bluuebunny/crossref_metadata_2025_split huggingface dataset:
Collision of cesium atoms on helium nanodroplets: Unraveling mechanisms for surface capture at experimental velocities
Authors: Bonhommeau David A. 
Published: November 2024

The collision of cesium atoms on the surface of helium nanodroplets (HNDs) containing 1000 atoms is described by the ZPAD-mPL approach, a zero-point averaged dynamics (ZPAD) method based on a He–He pseudopotential adjusted to better reproduce the total energy of He1000. Four types of collisional patterns were identified depending on the initial projectile speed v0 and impact parameter b. At the lowest speeds (v0 ≲ 250 m s−1), Cs atoms are softly captured by the HND surface, while at the highest ones (v0 ≳ 500–600 m s−1), Cs atoms can travel through the droplet and move away. In between these two extreme cases, Cs atoms can be temporarily submerged in the HND before being expelled to the surface if b = 0 or cross the HND before being captured on its surface. The possibility for Cs capture at experimental velocities and droplet piercings at the highest ones contrasts with time-dependent density functional theory calculations, which predict Cs capture for velocities lower than 75 m s−1, and ring-polymer molecular dynamics (RPMD) or former ZPAD-like methods, which predict soft Cs capture up to 500 m s−1. ZPAD-mPL results are attributed to the liquid but non-superfluid nature of the droplet, which favors energy exchanges with the helium environment, and to low He–He binding energy and HND surface tension, which can stimulate helium ejections, especially at high projectile speeds. Despite the use of a pseudopotential to model He–He interactions, the heliophobicity of Cs atoms is maintained as demonstrated by their ability to remain localized on the HND surface or to be expelled to the HND surface after transient submersion in helium.
Redifining RADEN’s high-resolution neutron imaging capabilities
Authors: Sans-Planell Oriol , Shinohara Takenao , Grazzi Francesco , Cantini Francesco , Su Yuhua , Matsumoto Yoshihiro , Parker Joseph D. , Manke Ingo 
Published: November 2024

This study presents a significant development in the Energy-Resolved Neutron Imaging System RADEN, in the Japan Proton Accelerator Research Complex, Japan. Through a systematic study, the collimation power of the facility was reevaluated. What was initially considered to be values of 230, 420, and 760 have been proven to be much higher. To perform the calculation of the L/D factor of the beam, a state-of-the-art method has been used, along with a standard reference sample to measure the resolution of neutron images. To add robustness to the results, the study compares five different scintillators of different composition and thickness [6LiF:ZnS(Ag) of 50, 100, 200, and 300 μm and Gd2O2S of 50 μm]. The calculated collimating power of the beam ranges between 470 and 1520. These results place a spotlight on an existing discrepancy between the geometrically calculated L/D and the actual measurable quantity, as well as highlight the superior performance of the RADEN beamline.
Ultra-wideband tunable transparent all-dielectric absorber based on a saline water-filled PMMA periodic structure
Authors: Pan Zeyu , Wu Haibin , Tian Ye , Lv Bo 
Published: November 2024

With the rapid development of electromagnetic technology, the demand for high-performance microwave absorbing materials is increasing. This paper proposes a novel design for an ultra-wideband, transparent, and tunable all-dielectric absorber based on a saline water-filled polymethyl methacrylate periodic structure. The performance of the absorber is comprehensively evaluated through theoretical analysis, numerical simulations, and effective medium theory. The results demonstrate that the designed absorber achieves an absorption rate of over 90% in the frequency range of 23.75–47.01 GHz, showcasing excellent wideband characteristics. The absorber also exhibits good angular stability, maintaining high absorption efficiency even at a large incidence angle of 60°. By adjusting the temperature and concentration of the saline water, dynamic tuning of the absorption band can be achieved, providing flexibility for different application scenarios. Analysis of the electric field distribution and power loss density reveals the absorption mechanism, confirming the critical role of saline water in the absorption process. The effective medium theory further explains the wideband characteristics and excellent impedance matching of the absorber. This study not only presents a novel absorber design but also provides important theoretical and practical guidance for the development of high-performance, environmentally friendly, and tunable microwave absorbing materials, with potential applications in addressing electromagnetic environmental pollution and electromagnetic compatibility issues.
Functions of the particles tõesti ‘really’ and tõepoolest ‘indeed’ in the spoken,
online and printed registers of Estonian
Authors: Laanesoo-Kalk Kirsi 
Published: November 2024

Abstract
The article examines the use of two Estonian pragmatic particles, tõesti ‘really’ and
tõepoolest ‘indeed, truly’, in everyday conversations, online news comments and academic prose. The data is
drawn from the Estonian Pragmatic Corpus. The material analyzed reveals that the main function of both particles in the different
registers is to confirm and emphasize some statement, fact, general knowledge, etc., either in the previous text or in the current
utterance or sentence. Both particles can add an element of surprise or bewilderment to the sentence or utterance. The use of
tõepoolest is associated with higher degrees of formality, whereas tõesti, being a
relatively frequent particle, can occur in both formal and informal texts.
Functions of the particle üldse ‘at all’ in questions in Estonian everyday
conversations
Authors: Rääbis Andriela 
Published: November 2024

Abstract
The goal of the paper is to examine the use of the particle üldse ‘at all; ever; generally;
absolutely (not)’ in questions in Estonian everyday face-to-face and telephone conversations. The analysis is based on the
methodological framework of interactional linguistics.
The particle üldse is found to serve three central functions in questions: (a) marking topic
shifts and topic changes, (b) intensifying doubt or challenges, (c) emphasizing someone’s norm-violating behavior. Questions
containing the particle üldse can be divided into two groups: neutral information-seeking questions and
multifunctional questions that perform several social actions simultaneously. The particle üldse is commonly
backward looking and serves both interpersonal and textual functions. Its use is often associated with non-preference,
disagreement, or contradiction.
Hangul — A hybrid orthography
Authors: O’Grady William 
Published: November 2024

Abstract
Hangul is widely categorized as a classic example of a morphophonemic orthography, in which each morpheme receives
a single spelling, even if its pronunciation varies from context to context. This paper makes the case that Hangul is in fact a
hybrid orthography that adheres to a morphophonemic strategy for spelling consonants but adopts the conventions of a phonetic
writing system for representing vowels. Various explanations for this arrangement are considered.
The role of university in a Korean heritage language student’s identity development
Authors: Park Mi Yung , Chung Katalina 
Published: November 2024

Abstract
Drawing on autobiographical narratives of a second-generation Korean heritage language (HL) student who grew up in
Auckland, New Zealand, this study examines the role of university study in HL students’ identity development. In her narratives,
the participant describes feeling inferior about her Korean HL and culture as a child but finding that university offered space
for the (re)construction and positive reinforcement of her HL and cultural identities over time. The university provided her with
a multicultural community, and HL and other identity- and heritage-culture-related courses, which enabled her both to
(re)construct her sense of self and to critically examine the challenges she faced in a White European-dominated society. The
findings shed light on second-generation immigrant-background students’ HL and related identity development processes, and the
importance of creating bilingual and bicultural educational spaces in tertiary education for their ongoing identity
development.
Topological Hall effect instigated in kagome Mn3–xSn due to Mn-deficit induced noncoplanar spin structure
Authors: Low Achintya , Ghosh Susanta , Thirupathaiah Setti 
Published: November 2024

Abstract
Magnetic topological semimetals are manifestations of the interplay between electronic and magnetic phases of matter, leading to peculiar characteristics such as the anomalous Hall effect (AHE) and the topological Hall effect (THE). Mn3Sn is a time-reversal symmetry-broken magnetic Weyl semimetal showing topological characteristics within the Kagome lattice network. This study reveals a large THE in Mn2.8Sn (6% Mn deficit Mn3Sn) at room temperature in the xy-plane, despite being an antiferromagnet. We argue that the magnetocrystalline anisotropy induced noncoplanar spin structure is responsible for the observed THE in these systems. Further, the topological properties of these systems are highly anisotropic, as we observe a large AHE in the zx-plane. We find that Fe doping at the Mn site, Mn

3
−
x

Fe
x
Sn (x = 0.2, 0.25, & 0.35), tunes the topological properties of these systems. These findings promise the realization of potential topotronic applications at room temperature.
A three-stage cross-domain intelligent fault diagnosis method for multiple new faults
Authors: Zhang YeFeng , Tang HeSheng , Ren Yan 
Published: November 2024

Abstract
Recently, transfer learning (TL) has been widely investigated to tackle the cross-domain fault diagnosis issue in machinery, and most research works follow the same assumption that the diagnosis domain shares the same fault categories. However, due to the randomness and complexity of mechanical faults, the new fault modes usually occur unexpectedly in the actual scenarios. The emergence of new faults also presents severe challenges to TL. In response to these challenges, a three-stage cross-domain intelligent fault diagnosis method is presented in this article. First, partial domain alignment is achieved based on an improved target weighted mechanism, and an outlier identifier is constructed to automatically separate the new fault classes. Then, an unsupervised learning model with silhouette coefficients is built to determine the number of new fault categories. Finally, the simulation signals are further adopted to distinguish the specific fault categories. Sufficient experiments on axial piston pump and public bearing datasets validate that the proposed method can predict a number of new fault categories and identify specific fault categories. The results indicate that the proposed method outperforms the other methods and has promising practical applications in fault diagnosis with multiple new faults.
Research on damage identification of simply supported bridge based on effect size method for vehicle-bridge coupled vibration
Authors: Guo Yihang , Wang Jiangfei , Wu Yanni , Xu Jianguo , Huang Liang , Zhou Qi , Kou Lei 
Published: November 2024

Abstract
This paper presents a novel bridge damage identification method employing Cohen’s d as an effect size indicator, predicated on the detection of bridge damage through the coupled vibration between a vehicle and the bridge. By analysing the dynamic response of the bridge as a vehicle passes over, this method effectively extracts the modal parameters of the bridge and facilitates the identification of bridge damage. Numerical models of the bridge under various damage conditions, including no damage, mid-span damage, and damage at the 1/4 and 3/4 span locations, have been constructed to substantiate the efficacy and precision of the effect size as a damage indicator. Furthermore, to address the challenge of accurately identifying damage at boundaries, which is often confounded by boundary effects, a boundary subdivision method has been defined for the detection of boundary damage. Through the implementation of a real-bridge test based on indirect measurement technology, the self-vibration frequency of the bridge was successfully extracted. This empirical data was then compared and analysed against results from ANSYS numerical simulations, thereby validating the practicality and accuracy of the proposed method. In the final analysis, the influence of random traffic flow on the bridge damage identification results was examined. The findings indicate that the vibrations in simply supported beam bridges are intensified due to the impact of random traffic flow, which aids in enhancing the accuracy of damage identification. The introduction of this method provides a new quantitative tool for bridge health monitoring, enabling the rapid and accurate identification of bridge damage without interrupting traffic. It holds significant value for engineering applications in bridge maintenance and safety assessment.
A novel epilepsy detection approach using intrinsic multiscale entropy analysis and DSEAM-enhanced 1D-ResNets
Authors: Jing Xiaolong , Yuan Rui , Lv Yong , Liu Hanwen , Li Hewenxuan , Zhu Weihang 
Published: November 2024

Abstract
Epilepsy, a prevalent neurological disorder, typically requires a complex diagnostic process involving medical history inquiry, physical examination, head computed tomography, and electroencephalogram (EEG) visual examination. Among the existing epilepsy automated detection algorithms, machine learning methods require manual feature selection. Most of deep learning algorithms for automatic detection have high complexity and computational complexity. To address this issue, this study proposes a high-precision, robust, and low computational epilepsy automatic detection algorithm based on EEG signal processing. The study utilizes ensemble empirical mode decomposition to preprocess the original EEG signal, breaking it down into intrinsic mode functions (IMFs) across various frequency bands. These IMFs contain information about epilepsy occurrence within the signal at different frequency bands. To enhance computational efficiency and reduce data dimension, the refined composite multiscale dispersion entropy of each IMF is further computed at different scales, referred to as intrinsic multiscale entropy (IME) analysis. IME analysis consolidates epilepsy occurrence information from EEG signals across different frequency bands and scales, linking entropy values to generate feature vectors. Drawing inspiration from successful deep residual networks and Squeeze-and-Excitation (SE) networks, the study introduces a double SE attention module enhanced one-dimensional residual network to classify one-dimensional feature vectors. The proposed method was tested on epilepsy dataset from University of Bonn, and the results demonstrated superior classification performance. In the experiment, the distinction between normal and epileptic EEG signals achieved 100% accuracy rate, while distinguishing between normal, epileptic interval, and epileptic EEG signals achieved accuracy rate of 99.41%.
Turbulence statistics estimation across a step change in roughness via interpretable network-based modelling
Authors: Iacobello Giovanni , Placidi Marco , Ding Shan–Shan , Carpentieri Matteo 
Published: November 2024

Abstract
This study proposes a data-driven methodology to complement existing time-series measurement tools for turbulent flows. Specifically, a cluster-based transition network model is employed for the estimation of velocity time traces and their corresponding statistics. The method is tested on a laboratory-modelled turbulent boundary layer over a step change in surface roughness, where velocity time series are recorded for training and validation purposes via Laser Doppler Anemometry. Results show that our approach can estimate velocity and momentum flux statistics within experimental uncertainty over a rough surface through an unsupervised approach, and across the step change in roughness through a semi-supervised variant. The friction velocity across the domain is also estimated with 10% relative error compared to the measured value. The proposed methodology is interpretable and robust against the main methodological parameters. A reliable data-driven framework is hence provided that can be integrated within existing laboratory setups to supplement or partially replace measurement systems, as well as to reduce wind tunnel running times.
Research on oil and gas pipeline leak detection method based on 1DCNN-DBO-LSTM
Authors: Song Jinbo , Yan Rongxi , Lu Jingyi , Liu Xingyu , Yang Dandi 
Published: November 2024

Abstract
Leak detection of oil and gas pipelines is essential for safe pipeline operation. Existing methods for detecting oil and gas pipeline leaks are inadequate and do not exploit the temporal characteristics of the leak signal. In this paper, a fusion recognition model of a long short-term memory network (LSTM) optimized by a one-dimensional convolutional neural network (1DCNN) and dung beetle optimization algorithm (DBO) is proposed. First, the one-dimensional signal is input into the 1DCNN. Then the adaptively extracted data features from the 1DCNN are input into the optimized DBO-LSTM for classification. The model is evaluated using the precision and duration metrics. In comparison to extant advanced models, the proposed model improves recognition precision and decreases detection time. The model proposed in this paper is able to extract pipeline data features more rapidly and precisely, thereby improving classification accuracy.
Method for rail surface defect detection based on neural network architecture search
Authors: Min Yongzhi , Jing Qinglong , Li Yaxing 
Published: November 2024

Abstract
This study addresses the inherent limitations of implementing neural network architecture search algorithms for rail surface defect detection, including low search efficiency and the oversight of edge features on the rail surface. A sophisticated multi-level neural network architecture search framework is proposed that integrates and emphasizes rail surface edge features. The framework utilizes the Z-Score normalization method to quantify the edge concern of rail surface defect samples, combined with an Edge-Loss function to enhance edge feature recognition capabilities. Furthermore, acknowledging the sensitivity of defect features to spatial resolution changes, a multi-level neural network architecture search space is meticulously designed. In the cell-level search space, a method combining partial channel sampling with operation pruning is employed to enhance model search efficiency and regularization. In the network-level search space, optimal paths for resolution change are established, allowing for the screening and aggregation of defect features at various levels to facilitate the adaptive extraction of multi-scale edge defect features. Experimental outcomes indicate that this method significantly reduces computational resource usage by approximately 75% and increases mIOU by 2.6% relative to traditional architecture search methods. Moreover, it demonstrates robust capability in accurately recognizing defective edges on rail surfaces, thereby substantiating the method’s effectiveness.
High-precision roundness measuring system for tungsten ball tips with the diameter of less than 100 µm
Authors: Zu Chuan , Zhao Wenkai , Li Rui-jun , Liu Yunlong , Cheng Zhenying 
Published: November 2024

Abstract
Tungsten balls with a diameter of less than 100 μm can function as probe tips for micro-coordinate measuring machines to measure ultraprecise workpieces with complex features. A high-precision measuring system was developed to evaluate the roundness of self-made tungsten ball tips. The system, based on the two-point method, is composed of two oppositely placed interferometers and a turntable. The turntable rotates the tested ball, and the interferometers measure the variation in radius. A new model based on the minimum zone method is proposed to evaluate roundness. The fabrication principle of tungsten balls is introduced and an analysis is conducted on the maximum permissible contact force and bending stress of tungsten ball tips. The elastic mechanism is designed, based on the analysis. A ruby sphere with a sphericity of 130 nm is measured to verify the system’s effectiveness. Repeated experiments are performed for two tungsten ball tips. The results show that the roundness error of tungsten ball-A is between 550.9 and 586.2 nm with a standard deviation of 11.0 nm while tungsten ball-B’s roundness error is between 898.5 and 959.9 nm with a standard deviation of 21.7 nm. The uncertainty is calculated as 144.6 nm. The developed system can stably measure the roundness of tungsten ball tips.
From interacting agents to Boltzmann-Gibbs distribution of money
Authors: Cao Fei , Jabin Pierre-Emmanuel 
Published: November 2024

Abstract
We investigate the unbiased model for money exchanges: agents give at random time a dollar to one another (if they have one). Surprisingly, this dynamics eventually leads to a geometric distribution of wealth (shown empirically by Dragulescu and Yakovenko, and rigorously by several follow-up papers). We prove a uniform-in-time propagation of chaos result as the number of agents goes to infinity, which links the stochastic dynamics to a deterministic infinite system of ordinary differential equations. This deterministic description is then analyzed by taking advantage of several entropy–entropy dissipation inequalities and we provide a quantitative almost-exponential rate of convergence toward the equilibrium (geometric distribution) in relative entropy.
Resolution-dependent MRI-to-CT translation for orthotopic breast cancer models using deep learning
Authors: Ambaye Dagnachew Tessema , Tessema Abel Worku , Jeong Jiwoo , Ryu Jiwon , Yu Tosol , Lee Jimin , Cho Hyungjoon 
Published: November 2024

Abstract

Objective. This study aims to investigate the feasibility of utilizing generative adversarial networks (GANs) to synthesize high-fidelity computed tomography (CT) images from lower-resolution MR images. The goal is to reduce patient exposure to ionizing radiation while maintaining treatment accuracy and accelerating MR image acquisition. The primary focus is to determine the extent to which low-resolution MR images can be utilized to generate high-quality CT images through a systematic study of spatial resolution-dependent magnetic resonance imaging (MRI)-to-CT image conversion. Approach. Paired MRI-CT images were acquired from healthy control and tumor models, generated by injecting MDA-MB-231 and 4T1 tumor cells into the mammary fat pad of nude and BALB/c mice to ensure model diversification. To explore various MRI resolutions, we downscaled the highest-resolution MR image into three lower resolutions. Using a customized U-Net model, we automated region of interest masking for both MRI and CT modalities with precise alignment, achieved through three-dimensional affine paired MRI-CT registrations. Then our customized models, Nested U-Net GAN and Attention U-Net GAN, were employed to translate low-resolution MR images into high-resolution CT images, followed by evaluation with separate testing datasets. Main Results. Our approach successfully generated high-quality CT images (0.142 mm2) from both lower-resolution (0.282 mm2) and higher-resolution (0.142 mm2) MR images, with no statistically significant differences between them, effectively doubling the speed of MR image acquisition. Our customized GANs successfully preserved anatomical details, addressing the typical loss issue seen in other MRI-CT translation techniques across all resolutions of MR image inputs. Significance. This study demonstrates the potential of using low-resolution MR images to generate high-quality CT images, thereby reducing radiation exposure and expediting MRI acquisition while maintaining accuracy for radiotherapy.