Analysis of inter-segment coordination during the take-off of ski jumping

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Laboratory flume experiments and field investigations demonstrated the existence of dynamic equilibrium conditions for river channels, according to the hydraulic (flow discharge, water depth) and morphological (bed slope, grain size distribution, cross-sec ...

Background.Type 2 diabetes (T2D) is among the most frequent comorbidities in people with HIV (PWH) and occurs more often in PWH than in people without HIV. Polygenic risk scores (PRS) can be used to summarize the genetic risk for T2D, but it is unknown to ...

Central aortic diastolic pressure decay time constant ( ) is according to the two-element Windkessel model equal to the product of total peripheral resistance (R) times total arterial compliance (C ). As such, it is related to arterial stiffness, which has ...

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 ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

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 ...

AIM: To characterise the corticoreticular pathway (CRP) in a case -control cohort of adolescent idiopathic scoliosis (AIS) patients using high -resolution slice -accelerated readoutsegmented echo -planar diffusion tensor imaging (DTI) to enhance the discri ...

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 ...

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 ...

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 ...

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 ...

Background: A neurocognitive phenotype of post-COVID-19 infection has recently been described that is characterized by a lack of awareness of memory impairment (i.e., anosognosia), altered functional connectivity in the brain's default mode and limbic netw ...

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the pe ...

Point clouds are effective data structures for the rep- resentation of three-dimensional media and hence adopted in a wide range of practical applications. In many cases, the portrayed data is expected to be visualized by humans. After acquisition, point c ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

Correlated errors of experimental data are a common but often neglected problem in physical sciences. Various tools are provided here for thorough propagation of uncertainties in cases of correlated errors. Discussed are techniques especially applicable to ...

Endogenous and exogenous uncertainties exert significant influences on energy planning. In this study, we propose a systematic methodology to excavate the uncertainty space, by combining mix-integer linear programming (MILP), Monte Carlo simulation, and ma ...

Heteroatom-doped polyaromatic hydrocarbons (or nanographenes) are promising molecular electrocatalysts for the oxygen reduction reaction (ORR). Here, we use density functional theory to investigate the first step of the ORR pathway (chemisorption) for a se ...

Fluid antenna systems (FAS) are an emerging technology that promises a significant diversity gain even in the smallest spaces. It consists of a freely moving antenna in a small linear space to pick up the strongest received signal. Previous works in the li ...

Analysis of inter-segment coordination during the take-off of ski jumping

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Towards unified scaling relationships for gravel-bed and sand-bed rivers

Polygenic Risk Scores for Type 2 Diabetes in the Swiss HIV Cohort Study

Novel theory and potential applications of central diastolic pressure decay time constant

Random matrix methods for high-dimensional machine learning models

Partial discharge localization in power transformer tanks using machine learning methods

On distributional autoregression and iterated transportation

The hindbrain and cortico-reticular pathway in adolescent idiopathic scoliosis

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Iterative removal of sources to model the turbulent electromotive force

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

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Acute TNF alpha levels predict cognitive impairment 6-9 months after COVID-19 infection

Distributional Regression and Autoregression via Optimal Transport

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Towards a multiscale point cloud structural similarity metric

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Reliable uncertainties: Error correlation, rotated error bars, and linear regressions in three-isotope plots and beyond

Deep excavation of the impact from endogenous and exogenous uncertainties on long-term energy planning

Metal-Free Molecular Catalysts for the Oxygen Reduction Reaction: Electron Affinity as an Activity Descriptor

A New Analytical Approximation of the Fluid Antenna System Channel