Systematic Effects Research Articles

Design of sliding mode controller for servo feed system based on generalized extended state observer with reinforcement learning

Nonlinear friction, system uncertainty, and external disturbances have a significant impact on the performance of high-precision servo feed systems. In order to achieve higher tracking accuracy, a sliding mode controller based on a generalized extended state observer with the double critic deep deterministic policy gradient algorithm is designed. Firstly, a flexible two mass drive model (FTMDM) is established for the two-axis differential micro-feed system (TDMS). Next, a generalized extended state observer (GESO) is designed to estimate matched interference and mismatched interference. And it is proved that the observation error of GESO is bounded. A sliding mode controller based on GESO is further proposed. The stability of the controller has been proven through Lyapunov theory, and the error is bounded and converges to zero in finite time. The tuning process of controller parameters is simplified by using quadratic optimal control principle. Furthermore, a double critic deep deterministic policy gradient algorithm (DCDDPG) is proposed to achieve dynamic optimization of parameters about GESO. The simulation results show that GESO with DCDDPG can reduce the observation error of step signal and sinusoidal signal, and improve the observation accuracy of nonlinear friction significantly. Finally, experimental results show that the proposed control method achieves more accurate position tracking performance on TDMS.

Assessing the robustness of the Galactic rotation curve inferred from the Jeans equations using Gaia DR3 and cosmological simulations

Several authors have recently applied Jeans modelling to -based datasets to infer the circular velocity curve for the Milky Way. These works have consistently found evidence for a continuous decline in the rotation curve beyond $ kpc $, which may indicate the existence of a light dark matter (DM) halo. Using a large sample of Gaia DR3 data, we aim to derive the rotation curve of the Milky Way using the Jeans equations, and to quantify the role of systematic effects, both in the data and those inherent to the Jeans methodology under the assumptions of axisymmetry and time independence. We used data from the Gaia DR3 radial velocity spectrometer sample, supplemented with distances inferred through Bayesian frameworks, to determine the radial variation of the second moments of the velocity distribution for stars close to the Galactic plane. We used these profiles to determine the rotation curve using the Jeans equations under the assumption of axisymmetry and explored how they vary with azimuth and position above and below the plane of the Galactic disc. We applied the same methodology to an N-body simulation of a Milky Way-like galaxy impacted by a satellite akin the Sagittarius dwarf, and to the Auriga suite of cosmological simulations. The circular velocity curve we infer for the Milky Way is consistent with previous findings out to $ 15$ kpc, where our statistics are robust. Due to the larger number of stars in our sample, we are able to reveal evidence of disequilibrium and deviations from axisymmetry closer in. For example, we find that the second moment of $V_R$ flattens out at $R kpc, and that the second moment of $V_ is different above and below the plane for $R kpc. Our exploration of the simulations indicates that these features are typical of galaxies that have been perturbed by external satellites. From the simulations, we also estimate that the difference between the true circular velocity curve and that inferred from Jeans equations can be as high as $15<!PCT!>$, but that it is likely of the order of $10<!PCT!>$ for the Milky Way. This is higher than the systematic uncertainties associated with the observations or those linked to most modelling assumptions when using the Jeans equations. However, if the density of the tracer population were truncated at large radii instead of being exponential as often assumed, this could lead to the erroneous conclusion of a steeply declining rotation curve. We find that steady-state axisymmetric Jeans modelling becomes less robust at large radii, indicating that particular caution must be exercised when interpreting the rotation curve inferred in those regions. A more careful and sophisticated approach may be necessary for precision measurements of the DM content of our Galaxy.

The Mechanisms and Neural Signature of Time-averaged Numerosity Perception.

The animal brain is endowed with an innate sense of number allowing to intuitively perceive the approximate quantity of items in a scene, or "numerosity." This ability is not limited to items distributed in space, but also to events unfolding in time and to the average numerosity of dynamic scenes. How the brain computes and represents the average numerosity over time, however, remains unclear. Here, we investigate the mechanisms and EEG signature of the perception of average numerosity over time. To do so, we used stimuli composed of a variable number (3-12) of briefly presented dot arrays (50 msec each) and asked participants to judge the average numerosity of the sequence. We first show that the weight of different portions of the stimuli in determining the judgment depends on how many arrays are included in the sequence itself: the longer the sequence, the lower the weight of the latest arrays. Second, we show systematic adaptation effects across stimuli in consecutive trials. Importantly, the EEG results highlight two processing stages whereby the amplitude of occipital ERPs reflects the adaptation effect (∼300 msec after stimulus onset) and the accuracy and precision of average numerosity judgments (∼450-700 msec). These two stages are consistent with processes involved with the representation of perceived average numerosity and with perceptual decision-making, respectively. Overall, our findings provide new evidence showing how the visual system computes the average numerosity of dynamic visual stimuli, and support the existence of a dedicated, relatively low-level perceptual mechanism mediating this process.

Visual imagination can influence visual perception - towards an experimental paradigm to measure imagination.

During visual imagination, a perceptual representation is activated in the absence of sensory input. This is sometimes described as seeing with the mind's eye. A number of physiological studies indicate that the brain uses more or less the same neural resources for visual perception of sensory information and visual imagination. The intensity of visual imagination is typically assessed with questionnaires, while more objective measures are missing. Aim of the present study was, to test a new experimental paradigm that may allow to objectively quantify imagination. For this, we used priming and adaptation effects during observation of ambiguous figures. Our perception of an ambiguous stimulus is unstable and alternates spontaneously between two possible interpretations. If we first observe an unambiguous stimulus variant (the conditioning stimulus), the subsequently presented ambiguous stimulus can either be perceived in the same way as the test stimulus (priming effect) or in the opposite way (adaptation effect) as a function of the conditioning time. We tested for these conditioning effects (priming and adaptation) using an ambiguous Necker Cube and an ambiguous Letter /Number stimulus as test stimuli and unambiguous variants thereof as conditioning stimuli. In a second experimental condition, we tested whether the previous imagination of an unambiguous conditioning stimulus variant - instead of its observation - can have similar conditioning effects on the subsequent test stimulus. We found no systematic conditioning effect on the group level, neither for the two stimulus types (Necker Cube stimuli and Letter /Number stimuli) nor for the two conditions (Real and Imaginary). However, significant correlations between effects of Real and Imaginary Condition were observed for both stimulus types. The absence of conditioning effects at the group level may be explained by using only one conditioning time, which may fit with individual priming and adaptation constants of some of our participants but not of others. Our strong correlation results indicate that observers with clear conditioning effects have about the same type (priming or adaptation) and intensity of imaginary conditioning effects. As a consequence, not only past perceptual experiences but also past imaginations can influence our current percepts. This is further confirmation that the mechanisms underlying perception and imagination are similar. Our post-hoc qualitative observations from three self-defined aphantasic observers indicate that our paradigm may be a promising objective measure to identify aphantasia.

An Efficient Attentive-GRU Structure for Uncertainty Modelling of Crowdsourced Human Trajectories Under Building-obscured Urban Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of human mobility analysis and urban navigation, that can affect the performance of human behaviour modelling and location information acquisition. Existing uncertainty modelling algorithms towards the human movement trajectory are subjected to random and highly dynamic human motion characteristics and sampling and observation errors of Global Navigation Satellite System (GNSS) signals caused by the occlusion of buildings in urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of uncertainty modelling. To fill in this gap, this paper proposes an efficient attentive-GRU structure for uncertainty modelling of crowdsourced human trajectories under building-obscured urban scenes, that takes into account both temporal correlation and spatial correlation of human-originated GNSS trajectories and related motion features. A period of human motion data is modelled instead of only one or adjacent location points to avoid interference factors caused by the obstruction of urban buildings, and time-varying measurement and sampling errors are further estimated and combined with comprehensive human motion features to improve the accuracy of final uncertainty modelling. Comprehensive experiments indicate that compared with existing uncertainty modelling methods including physical models and deep-learning models, the proposed attentive-GRU structure realizes much better performance under different accuracy indexes.

An Enhanced Seamless Localization Framework Using Spatial-temporal Uncertainty Predictor Under Obscured Indoor and Outdoor Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of urban navigation, that can affect the performance of indoor and outdoor location information acquisition, especially under obscured scenarios. Existing multi-source fusion-based seamless positioning algorithms are subjected to random and highly dynamic human motion characteristics and changeable observation errors caused by the dynamic occlusions of human and buildings under urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of final multi-source fusion structure. To fill in this gap, this paper proposes an enhanced seamless localization framework using spatial-temporal uncertainty predictor under obscured indoor and outdoor scenes (ESL-STUP), that takes into account both temporal correlation and spatial correlation of trajectories provided by Wi-Fi, GNSS, and sensor-originated motion information. An iPDR-based trajectory estimation structure is proposed, using the integration of INS/PDR mechanizations, magnetic observations, and deep-learning based speed estimation to enhance the performance of traditional PDR algorithm. A period of human motion features extracted from hybrid location sources are modelled instead of only one or adjacent location points to realize time-varying measured uncertainty errors prediction, and the predicted uncertainty errors of different indoor and outdoor location sources are integrated with iPDR to realize robust seamless positioning performance. Comprehensive experiments indicate that compared with existing multi-source fusion-based seamless positioning structure, the proposed ESL-STUP realizes much better performance under different scenes.

Radiative acceleration calculation methods and abundance anomalies in Am stars

Atomic diffusion with radiative levitation is a major transport process to consider to explain abundance anomalies in Am stars. Radiative accelerations vary from one species to another, yielding different abundance anomalies at the stellar surface. Radiative accelerations can be computed using different methods: some evolution codes use an analytical approximation, while others calculate them from monochromatic opacities. We compared the abundance evolutions predicted using these various methods. Our models were computed with the Toulouse-Geneva evolution code, in which both an analytical approximation (the single-valued parameter method) and detailed calculations from Opacity Project (OP) atomic data are implemented for the calculation of radiative accelerations. The time evolutions of the surface abundances were computed using macroscopic transport processes that are able to reproduce observed Am star surface abundances in presence of atomic diffusion, namely an ad hoc turbulent model or a global mass loss. The radiative accelerations obtained with the various methods are globally in agreement for all the models below the helium convective zone, but can be much greater between the bottom of the hydrogen convective zone and that of helium. The time evolutions of the surface abundances mostly agree within the observational error, but the abundance of some elements can exceed this error for the least massive mass-loss model. The gain in computing time from using analytical approximations is significant compared to sequential calculations from monochromatic opacities for the turbulence models and for the least massive wind model; the gain is small otherwise. Test calculations of turbulence models with the tabulated OPAL opacities yield quite similar abundances as OP for most elements but in a much shorter time, meaning that determining Am star parameters can be done using a two-step method.

Non-Invasive Brain Stimulation Beyond the Motor Cortex: A Systematic Review and Meta-Analysis Exploring Effects on Quantitative Sensory Testing in Clinical Pain.

Non-invasive brain stimulation (NIBS) has been investigated increasingly as a means of treating pain. The effectiveness of NIBS in the treatment of pain has traditionally focused upon protocols targeting the primary motor cortex (M1). However, over time, the effectiveness of M1 NIBS has been attributed to effects on interconnected cortical and subcortical sites rather than M1 itself. While previous reviews have demonstrated the effectiveness of non-M1 NIBS in improving subjective reports of pain intensity, the neurophysiological mechanisms underlying these effects remain incompletely understood. As chronic pain is associated with pain hypersensitivity and impaired endogenous descending pain modulation, it is plausible that non-M1 NIBS promotes analgesic effects by influencing these processes. The aim of this systematic review and meta-analysis was therefore to evaluate the effect of NIBS over non-M1 sites on quantitative sensory testing measures in clinical pain populations. A systematic search of electronic databases was conducted from inception to January 2024. Included articles (13trials, n = 565 participants) were appraised using PEDro and GRADE and a random effects model was used to meta-analyse outcomes where possible. A small number of studies found that NIBS applied to DLPFC may improve pain modulation in patients with fibromyalgia, and that stimulation of the posterior superior insula and prefrontal cortex could improve pain sensitivity in chronic neuropathic and osteoarthritic pain, respectively. However, findings varied between studies and there remains a paucity of primary research. This review indicates that current literature does not provide clear evidence that NIBS over non-M1 sites influences pain processing.

Boosting Efficiency to 22.73%: Unraveling the Role of Solvent Environment in Low‐Dimensional Perovskites Through Competitive Bonding Interactions

AbstractIt is reported that the solvent environment exerts a significant influence on the property of perovskite precursor solution and resultant film, which is more pronounced in more complex low‐dimensional perovskites. Four solvent additives with varying basicity are introduced to instigate a tug‐of‐war among bonding interactions, thereby exploring the systematic effect of the solvent environment on the growth of quantum wells and the photoelectric properties of the resultant low‐dimensional perovskite films. A governing principle that diverges significantly from those previously documented for three‐dimensional perovskites is elucidated in low‐dimensional perovskites. When coordination interactions predominate in the solvent environment, the emergence of a two‐dimensional GA2PbI4 transitional phase is fostered to facilitate subsequent transformation into the desirable ACI phase, and the perovskite crystallization kinetics is retarded to improve the film quality. Hence, the highest power conversion efficiency (PCE) of 22.73% is obtained for GA(MA)nPbnI3n+1 (n = 5) based solar cells with a p‐i‐n structure. The PCE achieved in this work is a record among the reported low‐dimensional perovskite solar cells.

Dynamic correction of forest fire spread prediction using observation error covariance matrix estimation technique based on FLC-GRU

BackgroundData assimilation (DA) techniques have played a significant role in improving the prediction accuracy of forest fire spread. The dynamic correction technique weights the predicted and observed values to obtain an analytical value that better reflects the position of the fire perimeter. The weighted importance of each contribution is determined by the magnitude of its associated error. However, as a crucial parameter affecting prediction accuracy, the covariance matrix of observation errors is often inaccurate and neglects its own temporal correlation. This is unfriendly to spread prediction results. To address this issue, we proposed a targeted technique for estimating the observation error covariance matrix (R matrix) based on the Fire Line Convolutional Gated Recurrent Unit (FLC-GRU).ResultsWe integrated this method into the DA framework and validated its applicability and accuracy using Observing System Simulation Experiment (OSSE). Through comparisons with traditional methods, the results indicate that using the FLC-GRU estimated R matrix for correction calculations leads to wildfire prediction locations that are closer to the true values.ConclusionsThe proposed approach learns the covariance matrix directly from time-series observed fire line data, without requiring any prior knowledge or assumptions about the error distribution, in contrast to classical posterior tuning methods. The proposed method significantly improves the rationality and accuracy of R matrix estimation, enhances the utility of observational data, and thereby improves the correction accuracy of forest fire spread predictions. Moreover, the study also demonstrates the applicability of the proposed method within the DA framework.

τSPECT: a spin-flip loaded magnetic ultracold neutron trap for a determination of the neutron lifetime

The confinement of ultracold neutrons (UCNs) in three-dimensional magnetic field gradient or magneto-gravitational traps allows for a measurement of the free neutron lifetime τ n with superior control over loss channels related to UCNs interacting with material surfaces. The most precise measurement τ n has been achieved using a magneto-gravitational trap, in which UCN are prevented from escaping at the top of their trap by gravity. More compact horizontal confinement geometries with variable energy acceptance ranges can be obtained by using steep magnetic field gradients in all spatial directions, generated by combinations of either permanent or (variable) superconducting magnets. In this paper, we present the first successful implementation of a pulsed spin-flip based loading scheme to fill a three-dimensional magnetic trap with externally produced UCN. The measurements with the τSPECT experiment were performed at the pulsed UCN source of the research reactor TRIGA Mainz. The extracted neutron storage time constant of τ = 859(16) s is compatible with the most precise determinations of τ n . We report on detailed, but statistically limited, investigations of major systematic effects influencing the neutron storage time. The statistical limitations are mitigated by the relocation of the experiment to a stronger UCN source.

Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery

Abstract Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High‐resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double‐counted plants) error in counts from high‐resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post‐wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high‐resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting (p) adult plants (0.25 m tall) varied between sites within 0.52 < < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness. Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.

Self‐quantification and consumer well‐being: A meta‐analytic review

AbstractSelf‐quantification technology is increasingly and irrevocably transforming consumers' relationships with their own minds and bodies. However, existing research findings on the contribution of self‐quantification to consumer well‐being are disparate. Given the popularity of self‐quantification technology among consumers in the post‐pandemic era and its inherent transformative nature, it is surprising that this gap remains unaddressed. To resolve this inconsistency and to examine how and when self‐quantification influences consumer well‐being, we conduct a meta‐analysis of consumer well‐being in the context of self‐quantification technology. Our findings reveal that self‐quantification positively influences consumer well‐being. However, self‐quantification also negatively affects consumer well‐being through body image and self‐esteem. The systematic moderation effects of cultural dimensions (e.g., uncertainty avoidance and individualism), prior experience, data sharing, and sample characteristics on the relationship between self‐quantification and consumer well‐being are also confirmed. While uncertainty avoidance, prior experience, and data sharing accentuate the positive effects of self‐quantification on consumer well‐being, an individualistic culture attenuates this influence. This study contributes to the consumer well‐being literature and extends objectification theory in the context of self‐quantification. These findings will guide practitioners and policymakers in devising strategies and policies to allow self‐quantification technology to be used in a way that enhances consumers' health and well‐being.

Sensitivity of Bayesian 21 cm power spectrum estimation to foreground model errors

Abstract Power spectrum estimators are an important tool in efforts to detect the 21 cm brightness temperature fluctuations from neutral hydrogen at early times. An initial detection will likely be statistical in nature, meaning that it will not be possible to make a coherent map of the brightness temperature fluctuations; instead, only their variance will be measured against a background of noise and residual systematic effects. Optimal Quadratic Estimator (OQE)-based methods often apply an inverse covariance weighting to the data. However, inaccurate covariance modelling can lead to reduced sensitivity and, in some cases, severe signal loss. We recently proposed a Bayesian method to jointly estimate the 21 cm fluctuations, their power spectrum, and foreground emission. Instead of requiring a fixed a priori estimate of the covariance, we estimate the covariance as part of the inference. Choices of parametrization, particularly of the foregrounds, are subject to model errors and could lead to biases and other ill effects if not properly controlled. In this paper, we investigate the effects of inaccurate foreground models on 21 cm power spectrum recovery. Using simulated visibilities, we find that, even in the most extreme scenarios tested, our approach is capable of recovering 21 cm delay power spectrum estimates consistent with a known input signal for delays ≳ 300 ns (∼88% of the available Fourier modes). This is true even when using foreground models derived from modified foreground catalogs containing spatial and spectral perturbations at the quoted level of uncertainty on our foreground catalogs.

Study and Comparison of Hyperbolic and Pseudorange Positioning Algorithms in the eLoran System

The positioning algorithms of the Enhanced Long-Range Navigation (eLoran) system primarily include the hyperbolic positioning algorithm and the pseudorange positioning algorithm. However, the calculations present in the existing literature are inaccurate and lack empirical data, and a thorough and precise comparison of the two algorithms has yet to be conducted. Therefore, this paper employs a combination of simulation analysis and empirical analysis to explore these two positioning algorithms in depth, with an optimization of the initial position calculation in the pseudorange algorithm. Under ideal conditions without observational errors, through precise calculations and analysis, the positioning errors of both algorithms are approximately zero, and full-area solutions can be achieved. Under conditions with observational errors, this study shows that both algorithms exhibit positioning errors, with the pseudorange algorithm achieving a level of accuracy comparable to that of the hyperbolic algorithm. At the same time, the empirical analysis further verified this conclusion. Additionally, this study found that the pseudorange positioning algorithm demonstrated better applicability in practical applications, as it successfully resolved the multivalued and singularity issues present in the hyperbolic positioning algorithm.

Multi-bandwidth observer-based adaptive robust pressure control for electro-hydraulic brake system with uncertainties and measurement noise

Accurate pressure regulation of electro-hydraulic brake system (EHB) is essential for enhancing braking performance in automobiles. However, component wear and pressure measurement noise can cause model parameters and controlled states to drift, resulting in system chattering. This nonlinear and uncertain state can significantly degrade pressure control performance. Motivated by this, the article presents a controlled state reconstruction-based adaptive robust pressure control technique with noise suppression. Firstly, a reduced dynamics model is established for controller design, which effectively captures the fundamental characteristic of the EHB. Secondly, a multi-bandwidth extended state observer (MBESO) capable of noise suppression is designed to reconstruct controlled states, including unknown disturbances and unmeasured pressure change rates of the EHB. Thirdly, an MBESO-based adaptive robust pressure control technique is introduced, where the control gain and robust factor are updated based on control and observation errors. In addition, the overall performance of the proposed control strategy is analyzed in the frequency domain, and close-loop stability is demonstrated via the Lyapunov method. Finally, the pressure control precision and robustness of the proposed algorithm are validated in both dynamic and static scenarios through sinusoidal and step-response experiments on a hardware-in-loop test bench. The results indicate that the proposed algorithm reduces dynamic pressure-tracking error by up to 62% compared to traditional method under sinusoidal conditions, with steady-state error remaining within 1 bar under step-response conditions.

Evaluating the Prediction Performance of the WRF-CUACE Model in Xinjiang, China

Dust and air pollution events are increasingly occurring around the Taklimakan Desert in southern Xinjiang and in the urban areas of northern Xinjiang. Predicting such events is crucial for the advancement, growth, and prosperity of communities. This study evaluated a dust and air pollution forecasting system based on the Weather Research and Forecasting model coupled with the China Meteorological Administration Chemistry Environment (WRF-CUACE) model using ground and satellite observations. The results showed that the forecasting system accurately predicted the formation, development, and termination of dust events. It demonstrated good capability for predicting the evolution and spatial distribution of dust storms, although it overestimated dust intensity. Specifically, the correlation coefficient (R) between simulated and observed PM10 was up to 0.85 with a mean absolute error (MAE) of 721.36 µg·m−3 during dust storm periods. During air pollution events, the forecasting system displayed notable variations in predictive accuracy across various urban areas. The simulated trends of PM2.5 and the Air Quality Index (AQI) closely aligned with the actual observations in Ürümqi. The R for simulated and observed PM2.5 concentrations at 24 and 48 h intervals were 0.60 and 0.54, respectively, with MAEs of 28.92 µg·m−3 and 29.10 µg·m−3, respectively. The correlation coefficients for simulated and observed AQIs at 24 and 48 h intervals were 0.79 and 0.70, respectively, with MAEs of 24.21 and 27.56, respectively. The evolution of the simulated PM10 was consistent with observations despite relatively high concentrations. The simulated PM2.5 concentrations in Changji and Shihezi were notably lower than those observed, resulting in a lower AQI. For PM10, the simulation–observation error was relatively small; however, the trends were inconsistent. Future research should focus on optimizing model parameterization schemes and emission source data.

A-196 Using Aggregated Proficiency Testing Results to Identify Systematic Error

Abstract Background Proficiency testing (PT) should identify systematic errors, which are likely to recur. The Clinical Laboratory Improvement Amendments of 1988 (CLIA) acceptance limits (ALs) include 3SD and concentration limits. We investigated the ability of PT to detect systematic error, especially as affected by the AL type. Methods Ungradable, duplicate, and irregular scores were removed from 2008 to 2018 CLIA laboratory PT results. For analytes that had at least 200 events and at least 200 participating laboratories, we calculated the overall sample miss rate, the unsatisfactory event rate, i.e., less than 80, and event rates for Score 100 to 0. We used paired t-tests and the Wilcoxon Signed-Rank test to compare miss rates and unsatisfactory event rates between short- and long-term participants. We used the binomial distribution to model the expected event scores under the assumption that all errors may be independent (random); see Figure. For each analyte and each of the possible PT scores, we compared observed event scores with their expected values as a ratio. Results 40,596 laboratories produced 15,140,128 event scores for 75 analytes. The distribution of event scores was over-weighted with higher-order event errors (Score 0 - Score 60) compared to the predicted distribution. Miss rates and unsatisfactory rates were significantly higher for short-term laboratories. Plotting the log ratio of observed versus expected rates for event scores showed that the degree of systematic effect increased progressively for each analyte. The magnitude of the effect was less for 3SD ALs. Conclusions In an event, PT errors are often dependent. Concentration and 3SD ALs were similarly sensitive to detect systematic error; both were more sensitive than ALs for qualitative or dilution assays. Expressing systematic error using PT data could help to identify remediable analytical issues.

Temporal dynamics in vertical leaf angles can confound vegetation indices widely used in Earth observations

Earth observation data is key for monitoring vegetation dynamics across temporal and spatial scales. The most widely used method to estimate vegetation properties from Earth observation data is vegetation indices. However, temporal dynamics in vertical leaf angles can strongly alter reflectance signals and, hence, vegetation indices. Here, we derive leaf angles from plant photographs to simulate the effect on vegetation indices with radiative transfer models. We show that leaf angle dynamics systematically confound widely applied vegetation indices. Moreover, we demonstrate that these effects are not random but tightly linked to abiotic environmental conditions. These systematic effects of vertical leaf angles have implications for monitoring plant properties, biodiversity, and ecosystem functional properties. We discuss the related challenges and opportunities to assess spatio-temporal vegetation dynamics with remote sensing and vegetation indices.

Precision polishing force control technology of abrasive belt-wheel polishing servo control system based on HGO-SMC

Due to cylinder friction, valve dead zone effect, flow nonlinearity, and measurement noise exist in pneumatic system. In the actual machining, it is difficult to achieve precise control of the pneumatic servo polishing system using the traditional PID algorithm, so the machining accuracy of the blades cannot be guaranteed. A sliding mode control (SMC) method based on high gain observer (HGO) is proposed. The HGO observes the output signal of the system and feeds the estimated signal back to the SMC to ensure that the observation error is uniform ultimate boundedness. Simulation and experiments show that compared with the PID algorithm, the steady-state error of HGO-SMC can be reduced by 66.4%. Compared with SMC(sgn), the high-frequency chattering of HGO-SMC can be reduced by 72.5%. Moreover, the polishing processing experiment shows that HGO-SMC can improve the form and position accuracy by 52.6% and reduce the surface roughness by 55.6%, compared with the original PID control algorithm of the polishing machine tool.