Abstracts:Lubricant quality is a critical factor affecting the healthy operation of rotating equipment such as engines. During prolonged operation or abnormal conditions, lubricants undergo oxidation that compromises equipment safety. Therefore, monitoring lubricant oxidation status is crucial. However, current oxidation detection methods primarily rely on offline laboratory sampling, which is inefficient and unsuitable for real-time monitoring. This study proposes an olfactory-based lubricant oxidation detection method using artificial olfactory sensor arrays to collect volatile component response data during accelerated oxidation cycles. A support vector machine (SVM) model was established to identify oxidation status, achieving an average accuracy rate of 99.6%. Additionally, near-infrared (NIR) absorption spectra were simultaneously acquired. SVM models based on NIR data and integrated AOS-NIR data were developed for comparative analysis with our proposed method, demonstrating the superiority of the AOS-SVM model. The study also investigated the impact of kernel functions on machine learning performance and validated the AOS-SVM model's generalization capability through cross-validation experiments with two different lubricant brands. Consequently, this research provides a practical solution for rapid detection and monitoring of lubricant oxidation status.

Abstracts:In today's digital landscape, personal branding has become a critical imperative for differentiation and success. As early as the late 1990s, management thought leaders such as Tom Peters discussed “The Brand Called You,” fore-shadowing the idea that each of us must manage ourselves as a brand. Today, in the age of Artificial Intelligence (AI), this intuition is more valid than ever: “in the current world, everyone is a brand, and it is essential to develop and know how to promote your own.” This means that our online presence, the content we share, and the way others perceive us collectively form a “personal brand” that must be managed with both academic rigor and strategic intent.

Abstracts:The aging of critical civil infrastructure, such as bridges and buildings, and their vulnerability to damage often requires inspection and predictive maintenance tasks. Physical damage in the structure can represent danger and potentially lead to catastrophic consequences, such as the collapse of the structure. Cracks usually emerge as small fissures in the surface of infrastructure components, making them weak and potentially leading to structural failures. The early detection of such cracks and their continuous monitoring leads to prompt intervention and increases the safety and lifetime of the monitored infrastructure [1]. For this reason, several studies proposed automatic techniques to detect and monitor cracks by analyzing their length, width, depth, and severity [2]–[4]. The advent of Machine Learning (ML), Deep Learning (DL), and computer vision techniques created a new breed of image-based methods to detect cracks and monitor them over time [5]. However, ML/DL methods require a preliminary phase of model training, in which a crack image dataset must be collected and labeled. Moreover, risks significantly limit human accessibility for building inspections, particularly in external areas. Therefore, numerous researchers propose the integration of unmanned aerial vehicles (UAVs) to perform autonomous image collection from a target structure [6]–[8]. Although image-based methods are widely used for detection purposes [4], [5], they have many limitations. Areas that are always obscured prevent the utilization of those techniques. Further, detecting and monitoring cracks through images is challenging in non-uniform structures, such as masonry and cultural heritage (CH) buildings, as well as in large structures due to the extensive surface area needed to analyze. In those scenarios, the joint employment of sensor measurements with image-based techniques has the potential to enhance the accuracy and precision of crack monitoring systems [2].

Abstracts:The main objective of global metrological traceability is to ensure the international recognition of measurement results worldwide. The accuracy, reliability and compatibility of measurements made in laboratories in different countries are linked to national standards through a continuous chain of calibrations with a clearly defined measurement uncertainty. The highest level of global metrological traceability is provided by the International Bureau of Weights and Measures (BIPM) and National Metrology Institutes (NMIs), which use the most accurate standards of units of various quantities for measurements. At lower levels of global metrological traceability, accredited calibration and measurement laboratories play a major role, performing calibration of measuring instruments for various end users in different sectors of national economies. Each level of such traceability has its own significant practical aspects.

Abstracts:Deteriorating water quality threatens people's lives and the balance of ecosystems. Most existing water pollutants identification systems based on multispectral fusion have uncertainty of results caused by some inessential spectral characteristics with weak indications, owing to the neglect of the relative importance of each spectrum to the real pollutants. In this study, a system of water pollutants identification was presented with an innovative approach based on spectral-oriented sequential three-way decisions, in which indications of spectra were taken into consideration. In this system, an automatic scanning system for absorption and three-dimensional (3D) fluorescence spectra simultaneously was designed and built. A spectral importance valuation system was proposed based on misclassification cost and information gain ratio of multispectral information in the decision-making process to measure the contribution of spectral information. Specifically, a cost matrix was designed for the misclassification cost calculation based on the environmental hazards of pollutants, which can prioritize key pollutants. Finally, the best spectral decision sequence was obtained based on the VIKOR algorithm according to the spectral importance index. The effectiveness of this method in identification was assessed through experiments with five typical pollutants, which is better than other methods. The presented system can prioritize significant spectral characteristics with strong indication, which can reduce the uncertainty of water pollutants identification. This system can be applied to the detection of pollutants in natural river waters, providing data support for environmental protection departments.

Abstracts:The widespread diffusion of digital technologies and systems is making computational methods ubiquitous, and this implicitly challenges our understanding of measurement as an empirically-grounded process, as evidenced by the Introduction to the guide to the expression of uncertainty in measurement, which states that “measurement can be described as an experimental or computational process.” Given that models in measurement are unavoidable, does the very distinction between measurement and computation need to be reconsidered? where does measurement end and computation begin? This paper aims to propose some considerations on this issue, in the light of the key question of the role of measurement in the information-laden world we live in.

Abstracts:The IEEE Instrumentation and Measurement Society is a small society representing a vast group of people who perform measurements, sometimes with sophisticated instruments daily. What sets members of the I&M apart is their identity. They identify with the science of measurement and they seek to work with others who identify with the science of measurement. Within our collegial community, some feel driven to improve the science and some to improve the profession. The AdCom of the I&M consists of a wonderfully diverse group of people dedicated to improving the profession as well as the science.

Abstracts:Dear Readers, The Administrative Committee (AdCm) of the IEEE Instrumentation and Measurement Society (IMS) met in Vancouver during October 23–25, 2025. We had a very productive 3 days, discussing and making decision about many issues related to publications, conferences, membership, education, awards, and technical activities and standards.