Variable Error Research Articles

Data interpretation and forecasting of SHM heteroscedastic measurements under typhoon conditions enabled by an enhanced Hierarchical sparse Bayesian Learning model with high robustness

Rapid advances in Structural Health Monitoring (SHM) have led to a new era of Big Data in the past decade. However, collected data may include a huge amount of heteroscedastic data, particularly under extreme events (e.g., typhoons), which makes robust decisions remain a challenging problem. To this end, this article proposed an enhanced Hierarchical Sparse Bayesian Learning (eHSBL) model for SHM field data interpretation and forecasting, uncertainty analysis, as well as correlation analysis. The proposed eHSBL model incorporates the Gaussian kernel function and the Hierarchical Bayesian model for an operational large-scale suspension bridge, i.e., Tsing Ma Bridge (TMB) in Hong Kong, under extreme events exhibiting heterogeneities, strong volatilities, and high uncertainties. The Gaussian kernel function in the eHSBL model is capable of mapping SHM heteroscedastic data into high-dimensional space, and the Hierarchical Bayesian model can be iteratively updated by introducing hyperparameters to achieve the sparse expression of the model, so the model has strong generalization ability and high robustness. Through the incorporation of the extrapolation forecasting algorithm, the eHSBL can carry out the forecasting analysis for the future SHM data. Results show that the eHSBL regression and forecasting results under extreme events can well reflect the strain responses of the structure. The uncertainty and correlation analysis reveal that the eHSBL model error variation is influenced by temperature, traffic loads, and wind speeds, and the corresponding error variation curves and change rules are also provided and summarized, which will shed light on the performance evaluation and early warning for in-service long-span bridges.

Spatiotemporal variability after stroke reflects more than just slow walking velocity

BackgroundIncreased spatiotemporal gait variability is considered a clinical biomarker of ageing and pathology, and a predictor of future falls. Nevertheless, it is unclear whether the increased spatiotemporal variability observed in persons with stroke is directly related to the pathology or simply reflects their choice of walking velocity. Research questionDoes increased spatiotemporal gait variability directly relate to motor coordination deficits after stroke? MethodsForty persons with stroke participated in this cross-sectional study. Participants performed the lower-extremity motor coordination test (LEMOCOT) on an electronic mat equipped with force sensors. Then, participants walked for 120 s on a computerized treadmill at their comfortable walking velocity. For the LEMOCOT we used the traditional score of in-target touch count and computed the absolute and variable error around the targets. For gait variability, we extracted the standard deviation of step time, step length, step velocity, and step width. Using linear modeling, we tested the correlations of gait variability with the outcome measures from the LEMOCOT, after controlling for walking velocity. ResultsThe variability in step time, step length and step width correlated with walking velocity, while the variability in step velocity did not. After controlling for walking velocity, we observed that the LEMOCOT score correlated with the variance in step time, and the variable error in the LEMOCOT correlated with the variance in step length, in step width, and in step velocity. No significant correlation with any of the velocity-controlled step parameters was found for the absolute error in the LEMOCOT. SignificanceDecreased performance in the LEMOCOT was associated with increased spatiotemporal variability in persons with stroke, regardless of their walking velocity. Our results demonstrate the connection between lower-extremity coordination impairments and deficits in gait function.

A new diagram for performance evaluation of complex models

In the latest years the capacity and complexity of climate and environmental modeling has increased considerably. Therefore, tools and criteria for model performance evaluation are needed to ensure that different users can benefit from model selection. Among graphical tools, Taylor’s diagram is widely used to provide evaluation and comparison of model performances, with particular emphasis on climate models. Taylor’s diagram accounts for different statistical features of model outputs and observations, including correlation, variability and centered root mean square error. Not included is model bias, which is an essential feature for climate model evaluations, and it is usually calculated separately to complement the information embedded in Taylor’s diagram. In this paper a new diagram is proposed, referred to as Aras’ diagram, which allows for visual assessments of the correspondence between model outputs and reference data in terms of total error, correlation, as well as bias and variability ratios through an easy-to-interpret two-dimensional (2D) plot, allowing for proper weighting of different model features. The strengths of the new diagram are exemplified in a case study of performance evaluation of EURO-CORDEX historical experiment over Southern Italy using E-OBS as reference dataset, for three hydrological variables (i.e. daily precipitation, daily surface minimum temperature, and daily maximum surface temperature), and four popular climate indices (i.e. total annual precipitation, annual maxima of daily precipitation, annual minima of daily minimum temperatures, and annual maxima of daily maximum temperatures). The proposed diagram shows interesting properties, in addition to those already included in Taylor’s diagram, which may help promoting climate model evaluations based on their accuracy in reproducing the climatological patterns observed in time and space.

Experimental investigation, ANN modeling, and TOPSIS optimization of gasoline-alcohol blends for minimizing tailpipe emissions of a motorcycle

Two-wheelers contribute more than 32% of global pollution; particularly motorcycle idling and revving operations induce severe emissions in metropolitan cities. The current study conducts both experimental and modeling assessments of a motorcycle operated with alcohol fuel blends (10%, 20%, and 55% by vol.) with gasoline, to optimize the idling and revving emissions. An artificial neural network is modeled to predict the tailpipe emissions with blend percentage, and vehicle speeds as the model inputs. The measured and predicted data are used for optimizing the alcohol blends at various speeds using a multi-objective optimization based technique for order preference by similarity to ideal solution. The experimental results reveal that the maximum HC and CO emissions are observed at higher speeds of 3000 rpm, whereas the peak NO is noted in the range of 3000–3500 rpm for gasoline operation. The maximum percentage error variation with the measured and predicted values is 12.5% for CO, 4.2% for CO2, 1.1% for HC, and 1.7% for NO emissions. The optimized blend varies from M15 to M50 for methanol-gasoline and E15 to E55 for ethanol-gasoline blend for the speed range of 1000–3500 rpm. Hence, the developed models provide a novel approach for the prediction and optimization of motorcycle tailpipe emissions for light alcohol fuel blends.

Study on nonlinear dynamic characteristics of a two-speed transmission system at low speed.

A pure shear mechanical model of low gear of six-degree-of-freedom two-speed transmission system is established by using lumped parameter method. The Runge-Kutta method is used to numerically solve the aforementioned nonlinear system. The variation of transmission error between gears is analyzed by using global bifurcation, time domain diagram, phase diagram and Poincare cross section. Moreover, the transfer error bifurcation characteristics of the solar wheel and the first planetary wheel under different gear moduli are investigated. The results show that: by taking the excitation frequency as the control parameter, the system includes period-1 motion, period-2 motion, quasi-periodic motion, multiperiodic motion, and chaotic motion. With the increase of gear modulus, the system mainly presents chaotic motion in the medium frequency range (0.5<ωh≤2). It shows stable period-1 motion in the high frequency range (2<ωh≤3), and the higher the modulus, the wider the high frequency range of period-1 motion. The research results can provide reference for the design and optimization of this kind of two-speed transmission system in the future.

A new OSL dose model to account for post-depositional mixing of sediments

In applications of optically stimulated luminescence (OSL) dating to unconsolidated sediments, the burial age of a sample of grains is estimated using statistical models of the distribution of the experimentally determined equivalent doses of the grains, together with estimates of the environmental dose rate. For grains that have been vertically mixed after deposition (e.g., due to bioturbation), existing dose models may fail to appropriately account for the complexity of the mixing process, thus producing inaccurate age estimates of the original time of deposition of the ‘native’ grains in any particular sample (usually the quantity of most interest). Here we introduce a new dose model, the asymmetric Laplacian mixture model (ALMM), developed for vertically mixed samples with single-grain dose distributions. The approach is based on a continuous statistical mixture that models the displacement of grains in both upward and downward directions. The central dose of the native grains in each sediment sample is estimated by the ALMM, as well as the parameters associated with overdispersion of single-grain dose distributions and the (modelled) mixing process. Using Bayesian methodology, we apply the model to two series of vertically contiguous samples collected at the site of Nawarla Gabarnmang in northern Australia. Independent age estimates obtained from radiocarbon dating of charcoal fragments support the OSL ages for the native grains estimated by the ALMM. Moreover, our study includes sensitivity analyses that show the model is robust to variation in the experimental error of the OSL data, as well as a simulation study that demonstrates the model’s good ability to recover the simulated central dose and its excellent coverage properties. The ALMM is introduced in the context of compound Gaussian distributions, a broadly encompassing statistical framework that includes many of the most commonly used dose models. This unifying and accessible perspective on the statistical modelling of dose distributions will support practitioners in selecting an appropriate model for samples affected by post-depositional mixing, and hopefully stimulate further theoretical developments. A new R package rstanosl is provided that fits the ALMM and other commonly used dose models using Hamiltonian Monte Carlo methods via the Stan programming language.

Enhancing discharge estimation from SWOT satellite data in a tropical tidal river environment

The Surface Water and Ocean Topography (SWOT) mission aims to provide essential data on river width, height and slope in order to estimate worldwide river discharge accurately. This mission offers a powerful tool for monitoring river discharge in dynamic coastal areas, like the Saigon-Dongnai estuary in Southern Vietnam. However, estimating discharge of tidally-influenced rivers using SWOT measurements can be challenging when hydraulic variables have the same order of magnitude as SWOT measurement errors. In this paper we present a methodology to enhance discharge estimation accuracy from SWOT measurements based on simulated SWOT products at the 200 meter node resolution and varying river reach size. We assess measurement error variability and its impact on discharge estimation by employing a Monte Carlo analysis. Our approach significantly improved discharge estimation in the Saigon tidal river, reducing RMSE from 1400 m3/s to 180 m3/s and increasing R² from 0.31 to 0.95. Notably, the percentage of Monte Carlo particles meeting the 30% rRMSE threshold rose from 0% to 79%. This study underscores the feasibility of obtaining reliable discharge estimates from SWOT data in complex coastal areas where hydraulic variables are of the same order of magnitude as SWOT errors. Additionally, the proposed methodology to improve discharge estimation from SWOT measurements is widely adaptable as it can be applied to similar regions and can be combined with any discharge estimation method.

Table tennis players use superior saccadic eye movements to track moving visual targets.

Table tennis players perform visually guided visuomotor responses countlessly. The exposure of the visual system to frequent and long-term motion stimulation has been known to improve perceptual motion detection and discrimination abilities as a learning effect specific to that stimulus, so may also improve visuo-oculomotor performance. We hypothesized and verified that table tennis players have good spatial accuracy of saccades to moving targets. University table tennis players (TT group) and control participants with no striking-sports experience (Control group) wore a virtual reality headset and performed two ball-tracking tasks to track moving and stationary targets in virtual reality. The ball moved from a predetermined position on the opponent's court toward the participant's court. A total of 54 conditions were examined for the moving targets in combinations of three ball trajectories (familiar parabolic, unfamiliar descent, and unfamiliar horizontal), three courses (left, right, and center), and six speeds. All participants primarily used catch-up saccades to track the moving ball. The TT group had lower mean and inter-trial variability in saccade endpoint error compared to the Control group, showing higher spatial accuracy and precision, respectively. It suggests their improvement of the ability to analyze the direction and speed of the ball's movement and predict its trajectory and future destination. The superiority of the spatial accuracy in the TT group was seen in both the right and the left courses for all trajectories but that of precision was for familiar parabolic only. The trajectory dependence of improved saccade precision in the TT group implies the possibility that the motion vision system is trained by the visual stimuli frequently encountered in table tennis. There was no difference between the two groups in the onset time or spatial accuracy of saccades for stationary targets appearing at various positions on the ping-pong table. Table tennis players can obtain high performance (spatial accuracy and precision) of saccades to track moving targets as a result of motion vision ability improved through a vast amount of visual and visuo-ocular experience in their play.

Time Variability Correction of CMIP6 Climate Change Projections

AbstractAccurate projections of climate change and associated extreme events under differing emission scenarios are linked to realistic representations of the temporal variability of the atmosphere at a variety of time scales, for example, annual, seasonal, synoptic, and daily. Here a new method is employed to explicitly quantify a model's ability to accurately represent covariance at and between differing time scales. From our global‐scale analysis, on average, raw Coupled Model Intercomparison Project Phase 6 (CMIP6) models misrepresent temporal variances at differing time scales for maximum temperature (tasmax) by a considerable margin, particularly at 183‐, 92‐ and 46‐day time scales. To ameliorate such variability errors, we propose a novel Time Variability Correction (TVC) method that corrects temporal covariances while preserving the essential time‐event sequence of the model simulations. We adopt a model‐as‐truth framework to evaluate the effectiveness of the TVC method under future forcing conditions when applied to daily tasmax simulations from 23 CMIP6 models for 1% of the global grid cells. TVC‐corrected temperatures generally show improved matching of temporal variance and lag correlations in the out‐of‐sample projection period compared to simple mean‐corrected projections. By imparting more realistic temporal‐correlations to model series, TVC is expected to improve the projections of extreme events associated with persistent heat, such as heatwaves. Applying TVC to future temperature projections using actual observations significantly increases the temperature variance in most middle to high latitude land regions in the Northern Hemisphere, while decreasing it in most low to middle latitude land regions, compared to simple mean‐corrected projections.

Research on Speed Cooperative Control Strategy for Rudderless Dual-PMSM Propulsion Ships

In the navigation of rudderless dual-motor propulsion ships, it is often necessary for the dual motor to run stably at different given speeds. However, the existing coupling strategies cannot achieve precise cooperative control of the dual motor under different given commands. Therefore, in this study, we proposed a variable error difference compensation-coupling control (VEDC-CC) strategy. Simultaneously, a coupling compensation coefficient determination method based on the self-error difference of the dual motor was introduced. The VEDC-CC utilizes the self-error difference of the dual motor as the input for the coupling compensation controller, which enables the mutual coupling of self-errors, and effectively improves the cooperative controlling performance under different given commands. Moreover, the employed variable compensation coefficient determination method enhances the disturbance coupling performance and the response speed of the system. In this study, we analyzed the different navigation conditions and dual-propeller load characteristics of a rudderless dual-motor propulsion ship. Taking a rudderless dual-motor propulsion ship designed based on the cruise ship “Feng Hua Xue Yue” as the object, the VEDC-CC strategy was validated through simulation tests, considering the propeller load characteristics and actual operating conditions. The experimental results demonstrated that the VEDC-CC strategy could meet the practical control needs of the target ship.

Optimization and validation of a suprasystolic brachial cuff-based method for noninvasively estimating central aortic blood pressure.

Clinical studies have extensively demonstrated that central aortic blood pressure (CABP) has greater clinical significance in comparison with peripheral blood pressure. Despite the existence of various techniques for noninvasively measuring CABP, the clinical applications of most techniques are hampered by the unsatisfactory accuracy or large variability in measurement errors. In this study, we proposed a new method for noninvasively estimating CABP with improved accuracy and reduced uncertain errors. The main idea was to optimize the estimation of the pulse wave transit time from the aorta to the occluded lumen of the brachial artery under a suprasystolic cuff by identifying and utilizing the characteristic information of the cuff oscillation wave, thereby improving the accuracy and stability of the CABP estimation algorithms under various physiological conditions. The method was firstly developed and verified based on large-scale virtual subject data (n = 800) generated by a computational model of the cardiovascular system coupled to a brachial cuff, and then validated with small-scale in vivo data (n = 34). The estimation errors for the aortic systolic pressure were -0.05 ± 0.63 mmHg in the test group of the virtual subjects and -1.09 ± 3.70 mmHg in the test group of the patients, both demonstrating a good performance. In particular, the estimation errors were found to be insensitive to variations in hemodynamic conditions and cardiovascular properties, manifesting the high robustness of the method. The method may have promising clinical applicability, although further validation studies with larger-scale clinical data remain necessary.

Vertical accuracy assessment of freely available global DEMs (FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM) in flood-prone environments

ABSTRACT Flood models rely on accurate topographic data representing the bare earth ground surface. In many parts of the world, the only topographic data available are the free, satellite-derived global Digital Elevation Models (DEMs). However, these have well-known inaccuracies due to limitations of the sensors used to generate them (such as a failure to fully penetrate vegetation canopies and buildings). We assess five contemporary, 1 arc-second (≈30 m) DEMs -- FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM -- using a diverse reference dataset comprised of 65 airborne-LiDAR surveys, selected to represent biophysical variations in flood-prone areas globally. While vertical accuracy is nuanced, contingent on the specific metrics used and the biophysical character of the site being assessed, we found that the recently-released FABDEM consistently ranked first, improving on the second-place Copernicus DEM by reducing large positive errors associated with forests and buildings. Our results suggest that land cover is the main factor explaining vertical errors (especially forests), steep slopes are associated with wider error spreads (although DEMs resampled from higher-resolution products are less sensitive), and variable error dependency on terrain aspect is likely a function of horizontal geolocation errors (especially problematic for AW3D30 and Copernicus DEM).

Development and performance evaluation of intelligent algorithm for optimal control of a hybrid heat pump system during the cooling season

The purpose of this study is to develop a control method for a hybrid heat pump system based on an artificial neural network (ANN) to reduce energy use and create a more comfortable thermal environment. The proposed optimal control method uses an ANN-based predictive model to predict the heat storage tank and indoor temperature during the cooling period and controls the flow rate of the circulation pump on the heat source and load side of the system. The performance of the predictive model for the heat storage tank temperature (R2 = 0.9988; coefficient of variation of the root mean square error [CV(RMSE)] = 1.06 %; normalized mean bias error [NMBE] = 0.16 %; and mean absolute error [MAE] = 0.09℃) and the indoor temperature (R2 = 0.9893; CV(RMSE) = 1.66 %; NMBE = 0.16 %; and MAE = 0.15℃) was excellent. The temperature control of the heat storage tank using the optimal algorithm exhibited an improvement of 18.39 % for the CV(RMSE), 3.10 % for the NMBE, and 1.31 °C for MAE compared with rule-based. For the indoor temperature, the optimal algorithm improved the CV(RMSE) by 1.30 %, NMBE by 0.42 %, and MAE by 0.29℃ compared to rule-based. The energy use was reduced by 52.85 % for the entire system using the optimal control method compared with the existing control strategy under similar outdoor conditions. Using the proposed control method, it is thus possible to improve thermal comfort and reduce carbon emissions in the building sector by improving the control and energy performance of hybrid heat pump systems.

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

The technological advancement in all countries leads to massive energy demand. The energy trading companies struggle daily to meet their customers’ power demands. For a good quality, disturbance-free, and reliable power supply, one must balance electricity generation and consumption at the grid level. There is a profound change in distribution networks due to the intervention of renewable energy generation and grid interactions. Renewable energy sources like solar and wind depend on environmental factors and are subject to unpredictable variations. Earlier, energy distribution companies faced a significant challenge in demand forecasting since it is often unpredictable. With the prediction of the ever-varying power from renewable sources, the power generation and distribution agencies are facing a challenge in supply-side predictions. Several forecasting techniques have evolved, and machine learning techniques like the model predictive controller are suitable for arduous tasks like predicting weather-dependent power generation in advance. This paper employs a Model Predictive Controller (MPC) to predict the solar array’s power. The proposed method also includes a system identification algorithm, which helps acquire, format, validate, and identify the pattern based on the raw data obtained from a PV system. Autocorrelation and cross-correlation value between input and predicted output 0.02 and 0.15. The model predictive controller helps to recognize the future response of the corresponding PV plant over a specific prediction horizon. The error variation of the predicted values from the actual values for the proposed system is 0.8. The performance analysis of the developed model is compared with the former existing techniques, and the role and aptness of the proposed system in smart grid digitization is also discussed.

Metrology for Indoor Radon Measurements and Requirements for Different Types of Devices.

Indoor radon measurements have been conducted in many countries worldwide for several decades. However, to date, there is a lack of a globally harmonized measurement standard. Furthermore, measurement protocols in the US (short-term tests for 2-7 days) and European Union countries (long-term tests for at least 2 months) differ significantly, and their metrological support is underdeveloped, as clear mathematical algorithms (criteria) and QA/QC procedures considering fundamental ISO/IEC concepts such as "measurement uncertainty" and "conformity assessment" are still absent. In this context, for many years, the authors have been advancing and refining the theory of metrological support for standardizing indoor radon measurements based on a rational criterion for conformity assessment within the ISO/IEC concepts. The rational criterion takes into account the main uncertainties arising from temporal variations in indoor radon and instrumental errors, enabling the utilization of both short- and long-term measurements while ensuring specified reliability in decision making (typically no less than 95%). The paper presents improved mathematical algorithms for determining both temporal and instrumental uncertainties. Additionally, within the framework of the rational criterion, unified metrological requirements are formulated for various methods and devices employed in indoor radon measurements.

Pinch force sense test–retest reliability evaluation using contralateral force matching task

A high test–retest reliability in measurement of pinch force sense is required to assess a clinical parameter accurately over a longitudinal study. Ipsilateral reproduction (IR) task and contralateral matching (CM) task have commonly been used for the assessment of force sense. To date, there has been little research on the test–retest reliability of pinch force sense utilizing the contralateral force matching task. This research aimed to explore this phenomenon across a spectrum of reference force levels (10, 30, and 50 percent maximum voluntary isometric contraction (MVIC)) using a contralateral matching task. Every participant in the study was tested twice by the same skilled experts, with each session separated by one week. Although normalized variable error indicated a poor level of reliability (intraclass correlation coefficient (ICC) = − 0.25 to 0.05) for these force sense tests, normalized constant error (ICC = 0.76–0.85) and normalized absolute error (ICC = 0.61–0.81) results indicated a fair to good of reliability. The lower bound of 95% CI of ICC for NAE and NCE indicated fair test–retest reliability (0.41–0.69). These findings suggest that investigators can reasonably obtain a fair to good test–retest reliability when investigating pinch force sense using the contralateral matching task. The Bland–Altman plots, SEM, and MDD95% were lower at these lower reference force level (10% MVIC) compared to the level of higher reference forces (30% and 50% MVIC). Therefore, when the reference force level increases, the participant needs a larger NAE or NCE decrease to show that their pinch force sense has indeed improved.

Determining the Anticipation Times of Athletes Participating and Non-Participating in Volleyball, Handball, and Basketball School Sports at Secondary Schools

The aim of this study is to find out the anticipation times of fifth-to eighth-grade students who participate and those who do not participate in school sports in volleyball, handball, and basketball in secondary schools, and also to determine whether the anticipation time varies according to gender, different speeds and sports experience of students participating in school sports. A total of 267 athletes (150 girls and 117 boys) participated in volleyball, handball, and basketball from the 5th to 8th grades of secondary schools, and a total of 214 athletes (90 girls and 124 boys) who did not participate in school sports in these branches were studied. The “Basin Anticipation Timer” device measured the anticipation time. As a result of the analysis, it was found that female students participating in school sports have better anticipation time than that of male students. The anticipation times of students participating in school sports were determined at speeds of 5 m/s and 8 eight m/s with average constant, absolute, and variable errors of (0.018, 0.044, 0.059) and (0.017, 0.041, 0.055), respectively. Even though the 8 m/s error results seem better than the 5 m/s results, no statistically significant difference was found between them.

Predicting melting temperature of inorganic crystals via crystal graph neural network enhanced by transfer learning

The melting temperature, a crucial material property, is particularly challenging to measure accurately for inorganic crystals. The data-driven approach emerges as a potential solution, although its effectiveness is hindered by the limitation of available data. To counter this challenge, we implement transfer learning, leveraging a vast computational database of atomization energy. We first pre-train a geometric-information-enhanced crystal graph neural network (GeoCGNN) using atomization energies of approximately 36,000 materials that are computed by the density functional theory. Subsequently, the pre-trained model is fine-tuned using melting temperatures measured for 799 crystals, encompassing 83 elements, ranging from unary to quaternary systems. This transfer learning strategy decreases the root mean square error from 407 to 218 K, attesting to a marked improvement in prediction accuracy. Furthermore, transfer learning significantly mitigates error variability across unary, binary, and ternary (or higher-order) systems, thereby enhancing the reliability of predictions across a broader range of crystals. We also show that transfer learning allows effective task adaptation by leveraging representation learned from pre-training. Therefore, it can achieve better prediction performance even with a limited number of data for predicting melting points.

The influence of elbow and forearm posture on grip force perception in healthy individuals

AbstractThis study aimed to examine the influence of elbow and forearm postures, as well as sex, on the perception of grip force in a sample of individuals without any known health conditions. A total of 21 healthy participants (10 women and 11 men) from college were included and completed a force reproducibility assignment with four elbow and forearm positions (full pronation, supination, and extension, and at 90° of flexion) at three force levels (10%, 30%, and 50% of maximal voluntary isometric contraction [MVIC]). Our results show that participants were more sensitive in detecting variations in their grip force when their elbow was in full supination (14.1 ± 8.5% MVIC, p &lt; .05) and full extension (13.8 ± 10.1% MVIC, p &lt; .01) than when it was at 90° of flexion (19.9 ± 20.1% MVIC). The normalized absolute error exhibited comparable patterns among both male and female participants. Specifically, when the working range of the muscles increased (as indicated by higher maximum voluntary isometric contraction values in males), the accuracy decreased (as reflected by the more significant absolute error in men). Moreover, men exhibited a greater degree of both constant and variable error than women. Recent research indicates that the prevalence of musculoskeletal disorders is higher in women than in males. The results we obtained may contribute to developing strategies to reduce injury risk.

General low alertness in people with obstructive sleep apnea.

In patients with obstructive sleep apnea (OSA), a previous study using a Go/No-Go task reported an average attention deficit. However, the temporal dynamics of such a deficit are unknown. Here, we investigated whether attention deficits in different subdomains increased as the test progressed. We also investigated the effect of target frequency and speed of stimulus presentation on performance. Twenty-seven untreated people with OSA and 27 age- and sex-matched controls underwent a 15-minute Go/No-Go task, divided into 6 blocks. Each block was subdivided into 3 different interstimulus intervals (1, 2, and 4 seconds). Three blocks had a low and three had a high target probability (20% and 80%, respectively). Reaction time (alertness), variability of reaction time (sustained attention), commission errors (response inhibition), and omission errors (focused attention) were measured. Alertness was lower in the group with OSA compared with controls, as evidenced by a significantly higher average reaction time. This effect was seen from the start of the task and continued until the end but did not increase in test progression. The temporal pattern of intrinsic alertness deficits in patients with OSA was found to be independent of target frequency or interstimulus interval. The primary attention problem in OSA is on the alertness subdomain irrespective of the number of required responses or speed of stimulus presentation. The present results support the notion that OSA is distinct from other neurological and psychiatric conditions, such as depression or chronic pain. The results also suggest significant concerns regarding daily life activities (eg, driving). de Souza Bezerra ML, van Duinkerken E, Simões E, Schmidt SL. General low alertness in people with obstructive sleep apnea. J Clin Sleep Med. 2024;20(5):689-698.