bluuebunny/crossref_metadata_2025_split huggingface dataset:
A modified perturbation method for global dynamic analysis of generalized mixed Rayleigh–Liénard oscillator with cubic and quintic nonlinearities
Authors: Li Zhenbo , Hou Linxia , Zhang Yiqing , Xu Feng 
Published: May 2024

Abstract
Deriving analytical relationship between the system parameters and amplitude of the limit cycle is a meaningful and challenging task. Currently, numerous existing analytical approximate methods struggle to achieve this goal when expressions of restoring force or nonlinear damping is complicated. To overcome this shortcoming, this study proposes a modified generalized harmonic function perturbation method. Using the proposed method, a generalized mixed RayleighLiénard oscillator with cubic and quintic nonlinearities was investigated. The analytical relationships between the system parameters and amplitude of the limit cycle, as well as the expression of its characteristic quantity, were derived. By employing these analytical relationships, the existence, stability, number, position, and amplitude of each limit cycle are quantitatively analysed. The homoclinic and heteroclinic bifurcations were also predicted using the above analytical relationships. Additionally, analytical approximate solutions for this oscillator were calculated using the proposed method. All results obtained in this study were subsequently confirmed numerically to demonstrate their feasibility and validity. Consequently, the proposed method can be considered an effective supplement to perturbation-based methods. This also implies that the work presented in this paper has a certain theoretical significance and application value in the research area of quantitative analysis methods for strongly nonlinear oscillators.
Interfacial stress engineering toward enhancement of ferroelectricity in Al doped HfO2 thin films
Authors: Chen 陈 S X 思学 , Chen 陈 M M 明明 , Liu 刘 Y 圆 , Cao 曹 D W 大威 , Chen 陈 G J 国杰 
Published: May 2024

Abstract
Ferroelectric HfO2 has attracted much attention owing to its superior ferroelectricity at an ultra-thin thickness and good compatibility with Si-based complementary metal–oxide–semiconductor (CMOS) technology. However, the crystallization of polar orthorhombic phase (o-phase) HfO2 is less competitive, which greatly limits the ferroelectricity of the as-obtained ferroelectric HfO2 thin films. Fortunately, the crystallization of o-phase HfO2 can be thermodynamically modulated via interfacial stress engineering. In this paper, the growth of improved ferroelectric Al doped HfO2 (HfO2:Al) thin films on (111)-oriented Si substrate has been reported. Structural analysis has suggested that nonpolar monoclinic HfO2:Al grown on (111)-oriented Si substrate suffered from a strong compressive strain, which promoted the crystallization of (111)-oriented o-phase HfO2 in the as-grown HfO2:Al thin films. In addition, the in-plane lattice of (111)-oriented Si substrate matches well with that of (111)-oriented o-phase HfO2, which further thermally stabilizes the o-phase HfO2. Accordingly, an improved ferroelectricity with a remnant polarization (2P
r) of 26.7 μC/cm2 has been obtained. The results shown in this work provide a simple way toward the preparation of improved ferroelectric HfO2 thin films.
Frequency combs based on magnon-skyrmion interaction in magnetic nanotubes
Authors: Abdulrazak Tijjani , Liu 刘 Xuejuan 雪娟 , Jin 金 Zhejunyu 哲珺雨 , Cao 曹 Yunshan 云姗 , Yan 严 Peng 鹏 
Published: May 2024

Abstract
Within the magnonics community, there has been a lot of interests in the magnon–skyrmion interaction. Magnons and skyrmions are two intriguing phenomena in condensed matter physics, and magnetic nanotubes have emerged as a suitable platform to study their complex interactions. We show that magnon frequency combs can be induced in magnetic nanotubes by three-wave mixing between the propagating magnons and skyrmion. This study enriches our fundamental comprehension of magnon–skyrmion interactions and holds promise for developing innovative spintronic devices and applications. This frequency comb tunability and unique spectral features offer a rich platform for exploring novel avenues in magnetic nanotechnology.
Nonlinear time-reversal interferometry with arbitrary quadratic collective-spin interaction
Authors: Hu 胡 Zhiyao 知遥 , Li 李 Qixian 其贤 , Zhang 张 Xuanchen 轩晨 , Zhang 张 He-Bin 贺宾 , Huang 黄 Long-Gang 龙刚 , Liu 刘 Yong-Chun 永椿 
Published: May 2024

Abstract
Atomic nonlinear interferometry has wide applications in quantum metrology and quantum information science. Here we propose a nonlinear time-reversal interferometry scheme with high robustness and metrological gain based on the spin squeezing generated by arbitrary quadratic collective-spin interaction, which could be described by the Lipkin–Meshkov–Glick (LMG) model. We optimize the squeezing process, encoding process, and anti-squeezing process, finding that the two particular cases of the LMG model, one-axis twisting and two-axis twisting outperform in robustness and precision, respectively. Moreover, we propose a Floquet driving method to realize equivalent time reverse in the atomic system, which leads to high performance in precision, robustness, and operability. Our study sets a benchmark for achieving high precision and high robustness in atomic nonlinear interferometry.
Three-dimensional crystal defect imaging by STEM depth sectioning
Authors: Ishikawa Ryo , Shibata Naoya , Ikuhara Yuichi 
Published: May 2024

Abstract
One of the major innovations awaiting in electron microscopy is full three-dimensional imaging at atomic resolution. Despite the success of aberration correction to deep sub-ångström lateral resolution, spatial resolution in depth is still far from atomic resolution. In scanning transmission electron microscopy (STEM), this poor depth resolution is due to the limitation of the illumination angle. To overcome this physical limitation, it is essential to implement a next-generation aberration corrector in STEM that can significantly improve the depth resolution. This review discusses the capability of depth sectioning for three-dimensional imaging combined with large-angle illumination STEM. Furthermore, the statistical analysis approach remarkably improves the depth resolution, making it possible to achieve three-dimensional atomic resolution imaging at oxide surfaces. We will also discuss the future prospects of three-dimensional imaging at atomic resolution by STEM depth sectioning.
Two-fold symmetry of the in-plane resistance in kagome superconductor Cs(V1–xTax)3Sb5 with enhanced superconductivity
Authors: Zhao 赵 Zhen 振 , Wang 王 Ruwen 汝文 , Zhang 张 Yuhang 宇航 , Zhu 祝 Ke 轲 , Yu 余 Weiqi 维琪 , Han 韩 Yechao 烨超 , Liu 刘 Jiali 家利 , Hu 胡 Guojing 国静 , Guo 郭 Hui 辉 , Lin 林 Xiao 晓 , Dong 董 Xiaoli 晓莉 , Chen 陈 Hui 辉 , Yang 杨 Haitao 海涛 , Gao 高 Hong-Jun 鸿钧 
Published: May 2024

Abstract
The kagome superconductor CsV3Sb5 has attracted widespread attention due to its rich correlated electron states including superconductivity, charge density wave (CDW), nematicity, and pair density wave. Notably, the modulation of the intertwined electronic orders by the chemical doping is significant to illuminate the cooperation/competition between multiple phases in kagome superconductors. In this study, we have synthesized a series of tantalum-substituted Cs(V1−x
Ta
x
)3Sb5 by a modified self-flux method. Electrical transport measurements reveal that CDW is suppressed gradually and becomes undetectable as the doping content of x is over 0.07. Concurrently, the superconductivity is enhanced monotonically from T
c ∼ 2.8 K at x = 0 to 5.2 K at x = 0.12. Intriguingly, in the absence of CDW, Cs(V1−x
Ta
x
)3Sb5 (x = 0.12) crystals exhibit a pronounced two-fold symmetry of the in-plane angular-dependent magnetoresistance (AMR) in the superconducting state, indicating the anisotropic superconducting properties in the Cs(V1−x
Ta
x
)3Sb5. Our findings demonstrate that Cs(V1−x
Ta
x
)3Sb5 with the non-trivial band topology is an excellent platform to explore the superconductivity mechanism and intertwined electronic orders in quantum materials.
GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals
Authors: Wang Yuwen , Peng Yudan , Han Mingxiu , Liu Xinyi , Niu Haijun , Cheng Jian , Chang Suhua , Liu Tao 
Published: May 2024

Abstract

Objective. Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge. Approach. Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance. Main results. The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls, in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross-validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks. Significance. The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical-assisted diagnosis.
Neural Networks for position reconstruction in liquid argon detectors
Authors: Cárdenas-Montes Miguel , Santorelli Roberto 
Published: May 2024

Abstract
This article explores the integration of Deep Learning and Explainable Artificial Intelligence in Particle Physics, focusing on their application in position reconstruction within dual-phase liquid argon detectors for Dark Matter search. Facing challenges like pile-up scenarios, Neural Networks prove crucial for refining algorithms. This article emphasizes Deep Learning's role in addressing regression and classification problems, such as position reconstruction and particle identification, particularly in Time Projection Chambers. Explainable Artificial Intelligence emerges as pivotal in unraveling Deep Learning's complex decisions, exposing biases, and facilitating improvement cycles. Innovations like Evolutionary Neural Networks and topology-driven dataset reduction offer potential efficiency gains. The conclusion highlights challenges in analyzing massive data volumes, emphasizing the need for adaptability and ethical considerations in the pursuit of understanding Dark Matter.
Design, fabrication and validation of an electrical conductivity principle based two-phase detection sensor array for molten lead (Pb) based heavy metal coolants up to 600°C
Authors: Saraswat A. , Bhattacharyay R. , Gedupudi S. , Chaudhuri P. 
Published: May 2024

Abstract
Molten lead (Pb) and its alloys (PbBi and PbLi) are of
immense interest for various nuclear engineering applications,
including but not limited to advanced Lead-cooled Fast Reactors
(LFRs), tritium Breeding Blankets (BBs) of fusion power plants and
spallation targets for Accelerator-Driven Systems (ADS). Owing to
their attractive thermophysical properties, these advanced fluids
assert their candidacy to address the critical requirements of
neutron multiplication, neutron moderation, high temperature
coolants and tritium breeders, enabling the operation of next
generation nuclear systems at high temperatures with better
efficiencies. However, for numerous reasons such as a compromise of
structural integrity at the heat transfer interface, presence of an
inert cover gas during charging of molten metal in the loop, and the
fusion fuel cycle itself may lead to molten metal-gas two-phase
flows with high density ratios. At present, no effective diagnostics
exist to detect such operational and accidental occurrences in high
temperature molten metal systems resulting in a severe lack of
relevant experimental studies. To address these limitations and to
advance the current understanding toward two-phase regimes in high
temperature Pb-based melts, the present work focuses on the design
and assembly aspects of an electrical conductivity-based two-phase
detection sensor array, utilizing high purity
α-Al2O3 coatings with AlPO4 binder as
electrical insulation layers. This paper discusses the design
considerations, thermal analysis, systematic selection of
structural/functional components along with preliminary results from
the probe performance tests in very high temperature
(600°C) static molten Pb column for real time detection of
argon gas bubbles rising within the melt. Quantitative estimations
of time-averaged void fraction, average bubble impaction frequency
and average bubble residence time are presented from the preliminary
experimental investigations.
Unappreciated healthcare inequality against PM2.5-related mortality risk
Authors: Zhang Haofan , Zhu Dianyu , Liu Miaomiao , Yang Jianxun , Ma Zongwei , Fang Wen , Ji John S , He Pan , Bi Jun 
Published: May 2024

Abstract
Understanding the inequality of PM2.5-related health is crucial for promoting health, building a just society, and advancing multiple Sustainable Development Goals goals. However, previous research has predominantly concentrated on PM2.5 exposure inequality, neglecting varied prompt responses and protective behaviors against it. Here, we established the relationship between short-term healthcare expenditure and PM2.5 concentration using the number and amount of healthcare transactions across all healthcare categories based on the Union Pay data. We also assessed daily city-specific PM2.5-related mortality and healthcare expenditures and evaluated their inequalities among cities according to the income inequality index, the Gini coefficient. The results show that short-term exposure to PM2.5 leads to severe physiological and health-related economic burdens on Chinese residents. From 2017 to 2019, 77.8 (34.5–121.1) thousand deaths were attributed to daily PM2.5, with healthcare expenditures reaching 93.7 (69.1–118.3) billion Chinese Yuan. Additionally, there were significant inequalities in PM2.5-related mortality and healthcare expenditures among cities. The inequality index for PM2.5-related healthcare expenditures was 0.53, while the inequality index for PM2.5-related mortality was 0.13. The greater inequality in healthcare expenditures than in mortality, implying inadequate healthcare resources amplify the health inequality related to PM2.5 exposure. 28.6% of Chinese cities lacked affordable healthcare resources to address the high physiological burden attributable to PM2.5. Our multidimensional exploration is essential for formulating effective policies addressing environmental health inequality. Focusing on these cities with disproportionate challenges is crucial for creating a more equitable and sustainable society.
Dynamical-statistical method for seasonal forecasting of wintertime PM10 concentration in South Korea using multi-model ensemble climate forecasts
Authors: Choi Jahyun , Woo Sung-Ho , Yoon Jin-Ho , Choi Jin-Young , Lee Daegyun , Jeong Jee-Hoon 
Published: May 2024

Abstract
Climate conditions and emissions are among the primary influences on seasonal variations in air quality. Consequently, skillful climate forecasts can greatly enhance the predictability of air quality seasonal forecasts. In this study, we propose a dynamical-statistical method for seasonal forecasting of particulate matter (PM10) concentrations in South Korea in winter using climate forecasts from the Asian Pacific Climate Center (APCC) multi-model ensemble (MME). We identified potential climate predictors that potentially affect the wintertime air quality variability in South Korea in the global domain. From these potential climate predictors, those that can be forecasted skillfully by APCC MME were utilized to establish a multiple-linear regression model to predict the winter PM10 concentration in South Korea. As a result of evaluating the forecast skill through retrospective forecasts for the past 25 winters (1995/96-2019/20), this model showed statistically significant forecast skill at a lead time of a month to a season. The skill of PM10 forecast from the MME was overall better than that from a single model. We also found that it is possible to improve forecast skills through optimal MME combinations.
Enhanced repetition codes for the cross-platform comparison of progress towards fault-tolerance
Authors: Liepelt Milan , Peduzzi Tommaso , Wootton James R 
Published: May 2024

Abstract
Achieving fault-tolerance will require a strong relationship between the hardware and the protocols used. Different approaches will therefore naturally have tailored proof-of-principle experiments to benchmark progress. Nevertheless, repetition codes have become a commonly used basis of experiments that allow cross-platform comparisons. Here we propose methods by which repetition code experiments can be expanded and improved, while retaining cross-platform compatibility. We also consider novel methods of analyzing the results, which offer more detailed insights than simple calculation of the logical error rate.
Aspects of non-topological soliton stars in a class of induced gravity theories
Authors: Asri Seema , Lohiya Daksh , Chandra Suresh 
Published: May 2024

Abstract
We explore some interesting properties of soliton stars arising in a class of effective gravity theories having a Higgs scalar field that serves a dual role of generating an effective mass of a fermion field as well as generating an effective gravitational constant. With a suitable choice of non-minimal coupling, such solitons have the canonical effective gravitational constant in their exterior while in the interior the effective gravitational constant could be arbitrarily smaller. Such a choice enables an exact determination of gravitational effects on the total conserved energy of the soliton. While the external metric of such a soliton is the standard Schwarzschild metric, the interior entropy is much larger than that of a black hole of the same equilibrium temperature.
Entropy production from waiting-time distributions for overdamped Langevin dynamics
Authors: Meyberg Ellen , Degünther Julius , Seifert Udo 
Published: May 2024

Abstract
For a Markovian dynamics on discrete states, the logarithmic ratio of waiting-time distributions between two successive, instantaneous transitions in forward and backward direction is a measure of time-irreversibility. It thus serves as an entropy estimator, which is exact in the case of a uni-cyclic network. We adopt this framework to overdamped Langevin dynamics, where such transitions have finite duration. By introducing milestones based on the observation of a particle at at least two milestones and an additional third event, we identify an entropy estimator that becomes exact for driven motion along a one-dimensional potential.
Laser-induced breakdown spectroscopy as a method for millimeter-scale inspection of surface flatness
Authors: YE 叶 Jinrui 晋瑞 , LI 李 Yaju 亚举 , ZHANG 张 Zhao 朝 , WANG 王 Xinwei 新威 , TAO 陶 Kewei 科伟 , ZENG 曾 Qiang 强 , CHEN 陈 Liangwen 良文 , QIAN 钱 Dongbin 东斌 , ZHANG 张 Shaofeng 少峰 , YANG 杨 Lei 磊 , MA 马 Xinwen 新文 
Published: May 2024

Abstract
A non-contact method for millimeter-scale inspection of material surface flatness via Laser-Induced Breakdown Spectroscopy (LIBS) is investigated experimentally. The experiment is performed using a planished surface of an alloy steel sample to simulate its various flatness, ranging from 0 to 4.4 mm, by adjusting the laser focal plane to the surface distance with a step length of 0.2 mm. It is found that LIBS measurements are successful in inspecting the flatness differences among these simulated cases, implying that the method investigated here is feasible. It is also found that, for achieving the inspection of surface flatness within such a wide range, when univariate analysis is applied, a piecewise calibration model must be constructed. This is due to the complex dependence of plasma formation conditions on the surface flatness, which inevitably complicates the inspection procedure. To solve the problem, a multivariate analysis with the help of Back-Propagation Neural Network (BPNN) algorithms is applied to further construct the calibration model. By detailed analysis of the model performance, we demonstrate that a unified calibration model can be well established based on BPNN algorithms for unambiguous millimeter-scale range inspection of surface flatness with a resolution of about 0.2 mm.
Extremes in Other Correlated Systems
Authors: Majumdar Satya N , Schehr Grégory 
Published: May 2024

Abstract
In order to provide examples of various applications of EVS, we go beyond the two basic models (IID and random walk sequences) and discuss the extreme value statistics for other models with weak and strong correlations. Many of these examples are rapidly evolving topics of current interest. We do not provide much detail for these examples, but instead discuss some general results and refer the reader to the original articles.
Introduction
Authors: Choudhry Sujit , Hailbronner Michaela , Kumm Mattias 
Published: May 2024

Abstract
The introduction situates the project in the broader comparative law literature and in the context of contemporary political developments. It discusses the meaning and functions of a canon of comparative law. Acknowledging the challenges involved with such a concept, in particular its Western bias and questions of exclusion, it argues for understanding a canon as not as a selection of the best interpretation of certain shared values or principles, but a space for a conversation about central normative underpinnings of democratic constitutionalism. At a time when we observe a retreat from universal categories towards more particularist understandings, sometimes coupled with the erosion of democratic constitutionalism, this kind of conversation is more necessary than ever. The second part of the introduction offers an outline of the volume and a detailed summary of the following chapters.
A Haitian Turn
Authors: Frankenberg Günter 
Published: May 2024

Abstract
This chapter sheds light on one of the hidden faces of modernity—Haiti’s founding constitutions of 1801 and 1805. It is argued that these “maroon constitutions” belong in the canon because of their normative innovations: By declaring all Haitians to be “Blacks,” they proposed a new black universalism, and document with their layered narrative as postcolonial social contracts and founding charters the first successful revolt against slavery and a colony’s declaration of independence. With this, their global importance can hardly be overrated and, accordingly, they should form a central part of any discussion of a canon of constitutionalism, alongside the standard texts from the American and French Revolutions.