bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cross-domain fault diagnosis network based on attributes and features transfer with dual classifier under limited and unbalanced datasets
Authors: Song Shanshan , Zhang Shuqing , Liu Haitao , Wu Xiang 
Published: May 2024

Abstract
Deep learning (DL)-based methods have demonstrated significant success in fault diagnosis owing to their robust feature extraction and non-linear fitting capabilities. Meanwhile, their remarkable performance is accompanied by constant operating conditions and sufficient monitoring data. However, in real engineering environments, variable working conditions or limited and unbalanced data are common, which can widen the gap between fault diagnosis methods and real industrial applications. In this paper, we proposed a cross-domain fault diagnosis network based on a dual classifier (CFDNet) with input being limited and unbalanced data to learn attributes and features for unsupervised domain adaptation. We found that the diagnostic performance is commonly bounded by the underlying knowledge, especially feature extraction from original data. Therefore, we designed a new feature encoder with features and relationships, i.e. using a convolutional neural network and graph convolutional network, which improves extraction efficiency while retaining valuable information. Then, we discovered that enforced feature transfer can lead to negative transfer. To mitigate this, we present a feature and attribute transfer framework, which not only achieves features transfer but also enables attributes transfer. Furthermore, it was noted that limited and unbalanced datasets can introduce label bias and lead to biased model training. Hence, we designed dual classifiers to improve the probability of high-confidence final prediction by synthesizing diagnostic results. Comprehensive experiments conducted on three case studies demonstrate the effectiveness and superiority of our method for cross-domain fault diagnosis under limited and unbalanced datasets, which outperforms state-of-the-art methods in this study.
A fast point cloud registration method based on spatial relations and features
Authors: Liao Zhuhua , Zhang Hui , Zhao Yijiang , Liu Yizhi , Yang Jiyuan 
Published: May 2024

Abstract
Point cloud registration plays a crucial role in mobile robot localization, map building and three-dimensional (3D) model reconstruction. However, it remains challenged by issues such as compromised accuracy and sluggish efficiency, posing significant obstacles in achieving precise and timely alignments. Therefore, we propose a lightweight and fast point cloud registration method. Firstly, we mesh the 3D point cloud, compared with the traditional gridded point cloud method, it achieves initial point cloud registration by preserving the curvature characteristics of the internal point cloud, and utilizing the spatial relationship between grid cells and the quantitative relationship between the internal point cloud. Moreover, we adopt an iterative nearest point based on KD-Tree to realize the fine registration. So, our method does not necessitate intricate feature analysis and data training, and is resilient to similar transformations, non-uniform densities and noise. Finally, we conduct point cloud registration experiments using multiple publicly available point cloud datasets and compare them with several point cloud registration methods. The results demonstrate it is able to accomplish the point cloud registration quickly and exhibit high accuracy. More importantly, it maintains its efficacy and robustness even in the presence of noisy and defective point clouds.
Dynamic simulation-assisted Gaussian mixture alignment approach for fault diagnosis of rotation machinery under small samples
Authors: Yu Shubo , Liu Zhansheng , Zhang Gaorong , Wang Saibo 
Published: May 2024

Abstract
Obtaining a substantial number of actual samples for rotating machinery in an industrial setting can be challenging, particularly when faulty samples are acquired under hazardous working conditions. The issue of insufficient samples hinders the effective training of reliable fault diagnosis models, impeding the industrial implementation of advanced intelligent methods. This study proposes an innovative dynamic simulation-assisted Gaussian mixture alignment model (DSGMA) to address the challenge of applying fault diagnosis technologies, with its performance mined by advanced transfer algorithms. Specifically, we establish a fault dynamics model for rotating machinery and acquire a substantial amount of simulated data as the source domain to facilitate the training of the deep neural network model. Subsequently, we propose a Gaussian mixture-guided domain alignment approach that assigns a domain-independent Gaussian distribution to each category as prior knowledge, with the parameters calculated using limited actual samples. Diagnostic knowledge is transferred from the source domain to the target domain by minimizing the Kullback–Leibler divergence between the features of the simulated samples and the Gaussian mixture priors. Furthermore, the DSGMA model incorporates Gaussian clustering loss to augment the clustering capability of samples belonging to the same category from real devices and enhances the computational stability of the parameters in the Gaussian mixture model. The efficacy of the DSGMA method is validated using three publicly available datasets and compared against five widely adopted methods. The experimental findings illustrate that DSGMA exhibits superior diagnostic and robust capabilities, facilitating efficient fault diagnosis under scenarios of small samples.
Enhanced low-temperature resistance of lithium-ion batteries based on methyl propionate-fluorinated ethylene carbonate electrolyte
Authors: Xiang Zhengping , Wang Liying , Yang Xijia , Yang Yue , Qu Ying , Gao Yang , Li Xuesong , Lü Wei 
Published: May 2024

Abstract
Low temperature has been a major challenge for lithium-ion batteries to maintain satisfied electrochemical performance, as it leads to poor rechargeability and low capacity retention. Traditional carbonate solvents, vinyl carbonate and dimethyl carbonate are indispensable components of commercial electrolytes. However, the higher melting point of these carbonate solvents causes their electrical conductivity to be easily reduced when temperatures drop below zero, limiting their ability to facilitate lithium ion transport. In this work, we demonstrate that the use of methyl propionate (MP) carboxylate and fluorocarbonate vinyl (FEC) electrolytes can overcome the limitations of low temperature cycling. Compared with carbonate electrolyte, MP has the characteristics of low melting point, low viscosity and low binding energy with Li+, which is crucial to improve the low temperature performance of the battery, while FEC is an effective component to inhibit the side reaction between MP and lithium metal. The carefully formulated MP-based electrolyte can generate a solid electrolyte interface with low resistance and rich in inorganic substances, which is conducive to the smooth diffusion of Li+, allowing the battery to successfully cycle at a high rate of 0.5 C at −20 °C, and giving it a reversible capacity retention rate of 65.3% at −40 oC. This work designs a promising advanced electrolyte and holds the potential to overcome limitations of lithium-ion batteries in harsh conditions.
Improved high voltage operation of amorphous In–Ga–Zn–O thin film transistor by carrier density enhancement
Authors: Ju Hyoungbeen , Bang Jiyoung , Sun Hyeonjeong , Lee Yeonghun , Kim Sangduk , Choi Seungmin , Noh Youngsoo , Kim Hyo Won , Choi Eunsuk , Jeong Jae Kyeong , Lee Seung-Beck 
Published: May 2024

Abstract
We report on improved high voltage operation of amorphous-In–Ga–Zn–O (a-IGZO) thin film transistors (TFTs) by increasing carrier density and distributing the high bias field over the length of the device which utilizes an off-set drain structure. By decreasing the O2 partial pressure during sputter deposition of IGZO, the channel carrier density of the high voltage a-IGZO TFT (HiVIT) was increased to ∼1018 cm−3. Which reduced channel resistance and therefore the voltage drop in the ungated offset region during the on-state. To further decrease the electric field in the offset region, we applied Ta capping and subsequent oxidation to locally increase the oxygen vacancy levels in the offset region thereby increasing local carrier density. The reduction of the drain field in the offset region from 1.90 V μm−1 to 1.46 V μm−1 at 200 V drain voltage, significantly improved the operational stability of the device by reducing high field degradation. At an extreme drain voltage of 500 V, the device showed an off-state current of ∼10−11 A and on-state current of ∼1.59 mA demonstrating that with further enhancements the HiVIT may be applicable to thin-film form, low leakage, high voltage control applications.
Wetting behavior of stainless-steel surface with zinc oxide nanorod coatings: modulations in nanorod alignment through atomic layer deposition
Authors: Kim Kyungmin , Hong Minpyo , Song Yongnam 
Published: May 2024

Abstract
This study investigated the hydrophobic-hydrophilic characteristics of zinc oxide (ZnO) nanorod coatings for potential biomedical applications. We examined the effects of different alignments of ZnO nanorods on the wetting and mechanical characteristics of the coatings. ZnO seed layers were prepared on stainless-steel plates using atomic layer deposition (ALD) at five different temperatures ranging from 50 to 250 °C. The ZnO nanorod coatings were then deposited on these seed layers through chemical bath deposition. The polycrystalline structure of the seed layers and the morphology of the nanorods were analyzed using grazing incidence angle x-ray diffraction, transmission electron microscopy, and scanning electron microscopy. Mechanical and wetting properties of the nanorod coatings were examined using nanoindentation and water-droplet tests. The seed layers produced at 50 and 250 °C showed stronger (0 0 2) peaks than the other layers. ZnO nanorods on these seed layers exhibited greater vertical orientation and lower water contact angles indicating a more hydrophilic surface. Additionally, vertically oriented nanorod coatings demonstrated greater elastic modulus and hardness than those of oblique nanorods. Our findings indicate that ALD technology can be used to control the spatial arrangement of ZnO nanorods and optimize the hydrophobic-hydrophilic and mechanical properties of coating surfaces.
Improving resonant ice protection systems with substrate optimization
Authors: Rafik Younes , Palanque Valerian , Budinger Marc , Pommier-Budinger Valerie , Olivier Philippe 
Published: May 2024

Abstract
Electro-mechanical de-icing systems are low-energy ice protection solutions based on ice fracture mechanisms. This article focuses on resonant electro-mechanical de-icing systems that actuate modes of flexion, which require low energy compared to extension modes. However, fracture propagation limits are encountered when using such flexural modes, preventing the ice from being completely detached from the substrate. This study demonstrates the feasibility of extending the ice detachment area by optimizing the thickness of the substrate. First, the interest and the limits of flexural resonant modes are discussed. Then the de-icing of a simple metallic beam in free conditions using a flexural mode is improved owing to the parametric optimization of the substrate thickness. The optimization is verified by tests on aluminum prototypes. The optimization results are then extended to a clamped composite plate and then to a NACA profile, showing interest in the approach to fully de-ice structures with modes of flexion, even in the case of complex geometries. With this last example, the study also demonstrates the feasibility of electro-mechanical ice protection systems for carbon fiber reinforced Polymer composite structures.
Modeling phase separation and composition patterning in FeCrAl alloys at neutron irradiation
Authors: Kharchenko D O , Kharchenko V O , Qin Jiantao , Wu Lu , Lysenko B O , Dvornichenko A V 
Published: May 2024

Abstract
Possible scenarios of the microstructural evolution of FeCrAl alloys under neutron irradiation are studied by using the phase-field method. The exploited approach is generalized by considering ballistic mixing of atoms, spatial rearrangement of point defects, and dynamics of their sinks (dislocation loops), self-consistently. Stability diagrams manifesting domains of main control parameters (irradiation temperature, damage rate, atomic relocation distance) governing precipitation are obtained. It is shown that depending on irradiation conditions a precipitation of the Cr-rich

α
′

-phase proceeds by either phase decomposition or composition patterning mechanism. Statistical analysis of precipitation kinetics is provided by studying precipitate number density, mean precipitate size and Chromium content in

α
′

-phase for a model alloy Fe-30%Cr-5%Al. A locality of point defects distribution with high concentration and dislocation loops growth are discussed. The hardening change is estimated for two possible scenarios of precipitation. Obtained results are verified with experimental observations. This study provides a deep insight into details of microstructural transformation in FeCrAl alloys during neutron irradiation.
Polarization-independent tunable multi-band terahertz absorber based on graphene structure to design the ultra-high sensitive biosensors
Authors: Rafighirani Yousef , Javidan Javad , Heidarzadeh Hamid 
Published: May 2024

Abstract
In this paper, we investigated the heterogeneous structure of a multi-band perfect absorber based on graphene in the terahertz range, benefiting from polarization independence. The proposed structure comprises three layers: copper, silicon dioxide, and an inhomogeneous graphene structure with an analyte. By altering the sub-layers dimensions and the graphene slices' geometric shape, we can modify the number of bands, quality, and absorption levels. Additionally, adjusting the chemical potential of graphene enables the customization of absorption frequencies as needed. The application of this structure in biological sensors extends to the detection of proteins, viruses, and cancer cells, as well as filtering telecommunication waves and imaging. Through geometrically shaping the graphene cuts at frequencies of 4.89 THz, 9.14 THz, and 10.76 THz, absorption values of 99.54%, 99.64%, and 98.3% have been achieved, respectively. Introducing the analyte to the biosensor structure causes a shift in absorption frequency values due to varying refractive index values in different materials. This property has been utilized for biosensor design. Within the refractive index range of biological analytes (e.g., 1.3), the first band achieved a sensitivity value of 2700 GHz/RIU and FoM = 13.08, while the second band achieved a sensitivity value of 2200 GHz/RIU and FoM = 14.02. An important characteristic of this structure is its insensitivity to polarization. Simulations were conducted using Computer Simulation Technology (CST) Microwave Studio Suite 2023.
Relativistic energies for the q-deformed Scarf potential with Feynman path integrals formulation
Authors: Diaf Ahmed , Hachama Mohammed 
Published: May 2024

Abstract
In this paper, the Dirac equation with the q-deformed Scarf potential for spin symmetry is solved for an arbitrary spin-orbit quantum number κ, in the presence of Coulomb-like potential tensor. Using the Feynman path integral formalism and the Pekeris approximation of the centrifugal term, we obtain the bound state energy eigenvalues and the associated spinor of the Dirac particle. Furthermore, this method is used to determine the spectrum of two diatomic molecules Li
2(61Π
u
) and KRb(B
−1Π). The obtained results are compared to the experimental and numerical ones.
Controlling vertical phase separation and crystallization via solvent synergy strategy to empower layer-by-layer processed organic solar cells
Authors: Li Hengyue , Yang Yu , Bai Xue , Zha Wusong , Chen Haopeng , Yang Yingguo , Lu Guanghao , Luo Qun , Ma Chang-Qi , Yang Junliang 
Published: May 2024

Abstract
The novel sequential layer-by-layer (LbL) processed organic solar cells (OSCs) have attracted continuous attention due to their advantages of ideal vertical phase separation, efficient charge transport and collection, and potentiality for large-scale production from laboratory to factory. Herein, a solvent synergy strategy is put forward to control morphology, crystallization and vertical phase distribution of blend films, which means the donor PM6 and acceptor Y6 treated by high/low boiling point solvents are fabricated using LbL solution process, respectively. Based on device with a configuration of ITO/ZnO/PM6:Y6/MoO3/Ag, OSCs derived from the solvent synergy strategy can obtain a remarkable power conversion efficiency (PCE) up to 15.03%, which is comparable to that of the bulk heterojunction devices prepared by conventional one-step solution method. This impressive result provides an insightful understanding of phase segregation and crystalllization in LbL processed OSCs assisted by the solvent synergy strategy. It lays the foundation for fabrication and optimization of high-performance, large-area OSCs in industrial production.
Sub-GeV millicharge dark matter from the U(1)
X
hidden sector
Authors: Feng Wan-Zhe , Zhang Zi-Hui , Zhang Kai-Yu 
Published: May 2024

Abstract
We conduct a comprehensive study on the sub-GeV millicharge dark matter
produced through the freeze-in mechanism.
We discuss in general the mixing mechanism,
encompassing both kinetic mixing and mass mixing,
between the U(1)
X
hidden sector and the standard model,
which can generate millicharge carried by the dark fermions from the hidden sector.
We discuss in depth how such millicharge is generated,
and clarify several misunderstandings regarding this subject in the literature.
Without employing an effective field theory approach,
where the photon field directly mixed with the additional U(1),
we analyze a general renormalizable model and
investigate the complete evolution of the hidden sector particles.
Due to the substantial self-interactions among hidden sector particles,
the evolution of the hidden sector temperature plays a crucial role,
which is addressed concurrently with the number densities of hidden sector particles
by solving a set of coupled Boltzmann equations.
We thoroughly examine eight benchmark models from six distinct cases.
Some of our key findings from the analysis of these benchmark models may be generalizable
and applicable to broader freeze-in scenarios.
We also explore the possibility that the 𝒪(keV) U(1)
X
dark photon is a viable dark matter candidate,
even though it can contribute at most ∼ 5% to the total observed dark matter relic density.
BAO cosmology in non-spatially flat background geometry from BOSS+eBOSS and lessons for future surveys
Authors: Sanz-Wuhl Santiago , Gil-Marín Héctor , Cuesta Antonio J. , Verde Licia 
Published: May 2024

AbstractWe study the impact of the assumption of a non-flat fiducial cosmology on the measurement, analysis and interpretation of BAO distance variables, along and across the line-of-sight. The assumption about cosmology enters in the choice of the base template, as well as on the transformation of tracer's redshifts into distances (the catalog cosmology): here we focus on the curvature assumption, separately and jointly, on both. We employ BOSS and eBOSS publicly available data and show that for the statistical precision of this data set, distance measures and thus cosmological inference are robust to assumptions about curvature both of the template and the catalog. Thus the usual assumptions of flat fiducial cosmologies (but also assumptions of non-flat cosmologies) do not produce any detectable systematic effects. For forthcoming large-volume surveys, however, small but appreciable residual systematic shifts can be generated which may require some care. These are mostly driven by the choice of catalog cosmology if it is significantly different from true cosmology. In particular, the catalog (and template) cosmology should be chosen, possibly iteratively, in such a way that the recovered BAO scaling variables are sufficiently close to unity. At this level of precision, however, other previously overlooked effects become relevant, such as a mismatch between the sound horizon as seen in the BAO and the actual sound horizon in the early Universe. If unaccounted for, such effect may be misinterpreted as cosmological and thus bias the curvature (and cosmology) constraints. We present best practices to avoid this.
Quantitative analysis of laser-generated ultrasonic wave characteristics and their correlation with grain size in polycrystalline materials
Authors: Xu 徐 Zhaowen 兆文 , Bai 白 Xue 雪 , Ma 马 Jian 健 , Wan 万 Zhuangzhuang 壮壮 , Wang 王 Chaoqun 超群 
Published: May 2024

Abstract
Quantitative relationship between nanosecond pulsed laser parameters and the characteristics of laser-generated ultrasonic waves in polycrystalline materials was evaluated. The high energy of the pulsed laser with a large irradiation spot simultaneously generated ultrasonic longitudinal and shear waves at the epicenter under the slight ablation regime. An optimized denoising technique based on wavelet thresholding and variational mode decomposition was applied to reduce noise in shear waves with a low signal-to-noise ratio. An approach for characterizing grain size was proposed using spectral central frequency ratio (SCFR) based on time-frequency analysis. The results demonstrate that the generation regime of ultrasonic waves is not solely determined by the laser power density; even at high power densities, a high energy with a large spot can generate an ultrasonic waveform dominated by the thermoelastic effect. This is ascribed to the intensification of the thermoelastic effect with the proportional increase in laser irradiation spot area for a given laser power density. Furthermore, both longitudinal and shear wave SCFRs are linearly related to grain size in polycrystalline materials; however, the shear wave SCFR is more sensitive to finer-grained materials. This study holds great significance for evaluating metal material properties using laser ultrasound.
Preparation of entangled W states based on the cavity QED system
Authors: Li 李 Ke 可 , Zhao 赵 Jun-Long 军龙 
Published: May 2024

Abstract
We present a qubit-loss-free (QLF) fusion scheme for generating large-scale atom W states in cavity quantum electrodynamics (QED) system. Compared to the most current fusion schemes which are conditioned on the case where one particle can be extracted from each initial W state to the fusion process, our scheme will access one or two particles from each W state. Based on the atom–cavity-field detuned interaction, three |W〉
n+m+t
states can be generated from the |W〉
n
, |W〉
m
, and |W〉
t
states with the help of two auxiliary atoms, and three |W〉
n+m+t+q
states can be generated from |W〉
n
, |W〉
m
, |W〉
t
, and a |W〉
q
state with the help of three auxiliary atoms. Comparing the numerical simulations of the resource cost of fusing three small-size W states based on the previous schemes, our fusion scheme seems to be more efficient. This QLF fusion scheme can be generalized to the case of fusing k different or identical particle W states. Furthermore, with no qubit loss, it greatly reduces the number of fusion steps and prepares W states with larger particle numbers.