Sources Of Uncertainty Research Articles

Soil and Water Assessment Tool (SWAT)-Informed Deep Learning for Streamflow Forecasting with Remote Sensing and In Situ Precipitation and Discharge Observations

In order to anticipate residual errors and improve accuracy while reducing uncertainties, this work integrates the long short-term memory (LSTM) with the Soil and Water Assessment Tool (SWAT) to create a deep learning (DL) model that is guided by physics. By forecasting the residual errors of the SWAT model, the SWAT-informed LSTM model (LSTM-SWAT) differs from typical LSTM approaches that predict the streamflow directly. Through numerical tests, the performance of the LSTM-SWAT was evaluated with both LSTM-only and SWAT-only models in the Upper Heihe River Basin. The outcomes showed that the LSTM-SWAT performed better than the other models, showing higher accuracy and a lower mean absolute error (MAE = 3.13 m3/s). Sensitivity experiments further showed how the quality of the training dataset affects the performance of the LSTM-SWAT. The results of this study demonstrate how the LSTM-SWAT may improve streamflow prediction greatly by remote sensing and in situ observations. Additionally, this study emphasizes the need for detailed consideration of specific sources of uncertainty to further improve the predictive capabilities of the hybrid model.

Out-of-distribution Detection in Dependent Data for Cyber-physical Systems with Conformal Guarantees

Uncertainty in the predictions of learning-enabled components hinders their deployment in safety-critical cyber-physical systems (CPS). A shift from the training distribution of a learning-enabled component (LEC) is one source of uncertainty in the LEC’s predictions. Detection of this shift or out-of-distribution (OOD) detection on individual datapoints has therefore gained attention recently. But in many applications, inputs to CPS form a temporal sequence. Existing techniques for OOD detection in time-series data for CPS either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We propose using deviation from the in-distribution temporal equivariance as the non-conformity measure in conformal anomaly detection framework for OOD detection in time-series data for CPS. Computing independent predictions from multiple conformal detectors based on the proposed measure and combining these predictions by Fisher’s method leads to the proposed detector CODiT with bounded false alarms. CODiT performs OOD detection on fixed-length windows of consecutive time-series datapoints by using Fisher value of the input window. We further propose performing OOD detection on real-time time-series traces of variable lengths with bounded false alarms. This can be done by using CODiT to compute Fisher values of the sliding windows in the input trace and combining these values by a merging function. Merging functions such as Harmonic Mean, Arithmetic Mean, Geometric Mean, Bonferroni Method, and so on, can be used to combine Fisher values of the sliding windows in the input trace, and the combined value can be used for OOD detection on the trace with bounded false alarm rate guarantees. We illustrate the efficacy of CODiT by achieving state-of-the-art results in two case studies for OOD detection on fixed-length windows. The first one is on an autonomous driving system with perception (or vision) LEC. The second case study is on a medical CPS for walking pattern or GAIT analysis where physiological (non-vision) data is collected with force-sensitive resistors attached to the subject’s body. For OOD detection on variable length traces, we consider the same case studies on the autonomous driving system and medical CPS for GAIT analysis. We report our results with four merging functions on the Fisher values computed by CODiT on the sliding windows of the input trace. We also compare the false alarm rate guarantees by these four merging functions in the autonomous driving system case study. Code, data, and trained models are available at https://github.com/kaustubhsridhar/time-series-OOD .

Adapting to Climate Change with Machine Learning: The Robustness of Downscaled Precipitation in Local Impact Analysis

The skill, assumptions, and uncertainty of machine learning techniques (MLTs) for downscaling global climate model’s precipitation to the local level in Bolivia were assessed. For that, an ensemble of 20 global climate models (GCMs) from CMIP6, with random forest (RF) and support vector machine (SVM) techniques, was used on four zones (highlands, Andean slopes, Amazon lowlands, and Chaco lowlands). The downscaled series’ skill was evaluated in terms of relative errors. The uncertainty was analyzed through variance decomposition. In most cases, MLTs’ skill was adequate, with relative errors less than 50%. Moreover, RF tended to outperform SVM. Robust (weak) stationary (perfect prognosis) assumptions were found in the highlands and Andean slopes. The weakness was attributed to topographical complexity. The downscaling methods were shown to be the dominant source of uncertainties. This analysis allowed the derivation of robust future projections, showing higher annual rainfall, shorter dry spell duration, and more frequent but less intense high rainfall events in the highlands. Apart from the dry spell’s duration, a similar pattern was found for the Andean slopes. A decrease in annual rainfall was projected in the Amazon lowlands and an increase in the Chaco lowlands.

Novel analysis of alignment error on spherical Fizeau interferometer and uncertainty evaluation of sphericity calibration system based on random ball test

The National Metrology Institute of Japan (NMIJ) has developed a high-precision sphericity calibration system utilizing a Fizeau interferometer and conducted a thorough evaluation of the system's measurement uncertainty. Generally, there are two principal sources of uncertainty in the measurement outcomes when utilizing a spherical Fizeau interferometer. First, the accuracy of the system is significantly influenced by the prior knowledge of the reference sphere surface's absolute profile. To address this, the study established a straightforward yet effective calibration system for the reference sphere surface using a random ball test method. Second, the system's precision is affected by misalignment aberration, which occurs when there is any lateral or longitudinal displacement of the test sphere from the confocal position relative to the reference sphere. This misalignment can introduce both high-order shape errors (misalignment aberrations) and low-order shape errors (alignment errors). Through analytical consideration of an observation coordinate system on the camera plane, this study delves into misalignment aberrations, suggesting that the impact of misalignment should be determined experimentally for each reference sphere unit due to potential imperfections that may be revealed by misalignment. Furthermore, the study proposes that uncertainties related to misalignment aberrations are theoretically confirmed to be smaller than previous studies by conducting an in-depth uncertainty analysis of the misalignment, with a focus on the observation coordinate system on the camera plane. Notably, the research demonstrates that a measurement uncertainty level of λ/100 is achievable, maintaining a broader tolerance for misalignment than previously reported studies. The uncertainty of calibration of the reference sphere unit's absolute profile was 4.2 nm and the uncertainty of sample measurement was determined to be 4.6 nm with misalignment tolerance of ±λ/10. This advancement marks a stride toward improving the accuracy and reliability of sphericity measurements, offering potential for widespread application in precision engineering and metrology.

Estimating wetland CH4 emissions on the Qinghai-Tibetan plateau based on a processed model coupled with inundation dynamics between 1960 and 2100

Quantifying the impacts of climate change and wetland area dynamics on methane (CH4) emissions in the Qinghai-Tibetan Plateau (QTP) is crucial for enhancing our understanding of regional and global CH4 budgets. However, a major source of uncertainty in estimating wetland methane emissions stems from the limited delineation of wetland area dynamics. In this study, we integrated dynamic wetland inundation schemes into a process-based model to quantify the contributions of wetland area variations to natural wetland CH4 emissions on the QTP, both historically and in the future. The results revealed that climate change significantly influenced the spatiotemporal distribution of wetland CH4 emissions, with wetland inundation dynamics contributing between 21.84 % and 27.18 % of the increase in CH4 emissions from 1960 to 2020. Under future climate change scenarios, wetland CH4 emissions are projected to exhibit a more pronounced increasing trend, with wetland inundation dynamics projected to contribute 18.94 % to 21.80 % of the increase in CH4 emissions in the QTP under seven Shared Socioeconomic Pathways scenarios.

TreeCompR: Tree competition indices for inventory data and 3D point clouds

Abstract In times of more frequent global change‐type droughts and associated tree mortality events, competition release is one silvicultural measure discussed to have an impact on the resilience of managed forest stands. Understanding how trees compete with each other is therefore crucial, but different measurement options and competition indices (CI) leave users with a difficult choice, as no single competition index has proven universally superior. To help users with the choice and computation of appropriate indices, we present the open‐source TreeCompR package, which handles 3D point clouds and classical forest inventory data, enabling the calculation of both innovative point cloud‐based indices and traditional distance‐dependent indices. It serves as a centralized platform for exploring and comparing different CIs, allowing users to test and select the most suitable CI for their specific research questions within a common interface. To evaluate the package, we used TreeCompR to quantify the competition situation of 307 European beech trees from 13 sites in Central Europe. Based on this dataset, we discuss the interpretation, comparability and sensitivity of the different indices to their parameterization and identify possible sources of uncertainty and ways to minimize them. The compatibility of TreeCompR with different data formats and different data collection methods makes it accessible and useful for a wide range of users, specifically ecologists and foresters. Due to the flexibility in the choice of input formats as well as the emphasis on tidy, well‐structured output, our package can easily be integrated into existing data‐analysis workflows both for 3D point cloud and classical forest inventory data.

Multifractal Analysis for Evaluating the Representation of Clouds in Global Kilometer‐Scale Models

AbstractClouds are one of the largest sources of uncertainty in climate predictions. Global km‐scale models need to simulate clouds and precipitation accurately to predict future climates. To isolate issues in their representation of clouds, models need to be thoroughly evaluated with observations. Here, we introduce multifractal analysis as a method for evaluating km‐scale simulations. We apply it to outgoing longwave radiation fields to investigate structural differences between observed and simulated anvil clouds. We compute fractal parameters which compactly characterize the scaling behavior of clouds and can be compared across simulations and observations. We use this method to evaluate the nextGEMS ICON simulations via comparison with observations from the geostationary satellite GOES‐16. We find that multifractal scaling exponents in the ICON model are significantly lower than in observations. We conclude that too much variability is contained in the small scales leading to less organized convection and smaller, isolated anvils.

Tropical Indian Ocean drives Hadley circulation change in a warming climate

Abstract The weakening and poleward expansion of the Hadley circulation (HC) are considered robust responses of atmospheric meridional circulation to anthropogenic warming. Climate impacts arising from these changes enhance drought conditions and reduce food production in the affected regions. Therefore, understanding the mechanisms of HC changes is critical to anticipating the resultant climate risks. First, we demonstrate that robust future HC changes in boreal winter, and the uncertainty in their future projections, are both largely related to sea surface temperature (SST) warming. Next, we investigate the impact of anthropogenic regional ocean warming on the future HC. Accordingly, we conduct a large ensemble of individual ocean basin perturbation experiments at 1.5 °C, 2 °C, and 3 °C warming thresholds (as in the Paris Agreement). These experiments highlight (i) the leading role of tropical Indian Ocean warming in HC changes and (ii) inter-model differences in tropical Pacific warming as a source of uncertainty in HC projections.

Parallel multiple-chain DRAM probabilistic model for ultrasonic pulse velocity of concrete performance in the pipe of arch bridge

The existing models for predicting the compaction state of concrete inside steel tubes based on ultrasonic wave velocity have a major limitation in that they cannot comprehensively consider concrete compressive strength, instar, and various sources of uncertainties, both subjective and objective. To overcome this limitation, the study introduced a probability model for ultrasonic wave velocity in the concrete performance inside arch bridge steel tubes, based on the parallel multiple-chain Delayed Rejection Adaptive Metropolis (DRAM) algorithm. Initially, the study considers the influence of concrete compressive strength and instar, establishing a simplified deterministic model for predicting concrete compactness inside the tubes. Building on this, the study incorporates material stochastic uncertainty and cognitive uncertainty, deriving analytical expressions for a probabilistic prediction model of concrete compactness. Furthermore, using parallel processing alongside the DRAM algorithm, the study explores how the number of parallel chains and the order of acceptance probabilities influence the probabilistic model parameters. The study selected and updated the posterior distribution information of the probabilistic model parameters. Finally, the effectiveness of the model was validated using experimental data. As shown in the analysis, with better prediction accuracy and lower dispersion, the model not only selects the optimal Markov chain iteration path in a short time, but also corrects the parameters affecting the prediction accuracy of the existing model, and also provides a probabilistic method for correcting the confidence intervals of the existing model.

Ozone Detector Based on Ultraviolet Observations on the Martian Surface

Ozone plays a key role in both atmospheric chemistry and UV absorption in planetary atmospheres. On Mars, upper-tropospheric ozone has been widely characterized by space-based instruments. However, surface ozone remains poorly characterized, hindered by the limited sensitivity of orbiters to the lowest scale height of the atmosphere and challenges in delivering payloads to the surface of Mars, which have prevented, to date, the measurement of ozone from the surface of Mars. Systematic measurements from the Martian surface could advance our knowledge of the atmospheric chemistry and habitability potential of this planet. NASA’s Mars 2020 mission includes the first ozone detector deployed on the Martian surface, which is based on discrete photometric observations in the ultraviolet band, a simple technology that could obtain the first insights into total ozone abundance in preparation for more sophisticated measurement techniques. This paper describes the Mars 2020 ozone detector and its retrieval algorithm, including its performance under different sources of uncertainty and the potential application of the retrieval algorithm on other missions, such as NASA’s Mars Science Laboratory. Pre-landing simulations using the UVISMART radiative transfer model suggest that the retrieval is robust and that it can deal with common issues affecting surface operations in Martian missions, although the expected low ozone abundance and instrument uncertainties could challenge its characterization in tropical latitudes of the planet. Other space missions will potentially include sensors of similar technology.

Impact of gut permeability on estimation of oral bioavailability for chemicals in commerce and the environment.

Performance of pharmacokinetic models developed using in vitro-to-in vivo extrapolation (IVIVE) methods may be improved by refining assumptions regarding fraction absorbed (Fabs) through the intestine, a component of oral bioavailability (Fbio). Although in vivo measures of Fabs are often unavailable for non-pharmaceuticals, in vitro measures of apparent permeability (Papp) using the Caco-2 cell line have been highly correlated with Fabs. We measured bidirectional Papp for over 400 non-pharmaceutical chemicals using the Caco-2 assay. A random forest quantitative structure-property relationship (QSPR) model was developed using these and peer-reviewed pharmaceutical data. Both Caco-2 data (R²=0.37) and the QSPR model (R²=0.29) were better at predicting human bioavailability compared to in vivo rat data (R²=0.23). After incorporation into a high throughput toxicokinetics (HTTK) framework for IVIVE, the Caco-2 data were used to estimate in vivo administered equivalent dose (AED) for bioactivity assessed in vitro, The HTTK-predicted plasma steady state concentrations (Css) for IVIVE were revised, with modest changes predicted for poorly absorbed chemicals. Experimental data were evaluated for sources of measurement uncertainty, which were then accounted for using the Monte Carlo method. Revised AEDs were subsequently compared with exposure estimates to evaluate effects on bioactivity:exposure ratios, a surrogate for risk. Only minor changes in the margin between chemical exposure and predicted bioactive doses were observed due to the preponderance of highly absorbed chemicals.

Роль семейных взаимосвязей в формировании жизнеспособной и благополучной личности студента с инвалидностью и / или ограниченными возможностями здоровья

<p style="text-align: justify;"><strong>Objective</strong><strong>.</strong> To analyze the role of family relationships in the formation of a prosperous and viable personality of a student with disabilities. <br><strong>Background. </strong>Moral responsibility, mutual support and care, teaching principles, norms and values remain at all times a historically significant function of the family. The appearance of a person with a disability in the family becomes a source of uncertainty, which is a threat on the one hand, and a chal­lenge on the other. Creating conditions for the effective and harmonious development of the family will make it possible to raise a prosperous and resilient citizen, through the support of not just a family, but the institution of a family resiliencе. <br><strong>Study design. </strong>The paper examines the features of the formation a well-being and resilient person­ality of students with disabilities, depending on the degree of family support and the presence of close family ties. The interrelation of family relations with psychological well-being, resilience, and assess­ment of the quality of life of students with disabilities is determined. <br><strong>Participants. </strong>The study involved students with disabilities (n = 98; 37,7% of boys, 62,3% of girls; average age 21,13 ± 2,68) from different faculties of FSUE IVO RSU Sotstech and conditionally healthy students (n = 116; 51,7% of the sample; 31,4% of boys, 68,6% of girls; average age 21,5 ± 2,9). <br><strong>Measurements. </strong>The research is based on the following methods: questionnaire “Psychological well-being” by C. Riff in adaptation by T.D. Shevelenkova, P.P. Fesenko; questionnaire SF — 36 “Assessment of quality of life”; test “Human resilience” by A.V. Makhnach; questionnaire “Human resilience” by E.A. Rylskaya; questionnaire allowing to establish the socio-psychological characteristics of students (gender, age, faculty, disability). The results were analyzed using the IBM SPSS 23.0 package. The presence of significant differences was revealed using the nonparametric Mann-Whitney U-test for two independent groups and the Kruskal-Wallis analysis for k-independent groups. The relationship of family relationships with the assessment of living standards, parameters of psychological well-being and resilience was established on the basis of Spearman’s p-criterion. The results were considered significant at p < 0,05. <br><strong>Results. </strong>Students with disabilities have a correlation relationship between family relationships and indicators of psychological well-being and resilience. The level of resilience of students in families with high support corresponds to a high level, in groups with weak family ties — to an average level. In families with high family support, girls and boys are statistically more capable of self-regulation and self-development. The state of psychological well-being is statistically significantly higher among students with disabilities in the group with close family relationships. The analysis of the assessment of the quality of life significantly differs in the parameter of vital activity among respondents with disabilities. <br><strong>Conclusions. </strong>As a result of the study, it was found that a family with close emotional ties creates conditions for the effective use of available opportunities, self-regulation and self-development. Stu­dents with disabilities with high family support show higher life activity and adaptation in society. The results of the study serve as the basis for the development of a targeted trajectory of psychological and pedagogical support for students with disabilities and/or disabilities to maintain psychological well-being and resilience, improve the assessment of quality of life.</p>

Margin derivation from intrafraction patient motion of multi-target, single isocentre, brain stereotactic radiosurgery treatments.

Brain metastases are the most common intracranial malignancy and remain a substantial source of morbidity and mortality in cancer patients. Linear accelerator based stereotactic radiosurgery (SRS) is widely used and is frequently delivered by hypo-fractionnated volumetric modulated arc therapy using non-coplanar beams, where geometric accuracy and planning margins are a majorconcern. To give a practical analysis of intrafraction patient motion for multi-target, single isocentre, brain SRS treatments and to derive adapted GTV-to-PTV margins. Data of 154 lesions, spread over 85 fractions from 56 patients treated in our institution with the Varian HyperArc SRS solution was processed. Intrafraction patient motion were recorded using an Optical Surface Monitoring System during irradiation. The present study focuses on small tumor volumes, roughly equal or inferior to 1.5 , and frameless mask-based immobilization. For each treatment session, a tumor displacement vector matrix was calculated from the patient drifts as a function of time. Data were combined together into a representative treatment scenario and the dosimetric impact of GTV displacement was calculated. Recommended margins due to patient motion range between 0.3 and 1mm, depending on the distance tumor-isocentre, and the desired GTV edge dose coverage. Those values should be added quadratically with other sources of uncertainty, such as mechanical isocentre and kV-MVmisalignment. Thorough analysis of intrafraction patient motion was performed, the dosimetric impact was calculated for different scenarios, and adequate GTV-to-PTV margins were derived. These values vary according to the distance isocentre-to-GTV, as well as the desired dose coverage, and should be chosenadequately.

Sensitivity and Uncertainty Analysis of the GeeSEBAL Model Using High-Resolution Remote-Sensing Data and Global Flux Site Data

Actual evapotranspiration (ETa) is an important component of the surface water cycle. The geeSEBAL model is increasingly being used to estimate ETa using high-resolution remote-sensing data (Landsat 4/5/7/8). However, due to surface heterogeneity, there is significant uncertainty. By optimizing the quantile values of the reverse-modelling automatic calibration algorithm (CIMEC) endpoint-component selection algorithm under extreme conditions through 212 global flux sites, we obtained the optimized quantile values of 11 vegetation types of cold- and hot-pixel endpoint components (Ts and NDVI). Based on the observation data of the global FLUXNET tower, the sensitivity of 20 parameters in the improved geeSEBAL model was determined through Sobol’s sensitivity analysis. Among them, the parameters dT and SAVI,hot were confirmed as the most sensitive parameters of the algorithm. Subsequently, we used the differential evolution Markov chain (DE-MC) method to analyse the uncertainty of the parameters in the geeSEBAL model used the posterior distribution of the parameters to modify the sensitive parameter values or ranges in the improved geeSEBAL model and to simulate the daily ETa. The results indicate that by analysing the end element components of the geeSEBAL model (Ts and NDVI), quantile numerical optimization and parameter optimization can be performed. Compared with the original algorithm, the improved geeSEBAL model has significantly improved simulation performance, as shown by higher R2 values, higher NSE values, smaller bias values, and lower RMSE values. The most suitable values of the predefined parameter Zoh were determined, and the reanalysis of meteorological data inputs (relative humidity (RH), temperature (T), wind speed (WS), and net radiation (Rn)) was also found to be an important source of uncertainty for the accurate estimation of ETa. This study indicates that optimizing the quantiles and key parameters of the model end component has certain potential for further improving the accuracy of the geeSEBAL model based on high-resolution remote-sensing data in estimating the ETa for various vegetation types.

Predicting the variability of the dynamics of bolted joints using polynomial chaos expansion

Friction and contact occurring in bolted joints can lead to highly nonlinear dynamics. Moreover, joints are prone to high sample-to-sample variability as well as low test-to-test repeatability. Notably, experimental work is increasingly highlighting the most influential sources of uncertainties. Nevertheless, randomness due to some uncertainties, such as surface imperfections, cannot be completely eliminated. Others, such as the actual applied bolt load, can be laborious to control. In this work, we aim to computationally predict the variability of numerical models of joints given such uncertainties. We perform the dynamic analysis using the Multi-Harmonic Balance Method (MHBM) to compute the forced, periodic response of the system. For probabilistic surrogate modeling, we use Polynomial Chaos Expansion (PCE). We show that PCE can be successful in studying the impact of ISO tolerances and uncertain bolt loads in the practical case where only a few numerical samples can be generated. We also show that an empirical error metric is unreliable and a validation error metric must be used. In addition, we assess using PCE to propagate uncertainties due to mesoscale imperfections, and highlight that unsuccessful PCE models indicate sensitive structural designs.

Terrestrial photosynthesis inferred from plant carbonyl sulfide uptake.

Terrestrial photosynthesis, or gross primary production (GPP), is the largest carbon flux in the biosphere, but its global magnitude and spatiotemporal dynamics remain uncertain1. The global annual mean GPP is historically thought to be around 120 PgC yr-1 (refs. 2-6), which is about 30-50 PgC yr-1 lower than GPP inferred from the oxygen-18 (18O) isotope7 and soil respiration8. This disparity is a source of uncertainty in predicting climate-carbon cycle feedbacks9,10. Here we infer GPP from carbonyl sulfide, an innovative tracer for CO2 diffusion from ambient air to leaf chloroplasts through stomata and mesophyll layers. We demonstrate that explicitly representing mesophyll diffusion is important for accurately quantifying the spatiotemporal dynamics of carbonyl sulfide uptake by plants. From the estimate of carbonyl sulfide uptake by plants, we infer a global contemporary GPP of 157 (±8.5) PgC yr-1, which is consistent with estimates from 18O (150-175 PgC yr-1) and soil respiration ( PgC yr-1), but with an improved confidence level. Our global GPP is higher than satellite optical observation-driven estimates (120-140 PgC yr-1) that are used for Earth system model benchmarking. This difference predominantly occurs in the pan-tropical rainforests and is corroborated by ground measurements11, suggesting a more productive tropics than satellite-based GPP products indicated. As GPP is a primary determinant of terrestrial carbon sinks and may shape climate trajectories9,10, our findings lay a physiological foundation on which the understanding and prediction of carbon-climate feedbacks can be advanced.

Vertical Profile Analysis of Cloud Feedbacks

AbstractCloud feedback is the largest source of uncertainty in climate sensitivity. This feedback is generally discussed in terms of radiative flux at the top of the atmosphere, but the vertical distribution of the contribution of cloud changes to the top‐of‐atmosphere cloud feedback should be discussed to understand the feedback in greater detail. We have developed a simple analysis method called “vertical profile analysis of cloud feedback,” which simply uses radiative flux data from each level of the model. The analysis is applied for typical cloud regimes and the results are discussed together with cloud fraction change profiles. The advantages and disadvantages of the vertical profile analysis, compared with the commonly used International Satellite Cloud Climatology Project (ISCCP) histogram kernel method, are discussed. Our analysis has the advantage of resolving the detailed vertical profiles of the contribution to the top‐of‐atmosphere cloud feedback from the changes in cloud regimes associated with warming, such as reduced cloud cover and upward shifts of the cloud layer in subtropical low‐cloud regions as well as the increased height of the melting layer in tropical deep convection. The main disadvantage is that the vertical profile analysis cannot represent the feedback components sorted by cloud optical thickness as in the ISCCP histogram kernel method.

Quantifying measurement uncertainty in renal transplant biopsy assessment

IntroductionRenal transplant biopsies provide insights into graft health and support decision making. The current evidence on links between biopsy scores and transplant outcomes suggests there may be numerous factors affecting biopsy scores. Here we adopt measurement science approach to investigate the sources of uncertainty in biopsy assessment and suggest techniques to improve its robustness.MethodsHistological assessments, Remuzzi scores, biopsy processing and clinical variables are obtained from 144 repeat biopsies originating from 16 deceased-donor kidneys. We conducted sensitivity analysis to find the morphometric features with highest discriminating power and studied the dependencies of these features on biopsy and stain type. The analysis results formed a basis for recommendations on reducing the assessment variability.ResultsMost morphometric variables are influenced by the biopsy and stain types. The variables with the highest discriminatory power are sclerotic glomeruli counts, healthy glomeruli counts per unit area, percentages of interstitial fibrosis and tubular atrophy as well as diameter and lumen of the worst artery. A revised glomeruli adequacy score is proposed to improve the robustness of the glomeruli statistics, whereby a minimum of 104 µm2 of cortex tissue is recommended to keep type 1 and type 2 error probabilities below 0.15 and 0.2.DiscussionThe findings are transferable to several biopsy scoring systems. We hope that this work will help practitioners to understand the sources of statistical uncertainty and improve the utility of renal biopsy.

The Virgo Newtonian calibration system for the O4 observing run

Abstract After initial tests performed during previous observing runs, a Newtonian Calibrator (NCal) system was developed and installed on the Virgo gravitational wave detector for the O4 observing run. This system, which is continuously operated, provides the absolute calibration of Virgo for this run. Its 1-σ uncertainty of 0.17% on the amplitudes of the injected signals is better than that obtained with other calibration techniques like the photon calibrator (PCal). This paper presents this NCal system and details the different sources of uncertainties.

A novel frequency constrained unit commitment considering VSC-HVDC's frequency support in asynchronous interconnected system under renewable energy Source's uncertainty

The increasing share of renewable energy source (RES) poses a challenge to the frequency security of the power system. Frequency constrained unit commitment (FCUC) serves as an effective measure to address this challenge at the operational level. This paper introduces a novel FCUC model applicable to the asynchronous interconnected system connected by voltage source converter based HVDC (VSCHVDC), fully taking into account the frequency support capability of VSCHVDC in order to reduce the demand for synchronous generators' inertia and reserve while ensuring frequency security, thereby lowering operating costs. Additionally, the uncertainty of RES is also considered. The constraint expressions for three frequency indicators are derived based on a system frequency response model that includes frequency support from VSCHVDC. The proposed model optimizes unit commitment, generation and reserve dispatch and VSCHVDC transmission power and frequency response parameters, while addressing RES uncertainty using the distributionally robust chance constrained approach. In response to the highly nonlinear characteristics of the maximum frequency derivation (MFD) constraints, we propose an intelligent sampling-based support vector machine to convexify the MFD constraints and introduce a two-stage decomposition algorithm for solving the model. The effectiveness of the proposed model is demonstrated based on a modified IEEE RTS-79 system.