An international harmonisation protocol has been proposed to establish metrological traceability in ISO 21151:2020. This study aimed to discuss evaluation indicators mentioned in ISO 21151:2020 as well as more evaluation indicators introduced in this study for multiple scenarios, then to perform a clinical validation. Algorithm of Interpolation Calibration Based on Bias Correction of Current Results (ICBCCR) was applied to achieve harmonisation of 15 measurands on different invitro diagnostic measurement devices (IVD MDs). Various evaluation indicators were applied to evaluate the harmonisation effectiveness for single measurand of single IVD MD, single measurand across multiple IVD MDs and multiple measurands from single IVD MD, respectively. A clinical validation for harmonisation effectiveness was further performed with a multi-center real-world cohort and Bhattacharyya distance (DB) served as a robust metric to compare the similarity of two probability distributions of the harmonised results from the 2 IVDMDs. Among 15 measurands selected in this study, when Mean bias combined with S/I mentioned in ISO 21151:2020 were used as evaluation indicators, the acceptance rate of harmonisation results was 85.29 % (58/68). When Mean bias , S/I together with MAX |95%LoAs|, Mean |RD|, Bias% mdml and ρ PB were overall considered, the acceptance rate was 70.59 % (48/68). In real-world validation, DB of harmonised results for FT3 and FT4 with acceptable harmonisation performance were 0.0086 and 0.0175, significantly lower than 0.0438 and 0.0429 for CA19-9 and NT-proBNP with unacceptable harmonisation performance. Harmonisation effectiveness is able to be validated with various evaluation indicators according to different scenarios.

Computer models are used to solve complex problems in many scientific applications, such as nuclear physics and climate research. Markov chain Monte Carlo-based Bayesian calibration of computer models although a popular approach, is computationally expensive. This work proposes a fast and scalable posterior approximation algorithm for Bayesian computer model calibration via Variational Inference. We provide the statistical guarantee of the proposed algorithm in the form of a posterior contraction theorem for the estimated physical process. To this end, we establish that the variational posterior concentrates in ϵ n neighbourhoods of the true physical process under regularity assumptions on the variational family. The main results are shown in the two widely used classes of Gaussian process priors, the Squared Exponential covariance class and the Matérn covariance class. Finally, we provide a simulation study to demonstrate the proposed method's computational efficiency and fidelity compared to the standard Markov chain Monte Carlo method.

A visual-based point localization model achieving super-resolution in measurement is introduced, termed 3-D Moiré projection model. It leverages the 3-D Moiré effect generated by the shadow of the tandem masks fixed in front of a bare camera sensor to produce a highly sensitive response to the location of a target beacon. Using a new approach called casting map, the geometry relationship between the beacon and the Moiré pattern is derived. It is shown that the 3-D Moiré projection model can be interpreted as a pin-hole model with periodically extended scopes, and the frequencies of the mask patterns determine the analogical focal length. The calibration and localization algorithms are presented, and proof-of-concept prototypes are made. In experiments, the prototypes achieved a resolution of over 0.3 billion points using only 7mm of thickness, which is more than 40 times that of pin-hole based counterparts. They also show potential and examples to surpass lens-based cameras, which are highly mature implementations of the pin-hole based model, despite having under one-tenth the thickness, lower cost, underdeveloped hardware and algorithms.

Offset voltage () is a critical parameter of sense amplifiers (SAs), determining both the read reliability and performance of SRAM. This paper proposes SC-DISBSA, a low- SA that combines self-adaptive calibration with dynamic body bias technology. Based on the linear relationship between the transfer gate voltage and , a three-step self-adaptive calibration algorithm is established. Supported by the calibration control circuit, this approach quantitatively calibrates circuit mismatch while dynamic body bias further suppresses remaining variations. Under a 28 nm CMOS process, the standard deviation () of SC-DISBSA remains below 3.1 mV across a 0.7 V to 1.1 V supply range, representing reductions of 49.9% and 69.3% compared to the voltage-latch SA (VLSA) and current-latch SA (CLSA), respectively. At a typical case (TT/0.9 V/27 °C) with a BL differential () of , SC-DISBSA reduces the required bitline discharge delay by 51.7% and improves average read sensing power by 24.9% compared to VLSA. By adopting an non-conventional bitline power supply strategy, SC-DISBSA decreases worst case (FF/1.1 V/125 °C) static power by 36.8% relative to VLSA. Additionally, it reduces gate area by 18.9%. Overall, SC-DISBSA effectively optimizes SRAM read latency and power efficiency.

PPO-PF: An aero-engine performance degradation state calibration algorithm for turbine blade service loadings assessment

In bandwidth-limited and time-varying vehicle–road–cloud cooperative autonomous driving scenarios, real-time transmission and joint inference of high-dimensional multimodal perception data are simultaneously constrained by latency, reliability, and energy consumption. To address these challenges, this article proposes a task-oriented multimodal fusion framework named Multi-Agent Dynamic Diffusion Semantic Communication Network (MA-DDSCNet). On the vehicle side, we design a Task-Guided Multi-Modal Semantic Encoder (TG-MMSE) that performs spatio-temporal alignment, complementary memory gating, and differentiable discrete quantization to compress heterogeneous perception streams from cameras, Light Detection and Ranging (LiDAR), and vehicular state into task-weighted discrete token sequences. A hierarchical distillation scheme is further employed to maintain a unified semantic coordinate system across vehicles, Road Side Units (RSUs), and the cloud. On the communication side, a hierarchical controllable diffusion mechanism adaptively adjusts diffusion noise and time steps according to the importance of object detection, trajectory prediction, and motion planning tasks, as well as link-specific bandwidth budgets. A multi-agent deep scheduler enables collaborative utilization of communication resources among the cloud, RSUs, and vehicles, while an iterative joint semantic decoding and consistency calibration algorithm feeds residuals back into a global memory matrix to suppress semantic drift and yield isomorphic semantic representations at all three layers. Furthermore, we construct a learnable uncertainty-driven multi-objective loss function, combined with a gradient projection strategy, to achieve end-to-end joint optimization of detection, prediction, and planning within a single training loop. Simulation results demonstrate that, compared with baseline methods, MA-DDSCNet achieves average gains of 9.6–18.4% in mean Average Precision (mAP), Average Displacement Error (ADE), Final Displacement Error (FDE), Effective Bit Rate (EBR), and planning safety rate, while reducing the 95th-percentile end-to-end latency to 63 ms, indicating that the proposed framework can significantly enhance the overall performance of semantic perception tasks in complex vehicular networks.

Multispectral imaging (MSI) systems leverage the differing optical absorption properties of oxygenated and deoxygenated haemoglobin across various wavelengths to enable non-invasive dynamic monitoring of relative blood oxygen saturation. Existing systems struggle to meet demands for portable, efficient monitoring due to high costs and slow response times. This study developed a compact MSI system utilising a multi-band light-emitting diode array as its light source. Combined with a triple-isosbestic point calibration algorithm, it rapidly generates pseudo-colour maps of blood oxygen distribution. The system was validated in human finger and rabbit small intestine ischemia-reperfusion models. Following occlusion, relative blood oxygen saturation in the ischaemic regions decreased to 66.3% (finger) and 29.5% (small intestine), both significantly distinct from normal areas. Post-reperfusion, the ischaemic regions exhibited marked recovery with characteristic reperfusion response patterns. These findings demonstrate the system's capability to accurately identify hypoxemic zones, indicating its potential for dynamicin vivoblood oxygen monitoring applications.

Accurate calibration is essential for ensuring the performance of magnetic gradient tensor (MGT) arrays. Existing calibration methods generally rely on mechanical rotation to obtain magnetic responses under multiple orientations. However, for large-scale cubic MGT arrays, rotating the entire array using a high-precision non-magnetic turntable is often costly and impractical, while manual rotation is difficult to control and may introduce array-center offsets. To address these limitations, this paper proposes a rotation-free scalar calibration framework for cubic MGT arrays, in which a tri-axial Helmholtz coil system generates constant-magnitude magnetic fields with randomized orientations while compensating for ambient magnetic drifts. Based on the acquired data, a hierarchical calibration algorithm is developed to estimate sensor-level intrinsic errors and array-level misalignment errors. Experimental results show that the proposed method reduces the joint tensor invariant CT from 9.07×103 nT/m to 11.51 nT/m, corresponding to a 99.87% reduction. In addition, compared with a conventional rotation-based fast calibration method, the proposed framework further decreases the mean and RMS of the joint CT by 62.7% and 63.1%, respectively. These results demonstrate that the proposed framework improves the spatial consistency of the MGT array and provides a practical calibration solution for large-scale MGT array systems.

In this paper, we study the solution to the multi-camera robot-world handeye calibration problem by employing dual quaternions to represent transformation matrices. This approach yields a system of multi-unit dual quaternion equations of the form adzˇd =(−1)σd⊙xˇb,d=1,. . ., p. We propose a novel formulation for the subspace constrained least squares solution to ad ˇzd= ˇ xb to avoid discussing the unknown signs (−1)σd and derive the closed-formexpression for the solution. We prove that when the transformation matrix equation associated with the multi-camera robot-world handeye calibration admits a solution, the corresponding unit dual quaternion obtained from this matrix equation constitutes a subspace constrained least squares solution for the system of multi-unit dual quaternion vector equations. We present an algorithm formulti-camera robot-world hand-eye calibration, using the derived closed-formsubspace constrained least squares solution to the multi-unit dual quaternion equations. We introduce a correction strategy to handle real-world data scenarios where the basic assumption may not hold. Experimental results demonstrate that the proposed subspace constrained least squares solutions exhibit competitive performance compared to state-of-the-artmethods in multi-camera robot-world hand-eye calibration.

Retrofitting existing buildings is widely recognized as a critical strategy for achieving global decarbonization goals. As a part of this effort, several tools have been developed for building retrofit analysis, each offering distinct advantages and limitations. However, the current approaches and tools still lack the capability to generate well-calibrated detailed building energy models that can evaluate both individual and combined energy efficiency measures. Moreover, no existing analysis tool can identify the most cost-optimal combination of retrofit measures through a comprehensive optimization search using different objectives. To address these shortcomings, this paper describes a new Simplified and Automated Building Energy Retrofit (SABER) analysis approach and tool. The SABER tool is a Python-based interactive platform designed to assist users by automatically creating detailed energy models of existing buildings. It incorporates a novel automatic calibration algorithm that adjusts operational schedules using building energy signature characteristics, ensuring accurate model performance. In addition, SABER can assess various building energy efficiency measures using a sequential search technique to determine the most cost-effective retrofit packages. This paper describes the key functionalities of SABER and demonstrates its capabilities through two residential building case studies. By integrating several key features into a unified framework, SABER represents a significant step toward the next generation of building energy retrofit analysis tools that can effectively assist the industry’s transition to a sustainable future.

Accurate and stable extrinsic calibration is the foundation of high-quality fusion sensing and positioning of camera and Light Detection and Ranging (LiDAR). However, traditional targetless calibration methods suffer from limitations such as poor scene adaptability and unstable convergence, which significantly restrict calibration accuracy and robustness in complex environments. Aiming at solving those problems, we propose an online cascade-optimization-based extrinsic calibration method of combining motion trajectory alignment and edge feature alignment. In the initial calibration stage, a hand-eye calibration algorithm is designed by minimizing the residual discrepancies between camera odometry and LiDAR odometry sequences. It establishes a robust initialization for subsequent optimization. Then, in order to extract robust edge line features from sparse point clouds, we employ depth difference and planar edges of point clouds in the optimization process. Subsequently, principal component analysis (PCA) is applied to compute the principal direction of the extracted line features, enabling a decoupled optimization scheme that accounts for directional observability. This approach effectively mitigates the adverse effects of uneven environmental feature distributions. Experimental validation on typical urban datasets demonstrates the method's generalizability and competitive accuracy: rotational parameter errors are constrained within 0.25°, and translational errors are maintained below 0.05 m. This affirms the method's suitability for high-accuracy engineering applications.

Calibration is a critical process for reducing uncertainty in water distribution network hydraulic models (WDN HM). However, features of certain water distribution networks (WDNs), such as oversized pipelines, lead to shallow pressure gradients under normal daily conditions, posing a challenge for effective calibration. This study proposes a calibration methodology using short hydrant trials conducted at night, which increase the pressure gradient in the WDN. The data is resampled to align with hourly consumption patterns. In a unique real-world case study of a WDN zone, we demonstrate the statistically significant superiority of our method compared to calibration based on daily usage. The experimental methodology, inspired by a machine learning cross-validation framework, utilizes four state-of-the-art calibration algorithms, achieving a reduction in absolute error of up to 15% in the best scenario.

To address the accuracy degradation caused by inherent errors in fluxgate magnetometers, this study proposes a Multi-Strategy Hybrid Particle Swarm Optimization (MSPSO) algorithm. This method effectively balances global search scope with local search depth, overcoming the limitation of conventional Particle Swarm Optimization (PSO) algorithms that tend to become trapped in local optima, and achieves high-precision, highly robust magnetometer calibration. Experimental results demonstrate that compared to PSO, modified particle swarm optimization, dynamic hierarchical elite-guided particle swarm optimization, and robust ellipsoid fitting methods, MSPSO reduces the average root mean square error by 73%, 54%, 41%, and 49%, respectively. This work provides a reliable solution for magnetometer calibration.

A novel fast and wide range background timing mismatch calibration algorithm for TIADCs

This article presents the first co-designed MRI imaging and magnetic positioning system for real-time dynamic motion compensation, achieving sub-millimeter tracking accuracy while preserving diagnostic image quality. The core innovation lies in a system-level co-design of an MRI imaging system and a magnetic localization system, featuring a customized receiver IC for processing magnetic signals coupled by the frontend RF coils, enabling artifact-free MRI imaging in dynamic scenarios. This integration enables a median positioning accuracy of 0.66 mm across a 40 × 40 × 50 cm3 field-of-view with a total power consumption of 997 μW. The key innovations include: 1) a time-division multiplexing scheme to enable signal detection from different coils while achieving spectral isolation between 1.4 MHz positioning signals and MRI Larmor frequencies through FPGA-synchronized blanking; 2) a dynamic calibration algorithm fusing magnetic tracking data with multi-frame MRI imaging, reducing spatial blur radius by 40% via weighted averaging; 3) an MRI-optimized Levenberg-Marquardt algorithm incorporating dynamic magnetic beacon weighting and spatial constraints, improving localization accuracy by 53% versus conventional algorithm. The system utilizes planar magnetic beacons with a dimension of 3 × 3 cm2, reducing spatial occupancy compared to prior designs. This work bridges critical gaps between high-precision tracking and artifact-free MRI, enabling real-time imaging of non-autonomous motion and respiratory motion compensation, representing a paradigm shift for MRI-guided interventions.

BackgroundElectrocorticographic (ECoG) speech brain-computer interfaces (BCIs) show promise for restoring communication in amyotrophic lateral sclerosis (ALS), but the long-term stability of speech-related neural signals and decoding performance during disease progression remains unclear. We tracked signal characteristics and decoding over 25 months in a participant with ALS to determine how high-gamma (HG, 70–170 Hz) activity changes over time and whether these changes affect offline speech decoding.MethodsWe implanted two 8×8 subdural ECoG grids over left sensorimotor cortex (SMC) in a participant with slowly progressive bulbar variant ALS. Across 25 months, the participant performed an overt syllable-repetition task (12 consonant-vowel tokens) during simultaneous ECoG and audio recording. We quantified HG activation ratio (ActR), spectral signal-to-noise ratio (SNR; HG/HF, where HF = 300–499 Hz), and peak z-scored HG responses. Speech acoustics were evaluated using first/second formants (F1/F2) and the triangular vowel space area (tVSA). Offline EEGNet-based decoders were assessed in two stages: models trained on post-implant months 1–6 were tested on months 7–25, while models trained on stabilized data (months 7–11) were tested on the remaining period (months 12–25). Electrode-level saliency assessed spatial contributions to decoding.ResultsAcoustic analyses showed a significant reduction in tVSA over two years (-44.6 Hz²/day;P < 10−⁷), consistent with mild intelligibility decline. Neural metrics (ActR and SNR) followed a biphasic trajectory: increasing during the first 6 months, after which ActR stabilized (0.041%/day;P = 0.13), and SNR declined gradually (-0.46%/day,P < 10− 4). The model trained on months 1–6 achieved 55.7% accuracy (chance: 8.33%), but performance declined over time (-0.019%/day;P = 2.1×10−⁴). Conversely, the model trained on months 7–11 achieved higher accuracy (65.9%) on subsequent data with no significant temporal decline (P = 0.23).ConclusionsSpeech-related HG features exhibited an initial unstable period followed by a long-term gradual SNR reduction, potentially reflecting disease progression. Models trained after signal stabilization generalized robustly to data recorded over a year later. These findings confirm that despite reduced absolute HG power and mild acoustic degradation of speech, cortical features remain stable enough to support durable ECoG speech BCIs without frequent recalibration. These findings will motivate future adaptive calibration algorithms that account for slow signal changes while leveraging stable spatial representations in ventral SMC.ClinicalTrials.gov IdentifierNCT03567213

The Digital Twin Road initiative aims to improve road infrastructure monitoring and maintenance through real-time data integration, computational modelling, and predictive analytics. However, the reliability of such digital twins is significantly affected by uncertainties in both material properties and sensor data. This paper provides a comprehensive evaluation of uncertainty sources within the CRC/TRR 339 – Digital Twin Road project. Material-related uncertainties stem from the intrinsic variability of asphalt, concrete, and soil due to production methods, environmental exposure, and construction practices. These include aleatoric uncertainties from natural variability and epistemic uncertainties from knowledge gaps. Sensor-related uncertainties arise from limitations in sensor technology, calibration, environmental influences, and data processing algorithms. Detailed case studies, including weigh-in-motion systems, drone-mounted laser scanning, and smart materials such as mineral-impregnated carbon-fibre (MCF) reinforced low-clinker concrete, illustrate how uncertainties accumulate and propagate across the Digital Twin Road framework. The classification of uncertainty into aleatoric and epistemic categories is discussed. Understanding these uncertainty sources is essential for improving the predictive accuracy and ensuring a robust representation of real-world road systems.

Kinematically redundant parallel mechanisms (KR-PMs) exhibit complex error coupling and error amplification due to the existence of redundant branches, which poses enormous challenges to kinematic calibration. To address the key issue of insufficient excitation of error parameters caused by measurement noise and local convergence in traditional pose optimization algorithms, this study proposes a hybrid GOA-IOOPS measurement pose optimization algorithm for the kinematic calibration of the 2PUPR-PRPU KR-PM. First, based on the error mapping Jacobian matrix, a kinematic error model integrating error parameters of redundant branches is established using the closed-loop vector method and numerical differentiation. Innovatively, the grasshopper optimization algorithm (GOA) is fused with the iterative one-by-one pose search (IOOPS) algorithm: GOA undertakes global exploration to avoid local optima, while IOOPS performs local refinement to maximize the observability index O 3 . For parameter identification, a regularized nonlinear least squares method based on the Levenberg-Marquardt (LM) algorithm is adopted to balance convergence speed and robustness. Numerical simulations confirm that the GOA-IOOPS algorithm outperforms the traditional IOOPS algorithm and random selection method. Prototype experiments verify that the proposed calibration method significantly improves the positioning accuracy of the 2PUPR-PRPU KR-PM.

In the manufacturing process of advanced integrated circuits, electron beam inspection equipment is crucial for yield assurance, while vibration poses a core challenge affecting its precision and speed. Vibrations in production line equipment are mostly multi-frequency; However, research findings in this field remain limited. Moreover, existing compensation schemes often struggle to meet industrial-grade precision and real-time requirements. This paper presents the design and implementation of a high-speed electron beam vibration compensation system based on positioning. The system incorporates state-of-the-art laser positioning and electrostatic scanning deflectors, and features an integrated signal processing and compensation signal output module. The study involved improvements and optimizations to the positioning processing analysis and compensation module, control software and algorithms, and calibration software and algorithms, demonstrating superior performance compared to existing methods. System validation data demonstrates that the proposed scheme effectively compensates for both single-frequency and multi-frequency disturbances at frequencies below 200 Hz, achieving an average attenuation of 50% to 90% and a repetitive compensation accuracy of less than 0.3 nm. These metrics meet the industrial application requirements for electron beam inspection equipment. The overall error in long-term repeatability tests complies with the stability demands of industrial production lines, confirming its practical applicability in production environments.

During the clinical extracorporeal life support process, noninvasive optical measurements of hematocrit (HCT) and blood oxygen saturation (SO2) often suffer from limited accuracy and a need for frequent calibration, particularly for HCT, and cannot yet provide truly continuous, high-accuracy monitoring. To address these limitations, a hybrid calibration algorithm combining the reproducing kernel Hilbert space (RKHS) with partial least squares regression (PLSR) is proposed. The algorithm first constructs a physically interpretable baseline model via the modified Lambert-Beer law and then integrates an RKHS-based anomaly suppression mechanism to inversely correct outlier interference in the original optical signals. This framework ultimately leverages the PLSR algorithm to conduct multiparameter joint calibrations that couple multiwavelength light intensity features with physical prediction outcomes. This hybrid framework effectively reduces the recalibration frequency while increasing the detection accuracy. A custom-designed multiwavelength inline optical monitoring system was developed and validated using blood samples with varying HCT and SO2 levels. The experimental results demonstrate that, compared with conventional PLSR, the RKHS-PLSR algorithm reduces the root mean square errors (RMSE) of the HCT and SO2 measurements to 1.41% and 1.23%, respectively. These results demonstrate that this innovative fusion algorithm, which synergizes physical correction with data calibration, significantly enhances the accuracy and long-term stability of continuous optical monitoring within extracorporeal circuits, reduces the recalibration frequency, and improves clinical reliability.