The mining sector is undergoing a profound shift as Industry 4.0 technologies—IoT, AI, robotics—reshape operational planning and execution. This study explores the integration of Mine IoT to modernize mining practices, emphasizing metrology-driven advancements in real-time monitoring, predictive maintenance, and autonomous systems. Accurate measurement and standardized data are central to improving efficiency, enhancing safety, and advancing sustainability goals. A smart ventilation case study illustrates how RAMI 4.0, combined with digital metrology, enhances interoperability, enables seamless integration, and supports scalable, adaptable systems for improved energy use, safety, and long-term resilience. Metrological traceability is embedded throughout system layers to support interoperability and long-term performance. RAMI 4.0’s structured framework ensures traceable data from calibrated sensors and uncertainty-aware analytics, aiding reliable decision-making and regulatory compliance. The study also highlights the role of standardization in facilitating communication across devices, platforms, and vendors. Achieving these outcomes requires strategic planning, skilled personnel, and cross-sector collaboration.

A data driven methodology for sensor fault diagnosis in sensor network using principal component analysis (PCA) of coherence spectrum and convolutional neural network (CNN) deep learning is proposed. The methodology was evaluated with the measurement data of a sensor network for ambient relative humidity (RH) monitoring of a chemical laboratory. The results demonstrated accuracy up to 99% for sensor fault diagnosis in the sensor network functioning across a large spectrum of frequencies for environmental monitoring.

This work explores the automation of a photometry laboratory by integrating LabVIEW as the primary control and data acquisition platform, coupled with a Python-based artificial intelligence (AI) algorithm for intelligent analysis and optimization. The system is designed to measure and analyze critical photometric parameters such as luminous intensity, luminous responsivity, illuminance, and luminous flux—all of which are essential for evaluating the performance of light sources like tungsten halogen lamps, LEDs, displays, and optical sensors. By automating traditionally manual processes, the system enhances both the accuracy and efficiency of photometric testing. LabVIEW provides an intuitive graphical interface to control instruments, log data, and visualize results in real time. Meanwhile, the Python AI component improves decision-making by learning from historical data, predicting trends, and detecting anomalies or inconsistencies in measurements. The AI model optimizes calibration routines, reduces human error, and enables adaptive testing scenarios based on environmental conditions or device behavior. Together, LabVIEW and Python form a powerful, flexible platform that brings modern intelligence to photometry labs, making them faster, smarter, and more reliable. This approach demonstrates how combining industrial automation tools with machine learning can revolutionize traditional optical testing environments, paving the way for advanced lighting technologies and robust quality assurance systems.

Key and supplementary comparisons are a core component of the Mutual Recognition Arrangement. Digitalisation of data processing steps has the potential to reduce workloads of metrologists and to improve consistency of outcomes. The Digital Metrological Expert is an open-source software tool for automating the comparison data analysis and report creation. It is designed for use by metrology experts and for an integration into end-to-end digital workflows for machines. Automation is supported by applying FAIR principles for machine-actionable data and APIs that are based on the SI. The tool can also utilise digital standards from the quality infrastructure such as Digital Calibration Certificates and Smart Standards to fulfil its work.

Quantum Key Distribution (QKD) offers secure communication by leveraging core principles of quantum mechanics. Characterization of QKD system is fundamental for comparing the experimental results provided by researchers. Unfortunately, there is no standardized or unified approach; therefore, different researchers present results that provide heterogeneous information, making comparisons difficult. A systematic analysis of the literature is necessary to identify common approaches from a metrological perspective. This paper provides an overview of measurements used to characterize quantum channels, with a focus on parameters such as attenuation, polarization effects, and timing stability. To support this analysis, real-world case studies are examined.