bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical-relativity surrogate modeling with nearly extremal black-hole spins
Authors: Walker Marissa , Varma Vijay , Lovelace Geoffrey , Scheel Mark A 
Published: January 2023

Abstract
Numerical relativity (NR) simulations of binary black hole (BBH) systems provide the most accurate gravitational wave predictions, but at a high computational cost—especially when the black holes have nearly extremal spins (i.e. spins near the theoretical upper limit) or very unequal masses. Recently, the technique of reduced order modeling has enabled the construction of ‘surrogate models’ trained on an existing set of NR waveforms. Surrogate models enable the rapid computation of the gravitational waves emitted by BBHs. Typically these models are used for interpolation to compute gravitational waveforms for BBHs with mass ratios and spins within the bounds of the training set. Because simulations with nearly extremal spins are so technically challenging, surrogate models almost always rely on training sets with only moderate spins. In this paper, we explore how well surrogate models can extrapolate to nearly extremal spins when the training set only includes moderate spins. For simplicity, we focus on one-dimensional surrogate models trained on NR simulations of BBHs with equal masses and equal, aligned spins. We assess the performance of the surrogate models at higher spin magnitudes by calculating the mismatches between extrapolated surrogate model waveforms and NR waveforms, by calculating the differences between extrapolated and NR measurements of the remnant black-hole mass, and by testing how the surrogate model improves as the training set extends to higher spins. We find that while extrapolation in this one-dimensional case is viable for current detector sensitivities, surrogate models for next-generation detectors should use training sets that extend to nearly extremal spins.
The Gravito–Maxwell equations of General Relativity in the local reference frame of a GR-noninertial observer
Authors: Schmid Christoph 
Published: January 2023

Abstract
We show that the acceleration-difference of neighboring freefalling particles (= geodesic deviation) measured in the local reference frame of a noninertial observer in general relativity (GR) is not given by the Riemann tensor. With the gravito-electric field

of GR defined as the acceleration of freefalling quasistatic particles relative to the observer,

measured in the reference frame of a GR-noninertial observer is different from the curvature

R

0

0

. We derive our exact, explicit, and simple gravito-Gauss law for

in our new reference frame of a GR-noninertial observer with his LONB (Local Ortho-Normal Basis

e
ˉ

a
ˆ

) and his LONB-connections

(

ω

b
ˆ

a
ˆ

)

c
ˆ

in his time- and 3-directions: the sources of

are contributed by all fields including the GR-gravitational fields

. In the reference frame of a GR-inertial observer our gravito-Gauss law coincides with with Einstein’s

R

0

0

equation, which does not have gravitational fields as sources. We derive the gravito-Ampère law for

, the gravito-Faraday law for

, and the law for

. The densities of energy, momentum, and momentum-flow of GR-gravitational fields

are local observables, but they depend on the observer with his local reference frame: if measured by a GR-inertial observer on his worldline in his frame of LONB connections, these quantities are zero. For a GR-noninertial observer the sources of gravitational energy, momentum, and momentum-flow densities have the opposite sign from the electromagnetic and matter sources. The sources in the gravito-Gauss law contributed by gravitational energy and momentum-flow densities have a repulsive effect on the gravitational acceleration-difference of particles. This contributes to the accelerated expansion of our inhomogeneous Universe today.
High-performance flexible dual-function networks based on MXene hybrid film for human–machine interaction
Authors: Xu Hao , Zheng Yiqiang , Yuan Zeyu , Lou Zheng , Wang Lili , Han Wei 
Published: January 2023

Abstract
Flexible multifunctional devices can be used in many fields, but it needs to solve the limitations of their integration and mutual interference, which limits their practical applications. Therefore, it is urgent to develop a flexible sensor that can sense multiple signals at the same time and apply it to human physiological monitoring in real-time. Herein, we developed a flexible dual function sensor which used Ti3C2T
x
MXene/Ag nanowires (Ag NWs)/tissue hybrid film as the sensing materials. The flexible devices exhibit two types of sensing, including high-performance pressure sensing with a high sensitivity and fast response speed, and high-performance humidity sensing with wide detection range of relative humidity. These new functions enable the device to monitor human motion information and human–machine interaction in a personalized way like an electronic skin. The synthesis of Ti3C2T
x
MXene/Ag NWs/tissue hybrid film provides a material suitable for large-scale preparation for the development of wearable electronic devices with high performance and high integration.
Deep neural network for microstructured polymer fiber modeling
Authors: Li Hongwei , Chen Hailiang , Li Yuxin , Chen Qiang , Li Shuguang , Ma Mingjian 
Published: January 2023

Abstract
Microstructured polymer fibers have been widely studied for terahertz waveguides, sensing, environmental monitoring, and medicine. Time-consuming methods, including the finite element method and finite-difference time-domain, have been used to design and simulate microstructure polymer fibers. Designing optimal artificial neural networks (ANN) with various hyperparameters for each optical property considerably improve the prediction accuracy. We compared and explained the differences between predicting one quantity at one time and predicting multiple quantities at the same time. We utilized the successfully trained ANN to accurately evaluate the optical properties of the unknown geometry, including the effective refractive index (

n

eff

), and effective mode area (

A

eff

). In addition, we took logarithmic transformation to form a more uniform distribution, which allowed us to successfully predict confinement loss (α
c). We also demonstrated that ANN could predict the core mode properties of unknown geometric parameters in a considerably short time compared to the finite element method simulation. Moreover, ANN with ultrafast calculation speed and high prediction accuracy accelerate the optical function device design process.
Ga2O3 metal–insulator-semiconductor solar-blind photodiodes with plasmon-enhanced responsivity and suppressed internal photoemission
Authors: Zhang Chong-De , Ren Fang-Fang , Yu Mingbin , Zhang Baoshan , Gu Shulin , Zhang Rong , Zheng Youdou , Ye Jiandong 
Published: January 2023

Abstract
Metal-semiconductor-metal (MSM) architectures are popular for achieving high-responsivity Ga2O3 solar-blind photodetectors (SBPDs), however, the hot-electron-induced internal photoemission (IPE) effect restricts their detecting performance. Herein, we demonstrate the rational design of an Al/Al2O3/Ga2O3 metal–insulator-semiconductor (MIS) SBPD that has merits of enhanced responsivity, suppressed sub-gap response and ultralow dark current based on the simulation results obtained using Lumerical software. For the cylindrical patterned detectors with Al/Al2O3/Ga2O3 MIS structures, the optimized dimensions of Al electrodes with a conformed ultra-thin (2 nm) Al2O3 layer support the surface plasmon polariton resonances at 250 nm, thus improving the photoresponsivity to 74 mA W−1. Furthermore, the sandwiched Al2O3 layer lifts the barrier for hot electrons in electrodes, which significantly suppresses the IPE-induced sub-gap photoresponse by more than 105 in magnitude with respect to the Al/Ga2O3 MSM counterpart. Optical and electrical field distributions are overlapped in cylindrically patterned MIS detectors, simultaneously improving the excitation and collection efficiencies of excess carriers and resulting in the 103-boosted rejection ratio.
Calibration method for structural parameters of the articulated arm coordinate measuring machine utilizing a modified hybrid algorithm
Authors: Liu Kejin , Liang Yaru , Tang Miaomiao 
Published: January 2023

Abstract
In order to increase the precision and decrease the movement uncertainty of the articulated arm coordinate measuring machine (AACMM), a calibration method is proposed in this paper, where the accuracy of single-point repeatability and volumetric accuracy (indicated by distance precision) are simultaneously considered. Considering the good ability of global exploitation and local exploration granted by chicken swarm optimization (CSO) and tabu search (TS), respectively, the CSO and TS are hybridized to construct the tabu searching-based CSO (TSCSO). To help the TSCSO jump out of the local optimal area when it is trapped in it, a transformational criterion of convergence precision is designed to integrate TSCSO with simulated annealing (SA) to formulate the hybrid algorithm called TSCSO with TSCSO-SA, which simultaneously possesses the good ability to globally exploit, locally explore, and jump out of the local optimal area when the population is trapped in it. Moreover, calibration experiments are conducted based on the accuracy of single-point repeatability and volumetric accuracy to verify the effectiveness, efficiency and suitability of the method and algorithm used in this paper. The results of the experiments and verifications show that the precision and movement uncertainty of the AACMM are immensely increased and decreased, respectively, further revealing that the method and algorithm proposed in this paper are able to effectively, efficiently and suitably calibrate the structural parameters of the AACMM.
Electrical and mechanical data fusion for hydraulic valve leakage diagnosis
Authors: Conti Fabio , Madeo Federica , Boiano Antonio , Tarabini Marco 
Published: January 2023

Abstract
This paper describes a method for the identification of valves’ failure, with the final aim of creating a predictive maintenance architecture. After revising the scientific literature, we selected the electric current, the acoustic emission and the vibration signals as the most promising monitoring techniques. The processes of feature extraction and data fusion have been optimized to detect early symptoms of a failure. Performances of five different machine learning algorithms have been compared. Results, obtained in a specific case study, evidenced that a data fusion process based on vibration and current data, paired with a random forest model allowed a prediction accuracy and a Jaccard index close to 99%.
Data-augmented patch variational autoencoding generative adversarial networks for rolling bearing fault diagnosis
Authors: Wang Xin , Jiang Hongkai , Liu Yunpeng , Yang Qiao 
Published: January 2023

Abstract
Many recent studies have focused on imbalanced rolling bearing data for fault diagnosis. Complementing the imbalance dataset through data augmentation methods excellently solves this problem superior. In this paper, a patch variational autoencoding generative adversarial network (PVAEGAN) is proposed. Firstly, overlap sampling is designed to preprocess the input samples to alleviate noise interference. Secondly, the PVAEGAN is constructed, and the matrix discriminative output of the model allows it to focus on more features of the data during training. Thirdly, a stability-enhancing structure is designed for PVAEGAN to improve the stability of network parameter variations and inter-network stability for better model results. Furthermore, to verify the use of the multi-class comparison method, experiments are conducted. The results indicate that PVAEGAN can augment imbalanced datasets more effectively and with better robustness than other existing models.
A multilevel electrolyte-gated artificial synapse based on ruthenium-doped cobalt ferrite
Authors: Monalisha P , Li Shengyao , Jin Tianli , Kumar P S Anil , Piramanayagam S N 
Published: January 2023

Abstract
Synaptic devices that emulate synchronized memory and processing are considered the core components of neuromorphic computing systems for the low-power implementation of artificial intelligence. In this regard, electrolyte-gated transistors (EGTs) have gained much scientific attention, having a similar working mechanism as the biological synapses. Moreover, compared to a traditional solid-state gate dielectric, the liquid dielectric has the key advantage of inducing extremely large modulation of carrier density while overcoming the problem of electric pinholes, that typically occurs when using large-area films gated through ultra-thin solid dielectrics. Herein we demonstrate a three-terminal synaptic transistor based on ruthenium-doped cobalt ferrite (CRFO) thin films by electrolyte gating. In the CRFO-based EGT, we have obtained multilevel non-volatile conductance states for analog computing and high-density storage. Furthermore, the proposed synaptic transistor exhibited essential synaptic behavior, including spike amplitude-dependent plasticity, spike duration-dependent plasticity, long-term potentiation, and long-term depression successfully by applying electrical pulses. This study can motivate the development of advanced neuromorphic devices that leverage simultaneous modulation of electrical and magnetic properties in the same device and show a new direction to synaptic electronics.
Nanoimprint lithography-based replication techniques for fabrication of metal and polymer biomimetic nanostructures for biosensor surface functionalization
Authors: Nowduri Bharat , Britz-Grell Anette , Saumer Monika , Decker Dominique 
Published: January 2023

Abstract
Nanostructuring is a promising and successful approach to tailor functional layers and to improve the characteristics of biosensors such as signal transmission and tighter cell-surface coupling. One of the major objectives in biosensing and tissue engineering is the development of interfaces that mimic the natural environment of biosystems composed of extracellular matrix biomolecules. Nevertheless, effective techniques to reconstruct the random distribution of these biomolecules are still not well established. For this reason, the presented work demonstrates different methods based on nanoimprint lithography to replicate randomly distributed natural nanostructures with complex geometries into different polymers and metals. The fidelity of the replicated nanostructures has been evaluated by atomic force microscopy and the attributes of the fabrication processes have been discussed. Finally, different replication techniques have been combined for the biomimetic nanostructuring of the dielectric passivation layer as well the metal electrode surface to develop novel whole-surface-nanostructured microelectrode arrays.
Atomic layer deposition (ALD)-constructed TaS2 nanoflakes for cancer-related nucleolin detection
Authors: Huang Yazhou , Lv Junyan , Zhang Yunfei , Shao Yinfeng , Yang Dongfang , Cong Yuan , Huang Jiacai , Bian Rong 
Published: January 2023

Abstract
Sensitive detection of nucleolin (NCL) is of great significance for the early diagnosis of cancer. In this work, as a new type of two-dimensional (2D) transition metal dichalcogenides (TMDCs), TaS2 nanoflakes (NFs) were precisely constructed by atomic layer deposition (ALD) on carbon fiber paper (CFP) with high specific surface area. In situ observation showed that the nucleation and growth of TaS2 nanoflakes were precisely controlled by the number of ALD cycles, thereby regulating their electrochemical properties. The electrochemical performance of TaS2NFs was observed in depth, and compared with that of traditional 2D TMDCs. Due to the high surface area and conductivity, anodic/cathodic current of ∼1570 μA of TaS2NFs/CFP can be obtained. Subsequently, an electrochemical biosensor based on ALD-constructed TaS2NFs/CFP for cancer-related NCL detection was fabricated. Due to the excellent electrochemical performance of TaS2NFs/CFP, ultrasensitive detection of NCL in the linear range of 0.1 pM–10 nM with a detection limit of 0.034 pM was achieved.
Full-field in vivo imaging of nanoparticles using benchtop cone-beam XFCT system with pixelated photon counting detector
Authors: Li Liang , Zhang Siyuan , Zhang Wenli , Lu Hongbing 
Published: January 2023

Abstract

Objective. X-ray fluorescence computed tomography (XFCT) is a promising noninvasive technique for in vivo imaging of high-Z elements (e.g. gadolinium (Gd) or gold (Au)). In this study we upgraded our experimental XFCT system using a flat panel photon counting detector with redesigned pinhole collimation in order to achieve 3D XFCT images during one scan. Approach. Aiming at the characteristics of pinhole-collimated cone-beam XFCT imaging, a new scatter correction algorithm was proposed to estimate the normalized spectrum of scatter background based on K–N formula and realize correction by a weighted least squares method. Then, images were quantitatively reconstructed by a maximum likelihood iterative algorithm with the attenuation correction. Main results. The potential on full-field in vivo XFCT imaging of this new system was investigated. An imaging experiment of a PMMA phantom with the diameter of 35 mm was carried out for quantitative evaluation of the system performance. Results show that 2 mg ml−1 Gd solutions can be successfully reconstructed with a 45 min cone-beam XFCT scan. In vivo XFCT imaging experiments of mice with injection of Gd nanoparticles (GdNPs) were also performed and demonstrated in this paper. A mouse was injected through the tail vein with 20 mg ml−1 NaGdF4 solution and then anesthetized with isoflurane during the cone-beam XFCT scan. Significance. The distribution of the GdNPs inside the mouse can be well reconstructed so that the deposition of NPs in vivo can be clearly observed, which indicates the feasibility of the proposed system for full-field XFCT of small animals and further potential in relevant in vivo research.
Research on fatigue crack quantitative monitoring based on eddy current sensor with an interactive induction coil layout
Authors: Fan Xianghong , He Yuting , Chen Tao 
Published: January 2023

Abstract
To reduce the number of sensor channels, this paper proposes an eddy current sensor with an interactive induction coil layout. First, the crack monitoring principle of the sensor is analyzed, and fatigue crack monitoring experiment is carried out. It is found that the cracks can be quantified according to the characteristic points of the sensitivity variation curve. Then, a permalloy plate is used for reinforcing the sensor, and the crack monitoring accuracy test is carried out, which shows that the crack monitoring accuracy error of the sensor is no more than 0.1 mm. Finally, the sensor network is built by the sensor according to the dangerous parts of the butt joint structure determined by the finite element analysis. And fatigue crack monitoring experiment is carried out, which shows that the sensor network can determine the position and length of the fatigue crack, and the error is no more than 0.1 mm.
Novel polyhedral mechanical metamaterial exhibiting negative Poisson’s ratio
Authors: Sorrentino A , Castagnetti D 
Published: January 2023

Abstract
The work presents a novel polyhedral mechanical metamaterial based on rotating triangular prisms connected by their corners, which possesses the ability to attain large values of negative Poisson’s ratio (NPR). Through a kinematic model of the proposed rotating structure, we evaluate the auxeticity of the system by varying the geometrical parameters of the polyhedrons composing the elementary cell of the structure. The kinematic results highlight the peculiar NPR of the system, whose values are nearly constant over significant strain ranges. Focusing on the most promising auxetic mechanisms we designed chiral architectures that replace the ideal hinges at the corners with curved-shape ligaments, and validated these configurations through three-dimensional printed specimens. The specimens were tested under uniaxial compression and simulated through finite element analyses. Experimental results exhibited an excellent agreement with computational predictions in terms of elastic modulus and auxeticity, showing a value of Poisson’s ratio up to −1.3 for one of the designs. Our findings demonstrate the highly auxetic property of rotating polyhedral systems, which allow the design of novel architected materials useful, for example, in biomechanical applications.
Submicrometer-scale temperature sensing using quantum coherence of a superconducting qubit
Authors: Kakuyanagi Kosuke , Toida Hiraku , Abdurakhimov Leonid V , Saito Shiro 
Published: January 2023

Abstract
Interest is growing in the development of quantum sensing based on the principles of quantum mechanics, such as discrete energy levels, quantum superposition, and quantum entanglement. Superconducting flux qubits are quantum two-level systems whose energy is sensitive to a magnetic field. Therefore, they can be used as high-sensitivity magnetic field sensors that detect the magnetization of a spin ensemble. Since the magnetization depends on temperature and the magnetic field, the temperature can be determined by measuring the magnetization using the flux qubit. In this study, we demonstrated highly sensitive temperature sensing with high spatial resolution as an application of a magnetic field sensor using the quantum coherence of a superconducting flux qubit. By using a superconducting flux qubit to detect the temperature dependence of the polarization ratio of electron spins in nano-diamond particles, we succeeded in measuring the temperature with a sensitivity of 1.3 µKµ

Hz

−
1

at T = 9.1 mK in the submicrometer range.
Time-resolved measurements of sub-optical-cycle relativistic electron beams
Authors: Li Cheng , Zhang Haoran , Guo Zixin , Xu Xiazhen , He Zhigang , Zhang Shancai , Jia Qika , Wang Lin 
Published: January 2023

Abstract
We propose an all-optical technique to record the time information of relativistic electron beams with sub-optical-cycle duration. The technique is based on the interaction of the electron beam with the ponderomotive potential of an optical traveling wave generated by two counter-propagating circularly polarized optical fields at different frequencies in vacuum. One of the optical pulses is a vortex laser pulse, and the other is a normal Gaussian laser pulse. The time information of the electron beam is mapped into the angular information, which can be converted into a spatial distribution after a drift section. Thus, the temporal profile and arrival time of the electron beam can be retrieved from the spatial distribution of the electron beam. The measurement has a dynamic range comparable to the period of the optical intensity grating formed by two counter-propagating laser pulses. This technique may have wide applications in many research fields that require sub-optical-cycle electron beams.
Any four orthogonal ququad–ququad maximally entangled states are locally markable
Authors: Hsu Li-Yi 
Published: January 2023

Abstract
In quantum state discrimination, the observers are given a quantum system and aim to verify its state from the two or more possible target states. In the local quantum state marking as an extension of quantum state discrimination, there are N composite quantum systems and N possible orthogonal target quantum states. Distant Alice and Bob are asked to correctly mark the states of the given quantum systems via local operations and classical communication. Here we investigate the local state marking with N

4
⊗
4

systems, N = 4, 5, 6, and 7. Therein, Alice and Bob allow for three local operations: measuring the local observable either σ

z
or σ

x
simultaneously, and entanglement swapping. It shows that, given arbitrary four

4
⊗
4

systems, Alice and Bob can perform the perfect local quantum state marking. In the N = 5, 6 cases, they can perform perfect local state marking with specific target states. We conjecture the impossibility of the local quantum state marking given any seven target states since Alice and Bob cannot fulfill the task in the simplest case.