bluuebunny/crossref_metadata_2025_split huggingface dataset:
An origami-inspired energy absorber
Authors: Khazaaleh Shadi , Dalaq Ahmed S , Daqaq Mohammed F 
Published: March 2024

Abstract
The design of effective and compact energy absorption systems is key to the survivability and durability of many man-made structures and machines. To this end, this work presents the design, assessment, and implementation of a novel origami-inspired energy absorber that is based on the Kresling origami pattern. The absorber consists of a Kresling origami column positioned between the loading point and an energy dissipation module. By exploiting its unique inherent translation-to-rotation coupling feature, the primary function of the Kresling column is to transmit uniaxial incident loads (shock or impact) into localized rotational energy that can then be dissipated in a viscous fluid chamber. The proposed system has several unique advantages over traditional designs including the ability to (i) dissipate energy associated with both torsional and uniaxial loads, (ii) control the rotational velocity profile to maximize energy dissipation, and (iii) customize the restoring-force behavior of the Kresling column to different applications. Furthermore, the proposed design is more compact since it can realize the same stroke distance of the traditional translational design while being considerably shorter. Through extensive computational modeling, parametric studies, and experimental testing, it is demonstrated that the proposed design can be optimized to absorb all the imparted energy; and out of the absorbed energy, around 40% can be dissipated in the viscous fluid, while the rest is either dissipated by the viscoelasticity of the origami column or stored in it as potential energy.
Color image encryption algorithm based on Mackey–Glass time-delay chaotic system and quantum random walk
Authors: Mou Dingkang , Dong Yumin 
Published: March 2024

Abstract
To ensure the confidentiality and integrity of image data and prevent unauthorized data tampering and privacy leaks. This study proposes a new color image encryption scheme based on the Mackey–Glass time-delay chaotic system and quantum random walk. This approach fully leverages the unpredictability of quantum random walks to generate random values. It combines the differences in Hamming distance between the three RGB channels of color images to create a highly complex and random key. The overall image and the three independent RGB channels are arranged in ascending order using Logistic-tent chaotic mapping and the Mackey–Glass time-delay chaotic system to obfuscate the image data. The deformed fractional-order Lorenz chaotic system is introduced, integrated with DNA encoding and decoding technology, and XOR operations are performed to achieve encryption at the spatial and pixel levels, thereby increasing the complexity of decryption. Through extensive experimental research, this solution has demonstrated excellent results in tests such as adjacent pixel correlation, information entropy, and key sensitivity. It has an excellent ability to protect the privacy of images and provides a reliable guarantee for the security of image data.
Nonlocal symmetries and solutions of the multi-dimensional integrable long water wave equations
Authors: Jin Meng , Yang Jiajia , Xin Xiangpeng 
Published: March 2024

Abstract
In this paper, the (2+1)-dimensional integrable long water wave equations (LWWs) are constructed for the first time using the conservation law of the (1+1)-dimensional LWWs. The new (1+1)-dimensional LWWs can be obtained by introducing a constraint to the (2+1)-dimensional LWWs. This new (1+1)-dimensional LWWs are studied by using nonlocal symmetry methods for the first time. The closed system corresponding to nonlocal symmetry is established by the lax pairs of equations and the potential function determined using conservation laws. Exact solutions of the equations are obtained by finite symmetry transformation and symmetry approximation of this closed system. The dynamic behavior of the equations is studied by means of figures of the exact solutions.
Squeezing and entangling two microwave modes of electro-optomechanical systems with two driving lasers
Authors: Li Ang , Yang Song-Lin , Zhang Jian-Song , Cheng Guang-Ling , Zhong Wen-Xue 
Published: March 2024

Abstract
We propose an effective scheme to generate the squeezed vacuum states of two microwave fields, bipartite and genuine tripartite entanglement in a compound four-mode electro-optomechanical system with two driving lasers of different amplitudes. We find that two microwave fields can be squeezed simultaneously when the effective coupling constants between mechanical oscillator and two microwave fields are chosen appropriately. In particular, the squeezing of one microwave mode can be larger than 3 dB even at high temperature. We also find that bipartite and genuine tripartite entanglement can be generated in this system which are robust against thermal fluctuations. Our scheme may have potential applications in quantum information processing and quantum metrology.
Improved post-radiation behavior of FinFET based CMOS with workfunction modulated gate
Authors: Ray Abhishek , Naugarhiya Alok , Mishra Guru Prasad 
Published: March 2024

Abstract
The total ionizing dose (TID) effect of modulated gate workfunction (MGW) FinFET based CMOS inverter is designed and analyzed. The post radiation analysis of the voltage transfer characteristics, noise margin and propagation delay are demonstrated. The incorporation workfunction modulation engineering enhances the OFF-state performance, and maintains positive threshold voltage for both the pre and post radiation condition. 2-decade improvement is noticed for the both n-FinFET and p-FinFET. In addition, the proposed technique offers minimum propagation delay time and acceptable noise margin range even after 2000 krad of radiation dose. The analysis and comparison of the results is done by the 3-D TCAD simulator.
Dirac materials in parallel non-uniform electromagnetic fields generated by SUSY: a chiral Planar Hall Effect
Authors: Pérez-Pedraza J C , García-Muñoz Juan D , Raya A 
Published: March 2024

Abstract
Within a Supersymmetric Quantum Mechanics (SUSY-QM) framework, the (3+1) Dirac equation describing a Dirac material in the presence of external parallel electric and magnetic fields is solved. Considering static but non-uniform electric and magnetic profiles with translational symmetry along the y-direction, the Dirac equation is transformed into two decoupled pairs of Schrödinger equations, one for each chirality of the fermion fields. Taking trigonometric and hyperbolic profiles for the vector and scalar potentials, respectively, we arrive at SUSY partner Pöschl-Teller-like quantum potentials. Restricting to the conditions of the potentials that support an analytic zero-mode solution, we obtain a nontrivial current density perpendicular to the electric and magnetic fields, thus, defining a plane where these three vectors become coplanar, indicating the possibility of realizing the Planar Hall Effect. Furthermore, this non-vanishing current density is the sum of current densities for the left- and right-chiralities, suggesting that the net current is a consequence of chiral symmetry. Possible application in current steering of solitonic nature through a Type-I Weyl semimetal is discussed.
Black hole graviton and quantum gravity
Authors: Kimura Yusuke 
Published: March 2024

Abstract
Drawing from a thought experiment that we conduct, we propose that a virtual graviton gives rise to a black hole geometry when its momentum surpasses a certain threshold value on the Planck scale. This hypothesis implies that the propagator of a virtual graviton, that possesses momentum surpassing this threshold, vanishes. Consequently, a Feynman diagram containing this type of graviton propagator does not add to the overall amplitude. This mechanism suggests the feasibility of formulating an ultraviolet-finite four-dimensional quantum gravitational theory. The elementary particles including the gravitons are treated as point particles in this formulation.
Constraining primordial non-Gaussianity from DESI quasar targets and Planck CMB lensing
Authors: Krolewski Alex , Percival Will J. , Ferraro Simone , Chaussidon Edmond , Rezaie Mehdi , Aguilar Jessica Nicole , Ahlen Steven , Brooks David , Dawson Kyle , de la Macorra Axel , Doel Peter , Fanning Kevin , Font-Ribera Andreu , a Gontcho Satya Gontcho , Guy Julien , Honscheid Klaus , Kehoe Robert , Kisner Theodore , Kremin Anthony , Landriau Martin , Levi Michael E. , Martini Paul , Meisner Aaron M. , Miquel Ramon , Nie Jundan , Poppett Claire , Ross Ashley J. , Rossi Graziano , Schubnell Michael , Seo Hee-Jong , Tarlé Gregory , Vargas-Magaña Mariana , Weaver Benjamin Alan , Yèche Christophe , Zhou Rongpu , Zhou Zhimin 
Published: March 2024

Abstract
We detect the cross-correlation between 2.7 million DESI quasar targets across 14,700 deg2 (180 quasars deg-2) and Planck 2018 CMB lensing at ∼30σ. We use the cross-correlation
on very large scales to constrain local primordial non-Gaussianity via the scale dependence of quasar bias. The DESI quasar targets lie at an effective redshift of 1.51 and are separated into four imaging regions of varying depth and image quality.
We select quasar targets from Legacy Survey DR9 imaging, apply additional flux and photometric redshift cuts to improve the purity and reduce the fraction of unclassified redshifts, and use early DESI spectroscopy of 194,000 quasar targets to determine their redshift distribution and stellar contamination fraction (2.6%). Due to significant excess large-scale power in the quasar autocorrelation, we apply weights to mitigate contamination from imaging systematics such as depth, extinction, and stellar density.
We use realistic contaminated mocks to determine the greatest number of systematic modes that we can fit, before we are biased by overfitting and spuriously remove real power.
We find that linear regression with one to seven imaging templates removed per region accurately recovers the input cross-power, f
NL and linear bias.
As in previous analyses, our f
NL constraint depends on
the linear primordial non-Gaussianity bias parameter, bϕ
= 2(b - p)δc
assuming universality of the halo mass function.
We measure f
NL = -26+45
-40 with p = 1.6 (f
NL = -18+29
-27 with p = 1.0), and find that this result is robust under several systematics tests. Future spectroscopic quasar cross-correlations with Planck lensing can tighten the f
NL constraints by a factor of 2 if they can remove the excess power on large scales in the quasar auto power spectrum.
A GPR-based emulator for semi-numerical reionization code SCRIPT: parameter inference from 21 cm data
Authors: Choudhury T. Roy , Paranjape A. , Maity B. 
Published: March 2024

Abstract
Semi-numerical models of reionization typically involve a large number of unknown
parameters whose values are constrained by comparing with observations. Increasingly often,
exploring this parameter space using semi-numerical simulations can become computationally
intensive, thus necessitating the use of emulators. In this work, we present a likelihood emulator
based on Gaussian Process Regression (GPR) for our semi-numerical reionization code,
SCRIPT, and use it for parameter inference using mock 21 cm power spectrum data and
Bayesian MCMC analysis. A unique aspect of our methodology is the utilization of coarse resolution
simulations to identify high-probability regions within the parameter space, employing only a
moderate amount of computational time. Samples drawn from these high-probability regions are used
to construct the training set for the emulator. The subsequent MCMC using this GPR-trained
emulator is found to provide parameter posteriors that agree reasonably well with those obtained
using conventional MCMC. The computing time for the analysis, which includes both generation of
training sets and training the emulator, is reduced by approximately an order of magnitude. This
methodology is particularly advantageous in scenarios where one wants to use different
parametrizations of reionization models and/or needs to start with broad prior distributions on
the parameters, offering an efficient and effective means of parameter inference.
Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing
Authors: Li Hui , Li Rong-Wang , Shu Peng , Li Yu-Qiang 
Published: March 2024

Abstract
Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal. Analyzing light curves to determine attitude is the most commonly used method. In photometric observations, outliers may exist in the obtained light curves due to various reasons. Therefore, preprocessing is required to remove these outliers to obtain high quality light curves. Through statistical analysis, the reasons leading to outliers can be categorized into two main types: first, the brightness of the object significantly increases due to the passage of a star nearby, referred to as “stellar contamination,” and second, the brightness markedly decreases due to cloudy cover, referred to as “cloudy contamination.” The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive. However, we propose the utilization of machine learning methods as a substitute. Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination, achieving F1 scores of 1.00 and 0.98 on a test set, respectively. We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine, then conduct comparative analyses of the results.
Online Bayesian optimization of vagus nerve stimulation
Authors: Wernisch Lorenz , Edwards Tristan , Berthon Antonin , Tessier-Lariviere Olivier , Sarkans Elvijs , Stoukidi Myrta , Fortier-Poisson Pascal , Pinkney Max , Thornton Michael , Hanley Catherine , Lee Susannah , Jennings Joel , Appleton Ben , Garsed Phillip , Patterson Bret , Buttinger Will , Gonshaw Samuel , Jakopec Matjaž , Shunmugam Sudhakaran , Mamen Jorin , Tukiainen Aleksi , Lajoie Guillaume , Armitage Oliver , Hewage Emil 
Published: March 2024

Abstract

Objective. In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimulation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating a particular patient’s illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting due to the size of the parameter space. Restricting this space, however, may lead to suboptimal therapeutic results, reduced responder rates, and adverse effects. Approach. As an alternative to a full parameter search, we present a flexible machine learning, data acquisition, and processing framework for optimizing neural stimulation parameters, requiring as few steps as possible using Bayesian optimization. This optimization builds a model of the neural and physiological responses to stimulations, enabling it to optimize stimulation parameters and provide estimates of the accuracy of the response model. The vagus nerve (VN) innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate (HR), making it an ideal candidate for demonstrating the effectiveness of our approach. Main results. The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to VN stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target HRs and optimizing neural B-fiber activations despite high intersubject variability. Significance. An optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing.
A New Type of Expanded Graphite-Based Heat-Conducting Glue and Its Application in Heat-Non-Burning Cigarette
Authors: Chen Wenting , Li Yufei , Zhang Yining , Liu Yuantao , Li Lu , Gao Cheng , Li Dian , Liu Jialei 
Published: March 2024

Abstract
With the popularity of heat non-combustible cigarettes, a new type of tobacco product, higher requirements have been put forward for the thermal conductivity of tobacco materials compared to traditional tobacco. In order to make the tobacco uniformly heated, adding high thermal conductivity materials to the tobacco to improve the thermal conductivity of the tobacco in the heat-non-burning smoke is a conventional practice. Therefore, this article introduces expanded graphite with high specific surface area and high thermal conductivity as the main material of thermal conductivity, and the polymer obtained by esterification reaction of biomass material polylactic acid, citric acid and diol are utilized as the adhesive glue. Research shows that this material enhances the heating performance of tobacco in heat non-combustible cigarettes. The thermal conductivity measured by adding 3 ml, 4 ml, and 6 ml of the expanded graphite viscous mixture to the filter paper spacer increased sequentially both at room temperature and at 100 degrees Celsius, and all of them were higher than that of the blank filter paper. This method effectively realized the fixation of the expanded graphite thermal conductive material on the surface of the tobacco, which greatly improved the thermal conductivity performance of the tobacco and made this new type of heat cigarettes more uniformly heated.
Preparation and Ferroelectric Properties of Er Doped BaTiO3 Nanotubes
Authors: Wu Chengjun , Song Haibo , Dai Xinchao , Qiu Shuang , Deng Xiangyun 
Published: March 2024

Abstract
In this paper, erbium-doped barium titanate nanotubes with different concentrations were prepared by combining the anodic oxidation method with the low-temperature hydrothermal method. The TiO2 nanotubes produced by electrolysis using titanium flakes as substrate were put into barium hydroxide octahydrate solution for hydrothermal reaction, and different concentrations of Er-doped barium titanate nanotubes were prepared by adding different concentrations of Er(NO3)3·5H2O to the reaction solution. The samples with different doping concentrations were analyzed for their morphological structure, physical phase composition and ferroelectric properties. All samples were found to have complete barium titanate nanotube structure, and the diffraction peaks of XRD were compared with the standard Cope, and the doped samples were all tetragonal phase chalcogenide structure with no other impurities generated. From the hysteresis loop diagram of the samples measured by the TF-2000 ferroelectric analyzer, it can be found that the residual polarization intensity of the samples showed a characteristic of increasing and then decreasing with the increase of the doping concentration, and the residual polarization intensity was the largest at 0.151 uC/cm2 under the hydrothermal reaction condition of 180°C and 3 h with the Er3+ concentration of 0.0025 mol/L.
Fe Single-Atoms Combined with Chitosan for Enhanced Electromagnetic Wave Absorption
Authors: Cao Kunyao , Xue Weidong , Zhao Rui 
Published: March 2024

Abstract
On account of that the single carbon material possess exceeding electrical conductivity, which will cause impedance mismatch and thus seriously affects the electromagnetic wave (EMW) absorption performance of the material. Therefore, it is an effective method to combine carbon material with magnetic material to make the material have dielectric loss and magnetic loss at the same time. In this paper, magnetic Fe nanospheres were loaded onto the conductive network of chitosan and the composite 800Fe-Chitosan was obtained through freeze drying and calcination treatment. As a result, the composite shows superior EMW absorption performance with a reflection loss (RL) of -40.22 dB and a broad effective absorption bandwidth (EAB) of 4.24 GHz.
Structural Design and Life Prediction of Sheet Metal Parts for Gas Turbine Vane
Authors: Lan Jibing , Dai Bin , Wei Jiaming , Yao Wendan , Yu Peijiong 
Published: March 2024

Abstract
In the cooling design of gas turbine vane, sheet metal parts were used to arrange the impact cooling holes in the platform and inner cavity, and divided the chamber space for impact cooling. During the service period, due to the impact of temperature load and air flow, the sheet metal parts were prone to bulge, crack, fall off, which worsen the cooling effect. In order to ensure the safe and reliable operation of blade and sheet metal parts, it was necessary to design reasonable sheet metal structure and accurately predict the service life. In this paper, material mechanical property test of GH3536 was carried out to obtain its constitutive equation and fatigue performance parameters. The structure of sheet metal parts were designed, then the temperature field of the blade and the sheet metal was simulated by using the fluid-thermal-solid coupling method, and the stress-strain analysis was carried out by using the elastic-plastic constitutive model. Finally, the fatigue life of the sheet metal was obtained, and the service life meet the design requirements. This work was helpful to prolong the life of sheet metal structure and ensure the cooling effect of blade.
Electrical Property Improvements of Multiple Dopant ZnO/Bi2O3 Varistor By Liquid Phase Method
Authors: Wang Lei , Xu Jinxia , Sun Yi , Song Xingjuan , Liu Lingyun 
Published: March 2024

Abstract
Although ZnO Varistor is widely used in surge protection due to its unique I-V characteristics, its key influencing factors of grain size and uniformity are difficult to be guaranteed by traditional solid phase method. In this paper, the ZnO/Bi2O3 varistor was prepared by liquid phase co-deposition method and the sintering temperature also has been optimized. For samples prepared by the liquid-phase method, as the sintering temperature increases, the grain size gradually increases, the grain distribution becomes initially more uniform and later more disordered, and the crystallinity gradually increases. In comparison to solid-phase method, samples prepared by liquid-phase method exhibit smaller grain sizes and more uniform grain size distribution. Based on this, samples sintered at 1100 °C by the liquid-phase method demonstrate optimal electrical performance: the voltage gradient is 520 V/mm, the nonlinear coefficient is 44, and the leakage current is 5.8 μA/cm2. This paper has theoretical support for the study of preparation of varistor for achieving excellent electrical performance for being used in the field of surge protection.
Protective Effects of Collagen Peptide on Periodontal Ligament Fibroblasts
Authors: Yan Zheng , Jiang Bin , Dong Peng , Wang Haiyan 
Published: March 2024

Abstract
To study the protective effect of collagen peptide on human periodontal ligament fibroblasts (hPDLFs) induced by lipopolysaccharide and its possible mechanism. Lipopoly-saccharide was used as a pathogenic factor to stimulate hPDLFs. Then, interleukin-8 and tumor necrosis factor-α in the supernatant were quantified, and Toll-like receptor 4 expression and the activation of nuclear factor-κB pathway were also studied. The results showed that collagen peptide above 100 mg/L could effectively reduce the toxicity of lipopolysaccharide, and significantly reduce interleukin-8 and tumor necrosis factor-α produced by lipopolysaccharide. Suggesting that collagen peptide can provide effecitve and safe solution to oral cavity repair.