Abstract This study investigates the sensitivity of the thermosphere and ionosphere to variations in solar spectral irradiance. Using data from the SDO and SORCE missions collected between 2010 and 2018, we quantified the variability and uncertainty of solar spectral irradiance across wavelengths from 0.1 to 190 nm and developed a data‐driven method to generate perturbed versions of the irradiance. These perturbations were used to drive ensemble simulation experiments conducted during the 2021/2022 winter to assess sensitivity in the thermosphere and ionosphere. In addition to the experiment using statistically derived perturbations, three more ensemble experiments driven by different perturbation methods were also performed. Results show that both neutral temperature and electron density are highly sensitive to uncertainty in solar spectral irradiance, especially above 200 km altitude. Electron density is particularly influenced by soft X‐ray variability in the lower ionosphere. Comparisons with Swarm, ICON, and COSMIC‐2 satellite observations confirm the performance of ensemble simulation experiments on capturing the realistic thermospheric and ionospheric variability. Among the experiments, the one driven by statistically derived perturbations produces ensemble spreads that best match the observed variability. This experiment shows good agreement with all three data sets, while the others tend to overestimate or underestimate the variability. This work highlights the importance of accounting for uncertainty in external solar energy input in space weather models and demonstrates the value of data‐informed ensemble simulations in improving the accuracy and reliability of thermosphere and ionosphere forecasts.

Selection of the optimal intensity measure is an important contribution to reducing the numerous uncertainties in seismic inputs within the context of performance-based earthquake engineering, especially for unreinforced masonry buildings that exhibit strong nonlinear behaviour. While traditional metrics such as efficiency, sufficiency, and practicality have been successfully used to determine optimal intensity measures for seismic demand models and fragility curves, the impact of different intensity measures on the final vulnerability curves has not been sufficiently investigated. Therefore, a new vulnerability-based metric is proposed, based on the vulnerability curve variance and its first derivative, with the aim of determining the optimal intensity measure for new vulnerability models of mid-rise unreinforced masonry buildings. Both traditional and new metrics were used to evaluate the performance of common intensity measures, using a typical unreinforced masonry building located in Zagreb, Croatia as a case study. The new metric produced intensity measure rankings in line with traditional metrics, but additionally proved effective in quantifying the impact of intensity measure choice on the final vulnerability curve, making it a reliable tool for vulnerability modelling. Average spectral acceleration and peak ground velocity were among the best performing intensity measures, confirming their use for unreinforced masonry buildings.

Distributed multi-dimensional classification, where multiple nodes over a network induce a multi-dimensional classifier based on their own local data and a little information exchanged from neighbors, has received extensive attention in the academic community recently. Nevertheless, we observe that the classical distributed multi-dimensional classification formulation requires all training data to have definite feature attributes and complete labels. However, in real-world scenarios, due to measurement errors in distributed networks, the collected data samples consist of attributes with uncertainty. Additionally, a substantial proportion of multi-dimensional data faces challenges in label acquisition. Therefore, the key to achieving satisfactory performance in such a case is designing an effective method to model the input uncertainty and exploit weakly supervised information from the training data. Considering this, in this paper, we design a novel misclassification loss function that extracts effective information from uncertain data by treating it as the integral of misclassification loss over the potential data distribution. Additionally, we propose a new explicit feature mapping for constructing a nonlinear discriminant function. Based on this, we further put forward a novel manifold regularization term to recover multi-dimensional labels and simplify the original objective function to enable it to be optimized. By leveraging the gradient descent method, we optimize the simplified decentralized cost function and obtain the global optimal solution. We evaluate the performance of the proposed distributed semi-supervised multi-dimensional uncertain data classification algorithm, namely the dSMUDC algorithm, on several real datasets. The results of our experiments indicate that, in terms of all metrics, our proposed algorithm outperforms existing approaches to a significant extent.

Monocular visual measurement and vision-guided robotics technology find extensive application in modern automated manufacturing, particularly in aerospace assembly. However, during assembly pose measurement and guidance, the propagation and accumulation of multi-source errors—including those from visual measurement, hand–eye calibration, and robot calibration—impact final assembly accuracy. To address this issue, this study first proposes an uncertainty analysis method for monocular visual measurement systems in assembly pose, encompassing the determination of uncertainty propagation paths and input uncertainty values. Building on this foundation, the system’s uncertainty is analyzed. Inspired by the uncertainty analysis results, this study further proposes a direct one-step solution to a series of problems in robot calibration and hand–eye calibration using a nonlinear mapping estimation method. Through experiments and discussion, a high-performance, one-step, end-to-end pose estimation convolutional neural network (OECNN) is constructed. The OECNN achieves direct mapping from the pose variation of the target object to the drive volume variation of the positioner. The uncertainty analysis conducted in this study yields a series of conclusions that are significant for further enhancing the precision of assembly pose estimation. The proposed uncertainty analysis methodology may also serve as a reference for uncertainty analysis in complex systems. Experimental validation demonstrates that the proposed one-step end-to-end pose estimation method exhibits high accuracy. It can be applied to automated assembly tasks involving various vision-guided robots, including those with typical configurations, and it is particularly suitable for high-precision assembly scenarios, such as aircraft assembly.

Nearshoring in Mexico is expanding rapidly, yet chronic volatility in the national power grid threatens the reliability and cost-competitiveness of relocated manufacturing lines. To inform strategic mitigation, this study presents a hybrid Fuzzy–CES decision-support framework that embeds the Constant-Elasticity-of-Substitution (CES) production function within a Mamdani Fuzzy-Inference Engine, implemented in both Type-1 and Interval Type-2 variants, to evaluate and optimize production adaptability in energy-constrained environments. Using sector-wide data from Mexico’s automotive industry, key input variables (energy reliability, capital intensity, and labor availability) are objectively quantified and normalized to reflect the realities of regional plant operations. The system linguistically classifies each facility’s production elasticity as low, moderate, or high, and generates actionable recommendations for resource allocation, such as targeted investments in renewable microgrids or workforce strategies. Implemented in MATLAB, simulation results confirm that, while high capital and labor inputs are essential, energy reliability remains the primary bottleneck limiting adaptability; only states with all three strong factors achieve maximum resilience. The Type-2 fuzzy approach demonstrates superior robustness to input uncertainty, enhancing managerial decision-making under volatile grid conditions. In addition, a case study regarding the automotive industry is presented to illustrate how the proposed framework is implemented. The same structure can be used to deploy it in another industry. This research offers a transparent, data-driven tool to inform both firm-level investment and regional policy, directly supporting Mexico’s efforts to sustain competitiveness and resilience in the global shift toward nearshoring.

Reliable precipitation inputs are essential for hydrological modeling, yet global precipitation products often exhibit substantial discrepancies that introduce significant uncertainties into streamflow simulations and projections. In this study, we assessed the relative contribution of precipitation dataset uncertainty to discharge simulations and projections, in comparison with uncertainties from model structure, model parameters, and climate projections, in the Liujiang catchment, southwest China. Three widely used satellite-based products (CHIRPS, PERSIANN, and IMERG) and one reanalysis dataset (ERA5) were combined with three hydrological models of varying structural complexity to simulate streamflow. Using an ANOVA-based variance decomposition framework, we quantified the contributions of different uncertainty sources under both historical and future climate conditions. Results showed that precipitation input uncertainty dominates discharge simulations during the calibration period, contributing over 60% of total variance particularly at high flows, while interactions among precipitation, model structure, and parameters govern low-flow simulations. Under future climate scenarios, climate projection uncertainty overwhelmingly dominates discharge predictions with 50–80% of uncertainty contribution, yet precipitation products still contribute significantly across time scales. The compensation of precipitation biases by hydrological models can cause parameter values to deviate from their true physical meaning. This deviation may further amplify the differences in discharge projections driven by different precipitation products under future climate conditions and increase the overall uncertainty of streamflow projections. Overall, this study introduced an integrated approach to simultaneously assess precipitation uncertainty across flow regimes and future climate scenarios. These results emphasized the necessity of using ensemble approaches that incorporate multiple precipitation products in hydrological forecasting and impact studies, particularly in data-scarce regions reliant on global datasets.

ABSTRACT Uncertainty estimation in integrated dynamic models, especially for water quality simulations, is challenging due to its complexity and limited observations. This study employs variance decomposition analysis (VDA) to estimate multi-source input uncertainties (precipitation, temperature, point source pollution, and social economy) and their propagation for integrated water quantity and quality simulations in the HEQM model. The Zhugan River watershed, a typical agricultural area in southern Huai River, China, was chosen for the case study. Results showed that precipitation is the main source of uncertainty for both runoff and TN load simulations, with contributions of 64.1% and 61.3%, respectively. Temperature is the main direct uncertainty source for TN load simulation (80.3%), along with contributions from point source pollution (15.1%) and social economy (4.6%). Furthermore, 70.7% of TN load simulation uncertainty propagates from upstream runoff simulation. Estimating multi-source input uncertainties and their propagation is essential for improving integrated water system models.

Design and stability analysis of a robust model predictive control method for voltage regulation in DC-DC buck converters subject to the input and load uncertainties

Rapeseed-mustard (Brassica spp.) is one of the most significant oilseed crops globally, with India being a major contributor, accounting for 11% of world production. Despite advancements in mustard cultivation practices, there remains a lack of comprehensive analysis integrating resource efficiency and input interactions to optimize yields sustainably. Furthermore, limited studies have employed advanced methodologies to assess the impacts of input uncertainties on yield stability and risk management. Therefore, the study evaluated the resource use efficiency in mustard cultivation through the Cobb-Douglas production function, Monte Carlo simulations, offering insights into input utilization and yield variability under uncertain conditions and sensitivity analysis for specific inputs’ contribution to yield. Results revealed imbalances in resource utilization; land and soil qualities are underutilized, while labor, plant protection chemicals, and machinery are overutilized. Fertilizer and seed inputs emerged as significant positive influencers of yield, with sulphur and fertilizer identified as critical factors through sensitivity analysis. Monte Carlo demonstrates yield stability, predicting a 100% probability of achieving at least 6 quintals per acre (1483 Kg/ha) under current input conditions. Policymakers can design targeted interventions to reduce regional productivity disparities and foster sustainable growth in the rapeseed-mustard sector. Findings also underscore the need for optimizing input utilization to balance economic, agronomic, and environmental outcomes, as well as adopting better practices for India’s oilseed sector.

Following the onset of an index chikungunya case on July 8, 2025, a significant outbreak occurred in Foshan, Guangdong Province, China. This study aimed to quantify the outbreak's transmissibility between June 16 and July 21, 2025. Data were obtained from local Government, Statistics Bureau, Centers for Disease Control and Prevention, and the relevant literature. We employed a transmission dynamic model that integrated human host-vector transmission to estimate the basic reproduction number ( ). The key parameters of the model were calibrated using early-phase limited surveillance data on the cumulative number of cases. We calculated the correlation coefficient to evaluate the accuracy of this calibration. Sensitivity analyses were conducted to quantify the uncertainties in the parameter inputs. Between June 16 and July 31, 2025, cumulative cases reached 2658, with 92.96% concentrated in the Shunde District. Model simulations showed that a cumulative case count is consistent with local reports (Pearson r = 0.99, P < 0.001). The median overall of this outbreak was 7.2807 [interquntile range (IQR): 7.2809‒7.2811], suggesting sustained transmission. Human-to-mosquito transmission (Median: 22.79, IQR: 5.44‒40.14) had a higher median than mosquito-to-human transmission (Median: 2.33, IQR:0.58‒4.07) (Mann-Whitney U P < 0.001). Symptomatic infections (Median: 19.60, IQR: 4.68‒34.52) had a higher median than asymptomatic infections (Median: 3.19, IQR: 0.76‒5.62) (Mann-Whitney U P < 0.001). The model simulated cumulative cases were sensitive to parameters , , , and . The overall and mosquito-to-human were sensitive to parameters and . The human-to-mosquito and symptomatic human-to-mosquito were sensitive to parameter , while asymptomatic human-to-mosquito was sensitive to parameter CONCLUSIONS: The transmissibility of CHIKV is high. Human-to-mosquito transmission, especially symptomatic infections to mosquito transmission, was the main driver of chikungunya virus transmission. These findings underscore the critical need for enhanced screening of travellers from endemic regions, timely case isolation, and targeted vector control to mitigate autochthonous transmission.

Abstract Over the past two decades, the Amazon has experienced four severe large-scale droughts (i.e., 2005, 2010, 2015/16 and 2023), leading to drastically reduced water availability, slowed vegetation growth and higher forest mortality. As future droughts are expected to become more frequent and severe, accurately predicting the unprecedentedly low water storage levels and water shortages in advance is crucial. Herein, we developed a new approach to predict terrestrial water storage (TWS) during droughts, based on monthly changes in TWS (ΔTWS) and meteorological variables from 2003 to 2023. The model was trained during non-drought months and assessed during the four droughts when TWS values are well below the range of training data. The ΔTWS-based model excels in predicting drought-month TWS even only using precipitation and incoming solar radiation, with average correlation (R) over 0.9 and RMSE below 50 mm. The model also showed superior skills for predicting drought TWS months lead-time, with the 3-month prediction achieved high performance (R &gt; 0.8, RMSE &lt; 80 mm). We further examined TWS predictions during the large-scale 2023 drought and found that the predicted TWS showed high spatial agreement with observed TWS, with all 1-, 2-, and 3-month lead-times reaching average R values over 0.9. Then we evaluated water deficits in the driest months (September – December) in 2023. The model predicted the affected regions with reasonable accuracy, achieving an average of 72% even at 3-month lead-time. We also analysed how uncertainty in meteorological inputs affects model performance, revealing higher input uncertainty reduced the model performance. This study presents a reliable approach for estimating and predicting low water storage during severe large-scale droughts, enabling early warnings of water deficits across the Amazon. This study could be generalized to other regions, supporting proactive water resource management, water security policies, ecosystem protection and climate adaptation strategies.

Counteracting input uncertainty effects of crystallization process in achieving consistent crystal size distribution

A self-attention model for robust rigid slice-to-volume registration of functional MRI.

Background and ObjectiveThe burden of oral diseases is high in the Philippines. The global burden of disease study in 2019 estimated that 44 million Filipinos are affected by oral disorder. More specifically, 29 million Filipinos have untreated dental caries. Outpatients' dental health services are not covered by PhilHealth benefit package. There is a need to include key oral health interventions such as basic prevention and treatment in PhilHealth benefit package to be delivered at the primary health care settings (WHO TSA 153980). The study aimed to determine the incremental cost-effectiveness ratio (ICER) of a set of oral health care services to be delivered at different levels of health care within a comprehensive PhilHealth benefit package.MethodsThis study evaluates the cost-effectiveness of including basic oral health services in the PhilHealth benefit package using a Markov modelling approach. The target population consists of Filipino adults and children at risk for dental diseases who are potential beneficiaries of PhilHealth. The intervention under consideration includes dental consultation, oral prophylaxis, topical fluoride application, silver diamine fluoride application, dental filling, and tooth extraction. The comparator is the current standard of care, which involves out-of-pocket payments for oral health services or limited access to subsidized dental care. The primary outcomes assessed include the incremental cost-effectiveness ratio (ICER) per quality-adjusted life year (QALY) gained. A Markov model was constructed with a time horizon of 50 years to simulate the lifespan of Filipinos up to the average life expectancy of 70 years old, using a cycle length of one year to reflect disease progression and treatment effects overtime. Model parameters were derived from literature and expert opinion. Sensitivity analyses, including one-way and probabilistic sensitivity analyses, were conducted to assess uncertainty in model inputs. The analysis was carried out from a societal perspective incorporating direct medical and non-medical costs, and indirect costs.ResultsA Markov model showed that a subsidized package is a cost-effective approach compared to the current situation of no subsidy, with an ICER of PhP 75,636 (1,535.76 USD) per disability adjusted life year (DALY) averted. The computed ICER was considered good value for money as it was below 2021 GDP per capita of the Philippines of PhP 174,286 (3,538.80 USD). One-way sensitivity analysis showed that the cost of preventive treatment had the most significant impact on the model, and a price threshold of greater than PhP 3,062 (62.17 USD) for preventive treatment will render the subsidized package no longer cost-effective. The budget impact analysis showed a 1.63% increase in budget annually with the current situation of no subsidy. Rolling out a subsidized oral health package will entail a significant increase in government expenses during the first year but a decreasing trend of 1-2% annually for the following years as the program takes its effect.ConclusionA subsidized oral health package is a cost-effective approach from a societal perspective. It will entail a significant increase in government expenditure during the start of its roll out but will eventually result in a decreasing trend of expenses as the years progress.

The kinematic reliability analysis of robotic manipulators is crucial due to uncertainties such as joint variations, manufacturing tolerances, and external disturbances. Traditional methods often rely on analytical techniques that struggle with nonlinear performance functions and fail to account for trajectory-based reliability. To overcome these limitations, this paper proposes a novel surrogate model-based approach using Kriging to estimate the reliability of robotic manipulator kinematics while considering end-effector trajectories. The methodology begins with building an initial Kriging surrogate model to analyze reliability, effectively capturing how input uncertainties influence trajectory accuracy. This model is then refined through statistical sampling techniques, ensuring an efficient evaluation of manipulator performance against specified tolerances. The approach reduces computational complexity while maintaining prediction accuracy. Compared to Monte Carlo Simulation (MCS), the proposed Kriging-based method reduces the number of function evaluations by over 98%, achieving comparable reliability predictions with significantly fewer function calls, and enhancing efficiency in kinematic reliability analysis. The proposed method is validated on two 6-DOF industrial robots, including the UR5, demonstrating improved computational efficiency and accuracy. This work has practical applications in manufacturing and healthcare, where enhanced kinematic reliability leads to greater operational efficiency and safety.

The kinematic reliability analysis of robotic manipulators is crucial due to uncertainties such as joint variations, manufacturing tolerances, and external disturbances. Traditional methods often rely on analytical techniques that struggle with nonlinear performance functions and fail to account for trajectory-based reliability. To overcome these limitations, this paper proposes a novel surrogate model-based approach using Kriging to estimate the reliability of robotic manipulator kinematics while considering end-effector trajectories. The methodology begins with building an initial Kriging surrogate model to analyze reliability, effectively capturing how input uncertainties influence trajectory accuracy. This model is then refined through statistical sampling techniques, ensuring an efficient evaluation of manipulator performance against specified tolerances. The approach reduces computational complexity while maintaining prediction accuracy. Compared to Monte Carlo Simulation (MCS), the proposed Kriging-based method reduces the number of function evaluations by over 98%, achieving comparable reliability predictions with significantly fewer function calls, and enhancing efficiency in kinematic reliability analysis. The proposed method is validated on two 6-DOF industrial robots, including the UR5, demonstrating improved computational efficiency and accuracy. This work has practical applications in manufacturing and healthcare, where enhanced kinematic reliability leads to greater operational efficiency and safety.

The present work focuses on the sensitivity assessment of simulation results for a severe accident scenario in the Spent Fuel Pool of Fukushima Daiichi Unit 4, using a simplified case study. The analysis is performed using the ASTEC code, based on an input deck developed by INRNE-BAS under the EU Horizon 2020 MUSA project. A probabilistic approach is applied for the propagation of input uncertainties using the SUNSET statistical tool. A total of 100 ASTEC simulations are conducted, enabling the estimation of 95%/95% probability and confidence levels for key outputs using Wilks' formula. The paper outlines the step-by-step application of the ASTEC/SUNSET methodology, its adaptation, and the main results.

Abstract Erosion measurements inherently involve uncertainty due to the complex interaction of fluid and particle dynamics. Although a variation factor of 0.5–2 is typically considered acceptable between repeated tests, experimental erosion often shows deviations by orders of magnitude. This highlights a critical gap in the literature regarding the reliability and repeatability of erosion measurements. The present work addresses this challenge by applying statistical techniques to quantify and reduce uncertainty, thereby improving confidence in both experimental and computational fluid dynamics (CFD)-based erosion predictions. Gas-sand erosion experiments selected from the literature are repeated to validate and refine experimental data. A standard 3-in. (76.2 mm) stainless steel elbow with a bend radius to pipe diameter ratio (r/D) equal to 1.5 is used. Wall thickness losses are measured using fixed-mounted ultrasonic transducers at six locations on the outer wall for gas velocities of 15, 23, and 31 m/s and sand particle sizes of 75 and 300 μm. A statistical approach employing the 99% confidence interval is used to conservatively identify and assess statistical anomalies. Upper and lower bounds of erosion are presented to visualize the bands of uncertainty around the average profile. By applying this analysis, the variance of the experimental erosion values is reduced by 20–99% at different locations. Input, modeling, and numerical uncertainties related to CFD simulations are also quantified. Finally, experimental erosion profiles are compared with CFD results, and the propagation of experimental and CFD uncertainties is analyzed for increasing gas velocities and both sand particle sizes.

Calcium supplementation during pregnancy can reduce the risk of preeclampsia and preterm birth. Few countries have implemented WHO-recommended high-dose calcium supplementation (1500–2000mg/day), due to adherence and cost concerns. However, low-dose calcium supplementation (one 500mg tablet daily) has recently been shown to be similarly efficacious as high-dose supplementation. We assessed the cost-effectiveness of low-dose calcium supplementation during pregnancy, in low- and middle-income countries (LMICs) with low dietary calcium intake. To do so, we conducted a mathematical modelling analysis in which we estimated the lifetime health outcomes (cases, deaths, and DALYs averted) and costs of low-dose calcium supplementation provided through routine antenatal care to women giving birth in 2024, as compared to no supplementation. We assessed costs (2022 USD) from a health system perspective, including cost-savings from averted care for preeclampsia and preterm birth. This analysis showed that low-dose calcium supplementation could prevent 1.3 (95% uncertainty interval: 0.2, 2.6) million preterm births (a 10% (2, 18) reduction), 1.8 (1.0, 2.8) million preeclampsia cases (a 23% (14, 32) reduction), as well as 5.9 (1.3, 12.9) million disability-adjusted life years (DALYs). Intervention costs would be $267 (220, 318) million and produce cost-savings of $56 (26, 86) million, with incremental costs per DALY averted of $90 (38, 389) across all countries, and a return on investment of 19.1 (3.8, 39.5). The intervention was cost-effective in 119 of 129 countries modeled when compared to setting-specific cost-effectiveness thresholds. While there was substantial uncertainty in several inputs, cost-effectiveness conclusions were robust to parameter uncertainty and alternative analytic assumptions. Based on these results, low-dose calcium supplementation provided during pregnancy is cost-effective for prevention of preeclampsia and preterm birth in most LMICs.

Seafloor scattering measurements are analyzed from a series of experiments (lasting from 3 weeks to 5 months) located in two shallow-water locations near Portsmouth, New Hampshire, USA. The experimental setup consisted of a tripod with transducers (operating at 38, 70, and 200 kHz) mounted ∼2 m above the seafloor and oriented at 17.5° grazing angle. Environmental measurements were obtained using a wave-sensing conductivity, temperature, and depth probe. Seafloor roughness was estimated using stereo imaging techniques on underwater photographs. The measured seafloor scattering strengths were averaged over calm periods between storm events. The overall levels and trends are compared to predictions from the small-slope approximation model. Data-model differences may be due to uncertainty in the model inputs and/or scattering mechanisms not captured in the model. In addition, the results are compared to reported estimates of scattering strength in sand-sized sediments. At a given frequency, grazing angle, and grain size, scattering strengths among all measurements vary up to ∼ 20 dB.