Soil moisture (SM) is a key variable regulating land–atmosphere energy exchange, hydrological processes, and ecosystem functioning. Though important, there are still unresolved problems in accurate SM monitoring and the practical application and validation of existing methods. In this review, we integrate mechanistic classification and applicability and constraint discussions to develop a coherent understanding of current SM monitoring approaches. Within this framework, in situ measurements, optical and thermal infrared methods, active and passive microwave remote sensing (RS) techniques, and model-based simulations are compared, and publicly accessible SM dataset products are comparatively analyzed in terms of product characteristics and application limitations. Different from other published reviews, this study covers a large scope of SM monitoring methods varying from in situ observation to RS inversion, and classifies them based on their mechanisms, thereby constructing a complete comparative framework for SM research. Moreover, three types of open-access SM dataset products are investigated, optical and microwave RS products, model simulation and data fusion products, and reanalysis dataset products, and evaluated according to their resolution, depth, applicability, advantages, and limitations. By doing so, it is concluded that in situ observations remain essential for calibration and validation but are spatially limited. Optical and thermal infrared methods are restricted by atmospheric conditions and a shallow penetration depth, while microwave techniques exhibit varying performances under different vegetation and soil conditions. Existing datasets differ significantly in resolution, consistency, and coverage, making no single product universally applicable. Future research should focus on multi-source and spatiotemporal data fusions, the integration of machine learning with physical mechanisms, enhancement for cross-sensor consistency, the establishment of standardized uncertainty evaluation frameworks, and the refinement of high-order RTMs and parameterization.

Abstract Machine learning (ML) classification models are increasingly being used in a wide range of applications where it is important that predictions are accompanied by uncertainties, including in climate and earth observation, medical diagnosis and bioaerosol monitoring. The output of an ML classification model is a type of categorical variable known as a nominal property in the International Vocabulary of Metrology (VIM). However, concepts related to uncertainty evaluation for nominal properties are not defined in the VIM, nor is such evaluation addressed by the Guide to the Expression of Uncertainty in Measurement (GUM). In this paper we propose a metrological conceptual uncertainty evaluation framework for nominal properties. This framework is based on probability mass functions and summary statistics thereof, and it is applicable to ML classification. We also illustrate its use in the context of two applications that exemplify the issues and have significant societal impact, namely, climate and earth observation and medical diagnosis. Our framework would enable an extension of the GUM to uncertainty for nominal properties, which would make both applicable to ML classification models.

Evaluation of uncertainty and risk control for trace analysis of superalloys using inductively coupled plasma mass spectrometry with micro-reaction

Method validation and uncertainty evaluation in trace element analysis of high-purity silver by ICP-OES.

Uncertainty evaluation for wind tunnel test based on new flow field and balance models

Strength degradation and uncertainty evaluation of heated GFRP and BFRP rebars with or without seawater sea-sand mortar cover

The publication provides a critical analysis of fundamental documents concerning the determination of measurement uncertainty from the perspective of the machinery industry. The requirements contained in the documents JCGM 104, JCGM 100, and JCGM 101 were compared with important documents used in geometrical measurements, particularly with EA-4/02, ISO 14253-2, ISO/TS 15530-1, ISO 15530-3, ISO/TS 15530-4, and VDI/VDE 2617-11. Significant differences between the documents analysed, both terminological and interpretative, were highlighted. The analysis was performed in the sequence of stages for determining measurement uncertainty: formulation, propagation, and summarizing. Special attention was paid to the problem of defining the measurement model and the insufficient reference to the measurement model in the analysed documents. Attention was drawn to the wide range of characteristics measured in the machinery industry, such as linear and angular dimensions and form, orientation, position, and runout deviations, as well as the wide range of measurement equipment used, from simple instruments like callipers, micrometers, and mechanical dial gauges, to coordinate measuring machines and measurement systems. The current approach to the uncertainty of coordinate measurements, including the new possibility of modelling coordinate measurement, was discussed.

Measurement uncertainty (MU) plays an important role in the interpretation of laboratory results, but data on serum proteins analyzed by immunoturbidimetry according to ISO/TS 20914 are limited. MU of 11 serum proteins, including CRP, RF, ASO, IgG, IgA, IgM, C3, C4, ceruloplasmin, transferrin, and β2-microglobulin, were estimated using 1-year internal quality control (IQC) data obtained from Roche Cobas analyzers. MU was calculated using uncertainty and calibrator uncertainty according to ISO/TS 20914, assuming negligible deviation from external quality assessment data. Analytical performance specification (APS) models were selected according to the EFLM APS selection criteria, and maximum allowable uncertainty (MAU) values were determined based on sources such as EFLM models and literature. IgA and RF were the only two analytes that met the required and minimum MAU values, respectively, at both IQC levels. MU values for CRP, ceruloplasmin, transferrin, and β2-microglobulin exceeded targets at both levels. MU for C3, C4, IgG, and IgM exceeded the minimum MAU at IQC1 but remained acceptable at IQC2. MU values for ASO were calculated as 10.01 and 7.22 % but could not be evaluated due to a lack of reference data. Assay precision should be improved for CRP, IgG, IgM, ceruloplasmin, transferrin, and β2-microglobulin. Use of updated calibration materials for CRP may help reduceMU. Maintaining acceptable precision over a long period remains a challenge for serum proteins analyzed by immunoturbidimetry, highlighting the need for methodological improvements and stricter quality monitoring. In this context, MU assessment is crucial.

Abstract. The Radio Occultation Modeling EXperiment (ROMEX) is an international collaboration to test the impact of varying numbers of radio occultation (RO) profiles in operational numerical weather prediction (NWP) models. An average of 35 000 RO profiles d−1 for September–November 2022 from 13 different missions are being used in experiments at major NWP centers. This paper evaluates properties of ROMEX data, with emphasis on the three largest datasets: COSMIC-2 (Constellation Observing System for Meteorology, Ionosphere and Climate-2 or C2), Spire, and Yunyao. The penetration depths (percent of profiles reaching different levels above the surface) of most of the ROMEX datasets are similar, with more than 80 % of all occultations reaching 2 km or lower and more than 50 % reaching 1 km or lower. The relative uncertainties of the C2, Spire, and Yunyao bending angles and refractivities are estimated using the three-cornered hat method. They are similar on the average in the region of overlap (45–45° N). Larger uncertainties occur in the tropics compared to higher latitudes below 20 km. Relatively small variations in longitude exist. We investigate biases in the observations by comparing them to each other and to models. C2 bending angles appear to be biased by about 0.15 % compared to Spire and other ROMEX data between 10 and 30 km altitude. These biases, most of which are representativeness or sampling differences, are caused by the different orbits of C2 and other ROMEX missions around the non-spherical Earth and the associated varying radii of curvature.

In recent years, fundus image segmentation has become a fundamental task in computer-aided diagnosis of ophthalmic diseases. However, the performance of segmentation models severely degrades when they are transferred across domains, primarily due to domain shift and the lack of reliable annotations in the target domain. Source-Free Domain Adaptation (SFDA) provides a feasible solution by adapting a pre-trained source model to the target domain without requiring access to source data. Nevertheless, the presence of noisy pseudo-labels and the absence of structural alignment remain challenging issues that limit the effectiveness of existing methods. To address these problems, this paper proposes a Semantic-Aware Adversarial Learning (SAAL) framework for source-free domain adaptation in fundus image segmentation, which is designed with two main components. First, a triple pseudo-label filtering mechanism is introduced, integrating confidence estimation, uncertainty evaluation, and class prototype consistency to ensure high-quality supervision. Second, a dual-branch discriminator is developed, which performs both domain discrimination and semantic classification, achieving pixel-level semantic alignment while maintaining domain-invariant representations. This design mitigates the impact of noisy labels and enhances structural consistency across domains. Experimental evaluations on multiple benchmark fundus image datasets demonstrate that the proposed method outperforms existing SFDA approaches, particularly in handling ambiguous cup/disc boundaries.

Evaluation of measurement uncertainties in quantifying urinary aripiprazole and dehydroaripiprazole via isotope dilution–LC–MS/MS

Vocational mobility (VM) is one of the most definitive and determinative factors in career advancement and flexibility, especially for college graduates starting their careers in competitive job markets. Previous strategies for modelling career paths cannot incorporate uncertainty and variability into decisions and consequently tend to provide imprecise assessments. To address these shortcomings, this article introduces an effective decision support system based on the interval-valued spherical fuzzy MARCOS (IVSF-MARCOS) method, integrated with multi-criteria group decision-making (MCGDM). This will enable the model to systematically combine different and disparate expert judgments, allowing it to deal with imprecise, vague, or incomplete information in complex decisions involving the environment. The judgments of five decision-makers are used to assess fifteen career options based on ten factors, including potential income, employment security, advancement opportunities, and market saturation levels. The proposed model has fewer uncertainties and higher levels of precision and accuracy in handling the findings, unlike traditional models of decision-making. The study’s practical implications are presented in the form of a ranking of career fields relevant to individuals and market needs. With the help of research that utilizes the IVSF-MARCOS method as an integral part of a larger study conducted within an MCGDM framework, this study contributes to the theory of career path prediction and VM by proposing a new decision-support process capable of managing uncertainty and group evaluation.

Uncertainty evaluation of the debris flow impact considering spatially varying basal friction and solid concentration

Uncertainty evaluation of absorbed dose measurement based on temperature rise using a water calorimeter for 60Co gamma rays

We report on a 88 Sr + single-ion optical clock with an estimated fractional systematic uncertainty of 7.9× 10 − 19 . The low uncertainty is enabled by small rf losses, a thorough evaluation of the blackbody-radiation temperature, and our recent measurement of the differential polarizability. A detailed uncertainty evaluation is presented. We also report on two absolute frequency measurements: one against a remote cesium fountain clock, and one against International Atomic Time (TAI). The former lasted 12 d and resulted in a frequency value of 444 779 044 095 485.49(15) Hz. The latter spanned 10 months with monthly optical-clock uptimes between 68% and 99%, and yielded a frequency value of 444 779 044 095 485.373(44) Hz. With a fractional uncertainty of 9.8× 10 − 17 , it is, to our knowledge, the most accurate optical frequency measurement reported to date. Both frequency values are in agreement with other recent measurements, providing further evidence that the 2021 CIPM recommended frequency value is too high by 1.6 times its uncertainty.

Quantitative NMR spectroscopy (qNMR) can be used to determine chemical purity. This applies to the resonating nuclei of all the present chemical species, enabling quantitation of the analyte against chemically non-identical calibrator molecules. Validation approaches for determining chemical purity with qNMR are being endorsed by major pharmacopoeias and other standard-setting bodies. In this study, we investigated the purity determination, uncertainty evaluation, and method validation of imazosulfuron using qNMR to gain ISO 17034 accreditation. We ensured the NIST traceability of imazosulfuron by calibrating 1,4-BTMSB-d 4 (determining its purity and uncertainty) using NIST PS 1 and then calibrating imazosulfuron using the calibrated 1,4-BTMSB-d 4. Purity and uncertainty determinations were performed using qNMR, as per the proposed revisions to the USP General Chapters <761> and <1761>. Method development and validation were performed as described in these chapters using the principles of Analytical Quality by Design (AQbD). First, we defined a target measurement uncertainty of ± 2.0% (k = 2) as the Analytical Target Profile (ATP). Next, we established robust operating parameters for qNMR and determined the purity and uncertainty of 1,4-BTMSB-d 4. Subsequently, we determined the purity and uncertainty of imazosulfuron using the calibrated 1,4-BTMSB-d 4 to verify that the qNMR method produced reportable values that met the ATP criteria. The purity and uncertainty of imazosulfuron were 98.2% ± 1.2% (k = 2), meeting the ATP criteria. We then moved on to the next stage to monitor and ensure that the qNMR method remains properly controlled and satisfy the ATP criteria during routine use. Based on the above, we established a validation scheme that meets the requirements of ISO 17034 by leveraging AQbD considerations. The AQbD principles shift the focus of method validation toward procedure design and development, resulting in more rational design, efficient development, and validation.

Active Noise Cancellation (ANC) technology in headsets has revolutionized our audio experience in noisy environments. This paper presents a comprehensive study on the calibration of ANC headsets in the Standards and Calibration Laboratory. The calibration process involves precise measurements of passive and active insertion losses, both critical to the noise-cancelling effectiveness of the headsets. Additionally, the frequency response and total harmonic distortion characteristics are evaluated to ensure high-fidelity sound reproduction. The calibration frequency ranges from 100 Hz to 20 kHz. The calibration processes utilized a Head and Torso Simulator equipped with embedded Type 3.3 artificial ears within a free-field anechoic chamber, allowing for precise measurements and reliable results. This method allows for a comprehensive evaluation of ANC technology by examining both passive and active insertion losses, thereby assessing its effectiveness in attenuating external noise. Furthermore, the analysis of frequency response and total harmonic distortion ensures that the audio output remains true to the original signal across various frequency ranges. This paper describes (i) methods for measuring the acoustic parameters, (ii) the measurement model and uncertainty evaluation, and (iii) the measurement results and details of the calibration system.

Label noise poses a significant challenge in Earth Observation (EO), often degrading the performance and reliability of supervised Machine Learning (ML) models. Yet, given the critical nature of several EO applications, developing robust and trustworthy ML solutions is essential. In this study, we take a step in this direction by leveraging probabilistic ML to model input-dependent label noise and quantify data uncertainty in EO tasks, accounting for the unique noise sources inherent in the domain. We train uncertainty-aware probabilistic models across a broad range of high-impact EO applications—spanning diverse noise sources, input modalities, and ML configurations—and introduce a dedicated pipeline to assess their accuracy and reliability. Our experimental results show that the uncertainty-aware models outperform standard deterministic approaches across most datasets and evaluation metrics. Moreover, through rigorous uncertainty evaluation, we validate the reliability of the predicted uncertainty estimates, enhancing the interpretability of model predictions. Our findings emphasize the importance of modeling label noise and incorporating uncertainty quantification in EO, paving the way for more reliable solutions in the field.

This study aims to analyze the influence of self-efficacy and job characteristics on the job satisfaction of freelance photographers in Surabaya. The background of this research is based on the growth of the gig economy, which has encouraged the emergence of independent work patterns where workers have the freedom to manage their own time, methods, and projects. However, this freelance work system also presents various challenges, such as project uncertainty, fluctuating workloads, and limited structured support or evaluation. In the context of freelance photography, these dynamics are particularly evident, as the work demands not only creativity but also managerial skills to organize work processes independently. This study employs a quantitative approach using a survey method with questionnaires distributed to 100 freelance photographers selected through purposive sampling. Data were analyzed using the Partial Least Squares (PLS) method. The results indicate that self-efficacy and job characteristics significantly contribute to shaping job satisfaction. Strong self-efficacy particularly previous work experience enhances freelancers’ confidence in facing challenges, while clear job characteristics, especially in terms of task identity, make them feel more directed, valued, and satisfied with their work. These findings emphasize the importance of both internal and external factors in determining job satisfaction and provide theoretical as well as practical contributions to the management of freelance workers in the creative sector, particularly in designing adaptive work strategies in the digital economy era.

The spatial variability of soil shear wave velocity (Vs) significantly influences the results of site seismic response analysis. Based on the collected measured Vs values of silty clay in a certain sea area in China, this study divides the Vs data into one set of on-site sample data and six sets of historical data. A power function is used to establish the regression equation between Vs and depth h, and the joint prior distribution of the mean and variance for parameters a and b in the power function is derived using historical data. The joint posterior distribution of parameters a and b is obtained by applying the Bayesian formula to the on-site sample data. Using the maximum a posteriori mean values of a and b combined with the point estimation method, the mean and standard deviation of the predicted Vs values as functions of depth h are derived. The accuracy of the point estimation results is verified using Monte Carlo simulation. Compared to the Vs values predicted using only the mean values of a and b derived from on-site sample data, the Vs values predicted based on the maximum a posteriori mean values of a and b are closer to the measured Vs values. Accordingly, the results of the site seismic response analysis also align more closely with those calculated using the true Vs values.