bluuebunny/crossref_metadata_2025_split huggingface dataset:
A practitioners view on the ICRP review of the system of radiological protection—feedback from SRP 2023 workshop
Authors: Bryant Peter A , Bannon Amber , Castle Matthew , Jeffries Cameron , Nisbet Anne , Hondros Jim , Humphries Jennifer 
Published: September 2024

Abstract
In 2018 the International Commission on Radiological Protection (ICRP) initiated a review and revision of the System of Radiological Protection which will lay the foundation for radiation protection (RP) standards, regulations, guidance and practice worldwide for the next 40 years. On the 25 April 2023 the Society for Radiological Protection ran a workshop at their Annual Conference presenting the current status and progress in the ICRP Review and Revision, along with inviting a number of panellist’s across different areas of the profession and wider audience to share their thoughts. The outputs of the workshop are summarised in this paper showing the views from a variety of practitioners working across the RP sectors on the key factors to be considered in the review.
A fault diagnosis method for bearings and gears in rotating machinery based on data fusion and transfer learning
Authors: Zhang Yi , Yan Xiaoxiang , Xiao Ping , Zou Jialing , Hu Ling 
Published: September 2024

Abstract
Rotating machinery is a crucial component of industrial equipment, and the fault diagnosis of bearings and gears, as vital elements of rotating machinery, is essential since they often fail under harsh working conditions, leading to significant property losses and serious personal safety problems. However, fault data for gears and bearings are often sparse in actual condition, and it is a challenge to ensure the reliability and stability of fault diagnosis results by extracting the features of a single data. To solve the above problems, this paper proposes a fault diagnosis method that combines Transfer Learning and data fusion techniques. Firstly, in this method, two kinds of fault signals are transformed into Gramian Angular Difference Fields and Recurrence Plot. Next, a U-shaped feature fusion dual discriminator generative adversarial network is used to fuse two-dimensional images from multiple sensor data. Its feature fusion module deeply integrates the features of the two images, thereby solving the impact of single data on the reliability and stability of fault diagnosis. Moreover, open-source datasets are used for Transfer Learning training to tackle the small sample problem. Finally, a decision-level information fusion classifier, the Dual-Branch Dempster-Shafer Classifier (DB-DSC), classifies the fused images. This classifier incorporates an improved soft threshold function and D-S evidence theory to achieve adaptive gradient changes and improve the robustness and accuracy of classification results. The experimental results show the effectiveness and stability of the proposed method, and the generated images get high score in several metrics. The average classification accuracy of the classification network reaches 93% and 92.5% on the two datasets, Therefore, the proposed method exhibits strong fault diagnosis capabilities under the small sample conditions of bearings and gears.
Research on abnormal object detection network of computer room inspection robot based on depth vision
Authors: Zhao Bin , Li Ziheng , Wu Chengdong , Zhang Xuejiao , Du Zhenjun 
Published: September 2024

Abstract
This paper investigates a deep learning-based anomaly object detection network for identifying and alerting on abnormal items within computer room. First, the framework of the data center inspection robot system is outlined, and the anomaly detection task is decomposed. Next, a dataset of abnormal objects based on data center environmental information is established, and augmentation operations are performed on the created dataset. Subsequently, a SqueezeNet network model based on Residual Squeeze Excitation and Atrous Spatial Pyramid Pooling (RSE-ASPP) is proposed to optimize and improve the SqueezeNet network model. Finally, this paper employs transfer learning to address the issue of insufficient data volume. By pre-training on a large-scale dataset and fine-tuning on the constructed dataset, the accuracy and stability of abnormal object recognition can be significantly enhanced. Ultimately, the proposed RSE-ASPP-SqueezeNet network achieves high-precision detection of abnormal items in the data center inspection robot’s anomaly detection task.
Characterization of identical lithium-ion battery electrodes before and after charge/discharge cycles via in-plane large-area polishing
Authors: Cho Mingi , Park So yeon , Jung Heechul , Kim Seong Heon 
Published: September 2024

Abstract
As an effective method to fabricate a large-area cross-sectional sample for lithium-ion battery electrodes, we perform in-plane polishing of LiNi0.8Co0.15Al0.05O2 (NCA) cathode samples and obtain a large cross-sectional area with a diameter of 1.5 mm. The polished cross-sections of NCA cathode particles are sufficiently flat to perform the atomic force microscopy (AFM) measurements on each cathode particle. Following AFM-based Kelvin probe force microscopy and scanning spreading resistance microscopy measurements, an identical in-plane polished NCA sample is assembled into a coin cell for the charge and discharge processes. After 90 charge/discharge cycles, the in-plane-polished sample is successfully disassembled from the coin cell without causing critical damage. In addition, a microcrack structure, which is a typical degradation feature of the cycles of NCA particles, is observed for the identical in-plane polished NCA sample. This indicates that the in-plane polishing method is effective for investigating identical NCA electrode samples before and after the charge/discharge process. Furthermore, the in-plane polishing method can be successfully applied to the large-area polishing of a Si-based anode which is a mixture of Si carbon complexes and graphite particles. This study presents a novel methodology for analyzing the degradation of lithium-ion battery electrode materials.
Analysis of coupling effect between TID and SET in SOI Tri-Gate nanowire field-effect transistors
Authors: Zhou Xilong , Yin Chenyu , Liu Hongxia , Chen Shupeng , Wang Shulong , Huang Junjie , Yan Zhanpeng , Liu Chang 
Published: September 2024

Abstract
This study investigates the response of nanowire field-effect transistors (NWFETs) to total ionizing dose (TID), single-event transient (SET), and their coupling effects in junctionless (JL), inversion (IM), and junctionless accumulation (AC) modes. The degradation of the three modes under irradiation and the effect of device bias configuration on the electrical properties of NWFETs are analyzed, and the different effects of SET on the three modes are compared. On this basis, the influence of TID on SET current generation and the charge collection mechanism are studied, and the changes in peak current, pulse width, and collected charge of transient current under different TIDs are compared. The results show that JL mode has the worst resistance to TID and SET coupling effects, followed by IM and AC modes.
BP-diff: a conditional diffusion model for cuffless continuous BP waveform estimation using U-Net
Authors: Liu Yinsong , Yu Junsheng , Mou Hanlin 
Published: September 2024

Abstract

Objective. Continuous monitoring of blood pressure (BP) is crucial for daily healthcare. Although invasive methods provide accurate continuous BP measurements, they are not suitable for routine use. Photoplethysmography (PPG), a non-invasive technique that detects changes in blood volume within the microcirculation using light, shows promise for BP measurement. The primary goal of this study is to develop a novel cuffless method based on PPG for accurately estimating continuous BP. Approach. We introduce BP-Diff, an end-to-end method for cuffless continuous BP waveform estimation utilizing a conditional diffusion probability model combined with a U-Net architecture. This approach takes advantage of the stochastic properties of diffusion models and the strong feature representation capabilities of U-Net. It integrates the continuous BP waveform as the initial status and uses the PPG signal and its derivatives as conditions to guide the training and sampling process. Main results. BP-Diff was evaluated using both uncalibrated and calibrated schemes. The results indicate that, when uncalibrated, BP-Diff can accurately track BP dynamics, including peak and valley positions, as well as timing. After calibration, BP-Diff achieved highly accurate BP estimations. The mean absolute error of the estimated BP waveforms, along with the systolic BP, diastolic BP, and mean arterial pressure from the calibrated BP-Diff model, were 2.99 mmHg, 2.6 mmHg, 1.4 mmHg, and 1.44 mmHg, respectively. Consistency tests, including Bland–Altman analysis and Pearson correlation, confirmed its high reliability compared to reference BP. BP-Diff meets the American Association for Medical Instrumentation standards and has achieved a Grade A from the British Hypertension Society. Significance. This study utilizes PPG signals to develop a novel cuffless continuous BP measurement method, demonstrating superiority over existing approaches. The method is suitable for integration into wearable devices, providing a practical solution for continuous BP monitoring in everyday healthcare.
Delamination and thrust force mitigation during drilling of unidirectional carbon fibre-reinforced plastic laminate using magnetorheological elastomer
Authors: Patel Punit , Chaudhary Vijaykumar , Patel Dipal , Upadhyay Ramesh , Mazlan Saiful Amri , Nordin Nurazmah , Johari Mohd Aidy Faizal 
Published: September 2024

Abstract
In new improvements to the aviation industry, carbon fibre-reinforced plastic (CFRP) is a buoyant material due to its noteworthy and application-friendly properties. The behaviour of transversely isotropic CFRP, which prompts drilling-induced delamination, causes critical damage that leads to the rejection of the final product. The cause of the delamination damage is the thrust force generated by the drilling tool during the machining operation. The present work proposes an indigenous approach to suppress delamination significantly using magnetorheological elastomer (MRE). The thrust force generated by the drilling tool is recorded for varying magnetic field strengths. Delamination damage was computed using the MATLAB script. Meanwhile, specific focus was given to studying the interlaminar mechanics of a drilled hole through scanning electron microscopy. The results show that nearly 45% of the thrust force is reduced using this MRE at a maximum field strength of 0.4T compared to a conventional one. The results are further supported by a 22% and 30% smoothening of the delamination at the hole’s entry and exit, respectively. Thus, this approach helps to reduce delamination during drilling.
Bi-intercalated epitaxial graphene on SiC(0001)
Authors: Wolff Susanne , Hutter Mark , Schädlich Philip , Yin Hao , Stettner Monja , Wenzel Sabine , Stefan Tautz F , Bocquet François C , Seyller Thomas , Kumpf Christian 
Published: September 2024

Abstract
The intercalation of graphene with suitable atomic species is one of the most frequently applied methods to decouple the graphene layer from the substrate in order to establish the classical electronic properties of graphene. In this context, we studied the bismuth (Bi) intercalation of the

(
6

3

×
6

3

)

R

30
∘

reconstructed so-called ‘zeroth layer graphene’ on SiC

(
0001
)

. As reported earlier by Sohn et al (2021 J. Korean Phys. Soc.
78 157) two phases are formed depending on the amount of intercalated Bi, which in turn is controlled by the annealing temperature: The α phase, showing a

(
1
×
1
)

periodicity with respect to the substrate, and, at higher temperatures, the

(

3

×

3

)

reconstructed β phase. We characterise both phases and the transformation from the α to the β phase by photoelectron spectroscopy, normal incidence x-ray standing waves, electron diffraction and electron microscopy. We clearly see an almost complete intercalation of the graphene layers in both phases, with strong (covalent) interaction between the topmost Si atoms of the substrate and the Bi intercalant, but only weak (van der Waals) interaction between Bi and the graphene layer. The n-doping of the graphene found for the α phase decreases continuously during the phase transformation, in agreement with a reduced density of the Bi intercalating layer. Missing core level shifts of the surface species as well as the normal incidence x-ray standing waves results indicate that all surface Si atoms remain saturated during the transition and no dangling bonds are formed. Low energy electron microscopy and diffraction reveal the coexistance of both phases after annealing to intermediate temperatures and allow a quantitative analysis of island sizes and numbers.
Exploration of time-fractional cancer tumor models with variable cell killing rates via hybrid algorithm
Authors: Qayyum Mubashir , Ahmad Efaza 
Published: September 2024

Abstract
Cancer is marked by abnormal cell growth that invades healthy tissues, potentially spreading throughout the body through bloodstream or lymphatic system. It arises when body cells show irregularities in the genes that control cell growth. To treat and minimize the growth of these abnormal cells, different models have been proposed to predict and analyze cancer-tumor. The current study contains analysis of fractional cancer-tumor with different uncertain conditions. To include the uncertainties in the model, Pentagonal fuzzy numbers (PFNs) approach is utilized. A hybrid mechanism, combining homotopies with perturbation technique and a generalized integral transform, is proposed to efficiently handle fractional derivatives with fuzzified conditions. The validity of obtained solutions is checked by calculating residual errors. Graphical analysis assesses the impact of important parameters on the solution profiles, and confirms the reliability of the proposed methodology for complex fractional tumor models and other intricate physical phenomena.
Numerical simulation and neural network model for hydromagnetic nanofluid convection in a porous wavy channel with thermal non-equilibrium model
Authors: Rani H P , B Shilpa , V Leela , Gangadhara Reddy R 
Published: September 2024

Abstract
The present study aims to analyze the nanofluid MHD convective heat transfer in a porous wavy channel with a local thermal non-equilibrium (LTNE) model. Such a model finds applications related to enhancement in thermal performance, increasing the heat transfer coefficient in the compact design of heat exchangers for the aerospace and automotive industries and elevation in the efficiency of the solar collector. A sinusoidal porous wavy LTNE channel containing nanofluid and subjected to the induced and applied magnetic fields is considered. A uniform magnetic field is applied orthogonal to the channel and the induced magnetic field effects are considered due to the large magnetic Reynolds number. The momentum, continuity, energy, and nanoparticle volume fraction equations constitute the coupled nonlinear system of differential equations and are solved using the Galerkin finite element method. The reliability of the technique is assessed by comparing the proposed procedure with the results from earlier sources. A detailed analysis is presented to determine the effects of different physical parameters arising in the system on temperature, nanoparticle concentration, and velocity profiles. As an illustration, the findings exhibit that increasing the modified diffusivity ratio increases the values of the nanoparticle volume fraction whereas, reducing the modified diffusivity ratio enhances the temperature distribution. A higher value of thermal Rayleigh number presents a significant involvement of buoyancy forces, potentially resulting in the development of convective currents. A higher Nield number indicates more effective heat transport from the solid surface to the nanofluid. Consequently, there is a minimal thermal difference between the solid surface and the bulk nanofluid. Effective heat transmission enhances the nanofluid ability to absorb heat and generates a more consistent dispersion of temperature inside the fluid. The performance of the designed algorithms of the artificial neural network, namely, the Levenberg—Marquardt algorithm, in the problem under consideration is evaluated and the methodology is found reasonably precise with the matching of order around 6 to 7 decimal places of accuracy.
Oblique propagation of dust acoustic solitary waves in a magnetized plasma in the presence of cairns-tsallis distributed ions
Authors: Benmadani Yanis , Kechidi Ziane , Rabia Amour , Ibrir Miloud 
Published: September 2024

Abstract
We investigate the effects of non-extensivity (q), non-thermality (α), obliqueness (l

z
), the strength of the magnetic field (ω

c
), and dust grain temperature (σ

d
) on the basic features (viz., amplitude, width, velocity, and soliton energy) of obliquely propagating dust-acoustic solitary waves (DASWs) in a magnetized dusty plasma, which consists of highly negatively charged dust grains, Boltzmann-distributed electrons, and nonthermal non-extensive Cairns-Tsallis(C-T)-distributed ions. First, we derived the expression of the C-T polarization force and analyzed the combined effects of the ions’ non-extensivity (q) and non-thermality (α) parameters on the magnitude (R) of this polarization force. Our results show that R strongly depends on both the q-parameter and the α-parameter. Specifically, for q < 1, the ions’ non-extensivity and non-thermality weaken the polarization force, whereas for q > 1, R shifts toward higher values. Thus, the obliquely propagating DASWs are more likely to form in a magnetized non-extensive plasma rather than in a magnetized extensive plasma q = 1. Subsequent key findings are as follows: The wave phase velocity increases linearly as the obliquity (l

z
) decreases. This implies that a reduced obliqueness results in faster soliton motion and spikier solitary structures. Moreover, the amplitude (width) of DASWs decreases (increases) with increasing l

z
. An increase in the magnetic field magnitude (ω

c
) affects only the width of the DASWs. The amplitude (width) of DASWs decreases (increases) with higher dust grain temperature (σ

d
). This indicates that dust temperature significantly affects wave excitation. Specifically, at higher dust temperatures, dispersion dominates over nonlinear effects, resulting in smoother solitary structures. The soliton’s energy increases with α and becomes more pronounced as q decreases (from 1 to 0.75). It increases also with higher ω

c
and dust temperature (σ

d
), especially in the presence of nonthermal energetic particles. This investigation provides valuable insights into the propagation mechanisms of nonlinear DASWs in both space and laboratory plasmas containing non-extensive, nonthermal C-T-distributed ions and dust grains.
Gravitational wave signatures of post-fragmentation reheating
Authors: Garcia Marcos A.G. , Pierre Mathias 
Published: September 2024

Abstract
After cosmic inflation, coherent oscillations of the inflaton field about a monomial
potential V(ϕ) ∼ ϕ

k
result in an expansion phase characterized by a stiff
equation-of-state w ≃ (k-2)/(k+2). Sourced by the oscillating inflaton condensate, parametric
(self)resonant effects can induce the exponential growth of inhomogeneities eventually
backreacting and leading to the fragmentation of the condensate. In this work, we investigate
realizations of inflation giving rise to such dynamics, assuming an inflaton weakly coupled to its
decay products. As a result, the transition to a radiation-dominated universe, i.e. reheating,
occurs after fragmentation. We estimate the consequences on the production of gravitational waves
by computing the contribution induced by the stiff equation-of-state era in addition to the signal
generated by the fragmentation process for k = 4,6,8,10. We find that the signal generated during
the fragmentation process gives a larger contribution than the one induced by the stiff
equation-of-state era in given frequency ranges for all values of k. Our results are independent
of the reheating temperature provided that reheating is achieved posterior to fragmentation. Our
work shows that the dynamics of such weakly-coupled inflaton scenario can actually result in
characteristic gravitational wave spectra with frequencies from Hz to GHz, in the reach of future
gravitational wave observatories, in addition to the complementarity between upcoming detectors in
discriminating (post)inflation scenarios. We advocate the need of developing high-frequency
gravitational wave detectors to gain insight into the dynamics of inflation and reheating.
Towards testing the general bounce cosmology with the CMB B-mode auto-bispectrum
Authors: Akama Shingo , Orlando Giorgio , Delgado Paola C.M. 
Published: September 2024

Abstract
It has been shown that a three-point correlation function of tensor perturbations from a bounce model in general relativity with a minimally-coupled scalar field is highly suppressed, and the resultant three-point function of cosmic microwave background (CMB) B-mode polarizations is too small to be detected by CMB experiments. On the other hand, bounce models in a more general class with a non-minimal derivative coupling between a scalar field and gravity can predict the three-point correlation function of the tensor perturbations without any suppression, the amplitude of which is allowed to be much larger than that in general relativity. In this paper, we evaluate the three-point function of the B-mode polarizations from the general bounce cosmology with the non-minimal coupling and show that a signal-to-noise ratio of the B-mode auto-bispectrum in the general class can reach unity for ℓ
max=100 in the full-sky case, with and without the lensing B-mode added to cosmic variance. Considering additionally the LiteBIRD experimental noise, we obtain a SNR smaller than unity.
Relativistic two-fluid hydrodynamics with quantized vorticity from the nonlinear Klein–Gordon equation
Authors: Xiong Chi , Huang Kerson 
Published: September 2024

Abstract
We consider a relativistic two-fluid model of superfluidity, in which the superfluid is described by an order parameter that is a complex scalar field satisfying the nonlinear Klein–Gordon equation (NLKG). The coupling to the normal fluid is introduced via a covariant current–current interaction, which results in the addition of an effective potential, whose imaginary part describes particle transfer between superfluid and normal fluid. Quantized vorticity arises in a class of singular solutions and the related vortex dynamics is incorporated in the modified NLKG, facilitating numerical analysis which is usually very complicated in the phenomenology of vortex filaments. The dual transformation to a string theory description (Kalb–Ramond) of quantum vorticity, the Magnus force, and the mutual friction between quantized vortices and normal fluid are also studied.
Average coherence and its monogamy in a two-dimensional XY model
Authors: Wei Xiu-Qin , Xie Yu-Xia 
Published: September 2024

Abstract
The critical behaviors of average coherence (AC) in a two-dimensional XY model are investigated by using the renormalization group method. It is found that there is an extremum for the AC of the renormalized blocks at the critical point of quantum phase transition (QPT), and a finite-scaling analysis shows that in the thermodynamic limit, there is a divergence for the AC susceptibility at the QPT point. Furthermore, by considering the bipartite division of the renormalized blocks, it is found that the AC is monogamous and the average correlated coherence is polygamous, and the corresponding monogamy score is also a reliable indicator of QPT. Some constraints on the shareability of AC among the renormalized blocks are also obtained.