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The Global Flood Awareness System (GloFAS) is a promising flood-modelling tool available for most river basins worldwide. However, it contains inherent biases, notably linked to its spatial resolution. This study propose...

In the present study, the transmission error (TE) behaviour of an identical involute spur gear pair dynamically coupled with an attached subsystem is investigated through numerical modelling. The surrounding mechanical s...

Distinguishing between the left- and right-handed versions of a chiral molecule (enantiomers) is vital, but also inherently difficult. Traditional optical methods using elliptically/circularly polarized light rely on lin...

Abstract Echo State Networks are recurrent neural networks that leverage a random reservoir’s dynamics, training only a simple readout layer. This approach is computationally efficient but limits network performance. B...

Deep learning has become the dominant approach in artificial intelligence to solve complex data-driven problems. Originally applied almost exclusively in computer-science areas such as image analysis and nature language ...

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) ...

Prediction invariance of causal models under heterogeneous settings has been exploited by a number of recent methods for causal discovery, typically focussing on recovering the causal parents of a target variable of inte...

The present work investigates the compressive mechanical response of three lattice structures manufactured via VAT photopolymerization. A bio-inspired architecture, derived from the observation of Euplectella aspergillum...

IntroductionThe COVID-19 pandemic has reduced system resilience, caused economic welfare loss, and widened disparities in healthcare access. This study aims to identify the macro-level factors contributing to COVID-19 va...

The Intergovernmental Panel on Climate Change (IPCC) is the leading international body for assessing the science related to climate change. It provides policymakers with regular assessments of the scientific basis of hum...

1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study pro...

Materie / parole chiave: nonlinear processes, nonlinear phenomena, nonlinear modelling, nonlinear analysis modelling, nonlinear analysis control; Science: Mathematics: Analysis

This study was conducted to potentially optimize the sustainable culture of Mozambique tilapia (Oreochromis mossambicus) in a periphyton-based aquaculture system with supplemental feeding to aqua dams using stochastic si...

Rapid progress of experimental biology has provided a huge flow of quantitative data, which can be analyzed and understood only through the application of advanced techniques recently developed in theoretical sciences. O...

Abstract The manufacturing of spiral groove structure of two-dimensional valve (2D valve) feedback mechanism has shortcomings of both high cost and time-consuming. This paper presents a novel configuration of rotary elec...

IntroductionLiver diseases continue to impose a major global health burden, and therapeutic progress is constrained by the limited availability of validated small-molecule modulators. TEAD4, a central Hippo-YAP effector,...

This work introduces a novel geometrical and analytical modeling framework for predicting the stiffness properties of knitted composites, a class of materials known for their complex architectures and versatile mechanica...

The Gray-Scott (GS) model is a nonlinear reaction diffusion system widely used in applied sciences. This paper delves into the investigation of a spectral method for the GS model with homogeneous Neumann boundary conditi...

Modelling and estimating relationships that combine time series and crosssectional data is often discussed in the statistical literature but in these considerations sampling errors are seldom taken into account. In the p...

Abstract Semi-autogenous grinding (SAG) mill performance prediction remains limited by empirical calibration requirements in conventional population balance models. This study presents a structured dynamic framework inte...

Accurate deformation forecasting is essential for concrete dam safety monitoring, yet real-world deformation series are often nonlinear, nonstationary, and contaminated by noise, missing values, and outliers, which limit...

This study presents a simulation-based evaluation of a hybrid fiber Bragg grating (FBG) temperature estimation framework combining the extended Kalman filter (EKF) and unscented Kalman filter (UKF) for electric vehicle (...