bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysing transitions from a Turing instability to large periodic patterns in a reaction-diffusion system
Authors: Brown Christopher , Derks Gianne , van Heijster Peter , Lloyd David J B 
Published: November 2023

Abstract
Analytically tracking patterns emerging from a small amplitude Turing instability to large amplitude remains a challenge as no general theory exists. In this paper, we consider a three component reaction-diffusion system with one of its components singularly perturbed, this component is known as the fast variable. We develop an analytical theory describing the periodic patterns emerging from a Turing instability using geometric singular perturbation theory. We show analytically that after the initial Turing instability, spatially periodic patterns evolve into a small amplitude spike in the fast variable whose amplitude grows as one moves away from onset. This is followed by a secondary transition where the spike in the fast variable widens, its periodic pattern develops two sharp transitions between two flat states and the amplitudes of the other variables grow. The final type of transition we uncover analytically is where the flat states of the fast variable develop structure in the periodic pattern. The analysis is illustrated and motivated by a numerical investigation. We conclude with a preliminary numerical investigation where we uncover more complicated periodic patterns and snaking-like behaviour that are driven by the three transitions analysed in this paper. This paper provides a crucial step towards understanding how periodic patterns transition from a Turing instability to large amplitude.
Robustness of higher-order networks with synergistic protection
Authors: Chen Qihang , Zhao Yang , Li Cong , Li Xiang 
Published: November 2023

Abstract
From chemical reactions to human communications, higher-order interactions are ubiquitous in real-world networks. Entities within higher-order interactions often exhibit collective behaviors that could create synergistic effects on robustness of the underlying system. Here we propose an analytical model to investigate the robustness of higher-order networks, in which potential higher-order synergistic protection is incorporated. In this model, higher-order networks are described with simplicial complexes, and robustness is studied under the proposed dynamics of extended bond percolation. We provide theoretical analysis for robustness quantities including the relative size of the giant component and percolation threshold. We discover that the percolation threshold could drop to zero, which is an indicator of notably strong robustness, with synergistic protective effects and dense higher-order simplices. We also find that higher-order interactions have strong impacts on the association between robustness and clustering. Specifically, a larger clustering coefficient could invariably indicate stronger robustness once the strength of protective effects exceeds a certain value. Our theoretical solutions are verified by simulation results in simplicial complexes with Poisson, exponential and power-law distributions.
The average trapping time of non-nearest-neighbor jumps on nested networks
Authors: Han Yuxi , Wu Bo 
Published: November 2023

Abstract
In this paper, we consider the trapping problem on the nearest-neighbor (NN) and non-nearest-neighbor (NNN) jumps on nested networks. Based on the nested construction of the network and the use of probability generating function tool, the iterative rules of two successive generations of the network are found, and the analytical expression of the average trapping time (ATT) is finally obtained. We allow two jump modes in the network at the same time, and the results show that the choice probability of the jump mode is not related to the exponential term of the scaling expression, but to its leading factor term. According to the analytic solution of ATT, we can find that the value of ATT expands superlinearly with the increase of network size. In addition, the numerical simulation results of parameters q (the probability of choosing NNN jump) and n (the generation of the network) show that with fixed n, ATT decreases with the increase of q; while with fixed q, ATT increases with the increase of n. In summary, this work can observe the effect of different hopping modes on random walk efficiency in complex networks.
Phase variation of Laguerre-Gaussian vortex beams in a homogeneous atmospheric medium under finite amplitude acoustic wave perturbation
Authors: Zhang Jialin , Wang Mingjun , Wang Wanrou , Huang Chaojun 
Published: November 2023

Abstract
In this article, we have derived the acoustic pressure and medium refractive index expressions in a homogeneous atmospheric medium perturbed by a planar finite amplitude acoustic wave. In a planar finite amplitude acoustic wave perturbation, we developed a Laguerre–Gaussian vortex beam transmission model in a homogeneous atmospheric medium. We investigated the effects of different acoustic source parameters on the phase of the Laguerre–Gaussian vortex beam transmission, considering the atmospheric medium’s viscous effect. The results show that acoustic waves of finite amplitude distort the refractive index distribution of a homogeneous atmospheric medium. At a given distance, the amplitude of the refractive index gradually increases with increasing acoustic wave transmission distance. At the same time, the phase of the Laguerre–Gaussian vortex beam is rotated by the perturbation of the finite-amplitude acoustic wave, and the phase always returns to its initial position. Unlike linear acoustic waves, changes in the homogeneous atmospheric refractive index distribution and the homogeneous phase of the Laguerre–Gaussian vortex light no longer satisfy the periodic variation when perturbed by finite-amplitude acoustic waves. Under the same conditions, the effect of finite-amplitude acoustic waves on the phase of the Laguerre–Gaussian vortex light is stronger than that of linear acoustic waves. Finally, the effects of different acoustic pressure and frequency of the source on the phase of the Laguerre–Gaussian vortex beam transmission are calculated. The results show that different acoustic parameters at the source can be used to achieve phase modulation at different distances and intensities.
Boundary effects on dispersion relations of ion-acoustic wave and physical properties of overlapping soliton in a plasma waveguide
Authors: Han Jiuning , Li Shengchang , Luo Junhua , Chen Hui , Li Junxiu 
Published: November 2023

Abstract
Taking into account the cylindrical boundary, a theoretical investigation has been made for the low frequency electrostatic waves in an electron-positron-ion plasma waveguide. The dispersion relation of ion-acoustic (IA) wave is obtained, and a predication for the linear interaction phenomenon of small-amplitude cylindrical IA solitons is presented. It is shown that the cylindrical boundary has significant effects on the dispersion property of IA waves, and the frequency for short wave is significantly modified by the plasma parameters. It has also been noted that cylindrical IA solitons add up linearly when they overlap and penetrate through each other, the maximum amplitude of the overlapping soliton is nearly the sum of the individual soliton amplitude, indicating an apparent linear interaction. Furthermore, the relationships between phase delay and kinetic energy of colliding solitons for an axisymetric cylindrical geometry are derived and discussed in detail. The work presented would be useful to enrich the solitons interaction theory in astrophysical and laboratorial plasma situations.
On pseudo-Riemannian quartics in Finsler geometry
Authors: Itin Yakov 
Published: November 2023

Abstract
Finsler geometry usually describes an extension of Riemannian geometry into a direction-dependent geometric structure. Historically, the well-known Riemann quartic length element example served as the inspiration for this construction. Surprisingly, the same quartic expression emerges as a fundamental dispersion relationcovariant Fresnel equationin solid-state electrodynamics. As a result, it is possible to conceive of the Riemann quartic length expression as a mathematical representation of a well-known physical phenomenon. This paper provides a number of Riemann quartic examples that show Finsler geometry to be overly constrictive for many applications, even when the signature space is positive definite in the Euclidean sense. The strong axioms of Finsler geometry are broken down on many more singular hypersurfaces for the spaces having an indefinite (Minkowski) signature. We suggest a more flexible definition of a Finsler structure that only has to hold for open subsets of a manifold’s tangent bundle. We demonstrate the distinctive singular hypersurfaces connected to the Riemann quartic and discuss the potential physics explanations for them. As an illustration of the pseudo-Riemannian quartic, we took into consideration the dispersion relation that appears in electromagnetic wave propagation in uniaxial crystal. Our analysis suggests that the signature of the Finsler measure may be altered for large anisotropy factors.
Electroplating of hydrophobic/hydrophilic ZnO nano-structural coatings on metallic substrates
Authors: Belamri Zehira , Boumaza Leila , Boudjadar Smail 
Published: November 2023

Abstract
In the present work, ZnO thin film is shown as a coating on an aluminum substrate. In order to synthesize ZnO thin films, electroplated Zn thin layers were thermally oxidized in atmospheric air for different times (1h–4h) at a fixed annealing temperature of 500 °C. The samples were characterized by scanning electron microscopy (FEG-SEM) equipped with energy dispersive x-ray analysis (EDX), a profilometer, x-ray diffraction (XRD), and Raman spectroscopy. The wettability properties of the synthesized films were evaluated by measuring the contact angle between the surface of the films and a deposited water drop (WCA). The FEG-SEM images show that the surface morphologies change throughout treatment time. The sample treated for 2 h shows flower-like microstructures with an average size of 100 μm, which are covered with spherical ZnO nanostructures with a size less than 50 nm. Measured surface roughness ranges from 5.800 μm to 6.560 μm. Layers thicknesses vary between 31 and 38 μm. Structural characterization by XRD demonstrates that the synthesized ZnO thin films were polycrystalline and have Wurtzite hexagonal structures, grown manly along the (101) plan. The estimated crystallite sizes are in the nanometric scale and reach their maximum value for the sample treated for 2 h. This annealing time corresponds to the low dislocation density (δ) and low lattice strain (ε), indicating fewer defects. The Raman analysis shows five normal vibrational modes, which correspond to the ZnO Wurtzite structure. It was possible to obtain both hydrophobic and hydrophilic surfaces; the shape and surface roughness of the as-prepared films had an impact on the results. The largest measured contact angle, of 97°, was obtained after annealing for 2 h at 500 °C.
Photon-phonon quantum cloning in optomechanical system
Authors: Mu Qingxia , Wang Ting , Zhang Wen-Zhao 
Published: November 2023

Abstract
Quantum cloning is an essential operation in quantum information and quantum computing. Similar to the ‘copy’ operation in classical computing, the cloning of flying bits for further processing from the solid-state quantum bits in storage is an operation frequently used in quantum information processing. Here we propose a high-fidelity and controllable quantum cloning scheme between solid bits and flying bits. In order to overcome the obstacles from the no-cloning theorem and the weak phonon-photon interaction, we introduce a hybrid optomechanical system that performs both the probabilistic cloning and deterministic cloning closed to the theoretical optimal limit with the help of designed driving pulse in the presence of dissipation. In addition, our scheme allows a highly tunable switching between two cloning methods, namely the probabilistic and deterministic cloning, by simply changing the input laser pulse. This provides a promising platform for experimental executability.
Breakdown of arcsine law for resetting brownian motion
Authors: Yan Hao , Chen Hanshuang 
Published: November 2023

Abstract
For a one-dimensional Brownian motion starting from the origin, the cumulative distribution of the occupation time V staying above the origin obeys the celebrated arcsine law. In this work, we show how the law is modified for a resetting Brownian motion, where the Brownian is reset to the position x

r
at random times but with a constant rate r. When x

r
is exactly equal to zero, we derive the exact expression of the probability distribution P

r
(V∣0, t) of V during time t, and the moments of V as functions of r and t. P

r
(V∣0, t) is always symmetric with respect to V = t/2 for arbitrary value of r, but the probability density of V at V = t/2 increases with the increase of r. Interestingly, P

r
(V∣0, t) at V = t/2 changes from a minimum to a local maximum at a critical value R
* ≈ 0.742 338, where R = rt denotes the average number of resetting during time t. Moreover, we consider the case when x

r
is a random variable and is distributed by a function g(x

r
), where g(x

r
) is assumed to be symmetric with respect to zero and possesses its maximum at zero. We derive the general expressions of the moments of V when the variance of x

r
is low. The mean value of V is always equal to t/2, but the fluctuation in x

r
leads to an increase in the second and third moments of V. Our results provide a quantitative understanding of how stochastic resetting destroys the persistence of Brownian motion.
Stepped partially acoustic dark matter: likelihood analysis and cosmological tensions
Authors: Buen-Abad Manuel A. , Chacko Zackaria , Kilic Can , Marques-Tavares Gustavo , Youn Taewook 
Published: November 2023

AbstractWe generalize the recently proposed Stepped Partially Acoustic Dark Matter (SPartAcous) model by including additional massless degrees of freedom in the dark radiation sector. We fit SPartAcous and its generalization against cosmological precision data from the cosmic microwave background, baryon acoustic oscillations, large-scale structure, supernovae type Ia, and Cepheid variables. We find that SPartAcous significantly reduces theH0tension but does not provide any meaningful improvement of theS8tension, while the generalized model succeeds in addressing both tensions, and provides a better fit than ΛCDM and other dark sector models proposed to address the same tensions. In the generalized model,H0can be raised to 71.4 km/s/Mpc (the 95% upper limit), reducing the tension, if the fitted data does not include the direct measurement from the SH0ES collaboration, and to 73.7 km/s/Mpc (95% upper limit) if it does. A version ofCLASSthat has been modified to analyze this model is publicly available athttps://github.com/ManuelBuenAbad/class_spartacous.
Application of graph networks to background rejection in Imaging Air Cherenkov Telescopes
Authors: Glombitza J. , Joshi V. , Bruno B. , Funk S. 
Published: November 2023

Abstract
Imaging Air Cherenkov Telescopes (IACTs) are essential to ground-based observations of gamma rays in the GeV to TeV regime. One particular challenge of ground-based gamma-ray astronomy is an effective rejection of the hadronic background.
We propose a new deep-learning-based algorithm for classifying images measured using single or multiple Imaging Air Cherenkov Telescopes.
We interpret the detected images as a collection of triggered sensors that can be represented by graphs and analyzed by graph convolutional networks.
For images cleaned of the light from the night sky, this allows for an efficient algorithm design that bypasses the challenge of sparse images in deep learning approaches based on computer vision techniques such as convolutional neural networks. We investigate different graph network architectures and find a promising performance with improvements to previous machine-learning and deep-learning-based methods.
Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals
Authors: Pan Lincong , Wang Kun , Xu Lichao , Sun Xinwei , Yi Weibo , Xu Minpeng , Ming Dong 
Published: November 2023

Abstract

Objective. Brain–computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography signals, limiting their practical application. Approach. We proposed a Riemannian geometry-based adaptive boosting and voting ensemble (RAVE) algorithm to address this issue. Our approach segmented the MI period into multiple sub-datasets using a sliding window approach and extracted features from each sub-dataset using Riemannian geometry. We then trained adaptive boosting (AdaBoost) ensemble learning classifiers for each sub-dataset, with the final BCI output determined by majority voting of all classifiers. We tested our proposed RAVE algorithm and eight other competing algorithms on four datasets (Pan2023, BNCI001-2014, BNCI001-2015, BNCI004-2015). Main results. Our results showed that, in the cross-session scenario, the RAVE algorithm outperformed the eight other competing algorithms significantly under different within-session training sample sizes. Compared to traditional algorithms that involved a large number of training samples, the RAVE algorithm achieved similar or even better classification performance on the datasets (Pan2023, BNCI001-2014, BNCI001-2015), even when it did not use or only used a small number of within-session training samples. Significance. These findings indicate that our cross-session decoding strategy could enable MI-BCI applications that require no or minimal training process.
Correlation and asynchronization of electroencephalogram and cerebral blood flow in active and passive stimulations
Authors: Li Weilong , Zhang Zihao , Li Zhiyi , Gui Zhiguo , Shang Yu 
Published: November 2023

Abstract

Objective. Real-time brain monitoring is of importance for intraoperative surgeries and intensive care unit, in order to take timely clinical interventions. Electroencephalogram (EEG) is a conventional technique for recording neural excitations (e.g. brain waves) in the cerebral cortex, and near infrared diffuse correlation spectroscopy (DCS) is an emerging technique that can directly measure the cerebral blood flow (CBF) in microvasculature system. Currently, the relationship between the neural activities and cerebral hemodynamics that reflects the vasoconstriction features of cerebral vessels, especially under both active and passive situation, has not been elucidated thus far, which triggers the motivation of this study. Approach. We used the verbal fluency test as an active cognitive stimulus to the brain, and we manipulated blood pressure changes as a passive challenge to the brain. Under both protocols, the CBF and EEG responses were longitudinally monitored throughout the cerebral stimulus. Power spectrum approaches were applied the EEG signals and compared with CBF responses. Main results. The results show that the EEG response was significantly faster and larger in amplitude during the active cognitive task, when compared to the CBF, but with larger individual variability. By contrast, CBF is more sensitive when response to the passive task, and with better signal stability. We also found that there was a correlation (p < 0.01, r = 0.866, R
2 = 0.751) between CBF and EEG in initial response during the active task, but no significant correlation (p > 0.05) was found during the passive task. The similar relations were also found between regional brain waves and blood flow. Significance. The asynchronization and correlation between the two measurements indicates the necessity of monitoring both variables for comprehensive understanding of cerebral physiology. Deep exploration of their relationships provides promising implications for DCS/EEG integration in the diagnosis of various neurovascular and psychiatric diseases.
Multipactor predictions for ion cyclotron resonance heating antennas of the tokamak WEST
*
Authors: Al Hajj Sleiman Eva , Hillairet Julien , Belhaj Mohamed , Dadouch Sarah , Maquet Vincent 
Published: November 2023

Abstract
During the plasma experimental campaigns performed on the tokamak WEST, a performance-limiting observation has been made on the Ion Cyclotron Resonance Heating (ICRH) system composed of three identical antennas. When only one antenna is powered while the remaining two are off, an increase in the internal pressure is measured within the non-powered antennas. The internal pressure of the non-powered antennas exceeds a safety pressure interlock whose threshold is

4.5
×

10

−
3

Pa, and consequently, the application of the RF power is prohibited to avoid plasma creation within these antennas. In this paper, the multipactor phenomenon caused by the inter-antennas’ coupling is shown to be responsible for the pressure increase observed in the non-powered antennas. Additionally, an analytic estimation of the pressure rise caused by the multipactor is compared to that observed experimentally.
Real time characterization of atmospheric turbulence using speckle texture
Authors: Lochab Priyanka , Kumar Basant , Ghai D P , Senthilkumaran P , Khare Kedar 
Published: November 2023

Abstract
A new method is proposed for the characterization of the atmospheric turbulence strength parameter (

C
n
2

) based on the principal component analysis (PCA) of the texture of the speckle intensity pattern obtained on long-range propagation of a focused charge +1 vortex beam. Employing the split-step propagation method, datasets containing instantaneous intensity images of the focused vortex beam are generated for three distinct

C
n
2

values, specifically

1
×

10

−
14

m

−
2

/

3

,

5
×

10

−
14

m

−
2

/

3

, and

1
×

10

−
13

m

−
2

/

3

, representing medium to high turbulence levels for a 2 km propagation distance. The gray level co-occurrence matrix (GLCM) methodology is employed to extract key texture attributes, like contrast, correlation, homogeneity, and energy from the intensity images. These extracted texture parameters serve as inputs for training a PCA model, enabling the identification of associated

C
n
2

values. The PCA analysis exhibits distinct clustering of the first three principal components for each of the three

C
n
2

values, forming individual clusters on the PCA plot. Standard deviational ellipses are drawn to clearly demarcate these clusters on the PCA plot. The texture-based PCA classification of atmospheric turbulence was also performed for a focused Gaussian beam. The comparison of PCA plots between vortex and Gaussian beams showed that a pronounced clear separation of

C
n
2

values is obtained for the vortex beam. This indicates that the non-zero orbital angular momentum of the vortex beam also plays an important role in achieving the distinct separation of

C
n
2

values on the PCA plot. The proposed method can provide efficient real-time turbulence estimation solely on the basis of texture of the instantaneous intensity speckle with prior training and therefore may simplify the estimation of turbulence strength parameter.