precision measurement

This paper addresses the reliability of calibration evaluation for machine learning models, identifying systematic biases in the widely used Expected Calibration Error (ECE) under distributional shift and varying binning strategies. Methodologically, it clarifies the logical hierarchy among multi-level calibration definitions, and systematically exposes ECE’s limitations through visualization, binning-based statistical analysis, and theoretical derivation—demonstrating its failure to satisfy key requirements of robustness and consistency in calibration assessment. Building on this critique, the paper introduces and explicates emerging calibration paradigms—including distribution-level and instance-level calibration—alongside their corresponding evaluation methodologies, thereby constructing a rigorous, interpretable, and practice-oriented calibration knowledge framework. The results equip researchers with principled guidance for selecting appropriate evaluation metrics and advance calibration assessment from ad hoc, heuristic practices toward standardization and formalization.

This study addresses the unreliable estimation of repeatability, between-laboratory, and reproducibility variance components under ISO 5725 standards when sample sizes are small or variance structures are extreme. To overcome this limitation, the authors propose a tailored Bootstrap resampling strategy adapted to a one-way random effects model. The approach refines point estimates by adjusting within-laboratory resampling and constructs confidence intervals via a two-stage resampling scheme integrated with bias-corrected and accelerated (BCa) techniques. Extensive simulations and validation using real data from ISO 5725-4 demonstrate that the proposed method substantially improves estimation accuracy and confidence interval coverage. It yields reliable, near-nominal or conservatively valid inferences for small- to moderate-sized experiments and clearly delineates optimal strategies across different practical scenarios.

In novel sensor development, conventional characterization and parameter optimization heavily rely on expert knowledge and are time-consuming, forming a critical bottleneck. This paper introduces the first closed-loop Bayesian optimization framework specifically designed for sensor characteristic characterization. By integrating real-time measurement feedback with a Gaussian process surrogate model, the method enables fully automated, human-in-the-loop-free exploration of high-dimensional parameter spaces and identification of optimal operating points. It eliminates manual trial-and-error, significantly improving optimization efficiency and reproducibility. Validated on a low-noise CCD sensor, the approach completes full-parameter-space characterization and optimization within two days—accelerating the process by over an order of magnitude compared to conventional methods—while maintaining comparable accuracy. This work establishes a generalizable, automation-first paradigm for intelligent instrument development.

In industrial quality inspection, anomaly detection suffers from poor robustness due to high noise levels and sparse defective samples. To address this, we propose Iterative Refinement of Pseudo-labels (IRP), a self-supervised method that alternately evaluates sample credibility and removes misleading instances under feature-space consistency constraints—effectively purifying the training set dynamically without human annotations and generating high-fidelity self-supervised signals. IRP introduces the novel paradigm of “iterative data refinement,” significantly enhancing model robustness against label noise and cross-domain generalization capability. Evaluated on KSDD2 and MVTec AD benchmarks, IRP consistently outperforms existing unsupervised and self-supervised methods. Notably, under high-noise conditions, it achieves substantial improvements in detection accuracy and reduces false positive rates by over 25%.

This study addresses the ambiguity in defining the Research Software Engineer (RSE) role and the absence of standardized competency criteria. Employing a Delphi method combined with multi-institutional case studies—and integrating educational competency mapping with career development theory—it constructs the first cross-institutional, hierarchical, and scalable RSE competency framework. The framework innovatively proposes a four-dimensional competency model encompassing technical proficiency, collaborative practice, research engagement, and research ethics. It systematically delineates core responsibilities, foundational competencies, professional values, and career progression pathways for RSEs, supporting role evolution and professionalization. The resulting framework has been established as an internationally recognized competency benchmark, formally adopted by multiple national RSE associations for training and certification, and has driven curriculum reform in RSE-related programs across over ten universities worldwide.

This study addresses the quality control challenges in additive manufacturing arising from its layer-by-layer fabrication process and high degree of customization. It presents the first systematic adaptation of the Six Sigma DMAIC methodology tailored to this context. By integrating multi-source sensing and measurement data across the entire manufacturing chain—including materials, design, process parameters, and post-processing—the work employs advanced techniques such as deep learning, machine learning, design of experiments, simulation, ontological analysis, and network science to model the complex relationships among design inputs, process variations, and final part quality. The proposed optimization framework enables real-time anomaly detection and simultaneously optimizes lead time and energy consumption, thereby significantly enhancing the quality stability and process controllability of additive manufacturing systems.

This study addresses the persistent ambiguity in classifying repeated measures experimental designs, which often arises from conceptual confusion. To resolve this issue, the authors systematically clarify the core characteristics of such designs and propose a novel classification framework grounded in experimental units and randomization strategies. For the first time in this context, Hasse diagrams are introduced to visually represent the hierarchical structure of these designs. This approach effectively distinguishes among various types of repeated measures designs, eliminates terminological ambiguities, and substantially enhances both the rigor and interpretability of experimental planning and reporting.

This study addresses the challenge of disentangling sources of inter-laboratory variability—specifically baseline offsets versus differences in sensitivity—in multi-laboratory assessments of linear dose–response relationships. To this end, the authors propose a precision evaluation framework based on linear mixed-effects models, integrating analysis of variance, F-tests, and ISO 5725 standards to define and estimate repeatability and between-laboratory variance components. Overall measurement precision is quantified via average dose-specific variance. Under a fully balanced design, the framework yields an exact decomposition of total sum of squares and closed-form ANOVA estimators, overcoming the limitation of conventional fixed-effects models that detect only the presence of differences without identifying their origin. The approach was successfully applied to bronchoalveolar lavage fluid data from a rat intratracheal instillation study involving nanomaterials, effectively distinguishing the sources of observed variability.

This study addresses the challenge of accurately assessing univariate process capability indices (PCIs) in manufacturing under atypical conditions where standard assumptions—particularly normality—are often violated. To overcome this limitation, the authors propose a systematic and unified PCI analysis workflow that integrates outlier detection, normality assessment, optimal distribution fitting, and corresponding PCI computation tailored to diverse distributional assumptions and data characteristics. By offering a structured and actionable framework, the approach enables practitioners to select the most appropriate PCI based on actual process behavior, thereby significantly enhancing both the accuracy and applicability of capability evaluations. This methodology provides a practical and streamlined guide for quality control and process improvement in real-world industrial settings where data frequently deviate from idealized statistical conditions.