bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influence of anatomical structure on antiscatter grid performance in 2D: application to x-ray angiography and a prototype 29:1 ratio grid
Authors: Fetterly Kenneth , Bernhardt Philipp , Schueler Beth 
Published: June 2024

Abstract

Objective. The use of uniform phantoms to assess the influence of x-ray scatter and antiscatter grids on x-ray angiography and fluoroscopy image quality disregards the influence of spatially variable x-ray attenuation of patients. The purpose of this work was to measure scatter to primary ratio (SPR) and antiscatter grid SNR improvement factor (K
SNR) using experimental conditions which better mimic patient imaging conditions. Approach. Three adult-sized anthropomorphic phantoms were used. AP and lateral projection images of the thorax and abdomen were acquired with and without an antiscatter grid. Grids with ratio 15:1 and 29:1 (r15, r29) and x-ray fields of view 20, 25 (thorax) and 32, 42 cm (abdomen) were tested. Combined with a-priori measurements of grid scatter and primary transmission fractions, these images were used to calculate 2D SPR and K
SNR maps. Main results. Results demonstrated that measurements by uniform phantom do not describe the complex 2D SPR and K
SNR distributions associated with anthropomorphic phantoms. The regions of the images with the lowest primary x-ray intensity (greatest attenuation) had the highest SPR and the highest K
SNR attributable to the grids. Considering all conditions, the 95th percentile of the SPR maps was in the range 42%–185% greater than the median values and that of the K
SNR maps was 4%–20% higher than the median values. The combined influences of SID 120 vs. 107 cm and r29 vs. r15 grid resulted in K
SNR in the range 1.05–1.49. Significance. Performance of anti-scatter grids using anatomically complex phantoms highlights the substantial variation of SPR and K
SNR within 2D images. Also, this work demonstrates the benefit of the prototype r29 grid for thoracic and abdominal angiography imaging conditions is substantial, especially for large patients and radiodense image regions.
Time-resolved analysis of Ar metastable and electron populations in low-pressure misty plasma processes using optical emission spectroscopy
Authors: Chouteau S , Durocher-Jean A , Granier A , Richard-Plouet M , Stafford L 
Published: June 2024

Abstract
Misty plasmas have recently emerged as a promising tool for nanocomposite thin films deposition. However, aerosol-plasma interactions remain poorly documented, especially at low working pressure. In this work, optical emission spectroscopy is used to probe the temporal evolution of three fundamental plasma parameters during pulsed liquid injection in an inductively coupled argon plasma at low-pressure. Time-resolved values of metastable argon density, electron temperature, and electron density are determined from radiation trapping analysis and particle balance equations of selected argon 1s and 2p levels. Pulsed liquid injection is found to induce a sudden drop in metastable density and electron temperature, and an increase in electron density. These results are attributed to the lower ionization thresholds of the injected molecular species compared to the one of argon. In addition, upstream liquid temperature is found to affect the transitory kinetics for non-volatile solvents more than volatile ones, in accordance with a previously reported flash boiling atomization mechanism.
Solitary waves of M-fractional low-pass nonlinear electrical transmission line model arising in network system
Authors: Yel Gülnur 
Published: June 2024

Abstract
In this study, we analyze the solitary wave behavior of a truncated M-fractional low-pass nonlinear electrical transmission line (NLETLs) model. NLETL models are relevant to computer network systems, particularly for high-speed data transmissions. They influence the behavior of signals traveling through network cables. To investigate the dynamics of solitary waves in the model, we applied the modified Sardar sub-equation and extended the sinh-Gordon equation expansion methods. We illustrated the 2D, 3D, and contour shapes of selected solutions for appropriate values of the NLETLs dynamics using Mathematica-14. Kink, anti-kink, bright-dark bell, dark bell, M-shaped periodic soliton, and logarithmic wave solutions were obtained. The results indicate that the proposed techniques may provide valuable, powerful, and efficient insights into the dynamics of nonlinear evolution models. The role of the fractional order derivative in making optical solutions is investigated in detail, which opens up opportunities for the creation of more complex models that can more accurately simulate optical phenomena in the real world.
Machine learning-assisted prediction of the electronic features of a Schottky diode interlaid with PVP:BaTiO3 composite
Authors: Azizian-Kalandaragh Yashar , Barkhordari Ali , Özçelik Süleyman , Altındal Şemsettin 
Published: June 2024

Abstract
This study employs two Machine Learning (ML) models to predict the electronic current and then analyze the main electronic variables of Schottky diodes (SDs), including leak current (I0), potential barrier height (ΦB0), ideality factor (n), series resistance (Rs), shunt resistance (Rsh), rectifying ratio (RR), and interface states density (Nss). The I-V characteristics are examined for both without and with an interlayer. The polyvinylpyrrolidone (PVP) polymer and BaTiO3 nanostructures are combined to form the nanocomposite interface. The ML algorithms that are employed include the Gaussian Process Regression (GPR) and Kernel Ridge Regression (KRR). The thermionic emission theory is used to gather training data for ML algorithms. Ultimately, the effectiveness of these ML methods in anticipating the electric characteristics of SDs is evaluated by contrasting the predicted and experimental findings in order to identify the optimal ML model. Whereas the GPR algorithm has given values that are closer to the actual values, the ML predictions of fundamental electric variables by practically both algorithms have the best level of agreement with the actual values. Also, the obtained findings indicate that when the nanocomposite interface is used, the amount of I0 and Nss for metal-semiconductor (MS) Schottky diodes reduces and φ
B0 increases.
Bayesian inference methodology for primordial power spectrum reconstructions from Large Scale Structure
Authors: Martínez-Somonte G. , Marcos-Caballero A. , Martínez-González E. , Cañas-Herrera G. 
Published: June 2024

Abstract
We use Bayesian inference to develop a non-parametric method to reconstruct the primordial power spectrum Pℛ
(k) from Large Scale Structure (LSS) data. The performance of the method is assessed by testing it against simulations of the clustering of high-z (QSOs) objects. Their clustering is derived from different templates of the primordial power spectrum motivated by models of inflation: the Standard Model power law characterized by the two parameters As
and ns
; a local feature template; and a global oscillatory template. The primordial power spectrum is reconstructed using N knots in the log {k, Pℛ
(k)} plane while sampling the cosmological parameters {H
0, Ω
b
, Ω
c
}. We use two statistical tests to examine the reconstructions for signs of primordial features: a global test comparing the evidences and a novel local test quantifying the power of the hypothesis test between the power law model and the marginalized probability over N model. We also discuss results of an application to low-z (ELGs) objects with two different photometric errors keeping the cosmology fixed. The method shows good performance in all scenarios considered. In particular, the tests show no feature detection for the standard power-law primordial power spectrum; yet, the method is able to detect power spectrum deviations at a percent level for all considered features, combining either the low-z or the high-z redshift bins. In addition, we include a test proof-of-concept application to real data from the Sloan Digital Sky Survey Luminous Red Galaxy Data Release 4 (SDSS LRG 04), finding no preference for deviations from the primordial power law. The method is flexible, model independent, and suitable for its application to existing and future LSS surveys.
Design and optimisation of 3D-printed energy harvesters for railway bridges
Authors: Cámara-Molina J.C , Romero A , Galvín P , Moliner E , Martínez-Rodrigo M.D 
Published: June 2024

Abstract
This paper investigates energy harvesting on railway bridges. The type of harvester studied is a cantilever bimorph beam with a mass at the tip and a load resistance. These parameters are adjusted to find the optimal design that tunes the harvester to the fundamental frequency of the bridge. An analytical model based on a variational formulation to represent the electromechanical behaviour of the device is presented. The optimisation problem is solved using a genetic algorithm with constraints of geometry and structural integrity. Additive manufacturing to 3D print energy harvesters is used to maximise design flexibility and energy performance. The proposed procedure is implemented in the design and manufacture of an energy harvesting device for a railway bridge on an in-service high-speed line. The structure under study corresponds to a six-arched concrete bridge with double ballasted track. The proposed design is experimentally validated and the performance under operating conditions is evaluated in a test field.
Estimating the foundation parameters of offshore wind turbines through Bayesian model updating
Authors: Simpson H A , Tatsis K E , Abdallah I , Chatzi E N , Chatzis M N 
Published: June 2024

Abstract
The rapid growth of the wind industry has resulted in larger wind turbines with modal properties that lie in the lower frequency range, rendering accurate fatigue assessment increasingly important. However, high uncertainty associated with the support conditions and foundation properties can pose challenges in the condition assessment and fatigue life estimation. One approach to improve these estimates is to use structural monitoring data (e.g. from sensors mounted on the towers) to update the foundation parameters of offshore wind turbine models. However, the low identifiability of the parameters to be estimated can lead to divergent estimates across different frameworks, which, combined with uncertainty in foundation properties, can compromise remaining useful life estimates. In this work, a Bayesian model updating framework is applied to update the foundation parameters of an offshore wind turbine, and its results are compared against a deterministic framework in a numerical example. The advantages of the Bayesian framework over the deterministic framework are discussed in detail and the importance of accurately accounting for uncertainties as part of the model updating process is highlighted.
Stochastic flutter analysis of a torsional-vibration-based energy harvester affected by random aeroelastic loads and wind turbulence
Authors: Piciucco Davide , Caracoglia Luca 
Published: June 2024

Abstract
This paper investigates the energy production of a “meso-scale”, wind-based energy harvester that exploits the torsional aeroelastic instability of a rigid blade-airfoil, elastically supported at equidistant supports. Torsional flutter is a single mode aeroelastic instability phenomenon, in which a diverging dynamic angular rotation of a body occurs. The apparatus relies on a simple mechanism that uses flow-induced pitch motion to extract and convert airflow kinetic energy to electrical energy. The system is composed by a rigid blade-airfoil, connected to a support structure through a non-linear restoring force (torsional spring-like) mechanism that enables the rotation about a reference pivot axis. The proposed technology is designed to be efficient in the range of low and medium wind speeds (10-13 m/s), in which horizontal-axis wind turbines and other harvesters are not efficient. Deterministic pre-flutter, incipient flutter and post-critical vibrations of the apparatus have been already explored in a previous study. This work aims to further investigate the aeroelastic behavior of the “flapping foil” by examining the effect of turbulence, random experimental error and modeling simplifications of the aeroelastic forces. The analysis is conducted at incipient flutter in the frequency domain using classical unsteady force models. Monte Carlo methods are employed to solve for the probability of incipient flutter speed. Several configurations are considered to improve the efficiency of the energy harvester.
Identification of human-structure interaction from full-scale observations: The impact of modeling errors
Authors: Nimmen Katrien Van , Broeck Peter Van den 
Published: June 2024

Abstract
The added-damping effect of human-structure interaction (HSI) is of great importance on the vibration serviceability of footbridges. To account for the interaction effects between the human body and the footbridge, the former is often described by a mass-spring-damper (MSD) system. The parameters of this MSD system are in this contribution estimated by minimizing the discrepancy between the experimentally derived and the numerically simulated power spectral density of the structural response. The methodology assumes that the dynamic behavior of the empty structure, the average weight and the distribution of step frequencies in the crowd are known. This contribution furthermore focuses on the impact of modeling errors. It is shown that the result is mostly sensitive to errors related to the mean value of the distribution of step frequencies and the structural natural frequencies. It is also shown that the impact of modeling errors decreases as the impact of HSI increases. The results are used to refect on the suitability of two real footbridges for the parameter estimation problem. When the impact of the reasonably expected modeling errors is taken into account and the results of both case studies are combined, a natural frequency and damping ratio of approximately 3.0±0.2 Hz and 34±8% are estimated for the MSD interaction model.
Physics-informed machine learning for moving load problems
Authors: Kapoor Taniya , Wang Hongrui , Núñez Alfredo , Dollevoet Rolf 
Published: June 2024

Abstract
This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML). Physics-informed neural networks (PINNs) utilize the underlying physics of moving load problems and aim to predict the deflection of beams and the magnitude of the loads. The mathematical representation of the moving load considered involves a Dirac delta function, to capture the effect of the load moving across the structure. Approximating the Dirac delta function with PINNs is challenging because of its instantaneous change of output at a single point, causing difficulty in the convergence of the loss function. We propose to approximate the Dirac delta function with a Gaussian function. The incorporated Gaussian function physical equations are used in the physics-informed neural architecture to simulate beam deflections and to predict the magnitude of the load. Numerical results show that PIML is an effective method for simulating the forward and inverse problems for the considered model of a moving load.
Interaction of a simple linear structure and a ball-type vibration absorber for harmonic and random loads
Authors: Náprstek Jiří , Fischer Cyril 
Published: June 2024

Abstract
Ball-type tuned mass absorbers are attractive for use in lightweight and slender designs because they combine multi-directional action with compact dimensions. However, their main disadvantage is the limited ability to adjust the damping level to a prescribed value, making them susceptible to undesirable effects arising from their non-linear nature. A 2D and 3D mathematical models of a ball-type absorber coupled to a simple linear elastic structure are presented, which show the effectiveness of such a damper for harmonic and random excitation. To prevent the occurrence of auto-parametric resonance conditions for safety reasons, the structure and damping equipment must be designed based on system parameters and the characteristics of possible excitation. The paper presents the dependence of the damped structure’s response on damper parameters for each type of resonant behaviour, the origin and nature of longitudinal and spatial response, and the influence of resonant conditions on the behaviour of the damped structure. The work is complemented by a simplified analysis of the response to random excitation, with examples of typical auto-parametric response patterns and possible applications in practical engineering.
The role of spine elasticity on legged locomotion
Authors: Zanotti A , Laurenza M , Pepe G , Carcaterra A 
Published: June 2024

Abstract
Robot locomotion has become increasingly common in transporting and assisting humans in complex environments. However, most existing quadrupedal robots have been developed with only a single rigid trunk and limited actuation to the legs and associated joints to reduce design and controls complexity. This paper presents a non-linear optimization process to identify the highest performance gait parameters of a quadruped robot with a flexible trunk. The optimization process is conducted using evolutionary algorithms, which are based on a heuristic search inspired by Charles Darwin’s theory of natural evolution and reflect the process of natural selection. Since identifying optimal gaits requires the complete definition of ground reaction forces coupled with the optimal trajectories of the body and its vibrational modes, it is possible to determine the influence of the main parameters linked to the vibration of the trunk on the optimal gait resulting from the optimization process. The paper’s final part presents a comparison between results obtained in the case of a flexible spine and the case of a rigid trunk to demonstrate the benefits of the elastic model in terms of efficiency.
A DDA enhanced locally resonant metamaterial: experimental testing on a Lego® Technic assembly
Authors: Kalderon M. , Mantakas A. , Chondrogiannis K.A. , Antoniadis I.A. 
Published: June 2024

Abstract
Locally resonant metamaterials (LRMs), with periodic elements that exhibit local resonance, have been recently investigated by numerous researchers as a means to pursue vibration attenuation and wave manipulation. These structures are able to generate bandgaps in specific frequency ranges depending on their mass, stiffness and geometrical characteristics; however, they present certain limitations when bandgaps in the low-frequency domain are sought, since they require heavy oscillating masses. This research work harnesses the potency of a novel dynamic directional amplifier, namely the DDA, that is introduced as a means to artificially increase the inertia of an oscillating mass. The DDA is realized by imposing kinematic constraints to the degrees of freedom (DoFs) of the oscillator, hence inertia is increased by coupling the horizontal and vertical motion and forcing the model to move along a circumference. Herein, the DDA is applied on the resonating mass of a scaled LRM structure, assembled using LEGO® components. Experimental and analytical calculations are subsequently undertaken to investigate the dynamic properties of this DDA-enhanced metamaterial. Results showcase the low-frequency attenuation properties of the structure and serve as a proof of concept of the mechanism.
Non-linear 3D seismic analysis of a RC bridge and retrofit alternatives with passive protective systems
Authors: Nanclares G , Ambrosini D , Curadelli O 
Published: June 2024

Abstract
In this paper a proposal of a partially restrained seismic isolation (PRSI) system is presented for the retrofit of existing continuous girder RC bridges. A pure-friction system is used with auto centring force provided by the stiffness of the deck and the frictional forces generated at the contact acting as an energy dissipation mechanism. The proposal has the advantage that the very small height of the system allows its application to existing bridges. In order to study the proposed system, the dynamic response until collapse of an existing bridge subjected to seismic accelerations is assessed through a detailed 3D nonlinear numerical model. An explicit integration scheme is employed that avoids numerical convergence drawbacks due to strength and stiffness degradation of concrete structures. The numerical model identifies the collapse mechanisms, such as the development of cracks in the concrete elements. Two retrofit systems are studied: a) steel jacketing reinforcement and b) PRSI proposal. The efficiency of each alternative is evaluated in terms of displacements and shear forces.
Damage detection using different types of sensors
Authors: Kullaa Jyrki 
Published: June 2024

Abstract
Different types of sensors are installed in many structural health monitoring systems. Correlation between heterogeneous response quantities is often small, which makes it difficult to utilize all sensor data for damage detection. A possible solution to handle diverse response data in the time domain is to use autocovariance function (ACF) estimates instead of direct sensor data. If the excitation is stationary random, different response quantities yield functionally similar ACFs. Due to phase differences, applying only spatial correlation between different ACFs is insufficient, whereas spatiotemporal correlation is justified. Therefore, ACFs at different lags are used to estimate a spatiotemporal correlation model of the undamaged structure under different environmental or operational conditions. If the correlation model is violated, it is an indication of damage. A monitoring campaign was carried out using a finite element model of a bridge deck to validate the proposed method. Neither the excitation nor the environmental variables were measured. Noisy response was measured with displacement, velocity, acceleration, and strain sensors in different locations of the deck. Using diverse types of sensors resulted in almost similar damage detection performance than if only a single type of sensor was used.
Mode tracking for a steel railway bridge in presence of changing support conditions
Authors: Maes Kristof , Lombaert Geert 
Published: June 2024

Abstract
Vibration-based damage detection commonly relies on a monitoring of the modal characteristics of a structure over time. It is assumed that damage will result in a change of the structural stiffness, which will in turn lead to a change of the natural frequencies and the displacement/strain mode shapes. The monitoring of the modal characteristics generally consists of two consecutive steps. First, the modal characteristics are identified by application of operational modal analysis (OMA) on a time series of response data, typically accelerations. This process is repeated on a regular, e.g. hourly, basis. In a second step, which is referred to as the mode tracking, the modes obtained from multiple OMA are linked across time windows, resulting in a subset of modes which can be tracked in time. The mode tracking typically relies on criteria that measure the similarity of the natural frequencies and mode shapes, assuming these do not change significantly over two time windows. In this paper, we consider the case of a bowstring railway bridge in Halle, Belgium, where the modes of the bridge can be assigned to two different states. This is observed from the identified natural frequencies, which are characterized by sudden shifts between consecutive time windows, which complicate the mode tracking. Displacement measurements at the bearings show that the transition is due to a change in support conditions under thermal action. It is explained how these transitions are explicitly accounted for in the mode tracking.