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There is concern that the values provided by devices using infrared thermometry in emergency departments (EDs) do not reflect body core temperature accurately. Evaluation of three thermometers commonly used in the ED. Two infrared ear thermometers and an infrared forehead thermometer were evaluated using 1) the Voltcraft IRS-350 calibration device, 2) comparing temperature values to a rectal end-exercise temperature (T-RECT) of 38.1°C in 12 participants, and 3) comparing temperature values to rectal temperature in 133 ED patients. Calibration across the human core temperature range revealed that the ear thermometers underestimated radiant temperature by 0.77 ± 0.39°C and 1.84 ± 0.26°C, respectively, whereas the forehead thermometer overestimated radiant temperature by 0.90 ± 0.51°C. After cycling exercise, all thermometers underestimated T-RECT (0.54 ± 0.27°C and 1.03 ± 0.48°C for the ear thermometers and 1.14 ± 0.38°C for the forehead thermometer). In the ED, the ear thermometers underestimated T-RECT by 0.31 ± 0.37°C and 0.46 ± 0.50°C, whereas the forehead thermometer exhibited a nonsignificant overestimation of 0.04 ± 0.46°C. If the threshold for fever in all systems had been set to 37.5°C instead of 38.0°C, the sensitivity and specificity of the systems for real fever (T-RECT ≥ 38°C) are, respectively, 71% and 96% (ear thermometer 1), 57% and 97% (ear thermometer 2), and 86% and 90% (forehead thermometer). We conclude that the investigated thermometers are not reliable as devices to measure radiant temperature, cannot be used to assess body core temperature during exercise, but may be used as a screening device, with 37.5°C as a threshold for fever in emergency care settings. [ABSTRACT FROM AUTHOR]

In this paper, we prove that the product of a paired calibrated set and a set of codimension 1 calibrated by a coflat calibration with small singularity set is Almgren minimal. This is motivated by the attempt to classify all possible singularities for Almgren minimal sets–Plateau's problem in the setting of sets. In particular, a direct application of the above result leads to various types of new singularities for Almgren minimal sets, e.g. the product of any paired calibrated cone (such as the cone over the d − 2 skeleton of the unit cube in R d , d ≥ 4) with homogeneous area minimizing hypercones (such as the Simons cone). [ABSTRACT FROM AUTHOR]

The dual five-axis machining machine is state-of-the-art equipment designed for producing thin-walled aerospace parts like skins and panels. It comprises two five-axis machine tools with mirrored synchronous motions: one bi-rotary machine for milling and the other machine for follow-up support. The high absolute accuracy of the five-axis machine tools themselves and the high synchronous motion accuracy between the two machines are both crucial to ensure machining accuracy. This paper proposes an efficient method to measure and identify geometric and dynamic errors of dual five-axis machine tools. Using the R-test combined with three measurement trajectories to measure the absolute motion error of two five-axis machine tools and the synchronous motion error between the two machines. The designed trajectories include the main rotary axis linkage conditions and can fully reflect the primary motion forms of the dual five-axis machine tool. The main position-independent geometric errors and rotational axis linkage dynamic errors are identified by converting quasi-static and dynamic measurement data from three different trajectories into a unified reference coordinate system. The entire process requires only one instrument installation, effectively avoiding time-consuming repetitive installations and calibration processes while reducing the impact of installation errors on results. The effectiveness of the proposed method for measuring and identifying the geometric and dynamic errors is analyzed combined with Monte Carlo simulation. Experiments were carried out on a dual five-axis mirror milling system (MMS) with a 5000 mm X-stroke. Both simulations and experiments confirmed that the proposed method is efficient in quickly identifying key error sources of the MMS. [Display omitted] • An efficient method to measure and identify geometric and dynamic errors during synchronous motion of the MMS is proposed. • A geometric and dynamic error model is introduced for identifying key errors using error sensitivity theory. • A 3-step measurement strategy that enables rapid separation of linkage errors of the MMS is introduced. • Both simulation and experiment show the proposed method could obtain key error sources of the MMS with high efficiency. [ABSTRACT FROM AUTHOR]

Accurate information about the satellite transmit antennas is essential for high-precision GNSS applications. Such metadata of the GPS III satellites were publicly made available by the manufacturer Lockheed Martin. They comprise phase center offsets (PCOs), phase patterns, and directivity patterns. These calibrations are validated with ground-based measurements of a global GPS tracking network and observations of a high-gain antenna. Phase center offsets and patterns are estimated from dual-frequency observations of a ground tracking network. The ionosphere-free L1/L2 z-PCOs show a 10 cm offset w.r.t. the manufacturer calibrations due to already known inconsistencies with the ITRF2020 scale fixed in the estimation. The standard deviation of the differences between estimated and calibrated z-PCOs is 2.7 cm. The estimated phase patterns have a 0.7 mm RMS consistency with the manufacturer calibrations after minimizing the raw patterns for nadir angles between 0° and 14°. The directivity patterns are validated with the effective isotropic radiated power (EIRP) measured with a 30 m high-gain antenna. The measured EIRP for the L1 and L2 band is affected by M-code transmission from a distinct antenna. When modeling the additional contribution of this antenna, the nadir-angle variation of the EIRP matches the calibrated directivity patterns with a precision of 0.1 dBW. Representative transmit power values of the main navigation antenna amount to 125 W, 30 W, and 85 W in the L1, L2, and L5 bands, while the L1 and L2 M-code signals are transmitted with about 30 W and 25 W, respectively. [ABSTRACT FROM AUTHOR]

The study described here was aimed at assessing the capability of quantitative ultrasound (QUS) based on the backscatter coefficient (BSC) for classifying disease states, such as breast cancer response to neoadjuvant chemotherapy and quantification of fatty liver disease. We evaluated the effectiveness of an in situ titanium (Ti) bead as a reference target in calibrating the system and mitigating attenuation and transmission loss effects on BSC estimation. Traditional BSC estimation methods require external references for calibration, which do not account for ultrasound attenuation or transmission losses through tissues. To address this issue, we used an in situ Ti bead as a reference target, because it can be used to calibrate the system and mitigate the attenuation and transmission loss effects on estimation of the BSC. The capabilities of the in situ calibration approach were assessed by quantifying consistency of BSC estimates from rabbit mammary tumors (N = 21). Specifically, mammary tumors were grown in rabbits and when a tumor reached ≥1 cm in size, a 2 mm Ti bead was implanted in the tumor as a radiological marker and a calibration source for ultrasound. Three days later, the tumors were scanned with an L-14/5 38 array transducer connected to a SonixOne scanner with and without a slab of pork belly placed on top of the tumors. The pork belly acted as an additional source of attenuation and transmission loss. QUS parameters, specifically effective scatterer diameter (ESD) and effective acoustic concentration (EAC), were calculated using calibration spectra from both an external reference phantom and the Ti bead. For ESD estimation, the 95% confidence interval between measurements with and without the pork belly layer was 6.0, 27.4 using the in situ bead and 114, 135.1 with the external reference phantom. For EAC estimation, the 95% confidence intervals were –8.1, 0.5 for the bead and –41.5, –32.2 for the phantom. These results indicate that the in situ bead method has reduced bias in QUS estimates because of intervening tissue losses. The use of an in situ Ti bead as a radiological marker not only serves its traditional role but also effectively acts as a calibration target for QUS methods. This approach accounts for attenuation and transmission losses in tissue, resulting in more accurate QUS estimates and offering a promising method for enhanced disease state classification in clinical settings. [ABSTRACT FROM AUTHOR]

• The calibration problem of the general acupuncture robot is analyzed. • A simultaneous offline calibration method for problems defined with multiple closed kinematic chains is proposed. • A multi-objective optimization model covering all coupling components and constraints is constructed and solved. • A real-world platform with a customized calibration board is built. • The proposed method has higher calibration accuracy and convergence speed. Acupuncture robot is a new-era product combining traditional acupuncture and cutting-edge technology. The calibration of the vision system and the acupuncture mechanism is a crucial prerequisite for humanoid acupuncture control, which has not yet been explored. In this paper, a simultaneous offline calibration method is proposed for acupuncture robots. Analysis reveals that its calibration problem is defined with three closed kinematic chains, while the typical problems cover only a single chain. Decoupling them, a Kronecker product-based method is deduced to access the closed-form rotation component. As opposed to quaternion-based methods, it only experiences linear complexity in sign ambiguity, which can produce a more precise solution in finite time. Further, a simultaneous optimization model that encompasses all components is established, which can be solved with stochastic gradient descent-based methods. It is free of truncation errors and thus has higher calibration accuracy and convergence speed. Besides, the impairment in error propagation between different closed kinematic chains is mitigated compared to step-by-step methods. Finally, simulations and experiments are carried out. Notably, the proposed method can be easily extended to other robot calibration problems with multiple closed kinematic chains. [ABSTRACT FROM AUTHOR]

• Study surveyed vehicle owners who have had their crash avoidance features repaired. • Features of interest were front crash prevention, blind spot detection, and cameras. • Although repairs often improved feature performance, post-repair issues were common. • Calibration problems may have contributed to many of these post-repair issues. • The repair process needs to be simplified and repair solutions made more accessible. Introduction : Popularity of crash avoidance features is growing, but so too is confusion around how to repair them, how much repairs should cost, and who should pay for those repairs. This study's purpose was to capture how these issues are affecting consumers. Method : A total of 496 vehicle owners in the United States were surveyed online and by phone about their experiences repairing front crash prevention (n = 359), blind spot detection (n = 317), and/or driver assistance cameras (n = 348) equipped on their personal vehicles. Results : Owners tended to have multiple reasons for repairs. Repairs due to vehicle (i.e., crash or windshield) damage corresponded with the greatest likelihood of post-repair issues, especially if calibration was performed, and higher out-of-pocket costs (possibly because of deductibles or other repair work). About half of respondents who had calibrations performed on features repaired because of vehicle damage reported persisting issues with the features after repair. Post-repair issues were more common for repairs performed at independent repairers than dealership service centers, yet similar feature calibration rates were reported for both types of repairers. More people went to dealership service centers than independent repairers, and these respondents were more likely to say they would return to this type of facility for a similar repair in the future. Although most repairers explained why repairs occurred, less than half of respondents said they completely understood the reasons given. Conclusions : There are new complications in the repair cycle affecting consumers. Post-repair issues are more prevalent than previously assumed, regardless of the crash avoidance feature repaired. Practical applications : Post-repair issues risk undermining consumer acceptance and the safety potential of critical features. Simplifying the repair process and establishing affordable and accessible centralized databases with repair specifications and instructions from the manufacturers would be a start to addressing industry-wide challenges. [ABSTRACT FROM AUTHOR]

The Low Earth Orbit (LEO) region is populated by tens of thousands of objects, which include not only active satellites, but also space debris which can pose a real danger to space operations, and therefore have to be monitored closely. The work presented in this paper addresses the problem of wide field LEO detection and astrometric reduction using a portable, low cost wide field system. The work will address the challenges of astrometric reduction errors for wide FOV images, which lead to the need for multiple calibrations, which pose a challenge for real time operation. We propose a mechanism for aggregating calibration data as the system runs in real time, which does not slow down the real time operation but continuously increases the accuracy of the results. The proposed mechanism can be adapted for both staring and sidereal tracking observation modes, and to all types of astrometry software or streak detection algorithms. [ABSTRACT FROM AUTHOR]

At the European Space Agency (ESA), Modeling and Simulation (M&S) plays a fundamental role during the lifetime of a spacecraft, being used from the design phase to the testing and during operations in space. M&S tools embed general physics-based models and disciplines characterized by configurable parameters which have to be calibrated in order to mimic the behavior of the actual flying spacecraft. However, their calibration requires a large number of simulations which are unfeasible to be obtained through computationally expensive high-fidelity simulation models. Thus, the inability to calibrate the high-fidelity simulation models poses limitations for the use of M&S tools during spacecraft operations. In this light, the present work proposes the use of a surrogate model-based approach for the calibration of simulation models of spacecraft. The approach integrates a computationally inexpensive deep-learning-based surrogate model, which mimics the high-fidelity simulation model without requiring the same computational burden, and a metaheuristic optimization algorithm, to identify the optimal values of the simulation model configurable parameters. This enhances the capabilities of M&S tools and allows their use in operations. The approach's effectiveness is shown by its application to real flying Earth observation satellite data and simulation models. [ABSTRACT FROM AUTHOR]

In daily anaesthetic practice, there are recommendations on the minimum monitoring required for the safe provision of general anaesthesia. It is vitally important clinicians understand the components, processes and potential sources of error when interpreting or using these measurement devices. Inadequate knowledge could result in the misinterpretation of results, inaccurate decision-making, and subsequently patient harm. This article explores the sources of error and some limitations of the equipment frequently used in anaesthetic practice. [ABSTRACT FROM AUTHOR]

This manuscript describes reflectance-based vicarious calibration exercise of the Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) sensor during its second phase of observation campaigns over the Indian subcontinent. Vicarious calibration methods are one of the significant approaches that have been practiced successfully for the absolute radiometric calibration of various spaceborne and airborne sensors to ascertain its desired performance in terms of data quality and its accuracy. Calibration campaigns were performed for AVIRIS-NG sensor during its phase-2 campaign overpass at the Desalpar (25th March 2018) and Amarapur (27th March 2018) calibration sites of Gujarat, India. Results from ground-based measurements of atmospheric conditions and surface reflectance including descriptions of the test sites are summarized in this work. Based on the in-situ data and images from the AVIRIS-NG sensor, the at-sensor apparent radiances are simulated using the 6SV radiative transfer model. The comparison shows good agreement between AVIRIS-NG measured radiance and 6SV simulated apparent radiance at the two calibration sites using desert aerosol model. The gains (spectrally) derived agree to within ∼ 2 % each other. Furthermore, an analysis is performed to determine or identify systematic and random errors, and the overall uncertainty is evaluated for reflectance-based method (total uncertainty of less than 4 % spectrally). [ABSTRACT FROM AUTHOR]

Low-fidelity, cost-effective, physics-based models are useful for assessing the environmental performance of novel combustion systems, especially those utilizing alternative fuels, like hydrogen and ammonia. However, these models require calibration and quantification of their limitations to be reliable predictive tools. This paper presents a framework for calibrating a simplified Chemical Reactor Network model using higher-fidelity Computational Fluid Dynamics data from a micro-gas-turbine-like combustor fuelled with pure ammonia. A Bayesian inference strategy that explicitly accounts for model error is used to calibrate the most relevant CRN parameters based on NO emissions data from CFD simulations and to estimate the model's structural uncertainty. The calibrated CRN model accurately predicts NO emissions within the design space and can extrapolate reasonably well to conditions outside the calibration range. By utilizing this framework, low-fidelity models can be employed to explore various operating conditions during the preliminary design of innovative combustion systems. • Bayesian calibration with model error embedding of Chemical Reactor Network (CRN) parameters is performed. • 15 RANS simulations of an ammonia-fuelled, multi-stage, mGT-like combustor are used as higher-fidelity samples. • Polynomial Chaos Expansion is used for likelihood construction and global sensitivity analysis. • Calibration enables quantifying parametric and model-form uncertainties in the CRN predictions of NO. • CRN allows performing optimisation under uncertainty to minimise NO at the combustor outlet. [ABSTRACT FROM AUTHOR]

In order to improve the reliability of the time transfer link, Bureau International des Poids et Mesures (BIPM) has offcially taken the Galileo time comparison as the backup link for UTC calculation since 2020. Therefore, Galileo calibration of receiver is a necessary work for all timekeeping labs around the world to participate in the UTC link. Taking the GPS link calibrated by Physikalisch-Technische Bundesanstalt (PTB) and the National Time Service Center (NTSC) as reference, this paper sets PT09 as reference station to calibrate and verify the Galileo E3 (E1&E5a) total delay of NT02 and NT05 receivers from NTSC. The results show that the Galileo E3 total delay of NT02 and NT05 are 74.6 ns and 46.5 ns, respectively, the calibration uncertainty is 3.5 ns, and the calibration delay is relatively stable. After calibration, GPS P3 and Galileo E3 Common View results between NT02/NT05 and calibrated receivers of other timekeeping labs are basically consistent. Taking the GPS P3 links comparison results as reference between NTP3 and receivers from other labs, the average deviation is less than 1.5 ns, which is within the calibration uncertainty range. [ABSTRACT FROM AUTHOR]

• A frequency-domain retracker with interface to In-Flight Point-Target-Response. • Assessment of the impact of In-Flight Point-Target-Response for Sentinel-6 MF. • The range walk correction applied via a Chirp-Zeta-Transform algorithm. • Assessment of the impact of the range-walk correction for Sentinel-6 MF. • A full Cal/Val of the first year of Sentinel-6 MF altimetry science data. Poseidon-4 is a dual-frequency redundant radar altimeter on board the European Commission Copernicus Programme Sentinel-6 Michael Freilich satellite, that represents a significant breakthrough with respect to its predecessors Jason-class altimeters due to its digital architecture and to its innovative measurements and calibration modes. In the framework of the Sentinel-6 Michael Freilich commissioning preparatory activities, CNES has contracted CLS for the development of a Sentinel-6 Processing Prototype (S6PP) application. S6PP is a multi-chain processing suite able to process Sentinel-6 Level-1A and Level-1B data products up to Level-2. The novel algorithms developed in the CNES/CLS research and development activities are implemented within S6PP and validated to support the different thematic applications (in particular inland water and ocean) and in view of promoting them for possible implementation in the operational ground segment. The present work covers in particular the main results over open ocean for the main altimetric geophysical variables over the sea surface (sea surface height anomaly, significant wave-height, sigma-nought and wind speed) derived by the Low-Resolution Mode (LRM) and High-Resolution Mode (HRM) chains of S6PP in terms of precision, accuracy, spectral content and measurement stability. Given the reported variation of the payload in-orbit temperatures along with the reported instrumental ageing, and given the tight requirement to measure the GMSL (Global Mean Sea Level) in seamless continuity with Jason-3, the clear goal for S6PP was to process the S6-MF data with the minimum possible level of approximations along the processing pipeline but still maintaining a very efficient prototype from the computational point of view. For this scope, a novel and computationally efficient numerical retracking scheme with interface to the in-flight PTR (Point Target Response) provided by the instrument calibration chain has been put in place within S6PP for both the Low-Resolution and High-Resolution modes whereas the Delay-Doppler beam-forming is carried out by applying the range walk correction based on a computationally efficient algorithm (Chirp Zeta-Transform). The impact of the range walk correction and of the in-flight PTR interface is assessed for HRM and LRM, respectively. The paper shows that the proposed processing baseline ensures a dataset robust from the currently known instrumental degradation or ageing issues, both in LRM and HRM mode and, once this is done, that Sentinel-6 Michael Freilich global mean sea level measurement is in line with the one measured by Jason-3. [ABSTRACT FROM AUTHOR]

A new calibration device using a double-ball rotary structure and three contact displacement sensors is proposed to measure the spatial errors of six-axis serial robot. The error model is divided into kinematics part and static stiffness part, established by Modified DH (MDH) model and virtual joint method. The MDH parameter errors are compensated by modifying the angles of each axis with Newton Raphson compensation method and ridge estimation. The stiffness parameters are also identified by the least square method. The self-designed double-ball rotary device is calibrated by coordinate measuring machine (CMM), while the three-point measuring device is calibrated by gauge block. Then, the kinematic and static stiffness calibration and compensation experiments are carried out on EPSON C4 A901 robot in turn. The spatial error under none load and 1.0 kg, 1.5 kg, 2.0 kg gravity load are measured separately. The experiment results show that the motion accuracy of the robot is improved to the range from 0.0163 mm to 0.0236 mm under the gravity load conditions between weights of 1.0 kg and 2.0 kg. Finally, by comparing the measuring results from the laser displacement sensor and laser interferometer, the measurement accuracy of each axis of the self-designed device is equivalent to the laser displacement sensor and the laser interferometer in micron range. • The self-designed measuring device consisting of three-point probing component and double-ball rotary component is proposed for measuring the spatial error of TCP of 6-axis joint industrial robot. • The compensating processes of spatial error are performed by the kinematic calibration under no-load condition and the static stiffness calibration under different loads, respectively. • After dynamic compensation, the average errors of the robot's TCP are improved to the range from 0.0163 mm to 0.0236 mm under the gravity load conditions between 1.0–2.0 kg weight. [ABSTRACT FROM AUTHOR]

We propose a new calibration standard for a CMOS-based autocollimator using an optical angle generator with a submillimeter strip reflector (OAG-SSR). The OAG-SSR combines the advantages of existing standards while minimizing their limitations by generating reference angles in the form of a reflected diffraction pattern from the strip reflector. This method provides angular values along the measuring range of the autocollimator without any mechanical movement, enabling a faster calibration process. This feature is suitable for the autocollimator integrated with production lines. In this work, we applied the propagation of uncertainty method and the Monte Carlo simulation for uncertainty evaluations. The expanded uncertainty of the reference angle from the OAG-SSR is 2.5 ″ with an approximately 95% confidence level for the calibration range of 1270 ". • A new calibration method for CMOS-based autocollimator. • Optical angle generator with a submillimeter strip reflector. • Full-range autocollimator calibration without mechanical movement. • Manufacturing inline calibration for CMOS-based autocollimator. [ABSTRACT FROM AUTHOR]

Egypt is currently developing huge housing projects at the New Capital, Alamein, and other Egyptian cities. These projects often require soil replacement for roads and buildings. Among the causes of problems observed in some housing projects in the last decade are improper type of soil replacement material and inadequate compaction. Pit-run gravelly soils are commonly recommended for soil replacement because they have the advantages of providing high stiffness, low permeability inherited from their fines' content and wide availability in Egypt. It is common to use sand cone test and/or static plate load test (SPLT) to assess the quality of compaction. The sand cone test is simple, cheap and rapid; however, it is not reliable with gravel sizes usually found in pit-run gravel, which may exceed 38 mm. Although the SPLT is efficient to assess the quality of compaction and the deformation modulus, it is relatively expensive and time-consuming. The dynamic plate load test is a reliable solution to combine the advantages of both the sand cone test and SPLT. This research includes a detailed laboratory characterization of pit-run gravel collected from Suez deserts and a field testing program including static and dynamic plate load tests on pit-run gravelly soil carried out at several sites in the new capital of Egypt to obtain static and dynamic plate load tests moduli. A statistical approach based on percent of exceedance criterion is adopted to obtain a simple correlation between the static and dynamic plate load test deformation moduli. [ABSTRACT FROM AUTHOR]

This paper presents a phased array system (PAS) with built-in calibration to reduce cost and improve the accuracy for vehicle-to-everything (V2X) communications. Unlike other high-complexity and high-cost calibration methods, our proposed built-in calibration achieves a lower cost and low complexity by detecting only the magnitude. Conventional built-in calibration uses a mixer, thus causing high power consumption and high cost. However, our proposed built-in calibration method searches for the minimum of the vector-sum point from the signal by sampled lines on-board, resulting in a higher accuracy. The vector-sum point goes to a minimum when the phase and magnitude are in balance. Therefore, the phase and magnitude imbalance can be easily monitored by controlling phase shifters and attenuators in PAS. To verify our proposed built-in calibration in PAS, a T/Rx with four RF-chains and sampled lines was fabricated at 5.8 GHz. Its performance was then demonstrated through simulated and measured results. Moreover, a communication performance evaluation using an actual vehicle was performed both before and after calibration. As a result, we found that a signal without distortion was transmitted after calibration. [ABSTRACT FROM AUTHOR]

Computer-assisted dynamic navigation surgery could provide accurate implant placement. However, its low efficiency was always criticized by dental surgeons. The purpose of this study was to evaluate the accuracy and efficiency of a calibration approach with reflective wafers in dynamic navigation for implant placement. Eighty implants were placed in the standardized polyurethane mandibular models under dynamic navigation and divided into 2 groups according to the calibration methods (n = 40). The U-shaped tube (UT) group used a prefabricated U-shaped tube embedded with radiopaque markers. The reflective wafers (RW) group used a fixation with 3 round reflective wafers as markers. Postoperative cone beam computed tomography images were obtained for implants deviation analyses. The calibration time was used to evaluate the efficiency of the 2 methods. Significant differences were found in the trueness and efficiency between the 2 groups (P < 0.05). The 3D deviations at the implant platform and apex were smaller in UT group (0.89 ± 0.28 and 0.79 ± 0.30 mm, respectively) than in the RW group (0.99 ± 0.28 and 0.98 ± 0.30 mm, respectively). The angular deviation was larger in the UT group (2.16 ± 1.12°) than in the RW group (1.53 ± 0.88°). The calibration approach of RW group was more efficient than the UT group (2.05 ± 0.55 and 7.50 ± 0.71 min, respectively). The calibration method of RW improved the efficiency significantly and achieved equivalent trueness with UT for dynamic navigation during implant placement. [ABSTRACT FROM AUTHOR]

Satellite-Based Augmentation Systems (SBASs) are designed to enhance the performance of Global Navigation Satellite Systems (GNSSs) for civil aviation users. As SBAS Geostationary Earth Orbit (GEO) satellite signals are received in a wide area, far more non-aeronautical GNSS users utilize SBAS to improve navigation performance. Since ionospheric delay is the principal error source of GNSS and a leading correction aspect of SBAS, the GEO-broadcast ionospheric data quality of six SBASs over the past three years is assessed in this article. Results show that European Geostationary Navigation Overlay Service (EGNOS), System for Differential Correction and Monitoring (SDCM), and Wide-Area Augmentation System (WAAS) have the highest Grid Ionospheric Vertical Delay (GIVD) accuracy judged by the residual Standard Deviation (STD), then followed by BeiDou SBAS (BDSBAS) and Multi-functional Satellite Augmentation System (MSAS), while GPS-Aided GEO Augmented Navigation (GAGAN) is the worst. The 6 m Grid Ionospheric Vertical Error (GIVE) availability is over 90 percent of the time for most Ionospheric Grid Points (IGPs) of all tested SBASs. However, GAGAN and WAAS have a significant availability decline at 3 m GIVE. In addition, the integrity limits of MSAS and WAAS are fairly conservative. In contrast, the confidence bounds of BDSBAS and SDCM are excessively radical that fail to meet the integrity requirement. To improve the ionospheric data quality received from SBAS, a Multi-Layer Perceptron (MLP)-based user-applicable GIVD calibration method is proposed in the article. A well-trained MLP learned temporal and spatial correlations of the IGP delays and extracted transfer functions between broadcast and reference GIVDs to eliminate error. The SBAS processing errors in Differential Code Bias (DCB) compensation, GIVD estimation modeling, and GEO message quantization are rectified by the MLP trained by abundant historical data with Center for Orbit Determination in Europe (CODE) final Global Ionospheric Maps (GIMs) as reference. Test results on real-time data show that over 85 percent IGPs have STD decline after calibration. The STD declination rates of six SBASs except for SDCM are over 15 percent, and the Mean Absolute Error (MAE) and Root Mean Square Error (RMS) calibration effects are dramatic for all systems. Finally, the average GIVEs are reduced by more than 50 percent for EGNOS, GAGAN, MSAS, and WAAS with a 99.9 percent integrity level maintained. Despite no promotion on availability for BDSBAS and SDCM, the proposed method compensates for the integrity issues of the systems. [ABSTRACT FROM AUTHOR]

The Generalised N-Trailer (GNT) vehicle is a tool for field operations that optimises harvesting and transportation tasks, offering a highly scalable payload using only one tractor. Precise knowledge of the position and attitude of each segment in the chained vehicle is crucial for the controller's success during operation. In this study, we propose the use of a Nonlinear Moving Horizon Estimator (NMHE) to estimate the system's state when the GNT vehicle is equipped with incremental encoders on its joints. A first NMHE serves as a virtual calibration procedure, estimating initial joint angle values and the system's state using noisy and biased measurements of the joints and the tractor pose. This calibration is performed while the chained vehicle travels along a straight path, whose length is determined by the number of trailers and their geometrical properties. Subsequently, a second NMHE, with fewer optimisation variables and constraints replace the first to effectively reduce the computational burden. Moreover, it treats the incremental encoder measurements as if they were absolute encoders after the initial joint angles have been estimated by the first NMHE. The proposed method is compared against the Extended Kalman Filter (EKF) and validated through simulated and practical real-time experiments, showcasing its effectiveness in achieving precise control and enhancing operational efficiency. [Display omitted] • The state of chained vehicles with incremental encoders can be estimated by using MHE • The computational burden is reduced by switching from N M H E 1 to N M H E 2 • The state estimated is compatible with the system's model and constraints [ABSTRACT FROM AUTHOR]

Ionospheric delay is one of the most dominant error sources in GNSS fast relative positioning, especially for RTK positioning of medium and long baselines, which directly affects the ambiguity fixing successful rate and positioning accuracy. The strategy of combining ionospheric correction based on multi-reference station network with ionospheric weighted model is still one of the most effective methods for precise relative positioning. In this paper, a method of ionospheric weighted calibration model is presented. By using a large-scale reference station network at a certain period of time with ionospheric disturbance to obtain the prior ionospheric information required by the user, the GNSS precision relative positioning performance of the ionosphere-weighted calibration model in complex scenarios (the accuracy of prior ionospheric information will be reduced due to large-scale reference station network and ionospheric active scene) was systematically evaluated. The experimental results show that the ionosphere-weighted calibration (IWC) method can effectively reduce the influence of excessive ionospheric modeling residual on the positioning results, and significantly improve the fixed rate and positioning accuracy of RTK. Compared with the traditional ionosphere-fixed model based on virtual reference station (VRS) technology, the three-dimensional accuracy and fixed rate of the six rover stations are improved by 56% and 33.5% on average. Secondly, the prior ionospheric constraint intensity in IWC mode is controllable, which can effectively balance the relationship between initialization speed and positioning accuracy. On average, 94.3% of the time period of the six rover stations can complete the initialization and fixing within 10 epochs, which can basically meet the requirements of fast positioning. In addition, IWC model can ensure higher accuracy and better maintain fixed solution state after the ionospheric parameter convergence is completed. In the ionospheric disturbance stage, after a period of parameter convergence, high fixed rate and positioning accuracy can still be guaranteed. [ABSTRACT FROM AUTHOR]

This paper presents the process of designing a highly repeatable calibration station for the NaviFast 6D measuring arm. This is a new solution, characterized by repeatability at the level of 1 μm for access from one direction, the ability to change the setting of the base and effector of the arm, fully automatic measurement, and relatively low construction cost. In this solution, human error has been eliminated as much as possible. The stand consists of two Stewart platforms equipped with double-acting pneumatic actuators on which the calibration arm is placed. The stand is able to provide 4096 different positions – a sufficient number for calibrating the arms. In addition, the paper presents the methodology of designing stations for various types of measuring arms and the methodology of selecting parameters for the appropriate working space of a measuring arm. • The design of stand for calibration the NaviFast6D measuring arms. • The method of measuring the repeatability of the test stand. • Simulation and experimental studies of the repeatability of the test stand. [ABSTRACT FROM AUTHOR]

An accurate and traceable performance testing of the X-ray computed tomography (XCT) is crucial for the application of the XCT in various industrial fields. For the performance testing of the XCT, we proposed a novel gauge with spheres embedded in a cylindrical resin. The proposed gauge is free from the artifacts caused by the gauge structure, and the spheres can be arranged at any position of the gauge. Additionally, the gauge can be placed with different orientations and it can be produced at an economical price. We verified the feasibility of using the proposed gauge as a reference standard, in terms of the coefficient of thermal expansion and secular change of the resin. Furthermore, we developed the associated calibration method for the gauge through multiple orientation measurements using the XCT. From the results, the proposed gauge can be calibrated with an uncertainty of 1.9 μm when the gauge size is 30 mm. • A novel gauge was proposed for the performance testing of X-ray computed tomography. • We verified the feasibility of the developed gauge with the coefficient of thermal expansion and secular change of the resin. • The proposed gauge can be calibrated with an uncertainty of 2.9 μm. [ABSTRACT FROM AUTHOR]

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are membrane-spanning lipids produced by bacteria that are ubiquitous in natural sedimentary archives and preserved over geologic timescales. The main influence on their distributions in the environment appears to be temperature, thus making them a potentially powerful proxy for paleotemperature reconstruction. Application of recent lacustrine brGDGT-based temperature calibration models to specific regions results in inaccurate reconstructed temperatures suggesting that regional or site-specific temperature calibration models may be necessary. Using an extended data set of 692 lake sediment samples from across the globe we determined whether brGDGT distributions in samples from the same regions or sites are significantly different from one another via hierarchical agglomerative clustering analysis (HAC). Results of HAC analysis showed four significant clusters with varying geographic distributions. Cluster 1 samples are mainly located at high latitudes (Mean Annual Air Temperature; MAAT = 3.10 ± 5.91 °C). Cluster 2 samples are concentrated in the Tibetan Plateau (MAAT = 1.54 ± 5.91 °C). Cluster 3 samples span temperate-tropical latitudes (MAAT = 17.26 ± 8.16 °C). Cluster 4 samples are mainly located in Central and South America (MAAT = 24.56 ± 4.01 °C). The clustering led us to develop random forest regression models to predict temperature (MAAT and Months Above Freezing, MAF, air temperature) based on samples within each cluster (cluster-specific temperature models). Model performance was the highest for Cluster 3 (MAF: R2 = 0.78, RMSE = 2.85 °C, n = 261; MAAT: R2 = 0.76, RMSE = 4.07 °C, n = 270), followed by Cluster 1 (MAF: R2 = 0.54, RMSE = 1.67 °C; n = 219; MAAT: R2 = 0.62, RMSE = 3.62 °C, n = 226) and Cluster 4 (MAF: R2 = 0.58, RMSE = 2.46, n = 67; MAAT: R2 = 0.59, RMSE = 2.52 °C, n = 67). The Cluster 2 model had the lowest model performance (MAF: R2 = 0.42, RMSE = 4.51, n = 38; MAAT: R2 = 0.51, RMSE = 1.74 °C, n = 129). We also developed a random forest classification model to predict the cluster assignment for new samples (cluster prediction model with an overall accuracy of 95%), which informs the user as to which cluster-specific temperature model(s) to apply to their samples. Finally, we applied our approach (i.e., cluster assignment followed by cluster-specific temperature reconstruction) to seven published lacustrine (paleo)records and illustrate pitfalls among the temperature reconstructions from brGDGT-based temperature calibration models. Overall, our study defines broad geographic relationships among lacustrine brGDGT distributions and air temperature while underscoring model limitations for paleotemperature reconstruction and subsequent interpretation. [ABSTRACT FROM AUTHOR]

N-wire phantom-based ultrasound probe calibration has been used widely in many freehand tracked ultrasound imaging systems. The calibration matrix is obtained by registering the coplanar point cloud in ultrasound space and non-coplanar point cloud in tracking sensor space based on the least squares method. This method is sensitive to outliers and loses the coplanar information of the fiducial points. In this article, we describe a coplanarity-constrained calibration algorithm focusing on these issues. We verified that the out-of-plane error along the oblique wire in the N-wire phantom followed a normal distribution and used it to remove the experimental outliers and fit the plane with the Levenberg–Marquardt algorithm. Then, we projected the points to the plane along the oblique wire. Coplanarity-constrained point cloud registration was used to calculate the transformation matrix. Compared with the other two commonly used methods, our method had the best calibration precision and achieved 25% and 36% improvement of the mean calibration accuracy than the closed-form solution and in-plane error method respectively at depth 16. Experiments at different depths revealed that our algorithm had better performance in our setup. Our proposed coplanarity-constrained calibration algorithm achieved significant improvement in both precision and accuracy compared with existing algorithms with the same N-wire phantom. It is expected that calibration accuracy will improve when the algorithm is applied to all other N-wire phantom-based calibration procedures. [ABSTRACT FROM AUTHOR]

Sun sensors are commonly used attitude sensors because of their low cost, mass, volume, and power consumption. Analog Sun sensors (ANSS), which are smaller but usually less accurate than digital ones, are especially preferred for small satellite missions. One of the main reasons for the lesser accuracy of analog Sun sensors is being prone to external errors, most prominently the Earth's albedo. This study proposes an analog Sun sensor calibration method using the Deep Neural Network (DNN). The main contribution of the proposed algorithm is that it does not require any model for measurement correction. The method is tested with simulations and real data from an Earth-imaging spacecraft. Results show that the error in the Sun direction measurements, which can be as high as 10°, can be decreased to a level of 0.5° by using the DNN for calibration. Moreover, testing in different scenarios verifies that the DNN can correct the measurements for periods as long as 7 days without requiring excessive training periods, even when the spacecraft is not in the same flight configuration for which the DNN was trained. • Earth's albedo is one of the primary sources of error for Sun sensors. • Sun sensor calibration using the Deep Neural Network is proposed. • The method is tested with both the simulated and actual Sun sensor data. • DNN corrected measurements becomes almost 20 times more accurate. [ABSTRACT FROM AUTHOR]

Health technology assessment (HTA) is an established mechanism for explicit priority setting to support universal health coverage. However, full HTA requires significant time, data, and capacity for each intervention, which limits the number of decisions it can inform. Another approach systematically adapts full HTA methods by leveraging HTA evidence from other settings. We call this "adaptive" HTA (aHTA), although in settings where time is the main constraint, it is also called "rapid HTA." The objectives of this scoping review were to identify and map existing aHTA methods, and to assess their triggers, strengths, and weaknesses. This was done by searching HTA agencies' and networks' websites, and the published literature. Findings have been narratively synthesized. This review identified 20 countries and 1 HTA network with aHTA methods in the Americas, Europe, Africa, and South-East Asia. These methods have been characterized into 5 types: rapid reviews, rapid cost-effectiveness analyses, rapid manufacturer submissions, transfers, and de facto HTA. Three characteristics "trigger" the use of aHTA instead of full HTA: urgency, certainty, and low budget impact. Sometimes, an iterative approach to selecting methods guides whether to do aHTA or full HTA. aHTA was found to be faster and more efficient, useful for decision makers, and to reduce duplication. Nevertheless, there is limited standardization, transparency, and measurement of uncertainty. aHTA is used in many settings. It has potential to improve the efficiency of any priority-setting system, but needs to be better formalized to improve uptake, particularly for nascent HTA systems. • "Adaptive health technology assessment" (aHTA) produces efficient HTA results by adjusting for analytical time, data, capacity, and source of conduct, and is practiced in many countries but is not a well-defined field. • To our knowledge, this study is the first of its kind to map existing methods for aHTA, including the following: rapid review, rapid cost-effectiveness analysis, rapid manufacturer submissions, transfers, and de-facto HTA, which are triggered by urgency, certainty, and low budget impact. • aHTA should be formalized so that it can play a critical, but underserved, role in supporting nascent HTA systems. [ABSTRACT FROM AUTHOR]

With significant technological progress in optics fields, such as VR and 3D sensing technologies, the demand for optical components with complex shapes (e.g., freeform surfaces) has expanded significantly in recent years. Ultra-precision diamond turning is one of the common manufacturing methods used for freeform optics. There are two tool-motion mechanisms for freeform diamond turning: slow tool servo (STS), which moves the entire tool rest, and fast tool servo (FTS), which only drives the diamond tool. In particular, high-frequency and long-stroke FTS units driven by a separate control system from the machine tool controller have recently been developed as a new trend in FTS-based diamond turning. However, this type of independent FTS control system causes a time delay between the machine tool instruction and the FTS tool motion. Even a millisecond-scale time delay can cause a considerable angular misalignment owing to the high rotation rate of the spindle, resulting in a micron-scale form error on the workpiece surface. In this paper, a novel method for precisely measuring the time delay from the machined workpiece surface and compensating for the misalignment based on the measured time delay was proposed. To measure the time delay, spherical dimples were machined on the workpiece surface by using STS and FTS. Subsequently, the angular misalignment and time delay were obtained by comparing the measured surface profiles at specific positions of the spherical dimples. Thus, the time delay can be measured with microsecond-scale accuracy. Based on the measured time delay, the shifted tool position along the spindle axis by the time delay was compensated to the ideal tool position by considering the cutting velocity. Consequently, the angular misalignment was completely eliminated, and submicron-level form accuracy was achieved. This calibration method of angular misalignment for an independent FTS unit is expected to significantly improve the accuracy of ultra-precision machining of freeform optics. [Display omitted] • A method for measuring the time delay of FTS from a machined surface was proposed. • The factors determining the time delay measurement accuracy were identified. • A program was developed to compensate for the time delay and spindle angular misalignment. • The residual time delay was reduced to ∼0.1 μs and angular deviation to ∼0.0001°. • The machining position error was successfully reduced from 321 μm to 97 nm. [ABSTRACT FROM AUTHOR]

Although current methods based on deep learning (DL) have been widely employed in shadow detection tasks, the cluttered background and complex shadow features in remote sensing images (RSIs) make shadow detection still a challenging task. In this paper, we apply a neural network combined with a distance transformation algorithm to RSI shadow detection for the first time and propose a novel slice-to-slice context transfer and uncertainty region calibration network (SCUCNet). First, an interslice context semantic transfer (SCT) module is proposed to explore the connections among spatial pixel information and extract sufficient global contextual information via interslice feature transfer. This process helps alleviate the interference of multiscale differences and complex backgrounds on the detection results. Second, to further optimize the initial output mapping, calibrate the uncertainty region, and enhance the feature representation, we propose a plug-and-play uncertainty region awareness and calibration (URAC) module combined with a distance transform algorithm, which introduces only 0.09 M parameters. The quantitative results show that the proposed model outperforms state-of-the-art methods for shadow detection in RSIs, achieving an Intersection over Union (IoU) of 88.33% on the aerial imagery dataset for shadow detection (AISD). In addition, extensive experiments and visualizations further prove the validity and interpretability of our method, and the proposed method can effectively mitigate the interference caused by multiscale differences, morphological diversity, spectral similarity and heterogeneity. [ABSTRACT FROM AUTHOR]

Hydrogen leakage concentration rapid measurement is the key issue for hydrogen application as hydrogen leakage is easy to cause hydrogen safety issues such as hydrogen explosions. Non-invasive visual measurement method such as the schlieren measurement technique is the prospective solution. However, the specific relationship between the hydrogen leakage concentration and schlieren image gray remains unclear, which leads that the schlieren technique procedure is developed for visualization and acquiring qualitative information only. This paper aims to decouple the hydrogen leakage concentration from the complicated schlieren image information, and find the mapping relationship between the hydrogen leakage concentration and schlieren image gray, hence realizing a quantitative hydrogen leakage concentration analysis. Therefore, a hydrogen leakage visualization experimental bench is established to simulate and measure hydrogen leakage by a series of experiments under different leakage concentrations. The mapping relationship between the hydrogen leakage concentration and schlieren image gray is obtained by the experiments using the schlieren technique. Then, a new calibration schlieren technique with the function of visually measuring hydrogen leakage concentration is developed and verified under 80% hydrogen leakage concentration. Results of the trials demonstrated the ability of the proposed technique successfully measure concentration distributions with satisfactory accuracy. • The relationship between hydrogen concentration and schlieren image gray is decoupled. • A new calibration schlieren method is proposed to measure the hydrogen concentration. • A hydrogen visualization experimental bench is established for results verification. • A series of visualization experiments are studied to summarize the distribution rules. [ABSTRACT FROM AUTHOR]

The accuracy of estimated sea conditions, specifically wave height and peak/average wave periods, affects the estimation of electrical energy production from wave energy converters. This study investigates the uncertainty in wave energy harvesting estimated by the SWAN wave model and determines possible improvements by adjusting the model's tunable parameters. Three different wave energy converters (OEBuoy, WaveBob, and Pontoon) and ten different locations along the Atlantic coast of the Iberian Peninsula are used in the study. The SWAN model is calibrated using the ST6 term package based on both wave height and peak period wave parameters. Different wave hindcast data produced by different model settings are used to estimate the wave energy produced by the wave energy converters at ten buoy locations and compared to wave energy produced estimated based on wave observations. The study finds that the physical settings of the SWAN model have a considerable influence on the uncertainty in the estimation of the power output produced by the device. The best-fitting calibrated model improved the mean energy output value of all locations compared to the SWAN default settings. The study concludes that adjusting the SWAN model parameters can improve the accuracy of the estimation of the energy output. • SWAN model calibration significantly improves wave resource accuracy. • ST6 physical source terms of the SWAN model were adjusted for the Atlantic coast of the Iberian Peninsula. • Estimation of wave power harvested can amplify wave resource uncertainties. • OEBuoy, WaveBob, and Pontoon WECs were used as case studies. • Calibration of the SWAN model corrected mean energy output by around 10% for the 3 considered WECs. [ABSTRACT FROM AUTHOR]

Nonparametric estimation for semilinear SPDEs, namely stochastic reaction–diffusion equations in one space dimension, is studied. We consider observations of the solution field on a discrete grid in time and space with infill asymptotics in both coordinates. Firstly, we derive a nonparametric estimator for the reaction function of the underlying equation. The estimate is chosen from a finite-dimensional function space based on a least squares criterion. Oracle inequalities provide conditions for the estimator to achieve the usual nonparametric rate of convergence. Adaptivity is provided via model selection. Secondly, we show that the asymptotic properties of realized quadratic variation based estimators for the diffusivity and volatility carry over from linear SPDEs. In particular, we obtain a rate-optimal joint estimator of the two parameters. The result relies on our precise analysis of the Hölder regularity of the solution process and its nonlinear component, which may be of its own interest. Both steps of the calibration can be carried out simultaneously without prior knowledge of the parameters. [ABSTRACT FROM AUTHOR]

Class-incremental learning (CIL) aims to recognize classes that emerged in different phases. The joint-training (JT), which trains the model jointly with all classes, is often considered as the upper bound of CIL. In this paper, we thoroughly analyze the difference between CIL and JT in feature space and weight space. Motivated by the comparative analysis, we propose two types of calibration: feature calibration and weight calibration to imitate the oracle (ItO), i.e., JT. Specifically, on the one hand, feature calibration introduces deviation compensation to maintain the class decision boundary of old classes in feature space. On the other hand, weight calibration leverages forgetting-aware weight perturbation to increase transferability and reduce forgetting in parameter space. With those two calibration strategies, the model is forced to imitate the properties of joint-training at each incremental learning stage, thus yielding better CIL performance. Our ItO is a plug-and-play method and can be implemented into existing methods easily. Extensive experiments on several benchmark datasets demonstrate that ItO can significantly and consistently improve the performance of existing state-of-the-art methods. Our code is publicly available at https://github.com/Impression2805/ItO4CIL. • We explore and study how class incremental learning (CIL) differs from joint training (i.e., the oracle), and identify the crucial difference in both feature space and weight space. Therefore, we propose to improve CIL by imitating the oracle (ItO). • In the feature space, the proposed feature calibration introduces deviation compensation to maintain the class decision boundary of old classes for CIL. • In the weights space, the proposed weight calibration leverages forgetting-aware weight perturbation to increase transferability and reduce forgetting for CIL. • Extensive experiments demonstrate that our ItO can significantly and consistently improve the performance of existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The nonlinear Hawkes process is a point process for which the occurrence of future events depends on its history, either by excitation or inhibition. This property made it popular in many fields, such as neuro-sciences and social dynamics. In this paper we propose a tractable nonlinear Poisson autoregression as a discrete-time Hawkes process. Our model allows for cross-excitation and inhibition between components, as well as for exogenous random noise on the intensity. We then prove a convergence theorem as the time step goes to zero. Finally, we suggest a parametric calibration method for the continuous-time Hawkes process based on the discrete-time approximation. [ABSTRACT FROM AUTHOR]

Statistical matching aims to integrate two statistical sources. These sources can be two samples or a sample and the entire population. If two samples have been selected from the same population and information has been collected on different variables of interest, then it is interesting to match the two surveys to analyze, for example, contingency tables or covariances. In this paper, we propose an efficient method for matching two samples that may each contain a weighting scheme. The method matches the records from the two sources. Several variants are proposed in order to create a directly usable file integrating data from both information sources. • Optimal transport using calibration can produce an efficient statistical match. • Method uses calibration to create a weighting system respecting matching variables. • Method can be used for micro and macro approaches. [ABSTRACT FROM AUTHOR]

The purpose of this work was to provide guidance for the lack of an air-kerma rate standard for the S7600 Xoft Axxent® source by providing a correction factor to apply to the National Institute of Standards and Technology (NIST) traceable S7500 well chamber (WC) calibration coefficient before the development of an S7600 standard at NIST. The Attix free air chamber (FAC) at the University of Wisconsin Medical Radiation Research Center was used to measure the air-kerma rate at 50 cm for six S7500 and six S7600 sources. These same sources were then measured using five standard imaging HDR1000+ WCs. The measurements made with the FAC were used to calculate source-specific WC calibration coefficients for the S7500 and S7600 source. These results were compared to the NIST traceable calibration coefficients for the S7500 source. The average results for each WC were then averaged together, and a ratio of the S7600 to S7500 WC calibration coefficients was determined. The average S7600 air-kerma rate measurement with the FAC was 7% lower than the average air-kerma rate measurements of the S7500 source. On average, the S7500 determined WC calibration coefficients agreed within ±1% of the NIST traceable S7500 values. The S7600 WC calibration coefficients were up to 16% less than the NIST traceable S7500 values. The final correction factor determined to be applied to the NIST traceable S7500 value was 0.8415 with an associated uncertainty of ±8.1% at k = 2. This work provides a suggested correction factor for the S7600 Xoft Axxent source such that the sources can be accurately implemented in the clinical setting. [ABSTRACT FROM AUTHOR]

The Discrete Element Method (DEM) has become increasingly popular for simulating breakage operations and grinding equipment performance. Two main breakage approaches can be found in literature: bonded particle models and particle replacement models. This work is focused on fitting the parameters of a DEM particle replacement breakage model for maize, implemented in software Rocky DEM. Once all required experimental and calibration procedures were developed for maize specific DEM properties and parameters, single particle compression tests were carried out to fit the parameters of the breakage model, which was extensively analyzed for validating its capability of representing the material. Maize properties showed to be in agreement with literature sources. Compression tests simulated in DEM led to the same results as their experimental counterparts, validating the model's capability of representing maize breakage. These results are the basis for further simulation of a full milling process in a hammer mill. [Display omitted] • Maize breakage in DEM is addressed by a particle replacement model. • Parameters are fitted based on single particle compression tests. • The breakage model is validated by means of DEM simulations. • DEM successfully predicts maize kernels' breakage. • The breakage model can be used in simulations of a complete maize milling process. [ABSTRACT FROM AUTHOR]

The brain–computer interface (BCI) provides a direct communication pathway between the human brain and external devices. However, the models trained for existing subjects perform poorly on new subjects, which is termed the subject calibration problem. In this paper, we propose a semi-supervised meta learning (SSML) method for subject-transfer calibration. The proposed SSML learns a model-agnostic meta learner with existing subjects and then fine-tunes the meta learner in a semi-supervised learning manner, i.e. using a few labelled samples and many unlabelled samples of the target subject for calibration. It is significant for BCI applications in which labelled data are scarce or expensive while unlabelled data are readily available. Three different BCI paradigms are tested: event-related potential detection, emotion recognition and sleep staging. The SSML achieved classification accuracies of 0.95, 0.89 and 0.83 in the benchmark datasets of three paradigms. The runtime complexity of SSML grows linearly as the number of samples of target subject increases so that is possible to apply it in real-time systems. This study is the first attempt to apply semi-supervised model-agnostic meta learning methodology for subject calibration. The experimental results demonstrated the effectiveness and potential of the SSML method for subject-transfer BCI applications. [ABSTRACT FROM AUTHOR]

Riser gas events appear during offshore drilling operations when there is a presence of gas in a marine riser above the subsea BOP, which has been recognized as a hazardous situation. The selection of pressure control methods during such events significantly impacts the safety and efficiency of riser gas handling (RGH). This paper performed a numerical simulation study on the fixed choke and constant outflow method as one of the most discussed and positively perceived pressure control methods for RGH currently in the industry. The processes of RGH with the proposed fixed choke and constant outflow method were simulated with a comprehensive riser gas influx simulator. The multiphase flow dynamics in a riser were simulated by a drift-flux model with the inclusion of various sub-models and coupled with a multiphase choke flow model. Measurement data (including distributed fiber optic sensing data) from a set of full-scale experiments conducted at the Petroleum Engineering Research, Training, & Testing (PERTT) Laboratory at Louisiana State University (LSU) was used for the calibration and validation of the developed simulator. The performance of the proposed method is evaluated based on the maximum gas and liquid outflow rates and the peak choke upstream pressure during RGH. A series of sensitivity analyses and a contingency case study was conducted to evaluate the applicability and the extendibility of the fixed choke and constant outflow method. The proposed pressure control method shows great potential in enhancing the safety of RGH for a wide range of riser lengths and gas influx sizes with the available managed pressure drilling (MPD) and riser gas handling equipment. The sensitivity analyses revealed that the selection of the initial circulation rates and choke positions has a significant impact on the safety and time efficiency of the RGH operations and should be carefully determined based on the influx and riser system status. This study deepens the understanding of gas influx movement and pressure behaviors in a marine riser during riser gas events. The numerical simulations in this study help to improve the design of riser gas handling strategies. [ABSTRACT FROM AUTHOR]

The determination of the knee joint axis (KJA) via specific calibration movements has become a promising alternative to the conventional approach to determine this axis based on regression equations or directly via marker placement on bony landmarks of the knee. Since the orientation of the KJA may differ between methods, it has direct influence on hip rotation and may therefore influence clinical decision making in context of transverse plane gait deviations. Research question: Does a functional KJA calibration lead to clinically relevant differences in hip rotation estimates during gait compared to the conventional method? 95 subjects (age: 19.9 years; BMI: 21.1 kg/m2), including 71 patients with potential rotation malalignment, were prospectively examined and underwent 3D gait analysis. For the conventional approach the KJA was determined by applying a knee alignment device, for the functional approach subjects were asked to perform two different calibration movements. Each procedure was performed twice. Mean hip rotation in stance (mHipRotSt) was determined following the conventional and the functional KJA calibration. Deming regression analysis for the comparison of conventional and functionally measured hip rotation revealed a systematic and substantial difference between methods (slope: 0.63; intercept: 0.31°). Measurement repetition with the knee alignment device revealed typical errors around 5°, whereas the functional methods lead to profoundly smaller errors around 1-2° with slightly inferior results for SQUAT compared to FLEX. However, when compared to conventional frontal plane video-taping, the conventional method seemed to reflect the more consistent results. The systematic linear discrepancy in mHipRotSt obtained by a functional approach as compared to the conventional approach appears critical since patients with severe internal or external rotation gait may be misjudged in function when receiving corrective procedures such as femoral derotation osteotomy. • Gait analysis was performed in 95 subjects with and without rotational malalignments. • A conventional and two functional approaches were applied for knee axis determination. • Hip rotation differs almost by a factor of two between approaches (linear slopes 0.63; 0.71). • The outcome with a functional approach (FLEX, SQUAT) leads to a higher precision. • The outcome with the conventional approach corresponds better with clinical evaluation. [ABSTRACT FROM AUTHOR]

The objective was to develop a novel scoring system that would be predictive of postoperative pulmonary complications in critically ill patients after cardiac and major vascular surgery. A total of 17,433 postoperative patients after coronary artery bypass graft, valve, or thoracic aorta repair surgery admitted to the cardiovascular intensive care units at Cleveland Clinic Main Campus from 2009 to 2015. The primary outcome was the composite of postoperative pulmonary complications, including pneumonia, prolonged postoperative mechanical ventilation (>48 hours), or reintubation occurring during the hospital stay. Elastic net logistic regression was used on the training subset to build a prediction model that included perioperative predictors. Five-fold cross-validation was used to select an appropriate subset of the predictors. The predictive efficacy was assessed with calibration and discrimination statistics. Post hoc, of 13,353 adult patients, we tested the clinical usefulness of our risk prediction model on 12,956 patients who underwent surgery from 2015 to 2019. Postoperative pulmonary complications were observed in 1669 patients (9.6%). A prediction model that included baseline and demographic risk factors along with perioperative predictors had a C-statistic of 0.87 (95% confidence interval, 0.86-0.88), with a corrected Brier score of 0.06. Our prediction model maintains satisfactory discrimination (C-statistics of 0.87) and calibration (Brier score of 0.07) abilities when evaluated on an independent dataset of 12,843 recent adult patients who underwent cardiovascular surgery. A novel prediction nomogram accurately predicted postoperative pulmonary complications after major cardiac and vascular surgery. Intensivists may use these predictors to allow for proactive and preventative interventions in this patient population. Strong multivariate baseline and perioperative predictors were factored into a novel nomogram to predict PPCs. Bootstrapped calibration curve demonstrated the model calibration showing ideal (green line), apparent (blue line), and bias-corrected (red line) model. High agreement was noted between observed (frequencies), and predicted pulmonary complications events were plotted against predicted probabilities. The prediction nomogram was further internally validated in an independent dataset to estimate clinical usefulness that demonstrated excellent discrimination with preserved calibration ability. Early identification of pulmonary complications in at-risk patients provides latitude to the clinicians to use preemptive measures and may help modify postoperative outcomes. [Display omitted] [ABSTRACT FROM AUTHOR]

• Propose a unified model framework for location agnostic outlier detection. • Integrate implicit/explicit confidence calibration into a reconstruction based model. • Propose to use Uniform and Dirichlet distributions for generating known outliers. • Validate our proposed model on both the image and network traffic datasets. Novelty detection detects outliers located at any location, such as abnormalities (i.e., far distance outliers) and novel/unobserved patterns (i.e., close distance outliers). While many novelty detection approaches have been proposed in the literature, they generally focus on detecting one specific type of outlier, e.g., Multi-Class Open Set Recognition (MCOSR) and One-Class Novelty Detection (OCND) approaches are applied for far and close distance outlier detection, respectively. However, in practice, it is difficult to measure in advance whether the distance between outliers and inliers is far or close. Recent work on outlier detection at any location with a unified model has yielded mixed performance. In this paper, we propose a new unified model, named Calibrated Reconstruction Based Adversarial AutoEncoder (CRAAE), for location agnostic outlier detection. The key idea is to integrate implicit and explicit confidence calibration strategies into a reconstruction based model for building a more accurate decision boundary. We leverage the category information disentangled from feature space to calibrate the decision metric (i.e., reconstruction error) constructed in the original data space. CRAAE also adds Uniform or Dirichlet noise into the artificial outlier generation process to represent various outliers. Experimental results show that CRAAE can outperform state-of-the-art unified models (e.g., GPND) and achieve similar performance with OCND and MCOSR methods in close and far distance outlier detection, respectively. [ABSTRACT FROM AUTHOR]

Instrumented Indentation Test (IIT) is a non-conventional mechanical characterization technique to evaluate hardness, Young modulus, creep and relaxation of materials. In the macro range, it represents a cheaper and faster alternative to conventional tensile-based tests. IIT is a metrological scale; thus, to establish traceability, calibration is essential. Frame compliance calibration is critical because it is a major contribution to the measurement uncertainty. This work discusses the limits of the current state-of-the-art and proposes a novel methodology for frame compliance calibration. The introduced approach demonstrates the source of common systematic errors in the mechanical characterization reported in the literature, i.e. edge effect, while first highlighting a relevant frame compliance nonlinearity. The proposed procedure is cost-effective and relies upon constitutive spring modelling of IIT and calibration of reference block by nanoindentation. Results show that the novel approach corrects systematic trends in the characterization and yields a relative measurement uncertainty of 5%. • Macro-Instrumented Indentation can conveniently replace conventional tests. • Frame compliance calibration is critical for accurate and precise characterization. • Current calibration methods neglect frame compliance nonlinearity. • A method is proposed and applied to calibrate frame compliance nonlinearity. • An approach to calibrate reference material by nanoindentation is demonstrated. [ABSTRACT FROM AUTHOR]

An artefact-based fast and automated volumetric error mapping solution for medium size 3-axis machine tools that enables the calibration of a 1m3 workspace in less than 1 h is proposed for characterizing how temperature variations affect the volumetric accuracy of the machine without a priori knowledge of the temperature variations. A continuous measurement during seven days is performed on a machine affected by different heat sources and a volumetric error variation model is identified. Residual errors remaining from the identification process are used to estimate the uncertainties of individual parameters and motion errors, and Monte Carlo simulations are used to propagate them to the TCP. This model is used to understand how the volumetric positioning error of the machine changes over time and how it is generated within the kinematic chain of the machine. • Artefact-based measurement strategy is used to characterize volumetric thermal behaviour of a milling machine. • Thermal variation is captured during a 7-day experimental test. • Uncertainties are estimated at parameter, motion error and TCP level. • Machine behaviour is analysed based on parameter and motion error variations. [ABSTRACT FROM AUTHOR]

In domain generalization (DG) problem, we hope to train a robust model from multiple source domains and generalize it to unseen domains. However, the trained model can not perform well in the unseen domain since the domain-invariant representation is hard to learn across multiple source domains. In this work, we propose a novel gradient optimization strategy named gradient calibration (GradCa) which optimizes the dominant gradients and conflicting gradients without learning extra parameters. The dominant gradients are suppressed for addressing to be inclined to the optimization directions of specific domains. Then two strategies (average and sign-mask) are designed for further alleviating the conflicting gradients. These types of gradients are effectively calibrated for improving the domain-invariant representation learning. Extension experiments on four benchmark datasets have shown the competitive results and the effectiveness of improving the model generalization with GradCa. [ABSTRACT FROM AUTHOR]

Ultrafine dry powder agents (UDPAs) are the most potential Halon substitutes due to the high extinguishing effectiveness and excellent environmental friendliness. Nevertheless, the method for obtaining minimum extinguishing concentration (MEC) is flawed and inaccurate. An improved method is proposed by developing the special laser measurement system without powder contamination, the concentration calibration test bench and the cup burner with uniform aerosol generating. The results show that the optimum calibration position is 0.35–0.65 m from the inlet in the y-axis coordinate. More accurate mathematical relationships between the transmittance of the laser measurement system and the NaHCO 3 and KHCO 3 powders are established, with the errors less than 12%. Upon reaching MECs in the modified cup burner, the flames pulsate, then fade and eventually die. Based on the phenomenon of the sudden change in the transmittance of the laser measurement system at the moment of flame extinguishment, i.e. the "step shape" curve, a more accurate critical criterion point for the MEC is proposed. The measured MECs for methane flames by NaHCO 3 (32.2 g/m3) are approximately 4 times that of KHCO 3 (8.2 g/m3), consistent with previous studies. The obtained MECs of NaHCO 3 and KHCO 3 for kerosene flame are verified by 1 m3 total flooding fire extinguishing experiments. The improved method may guide the design of industry fire protection systems based on UDPAs to achieve process safety. [Display omitted] [ABSTRACT FROM AUTHOR]

The ability to precisely constrain the physical–chemical conditions of serpentinization, such as temperature and fluid source, is limited by the accuracy of calibrations for oxygen isotope fractionation between serpentine and water – i.e., 1000 lnα (serpentine-water) – which disagree by up to 20‰ when extrapolated to T < 200 °C. In this study, we present a new empirical calibration of 1000 lnα (serpentine-water) aiming to improve applications of oxygen isotope geochemistry to very low- T serpentinization (T < 100 °C). We used the high-spatial resolution capabilities of Secondary Ion Mass Spectrometry (SIMS) to analyze oxygen isotope ratios in mineral pairs of calcite + serpentine, quartz + serpentine and talc + serpentine co-crystallized at scales ≤ 50 μm in six serpentinite samples from the Samail ophiolite (Oman). SIMS analysis shows that the mineral pairs are relatively homogenous in oxygen isotope ratios with variability in δ18O values ≤2‰ (2 s). Clumped isotope thermometry and petrological constraints indicate crystallization temperatures from 25 to 100 °C for the investigated samples. These independent constraints on temperature allowed us to derive 1000 lnα (serpentine-water) by combining Δ18O (quartz-serpentine) , Δ18O (talc-serpentine) and Δ18O (calcite-serpentine) measured by SIMS in the investigated samples with Δ18O (quartz-water) , Δ18O (talc-water) and Δ18O (calcite-water) calibrations available in the literature. Our empirical calibration of 1000 lnα (serpentine-water) = 1.04 ± 0.20 (2SE) × 106/ T 2 (T in K), from T = 25 to 100 °C, is within uncertainty of former high-temperature empirical calibrations extrapolated to T < 100 °C and matches experimental calibrations when extrapolated to T = 250 °C. The new serpentine-water calibration enables more accurate reconstructions of fluid-rock interactions occurring during low-temperature serpentinization processes in various tectonic settings. [ABSTRACT FROM AUTHOR]