurement errors. In this context, this study aims to propose a method for extracting reliable understory elevation control points under varying forest conditions, based on the parameter attributes of ICESat-2/ATLAS data. The overall filtered data resulted in a coefficient of determination (R2), root mean square error (RMSE), and standard deviation (STD) of 0.99, 2.77 m, and 2.42 m, respectively. The greatest accuracy improvement was found in the Puerto Rico study area, showing decreases in the RMSE and STD values by 2.68 and 2.67 m, respectively. On the other hand, canopy heights and slopes exhibited relatively large impacts on noise interferences. In addition, there were decreases in the RMSE and STD values by 4.57 and 4.64 m, respectively, under the very tall canopy category, whereas under steep slope conditions, the RMSE and STD values of the filtering results decreased by 4.59 and 4.34 m, respectively. The proposed method can enhance the overall accuracy of elevation data, allowing for the significant extraction of understory elevation control points, ultimately optimizing forest management practices and improving ecological assessments. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effects of wind turbulence on CO2 transport within a medium and the extent of measurement errors in a closed chamber. Therefore, in a laboratory with controllable environmental conditions, the measurement performance of the closed chamber at various wind speeds was assessed using a soil respiration calibration apparatus and four types of porous media. The experimental results indicated that the closed chamber under the influence of wind turbulence exhibited varying degrees of underestimation, ranging from −51 to −6%. The effects of wind turbulence were more pronounced in sandy soils. As wind turbulence enhanced gas transport within the medium, the flux measurements of the closed chamber were biased, and this phenomenon was closely related to the medium's particle size and surface wind speed. To address this issue, it is recommended to conduct long-term monitoring and eliminate errors by averaging repeated measurements, which will improve the accuracy of the ecosystem carbon budget. [ABSTRACT FROM AUTHOR]

Assessing the reliability of measurement instruments and equipment is essential to ensure the accurate tracking of athletes over extended periods, minimizing the measurement errors caused by chance or other factors. However, a less common but equally important analysis is the verification of inter-measurement agreement, which complements the reliability results. Purpose: To evaluate the intra- and inter-test reliability of an isometric hip adduction strength and asymmetries test in professional soccer players. Methods: Twenty-three professional male soccer players were evaluated on two occasions, 1 week apart. The force signal was collected using force plates (Pasco PS-2141), and the data processing was performed using Matlab software (R2023a, MathWorks, Natick, MA, USA). The peak force, interval RFD, peak RFD, peak force asymmetry and RFD were analyzed. Intraclass correlation coefficients (ICCs) and coefficients of variation (CV) were calculated to corroborate the intra- and inter-test reliability. In addition, the degree of agreement of the asymmetries was corroborated through the kappa index. Results: The peak force demonstrated an acceptable absolute reliability (CV < 10%) for the intra-test and test–retest condition, an excellent relative intra-test reliability and a good to moderate reliability for the test–retest reliability. However, the peak force asymmetry showed a moderate test–retest reliability and agreement. For the intra-test condition, the RFD variables demonstrate a moderate to excellent relative reliability; however, all demonstrate unacceptable absolute reliability (CV > 10%) in at least one of the evaluation sessions. A moderate to poor test–retest reliability and unacceptable absolute reliability were observed for all the RFD variables. Conclusions: The peak force is the variable with the highest intra- and inter-test reliability, so its use is recommended to longitudinally assess the maximum strength of the adductors in professional soccer players, but not the asymmetry orientation of the peak force. The RFD variables should be interpreted with caution due to their inconsistent reliability, and it is necessary to improve the methods used to achieve adequate reliability. [ABSTRACT FROM AUTHOR]

Frequency-modulated continuous wave (FMCW) interferometry, an emerging laser interferometry technology, can be applied in the field of fibre-optic sensing to achieve high-precision micro-displacement measurements. To address nonlinearity issues in laser frequency modulation and localisation deviations of feature points in traditional algorithms, this paper proposes a demodulation algorithm suitable for sinusoidal frequency modulation schemes, incorporating the principle of orthogonal phase-locked amplification. The algorithm includes signal preprocessing, phase-locked amplification, error correction, and phase calculation. Experimental results show that the system achieves a measurement error standard deviation of 3.23 nanometres for static targets. The displacement measurement error at 100 μm is 0.057% F.S., and the linearity between the measured values and the actual displacement values is 0.99997. Compared with conventional methods, the approach introduced in this paper eliminates the need for separate nonlinear corrections of the current-to-optical frequency relationship and minimises the issue of feature point localization deviations, showing significant potential for practical applications. [ABSTRACT FROM AUTHOR]

We consider evaluating biomarkers for treatment selection under assay modification. Survival outcome, treatment, and Affymetrix gene expression data were attained from cancer patients. Consider migrating a gene expression biomarker to the Illumina platform. A recent novel approach allows a quick evaluation of the migrated biomarker with only a reproducibility study needed to compare the two platforms, achieved by treating the original biomarker as an error‐contaminated observation of the migrated biomarker. However, its assumptions of a classical measurement error model and a linear predictor for the outcome may not hold. Ignoring such model deviations may lead to sub‐optimal treatment selection or failure to identify effective biomarkers. To overcome such limitations, we adopt a nonparametric logistic regression to model the relationship between the event rate and the biomarker, and the deduced marker‐based treatment selection is optimal. We further assume a nonparametric relationship between the migrated and original biomarkers and show that the error‐contaminated biomarker leads to sub‐optimal treatment selection compared to the error‐free biomarker. We obtain the estimation via B‐spline approximation. The approach is assessed by simulation studies and demonstrated through application to lung cancer data. [ABSTRACT FROM AUTHOR]

AbstractWe examine the symmetry or asymmetry of the density function of a continuous variable under the setting of additive distortion measurement errors. The unobservable variable is distorted in an additive manner by an observed confounding variable. We adopt the covariance ratio-based measure under conditional mean calibration and conditional exponential mean calibration methods. Using the empirical likelihood method, we consider statistical inference of the covariance ratio-based measure under additive distortions to check for symmetry. The calibrated covariance ratio-based measures are shown to be asymptotically efficient as if there were no additive distortion effects. Additionally, Gupta’s skewness with conditional exponential mean calibration is also studied. We conduct Monte Carlo simulation experiments to access the performance of the calibrated measures and test procedures. These methods are applied to analyze a real dataset for illustrative purposes. [ABSTRACT FROM AUTHOR]

Satellite measurements are essential for understanding Earth's complex system, yet they often lack a reliable a‐priori covariance matrix. This study presents a new methodology to enhance the reliability of satellite measurements by deriving experimental covariance matrices from the original observation. We focus on the accelerometer measurements (1A dataset) from the GRACE (Gravity Recovery and Climate Experiment) C satellite. Using autocorrelation analysis, we create a block‐diagonal covariance matrix for the 1A dataset. We then apply a low‐pass Gaussian filter that integrates this covariance matrix into the least squares estimation, resulting in a refined 1B dataset that minimizes spikes and spurious accelerations while preserving measurement error. Our variance analysis uncovers disturbances linked to geomagnetic storms and the satellite's transitions through Earth's shadow and terminator, with fluctuations notably peaking during the equinoxes. Plain Language Summary: We developed an effective method to generate covariance matrices directly from the raw accelerometer measurements of Gravity Recovery and Climate Experiment (GRACE) C (10 Hz $Hz$ sampling rate) that can be applied to any satellite measurements structured as time or data series. The result of this method is a weighted accelerometer 1B dataset, free of spurious spikes typically present in accelerometer measurements, while preserving error information through the inclusion of measurement variances and correlations. Our approach excels at detecting errors and preserving important signals. Notably, the accelerometer variances depend on latitude and local time, increase during equinoxes and geomagnetic storms, and are highest in the radial direction. Key Points: A new post‐processed 1B accelerometer dataset has been developed for GRACE C satelliteThe dataset variances exhibit significant dependence on equinoxes, terminator crossings, latitude, local time, and β' angleThese variances show strong signals associated with geomagnetic storms [ABSTRACT FROM AUTHOR]

With the continuous development of the water transportation and shipping industries, the number of ships in rivers has steadily multiplied, followed by the increasing complexity of the ship routes. These changes have highlighted the growing importance of ship detection and water level measurement systems. Such systems not only enhance the management efficiency of waterborne traffic, ensure navigation safety, and reduce congestion and collision accidents, but also effectively safeguard the integrity of riverside and bridge structures. Ship detection and river elevation measurement based on 3D point clouds can directly acquire depth information without being affected by lighting conditions. So it has a good research prospect. Therefore, this paper proposes a novel ship detection algorithm based on improved PointRCNN and a novel method for riverbank line extraction and water level measurement based on 3D point clouds, respectively. For ship detection, the improved PointRCNN algorithm can increase the performance of data processing and keypoint extraction techniques, and make the network to keep more foreground point clouds and learn more effective features. This improves the recognition capability of distant ships. Compared to the original PointRCNN algorithm, the improved PointRCNN algorithm has achieved a 3.84% increase in detection precision in practical scenarios. Regarding riverbank extraction and water level measurement, the proposed method based on 3D point clouds can directly extract riverbank lines with depth information, obtaining water level height without direct contact with the river surface. Within a distance range between 15 and 45 m from the LiDAR, the average absolute error using this measurement method is less than 5 cm, demonstrating the good detection accuracy of this method. [ABSTRACT FROM AUTHOR]

Corrections for wind tunnel experimental results are crucial when accounting for tunnel wall interference. This study introduces a new method, the non-uniform wall pressure signature method (NUWPSM), which is designed to address tunnel wall interference in airfoil. The improved wall pressure signature method (WPSM), an enhanced version of the WPSM, is developed to address the velocity disparities and systematic errors in pressure measurements between with and without model conditions. Furthermore, the NUWPSM considers the non-uniformity of the flow induced by the limited far-field effect in wind tunnel experiments. Utilizing experimental data from three different scaled models of the WA210 airfoil, the efficacy of both the Improved WPSM and NUWPSM is verified. Results indicate that the Improved WPSM exhibits superior capabilities in simulating the distribution of axial induced velocity along the wall compared to the traditional WPSM. Additionally, both the Improved WPSM and NUWPSM demonstrate comparable abilities in correcting tunnel wall interference, achieving precise corrections within an angle of attack range of −180° to +180°. Notably, the NUWPSM effectively captures the velocity non-uniformity induced by the limited far-field effect, thereby extending its applicability to a broader range of scenarios. Furthermore, the NUWPSM showcases enhanced robustness by eliminating human intervention in the singularity quantity and distribution. [ABSTRACT FROM AUTHOR]

The rapid advancement in the modern automotive industry has put forward higher requirements for the quality of various parts. This paper explores automated dimensional measurement technique based on point cloud models and develops a novel digital inspection system to enhance measurement accuracy and efficiency. By working out specialized inspection schemes, creating local cross-sections at the corresponding inspection positions on the scanned point cloud model of the part to be measured, and adopting geometric analysis algorithms, such as random sample consensus and progressive iterative optimization, the proposed digital inspection method constructs key points for measuring dimensions. This process combines heuristic information from the design or theoretical model with local features of the actual part, enabling rapid and accurate acquisition of inspection results and errors. Besides, to meet the actual production needs for achieving efficient product inspection and quality evaluation, an open system development framework has been designed. It integrates independently developed core functionalities for collecting, sampling point cloud data, performing dimensional measurement and error analysis, with auxiliary functions developed on the basis of commercial software by applying automated scripts for the preprocessing of scanned data and visualizing dimensional measurement and error analysis results. Experimental and application results on actual components demonstrate that, compared to traditional inspection methods, the digital virtual inspection method maintains high inspection accuracy, enhances the overall intelligence level of quality evaluation, and significantly shortens measurement time. Additionally, it exhibits universality and is especially suitable for dimensional measurement where traditional methods are not applicable. [ABSTRACT FROM AUTHOR]

Background: Oxford Shoulder Instability Score (OSIS) is a patient reported outcome measure designed specifically to assess functional difficulties resulting from shoulder instability. The main aim of this study was to cross-culturally adapt the OSIS to Spanish. Secondary, it aimed to analyse its methodological quality. Methods: A cross-cultural adaptation to Spanish has been carried out following the recommendations of COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) with a sample of 167 cases of shoulder instability. Inclusion criteria were: subjects with instability symptoms in at least one shoulder with or without clinical diagnosis; aged between 18 and 60. The following psychometric properties were evaluated: validity (construct, internal and external), reliability (internal consistency, test-retest and measurement error), discriminant ability and feasibility. The methodological quality was addressed with Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and COSMIN Risk of Bias checklist (COSMIN RoB). Results: The construct validity obtained an OSIS correlation with the Simple Shoulder Test of r = 0.636 and with the Western Ontario Shoulder Instability of r = 0.80. The unidimensionality of the OSIS was confirmed through second-order factor analysis. Cronbach's alpha and intraclass correlation coefficient were both 0.93 (IC95%: 0.91–0.94). Standard error of measurement was 0.70, and the percentage of error and smallest detectable change were 1.46% and 1.94, respectively. No floor or ceiling effects were found. Assessing feasibility of OSIS, the participants answered all questions, had no questions and completion time was: mean 2 min 30 s; SD ± 1 min. Regarding methodological quality, the study showed low risk of bias in the areas of patient selection, index test, reference standard and flow and timing, as well as low concern regarding applicability in the domains of patient selection, index test and reference standard according to QUADAS-2. In relation to COSMIN RoB, all psychometric properties, except for content validity, obtained very good results. Conclusions: The Spanish version of OSIS offers valid, reliable and feasible functional outcome measures for Spanish-speaking subjects with shoulder instability, based on its psychometric properties and its methodological quality. [ABSTRACT FROM AUTHOR]

This paper proposes a mixture regression model for competing risks data, where the logistic regression model is specified for the marginal probabilities of the failure types and the mean residual lifetime (MRL) model is assumed for the failure time given the failure of interest. The estimating equations (EEs) are derived to infer the logistic regression and MRL model separately. We further consider the situation where the covariates are subject to measurement error. The presence of measurement error imposes extra challenges for the analysis of complex time-to-event data. By using the above EEs as the correction-amenable original estimating functions, we propose a corrected score estimation, which does not require specifying the distributions for unobserved error-prone covariates. The proposed estimators are shown to be consistent and asymptotically normally distributed. The performance of the method is investigated by intensive simulation studies and two real examples are presented to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

Baicalin concentration is concerned in manufacture of scutellaria spray drying powder as a traditional Chinese medicine, and the quality control based on high‐performance liquid chromatography is inconvenience. In this study, terahertz time domain spectroscopy was employed to achieve quality control of scutellaria spray drying powder; however, an acute difficulty was found that terahertz spectra overlapped due to the disturbance in both content matrix and measurement error. In this study, similar terahertz spectra of scutellaria spray drying powder were classified with the help of Gaussian mixture model and built a classifier based on probability feature instead of spectral features conventionally employed in previous investigations. To explore the feasibility of GMM, principal component analysis was given, indicating that it is possible to train GMM with original features and proper principal components. Probable advantage of training GMM based on PCA feature was discussed and so it was with the capacity of the model to identify the linear combined spectra by comparing the performance of GMM and a decision tree model. Above all, the reason why GMM shows potential in the analysis of TCM terahertz spectra was illustrated by comparing the thought of discriminative model and generative model. This study implied that generative model may have natural advantage of overcoming the inherent disturbance of terahertz spectroscopy, which would be promising in future studies. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the feasibility of using dynamic MRI to measure the features of cranial rhythmic impulse (CRI). Design and setting: Fifteen healthy participants (9 females and 6 males, aged 25 to 77) underwent dynamic MRI in a sagittal T2 HASTE view at a rate of 0.60 Hz for 30 s. The MRI videos were analyzed using video tracking software. Three points were marked: the glabella, the midpoint of the sella turcica, and a symmetrical point of the glabella on the occiput. The distances between these points were measured across 46 frames. Amplitudes and rates of asymmetrical CRI waves were calculated using Excel formulas. Results: The mean wave frequencies were 5.65 Hz for the anteroposterior distance, 6.2 Hz from sella turcica to occiput, and 6.76 Hz from sella turcica to glabella. The mean wave amplitudes were 0.39 mm, 0.6 mm, and 0.49 mm for the respective distances. Both intraclass correlation coefficients (ICC) and reliability coefficient (R) indicated excellent reliability (R, ICC > 0.90). The technical error of measurement (TEM) exceeded 1 mm for the anteroposterior and sella-to-occiput distances, while it was 0.32 mm for the sella-to-glabella distance. Conclusions: Dynamic MRI demonstrates potential in measuring the features of CRI, particularly in assessing CRI wave rate. While the ICC values indicate high reliability, the TEM values suggest that using MRI to measure CRI wave amplitude may only be dependable for the distance from the sella to the glabella. [ABSTRACT FROM AUTHOR]

Fluctuating asymmetry, the non-directional deviation from bilateral symmetry resulting from developmental instability, can indicate early-life environmental stress. While fluctuating asymmetry can affect individual survival and reproductive success, its effect on fitness differs between species. Here, we analyzed up to 27 years of mark-recapture data from 894 RFID tagged individuals of three forest-living bat species in southern Germany to investigate the degree of fluctuating asymmetry in forearm length. In Bechstein's bats, Myotis bechsteinii, the species with the highest sample size, we furthermore investigated if fluctuating asymmetry has become more frequent over the study period, a time when juvenile bats have grown larger forearms in response to warmer summers. We also investigated whether fluctuating asymmetry affects individual lifespan and lifetime reproductive success in female Myotis bechsteinii. The degree of fluctuating asymmetry clearly exceeding the measurement error estimated on recaptured individuals was similar in all three species (1.8%). In female Myotis bechsteinii, the frequency of fluctuating asymmetry did not increase over the course of the study and even strong asymmetry had no effect on individual reproductive success and life expectancy. Our data suggest that fluctuating asymmetry is a poor predictor of fitness in the female Myotis bechsteinii studied, and is so far unaffected by the warming environment which is leading to larger individuals in our study population. [ABSTRACT FROM AUTHOR]

AbstractAimMethodResultsConclusionClinicalTrials.gov ID\nIMPLICATIONS FOR REHABILITATIONThis study aimed to investigate the validity and reliability of the 2-Minute Step Test (2MST) in patients with lumbar spinal stenosis (LSS).This cross-sectional study involved 55 volunteers, aged 18–80, diagnosed with lumbar spinal stenosis. The participants were assessed using the 2MST, the 2-Minute Walk Test (2MWT), the 6-Minute Walk Test (6MWT), the Visual Analogue Scale (VAS), and the Oswestry Disability Index (ODI). To evaluate the reliability of the 2MST, test-retest reliability was determined by administering the 2MST again after a 7-day interval. The reliability analysis included the intraclass correlation coefficient (ICC) with a 95% confidence interval (CI), standard error of measurement (SEM), and minimum detectable change at the 95% confidence level (MDC95). For concurrent validity, the correlations of the 2MST with the 2MWT, 6MWT, and ODI were assessed.The 2MST exhibited excellent test-retest reliability (ICC = 0.94, SEM = 5.56, MDC95=15.41) in LSS patients. Additionally, the 2MST showed significant correlations with the 2MWT (r = 0.842, p < 0.001), 6MWT (r = 0.819, p < 0.001), and ODI score (r = −0.722, p < 0.001).This study demonstrated that the 2MST is a valid and reliable tool for assessing exercise capacity in individuals with LSS.NCT06060821The 2-Minute Step Test (2MST) is a valid and reliable method for assessing exercise capacity in patients with lumbar spinal stenosis (LSS).The 2MST is a practical, low-cost test that does not require equipment and can be easily administered in small clinical spaces, making it a convenient tool for functional capacity assessments.Clinicians should consider using the 2MST to monitor functional improvements and guide rehabilitation interventions for patients with LSS.The 2-Minute Step Test (2MST) is a valid and reliable method for assessing exercise capacity in patients with lumbar spinal stenosis (LSS).The 2MST is a practical, low-cost test that does not require equipment and can be easily administered in small clinical spaces, making it a convenient tool for functional capacity assessments.Clinicians should consider using the 2MST to monitor functional improvements and guide rehabilitation interventions for patients with LSS. [ABSTRACT FROM AUTHOR]

Background Given rising youth sport participation, 8 to 10-year-olds increasingly display comparable lower-extremity injury incidence to 11 to 17-year-olds and require effective return to sport criteria. One such criterion which quantifies dynamic stability is the Y-Balance Test (YBT), though it has not been validated in children under age 11. Hypothesis/Purpose The purpose of this study was to examine the performance of 8 to 10-year-old patients on the YBT after lower-extremity injury and determine how these results compare to larger samples of age-grouped athletes within the validated 11 to 17-year-old range. It was hypothesized that 8 to 10-year-olds would display different normalized YBT distances compared to 11 to 17-year-olds. Study Design Cross-sectional Study. Methods Patients (N=1093) aged 8 to 17 who presented to a pediatric sports medicine practice with a lower-extremity injury and completed the YBT between December 2015-May 2021 were included. Anterior, posteromedial, and posterolateral YBT scores were collected at return-to-sport for affected and unaffected limbs. Scores were normalized to limb length, and composite scores were created. Between-limb differences were calculated in groups of ages 8-10, 11-12, 13-14, and 15-17. Groups were also evaluated for differences by sex. Results A rise in performance was observed in unaffected limb anterior reach from ages 8 to 10 years to 11 to 12 years followed by a subsequent significant decrease at older ages (p<0.001). Affected limb anterior reach differed between the youngest group and two oldest groups (p=0.004). Anterior and composite difference were significantly different between the oldest three groups (p=0.014 anterior; p=0.024 composite). No differences were observed between sexes in 8 to 10-year-olds, though 11 to 12-year-old females reached further during all eight distances. In the older three groups, males generally displayed greater between-limb differences. Conclusion YBT scores, specifically anterior reach, demonstrated inconsistency by age and sex across a large adolescent cohort. Existing return-to-sport standards should not be used with younger athletes, and individual validation is required. Level of Evidence Level III [ABSTRACT FROM AUTHOR]

Landslides are a severe geological disaster that can cause casualties and property damage. Surface deformation displacement monitoring plays a crucial role in landslide monitoring and early warning systems. However, the shortage of power supply at landslide monitoring sites limits the widespread installation of automated landslide displacement monitoring sensors. In this research, we propose a self‐powered sensor (displacement monitoring sensor‐triboelectric nanogenerator [DIS‐TENG]) for monitoring landslide displacement based on the TENG. The DIS‐TENG utilizes the principle of frictional electrification and electrostatic induction to convert landslide displacement into voltage signal output, thereby achieving the monitoring of landslide displacement. Test results show that the output voltage of the DIS‐TENG is directly proportional to the landslide displacement, and the maximum output voltage is 26.98 V. The measurement range is 1–12 cm, the measurement errors are below 5%, the maximum output power is 9.02 µW with an external load resistance of 107 Ω, and the sensor operates reliably within a temperature range of 5°C to 85°C and a humidity range of 10%–80%. Compared to traditional landslide displacement monitoring sensors, the DIS‐TENG possesses the distinct advantage of not requiring an external power supply. Given the absence of reliable power sources in outdoor working environments, a self‐powered sensor is undeniably more suitable for such conditions. [ABSTRACT FROM AUTHOR]

Background: COSMIN (Consensus-based Standards for the selection of health Measurement INstruments) provides a framework for selecting and validating patient-reported outcome measurements (PROMs). This study aims to validate the Hungarian version of the GOHAI and, for the first time, to assess its Standard Error of Measurement (SEM), Smallest Detectable Change (SDC), and Measurement Invariance (MI) across general and clinical populations as well as different age groups, following COSMIN guidelines. Materials and methods: The translation was performed using a forward-backward process. A mixed sample (n = 306) was recruited in Budapest from May 2023 to February 2024, consisting of the general population (45.1%), recruited from health kiosks and a nursing home, and the clinical population (54.9%), sourced from Semmelweis University's care units. The sample was further divided into two age groups: 18–64 years old (54.9%) and 65 + years old (45.1%). GOHAI was administered twice to 108 stable participants. For both the additive score (ADD-GOHAI) and simple count (SC-GOHAI), structural validity and measurement invariance by subgroups were assessed via Confirmatory Factor Analysis (CFA). Internal consistency was evaluated using Cronbach's alpha, and test-retest reliability was measured using the intraclass correlation coefficient (ICC). SEM was calculated using the SEM agreement formula, and SDC using: . Convergent and known-group validity were tested against predefined hypotheses for structural validity. Results: Contrary to a three factor model, a single-factor model showed good fit in all subgroups for both scoring methods, with adequate internal consistency (Cronbach 훼: 0.76–0.85). Four of the six hypotheses for convergent validity and all ten hypotheses for known-groups validity supported the predefined criteria. Measurement invariance between clinical and general populations, or by age, was not demonstrated, so GOHAI's different measurement properties should be considered when comparing subpopulations. Test-retest reliability was adequate (ICC: 0.87–0.96). SDC was ≈5 points using ADD-GOHAI and 2–3 points using SC-GOHAI. Conclusion: The Hungarian version of GOHAI demonstrates satisfactory psychometric properties across both general and clinical populations, as well as among both younger and older age groups. While the measurement properties of SC-GOHAI may be more stable between populations, ADD-GOHAI seems more suitable for individual follow-up. However, observed changes must be considered in relation to the measurement error associated with GOHAI. [ABSTRACT FROM AUTHOR]

The primary source of measurement error from widely used particulate matter (PM) PurpleAir sensors is ambient relative humidity (RH). Recently, the US EPA developed a national correction model for PM2.5 concentrations measured by PurpleAir sensors (Barkjohn model). However, their study included few sites in the southeastern US, the most humid region of the country. To provide high-quality spatial and temporal data and inform community exposure risks in this area, our study developed and evaluated PurpleAir correction models for use in the warm–humid climate zones of the US. We used hourly PurpleAir data and hourly reference-grade PM2.5 data from the EPA Air Quality System database from January 2021 to August 2023. Compared with the Barkjohn model, we found improved performance metrics, with error metrics decreasing by 16 %–23 % when applying a multilinear regression model with RH and temperature as predictive variables. We also tested a novel semi-supervised clustering method and found that a nonlinear effect between PM2.5 and RH emerges around RH of 50 %, with slightly greater accuracy. Therefore, our results suggested that a clustering approach might be more accurate in high humidity conditions to capture the nonlinearity associated with PM particle hygroscopic growth. [ABSTRACT FROM AUTHOR]

For meter-wave multiple-input multiple-output (MIMO) radar, the multipath of target echoes may cause severe errors in height measurement, especially in the case of complex terrain where terrain fluctuation, ground inclination, and multiple reflection points exist. Inspired by a sparse array with greater degrees of freedom and low mutual coupling, a height measurement method based on a sparse array is proposed. First, a practical signal model of MIMO radar based on a sparse array is established. Then, the modified multiple signal classification (MUSIC) and maximum likelihood (ML) estimation algorithms based on two classical sparse arrays (coprime array and nested array) are proposed. To reduce the complexity of the algorithm, a real-valued processing algorithm for generalized MUSIC (GMUSIC) and maximum likelihood is proposed, and a reduced dimension matrix is introduced into the real-valued processing algorithm to further reduce computation complexity. Finally, sufficient simulation results are provided to illustrate the effectiveness and superiority of the proposed technique. The simulation results show that the height measurement accuracy can be efficiently improved by using our proposed technique for both simple and complex terrain. [ABSTRACT FROM AUTHOR]

Aiming to address errors in the estimation of the position and attitude of an unmanned vessel, especially during vibration, where the rapid loss of feature point information hinders continuous attitude estimation and global trajectory mapping, this paper improves the monocular ORB-SLAM framework based on the characteristics of the marine environment. In general, we extract the location area of the artificial sea target in the video, build a virtual feature set for it, and filter the background features. When shaking occurs, GNSS information is combined and the target feature set is used to complete the map reconstruction task. Specifically, firstly, the sea target area of interest is detected by YOLOv5, and the feature extraction and matching method is optimized in the front-end tracking stage to adapt to the sea environment. In the key frame selection and local map optimization stage, the characteristics of the feature set are improved to further improve the positioning accuracy, to provide more accurate position and attitude information about the unmanned platform. We use GNSS information to provide the scale and world coordinates for the map. Finally, the target distance is measured by the beam ranging method. In this paper, marine unmanned platform data, GNSS, and AIS position data are autonomously collected, and experiments are carried out using the proposed marine ranging system. Experimental results show that the maximum measurement error of this method is 9.2%, and the average error is 4.7%. [ABSTRACT FROM AUTHOR]

The integrity of the 12 V dc power distribution system on a vehicle is essential to guarantee continuous power supply to safety-relevant consumers. Safety-relevant consumers are critical loads, for example, electric power steering, braking systems with functionalities like Anti-Lock Braking or Electronic Stability Control, and autonomous drive systems. To prevent insufficient power supply for safety-relevant consumers due to an increased wiring harness resistance, a novel diagnostic approach is developed to determine the condition of the power distribution, especially the electrical resistance. The influence of measurement errors and bus commutation on the estimation is investigated by using a simulation. By using the diagnostic, a resistance determination in the milliohm range with a standard deviation of σ = 0.3 m Ω can be achieved under realistic conditions. This ensures that failures in the wiring harness can be identified, avoiding cascading effects and minimizing recalls. Compared to the state of the art, redundancies, costs, and weight can be saved with the proposed diagnostic system based on electrical resistance estimation. [ABSTRACT FROM AUTHOR]

Background/Objectives: Blood pressure (BP) readings are often rounded to the nearest zero end-digit. Guidelines permit rounding to the closest 2 mmHg. This paper investigated the effect of rounding systolic blood pressure (SBP) values on the prediction of cardiovascular disease (CVD) risk among the New Zealand population. A total of 427,299 individuals received opportunistic cardiovascular disease risk assessments at primary care facilities in New Zealand. Method: A total of 292,122 SBP readings possessed a non-zero terminal digit. These were rounded to the nearest zero end-digit. A survival model estimating a 5-year CVD risk was applied to both datasets, i.e., with and without rounding. Hazard ratios and misclassification rates were analysed to emphasise the notable differences. Financial impact was assessed by examining healthcare expenditures. Results: In total, 32% of SBP values exhibited a terminal digit of zero, and 2.85% and 4.24% of men were misclassified as moderate and high risk, respectively, while approximately 3.21% of women were misclassified into the same risk categories. Likewise, 1.19% and 0.47% of men, as well as 0.62% and 0.20% of women, were misclassified into the low and moderate risk categories, respectively. Conclusions: Precisely measuring SBP is crucial in accurately assessing CVD risk and managing healthcare resources effectively. [ABSTRACT FROM AUTHOR]

Background: Early childhood caries (ECC) is a significant oral health issue affecting children under six years old. This study aimed to determine the prevalence and distribution of ECC in Nigeria through a systematic review and meta-analysis. Methods: A systematic search was conducted across multiple databases for studies on ECC prevalence in Nigeria. The Joanna Briggs Institute (JBI) checklist was used to assess study quality. A random-effects model was employed to calculate pooled prevalence estimates and mean dmft scores. Subgroup analyses and meta-regression were performed to explore sources of heterogeneity. Results: Twenty-three studies met the inclusion criteria, comprising 7,362 participants. The pooled ECC prevalence in Nigeria was 17% (95% CI 11–24%), with significant heterogeneity (I2 = 97%). Subgroup analyses revealed variations across geopolitical zones and study settings. The pooled mean dmft score was 0.44 (95% CI 0.24–0.74). Age group and gender showed minimal impact on ECC prevalence. Conclusion: With a 17% prevalence rate, ECC remains a significant public health concern in Nigeria. The study highlights the need for targeted interventions, standardized national surveys, and further research into region-specific risk factors to address ECC effectively. [ABSTRACT FROM AUTHOR]

Because of remarkable reactivity and strong electron‐electron correlation effects, the precise prediction of ground state energy and chemical reactivity of hydride ion is an essential objective in quantum chemistry. Leveraging variational quantum algorithms offers a promising avenue for studying molecular properties using current noisy intermediate‐scale quantum devices. This work utilises the variational approach to anticipate the ground state, reactivity, and single‐electron detachment energy of the three‐body hydride ion. We investigated both Hardware‐Efficient Ansatz (HEA) and Chemistry‐inspired ansatz based on a Unitary Coupled Cluster (UCC) on both noiseless and noisy IBM simulators. Modern error‐mitigating techniques, such as Zero‐Noise Extrapolation (ZNE) with unitary folding and measurement error mitigation, have been implemented to significantly reduce errors in noisy environments. This study contributes to our understanding of the quantum computational nuances of the hydride ion and addresses the question of whether quantum computers can retain the correlation energies for these correlated ions. [ABSTRACT FROM AUTHOR]

ABSTRACT Aims Design Methods Results Conclusions Relevance to Clinical Practice Impact No Patient or Public Contribution This study identifies instruments for assessing the burden on family caregivers of cancer patients and evaluates their psychometric properties using COSMIN criteria.A systematic review based on COSMIN methodology.The review adhered to PRISMA guidelines. Relevant studies were identified through searches in PubMed, Web of Science, Cochrane Library, Embase, China National Knowledge Infrastructure, Wanfang Database, and China Science and Technology Journal Database, covering literature from inception to September 2024. Included studies focused on developing or validating measurement tools and assessing psychometric properties such as reliability, internal consistency and construct validity. Two independent researchers screened the literature, extracted data and evaluated psychometric properties using COSMIN criteria while assessing evidence quality via the GRADE method.A total of 32 studies were included, encompassing 17 tools for assessing caregiver burden. None reported measurement error, cross‐cultural validity or responsiveness. The Caregiver's Burden Scale in End‐of‐Life Care (CBS‐EOLC) demonstrated strong reliability, validity and internal consistency, receiving a strong recommendation for clinical practice. In contrast, the Zarit Burden Interview (ZBI), Family Caregiver Burden Interview Scale (FBIS) and Bakas Caregiving Outcomes Scale (BCOS) are not recommended due to insufficient supporting evidence. Other tools showed weak evidence, leading to weak recommendations.The CBS‐EOLC exhibits comprehensive psychometric properties suitable for clinical applications. The ZBI, FBIS and BCOS lack adequate supporting evidence and are not recommended. Future research should focus on measurement error, cross‐cultural validity and responsiveness to enhance these tools' applicability and reliability.This review provides evidence for healthcare providers to select instruments for assessing caregiver burden in cancer patients.This systematic review highlights the need for comprehensive assessments of caregiver burden based on COSMIN guidelines.This study is a systematic review with no patient or public participation. [ABSTRACT FROM AUTHOR]

Aim: To systematically appraise, compare, and summarize the measurement properties of intimate partner violence screening instruments for the general population and provide recommendations. Methods: We searched PubMed, Embase, Web of Science, ProQuest Dissertations & Theses Global and EBSCO Psychology Behavioral Sciences Collection from their establishment to March 2024 using systematic search strategies. The methodological quality of the instruments that met the inclusion criteria and their measurement properties were assessed using the COSMIN methodology, and the COSMIN recommendations were followed. We reported this study using PRISMA 2020 checklist. Results: A total of 23 studies were eventually included, and 18 instruments were identified. The evaluation of the methodological quality indicates poor content validity but good structural validity; however, that of the criterion validity in most of the studies was inadequate. Measurement error and responsiveness were not assessed. Four screening instruments could be used in the interim. The remaining 14 instruments were not recommended for use. Conclusions: The overall methodological quality of most of the assessed instruments was insufficient. A rigorous intimate partner violence screening instrument with good measurement properties is urgently required to identify and screen for intimate partner violence in the general population. PROSPERO number: CRD42022365247. [ABSTRACT FROM AUTHOR]

The process of engineering construction typically impacts the nearby existing buildings, causing either localized or widespread deformation of these structures. The application of deformation monitoring technology enables the timely identification of damage or uneven deformation, thereby facilitating the implementation of appropriate maintenance and repair actions. This paper explores the application of ground-based synthetic aperture radar (GB-SAR) in building facade deformation monitoring. GB-SAR acquires two-dimensional (2D) images and measures deformation along the line of sight (LOS) direction through interference calculation. In order to accurately determine the location of deformation and retrieve the elevation information of image pixels, it is usually required to utilize external three-dimensional (3D) data to reconstruct the 3D coordinate of these pixels, which is known as geocoding, or georeferencing. In the case of the GB-SAR system, it is necessary for the observer to measure the angles between the antenna orientation, the radar movement direction and the reference plane of the local surveying coordinate system. Nevertheless, the measurement of these attitude angles may be overlooked in practice, and the impact of small attitude angles or their measurement errors on GB-SAR geocoding may be disregarded, which directly affects the slant range projection (SRP) calculation of the external 3D data, thereby leading to biased 3D coordinate reconstruction of GB-SAR pixels. This paper proposes a method to calculate the SRP coordinates of point cloud data considering the attitude inclination angles of radar sensor, and perform matching calculations on the high-quality pixels (HQPs) with the point cloud after SRP to extract building facade deformation. The results demonstrate that the method is capable of effectively mitigating the influence of the radar attitude inclination angles, resulting in a reduction of the average deviation of 3D coordinates to approximately one-third of the value without consideration of the sensor's attitude in a real-world building monitoring experiment. [ABSTRACT FROM AUTHOR]

Transthoracic echocardiography (TTE) is widely recognized as one of the principal modalities for diagnosing tricuspid regurgitation (TR). The diagnostic procedures associated with conventional methods are intricate and labor-intensive, with human errors leading to measurement variability, with outcomes critically dependent on the operators' diagnostic expertise. In this study, we present an innovative assessment methodology for evaluating TR severity utilizing an end-to-end deep learning system. This deep learning system comprises a segmentation model of single cardiac cycle TR continuous wave (CW) Doppler spectra and a classification model of the spectra, trained on the TR CW Doppler spectra from a cohort of 11,654 patients. The efficacy of this intelligent assessment methodology was validated on 1500 internal cases and 573 external cases. The receiver operating characteristic (ROC) curves of the internal validation results indicate that the deep learning system achieved the areas under curve (AUCs) of 0.88, 0.84, and 0.89 for mild, moderate, and severe TR, respectively. The ROC curves of the external validation results demonstrate that the system attained the AUCs of 0.86, 0.79, and 0.87 for mild, moderate, and severe TR, respectively. Our study results confirm the feasibility and efficacy of this novel intelligent assessment method for TR severity. [ABSTRACT FROM AUTHOR]

This paper presents new method for traceability assurance of measurements performed using hybrid measuring systems built using one system that is based on tactile point measurement method (for example: coordinate measuring machine, articulated arm coordinate measuring machine, laser tracker system) and the second one that is based on optical field measurement method (for example: structured light scanners, digital image correlation systems). Within works described in this paper a series of tests aimed at determining task-specific errors for measurements performed using such composed systems were run. Measurement tasks for which such errors were determined include length measurements and measurements of form deviations (roundness, flatness, etc). Measurements were performed using material standards representing various shapes, dimensions and geometric relations. Measurements were run in different orientations and positions of the standards. Types of standards along with orientations and positions used were chosen basing on the guidelines of the ISO 10360 standard, parts 2, 5, 7, 8, 9, 10, 12, VDI/VDE 2634 and longtime experience of authors of this paper. At the end, results of performed measurements were checked for consistency with results of material standards calibration and values of task- specific maximum permissible errors were established. Guidelines for using developed method in other hybrid systems were also presented in the paper. [ABSTRACT FROM AUTHOR]

The physicochemical properties of petroleum products in commercial transactions are crucial for quality control in the oil and gas industry. However, different laboratories often produce slightly different measurement results. These variations can be significant when approving or rejecting properties based on regulatory agency and environmental body specifications. A simple arithmetic average is typically used to determine the most probable value in disputes. This study proposed using a Data Reconciliation approach to address the disparity between the projected model and empirical data. An objective function was employed to optimize and evaluate parameters using maximum likelihood estimation, considering the experimental uncertainty values. This study found that the flash point of jet fuel, as determined by the Tag Closed Cup Tester, was within the specified range (maximum of 40 °C). The application of this tool resolved a dispute between a supplier and a customer, as the reconciled value with minimized uncertainty was determined to be 37.5 ± 2.0 °C. Additionally, the study utilized experimental results from 12 accredited laboratories to determine a single reconciled value for the final boiling point of gasoline. Despite the varying experimental uncertainties ranging from 6.0 °C to 13 °C, the reconciled uncertainty was minimized to 2.6 °C. The last case study identified that ASTM D4294 was incompatible with other test methods for evaluating the mass fraction in diesel oil. In this manner, Data Reconciliation enhanced the accuracy and effectively reduced measurement uncertainties, rendering it a potent tool for resolving legal disputes when evaluating the compliance of the physicochemical properties of petroleum products in commercial transactions. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to enhance pose estimation accuracy and precision in mobile robotics using multiple graphical markers. While traditional single-marker methods using graphical markers such as ArUco offer simple implementation, their performance is susceptible to environmental variations and measurement errors. To address these limitations, we propose a robust pose estimation algorithm that leverages multiple markers simultaneously. Our approach incorporates two key mechanisms: spatial consistency verification to detect invalid markers within the marker array, and temporal stability analysis to identify and exclude outlier measurements. The algorithm enhances pose estimation accuracy by averaging measurements from valid markers while preventing any single marker from dominating the estimation process. The effectiveness of our approach was validated through experiments using both fixed and drone-mounted camera configurations. The results demonstrated that the pose estimation using multiple markers significantly improved both accuracy and precision compared with single-marker approaches. In fixed-camera experiments, the proposed method showed reduced mean errors and standard deviations in both position and orientation measurements across various camera poses. Similarly, in drone-mounted camera experiments, our approach exhibited superior stability with significantly lower measurement variations during hovering maneuvers. These improvements were particularly pronounced in challenging scenarios, such as when the camera was tilted at large angles relative to the marker plane. This research contributes to the advancement of reliable pose estimation methodologies in mobile robotics and autonomous systems, with potential applications across diverse environments where precise position and orientation measurements are crucial. [ABSTRACT FROM AUTHOR]

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Without a GNSS (global navigation satellite system) signal, the integrated navigation system in vehicles with a fixed trajectory (e.g., railcars) is limited to the use of micro-electromechanical system-inertial navigation system (MEMS-INS) and odometer (ODO). Due to the significant measurement error of the MEMS inertial device and the inability of ODO to output attitude, the positioning error is generally large. To address this problem, this paper presents a new integrated navigation algorithm based on a dynamically constrained Kalman model. By analyzing the dynamics of a railcar, several new observations have been investigated, including errors of up and lateral velocity, centripetal acceleration, centripetal D-value (difference value), and an up-gyro bias. The state transition matrix and observation matrix for the error state model are represented. To improve navigation accuracy, virtual noise technology is applied to correct errors of up and lateral velocity. The vehicle-running experiment conducted within 240 s demonstrates that the positioning error rate of the dead-reckoning method based on MEMS-INS is 83.5%, whereas the proposed method exhibits a rate of 4.9%. Therefore, the accuracy of positioning can be significantly enhanced. [ABSTRACT FROM AUTHOR]

In the multi-user multiple-input-multiple-output (MU-MIMO) beamforming (BF) training defined by the 802.11ay standard, since a single initiator transmits a significant number of action frames to multiple responders, inefficient configuration of the transmit antenna arrays when sending these action frames increases the signaling and latency overheads of MU-MIMO BF training. To configure appropriate transmit antenna arrays for transmitting action frames, the initiator needs to accurately estimate the signal to noise ratios (SNRs) measured at the responders for each configuration of the transmit antenna arrays. In this paper, we propose a two-phase deep learning approach to improve the accuracy of SNR estimation for multiple concurrent beams by reducing the measurement errors of the SNRs for individual single beams when each action frame is transmitted through multiple concurrent beams. Through simulations, we demonstrated that our proposed scheme enabled more responders to successfully receive action frames during MU-MIMO BF training compared to existing schemes. [ABSTRACT FROM AUTHOR]

As an essential part of modern smart manufacturing, road transport with large and heavy trucks has in-creased dramatically. Due to the inside wheel difference in the process of turning, there is a considerable safety hazard in the blind area of the inside wheel difference. In this paper, multiple cameras combined with deep learning algorithms are introduced to detect pedestrians in the blind area of wheel error. A scheme of vehicle-pedestrian safety alarm detection system is developed via the integration of YOLOv5 and an improved binocular distance measurement method. The system accurately measures the distance between the truck and nearby pedestrians by utilizing multiple cameras and PP Human recognition, providing real-time safety alerts. The experimental results show that this method significantly reduces distance measurement errors, improves the reliability of pedestrian detection, achieves high accuracy and real-time performance, and thus enhances the safety of trucks in complex traffic environments. [ABSTRACT FROM AUTHOR]

AbstractObjectiveDesignStudy SampleResultsConclusionsThis study developed and validated the Cantonese integrated Digit-in-Noise Test (iDIN) for speech-in-noise measurement and hearing screening.The development of the Cantonese iDIN followed the International Collegium of Rehabilitative Audiology guidelines. Its psychometric properties, including normative data, psychometric function, test-retest reliability, and criterion validity, were established. Additionally, a preliminary comparison of the effectiveness of 2- and 3-digit SRTs in hearing screening was conducted.The sample included 40 young adults with normal hearing and 65 older adults, with and without hearing loss.Mean SRTs for 2-, 3-, and 5-digit with forward recall were −11.5 ± 1.3, −12.0 ± 1.0, and −11.3 ± 0.9 dB SNR in young adults, respectively. The slopes for the psychometric functions of these sequences were 20.26, 18.61, and 19.10 %/dB, respectively, and measurement errors were 0.80, 0.61, and 0.71 dB. The area under the receiver operating characteristic curves for the 2- and 3-digit SRTs were .941 and .974, indicating high discriminative ability in hearing screening.The Cantonese iDIN shows robust psychometric properties. Despite the 3-digit test’s slightly better screening performance, shorter test during associated with the 2-digit suggests its potential for hearing screening. [ABSTRACT FROM AUTHOR]

Highlights In the fiber winding process, the maximum static friction coefficient significantly influences the designable range of non‐geodesic winding trajectories and the behavior of fiber stacking and sliding. To accurately and conveniently measure the maximum static friction coefficient for fiber winding, a method based on semi‐geodesic trajectories was proposed. This method assesses the maximum static friction coefficient between fibers and mandrels, as well as between fibers, without requiring additional component fabrication. Its effectiveness was investigated, and two methods for determining fiber yarn slip were proposed. Additionally, measurement errors and variations in the slip coefficient before and after fiber yarn slip were examined. The research results indicate that the proposed measurement method has an error margin of approximately ±6%. After fiber yarn slip, the slip coefficient gradually decreases along the winding trajectory. The maximum static friction coefficient is independent of both winding speed and winding tension. A method for measuring the maximum static friction coefficient was proposed. Two methods were proposed for determining the slip of fiber yarn. The method allows for direct measurement using the mandrel to be wound. The maximum static friction coefficient between fibers can be measured. [ABSTRACT FROM AUTHOR]