Search Results (122)

Measuring the small tree trunk variations during the day–night cycle, seasonal cycles, as well as those caused by the plant’s growth and health regime is a very important action in horticulture or forestry because by analyzing the collected data, assessments can be made on the health of the trees, but also on the climatic conditions and changes in a certain region. This can be performed with devices called dendrometers. This paper presents a new type of approach to these measurement types in which the trunk volume changes are highly sensitively converted into the axial stress on sensitive elements made of magnetic materials in wire form in which the giant stress impedance effect occurs. Finally, by electronic processing of the signals provided by the sensitive elements, digital words with a decimal value proportional to the diameter variations are obtained. This paper presents the operating principle, the constructive details and the experimental results obtained by testing the device in the laboratory and in-field. The proposed dendrometer, compared to those available commercially, has the advantage of good resolution and sensitivity, good immunity to temperature variations, the possibility of transmitting the result remotely, robustness and low price. Some metrological parameters obtained from the experimental testing are the following: resolution 1.6 µm, linearity 1.4%, measurement range 0 to 5 mm, temperature coefficient 0.012%/°C. Full article

Accurate measurement uncertainty quantification and its propagation are critical for dimensional compliance in precision manufacturing. This study presents a novel framework that examines the evolution of measurement error along the axial length of CNC-turned components, focusing on spatial and material-specific factors. A systematic experimental comparison was conducted between a manual Digital Vernier Caliper (DVC) and an automated Coordinate Measuring Machine (CMM) using five engineering materials: Aluminum Alloy 6061, Brass C26000, Bronze C51000, Carbon Steel 1020 Annealed, and Stainless Steel 304 Annealed. Dimensional measurements were taken from five consecutive machining zones to capture localized metrological behaviors. The results indicated that the CMM consistently achieved lower expanded uncertainty (as low as 0.00166 mm) and minimal propagated uncertainties (≤0.0038 mm), regardless of material hardness or cutting position. In contrast, the DVC demonstrated significantly higher uncertainty (up to 0.03333 mm) and propagated errors exceeding 0.035 mm, particularly in harder materials and unsupported zones affected by surface degradation and fixture variability. Root-sum-square (RSS) modeling confirmed that manual measurements are more prone to operator-induced error amplification. While the DVC sometimes recorded lower absolute errors, its substantial uncertainty margins hampered measurement reliability. To statistically validate these findings, a two-way ANOVA was performed, confirming that both the measurement system and machining zone significantly impacted uncertainty, as well as their interaction. These results emphasize the importance of material-informed and zone-sensitive metrology, highlighting the advantages of automated systems in sustaining measurement repeatability and dimensional stability in high-precision applications. Full article

Traditional Chinese medicine (TCM), as a vital component of traditional healthcare systems, relies heavily on its chemical constituents, which serve as a bridge between ancient therapeutic theories and modern biomedical science. Efficient access to compound-related information is crucial for promoting the modernization and scientific understanding of TCM. However, existing approaches primarily rely on fragmented databases and literature-based retrieval methods, which suffer from low intelligence, poor data integration, and limited retrieval efficiency.This study presents a novel AI agent-based retrieval system tailored for compound information in TCM. The core innovation of the system lies in its hybrid retrieval-augmented generation framework, which seamlessly combines structured database queries with semantic vector retrieval. Furthermore, it integrates knowledge from three complementary sources—locally built knowledge bases, domain-specific APIs, and open web search—allowing for comprehensive coverage and adaptive handling of diverse natural language queries. Experiments conducted on a benchmark dataset of 150 compound-related queries demonstrate that the system achieves a peak accuracy of 96.67% across multiple mainstream LLMs. Ablation studies further reveal that removing either the hybrid RAG or multi-source knowledge module leads to a notable accuracy decline, while the full system outperforms typical RAG baselines by over 25%. These results confirm the effectiveness and robustness of the proposed architecture in TCM compound retrieval, and highlight the advantage of combining structured matching with dynamic knowledge access in specialized biomedical applications. Full article

Accurate and non-invasive optical metrology of transparent objects is essential in several commercial and research applications, from fluid dynamics to biomedical imaging. In this work, a digital holography approach for thickness measurement of glass plate and temperature mapping of candle flame is presented that leverages a double-field-of-view (FOV) configuration combined with high spatial bandwidth utilization (SBU). By capturing a multiplexed hologram from two distinct objects in a single shot, the system overcomes the limitations inherent to single-view holography, enabling more comprehensive object information of thickness measurement and temperature-induced refractive index variations. The method integrates double-FOV digital holography with high SBU, allowing for accurate surface profiling and mapping of complex optical path length changes caused by temperature gradients. The technique exhibits strong potential for applications in the glass industry and microfluidic thermometry, convection analysis, and combustion diagnostics, where precise thermal field measurements are crucial. This study introduces an efficient holographic framework that advances the capabilities of non-contact measurement applications by integrating double-FOV acquisition into a single shot with enhanced spatial bandwidth exploitation. The approach sets the groundwork for real-time, volumetric thermal imaging and expands the applicability of digital holography in both research and industrial settings. Full article

Conventional methods for transporting biological materials typically use dry ice or ice for preservation but often overlook important aspects of temperature monitoring and metrological control. These methods generally do not include temperature sensors to track the thermal conditions of the materials during transport, nor do they apply essential metrological practices such as regular sensor calibration and stability checks. This lack of precise monitoring poses significant risks to the integrity of temperature-sensitive biological materials. This study presents a statistical analysis of DS18B20 digital temperature sensors used in an experimental refrigeration system based on thermoelectric modules. The aim was to verify sensor consistency and investigate sources of measurement error. The research was motivated by a prior phase of study, which revealed significant discrepancies of approximately 3 °C between experimental temperature data and numerical simulations. To investigate a potential cause, we conducted a case study analyzing measurements from three identical temperature sensors (same model, brand, and manufacturer). Statistical analyses included ANOVA (analysis of variance) and Tukey’s test with a 95% confidence interval. Since the data did not follow a normal distribution (p-value < 0.05), non-parametric methods such as the Kruskal–Wallis and Levene’s procedures were also applied. The results showed that all sensors recorded statistically significant different temperature values (p-value < 0.05). Although experimental conditions were kept consistent, temperature differences of up to 0.37 °C were observed between sensors. This finding demonstrates an inherent inter-sensor variability that, while within manufacturer specifications, represents a source of systematic error that can contribute to larger discrepancies in complex systems, highlighting the need for individual calibration. Full article

Attaining dimensional accuracy in CNC-machined parts is essential for high-precision manufacturing, especially when working with materials that exhibit varying mechanical and thermal characteristics. This research provides a thorough experimental comparison of manual and automated metrological systems, specifically the Digital Vernier Caliper (DVC) and Coordinate Measuring Machine (CMM), as applied to five different engineering alloys through five progressively machined axial zones. The study assesses absolute error, relative error, standard deviation, and measurement repeatability, factoring in material hardness, thermal conductivity, and surface changes due to machining. The results indicate that DVC performance is significantly affected by operator input and surface irregularities, with standard deviations reaching 0.03333 mm for Bronze C51000 and relative errors surpassing 1.02% in the initial zones. Although DVC occasionally showed lower absolute errors (e.g., 0.206 mm for Aluminum 6061), these advantages were countered by greater uncertainty and poor repeatability. In comparison, CMM demonstrated enhanced precision and consistency across all materials, with standard deviations below 0.0035 mm and relative errors being neatly within the 0.005–0.015% range, even with challenging alloys like Stainless Steel 304. Furthermore, a Principal Component Analysis (PCA) was conducted to identify underlying measurement–property relationships. The PCA highlighted clear groupings based on sensitivity to error in manual versus automated methods, facilitating predictive classification of materials according to their metrological reliability. The introduction of multivariate modeling also establishes a new framework for intelligent metrology selection based on material characteristics and machining responses. These results advocate for using CMM in applications requiring precise tolerances in the aerospace, biomedical, and high-end tooling sectors, while suggesting that DVC can serve as an auxiliary tool for less critical evaluations. This study provides practical recommendations for aligning measurement techniques with Industry 4.0’s needs for accuracy, reliability, and data-driven quality assurance. Full article

Metrological traceability is essential for ensuring the accuracy of measurement results and enabling a comparison of results to support decision-making in society. This paper explores a structured approach to modelling traceability chains, focusing on the role of residual measurement errors and their impact on measurement accuracy. This work emphasises a scientific description of these errors as physical quantities. By adopting a simple modelling framework grounded in physical principles, the paper offers a formal way to account for the effects of errors through an entire traceability chain, from primary reference standards to end users. Real-world examples from microwave and optical metrology highlight the effectiveness of this rigorous modelling approach. Additionally, to further advance digital systems development in metrology, the paper advocates a formal semantic structure for modelling, based on principles of Model-Driven Architecture. This architectural approach will enhance the clarity of metrological practices and support ongoing efforts toward the digital transformation of international metrology infrastructure. Full article

This study investigates the reliability of Francis turbines and highlights the critical need for an improved deformation monitoring system for bolts that fasten a hydroturbine head-cover to its casing. During different operational stages of the hydraulic unit, such as start-up, partial load, and full load, the hydroturbine head-cover and its fastening bolts are subjected to static and cyclic loads. The loads generate vibrations and different deformations that must be monitored. Although various measuring instruments, such as vibration sensors and accelerometers, have been developed to monitor hydroturbine vibrations, only two systems—KM-Delta-8-CM and PTK KM-Delta—are currently applied to measure fastening bolt deformation. Furthermore, only one system, SKDS-SISH, was found to monitor the forces inducing this deformation. After analysis, it is evident that the described systems for monitoring the deformation of the fastening bolts do not guarantee the trustworthiness of the measuring sensors and there is a need for their improvement. The implementation of a self-checking function (including metrological features), the development of a digital twin of the sensor, and the application of technologies based on artificial intelligence could solve this problem. Full article

To push the boundaries of confocal microscopy beyond its current limitations by predicting sensor responses for complex surface geometries, we build digital twins using three rigorous models, the finite element method (FEM), Fourier modal method (FMM), and boundary element method (BEM) to model light–surface interactions. Fourier optics are then used to calculate the sensor signals at the back focal plane and at the detector. A 3D illumination model is applied to 2D periodic structures for FEM and FMM modelings and to 3D aperiodic structures for BEM modeling. The lateral and vertical scanning processes of the confocal microscope are achieved through focal-point shifts of the objective, using plane-wave illuminations with varying incident and azimuthal angles. This approach reduces the need for repeated, time-intensive rigorous simulations of the scattering process when a fine scanning is desired. Furthermore, we give an in-depth description of a novel confocal microscopy method using FMM. For rectangular grating surfaces, the three models yield identical, highly accurate results, as validated by measured results. Simulations of the instrument transfer function, tilted gratings, and gratings with edge rounding offer insights into some experimentally observed effects. This research therefore provides a promising approach for correcting systematic errors in confocal microscopy. Full article

Mass spectrometry (MS) data present challenges for machine learning (ML) classification due to their high dimensionality, complex feature distributions, batch effects, and intensity discrepancies, often hindering model generalization and efficiency. To address these issues, this study introduces the Efficient Quick 1D Lite Convolutional Neural Network (CNN) Ensemble Classifier (EQLC-EC), integrating 1D convolutional networks with reshape layers and dual voting mechanisms for enhanced feature representation and classification performance. Validation was performed on five publicly available MS datasets, each featured in high-impact publications. EQLC-EC underwent comprehensive evaluation against classical machine learning (ML) models (e.g., support vector machine (SVM), random forest) and the leading deep learning methods reported in these studies. EQLC-EC demonstrated dataset-specific improvements, including enhanced classification accuracy (1–5% increase) and reduced standard deviation (1–10% reduction). Performance differences between soft and hard voting mechanisms were negligible (<1% variation in accuracy and standard deviation). EQLC-EC presents a powerful and efficient tool for MS data analysis with potential applications across metabolomics and proteomics. Full article

The development and study of Smart Grid technologies rely heavily on high-fidelity data from Phasor Measurement Units (PMUs). However, the scarcity of real-world PMU data due to privacy, security, and variability issues poses significant challenges to researchers, developers, and related industries. To address these challenges, this article introduces the bases for a digital metrology framework, focusing on a newly designed and developed synthetic PMU data generator, that is both metrologically accurate and easy to adapt to various grid configurations for data generation from point-on-wave (PoW) data. This initial phase for a Smart Grid research framework aligns with Open Science principles, ensuring that the generated data are Findable, Accessible, Interoperable, and Reusable (FAIR). By embracing these principles, the generated synthetic data not only facilitate collaboration for Smart Grid research but also ensure their easy integration into existing Smart Grid simulation environments. Additionally, the proposed digital metrology framework for Smart Grid research will provide a robust platform for simulating real-world scenarios, such as grid stability, fault detection, and optimization. Through this open science approach, future digital metrology frameworks can support the acceleration of research and development, overcoming current limitations, e.g., lack of significant amounts of real-world scenarios by PMU data. This article also presents an initial case study for situational awareness and control systems, demonstrating the potential for future Smart Grid research framework and its direct real-world impact. All research outcomes are provided to highlight future opportunities for reusability and collaborations by a novel approach for research on sensor network metrology. Full article

The intensification of global climate change and the resulting environmental challenges have made carbon emission control a focal point of global attention. As one of the major sources of carbon emissions, the power sector plays a critical role in accurately quantifying CO₂ emissions, which is essential for formulating effective emission reduction policies and action plans. The average CO₂ emission factor of electricity (AEF), as a key parameter, is widely used in calculating indirect carbon emissions from purchased electricity in various industries. The International Energy Agency (IEA) reported an AEF of 0.6093 kgCO₂/kWh for China in 2021, while the Ministry of Ecology and Environment of China (MEE) officially reported a value of 0.5568 kg CO₂/kWh, resulting in a discrepancy of 9.43%. This study conducts an in-depth analysis of the calculation methodologies used by the MEE and IEA, comparing them from two critical dimensions: calculation formulas and data sources, to explore potential causes of the observed discrepancies. Differences in formula components include factors such as electricity trade, the allocation of emissions from combined heat and power (CHP) plants, and emissions from own energy use in power plants. Notably, the IEA’s inclusion of CHP allocation reduces its calculated emissions by 10.99%. Regarding data sources, this study focuses on total carbon emissions and total electricity generation, revealing that the IEA’s total carbon emissions exceed those of the MEE by 9.71%. This exploratory analysis of the discrepancies in China’s AEFs provides valuable insights and a foundational basis for further research. Full article

China is a leading producer and consumer of coal, with coal being the dominant energy source. The accurate calculation of the mass carbon emission factor (EF_m) of coal is crucial as the carbon emissions from its combustion influence carbon emission assessment and policy formulation. However, discrepancies in EF_m values across documents, due to varying net calorific values (NCVs), carbon contents (CCs), and carbon oxidation factors (COFs), have posed challenges for enterprises in carbon emission calculations. By analyzing different coal types, it is found that for anthracite, the EF_m difference in different documents can reach 38.5%; for bituminous coal, it can reach 42.3%; and for lignite, it can reach 18.6%. These differences significantly affect carbon emission calculation accuracy, carbon allowance allocation fairness, and enterprise costs under the Carbon Border Adjustment Mechanism (CBAM). For instance, in 2023, the calculated carbon emissions of anthracite vary by over 300 million tons depending on the EF_m used. To address these issues, relevant departments should establish a unified EF_m release system, build a data sharing platform, and standardize enterprise testing standards to enhance the accuracy of carbon-related calculations and drive the low-carbon development of the coal industry. Full article

In various types of industrial applications, such as reverse engineering, machine operation, technical metrology, or modern factory maintenance, it is important to have systems that enable the quick and easy scanning of selected mechanical parts. This study presents the design process and analysis of a low-cost, 3D scanning system which can be used in industrial applications. The system collects point cloud data using an infrared distance sensor based on optical triangulation, controlled by a 32-bit microcontroller. Communication with the system is enabled through a serial interface and a dedicated window application, allowing users to monitor and adjust scanning parameters. The output data in the form of a point cloud are saved in a text file in the scanner’s controller memory and then sent wirelessly to an external device, e.g., cloud and/or a diagnostic controller. The electronic system is equipped with a radio module that can be used to communicate with other devices in line with the idea of the Internet of Things and the concept of Industry 4.0. The results of the study are based on the accuracy of the three-dimensional digitization of the tested object and on the determination of the average measurement uncertainty. Full article

Dentistry is steadily evolving along the digital pathway at a constant and sure pace. Intraoral scanners (IOSs) started to enhance the precision and trueness of the restorations, making prosthodontics treatment more predictable. The objective of this study was to compare the trueness and internal fit of the printed provisional veneers for 60 preparations with three different types of finish lines. The abutments were scanned with the same intraoral scanner, and the resulting meshes were overlapped and analyzed in metrology software. The comparison was executed using two methods: (1) CAD (computer-aided design) designs vs. scanned veneers and (2) inverted surfaces of the preparations vs. scanned veneers. The butt joint preparation had the best trueness out of the three groups of preparation during both comparison methods, although there was no statistically significant difference between the butt joint and feather edge groups in the CAD design vs. scanned veneers analysis. Also, there was no statistically significant difference between palatal chamfer and feather edge groups in the scanned veneers vs. inverted surfaces of the preparations analysis. Full article