The decision-making behavior of drivers during the driving process is influenced by various factors, including road conditions, traffic situations, weather conditions, and so on. However, our understanding and quantification of the driving environmen...
The aim of our work is to quantify two gases (acetone and ethanol) diluted in an air buffer using only a single metal oxide (MOX) sensor. We took advantage of the low selectivity of the MOX sensor, exploiting a dual-temperature mode. Working at two t...
In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes al...
As magnetic field strength in Magnetic Resonance Imaging (MRI) technology increases, maintaining the specific absorption rate (SAR) within safe limits across human head tissues becomes challenging due to the formation of standing waves at a shortened...
Road infrastructure is a significant factor in the development of any country, affecting economic growth, social development, and environmental sustainability. Large infrastructure projects often face significant risks and uncertainties, which can le...
The credibility of Computational Fluid Dynamics (CFD) has been a topic of debate due to the significant uncertainties inherent in its modeling processes and numerical implementations. Uncertainty Quantification (UQ) offers a scientific framework for...
The modelling and simulation process in the automotive domain is transforming. Increasing system complexity and variant diversity, especially in new electric powertrain systems, lead to complex, modular simulations that depend on virtual vehicle deve...
This paper provides a review of current and upcoming innovations in development, validation, and uncertainty quantification of nuclear reactor multi-physics simulation methods. Multi-physics modelling and simulations (M&S) provide more accurate a...
The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticult...
The accurate prediction of shear wave velocity (Vs) is critical for earthquake engineering applications. However, the prediction is inevitably influenced by geotechnical variability and various sources of uncertainty. This paper investigates the effe...
In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and...
In this paper, we review some of the most recent research regarding design, simulation, implementation and evaluation of dynamic tolling schemes. Analyzing the structure of the reviewed studies, we identify the common elements and the differences in...
A novel algorithmic discussion of the methodological and numerical differences of competing parametric model reduction techniques for nonlinear problems is presented. First, the Galerkin reduced basis (RB) formulation is presented, which fails at pro...
Reliability quantification of deep reinforcement learning (DRL)-based control is a significant challenge for the practical application of artificial intelligence (AI) in safety-critical systems. This study proposes a method for quantifying the reliab...
Background/Objectives: Accurate quantification of left-to-right shunt volume is central to clinical decision-making in patients with atrial septal defect (ASD). Conventional echocardiographic Qp:Qs estimation is widely used but limited by operator de...
Inlet and outlet boundary conditions (BCs) play an important role in newly emerged image-based computational hemodynamics for blood flows in human arteries anatomically extracted from medical images. We developed physiological inlet and outlet BCs ba...
Uncertainty quantification (UQ) in medical image segmentation is essential for ensuring the reliability and interpretability of deep learning models in clinical decision-making. While convolutional neural networks (CNNs) and transformer-based archite...
Toxicity testing in populations probes for responses in demographic variables to anthropogenic or natural chemical changes in the environment. Importantly, these tests are primarily performed on species in isolation of adjacent tropic levels in their...
Understanding the carbon emission characteristics of fused filament fabrication (FFF) is important for the development of more sustainable additive manufacturing practices. This study presents a framework for quantifying, modelling, and optimizing th...
Quantifying uncertainty in complex systems is a central problem in reliability analysis and engineering applications. In this work, we develop an information-theoretic framework for analyzing linear consecutive k-out-of-n:G systems using the cumulati...
Reliable hydrologic models are essential for planning, designing, and management of water resources. However, predictions by hydrological models are prone to errors due to a variety of sources of uncertainty. More accurate quantification of these unc...
With the objectives of achieving “peak carbon” and “carbon neutrality”, accurately quantifying the carbon emissions of road transportation becomes crucial. It is challenging to accurately describe the energy consumption of veh...
Low-cost air quality sensors can expand PM2.5 monitoring networks but require calibration against regulatory-grade monitors to correct systematic bias towards trustworthy observations. Although interest in predictive uncertainty for air quality estim...
The wind parameter distribution law at a bridge midspan in a U-shaped valley has important influence on the wind strength of this bridges. In this paper, the wind characteristics were researched by computational fluid dynamics (CFD). The surface roug...
Building information modeling (BIM) in the planning and construction of infrastructure projects, such as roads, tunnels, and excavations, requires the generation of comprehensive 3D subsoil models that encompass relevant geological and geotechnical i...
This paper presents a preliminary version of an active learning (AL) scheme for the sample selection aimed at the development of a surrogate model for the uncertainty quantification based on the Gaussian process regression. The proposed AL strategy i...
During microwave design, it is of practical interest to obtain insight in the statistical variability of a device’s frequency response with respect to several sources of variation. Unfortunately, the frequency response acquisition can be partic...
In transonic high-pressure turbine stages, oblique shocks originating from vane trailing edges impact the suction side of each adjacent vane. High-pressure vanes are cooled to tolerate the combustor exit-temperature levels, then it is highly probable...
Background/Objectives: Gastrointestinal bleeding is a critical finding in wireless capsule endoscopy (WCE), but manual examination of thousands of image frames is labor-intensive, time-consuming, and susceptible to missed lesions. This study aimed to...
Meta-analytic deconvolution seeks to recover the distribution of true effects from noisy site-specific estimates. While Efron’s log-spline prior provides an elegant empirical Bayes solution with excellent point estimation properties, its plug-i...
In model calibration, the identification of the unknown parameter values themselves, but also the uncertainty of these model parameters, due to uncertain measurements or model outputs might be required. The analysis of parameter uncertainty helps us...
The quantification of the irregular morphology and distribution pattern of mineral grains is an essential but challenging task in ore-related mineralogical research, allowing for tracing the footprints of pattern-forming geological processes that are...
This study proposes a novel and improved numerical approximation of the simulation of Gaussian process autoregressive models. As a Bayesian nonparametric regression method, Gaussian process models offer the unique advantage of providing closed-form u...
In the realm of geotechnical engineering, understanding the mechanical behavior of soil particles under external forces is paramount. The main topic of this study is how to use deep learning image analysis techniques, especially transfer learning mod...
The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many ener...
As renewable energy is increasingly integrated into our electricity supply, it becomes more challenging to ensure reliability and security due to the intermittent nature of these resources. With the electrification of buildings and technological adva...
Accurate crop yield forecasting is essential for ensuring food security, optimizing agricultural resource allocation, and supporting climate-resilient farming systems. Recent advances in deep learning have improved yield prediction accuracy; however,...
This study deals with the morphometric characterization and quantification of earthquake damage in the ancient Roman city of Baelo Claudia in South Spain (Gibraltar Arc) by means of the use of 3D modelling from drone imagery. Baelo Claudia is a world...
The quantification of uncertainties of computer simulations due to input parameter uncertainties is paramount to assess a model’s credibility. For computationally expensive simulations, this is often feasible only via surrogate models that are learne...
This paper presents a probabilistic sensitivity analysis of a nonlinear electrochemical model for lithium-ion batteries. The model is treated as a reduced virtual replica for uncertainty-aware analysis rather than as a full digital twin. A reduced el...
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This study aims to explore the efficacy of Bayesian Neural Networks (BNNs) in enhancing predictive modeling for healthcare applications. Advancements in artificial intelligence have significantly improved predictive modeling capabilities, with BNNs o...
Soil water models are increasingly required to support irrigation, drought assessment and sustainable water management, yet physical, artificial intelligence (AI)-based and hybrid approaches differ in process representation, data demand and transfera...
This study aims to evaluate non-invasive PET quantification methods for (R)-[11C]PK11195 uptake measurement in multiple sclerosis (MS) patients and healthy controls (HC) in comparison with arterial input function (AIF) using dynamic (R)-[11C]PK11195...
South Africa faces significant challenges in monitoring air pollution from different provinces due to the sparse nature of the sensor network and heterogeneous pollutant sources. Notably, some provinces continue to record a limited amount of data on...
Uncertainty quantification is widely used in engineering domains to provide confidence measures on complex systems. It often requires to accurately estimate extreme statistics on computationally intensive black-box models. In case of spatially or tem...
In this paper, we deal with computational uncertainty quantification for stochastic models with one random input parameter. The goal of the paper is twofold: First, to approximate the set of probability density functions of the solution stochastic pr...
Satellite remote sensing has become essential for water quality assessment across inland and coastal environments, with rapid improvements in recent years. Significant advances have been made in detecting optically active parameters (such as chloroph...
In this study, a concentration overvoltage model that focuses on describing variable-temperature operating condition properties for PEMFCs is established. Sensitivity analysis and a quantification study of oxygen transport resistance are carried out...
Methane (CH4) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing gl...
Sustainable soil–water management under climate and socio-economic pressures requires predictive capability that is both mechanistic and continuously corrected by observations. Data assimilation (DA) provides the formal machinery to merge model...
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