Combined Model-Free Data-Interpretation Methodologies for Damage Detection during Continuous Monitoring of Structures

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AimPlant senescence largely influences the global carbon cycle by regulating the growing season length. However, the driving mechanisms of plant senescence remain unclear, particularly the role of developmental factors. This study aims to investigate how e ...

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

Context. Geminga is the third gamma-ray pulsar firmly detected by imaging atmospheric Cherenkov telescopes (IACTs) after the Crab and the Vela pulsars. Most of its emission is expected at tens of giga-electronvolts, and, out of the planned telescopes of th ...

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Our understanding of the patterns of internal migration that underlie the fast urbanization in developing countries is mainly based on theory and country case studies. We test the hypothesis of a mobility transition from a comparative perspective by analyz ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

Aims. We validate a semi-analytical model for the covariance of the real-space two-point correlation function of galaxy clusters. Methods. Using 1000 PINOCCHIO light cones mimicking the expected Euclid sample of galaxy clusters, we calibrated a simple mode ...

We describe a novel method to compute the components of dynamo tensors from direct magnetohydrodynamic (MHD) simulations. Our method relies upon an extension and generalization of the standard H & ouml;gbom CLEAN algorithm widely used in radio astronomy to ...

Zoning reform is a crucial tool for cities to adapt to contemporary challenges. However, its implementation remains challenging. Property owners, with a vested interest in the value of their neighborhoods, are sensitive to local developments and the potent ...

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

We propose a local, non -intrusive model order reduction technique to accurately approximate the solution of coupled multi -component parametrized systems governed by partial differential equations. Our approach is based on the approximation of the boundar ...

Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby facilitating the exploration of informative patterns and structures across var ...

In tokamak experiments, sufficiently strong E x B flow shear reduces turbulent transport, thereby improving the prospects for fusion power plants. It is therefore of great importance to efficiently explore parameter space to find where strong plasma flow c ...

In this study, we examine the potential of several self-supervised deep learning models in predicting forest attributes and detecting forest changes using ESA Sentinel-1 and Sentinel-2 images. The performance of the proposed deep learning models is compare ...

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

Combined Model-Free Data-Interpretation Methodologies for Damage Detection during Continuous Monitoring of Structures

Chattez avec Graph Search

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Secure and federated genome-wide association studies for biobank-scale datasets

Internal Migration Over the Course of Urbanization in Developing Countries

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Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps

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A new quasilinear model for turbulent momentum transport in tokamaks with flow shear and plasma shaping

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach