bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated surface profile measurement with digital holographic microscopy and large mask inpainting networks
Authors: Ma Shujun , Wang Minting , Gao Lin , Yin Junzuo , Yang Meng , Ren Lei 
Published: November 2023

Abstract
Digital holographic microscopy (DHM) allows for highly precise 3D surface measurements in a non-invasive way, but phase aberrations from off-axis DHM recordings can compromise image accuracy. Traditional compensation methods require manual intervention, hindering further automated use of DHM. Other methods based on background segmentation and Zernike polynomials have been proposed, but identifying the sample and background regions can lead to inaccurate compensation results. Additionally, traditional image restoration algorithms struggle with restoring sample-free holograms involving large or multiple vacant areas. A new automated aberration compensation method is proposed using large-mask inpainting networks. This method restores sample-free holograms and compensates for phase aberrations, leveraging deep learning to enable real-time measurements. In the study, the network was trained with holograms of varying fringes, and experimental results show its effectiveness in improving image accuracy and detail. This approach could have wide applications in industries such as micro-electromechanical systems and micro- integrated circuits.
Green fabrication of high-performance silver nanoparticles/reduced Ti3C2T
x
MXene nanocomposite catalyst for 4-nitrophenol reduction
Authors: Peng Wei , Zhao Wenkui , Jiang Peicheng , Yao Jie , Le Bin , Lei Song , Peng Chang , Chen Shu 
Published: November 2023

Abstract
The outstanding electrical conductivity of transition metal carbides Ti3C2T
x
(MXene) makes it as an excellent electron transfer medium for fabrication of efficient catalysts. However, the poor stability of MXene may restrict its application. Herein, a novel silver nanoparticles/reduced MXene nanocomposite (AgNPs/rMXene) catalyst was prepared by using L-arginine (L-Arg) as a green reducing agent. In the AgNPs/rMXene catalyst, the silver nanoparticles (AgNPs) and reduced MXene (rMXene) acted as catalytic active species and electron transfer medium, respectively. The composite catalyst exhibited superior catalytic activity in the conversion of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP), and the conversion frequency (TOF) was high up to 1109.4 h−1. Notably, the composite catalyst also showed high stability due to the reduction of L-Arg (i.e. the repair of defect groups on MXene surface). The conversion efficiency for 4-NP reduction by AgNPs/rMXene was high up to 90% after five recycles. This present study offers a simple and green approach for the design and development of efficient MXene-based catalysts.
Development of a system for detecting cardiac troponin I by background fluorescence quenching based on internal filtration effect
Authors: Wang Lei , Zhang Wei , Song Bailing , Zhang Ziyi , Li Jiutong , Li Xinxia , Yao Jun 
Published: November 2023

Abstract
The present study sought to develop a cardiac troponin I (cTnI) detection system based on background fluorescence quenching of internal filtration effect (IFE) and study the influence of IFE on the sensitivity of cTnI detection. Three nanogold materials were synthesized as fluorescence quenchers, and rhodamine 6 G (R6G) and Cy5 were used as fluorescence probes. Six experimental systems were established to detect cTnI in negative serum test solutions and clinical serum samples. The sensitivity of each system was compared to explore the contribution of IFE to the detection sensitivity of cTnI. When applied to negative serum test solutions, the R6G-nanogold material I system exhibited a superior detection effect for cTnI, with a limit of detection (LOD) of 0.15 ng ml−1. When applied to clinical serum samples, the Cy5-nanogold material Ⅲ system yielded a better detection effect for cTnI, with the lowest concentration of cTnI detected at 2 ng ml−1. The first and second internal filtering effects in the proposed system can be achieved simultaneously, effectively avoiding light absorption interference from clinical serum samples and enhancing the sensitivity of the background fluorescence quenching detection of cTnI.
Sparse annotation learning for dense volumetric MR image segmentation with uncertainty estimation
Authors: Osman Yousuf Babiker M , Li Cheng , Huang Weijian , Wang Shanshan 
Published: November 2023

Abstract

Objective. Training neural networks for pixel-wise or voxel-wise image segmentation is a challenging task that requires a considerable amount of training samples with highly accurate and densely delineated ground truth maps. This challenge becomes especially prominent in the medical imaging domain, where obtaining reliable annotations for training samples is a difficult, time-consuming, and expert-dependent process. Therefore, developing models that can perform well under the conditions of limited annotated training data is desirable. Approach. In this study, we propose an innovative framework called the extremely sparse annotation neural network (ESA-Net) that learns with only the single central slice label for 3D volumetric segmentation which explores both intra-slice pixel dependencies and inter-slice image correlations with uncertainty estimation. Specifically, ESA-Net consists of four specially designed distinct components: (1) an intra-slice pixel dependency-guided pseudo-label generation module that exploits uncertainty in network predictions while generating pseudo-labels for unlabeled slices with temporal ensembling; (2) an inter-slice image correlation-constrained pseudo-label propagation module which propagates labels from the labeled central slice to unlabeled slices by self-supervised registration with rotation ensembling; (3) a pseudo-label fusion module that fuses the two sets of generated pseudo-labels with voxel-wise uncertainty guidance; and (4) a final segmentation network optimization module to make final predictions with scoring-based label quantification. Main results. Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image segmentation tasks and compared to five state-of-the-art methods. Significance. Results demonstrate that our proposed ESA-Net can consistently achieve better segmentation performances even under the extremely sparse annotation setting, highlighting its effectiveness in exploiting information from unlabeled data.
A study on solitary wave solutions for the Zoomeron equation supported by two-dimensional dynamics
Authors: Duran Serbay , Yokus Asıf , Kilinc Gulsen 
Published: November 2023

Abstract
This study emphasizes the importance of understanding natural phenomena through various observations and relating them to scientific studies. Nonlinear partial differential equations serve as fundamental tools for modeling these phenomena, with a focus on nonlinear evolution equations when involving time. This paper investigates the dynamics of the Zoomeron equation, highlighting not only a result derived from the KdV and Schrödinger equations but also its contribution to the modeling of Boomeron and Trappon solitons. Different analytical techniques yield various solutions for the Zoomeron equation, each offering unique insights. The behavior of the solutions generated in kink and singular soliton forms in various dynamics using the

G
′

/
G

2

-expansion method is investigated and compared under restrictive conditions, and the distribution of energy density is explained with the assistance of the gradient function. It is aimed at gaining a physically unique perspective by revealing the connections between the velocities of solitons produced for this equation and the effects of gradient flow directions. In addition, stability analysis for some of the solutions generated in the kink form is investigated. The structure of the paper includes method introduction, application, stability property, results and discussion, resulting in a unique perspective on understanding the physical dynamics of the Zoomeron model.
Uhlmann phases in the Kitaev chain with NNN hopping interaction
Authors: Liu Sheng , Hou Qi-Zhe 
Published: November 2023

Abstract
We study the extended Kitaev chain with both nearest-neighbor and next-nearest-neighbor hopping terms and find the model system exhibiting nontrivial phases, which can be characterized by a nonzero Berry phase and winding number when the system is in a pure state. While in a mixed state, we investigate the robustness of the topological Uhlmann phases and show how it responses to the presence of next-nearest-neighbor hopping terms. Furthermore, we analyse the complicated behavior of the Uhlmann phase of the extended Kitaev chain at finite temperature as k moves along the Brillouin zone, and we think this may serve as a topological indicator for mixed states in condensed matter systems.
Exploring axions through the photon ring of a spherically symmetric black hole
Authors: Roy Sourov , Sarkar Pratick , Sau Subhadip , SenGupta Soumitra 
Published: November 2023

Abstract
In this study, we examine the phenomenon of photon axion conversion occurring in the
spacetime surrounding a black hole. Specifically, we focus on the potential existence of a
magnetic field around the supermassive black hole M87*, which could facilitate the conversion of
photons into axions in close proximity to the photon sphere. While photons traverse through the
curved spacetime, they spend time near the photon sphere, where conversion of these photons into
axions takes place. Consequently, this process leads to a decrease in the intensity of the black
hole's photon ring. To explore the possibilities of detecting these hypothetical axion particles,
we propose observing the photon sphere using higher resolution telescopes. By doing so, we can
gain valuable insights into the conversion mechanism as well as the nature of the spherically
symmetric black hole geometry. Moreover, we also investigate how the photon ring luminosities are
affected if the black hole possesses a charge parameter. For instance apart from U(1) electric
charge, the presence of extra dimension may induce a tidal charge with a characteristic
signature. It is important to note that the success of the conversion mechanism relies on the
axion-photon coupling and mass. As a result, the modified luminosity of the black hole's photon
ring offers a valuable means of constraining the axion's mass and coupling parameter within a
certain range. Thus our findings contribute to a better understanding of photon axion conversion
in the environment of a black hole spacetime and helps us explore the possible existence of extra
spatial dimension.
Dymnikova GUP-corrected black holes
Authors: Alencar G. , Estrada Milko , Muniz C.R. , Olmo Gonzalo J. 
Published: November 2023

Abstract
We consider the impact of Generalized Uncertainty Principle (GUP) effects on the Dymnikova regular black hole. The minimum length scale introduced by the GUP modifies the energy density associated with the gravitational source, referred to as the Dymnikova vacuum, based on its analogy with the gravitational counterpart of the Schwinger effect. We present an approximated analytical solution (together with exact numerical results for comparison) that encompasses a wide range of black hole sizes,
whose properties crucially depend on the ratio between the de Sitter core radius and the GUP scale. The emergence of a wormhole inside the de Sitter core in the innermost region of the object is one of the most relevant features of this family of solutions. Our findings demonstrate that these solutions remain singularity free, confirming the robustness of the Dymnikova regular black hole under GUP corrections. Regarding energy conditions, we find that the violation of the strong, weak, and null energy conditions which is characteristic of the pure Dymnikova case does not occur at Planckian scales in the GUP corrected solution. This contrast suggests a departure from conventional expectations and highlights the influence of quantum corrections and the GUP in modifying the energy conditions near the Planck scale.
Particles creation from JNW quantum perturbed black holes
by minimally coupled Klein Gordon scalar free fields
Authors: Ghaffarnejad Hossein , Faghani Hamid Reza 
Published: November 2023

Abstract
In this work, we choose a minimal coupling interaction between massive Klein Gordon (KG)
quantum scalar free fields and Janis-Newman-Winicour (JNW) spherically symmetric static black
hole, to produce its Hawking temperature and luminosity. This is done by calculating asymptotic
wave solutions at near and far from the black hole horizon. They are orthogonal mode solutions of
local Hilbert spaces. By using these mode solutions, we calculated Bogolubov coefficients and
then, we investigated number density matrix of created particles. Mathematical calculations show
that this is not exactly similar to the Planck`s black body radiation energy density distribution
but, it is “gray” body radiation distribution depended to the emitted Hawking particles
frequency. Their difference is a non-vanishing absorptivity factor of backscattered particles
after to form horizon of a collapsing body. Our motivation is determination of position of Hawking
created pairs in which, two different proposals are proposed, so called as “fairwall” and “quantum
atmosphere”.
The influence of a self-focused laser beam on the stimulated Raman scattering process in collisional plasma
Authors: Walia Keshav , Singh Taranjot 
Published: November 2023

Abstract
The influence of a self-focused beam on the stimulated Raman scattering (SRS) process in collisional plasma is explored. Here, collisional nonlinearity arises as a result of non-uniform heating, thereby causing carrier redistribution. The plasma density profile gets modified in a perpendicular direction to the main beam axis. This modified plasma density profile greatly affects the pump wave, electron plasma wave (EPW) and back-scattered wave. The well-known paraxial theory and Wentzel–Kramers–Brillouin approximation are used to derive second-order ordinary differential equations for the beam waists of the pump wave, EPW and the scattered wave. Further to this, the well-known fourth-order Runge–Kutta method is used to carry out numerical simulations of these equations. SRS back-reflectivity is found to increase due to the focusing of several waves involved in the process.
The Radcliffe Wave Parameters from Data on Open Star Clusters
Authors: Bobylev V. V. , Bajkova A. T. 
Published: November 2023

Abstract
A spectral analysis of the vertical positions and velocities of 374 open star clusters (OSCs) was carried out. We took these OSCs from the Hunt and Reffert catalog; they have an average age of about 10 million years, and are located on the galactic plane XY in a narrow zone inclined by 25° to the galactic axis Y. The following estimates of the parameters of the Radcliffe wave were obtained: (a) the maximum value in periodic perturbations of vertical coordinates

Z

max

=
92
±
10

pc with the wavelength of these perturbations λ

z
= 4.82 ± 0.09 kpc; (b) the maximum value of the velocity of vertical disturbances

W

max

=
4.36
±
0.12

km s−1 with disturbance wavelength λ

W
= 1.78 ± 0.02 kpc. Note that the results of the vertical velocity analysis are first-class in accuracy and completely new.
Analysis of dual laser Thomson scattering signals on W7-X
Authors: D'Isa F.A. , Pasch E. , Beurskens M.N.A. , Brunner K.J. , Fuchert G. , Pasqualotto R. , Giudicotti L. , W7-X team the 
Published: November 2023

Abstract
A dual laser Thomson scattering has been developed at W7-X: a unique system employing, in combination with a 1064 nm laser, a 1319 nm Nd:YAG laser. Dual-laser Thomson scattering (DLTS) is an advanced diagnostic technique in which two laser pulses of different wavelengths are sent to the plasma with a very short time delay and the two scattered signals are separately and independently measured with the same set of polychromators. For the first time during OP2.1, a dual laser Thomson scattering system was operated stably during the entire experimental campaign. In this work, the dual-wavelength signals recorded in several W7-X discharges are analyzed with different methods and the resulting electron temperature profiles and the experimental errors are discussed, with the aim of identifying the capabilities of this technique.
Digital holographic interferometry method for tracking detector modules displacement
Authors: Arena A. , Cantatore G. , Karuza M. ,   the MUonE collaboration
Published: November 2023

Abstract
In high energy particle physics scattering experiments, the precision of the reconstructed particle tracks can be fundamental. For this reason, a method for detecting the displacement of tracking detector modules is developed. The modules are silicon planes mounted on a frame and used in the MUonE project, which aims at a precision measurement of the scattering angle of elastic muon-electron scattering. From the scattering angle, the hadronic contribution to the anomalous magnetic moment of the muon is extracted. To achieve the desired accuracy, the position of the tracking detector planes must be monitored. The allowable relative displacements must be less than 10 μ m. To meet the specifications and to monitor as large an area of the detector as possible, a digital holographic interferometer was developed. It is based on a novel lens-less design in off-axis holographic geometry. Light from a fiber-coupled laser source is split by a fiber beam splitter, with one output used to illuminate the detector plane and the other for the reference beam. The two beams produce an interference pattern on a CMOS image sensor. To obtain relative displacement information, successive images are superimposed on an initial reference image and reconstructed by solving the Rayleigh-Sommerfeld diffraction integral taking into account the spherical wavefronts of the beams. The interference fringes that appear in the reconstructed holographic image provide a measure of the relative displacement of the detector plane compared to the initial position. The performance of the reconstruction method used was verified with the proposed setup at a real tracking station.
Parameter-free basis allocation for efficient multiple metric learning
Authors: Kim Dongyeon , Kan Yejin , Lee Seungmin , Yi Gangman 
Published: November 2023

Abstract
Metric learning involves learning a metric function for distance measurement, which plays an important role in improving the performance of classification or similarity-based algorithms. Multiple metric learning is essential for efficiently reflecting the local properties between instances, as single metric learning has limitations in reflecting the nonlinear structure of complex datasets. Previous research has proposed a method for learning a smooth metric matrix function through data manifold to address the challenge of independently learning multiple metrics. However, this method uses the basic distance-based clustering algorithm to set the anchor points, which are the basis for local metric learning, and the number of basis metrics is dependent on the user. We propose a new method that can assign sophisticated anchor points by iteratively partitioning to identify mixed clusters of multi-class instances and cluster the most similar class instances together. In an experiment, we demonstrate the reliability of the automatically set parameter by comparison with the distribution of error rates according to the number of basis metrics of the existing algorithm. Furthermore, we show the superior performance of the proposed method over a fixed parameter setting of existing algorithms and confirm the relative classification accuracy superiority through performance comparison with baseline algorithms.
VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation
Authors: Beltrán Jorge F , Belén Lisandra Herrera , Farias Jorge G , Zamorano Mauricio , Lefin Nicolás , Miranda Javiera , Parraguez-Contreras Fernanda 
Published: November 2023

Abstract
Throughout evolution, pathogenic viruses have developed different strategies to evade the response of the adaptive immune system. To carry out successful replication, some pathogenic viruses encode different proteins that manipulate the molecular mechanisms of host cells. Currently, there are different bioinformatics tools for virus research; however, none of them focus on predicting viral proteins that evade the adaptive system. In this work, we have developed a novel tool based on machine and deep learning for predicting this type of viral protein named VirusHound-I. This tool is based on a model developed with the multilayer perceptron algorithm using the dipeptide composition molecular descriptor. In this study, we have also demonstrated the robustness of our strategy for data augmentation of the positive dataset based on generative antagonistic networks. During the 10-fold cross-validation step in the training dataset, the predictive model showed 0.947 accuracy, 0.994 precision, 0.943 F1 score, 0.995 specificity, 0.896 sensitivity, 0.894 kappa, 0.898 Matthew’s correlation coefficient and 0.989 AUC. On the other hand, during the testing step, the model showed 0.964 accuracy, 1.0 precision, 0.967 F1 score, 1.0 specificity, 0.936 sensitivity, 0.929 kappa, 0.931 Matthew’s correlation coefficient and 1.0 AUC. Taking this model into account, we have developed a tool called VirusHound-I that makes it possible to predict viral proteins that evade the host’s adaptive immune system. We believe that VirusHound-I can be very useful in accelerating studies on the molecular mechanisms of evasion of pathogenic viruses, as well as in the discovery of therapeutic targets.