bluuebunny/crossref_metadata_2025_split huggingface dataset:
Realization of a strong ferroelectric metal by Nb-doping in strained EuTiO3
Authors: Xu Sheng , Gu Yanni , Zheng Chuanbo , Li Hui , Shen Xiao 
Published: January 2024

Abstract
Ferroelectric (FE) metals have been attracting attention as they possess both metallicity and ferroelectricity, the two seemingly incompatible physical properties. An important problem for both fundamental research and potential applications is how to realize a strong FE metal. One strategy is to dope a strong FE insulator and turn it into an FE metal, provided that the induced free electrons do not destroy FE distortion. Strained EuTiO3 is a strong FE ferromagnet and a promising candidate for such a strategy. Here, we calculate the structural, electronic, and polarization properties of Nb-doped strained EuTiO3 using the hybrid density-functional theory. The results show that the strained EuTi0.875Nb0.125O3 is a strong FE metal as the Nb doping induces metallicity without weakening the FE distortion. The underlying atomistic mechanism of the coexistence of metallicity and strong ferroelectricity is discussed. The findings show that combining doping and strain engineering is a promising way to realize new EuTiO3-based strong FE metals and may be used in other materials as well.&#xD;
Power-duality in path integral formulation of quantum mechanics
Authors: Inomata Akira , Junker Georg 
Published: January 2024

Abstract
Power duality in Feynman's path integral formulation of quantum mechanics is investigated. The power duality transformation consists of a change in coordinate and time variables, an exchange of energy and coupling, and a classical angular momentum replacement. Two physical systems connected by the transformation form a power-dual pair. The propagator (Feynman's kernel) expressed by Feynman's path integral cannot be form-invariant under the transformation, whereas the promotor constructed by modifying Feynman's path integral is found form-invariant insofar as the angular momentum is classical. Upon angular quantization, the power duality breaks down. To save the notion of power duality, the idea of quasi power duality is proposed, which constitutes of an ad hoc angular momentum replacement. The power-dual invariant promotor leads to the quasi-dual invariant Green function. A formula is proposed, which determines the Green function for one of a dual pair by knowing the Green function for the other. As examples, the Coulomb-Hooke dual pair and a family of two-term confinement potentials for a zero-energy state are discussed.
A comparative DFT analysis of chiral laterally semi-fluorinated liquid crystals
Authors: Khan Obeidullah , Gandhi Sona , Gupta Deepak , Bhattacharjee Ayon 
Published: January 2024

Abstract
Liquid crystalline materials have the property to flow like a liquid while maintaining their molecular order. They are valuable material for technological integration. In this work, we have investigated a chiral semi-fluorinated liquid crystal material with the chemical structure C21H17F2NCS in two distinct laterally fluorine-substituted configurations. Employing density functional theory computations, we undertake a comprehensive comparative analysis of both configurations, assessing their charge dynamics, electrical characteristics, thermochemical properties, and nonlinear attributes. Intriguingly, the configuration featuring fluorine substitution on adjacent benzene rings exhibits remarkably superior properties in contrast to its counterpart, where fluorine is substituted on the same benzene ring. This research underscores the significance of structural nuances in elucidating the enhanced performance of liquid crystalline materials.
21cm signal sensitivity to dark matter decay
Authors: Facchinetti G. , Lopez-Honorez L. , Qin Y. , Mesinger A. 
Published: January 2024

Abstract
The redshifted 21cm signal from the Cosmic Dawn is expected to provide unprecedented insights into early Universe astrophysics and cosmology. Here we explore how dark matter can heat the intergalactic medium before the first galaxies, leaving a distinctive imprint in the 21cm power spectrum. We provide the first dedicated Fisher matrix forecasts on the sensitivity of the Hydrogen Epoch of Reionization Array (HERA) telescope to dark matter decays. We show that with 1000 hours of observation, HERA has the potential to improve current cosmological constraints on the dark matter decay lifetime by up to three orders of magnitude. Even in extreme scenarios with strong X-ray emission from early-forming, metal-free galaxies, the bounds on the decay lifetime would be improved by up to two orders of magnitude. Overall, HERA shall improve on existing limits for dark matter masses below 2 GeV/c
2 for decays into e+e- and below few MeV/c
2 for decays into photons.
Physical informed memory networks for solving PDEs: implementation and applications
Authors: Sun Jiuyun , Dong Huanhe , Fang Yong 
Published: January 2024

Abstract
With the advent of physics informed neural networks (PINNs), deep learning has gained interest for solving nonlinear partial differential equations (PDEs) in recent years. In this paper, physics informed memory networks (PIMNs) are proposed as a new approach to solving PDEs by using physical laws and dynamic behavior of PDEs. Unlike the fully connected structure of the PINNs, the PIMNs construct the long-term dependence of the dynamics behavior with the help of the long short-term memory network. Meanwhile, the PDEs residuals are approximated using difference schemes in the form of convolution filter, which avoids information loss at the neighborhood of the sampling points. Finally, the performance of the PIMNs is assessed by solving the KdV equation and the nonlinear Schrödinger equation, and the effects of difference schemes, boundary conditions, network structure and mesh size on the solutions are discussed. Experiments show that the PIMNs are insensitive to boundary conditions and have excellent solution accuracy even with only the initial conditions.
Some remarks on compositeness of
*
Authors: Chen 陈 Chang 畅 , Meng 孟 Ce 策 , Xiao 肖 Zhiguang 志广 , Zheng 郑 Han-Qing 汉青 
Published: January 2024

Abstract
Recently, the LHCb experimental group found an exotic state

from the

process. A key question is whether it is just a molecule or may have a confined tetraquark ingredient. To investigate this, different methods were used, including a two-channel (

and

) K-matrix unitarization and a single-channel Flatté-like parametrization method analyzed utilizing the pole counting rule and spectral density function sum rule. These analyses demonstrated that

is a molecular state, although the possibility that there may exist an elementary ingredient cannot be excluded, according to an approximate analysis of its production rate.
De-Higgsing in eleven-dimensional supergravity on the squashed S
7
Authors: Nilsson B E W , Pope C N 
Published: January 2024

Abstract
In this paper we construct the subset of modes on S
7 that are relevant in the compactification of eleven-dimensional supergravity on a squashed S
7 when restricted to the sector that comprises singlets under the

S
p
(
1
)
×
S
p
(
2
)

isometry of the squashed sphere. Some of the properties of these modes, connected to the transition from the round S
7 to the squashed S
7, are analysed in detail. Special features of the Rarita–Schwinger operator, described in earlier work by Buchdahl, are explained and related to properties of the squashed S
7 operator spectrum obtained in previous work by the authors. We then discuss how the singlet modes give rise to supermultiplets in the left-squashed case, the phenomenon of de-Higgsing, and what happens to the AdS4 fields in these supermultiplets under an orientation reversal (‘skew-whiffing’) of the squashed S
7. Finally, we consider the possible choices of boundary conditions that appear for some of these fields in AdS4 in the case of the right-squashed non-supersymmetric compactification, and how these choices may affect the stability of the gravity theory.
Gauge theory for quantum XYZ spin glasses
Authors: Itoi C , Sakamoto Y 
Published: January 2024

Abstract
Nishimori’s gauge theory is extended to the quantum XYZ p-spin glass model in finite dimensions. This enables us to obtain useful correlation equalities, which show also that Duhamel correlation functions at an arbitrary temperature are bounded by those in the corresponding classical model on the Nishimori line. These bounds give that the spontaneous magnetization vanishes in any low temperature even if the model enters the

Z

2

-symmetry broken spin glass phase. This theory explains well-known fact from experiments and numerical calculations that the magnetic susceptibility does not diverge in the spin glass transition. The new gauge theory together with the known phase diagram of the Edwards–Anderson model can specify the spin glass region in the coupling constant space of the quantum Heisenberg XYZ spin glass model.
A high Q-factor metamaterial sensor based on electromagnetically induced transparency-like
Authors: Shen Zhaoyang , Zhang Hang , Zhang Jing 
Published: January 2024

Abstract
This paper proposes an electromagnetically induced transparency-like (EIT-like) metamaterial with a high-quality factor (143.54), which is composed of a square ring resonator (SRR) and a cross-shaped resonator (CR). The designed metamaterial generates a sharp transparency peak at 15.79 GHz. The electric field distributions reveal that appearance of the transparency window results from the coupling between bright mode and dark mode. Moreover, analysis of the surface current distributions verifies that the EIT-like effect is induced by the destructive interference of the electric dipoles . Furthermore, the metamaterial is equated to a circuit model, which provides a better fit to the simulated transmission spectrum. These theories fully explain the generation mechanism of the EIT-like phenomenon. In addition, the EIT-like metamaterial is able to detect the dielectric constant of the target analyte (glucose solution), and the sensing performance is evaluated. The results demonstrate that the sensitivity (S) is 4.80 GHz R−1IU−1 and the figure of merit (FOM) reaches 43.71. The proposed EIT-like metamaterial shows prominent sensing abilities, consequently it has potential applications in environmental monitoring, biological and chemical measurements.
GAD plasma-assisted synthesis of ZnO nanoparticles and their photocatalytic activity
Authors: Messai Ridha , Ferhat Mohammed Fouad , Belmekki Bilal , Alam Mir Waqas , Al-Othoum Mohd Al Saleh , Sadaf Shima 
Published: January 2024

Abstract
In this study we present an efficient method for synthesizing highly pure ZnO nanoparticles using a Gliding Arc Discharge (GAD) plasma system as a non-thermal plasma source. This approach offers distinct advantages over conventional techniques, including simplicity, a short synthesis time, utilization of readily available air as the source gas, and potential scalability, rendering it a promising alternative for sustainable ZnO nanoparticle production. The synthesized nanoparticles physicochemical properties were characterized using various techniques, such as scanning electron microscopy (SEM), energy dispersive x-ray analysis (EDAX), UV-visible spectroscopy (UV–vis), Fourier-transform infrared spectroscopy (FTIR), x-ray diffraction (XRD), Thermogravimetric Analysis (TGA), and Differential Scanning Calorimetry (DSC). Furthermore, we evaluated the effectiveness of the synthesized ZnO nanoparticles for wastewater treatment by assessing their photocatalytic activity against methylene blue (MB), Brilliant Cresyl Blue (BCB), and Congo Red (CR) under UV light irradiation for 2 h and 30 min. The results confirmed the successful synthesis of highly pure ZnO nano-powder with an average size of 27.18 nm and a band gap energy of 3.28 eV in an exceptionally brief duration and through straightforward steps. Additionally, GAD plasma-assisted ZnO nanoparticles exhibited a significant dye removal rate, showcasing their potential as highly effective materials for photocatalytic wastewater treatment. This study contributes new insights into the application of GAD plasma for nanoparticle synthesis.
Window size analysis in ultrasonic temperature estimation with timeshifts
Authors: Uyğun Mustafa , Küçüka M Serhan , Özkurt Ahmet 
Published: January 2024

Abstract
In this work, ultrasonic timeshift method for temperature change estimation was investigated for 2D simulated in-silico synthetic ultrasonic signals. Digital phantom tissue was created in MATLAB environment and acoustic simulation was running on k-Wave toolbox for two different temperature conditions. First temperature distribution was assigned to tissue as uniform 37 °C. Second temperature distribution is Gaussian form with peak at tissue center as 45 °C and tails of Gaussian curve is 37 °C. Signal was analyzed with ultrasonic timeshift method for temperature change estimation. This method is based on four steps, calibration with tissue constant, finding timeshift with cross correlation algorithm, find slope of timeshift vector respect to timestep, and multiply tissue constant and slope of local timeshift vector. This multiplication gives temperature change of local point. In this work, window size of smoothing filter of timeshift vector and linear fitting to timeshift—timestep data was analyzed as parametrically with range 3λ to 10λ with a 1λ increment for both windows equally. As a result, window parameters as 5λ to 7λ give best results, maximum absolute error is 0.82 °C, 0.97 °C and 0.92 °C respectively and mean absolute error is ∼0.35 °C. As a verify, different analysis was performed on different temperature distribution with discrete two peak curves.
Numerical investigation of pull in control of rectangular AA1050 rolling ingot through the design of convex mould during direct chill casting process
Authors: Fegade Ritesh , Tated Rajendrakumar , Nehete Rupendra 
Published: January 2024

Abstract
The pull in effect/concavity on the lateral faces of the ingot is formed during the solidification of the ingot due to the thermal stresses induced in the ingot. A part of rolling ingots with pull in effect are cut by a conventional cutting process which affects the recovery and increases wastage and cost of production. In the present work, an elemental death and birth technique was used to develop a numerical model to study mechanical and thermal behavior during the solidification of AA1050 rolling ingot. The results of the model are validated with the results of the Williams model available in the literature. Furthermore, different mould designs provided with convexity on lateral surfaces with a convexity radius of 16 mm to 26 mm were numerically analyzed. A series of numerical simulations were conducted with varying mould convexity with an increment value of one. The results revealed that, providing convexity to the mould is an effective method to control the pull in effect during the direct chill casting process. The convexity radius of 26 mm is enough to completely eliminate the pull in radius of 23.34 mm for the rectangular AA1050 ingot.
Rewiring driven evolution of quenched frustrated signed network
Authors: Kožić Sven Benjamin , Giampaolo Salvatore Marco , Zlatić Vinko 
Published: January 2024

Abstract
A framework for studying the behavior of a classically frustrated signed network in the process of random rewiring is developed. We describe jump probabilities for change in frustration and formulate a theoretical estimate in terms of the master equation. Stationary thermodynamic distribution and moments are derived from the master equation and compared to numerical simulations. Furthermore, an exact solution of the probability distribution is provided through suitable mapping of rewiring dynamic to birth and death processes with quadratic asymptotically symmetric transition rates.
Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison
Authors: Jeffrey Niall , Wandelt Benjamin D 
Published: January 2024

Abstract
Evidence Networks can enable Bayesian model comparison when state-of-the-art methods (e.g. nested sampling) fail and even when likelihoods or priors are intractable or unknown. Bayesian model comparison, i.e. the computation of Bayes factors or evidence ratios, can be cast as an optimization problem. Though the Bayesian interpretation of optimal classification is well-known, here we change perspective and present classes of loss functions that result in fast, amortized neural estimators that directly estimate convenient functions of the Bayes factor. This mitigates numerical inaccuracies associated with estimating individual model probabilities. We introduce the leaky parity-odd power (l-POP) transform, leading to the novel ‘l-POP-Exponential’ loss function. We explore neural density estimation for data probability in different models, showing it to be less accurate and scalable than Evidence Networks. Multiple real-world and synthetic examples illustrate that Evidence Networks are explicitly independent of dimensionality of the parameter space and scale mildly with the complexity of the posterior probability density function. This simple yet powerful approach has broad implications for model inference tasks. As an application of Evidence Networks to real-world data we compute the Bayes factor for two models with gravitational lensing data of the Dark Energy Survey. We briefly discuss applications of our methods to other, related problems of model comparison and evaluation in implicit inference settings.
Deep learning of crystalline defects from TEM images: a solution for the problem of ‘never enough training data’
Authors: Govind Kishan , Oliveros Daniela , Dlouhy Antonin , Legros Marc , Sandfeld Stefan 
Published: January 2024

Abstract
Crystalline defects, such as line-like dislocations, play an important role for the performance and reliability of many metallic devices. Their interaction and evolution still poses a multitude of open questions to materials science and materials physics. In-situ transmission electron microscopy (TEM) experiments can provide important insights into how dislocations behave and move. The analysis of individual video frames from such experiments can provide useful insights but is limited by the capabilities of automated identification, digitization, and quantitative extraction of the dislocations as curved objects. The vast amount of data also makes manual annotation very time consuming, thereby limiting the use of deep learning (DL)-based, automated image analysis and segmentation of the dislocation microstructure. In this work, a parametric model for generating synthetic training data for segmentation of dislocations is developed. Even though domain scientists might dismiss synthetic images as artificial, our findings show that they can result in superior performance. Additionally, we propose an enhanced DL method optimized for segmenting overlapping or intersecting dislocation lines. Upon testing this framework on four distinct real datasets, we find that a model trained only on synthetic training data can also yield high-quality results on real images–even more so if the model is further fine-tuned on a few real images. Our approach demonstrates the potential of synthetic data in overcoming the limitations of manual annotation of TEM image data of dislocation microstructure, paving the way for more efficient and accurate analysis of dislocation microstructures. Last but not least, segmenting such thin, curvilinear structures is a task that is ubiquitous in many fields, which makes our method a potential candidate for other applications as well.
Sparse optical flow outliers elimination method based on Borda stochastic neighborhood graph
Authors: Wang Yifan , Li Yang , Wang Jiaqi , Lv Haofeng , Guo Jinshi 
Published: January 2024

Abstract
During the tracking of moving targets in dynamic scenes, efficiently handling outliers in the optical flow and maintaining robustness across various motion amplitudes represents a critical challenge. So far, studies have used thresholding and local consistency based approaches to deal with optical outliers. However, there is subjectivity through expert-defined thresholds or delineated regions, and therefore these methods do not perform consistently enough under different target motion amplitudes. Other studies have focused on complex statistical-mathematical modeling which, although theoretically valid, requires significant computational resources. Aiming at the above problems this paper proposes a new method to calculate the optical outliers by using stochastic neighborhood graph combined with the Borda counting method, which reduces the computation amount on the basis of objectively eliminating the outliers. Sparse optical flow (SOF) values are used as the overall population and the outlier and inlier SOF values are used as samples. Analyze the dissimilarity between SOF data points, obtaining the dissimilarity matrix, introducing the Gaussian function to smooth and reduce the dimensionality of the dissimilarity matrix, and then normalizing the smoothing matrix to generate the binding matrix, where the probability sum of each node to other nodes in the matrix is equal to 1. Stochastic neighborhood graphs are then generated based on a binding matrix to obtain the outlier probabilities of data points in different neighborhood graphs, and outlier samples are obtained based on the probability. To avoid the subjectivity of the expert thresholds, the outlier probabilities are weighted and ranked to calculate the data point Borda scores to obtain accurate optical outliers. The experimental results show that the method in this paper is robust to different amplitude motions and real scenarios, and the accuracy, precision and recall of outliers elimination are better than the current mainstream algorithms.
Weyl asymptotics for functional difference operators with power to quadratic exponential potential
Authors: Qiu Yaozhong 
Published: January 2024

We continue the program first initiated by Laptev, Schimmer, and Takhtajan [Geom. Funct. Anal. 26 (2016), pp. 288–305] and develop a modification of the technique introduced in that paper to study the spectral asymptotics, namely the Riesz means and eigenvalue counting functions, of functional difference operators

H
0

=

F

−
1

M

cosh
⁡
(
ξ
)

F

H_0 = \mathcal {F}^{-1} M_{\cosh (\xi )} \mathcal {F}

with potentials of the form

W
(
x
)
=

|

x

|

p

e

|

x

|

β

W(x) = \left \lvert {x} \right \rvert ^pe^{\left \lvert {x} \right \rvert ^\beta }

for either

β
=
0

\beta = 0

and

p
>
0

p > 0

or

β
∈
(
0
,
2
]

\beta \in (0, 2]

and

p
≥
0

p \geq 0

. We provide a new method for studying general potentials which includes the potentials studied by Laptev, Schimmer, and Takhtajan [Geom. Funct. Anal. 26 (2016), pp. 288–305] and [J. Math. Phys. 60 (2019), p. 103505]. The proof involves dilating the variance of the gaussian defining the coherent state transform in a controlled manner preserving the expected asymptotics.