instrumentation

This study addresses the limitations of traditional fixed-interval calibration, which neglects operational condition–induced variations in sensor drift rates and consequently risks either excessive resource consumption or non-compliance. The work reframes calibration scheduling as a predictive maintenance problem, formally casting it as a joint optimization task integrating time-series forecasting and risk-aware decision-making. A compact Transformer architecture is proposed, coupled with quantile regression to predict Time-to-Drift (TTD) and enable an uncertainty-aware calibration policy that enhances robustness. Evaluated on a modified NASA C-MAPSS FD001 dataset, the method achieves state-of-the-art point prediction accuracy and significantly reduces violation rates under high-noise conditions, outperforming both fixed-interval and reactive strategies in terms of calibration cost efficiency.

Early-stage engineering design often neglects sensor integration, treating signal pathways and data flows as secondary considerations rather than core design dimensions. Method: This paper proposes a “sensor-native” Digital Systems Design (DSD) paradigm that intrinsically embeds sensing capabilities at the conceptual design stage. DSD holistically integrates multimodal sensor selection and placement optimization, embedded signal conditioning, edge–cloud collaborative data architecture, and real-time digital twin simulation. Unlike conventional “retrofitted sensing,” DSD treats the signal chain and data flow as first-class design variables, enabling end-to-end data闭环 across design, operation, and maintenance. Contribution/Results: Experiments demonstrate that DSD improves early-design predictability by over 40%, supports dynamic iterative optimization and predictive maintenance, and has been validated in mechanical systems and intelligent equipment education as well as prototype development.

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.

Quantum software testing presents unique challenges not addressed by classical software engineering practices, yet its professional scope and competency requirements remain poorly defined. Method: This study systematically analyzes job postings from 110 quantum hardware and software enterprises, integrating semantic role labeling, latent Dirichlet allocation (LDA)-based topic modeling, and cross-domain skill mapping to empirically characterize the profession. Contribution/Results: We propose the first empirically grounded occupational definition of quantum software testing, formalized as a three-dimensional competency framework—“calibration, control, and hybrid verification.” The framework identifies three core competencies: programming automation, quantum device literacy, and interdisciplinary collaboration. This work bridges the epistemic gap between industry demand and academic training, providing empirically validated foundations for curriculum design, workforce standardization, and career pathway development in quantum software engineering.

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.

Current vision-language models struggle to accurately interpret high-frequency pointer oscillations and trajectories in dynamic analog instruments, failing to meet metrology’s stringent requirements for traceability and reliability. This work introduces IEEE/ISO metrological standards into the evaluation framework for vision-language models, constructing a video dataset encompassing diverse instrument types and motion speeds. Leveraging video sequence analysis, zero-shot reasoning, uncertainty quantification, and multimodal fusion techniques, the study systematically evaluates state-of-the-art models such as GPT-5 and Gemini 3. Experimental results demonstrate that existing models cannot reliably interpret dynamic pointer behavior, lack the performance necessary to function as trustworthy synthetic instruments, and are therefore unsuitable for deployment in safety-critical industrial monitoring applications.

Existing agent evaluation benchmarks predominantly focus on virtual software interactions and fail to assess the multimodal interface coordination and feedback-driven parameter tuning required for scientific instrument control. This work introduces the first benchmark specifically designed for this domain, presenting a web-based, extensible, secure, and reproducible simulator suite encompassing eight instrument types and 96 subtasks that fully span the workflow from sample loading to result inspection. The benchmark supports flexible task configuration and execution-based evaluation, integrating vision-language models with a dedicated agent framework. Experimental results demonstrate that while current agents can handle structured GUI subtasks, they struggle significantly with feedback-driven operations and long-horizon workflows, thereby validating the benchmark’s necessity and its capacity to expose critical gaps in agent capabilities.

This work addresses the high instruction overhead and software complexity inherent in traditional soft processors, which rely on explicit instructions to read sensors and generate PWM signals within control loops. The authors propose an innovative architecture that maps high-frequency peripheral inputs directly to general-purpose registers and fully offloads PWM generation to dedicated hardware, thereby enabling zero-instruction sensor access and continuous actuation without software intervention. Implemented on a 32-bit, five-stage pipelined MIPS-style RISC soft core, the design incorporates direct peripheral-to-register-file write ports, a single-cycle multiplier, and hardware-based PWM logic. Experimental results demonstrate a reduction in the control loop cycle count from 91 to 43, eliminating five critical instructions; under a 20 ms control frame, the system achieves a real-time margin of 7,300–15,000×, substantially simplifying software and enhancing computational efficiency.