Extended reality head-mounted displays as measuring systems: Conceptualization and measurement uncertainty evaluation

Development of a Python-based tool for evaluation of multivariate decision risk considering measurement uncertainty and correlation effects

Measurement of the half-life of 214Po, including long-term studies to investigate potential seasonal effects.

Estimating reaction rate constants from impedance spectra: Simulating the multistep oxygen evolution reaction

Mercury in sediment of the upper Clark Fork River, Western Montana, USA.

Storage change ( S ) is an important component of the mass balance equation and quantifies the accumulation or depletion of matter in the studied control volume, under the eddy covariance (EC) sensor. The quantification of S is required to estimate surface fluxes. This study compared four methods for calculating S of CO 2 , based on EC and profile measurements at a Danish temperate forest ICOS site (DK-Sor). The 12-heights sequential sampling system quantified in- and above-canopy S . Its design and physical averaging properties were thoroughly described. Two vertical configurations of the profile system were analyzed: (i) top-tower, (ii) full profile (incorporating all levels), along with two alternative calculation methods based on top-tower EC data alone, including the method proposed in an often used software. Results showed that the deviations between the S methods had a seasonal course and that the top-tower profile was on average 21% lower than the full profile method. The choice of S method also impacted the surface flux estimations on an annual scale, with relative differences in net ecosystem exchanges of up to 8%, represented by 22 g-C m −2 yr −1 . The S methods impacted the friction velocity threshold determination, leading to a variation in the amount of data retained during low-turbulence filtering. The full profile retained the most data. Lastly, the tailor-made calculation from EC concentration measurements were shown to fit the top-tower profile measurements closer, compared to the EddyPro-calculated S . These results highlight the importance of accurate storage change measurements in high and dense forest sites. • Tall and dense forest sites require a profile storage change system. • The uncertainty of top-tower storage measurements shows a seasonal course. • Storage change calculation methods impact the net annual CO 2 ecosystem exchanges. • Accurate storage change estimation decreases the u ∗ threshold and retains more data.

• The paper presents a new methodology for evaluating the calibration of a coordinate measuring machine (CMM) using a polynomial calibration curve and a confidence interval. • The paper also introduces a method for assessing measurements performed by a CMM using an established calibration curve and compares the results with the current approach based on the Maximum Permissible Error (MPE). The article presents an alternative method for evaluating the calibration of a coordinate measuring machine (CMM) using a polynomial calibration curve, including its confidence interval, and its subsequent use in evaluating measurements. The methodology, based on the OEFPIL (Optimal Estimation of Functional Parameters by Iterated Linearization) algorithm and GUM principles, enables a more comprehensive and statistically sound evaluation of measurements using CMM. The procedure was applied to measurements along three coordinate axes and four diagonal directions, with confidence intervals set at a minimum coverage probability of 95 %. The proposed procedure allows for a more accurate and traceable determination of measurement uncertainty and provides a significant improvement over conventional MPE-based evaluation under conditions close to calibration. The paper presents quantitative results for a specific calibration and measurement case.

Agile human-centric manufacturing increasingly requires resilient robotic solutions that are capable of safe and productive interactions within unstructured environments of modern factories. While multi-modal sensor fusion provides comprehensive situational awareness yet robots must also contextualize their reasoning to achieve deep semantic understanding of complex scenes. Foundation model particularly Vision-Language-Action (VLA) models have emerged as promising approach on integrating diverse perceptual modalities and spatio-temporal reasoning abilities to ground physical actions to realize General Physical Intelligence (GPI) across various robotic embodiments. Although GPI has been conceptually discussed in literature but its pivotal role and practical deployment in agile manufacturing remain underexplored. To address this gap, this practical review systematically surveys recent advances in VLA models through the lens of GPI by offering comparative analysis of leading implementations and evaluating their industrial readiness via structured ablation study. The state of the art is organized into six thematic pillars including multisensory representation learning, sim2real transfer, planning and control, uncertainty and safety measures and benchmarking. Finally, the review highlights open challenges and future directions for integrating GPI into industrial ecosystems to align with the vision of Industry 5.0 for intelligent, adaptive and collaborative manufacturing ecosystem.

• Measuring deflection of reflected fringes or patterns is used to assess heliostat surfaces. • Performing this technique with computer vision targets has several key advantages. • Photogrammetry and camera advancements can also be leveraged improved cost and performance. • Testing this measurement system shows consistent surface slope results. Reflected computer vision targets are a powerful tool for measurement of mirror surface shape, with several important advantages over traditional fringe deflectometry methods. This method was first presented in 2021 and has undergone significant improvement and demonstration since. We describe a new baseline system using reflected computer vision targets, and present results from a large-scale measurement campaign conducted on both commercial heliostats and test mirrors in the laboratory. Calibration of the measurement system with photogrammetry allows for accurate measurement without careful control of target shape or camera position. Overall, the results show that a baseline setup using this method achieves measurement uncertainties in the slope error root-mean-square less than ±0.11 milliradian due to a series of repeatability conditions, varying sample position, rotation, lighting, camera settings, and system rebuild and recalibration. We present a detailed description of the setup, the results generated by this measurement tool, repeated measurement results, and the strengths and limitations of this metrology system.

• Real-LIFE test protocol for log wood stoves developed considering all phases. • Validation with two log wood stoves performed using the Real-LIFE test protocol. • Good repeatability of emissions while applying the new test protocol achieved. • Challenging to get comparable results from four laboratories using new protocol. • Study emphasizes the importance of measuring different combustion conditions. The use of log wood stoves is common in residential homes and are tested in a type test procedure following EN 16510-1:2022 at optimal combustion condition. Since this does not represent real-life operation, a novel test protocol was developed and validated using two different log wood stoves. The new test protocol includes the ignition phase (two batches) at natural draught, followed by three batches at nominal load, two batches at partial load and one final batch at overload. Typical emission parameters such as carbon monoxide (CO), nitrogen oxides (NO X ), organic gaseous carbon (OGC) emissions were recorded as well as TPM emissions in the hot undiluted flue gas. This study shows that it is challenging to get similar emission results for the same stove in different laboratories even when using the same fuel and well-defined test protocol, differences in results are due to measurement uncertainty, differences in appliance operations and not following exactly the defined Real-LIFE test protocol. Coefficients of variation for TPM, CO, OGC and NO X were 17.8 %, 20.1 %, 30.6 % and 8.7 %, respectively for stove A and 32.7 %, 13.9 %, 19.6 % and 10.0 %, respectively for stove B based on two to three repetitions per lab. The novel test protocol showed that combustion appliances may behave differently in different combustion phases, and this emphasizes the importance of measuring different combustion conditions in official testing to ensure that the appliances work properly in the field and that the measured emissions cover the whole operating range.

Systematic feature selection using three-level-fused of three-view uncertainty measures for multi-granularity fuzzy γ coverings

Plasticity and fracture of zinc are complex and highly anisotropic, because of the unusually large c/a ratio of the hexagonal-closed-packed (HCP) structure. Traditional semi-empirical interatomic potentials fail to capture both HCP stability and c/a ratio, thus they are not suitable to study plastic deformation. Recent machine learning-based potentials, including Rapid Artificial Neural Network (RANN) and Moment Tensor Potential (MTP), address this issue, however they struggle to accurately reproduce the Generalized Stacking Fault Energy (GSFE) surfaces, which control dislocation structure and glide. Also, these potentials do not accurately reproduce the basal traction-separation (T-S) curve, which determines crack propagation. In this work, we develop an Atomic Cluster Expansion (ACE) potential for Zn, trained on an extensive and well-converged DFT database. The training data is optimized using the recent HyperActive Learning (HAL) algorithm, enabling to achieve very low RMSE, accurate phonons for molecular dynamics simulations, and good transferability, assessed using model uncertainty measures, to extended defects such as dislocations and cracks. The validated potential is then used to clarify key observations of crack and dislocation slip behaviour in Zn. First, basal slip activates at a critical resolved shear stress below 0.5 MPa, consistent with single crystal experiments. Second, we find that pyramidal II slip is the next easier slip system, and it shows a pronounced compression/tension asymmetry in line with some experimental findings. Finally, we reveal why prismatic slip does not occur in Zn: screw cores are unstable and dissociate into basal dislocations, confirming previous DFT-based calculations. Our work demonstrates how carefully validated machine learning potentials can be used to unravel atomic-scale mechanisms of slip, that are beyond reach of DFT calculations.

Patients with cardiac implantable electronic devices (CIEDs) may receive replacement device components over time, resulting in mixed-manufacturer systems that pose unknown, potentially higher risk under MRI exposure. It is not clear how to efficiently evaluate whether adding a non-original implantable pulse generator (IPG) changes the system's radiofrequency (RF)-induced heating response. This study aimed to develop a least-burdensome RF safety evaluation method for MR Conditional labeling of CIEDs. Critical parameters were characterized and demonstrated as an IPG equivalency test method that supports leveraging existing single-manufacturer MR-Conditional labeling for mixed-manufacturer systems. The transfer function model of the pacing lead was measured using the original system (LeadA + IPGA) or a third-party IPGs (LeadA + IPGB or IPGC). Deposited power near the lead tip was compared for 50 Ω and open-end conditions, and lead or IPG impedances were measured at 64 and 128 MHz. Lead tip heating was not significantly different between original and third-party IPGs configurations (p < 0.001). RF-induced heating responses were outside of measurement uncertainty when the lead was connected with a 50 Ω resistor and some open-end conditions. The results showed that small changes in IPG impedance (< 2.5 Ω) to the lead-IPG system impedance (> 80.2 Ω) may not significantly alter RF-induced heating, remaining within measurement uncertainties. A simplified IPG impedance equivalency test has been developed to facilitate MRI access for patients with CIEDs that undergo IPG interchange. This test aids assessment of whether replacing an original IPG model with a different model may significantly increase RF-induced heating compared to the original system.

Measurement Uncertainty of Cadmium and Lead in PAEONIAE RADIX ALBA with AAS and ICP-MS: Comparison of Microwave Digestion, wet Digestion and Dry Ashing Approaches

The incorporation of artificial intelligence, multi-sensor perception, and cyber-physical control into mining operations offers tremendous opportunities for increasing productivity, safety, and sustainability. However, present frameworks focus on discrete subsystems rather than providing a unified, safety-constrained optimization method that has been verified in both surface and underground environments. This paper describes a scalable, hierarchical autonomous mining architecture that incorporates sensor fusion, edge intelligence, fleet coordination, and digital twin-based decision support. It is designed to operate in GNSS-denied conditions and extreme climatic constraints common to Nordic mining environments. A mathematical modeling approach formalizes vehicle dynamics, drilling mechanics, and multi-agent fleet coordination inside a safety-constrained multi-objective optimization formulation. The framework is validated using Monte Carlo simulation with uncertainty measurement, sensitivity analysis, and statistical hypothesis testing. The preliminary results show improvements over a typical baseline, with productivity increasing by approximately 24.3% ± 3.2%, energy consumption decreasing by 12.8% ± 2.5%, and safety risk decreasing by 48.6% ± 4.1%. A sensitivity study identifies localization accuracy, communication delay, and optimization weighting as the primary system performance drivers. The suggested framework serves as a reproducible and transferable reference model for next-generation intelligent mining systems, having direct applications to both industrial deployment and future research in autonomous resource extraction.

Properties of the hot intracluster and intragroup medium are mostly set by the underlying gravitational potential well, although complex astrophysical processes at play during their buildup may leave a significant imprint. Observational constraints on the degree and scales of such nongravitational processes require well-selected samples of objects and deep observations of their gas content. We aim to study the scaling relation between two global properties of the hot gas, namely its soft-band X-ray luminosity (L_X) and its temperature (T), by studying a sample of low-mass systems associated with precise redshifts, simultaneously accounting for sample selection biases and associated measurement uncertainties. This work takes as input a large catalog of X-ray-selected galaxy clusters (X-CLASS). We performed a thorough revision of the redshifts of sources using deep photometric data from the Legacy Surveys and our own tailored spectroscopic follow-up of 52 low-redshift systems. We devised a spectroscopically complete sample of 155 low-redshift (0.07&lt;z&lt;0.2) systems, and we measured properties of their X-ray emitting gas, with median overline T =1.7 keV and median overline L_X =10^ 43 -1 $. We inferred the relation between L_X and $T in a Bayesian framework. Our sample of groups and clusters with a median total mass of ∼ 6 M_⊙ reveals a relation L_X-T steeper than that predicted by the self-similar model, with a slope of B=3.2 ± 0.1. This result fits well within recent studies that together indicate a trend of an increasing slope with decreasing median halo mass. 10^ 13 This work supports a scenario of a stronger decrease in luminosity with decreasing mass in the group regime than for massive galaxy clusters. This effect is possibly due to strong and sustained feedback expelling gas efficiently from their shallower potential wells. We release the list of updated redshifts (photometric and spectroscopic) for the full X-CLASS sample and the gas properties of the low-redshift sample. The cluster photometric redshift code presented in the paper photXclus is made publicly available .

Abstract Redshift measurement uncertainty by direct reference of zeroth-order images of the CSST slitless spectrograph GI gratings is evaluated. Total optical throughput ratio of zeroth-order image with respect to its first order science photons is much lower for GI gratings compared with GU or GV gratings. Potential optimization of the GI gratings are investigated to enhance zeroth-order throughput without compromising their first-order scientific performance. Rigorous Coupled-Wave Analysis (RCWA) shows that groove profiles optimization could boost a gain factor of 2.5 in GI band zeroth-order image photons while preserving its first-order efficiency above 55%. Superior balance is achieved between centroid precision and spectral efficiency. Point source Signal-to-Noise Ratio (SNR) of 20-22 magnitude in I band demonstrate significant boost in zeroth-order image, yielding a centroid precision better than 0.353 pixels at 22 mag. A novel optimization strategy of grating parameters is reported as well.

Robust numerical frameworks are essential for the simulation, design, monitoring, and control of membrane-based separation units, particularly under highly nonlinear and industrially relevant operating conditions. In this context, a comprehensive phenomenological and numerical framework is proposed for the simulation of hollow-fiber membrane modules, incorporating coupled mass, momentum (through pressure drop), and energy transport equations. The governing equations are discretized using a rigorous orthogonal collocation formulation, and the performances of two numerical solution strategies are systematically investigated for the first time to allow the in-line and real-time implementation of the model: a steady-state approach based on the Newton-Raphson method with careful treatment of initial estimates, and a pseudotransient formulation. Particularly, an original and consistent numerical treatment is introduced for the energy balance at boundaries where the permeate flow vanishes, enabling the stable incorporation of thermal effects and Joule-Thomson phenomena. The results clearly show that the steady-state Newton-Raphson approach provides the best overall performance in terms of computational efficiency, numerical robustness, and accuracy when physically consistent initial profiles are employed. In particular, the combination of a linear initial guess and a numerical mesh constituted of four collocation points yielded the most favorable balance between convergence speed, numerical robustness, and accuracy for the base-case sensitivity analysis. For monitoring-oriented applications, the numerical choice should be weighted primarily toward computational performance once physical consistency and convergence criteria are satisfied, rather than toward maximum mesh-refinement accuracy. In this context, small differences in internal-fiber profiles can be compensated through real-time permeance estimation and are negligible when compared with measurement uncertainty in real industrial processes. Under extreme operating conditions involving low concentrations, low flow rates, and highly permeable species, the pseudotransient formulation proved to be a reliable auxiliary strategy, enabling robust convergence when suitable initial guesses were not readily available. The proposed framework is validated against experimental data from the literature and subjected to extensive convergence and sensitivity analyses, providing a reliable basis for simulation and for assessing computational feasibility in in-line and real-time monitoring-oriented applications. A full demonstration of digital-twin integration, online parameter updating, reduced-order coupling, and closed-loop control is beyond the scope of the present study and will be addressed in future work.

This paper addresses the problem of robust identification of gross errors affecting both measurements and network parameters in power system state estimation. The study is conducted within a steady-state framework and focuses on improving bad data identification in the presence of modeling and measurement uncertainties, explicitly accounting for the limited observability of gross errors. Building on an Extended Weighted Least Squares (EWLS) estimator and a theoretically refined eigenvalue-based clustering of dominant error components, a novel Bayesian identification framework is introduced. The proposed Bayesian approach assigns probabilities to competing gross error models, including scenarios involving multiple simultaneous errors, given the observed clusters of dominant errors. This probabilistic formulation enables a systematic and quantitative decision-making process for identifying the most likely sources of gross errors, extending existing deterministic or heuristic approaches. The methodology is evaluated through numerical simulations on the IEEE-14 bus test system, considering several gross error scenarios and significant parameter uncertainties. The results demonstrate that the proposed Bayesian framework enhances the interpretability and discriminative capability of gross error identification, highlighting its potential for robust bad data identification in power system state estimation.

This article analyzes modern methods for guiding aircraft toward maneuvering targets. The main guidance algorithms are considered, in particular geometric methods, the proportional guidance method, and its modifications. Based on a kinematic model of the relative motion of the interceptor and the target, the principles of generating control accelerations and the specifics of the practical implementation of the corresponding algorithms are analyzed. A comparative analysis of the considered methods is conducted in terms of their effectiveness, implementation complexity, and ability to ensure the interception of maneuvering targets. It is shown that the proportional guidance method is the most widespread in modern systems; however, its effectiveness may decrease in the case of active target maneuvering and the presence of measurement errors. It has been established that modified guidance algorithms, in particular Augmented Proportional Navigation, require estimation of the target’s acceleration, which in real-world conditions is accompanied by measurement uncertainty and noise. This paper identifies the main limitations of existing approaches and justifies the development of adaptive guidance methods capable of accounting for the uncertainty of target motion parameters and the dynamic limitations of control systems.