bluuebunny/crossref_metadata_2025_split huggingface dataset:
Snap-through behavior of bistable beam with variable sections: mechanical model and experimental study
Authors: Zhang Zhongwen , Chen Li-Wei , Xu Zhao-Dong 
Published: August 2022

Abstract
Bistable and multistable structures with snap-through behavior can achieve nonlinear and negative stiffness based on their geometric nonlinearity in elastic deformation. Traditionally, loading capacity and stiffness of this kind of structure tend to be relatively low, a drawback that can potentially be overcome by making section bistable beams varying along its length. However, there currently lacks a systematic evaluation on influences of the sectional alignment on its mechanical performance. Based on the chained constrained beam model, a simplified method for calculating the structure is derived in this paper. The method is validated by a series of tests. Using the method, a systematic parametric study is performed and optimization studies are conducted to establish the best achievable mechanical behavior of the bistable structure subjected to different design limitations. Influences of the stiffness of the lateral restraints on performance of the structure are also investigated. At last, a structure based on the bistable beams connected in parallel were designed using the proposed methodology and tested.
Analytical solutions of (2+1)-dimensional Calogero-Bogoyavlenskii-Schiff equation in fluid mechanics/plasma physics using the New Kudryashov method
Authors: Cinar Melih , Secer Aydin , Bayram Mustafa 
Published: August 2022

Abstract
This study investigates various analytic soliton solutions of the generalized (2+1)-dimensional Calogero-Bogoyavlenskii-Schiff (CBS) equation in fluid dynamics and plasma physics using a recently introduced technique which is the New Kudryashov method. Moreover, it is examined how the wave propagation in both directions represented by the CBS equation occurs. The considered equation describes the interaction of the long propagating wave in the x axis with the Riemann propagating wave along the y axis. To get traveling wave solutions of the CBS equation, it is transformed into a nonlinear ordinary differential equation (NLODE) using a proper wave transformation. Supposing that the NLODE has some solutions in the form provided by the method, one can obtain a nonlinear system of algebraic equations. The unknowns in the system can be found by solving the system via computer algebraic systems such as Mathematica and Maple, etc. Substituting the unknowns into the trial solutions provided by the method, we get the solutions of the NLODE. Then, putting wave transformations back into the solutions of NLODE, we get the solutions of the considered CBS equation. We present the 2D, 3D and contour plots to illustrate the physical behavior of the obtained solutions using the appropriate parameters. Besides, the schematic representation of wave motion of the soliton along both spatial axes and its interpretation are given. The used novel technique can be used for a wide range of partial differential equations (PDEs) in the real world. It is expected that the derived soliton solutions might be helpful for better understanding the wave behavior and so, it might contribute to future studies in various disciplines.
Magnon control of light transmission in a  -symmetric-like cavity magnomechanical system
Authors: Ding Ming-Song , Shi Ying , Liu Yu-Jie , Zheng Li 
Published: August 2022

Abstract
We theoretically study magnon control of light transmission in a cavity magnomechanical system, which consists of an active magnon mode and a passive microwave cavity mode. Phonon-magnon coupling and cavity photon-magnon coupling can be achieved via magnetostrictive interaction and magnetic dipole interaction respectively in this system. By driving a small ferromagnetic sphere which connected by an optical fiber to the inner wall of a microwave cavity, the effective magnon-gain can be obtained. We find that the transparent, absorptive and amplificatory windows can appear by adjusting the gain of the magnon mode, and the introduction of gain can realize significant amplification of transmitted probe power, especially at the EP point. In comparison with traditional optomechanically induced amplification, the amplification can be controlled not only by adjusting the driving field and magnon-microwave coupling, but also by adjusting the external magnetic field. Our results lays a theoretical foundation for the study of the magnon control in magnomechanical systems.
Negative differential thermal conductance between Weyl semimetals nanoparticles through vacuum
Authors: Sun Yasong , Hu Yang , Shi Kezhang , Zhang Jihong , Feng Dudong , Wu Xiaohu 
Published: August 2022

Abstract
In this work, the near-field radiative heat transfer (NFRHT) between two Weyl semimetal (WSM) nanoparticles (NPs) is investigated. The numerical results show that negative differential thermal conductance (NDTC) effect can be obtained in this system, i.e., when the temperature of the emitter is fixed, the heat flux does not decrease monotonically with the increase of the temperature of the receiver. Specifically, when the temperature of the emitter is 300 K, the heat flux is identical when the temperature of the receiver is 50 K or 280 K. The NDTC effect is attributed to the fact that the permittivity of the WSMs changes with the temperature. The coupling effects of polarizability of two WSM NPs have been further identified at different temperature to reveal the physical mechanism of the NDTC effect. In addition, the NFRHT between two WSM NPs can be greatly enhanced by exciting the localized plasmon and circular modes. This work indicates that the WSMs maybe promising candidate materials for manipulating NFRHT.
BAO scale inference from biased tracers using the EFT likelihood
Authors: Babić Ivana , Schmidt Fabian , Tucci Beatriz 
Published: August 2022

Abstract
The physical scale corresponding to baryon acoustic oscillations (BAO), the size of the
sound horizon at recombination, is precisely determined by CMB experiments. Measuring the
apparent size of the BAO scale imprinted in the clustering of galaxies gives us a direct estimate
of the angular-diameter distance and the Hubble parameter as a function of redshift. The BAO
feature is damped by non-linear structure formation, which reduces the precision with which we can
infer the BAO scale from standard galaxy clustering analysis methods. Many methods to undo this
damping via the so-called BAO reconstruction have so far been proposed; however, they all rely on
backward modeling. In this paper, we present the first results of isotropic BAO inference from
rest-frame halo catalogs using forward modeling combined with the EFT likelihood, in the case
where the initial phases of the density field are fixed. We show that the remaining systematic
bias is less than 2% when we consider cutoff values of Λ ≤ 0.25 h Mpc-1
for all halo samples considered, and below 1% and consistent with zero for all but the most
highly biased samples. We also demonstrate that, when compared to the standard power spectrum
likelihood approach under the same assumption of fixed phases, the 1σ errors associated to
the field level inference of the BAO scale are 1.1 to 3.3 times smaller, depending on the value of
the cutoff and the halo sample. Our analysis therefore unveils another promising feature of using
field-level inference for high-precision cosmology.
High energy particles from young supernovae: gamma-ray and neutrino connections
Authors: Sarmah Prantik , Chakraborty Sovan , Tamborra Irene , Auchettl Katie 
Published: August 2022

Abstract
Young core-collapse supernovae (YSNe)
are factories of high-energy neutrinos and gamma-rays as the shock accelerated protons efficiently interact with the protons in the
dense circumstellar medium. We explore the detection prospects of secondary particles from YSNe of Type
IIn, II-P, IIb/II-L, and Ib/c.
Type IIn YSNe are found to produce the largest flux of neutrinos and gamma-rays, followed by II-P YSNe.
Fermi-LAT and the Cherenkov Telescope Array (IceCube-Gen2) have the potential to detect Type IIn YSNe up to 10 Mpc (4 Mpc),
with the remaining YSNe Types being detectable closer to Earth.
We also find that YSNe may dominate the diffuse neutrino background, especially between 10 TeV and 103 TeV, while they do not constitute a dominant component to the isotropic gamma-ray background observed by Fermi-LAT. At the same time, the IceCube high-energy starting events and Fermi-LAT data already allow us to exclude a large fraction of the model parameter space of YSNe otherwise inferred from multi-wavelength electromagnetic observations of these transients.
Perturbative construction of mean-field equations in extensive-rank matrix factorization and denoising
Authors: Maillard Antoine , Krzakala Florent , Mézard Marc , Zdeborová Lenka 
Published: August 2022

Abstract
Factorization of matrices where the rank of the two factors diverges linearly with their sizes has many applications in diverse areas such as unsupervised representation learning, dictionary learning or sparse coding. We consider a setting where the two factors are generated from known component-wise independent prior distributions, and the statistician observes a (possibly noisy) component-wise function of their matrix product. In the limit where the dimensions of the matrices tend to infinity, but their ratios remain fixed, we expect to be able to derive closed form expressions for the optimal mean squared error on the estimation of the two factors. However, this remains a very involved mathematical and algorithmic problem. A related, but simpler, problem is extensive-rank matrix denoising, where one aims to reconstruct a matrix with extensive but usually small rank from noisy measurements. In this paper, we approach both these problems using high-temperature expansions at fixed order parameters. This allows to clarify how previous attempts at solving these problems failed at finding an asymptotically exact solution. We provide a systematic way to derive the corrections to these existing approximations, taking into account the structure of correlations particular to the problem. Finally, we illustrate our approach in detail on the case of extensive-rank matrix denoising. We compare our results with known optimal rotationally-invariant estimators, and show how exact asymptotic calculations of the minimal error can be performed using extensive-rank matrix integrals.
Socially-differentiated urban metabolism methodology informs equity in coupled carbon-air pollution mitigation strategies: insights from three Indian cities
Authors: Nagpure Ajay Singh , Tong Kangkang , Ramaswami Anu 
Published: August 2022

Abstract
A differentiated urban metabolism methodology is developed to quantify inequality and inform social equity in urban infrastructure strategies aimed at mitigating local in-boundary PM2.5 and co-beneficially reducing transboundary greenhouse gas (GHG) emissions. The method differentiates community-wide local PM2.5 and transboundary GHG emission contributions by households of different income strata, alongside commercial and industrial activities. Applied in three Indian cities (Delhi, Coimbatore, and Rajkot) through development of new data sets, method yields key insights that across all three cities, top-20% highest-income households dominated motorized transportation, electricity, and construction activities, while poorest-20% homes dominated biomass and kerosene use, resulting in the top-20% households contributing more than three times GHGs as the bottom-20% homes. Further, after including commercial and industrial users, top-20% households contributed as much or more in-boundary PM2.5 emissions than
all
commercial OR
all
industrial emitters (e.g. Delhi’s top-20% homes contributed 21% of in-boundary PM2.5 similar to industries at 21%. These results enabled co-benefit analysis of various infrastructure transition strategies on the horizon, finding only three could yield both significant GHG and PM2.5 reductions (>2%-each): (a) Modest 10% efficiency improvements among top-20% households, industry and commercial sectors, requiring a focus on wealthiest homes; (b) Phasing out all biomass and kerosene use within cities (impacting poorest); (c) Replacing gas and diesel vehicles with renewable electric vehicles. The differentiated PM2.5 and GHG emissions data-informed social equity in the design of the three co-beneficial infrastructure transitions by: (a)-prioritizing free/subsidized clean cooking fuels to poorest homes; (b)-increasing electricity block rates and behavioral nudging for wealthiest homes; and, (c)-prioritizing electrification of mass transit and promoting electric two-wheelers ahead of providing subsidies for electric cars, where the free-rider phenomenon can occur, which benefits wealthiest homes. The methodology is broadly translatable to cities worldwide, while the policy insights are relevant to rapidly urbanizing Asia and Africa to advance clean, low-carbon urban infrastructure transitions.
First passage in discrete-time absorbing Markov chains under stochastic resetting
Authors: Chen Hanshuang , Li Guofeng , Huang Feng 
Published: August 2022

Abstract
First passage of stochastic processes under resetting has recently been an active research topic in the field of statistical physics. However, most of previous studies mainly focused on the systems with continuous time and space. In this paper, we study the effect of stochastic resetting on first passage properties of discrete-time absorbing Markov chains, described by a transition matrix Q between transient states and a transition matrix R from transient states to absorbing states. Using a renewal approach, we exactly derive the unconditional mean first passage time (MFPT) to any absorbing state, the splitting probabilities and the conditional MFPT to a specific absorbing state without hitting any other absorbing state. All the quantities can be expressed in terms of a deformed fundamental matrix

Z

γ

=

I
−

(

1
−
γ

)

Q

−
1

and R, where I is the identity matrix, and γ is the resetting probability at each time step. We further show a sufficient condition under which the unconditional MPFT can be optimized by stochastic resetting. Finally, we apply our results to two concrete examples: symmetric random walks on one-dimensional lattices with absorbing boundaries and voter model on complete graphs.
The SAGEX review on scattering amplitudes Chapter 3: Mathematical structures in Feynman integrals
Authors: Abreu Samuel , Britto Ruth , Duhr Claude 
Published: August 2022

Abstract
Dimensionally-regulated Feynman integrals are a cornerstone of all perturbative computations in quantum field theory. They are known to exhibit a rich mathematical structure, which has led to the development of powerful new techniques for their computation. We review some of the most recent advances in our understanding of the analytic structure of multiloop Feynman integrals in dimensional regularisation. In particular, we give an overview of modern approaches to computing Feynman integrals using differential equations, and we discuss some of the properties of the functions that appear in the solutions. We then review how dimensional regularisation has a natural mathematical interpretation in terms of the theory of twisted cohomology groups, and how many of the well-known ideas about Feynman integrals arise naturally in this context.
Stochastic resetting of a population of random walks with resetting-rate-dependent diffusivity
Authors: Bertin Eric 
Published: August 2022

Abstract
We consider the problem of diffusion with stochastic resetting in a population of random walks where the diffusion coefficient is not constant, but behaves as a power-law of the average resetting rate of the population. Resetting occurs only beyond a threshold distance from the origin. This problem is motivated by physical realizations like soft matter under shear, where diffusion of a walk is induced by resetting events of other walks. We first reformulate in the broader context of diffusion with stochastic resetting the so-called Hébraud–Lequeux model for plasticity in dense soft matter, in which diffusivity is proportional to the average resetting rate. Depending on parameter values, the response to a weak external field may be either linear, or non-linear with a non-zero average position for a vanishing applied field, and the transition between these two regimes may be interpreted as a continuous phase transition. Extending the model by considering a general power-law relation between diffusivity and average resetting rate, we notably find a discontinuous phase transition between a finite diffusivity and a vanishing diffusivity in the small field limit.
Genetic relationship of microstructure and mechanical properties between ferrite/pearlite steel plate and billet
Authors: Feng Rui , Li Zongheng , Li Hui , Zhao Weichao , Gan Kefu , Wang Yucheng , Zhao Guiju , Ma Jin 
Published: August 2022

Abstract
This paper proposed the double-pass compression thermal/force simulation experiment to in situ analyze the phase transformation of continuous casting billet during the controlled rolling and controlled cooling process. The genetic relationship between the central segregation of the billet and solute element distribution of the steel plate, and also the genetic relationship of microstructure and mechanical properties between them were experimentally studied. The results show that the microstructure and mechanical properties between the billet and corresponding batch of steel plate have the genetic relationship, and the link of the relationship is central segregation. The phase transformation of supercooling austenite will be affected by the central segregation of Mn and C elements, and the granular bainite abnormal segregation band is the transformation product of Mn and C element segregation region.
PD-L1 Expression in Colorectal Adenocarcinoma Is Associated With the Tumor Immune Microenvironment and Epithelial-Mesenchymal Transition
Authors: Secinti Ilke Evrim , Ozgur Tumay , Dede Isa 
Published: August 2022

Abstract

Objectives
Colorectal carcinomas are the third-most common tumors in the world, and colorectal cancer ranks second in cancer-related deaths. Our aim in this study was to investigate the correlation between programmed cell death ligand 1 (PD-L1) expression and clinicopathologic parameters in colorectal carcinomas and their relationship to the tumor immune microenvironment, epithelial-mesenchymal transition (EMT), and microsatellite instability. We also investigated the predictive and prognostic role of PD-L1.

Methods
One hundred patients with a diagnosis of colorectal adenocarcinoma who did not receive neoadjuvant therapy were included in the study. The relationships among the altered expression of PD-L1; vimentin; E-cadherin; mismatch repair status; and pathologic microenvironmental features, including the presence of tumor budding and CD8-positive tumor infiltrating lymphocytes (TILs), were assessed.

Results
Increased PD-L1 expression in tumor cells was associated with increased TILs (P = .013), high histologic grade (P = .011), advanced pathologic T stage (P = .007), lymph node metastasis (P = .002), distant metastasis (P &lt; .001), perineural invasion (P = .009), high bud score (P = .023), EMT (P &lt; .001), and shorter disease-free survival (P = .029).

Conclusions
Overall, PD-L1 expression in colorectal carcinoma tumor cells is a marker of poor prognosis, and the positive correlation detected between EMT status and PD-L1 expression suggests that patients with the mesenchymal phenotype may be more likely to benefit from programmed cell death 1 protein/PD-L1 immunotherapy.
iEnhancer-DLRA: identification of enhancers and their strengths by a self-attention fusion strategy for local and global features
Authors: Zeng Li , Liu Yang , Yu Zu-Guo , Liu Yuansheng 
Published: August 2022

AbstractIdentification and classification of enhancers are highly significant because they play crucial roles in controlling gene transcription. Recently, several deep learning-based methods for identifying enhancers and their strengths have been developed. However, existing methods are usually limited because they use only local or only global features. The combination of local and global features is critical to further improve the prediction performance. In this work, we propose a novel deep learning-based method, called iEnhancer-DLRA, to identify enhancers and their strengths. iEnhancer-DLRA extracts local and multi-scale global features of sequences by using a residual convolutional network and two bidirectional long short-term memory networks. Then, a self-attention fusion strategy is proposed to deeply integrate these local and global features. The experimental results on the independent test dataset indicate that iEnhancer-DLRA performs better than nine existing state-of-the-art methods in both identification and classification of enhancers in almost all metrics. iEnhancer-DLRA achieves 13.8% (for identifying enhancers) and 12.6% (for classifying strengths) improvement in accuracy compared with the best existing state-of-the-art method. This is the first time that the accuracy of an enhancer identifier exceeds 0.9 and the accuracy of the enhancer classifier exceeds 0.8 on the independent test set. Moreover, iEnhancer-DLRA achieves superior predictive performance on the rice dataset compared with the state-of-the-art method RiceENN.
Complex genome assembly based on long-read sequencing
Authors: Zhang Tianjiao , Zhou Jie , Gao Wentao , Jia Yuran , Wei Yanan , Wang Guohua 
Published: August 2022

Abstract
High-quality genome chromosome-scale sequences provide an important basis for genomics downstream analysis, especially the construction of haplotype-resolved and complete genomes, which plays a key role in genome annotation, mutation detection, evolutionary analysis, gene function research, comparative genomics and other aspects. However, genome-wide short-read sequencing is difficult to produce a complete genome in the face of a complex genome with high duplication and multiple heterozygosity. The emergence of long-read sequencing technology has greatly improved the integrity of complex genome assembly. We review a variety of computational methods for complex genome assembly and describe in detail the theories, innovations and shortcomings of collapsed, semi-collapsed and uncollapsed assemblers based on long reads. Among the three methods, uncollapsed assembly is the most correct and complete way to represent genomes. In addition, genome assembly is closely related to haplotype reconstruction, that is uncollapsed assembly realizes haplotype reconstruction, and haplotype reconstruction promotes uncollapsed assembly. We hope that gapless, telomere-to-telomere and accurate assembly of complex genomes can be truly routinely achieved using only a simple process or a single tool in the future.