bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation on transition characteristics of hydrofoil boundary layer based on algebraic local-correlation-based transition modeling model
Authors: Ye Changliang , Wang Yang , An Dongsen , Kan Kan , Yan Hongyeyu , Zheng Yuan , van Esch B. P. M. (Bart)
Published: October 2024

Hydrofoil shapes are used for the marine turbine blades to capture kinetic energy from water currents effectively. Predicting transitions is a critical concern when studying the hydrofoil boundary layer. This paper analyzed the transitional behavior of the boundary layer in the National Advisory Committee of Aeronautics (NACA) hydrofoil, NACA0009, with a blunt trailing edge using the Algebraic Local-Correlation-based Transition Modeling (Algebraic LCTM) model. First, through sensitivity analysis, the effects of the maximum y+ (the dimensionless distance y to the wall), grid expansion ratio, number of normal and streamlined grids, and timescale on transition prediction were studied. The results indicate that finer y+ value and appropriate grid expansion ratios can improve the accuracy of transition prediction, while the influence of timescale on the prediction results is relatively small within the range of Courant number theory values. Second, further analysis was conducted on the transition prediction performance under different Reynolds numbers. It was found that the model predictions were consistent with experimental values at low Reynolds numbers, but the predicted transition position was advanced at high Reynolds numbers, mainly because of the significant disparity in eddy viscosity coefficients within the free flow field. In the study of leading-edge roughness bands' impact on boundary layer transition for hydrofoil, the introduction of roughness significantly expedited the transition process. The Algebraic LCTM model outperformed the gamma (γ) transition model, reducing prediction errors by 5–40% for boundary layer parameters and maintaining errors between 0.005 and 4% for wake vortex shedding frequency, as opposed to the γ model's 0–23%. The results of this study provide a theoretical basis for hydrofoil design.
Comparing metaphor elicitation models
Authors: Nacey Susan , Turunen Renata
Published: October 2024

Metaphor has increasingly been recognized as an effective investigative tool, especially in educational research,
because it allows for insight into implicit beliefs, values and assumptions about the world around us. This article discusses
methodological issues involved in ‘getting at’ students’ metaphors, the first step in metaphor analysis in educational research.
Our focus is on techniques of eliciting metaphor, where respondents are overtly prompted to produce a metaphor about a particular
issue. We discuss different elicitation techniques in four studies: study 1 without any scaffolding, just a prompt for eliciting
metaphor; study 2 with minimal scaffolding in the form of a definition of metaphor and illustrative examples; study 3 with a brief
classroom intervention before elicitation; and study 4 with a longer intervention over an entire semester. We gained the most
thorough results in study 4, which also arguably provided the most valuable learning experience for the respondents. This came at
a price however — a great investment of time over the course of a semester. In the event that no classroom time may be set aside
for work with metaphor, the non-interventional approach of study 2 offers an acceptable alternative. Even a few explanatory lines
about metaphor prove helpful in eliciting metaphor.
Linkage for Metrosert to CCT-K9 via the EURAMET-T.K9.1 bilateral comparison
Authors: Olsen Åge Andreas Falnes , Tammik Kristjan
Published: October 2024

Main text

Justervesenet (JV) and Metrosert carried out a bilateral comparison, EURAMET.T-K9.1, with the purpose of linking Metrosert to the CCT-K9 reference value. Metrosert realised a subset of the fixed points used in the CCT-K9, namely Hg TP, Ga MP, Sn FP and Zn FP. JV participated in the EURAMET.T-K9, whose topology was a set of distinct stars with a pilot that had participated in the CCT-K9. The linkage between EURAMET laboratories was thus via the pilot degree-of-equivalence. A consequence of this organisation was that there could be no linkage for Metrosert (and bilateral links between European laboratories in different stars) until the CCT-K9 was approved and closed. The EURAMET.T-K9.1 measurements finished in 2017, and the final report was published in 2020 after a prolonged review process. However, the CCT-K9 results were not ready, and therefore the EURAMET.T-K9 results could not be analysed. The consequence was that computation of the linkage had to be postponed.

The CCT-K9 final report was published in early 2023. The final EURAMET.T-K9 report followed in autumn 2024. This brief report extends the EURAMET.T-K9.1 report with details about the computation and the results of the linkage to the CCT key comparison.

To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.

The final report has been peer-reviewed and approved for publication by the CCT, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Discontinuities of Banana Integrals in Dispersion Relation Representation
Authors: Chen 陈 Xu-Liang 绪梁 , Yang 杨 Peng-Fei 鹏飞 , Chen 陈 Wei 伟
Published: October 2024

We derive the discontinuities of banana integrals using the dispersion relation iteratively, and find a series of identities between the parameterized discontinuities of banana integrals (p-DOBIs). Similar to elliptic integrals, these identities enable the reduction of various p-DOBIs to be a linear combination of some fundamental ones. We present a practical application of p-DOBIs for deriving the Picard–Fuchs operator. Then we establish the expression of generalized dispersion relation, which enables us to obtain the dispersion relation representation of arbitrary banana integrals. Moreover, we propose a hypothesis for generalized dispersion relation and p-DOBIs, which provides a simple way to calculate the discontinuities and transform dispersion relation representation to p-DOBIs.
Coherent control of dressing interaction of sequential-doubly-dressed four-wave mixing
Authors: Xiaoyu Ma , Suling Sang
Published: October 2024

We investigate the coherent control of dressing interaction in four-wave mixing (FWM) in a Y-type four-level atomic system based on the dressed states theory. We study the density matrix equation acted by two dressing fields simultaneously governing FWM signal intensity and the eigenvalues under the dressed state formalism. It is shown that the peak position and intensity of Autler–Townes splitting both originate from the dressing interaction as the detuning of the probe field is scanned. Moreover, it is observed that the interaction could be controlled by the dressing fields. Such flexibly controlled nonlinear signal has potential applications in optical communication and quantum information processing.
Optimizing multiferroic properties in 12% Sm-doped BiFeO3 through cobalt doping
Authors: Zala Jahnviba , Kanjariya P V , Prajapati Priya , Bera Kirti , Bera Surojit , Patel Himalay
Published: October 2024

Presented study explores the effect of varying cobalt doping on the enhancement of multiferroic behavior of 12% samarium (Sm)-doped bismuth ferrite (BiFeO3). Samples with the composition Bi0.88Sm0.12Fe1‒x
Co
x
O3 (x = 0.04, 0.06, 0.08, 0.12) were prepared using the auto-combustion technique. The structural, magnetic and dielectric characteristics were analyzed using x-ray diffraction, scanning electron microscopy (SEM), vibrating sample magnetometry (VSM), LCR analysis and polarization-electric field (P-E) measurements. Our investigation disclose that the 12% Co-doped sample exhibits improved ferromagnetic, ferroelectric and dielectric properties compared to samples with lower Co doping levels. These results shows that cobalt doping effectively optimizes the multiferroic behavior of 12% Sm-doped bismuth ferrite, suggesting new insights into the design of advanced multifunctional materials.
Reverse design and application of phononic crystals based on deep learning
Authors: Qi Wenchao , Ye Xi , Wang Xianzhong , Chen Lin , Zhan Bixin , Wang Weiwei , Shao Yuechuan , Sun Jie , Xu Longlong
Published: October 2024

This paper reverse-design phononic crystals with band gaps within a targeted frequency band using the trained conditional variational autoencoder (CVAE) and further studies the vibro-acoustic characteristics of a composite sandwich plate with a phononic crystal panel as the core layer. Firstly, a matrix composed of 0 s and 1 s, representing scatterers and substrates, is randomly generated by MATLAB to represent two-dimensional phononic crystals. The three-dimensional phononic crystals are obtained by stretching the two-dimensional phononic crystals along the average direction, and COMSOL Multiphysics is used to calculate the band gap. In order to maximize the production of phononic crystals with a band gap distribution, the convolutional neural network is trained to predict whether the generated phononic crystals have band gaps. Finally, using data on the structures of phononic crystals and their band gap distributions, the CVAE is trained to achieve the reverse design of artificial periodic structures based on the target band gap. To verify the effectiveness of the structures obtained through the reverse design method on vibration and noise reduction, the submerged vibro-acoustic characteristics of a composite sandwich plate are studied. The plate consists of a phononic crystal panel and carbon fiber panels. The model of the composite sandwich plate is fabricated, and its submerged vibro-acoustic characteristics are tested and compared with numerical results. Finally, the submerged vibro-acoustic response levels of composite sandwich plates with phononic crystal panels and honeycomb panels as core layers are compared using numerical methods. This comparison assesses the phononic crystal panel’s vibration and noise reduction effects.
Deep transfer learning in machinery remaining useful life prediction: a systematic review
Authors: Chen Gaige , Kong Xianguang , Cheng Han , Yang Shengkang , Wang Xianzhi
Published: October 2024

As a novel paradigm in machine learning, deep transfer learning (DTL) can harness the strengths of deep learning for feature representation, while also capitalizing on the advantages of transfer learning for knowledge transfer. Hence, DTL can effectively enhance the robustness and applicability of the data-driven remaining useful life (RUL) prediction methods, and has garnered extensive development and research attention in machinery RUL prediction. Although there are numerous systematic review articles published on the topic of the DTL-based approaches, a comprehensive overview of the application of DTL in the RUL prediction for different mechanical equipment has yet to be systematically conducted. Therefore, it is imperative to further review the pertinent literature on DTL-based approaches. This will facilitate researchers in comprehending the latest technological advancements and devising efficient solutions to address the cross-domain RUL prediction challenge. In this review, a brief overview of the theoretical background of DTL and its application in RUL prediction tasks are provided at first. Then, a detailed discussion of the primary DTL methods and their recent advancements in cross-domain RUL prediction is presented. Next, the practical application of the current research is discussed in relation to the research object and its open-source data. More importantly, several challenges and further trend are further presented to conclude this paper in the end. We have reason to hope this work can offer convenience and inspiration to researchers seeking to advance in the field of RUL prediction.
The unsupervised bearing fault diagnosis method based on the dual-framework Siamese network
Authors: Qu Xiaotian , Liu Yongqiang , Deng Feiyue , Yingying Liao , Qing Chengwei
Published: October 2024

Aiming at the problem of insufficient bearing fault samples in practical engineering, an unsupervised bearing fault diagnosis method with double frame twin network is proposed in this paper. This method only uses the source domain data to diagnose the target domain data, and can minimize the problem of domain difference. The concrete method is firstly to carry on continuous wavelet transform to the rolling bearing signal, and the processed signal is used as the input feature of convolutional neural network. The twin network structure is optimized and Manhattan distance is used as the loss function in the training process. The proposed CM network is applied to the diagnosis of inner ring fault, outer ring fault and normal state of rolling bearing. The results show that CM network can achieve over 90% accuracy on SQ variable speed vibration signal data set (SQV), Shijiazhuang Railway University data set (STD) and Case Western Reserve University data set (CWRU), which is suitable for bearing fault diagnosis under different working conditions and provides a new direction for fault diagnosis. The PyTorch code is available at https://github.com/xiaotianqu/The-unsupervised-bearing-fault-diagnosis-method-based-on-the-dual-framework-Siamese-network.
Deep learning-based acoustic emission source localization in heterogeneous rock media without prior wave velocity information
Authors: Cui Yi , Chen Jie , Chen Ziyang , Pu Yuanyuan , Yu Bin , Jiang Wei
Published: October 2024

Acoustic emission (AE) source localization is crucial for monitoring but often relies on prior information, such as wave velocity and arrival time. This study introduces a novel method for locating AE sources in rocks without such information, addressing challenges posed by heterogeneous sensor arrays. Experiments involving pencil led break (PLB) tests on sandstone cubes collected AE waveforms and their coordinates. A ResNet-50 based deep learning model was developed to correlate the time-frequency spectra of AE with PLB locations, expressed as spatial Gaussian distributions. The model, achieved a 79% prediction accuracy for AE localization in complex environments. While there is room for improvement in training data quantity and diversity, the results validate the model’s effectiveness, particularly in coal mines and tunnel engineering.
An enhanced mobile localization algorithm integrating multiple AUKF models for mixed indoor environments
Authors: Jiang Yi , Gao Heng , Zhang Pengpeng , Hu Qing
Published: October 2024

The positioning technology based on ultra-wideband ranging has been widely applied in the field of indoor positioning due to its excellent localization capabilities. However, mixed line-of-sight (LOS) and non-LOS (NLOS) indoor environments severely constrain positioning accuracy. To address this issue, we propose an innovative algorithm based on the adaptive unscented Kalman filter (AUKF) and interactive multiple model (IMM), designed to significantly enhance positioning accuracy in mixed indoor environments by mitigating the impact of NLOS errors and inaccurate process noise. Firstly, recognizing the distinct characteristics of ranging errors in indoor environments, we develop LOS and NLOS ranging models separately. Based on these models, the unscented Kalman filters are constructed for LOS and NLOS environments to accurately simulate the mixed LOS/NLOS indoor environments. Secondly, determining the statistical characteristics of process noise is challenging, often leading to degraded filter performance. We address this issue by proposing an environment-based AUKF algorithm, which significantly enhances the robustness and accuracy of the positioning system. Finally, the environment-based AUKFs are integrated into the IMM framework to constrain NLOS errors and achieve precise positioning effectively. Simulations, open-source dataset validation and experimental results demonstrate that the proposed algorithm significantly enhances the accuracy and stability of mobile target positioning in mixed LOS/NLOS indoor environments.
Sensing offset analysis and compensation of a capacitive displacement transducer for space inertial sensors
Authors: Yang Xiaotian , Wang Chengrui , Hu Ming , Lu Jinfeng , Bai Yanzheng , Qu Shaobo , Zhou Zebing
Published: October 2024

Capacitive displacement transducers play a crucial role in inertial sensors especially for space gravitational wave detection missions. One of the major requirements for the displacement transducer is to have a lower sensing offset. The sensing offset will adversely affect the accuracy of the absolute position measurement of the test mass, providing erroneous data to the drag-free controller and leading to spacecraft mispositioning. Additionally, the offset could introduce multiplicative noise to the sensing output and the closed loop output of the inertial sensor. The asymmetry of the sensing bridge within the front-end electronics has been identified as the primary source of these offsets. This paper analyzes the requirements of the main parameters in the sensing bridge. A method is proposed to reduce the sensing offset by utilizing differentiated resonant capacitors while maintaining the ability to tune the resonant frequency and ensure a sufficiently low sensing noise level. Experimental results show that this approach can effectively reduce the residual sensing offset to (−15.7 ± 5.0) ppm, which satisfies the stringent requirement for space inertial sensors in the TianQin mission.
Performance degradation assessment of rolling bearings for electrostatic monitoring based on IDDAE and ADPC
Authors: Wei Xinyue , Li Dewen , Li Zihan , Cai Jing , Li Ai , Zhang Ying
Published: October 2024

To improve the early degradation detection capability of the electrostatic monitoring system for rolling bearings, a performance degradation evaluation method based on improved deep denoising autoencoder and adaptive density peak clustering (ADPC) is proposed in this paper. Firstly, the fusion of electrostatic charge signal features with conventional time-domain, frequency-domain and time–frequency-domain features constitutes the characteristic parameter set of the electrostatic monitoring system indicating the status of the bearings. Then, in order to improve the feature extraction ability of DAE, the deep network DDAE is constructed, and L1 regularisation and Dropout mechanism are applied to avoid overfitting in the deep network, so as to achieve non-linear mapping dimensionality reduction of high-dimensional features. Moreover, to eliminate the error caused by manually selecting the clustering centre, the parameters are adaptively determined by entropy value method and comprehensive optimisation search on the basis of DPC, thus avoiding the ‘chain effect’ that occurs in traditional DPC when data are incorrectly aggregated due to incorrect assignment of clustering centres. Consequently, an improved ADPC algorithm is used to establish a model to measure the health status of bearings, calculate the Mahalanobis distance (MD) between the test set and the cluster centre, and quantitatively characterize the degree of performance degradation of rolling bearings. Finally, combining the

3
δ

principle, a repair method that can satisfy online monitoring and adapt to spurious fluctuations in different situations is established on a sliding window to obtain an improved index IMD that can accurately characterise the rolling bearing degradation process. The experimental results show that the proposed method can identify early bearing degradation earlier and has better monotonicity, robustness and tendency than other performance degradation assessment methods.
Laser-assisted synthesis and modification of 2D materials
Authors: Lee Yejun , Hong Sunhwa , Moon Issac , Kim Chan-Jin , Lee Yunseok , Hong Byung Hee
Published: October 2024

Two-dimensional (2D) materials with unique physical, electronic, and optical properties have been intensively studied to be utilized for the next-generation electronic and optical devices, and the use of laser energy in the synthesis and modification of 2D materials is advantageous due to its convenient and fast fabrication processes as well as selective, controllable, and cost-effective characteristics allowing the precise control in materials properties. This paper summarizes the recent progress in utilizations of laser technology in synthesizing, doping, etching, transfer and strain engineering of 2D materials, which is expected to provide an insight for the future applications across diverse research areas.
CRISPR-based strategies for sample-to-answer monkeypox detection: current status and emerging opportunities
Authors: Ahamed Md Ahasan , Politza Anthony J , Liu Tianyi , Khalid Muhammad Asad Ullah , Zhang Huanshu , Guan Weihua
Published: October 2024

The global health threat posed by the Monkeypox virus (Mpox) requires swift, simple, and accurate detection methods for effective management, emphasizing the growing necessity for decentralized point-of-care (POC) diagnostic solutions. The clustered regularly interspaced short palindromic repeats (CRISPR), initially known for its effective nucleic acid detection abilities, presents itself as an attractive diagnostic strategy. CRISPR offers exceptional sensitivity, single-base specificity, and programmability. Here, we reviewed the latest developments in CRISPR-based POC devices and testing strategies for Mpox detection. We explored the crucial role of genetic sequencing in designing crRNA for CRISPR reaction and understanding Mpox transmission and mutations. Additionally, we showed the integration of CRISPR-Cas12 strategy with pre-amplification and amplification-free methods. Our study also focused on the significant role of Cas12 proteins and the effectiveness of Cas12 coupled with recombinase polymerase amplification (RPA) for Mpox detection. We envision the future prospects and challenges, positioning CRISPR-Cas12-based POC devices as a frontrunner in the next generation of molecular biosensing technologies.
A simulation framework for preclinical proton irradiation workflow
Authors: Malimban Justin , Ludwig Felix , Lathouwers Danny , Staring Marius , Verhaegen Frank , Brandenburg Sytze
Published: October 2024

Objective. The integration of proton beamlines with x-ray imaging/irradiation platforms has opened up possibilities for image-guided Bragg peak irradiations in small animals. Such irradiations allow selective targeting of normal tissue substructures and tumours. However, their small size and location pose challenges in designing experiments. This work presents a simulation framework useful for optimizing beamlines, imaging protocols, and design of animal experiments. The usage of the framework is demonstrated, mainly focusing on the imaging part. Approach. The fastCAT toolkit was modified with Monte Carlo (MC)-calculated primary and scatter data of a small animal imager for the simulation of micro-CT scans. The simulated CT of a mini-calibration phantom from fastCAT was validated against a full MC TOPAS CT simulation. A realistic beam model of a preclinical proton facility was obtained from beam transport simulations to create irradiation plans in matRad. Simulated CT images of a digital mouse phantom were generated using single-energy CT (SECT) and dual-energy CT (DECT) protocols and their accuracy in proton stopping power ratio (SPR) estimation and their impact on calculated proton dose distributions in a mouse were evaluated. Main results. The CT numbers from fastCAT agree within 11 HU with TOPAS except for materials at the centre of the phantom. Discrepancies for central inserts are caused by beam hardening issues. The root mean square deviation in the SPR for the best SECT (90 kV/Cu) and DECT (50 kV/Al–90 kV/Al) protocols are 3.7% and 1.0%, respectively. Dose distributions calculated for SECT and DECT datasets revealed range shifts <0.1 mm, gamma pass rates (3%/0.1 mm) greater than 99%, and no substantial dosimetric differences for all structures. The outcomes suggest that SECT is sufficient for proton treatment planning in animals. Significance. The framework is a useful tool for the development of an optimized experimental configuration without using animals and beam time.
Hydrodynamic equations for a U(N) invariant superfluid
Authors: Zhang Yi-Cai , Zhang Shizhong
Published: October 2024

In this paper, we develop an appropriate set of hydrodynamic equations for a U(N) invariant superfluid that couple the dynamics of superflow and magnetization. In the special case when both the superfluid and normal velocities are zero, the hydrodynamic equations reduce to a generalized version of Landau–Lifshitz equation for ferromagnetism with U(N) symmetry. When both velocities are non-zero, there appears couplings between the superflow and magnetization dynamics, and the superfluid velocity no longer satisfies the irrotational condition. On the other hand, the magnitude of magnetization is no longer a constant of motion as was the case for the standard Landau–Lifshitz theory. In comparison with the simple superfluid, the first and second sounds are modified by a non-zero magnetization through various thermodynamic functions. For U(2) invariant superfluid, we get both (zero-) sound wave and a spin wave at zero temperature. It is found that the dispersion of spin wave is always quadratic, which is consistent with microscopic analysis. In the appendix, we show that the hydrodynamic equation for a U(N) invariant superfluid can be obtained from the general hydrodynamic equation with arbitrary internal symmetries.
Terahertz laser-fabricated all-polymer hole arrays with high-quality quasi-bound states in the continuum
Authors: Shen Yanchun , Wu Feng , Liu Dejun
Published: October 2024

Dielectric metasurfaces promise to realize ultrahigh-quality (Q) resonances due to their ultralow material absorption. Most of them are silicon-based metasurfaces, requiring complex fabricated steps and thus suffering high costs. Laser etching processing has simple steps accompanied by low time consumption and exemplary processing efficiency. Here, an all-polymer metasurface based on hole arrays fabricated by laser processing has been proposed and investigated. Such metasurfaces achieve sharp quasi-bound states in the continuum (quasi-BICs) via breaking structural symmetry, form two annular circulation electric fields in different directions, and thus allow strong coupling between holes. Owing to the low refractive index of polymer, the calculated Q-factor reaches 9555 while the diameter discrepancy is 4 μm. Simulated results proved that the Q-factors of quasi-BICs can be further improved by reducing the film thickness and refractive indices of materials, which can be predicted by the fitting equation. Also, the fields in holes can be enhanced by reducing the film refractive index. These results in simulations and experiments provide an alternative method for designing high-Q resonators in terahertz regions.
Effect of silver nanoparticles on the photostability and aging of CsPbBr3 nanocrystals
Authors: Krukov Vitali , Patsinko Olga , Ramanenka Andrei A , Kulakovich Olga , Canımkurbey Betül , Gaponenko Sergey V
Published: October 2024

Perovskite nanocrystals may become a promising replacement for current phosphors in light-emitting diodes (LEDs) and screens, but the question of the stability of their optical properties remains open. One way to solve this problem could be to use plasmonic nanoparticles. In this work, we investigate the combination of all-inorganic perovskite nanocrystals synthesized by the hot-injection method with spherical Ag nanoparticles (mean diameter 53 nm). 3-fold enhancement of photoluminescence (PL) has been implemented in hybrid ‘silver-CsPbBr3-polymethyl methacrylate’ structures. The presence of silver nanoparticles reduces the likelihood of Auger processes and forms a possible silver bromide barrier layer which prevents photoinduced ion migration in the perovskite-polymer film. Plasmonic enhancement of PL partially presents during long-term samples storage within 75 days. This work may be useful in the creation of perovskite LEDs using remote phosphor technology.
Low-energy (0–9 eV) electron interaction with gas phase 1,3-dichlorobenzene: an experimental and theoretical study
Authors: Abdoul-Carime H , G F de Miranda E , Varella M T do N
Published: October 2024

Dichlorobenzene used widely in industry for the synthesis of complex products, such as polymers. The processes using this compound require, as the initial step, the breakage of the C–Cl bond. In this work, we study the interaction of electrons with 1,3 dichlorobenzene molecules not only below 2 eV [M Mahmoodi-Darian et al 2001 J. Phys. Chem. A 113 11923–14929] but also at higher energies, i.e., up to 10 eV. In this investigated energy range, the electron induces the cleavage of the C–Cl bond producing essentially a Cl− anion and the chlorobenzene radical via dissociative electron attachment. The experimental measurements are completed with quantum scattering treatments providing the resonant states and also the integral scattering cross sections. These outcomes may potentially contribute to elaborate synthesis strategies using electrons (i.e., cold plasma, surface plasmon resonance, …).
Supression of shielding effect of large area field emitter cathode in radio frequency gun environment
Authors: Sathya A T , Sarkar Shreya G , Bakhtsingh R I , Mondal Jayanta
Published: October 2024

Utilisation of large area field emitters (LAFE) cathodes for rf gun injector hold promise for delivering compact, high power and high brightness electron beam for advanced accelerator technologies. LAFEs subjected to DC electric fields posses significant challenges due to the shielding effect which restricts emission from central emitters and decreases the overall current density. Mitigating the shielding effect of LAFE in rf gun environment is essential for meeting the desired beam quality requirement in an accelerator. The current distribution of LAFE under DC conditions depend on its various geometrical parameters such as emitter height, inter-emitter distance, aspect ratio, number of emitters. Additionally, in rf gunsetup, LAFEs are subjected to variable macroscopic electric field at different emitter position which can potentially alter the current distribution compared to DC fields. In this work, we have systematically studied the shielding effect properties of LAFE in rf gun environment under the influence of various LAFE parameters. A semi-analytical approachhas been adopted to estimate the current distribution which combines the analytically calculated field enhancement factor (γ) and numerically calculated applied rf field values. This new methodology was first validated using COMSOL simulation and then employed for field emission performance estimation of a LAFE cathode integrated in a½ cell S-band (2856 MHz) rf gun. The simulation results reveals that under favourable conditions, a Gaussian spatial distribution of beam can be obtained from LAFE thus countering the shielding effect typical in DC fields. By optimizing the LAFE parameters, the desired current and beam distribution pattern can be achieved. This study highlights the adoption of a promising approach for designing LAFE cathodes suitable for rf gun which can lead to advancement of field emission technologies for accelerator-related applications.
Structure-preserving analysis on chaotic characteristics of transverse vibration for embedded double-walled carbon nanotube
Authors: Hu Weipeng , Han Songmei , Wang Gangwei , Wazwaz Abdul-Majid
Published: October 2024

Analysing the ultra-high frequency vibrational characteristics of carbon nanotubes, especially on the chaotic characteristics, is a key scientific problem in the dynamic design of the carbon nanotube devices. Considering the van der Waals force between the inner layer and the outer layer of the embedded double-walled carbon nanotube, and the effects of the elastic medium as well as the effects of the simple harmonic external excitation, the coupling dybamic model describing the transverse vibration of the embedded double-walled carbon nanotube is presented. The generalized multi-symplectic formulations with an explicit multi-symplectic structure residual are deduced by introducing the dual momenta. The Preissmann approach, which has been proved to be a structure-preserving method that can be used to reproduce the chaotic characteristics of carbon nanotubes, is employed to discrete the generalized multi-symplectic formulations. The numerical results imply that, the transverse vibration of the embedded double-walled carbon nanotube subjected to the external excitation larger than the critical external excitation will enter the chaotic state through a period-doubling bifurcation path. In addition, the critical external excitation for the chaos of the inner layer carbon nanotube’s transverse vibration is larger than that of the outer layer carbon nanotube’s transverse vibration. The above findings reported in this paper provide some guidance for the dynamic design of the carbon nanotube devices directly.
New dynamics performance for established dark solitons in polariton condensate
Authors: Zahran Emad H M , Bekir Ahmet , Ibrahim Reda A
Published: October 2024

New diverse enormous soliton solutions to the Gross–Pitaevskii equation, which describes the dynamics of two dark solitons in a polarization condensate under non-resonant pumping, have been constructed for the first time by using two different schemes. The two schemes utilized are the generalized Kudryashov scheme and the (G’/G)-expansion scheme. Throughout these two suggested schemes we construct new diverse forms solutions that include dark, bright-shaped soliton solutions, combined bright-shaped, dark-shaped soliton solutions, hyperbolic function soliton solutions, singular-shaped soliton solutions and other rational soliton solutions. The two 2D and 3D figure designs have been configured using the Mathematica program. In addition, the Haar wavelet numerical scheme has been applied to construct the identical numerical behavior for all soliton solutions achieved by the two suggested schemes to show the existing similarity between the soliton solutions and numerical solutions.
Probing axion-like particles in leptonic decays of heavy mesons*
Authors: Yang 杨 Gang 钢 , Wang 王 Tianhong 天鸿 , Wang 王 Guo-Li 国利
Published: October 2024

We study the possibility of finding axion-like particles (ALPs) through the leptonic decays of heavy mesons. The Standard Model (SM) predictions of the branching ratios of the leptonic decays of heavy mesons are less than the corresponding experimental upper limits. This provides some room for the existence of decay channels, of which the ALP is one of the products. Three scenarios are considered: First, the ALP is only coupled to one single charged fermion, namely, the quark, the antiquark, or the charged lepton; second, the ALP is only coupled to quark and antiquark with the same strength; and third, the ALP is coupled to all the charged fermions with the same strength. The constraints of the coupling strength in different scenarios are obtained by comparing the experimental data of the branching ratios of leptonic decays of

,

, and

mesons with the theoretical predictions achieved by using the Bethe-Salpeter (BS) method. These constraints are further applied to predict the upper limits of the leptonic decay processes of the

meson in which the ALP participates.
Statistical Research on Megahertz-peaked Spectra Pulsars
Authors: Cai Hong-Bing , Chen Li , Li Shi-Dang , Wang Kai , Liu Hang
Published: October 2024

Megahertz-peaked spectra (MPS) pulsars are referred as cases whose radio spectra turn over around 100 MHz. We identified 53 MPS pulsars based on the spectral data from the literature, and statistically analyzed the spatial location distribution, the magnetic field-period distribution, peak frequencies, spectral indices, and dispersion measures of these MPS pulsars. We found that there is a strong positive correlation between the dispersion measures and the peak frequencies of MPS pulsars, and negative correlations of the dispersion measures with spectral indices and the ages are also found. Such correlations suggest that the interstellar medium is an important factor that affects observational properties of MPS pulsars.
An ANN models cortical-subcortical interaction during post-stroke recovery of finger dexterity
Authors: Kadry Ashraf , Solomonow-Avnon Deborah , Norman Sumner L , Xu Jing , Mawase Firas
Published: October 2024

Objective. Finger dexterity, and finger individuation in particular, is crucial for human movement, and disruptions due to brain injury can significantly impact quality of life. Understanding the neurological mechanisms responsible for recovery is vital for effective neurorehabilitation. This study explores the role of two key pathways in finger individuation: the corticospinal (CS) tract from the primary motor cortex and premotor areas, and the subcortical reticulospinal (RS) tract from the brainstem. We aimed to investigate how the cortical-reticular network reorganizes to aid recovery of finger dexterity following lesions in these areas. Approach. To provide a potential biologically plausible answer to this question, we developed an artificial neural network (ANN) to model the interaction between a premotor planning layer, a cortical layer with excitatory and inhibitory CS outputs, and RS outputs controlling finger movements. The ANN was trained to simulate normal finger individuation and strength. A simulated stroke was then applied to the CS area, RS area, or both, and the recovery of finger dexterity was analyzed. Main results. In the intact model, the ANN demonstrated a near-linear relationship between the forces of instructed and uninstructed fingers, resembling human individuation patterns. Post-stroke simulations revealed that lesions in both CS and RS regions led to increased unintended force in uninstructed fingers, immediate weakening of instructed fingers, improved control during early recovery, and increased neural plasticity. Lesions in the CS region alone significantly impaired individuation, while RS lesions affected strength and to a lesser extent, individuation. The model also predicted the impact of stroke severity on finger individuation, highlighting the combined effects of CS and RS lesions. Significance. This model provides insights into the interactive role of cortical and subcortical regions in finger individuation. It suggests that recovery mechanisms involve reorganization of these networks, which may inform neurorehabilitation strategies.
Review of deep representation learning techniques for brain–computer interfaces
Authors: Guetschel Pierre , Ahmadi Sara , Tangermann Michael
Published: October 2024

In the field of brain–computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest. Objective: This review synthesizes empirical findings from a collection of articles using deep representation learning techniques for BCI decoding, to provide a comprehensive analysis of the current state-of-the-art. Approach: Each article was scrutinized based on three criteria: (1) the deep representation learning technique employed, (2) the underlying motivation for its utilization, and (3) the approaches adopted for characterizing the learned representations. Main results: Among the 81 articles finally reviewed in depth, our analysis reveals a predominance of 31 articles using autoencoders. We identified 13 studies employing self-supervised learning (SSL) techniques, among which ten were published in 2022 or later, attesting to the relative youth of the field. However, at the time being, none of these have led to standard foundation models that are picked up by the BCI community. Likewise, only a few studies have introspected their learned representations. We observed that the motivation in most studies for using representation learning techniques is for solving transfer learning tasks, but we also found more specific motivations such as to learn robustness or invariances, as an algorithmic bridge, or finally to uncover the structure of the data. Significance: Given the potential of foundation models to effectively tackle these challenges, we advocate for a continued dedication to the advancement of foundation models specifically designed for EEG signal decoding by using SSL techniques. We also underline the imperative of establishing specialized benchmarks and datasets to facilitate the development and continuous improvement of such foundation models.
Vertical instability prediction and its direction control using a support vector machine in integrated commissioning of JT-60SA solely based on magnetics
Authors: Inoue S. , Kojima S. , Miyata Y. , Wakatsuki T. , Yokoyama T. , Takechi M. , Urano H. , Yoshida M. , Suzuki T. ,   the JT-60SA Integrated Project Team
Published: October 2024

We developed a redundant logic for predicting Vertical Displacement Events (VDEs) using only magnetic diagnostics with a Support Vector Machine (Inoue et al 2022 Nucl. Fusion
62 086007). This logic was experimentally validated in the integrated commissioning of JT-60SA. Triggering of VDEs results in significant asymmetric heat loads on first walls and electromagnetic loads on conducting materials. These concerns are mitigated by a newly proposed direction control for VDEs. We successfully detected VDEs in experiments and controlled their direction by setting the control voltage to zero upon detection, thus guiding VDEs in an intended direction and reducing the risk to the device from unmitigated VDEs in either direction, although completely avoiding VDEs remains the ideal. In the developed predictor, VDEs are predicted using proxies to monitor the controller’s performance with an adaptive voltage allocation scheme (Inoue et al 2021 Nucl. Fusion
61 096009). For future experiments, we plan to extend our approach to monitor more detailed information from the equilibrium controller, including control values of each PID component, which has been shown to enhance the prediction accuracy of VDEs.
Deterministic equivalent and error universality of deep random features learning*
Authors: Schröder Dominik , Cui Hugo , Dmitriev Daniil , Loureiro Bruno
Published: October 2024

This manuscript considers the problem of learning a random Gaussian network function using a fully connected network with frozen intermediate layers and trainable readout layer. This problem can be seen as a natural generalization of the widely studied random features model to deeper architectures. First, we prove Gaussian universality of the test error in a ridge regression setting where the learner and target networks share the same intermediate layers, and provide a sharp asymptotic formula for it. Establishing this result requires proving a deterministic equivalent for traces of the deep random features sample covariance matrices which can be of independent interest. Second, we conjecture the asymptotic Gaussian universality of the test error in the more general setting of arbitrary convex losses and generic learner/target architectures. We provide extensive numerical evidence for this conjecture. In light of our results, we investigate the interplay between architecture design and implicit regularization.
AI for biofabrication
Authors: Zhou Chang , Liu Changru , Liao Zhendong , Pang Yuan , Sun Wei
Published: October 2024

Biofabrication is an advanced technology that holds great promise for constructing highly biomimetic in vitro three-dimensional human organs. Such technology would help address the issues of immune rejection and organ donor shortage in organ transplantation, aiding doctors in formulating personalized treatments for clinical patients and replacing animal experiments. Biofabrication typically involves the interdisciplinary application of biology, materials science, mechanical engineering, and medicine to generate large amounts of data and correlations that require processing and analysis. Artificial intelligence (AI), with its excellent capabilities in big data processing and analysis, can play a crucial role in handling and processing interdisciplinary data and relationships and in better integrating and applying them in biofabrication. In recent years, the development of the semiconductor and integrated circuit industries has propelled the rapid advancement of computer processing power. An AI program can learn and iterate multiple times within a short period, thereby gaining strong automation capabilities for a specific research content or issue. To date, numerous AI programs have been applied to various processes around biofabrication, such as extracting biological information, designing and optimizing structures, intelligent cell sorting, optimizing biomaterials and processes, real-time monitoring and evaluation of models, accelerating the transformation and development of these technologies, and even changing traditional research patterns. This article reviews and summarizes the significant changes and advancements brought about by AI in biofabrication, and discusses its future application value and direction.
Multiple conformal-contact transfer of large-area crack-free transition metal dichalcogenide stacks
Authors: Fu Yu , Niu Yilin , Xue Guodong , Guo Quanlin , Liu Chang , Chen Jiale , Hong Hao , Liu Can , Liu Kaihui
Published: October 2024

Atomically-thin two-dimensional (2D) transition metal dichalcogenides (TMDs) have emerged as an ideal platform for both physics investigation and device applications. By stacking different layers into homo- or hetero-structures, an extra degree of freedom is involved in further tuning their properties, thereby boosting scenarios in twistronics, moiré photonics and optoelectronics. However, interfacial imperfections such as contaminations and cracks, frequently occur during the layer stacking sequence and accumulate layer by layer, greatly degenerating the interface quality. In this study, we developed a multiple conformal-contact transfer method to construct TMD stacks with crack-free intrinsic interfaces. The design of a deformable buffer layer is crucial to guarantee the conformal contact and intact transfer of each layer, contributing to the successful construction of centimetre-scale TMD stacks up to 8 layers. Precise control over spatial location and interlayer twist angle is also feasibly achieved, evidenced by the stacking-dependent interlayer exciton (IE) effects in WS2-WSe2 heterostructures. This work provides a facile and precise approach for architecting 2D stacks with perfect interfaces, which will further accelerate the customized design for their device functionalization.
High-pressure supercritical CO2-assisted fabrication of two-dimensional LaAlO3 and its room temperature ferromagnetism
Authors: Che Guoqi , Gao Bo , Xu Qun
Published: October 2024

Spintronics applications in two-dimensional (2D) magnetic materials can significantly contribute to the miniaturization and energy efficiency of semiconductor devices. However, current 2D ferromagnetic materials face challenges such as ferromagnetic instability and low Curie temperature (Tc), which limit their broader application. In this study, 2D room-temperature ferromagnetic LaAlO3 was successfully prepared with supercritical carbon dioxide (SC CO2). With the enhanced stress effect of CO2 molecules, the contents of oxygen vacancies OV, aluminium vacancies AlV, and AlO4 in the 2D structure are elevated, and then the ferromagnetic properties of LaAlO3 appeared to be significantly enhanced due to defects and spatial symmetry breaking of the octahedron. Notably, the 2D intrinsic ferromagnetic LaAlO3 exhibited a Tc above 350 K. Therefore, this work supplies a new means for SC CO2 to modulate the microstructure and ferromagnetic properties of 2D LaAlO3, which is conducive to expanding the applications of LaAlO3.