Structural Uncertainty Research Articles (Page 1)

Abstract This article proposes the design of an adaptive fractional‐order neural network controller based on the backstepping sliding mode control approach for fully actuated 3 degree‐of‐freedom (DOF) autonomous underwater vehicles (AUVs) in the presence of uncertainties and external disturbances. Compared to existing results, the design of an adaptive fractional‐order controller for tracking the desired trajectory of AUV is represented for the first time in this paper. Due to the presence of parametric structural uncertainties, a radial basis function neural network (RBFNN) approximation is employed in combination with adaptive control systems. Furthermore, the robustness of the control law is enhanced against unstructured uncertainties (external disturbances and modeling errors) by employing the ‐modification approach and proving their boundedness. Moreover, the control system stability is analyzed using the fractional Lyapunov approach, demonstrating that all closed‐loop control signals are uniformly bounded. Additionally, the convergence of the Lyapunov variables (tracking error) to a very small neighborhood of zero is proven by adjusting the design parameters of the controller.

ABSTRACT This article examines the role of mediation in emerging middle power conduct in an increasingly fragmented world. It asks why and how emerging middle powers seek mediator roles in international conflicts, focusing on Turkey and Indonesia's responses to the Russia–Ukraine war. Through content analysis of public documents and interviews with diplomats, it argues that the war has enabled emerging middle powers to seek influence by emphasising their bridge‐building capacity. While the conventional literature on middle powers explains such activism in terms of the traditional leadership–followership dynamic—where middle powers are seen as followers who act as ‘good international citizens’—our findings suggest that international systemic instability, the search for status and domestic factors provide better explanations for their actions. Specifically, we argue that the saliency of middle power followership diminishes as a source of status under conditions of structural uncertainty and manifests through stability‐seeking conduct like mediation. Concurrently, we show that mediatory approaches are motivated not only by international considerations but also significantly by domestic elite concerns. These findings contribute to middle power scholarship by illustrating how these states use mediation to seek both domestic regime support and international recognition, offering a more nuanced understanding of emerging middle power agency.

To accurately assess the seismic risk of bridges, this study systematically conducted probabilistic seismic hazard–fragility–risk assessments using a reinforced concrete continuous girder bridge as a case study. First, the CPSHA method from China’s fifth-generation seismic zoning framework was employed to calculate the Peak Ground Acceleration (PGA) with 2%, 10%, and 63% exceedance probabilities over 50 years as 171.16 gal, 98.10 gal, and 28.61 gal, respectively, classifying the site as being with 0.10 g zone (basic intensity VII). Second, by innovatively integrating the Response Surface Method with Monte Carlo simulation, the study efficiently quantified the coupled effects of structural parameter and ground motion uncertainties, a finite element model was established based on OpenSees, and the seismic fragility curves were plotted. Finally, the risk probability of seismic damage was calculated based on the seismic hazard curve method. The results demonstrate that the study area encompasses 46 potential seismic sources according to China’s fifth-generation zoning. The seismic fragility curves clearly show that side piers and their bearings are generally more susceptible to damage than middle piers and their bearings. Over 50 years, the pier risk probabilities for the intact, slight, moderate, severe damage, and collapse are 68.90%, 6.22%, 15.75%, 7.86%, and 1.27%, while the corresponding probabilities of bearing are 3.54%, 44.11%, 25.64%, 7.74%, and 18.97%, indicating significantly higher bearing risks at the moderate damage and collapse levels. The method proposed in this study is applicable to various types of bridges and has high promotion and application value.

Abstract We developed a physics‐aware denoising diffusion based probabilistic model for estimating subsurface soil moisture from surface observations. Unlike traditional physical‐based methods that rely on site‐specific soil parameters, our approach leverages a data‐driven framework constrained by smoothness and Fickian diffusion principles to ensure physically consistent predictions. The model is trained and evaluated on hourly soil moisture data from 20 globally distributed sites, and further validated on high‐resolution 10‐min observations from four African stations. The results demonstrate robust performance across depths (10–40 cm), with the model maintaining high accuracy and low bias, even under varying temporal resolutions. We also analyzed the effect of input noise through a structured uncertainty experiment, highlighting the model's stability and reliability. By eliminating the need for explicit physical inputs and enabling uncertainty quantification, this framework offers a scalable solution for operational soil moisture monitoring, particularly in data‐sparse or heterogeneous regions.

Masonry arch bridges supported by shallow foundations are vulnerable to foundation scour due to the low burial depth. Although current studies have investigated the structural behaviours and failure mechanism of masonry arch bridges under hydraulic effects, the three-dimensional (3D) nature of scour has not been treated accurately. Most studies focus on evaluating their structural performance under foundation scour through a deterministic approach without considering the structural, hydraulic and geological uncertainties. A lack of risk-based indicators hinders the anti-flood design of bridges. To bridge the gap, this study performed a probabilistic analysis considering the 3D scour morphology with a risk-based indicator. Firstly, a refined finite element model of a masonry arch bridge was built considering the spatial scour morphology. Then, possible failure modes of the bridge were summarised and assessed. Next, fragility evaluations were performed to deduce the potential failure mode. Finally, a risk-based indicator was proposed to include the uncertainty of hydraulic parameters, and the results of two masonry arch bridges with the same structural and geotechnical parameters but different hydraulic parameters were compared. The systematic analysis includes the uncertainty of hydraulic and structural parameters on the anti-flood risk of masonry arch bridges and can significantly improve their anti-flood performance.

Abstract. In marine ecosystems, net primary production (NPP) is important, not merely as a critical indicator of ecosystem health, but also as an essential component in the global carbon cycling process. Despite its significance, the accurate estimation of NPP is plagued by uncertainty stemming from multiple sources, including measurement challenges in the field, errors in satellite-based inversion methods, and inherent variability in ecosystem dynamics. This study focuses on the aquatic environs of Weizhou Island, located off the coast of Guangxi, China, and introduces an advanced probability prediction model aimed at improving NPP estimation accuracy while partially addressing its associated uncertainties within the current modeling framework. The dataset comprises eight distinct sets of monitoring data spanning January 2007 to February 2018. NPP values were derived using three widely recognized estimation methods – the Vertically Generalized Production Model (VGPM); the Carbon, Absorption, and Fluorescence Euphotic-resolving (CAFE) model; and the Carbon-based Productivity Model (CbPM) – serving as model outputs for further analysis. The study evaluates two probability prediction approaches: a Bayesian probability prediction model based on empirical distribution and a deep-learning-based probability prediction model. These methods are employed to meticulously quantify the uncertainty in NPP. The results highlight the effectiveness of probability prediction models in capturing the dynamic trends and uncertainties in marine NPP. Notably, the neural-network-based model demonstrates superior accuracy and reliability compared to the Bayesian approach. Furthermore, the models are applied to prognosticate NPP variations in specific marine regions, efficaciously elucidating interannual trends. This research advances the methodological precision in partially quantifying NPP uncertainty related to parameter and input data variability while highlighting the need for future structural uncertainty assessments through multi-model comparisons.

This review synthesizes the current knowledge of the various natural and human-caused processes that influence the evolution of sandy beaches and explores ways to improve predictions. Short-term storm-driven dynamics have been extensively studied, but long-term changes remain poorly understood due to a limited grasp of non-wave drivers, outdated topo-bathymetric (land–sea continuum digital elevation model) data, and an absence of systematic uncertainty assessments. In this study, we classify and analyze the various drivers of beach change, including meteorological, oceanographic, geological, biological, and human influences, and we highlight their interactions across spatial and temporal scales. We place special emphasis on the role of remote sensing, detailing the capacities and limitations of optical, radar, lidar, unmanned aerial vehicle (UAV), video systems and satellite Earth observation for monitoring shoreline change, nearshore bathymetry (or seafloor), sediment dynamics, and ecosystem drivers. A case study from the Langue de Barbarie in Senegal, West Africa, illustrates the integration of in situ measurements, satellite observations, and modeling to identify local forcing factors. Based on this synthesis, we propose a structured framework for quantifying uncertainty that encompasses data, parameter, structural, and scenario uncertainties. We also outline ways to dynamically update nearshore bathymetry to improve predictive ability. Finally, we identify key challenges and opportunities for future coastal forecasting and emphasize the need for multi-sensor integration, hybrid modeling approaches, and holistic classifications that move beyond wave-only paradigms.

A control perspective on parametric uncertainty of building structures: A literature study

ABSTRACT This study presents a trivariate flood frequency analysis (FFA) framework that integrates copula theory with both parametric and non-parametric marginal distributions to model the joint behavior of peak discharge, flood volume, and duration. The approach captures nonlinear interdependencies among flood characteristics without assuming specific marginal forms, enhancing realism in flood modeling. The model was applied to long-term hydrological records from the Zayandeh-Roud Basin in central Iran, a semi-arid watershed facing intensifying climatic and anthropogenic pressures. Performance evaluation based on root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) confirmed that the proposed trivariate model outperforms traditional univariate and bivariate approaches in predictive accuracy and parsimony. The framework also supports real-world decision-making by improving the reliability of flood risk estimation, infrastructure planning, and reservoir operations in non-stationary conditions. Future scenarios generated using the LARS-WG weather generator and MRI-ESM2-0 model under CMIP6 SSP2-4.5 and SSP5-8.5 pathways indicate increased compound flood risks in the coming decades. Moreover, the study highlights the importance of accounting for structural uncertainty in climate projections to ensure more robust and adaptive long-term flood risk management strategies under changing environmental conditions.

Groundwater is threatened by climate change and human activities, with depletion and contamination emerging as critical risks, necessitating the development of models to estimate its response to changes. Artificial intelligence (AI) is gaining increasing traction in groundwater applications on multiple scales. This review evaluates the potential of AI to model groundwater systems including flow and transport problems across various scales. AI has been leveraged to identify contamination sources and optimize remediation strategies at site scales. The substantial promise of AI in predicting groundwater levels and groundwater quality and conducting risk assessments has been evidenced from regionally to globally. AI has demonstrated potential in cross-scale modeling, with initial progress achieved in upscaling hydraulic parameters and downscaling groundwater levels and quality predictions. Quantifying uncertainties in input data, model structures, and predictive outcomes has been used to enhance model reliability. In addition, physics-consistent explainability decreases as the simulation scale expands due to the increasing challenges in describing boundary conditions and the limited applicability of governing equations. Establishing an evaluation system for multisource uncertainties, enhancing data accessibility, and integrating various post hoc techniques and physical constraints to enhance model explainability present opportunities for the applications of AI in groundwater modeling.

Understanding uncertainties in meteorological extremes induced by Atmospheric river (AR) structural uncertainties can help to develop effective strategies to mitigate AR induced hazards and adapt to changing climate conditions. As a first step, this study examines the statistical relationship between AR structural uncertainty and the characterisation of associated meteorological extremes over the Euro-Atlantic region, using long-term historical data from ECMWF Reanalysis v5 (ERA5) during 1940 to 2022. Leveraging the Bayesian AR detection (BARD), a form of statistical machine learning model in the Toolkit for Extreme Climate Analysis (TECA), we examine the impact of structural uncertainties in AR dimensions on daily precipitation (wet), wind speeds (windy), and temperature (warm/cold) anomalies and extremes over Europe, the UK and Scandinavia. A large spread in the aggregated detected AR probabilities (ARP) spatially and temporally led to differences in ARs' attributes, such as frequency, integrated water vapour transport (IVT; intensity), and their impact on weather parameters, anomalies and extremes at selected probability thresholds across space and time. The magnitude of AR impacts and associated meteorological phenomena over land varies based on the chosen deciles (dividing ARP into ten equal parts with a 0.1 increase) of ARPs, along with the default threshold from the model ([Formula: see text]). AR intensities and landfalling area are increasing over the study period, irrespective of the selected ARP. The effects of AR structural uncertainties are more prominent over inland Europe and Scandinavia than over coastal Europe and the UK. The physical and meteorological phenomena underlying these results require further exploration to understand the impact of landfalling ARs on land.

Abstract Computational fluid dynamics (CFD) simulations based on the Reynolds-Averaged Navier-Stokes (RANS) equations are essential for aerodynamic analysis and compressor design. However, structural uncertainties arising from the assumptions embedded in the constitutive model limit the precision of these simulations. This study proposes an improved eigenspace perturbation framework (EPF) to quantify structural uncertainties in RANS simulations of the transonic compressor NASA Rotor 67. By combining a non-uniform eigenvalue perturbation technique with relaxation regulation and a partial eigenvector perturbation method, the improved EPF enhances robustness while maintaining physical realizability. The study systematically assesses the impact of this strategy on overall performance metrics, including total pressure ratio and isentropic efficiency, as well as localized parameters like total pressure and temperature. Analysis of turbulent kinetic energy distribution, shock structure, and loss mechanisms reveals that eigenspace perturbation-induced changes in Reynolds stress significantly influence shock position, intensity, and flow structure near the blade trailing edge and channel. Moreover, the spatial variations in relative total pressure loss indicate that flow modulation patterns due to structural uncertainty differ across regions.

The main protease (Mpro) is a critical target in the design of antiviral drugs against coronaviruses, while accurately predicting the binding affinity between small molecules and this target remains a key challenge. In the recent Polaris challenge of blind drug-potency prediction targeting SARS-CoV-2 and MERS-CoV Mpro, we developed a multimodal multitask graph attention network based on the customized gate control framework (abbreviated as MultiMolCGC). Our team achieved top performance among all participating teams in the blind prediction challenge. In this paper, we detail the model development and further explorations in terms of pretraining, adjusting the model architecture, and many others. Our model consistently outperforms traditional machine learning baselines, demonstrating the effectiveness of end-to-end deep learning in capturing complex molecular interactions. Integrating multimodal representations proved essential, and the multitask specialized gating architecture outperformed both single-task and nonspecialized multitask variants, highlighting the value of tailored knowledge sharing. While auxiliary loss weighting and hyperparameter tuning offered modest improvements, incorporating predicted structural data unexpectedly reduced performance, likely due to structural uncertainty. Notably, pretraining on large-scale synthetic docking data sets significantly enhanced performance in low-data scenarios, reducing dependence on experimental pIC50 data. The numerical results highlight the potential of MultiMolCGC as a robust and accurate deep-learning framework for protein-ligand binding in future studies.

The rapid rate of virus evolution, while useful for outbreak investigations, poses a challenge for accurately estimating long-term viral evolutionary divergence and leaves us with little genomic traces at deep evolutionary timescales, complicating the reconstruction of deep virus evolutionary history. Recent advancements in protein structure prediction and computational biology have opened up new avenues and enabled us to peer back further in time and with greater clarity than ever before. Here, we review recent approaches to reconstructing the deep evolutionary history of viruses. In particular, we focus on how Bayesian models that account for evolutionary rates that are time-dependent may provide better estimates of the timescale of virus evolution. We then outline approaches to structural phylogenetics and their application to reconstructing the evolutionary history of viruses. Despite current limitations, including structural prediction uncertainty, conformational variation, and limited benchmarking, structural phylogenetics appears promising, particularly where sequence-level homology is eroded. The availability of and ease with which virus structures can now be predicted is likely to drive additional statistical and software developments in this area. Ultimately, answering fundamental questions of virus origins and early diversification, long-term host associations, virus classification, and the timescale of viral diseases will likely require unifying sequence and structural information into a temporally aware evolutionary inference framework.

Abstract Structural uncertainties and unresolved features in fault zones hinder the assessment of leakage risks in subsurface storage. Understanding multi‐scale uncertainties in fracture network conductivity is crucial for mitigating risks and reliably modeling upscaled fault leakage rates. Conventional models, such as the Cubic Law, which is based on mechanical aperture measurements, often neglect fracture roughness, leading to model misspecifications and inaccurate conductivity estimates. Here, we develop a physics‐informed, AI‐driven correction of these model misspecifications by automatically integrating roughness effects and small‐scale structural uncertainties. Using Bayesian inference combined with data‐driven and geometric corrections, we reconstruct local hydraulic aperture fields that reliably estimate fracture conductivities. By leveraging interactions across scales, we improve upon traditional empirical corrections and provide a framework for propagating uncertainties from individual fractures to network scales. Our approach thereby supports robust calibration of conductivity ranges for fault leakage sensitivity analyses, offering a scalable solution for subsurface risk assessment.

The United States–China trade war placed Vietnam in a strategic yet vulnerable position, as a non-involved country that significantly benefited from trade and investment relocation. This situation contributed to Vietnam’s relative capability growth and created new space for asserting its role in regional economic and political structures. This article analyzes Vietnam’s foreign policy strategy in response to the rivalry between two great powers by applying a geo-economic approach and qualitative research methods. The findings reveal that Vietnam adopts a hedging strategy by undertaking active neutrality, contradictory policy—simultaneously accepting and rejecting dominance—and by diversifying its global partnerships to preserve policy autonomy. Vietnam’s hedging reflects both a function of national interest protection and an effort to leverage the opportunity created by relative capability enhancement amidst structural uncertainty. Thus, hedging emerges as a relevant geo-economic instrument for middle states in navigating major power competition while maintaining strategic space and sovereign decision-making.

In this study, the key challenges faced by global navigation satellite systems (GNSSs) in the field of security are addressed, and an eye diagram-based deep learning framework for intelligent classification of interference types is proposed. GNSS signals are first transformed into two-dimensional eye diagrams, enabling a novel visual representation wherein interference types are distinguished through entropy-centric feature analysis. Specifically, the quantification of information entropy within these diagrams serves as a theoretical foundation for extracting salient discriminative features, reflecting the structural complexity and uncertainty of the underlying signal distortions. We designed a hybrid architecture that integrates spatial feature extraction, gradient stability enhancement, and time dynamics modeling capabilities and combines the advantages of a convolutional neural network, residual network, and bidirectional long short-term memory network. To further improve model performance, we propose an improved coati optimization algorithm (ICOA), which combines chaotic mapping, an elite perturbation mechanism, and an adaptive weighting strategy for hyperparameter optimization. Compared with mainstream optimization methods, this algorithm improves the convergence accuracy by more than 30%. Experimental results on jamming datasets (continuous wave interference, chirp interference, pulse interference, frequency-modulated interference, amplitude-modulated interference, and spoofing interference) demonstrate that our method achieved performance in terms of accuracy, precision, recall, F1 score, and specificity, with values of 98.02%, 97.09%, 97.24%, 97.14%, and 99.65%, respectively, which represent improvements of 1.98%, 2.80%, 6.10%, 4.59%, and 0.33% over the next-best model. This study provides an efficient, entropy-aware, intelligent, and practically feasible solution for GNSS interference identification.

Hydrological models are essential tools for water resource management and for mitigating extreme hydrological events risks. Although they are crucial for flood forecasting, these models often exhibit substantial uncertainties, including input data uncertainties (e.g., precipitation) and structural uncertainties of the models themselves. This study aims to explore the implications of different precipitation datasets and hydrological model structures on streamflow simulation, by evaluating the effects of multiple precipitation products and employing an enhanced model version to reduce structural uncertainty. This study evaluated the hydrological applicability of three representative precipitation products—reanalysis-based (the land component of the fifth-generation European Reanalysis, ERA5-Land), satellite-based (Integrated Multi-satellite Retrievals for GPM, IMERG), and machine learning-based (the first deep learning based spatio-temporal downscaling of precipitation data on a global scale, spateGAN-ERA5), using the offline version of WRF-Hydro, a distributed hydrological model. Additionally, this study evaluated the performance of an enhanced version of WRF-Hydro, incorporating an overbank flow module for reducing the model structural uncertainty in a large, flood-prone tropical river basin, Irrawaddy River Basin in Myanmar. The findings indicate that: (1) Simulations driven by IMERG precipitation outperformed those driven by ERA5-Land and spateGAN-ERA5 in terms of accuracy in streamflow, with average NSE values of 0.77, compared to 0.19 and 0.09, respectively; (2) The modified model with enabled overbank flow showed consistent improvements over the default model. The average NSE improved from 0.09–0.77 (default) to 0.31–0.78 (modified); (3) The water balance analysis reveals that incorporating the overbank flow module reduces surface runoff, accompanied by an increase in soil moisture storage, and slightly enhancing underground runoff and evapotranspiration (ET) during the rainy period. After the end of the rainy period, the increase soil moisture storage gradually contributes to an increase in surface runoff. These results highlight the significant impact of accurate precipitation data and the overbank flow module on hydrological processes, particularly in flood-prone areas, and suggest that the modified model and high quality precipitation data may enhance hydrological forecasting capabilities.

This study addresses a single-period multi-product inventory routing problem (IRP) considering reactive lateral transhipment with the optimisation objective of logistics ratio, defined as logistics cost per unit of product value. The problem is modelled in two stages: in the first stage, the distribution is planned based on imperfect forecasted demand. In the second stage, actual demand is revealed, and reactive lateral transhipment is executed based on first-stage decisions. We develop a basic fractional programming (BFP) model with deterministic demand for both stages. Considering demand uncertainty, we extend a robust fractional programming (RFP) model and a two-stage robust fractional programming (TSRFP) model for first-stage IRP and propose structural uncertainty sets. We apply Dinkelbach's algorithm and the robust counterpart transformation to tackle the RFP model. We employ the column and constraint generation algorithm to address the TSRFP model with Dinkelbach's algorithm. The results indicate that the proposed TSRFP is suits scenarios with high stockout penalties and at medium demand uncertainty levels. Managers can select between TSRFP, RFP, and BFP to optimise inventory management based on demand uncertainty and product attributes.

Development of a new L-fucose purity certified reference material.