bluuebunny/crossref_metadata_2025_split huggingface dataset:
Remote sensing of high energy particle currents (HEC): towards HEC radiography
Authors: Zygmanski Piotr , Rajapakse Arith , Outwater Coral , Sajo Erno , Brivio Davide 
Published: April 2024

AbstractHigh-energy charged particle current (HEC) originating in or passing through an object creates mirror currents in a system of electrodes surrounding it. The electrodes are not in an electrically conductive contact with the object; they can be placed at a distance from its surface. We employ this effect to probe the object by measuring HEC-radiographic signal for a megavoltage x-ray scanning beam and for a broad static beam. Unlike in standard radiography, where an external bank of detectors measures the emerging radiation from the object, the principle of the proposed HEC radiography relies on remotely sensing the current distribution inside the object. Thus, HEC radiography is capable of probing objects that absorb all or most of the primary incident beam and where transmitted radiation cannot be used for image formation or reconstruction. Potential applications of this technology include non-destructive imaging of radio-opaque objects andin-vivomonitoring of charged particle ranges or dose distribution during cancer radiotherapy.
Plasma emission spectroscopy and optical properties of reactive-sputtered silicon oxynitride films
Authors: Rodríguez-López R , Abundiz-Cisneros N , Sanginés R , Águila-Muñoz J , Machorro-Mejía R 
Published: April 2024

Abstract
Silicon oxynitride (SiO
x
N
y
) thin films were synthesized via reactive direct current magnetron sputtering under different reactive gas mass flow ratios (O2:N2) and discharge powers. The process was monitored by optical emission spectroscopy (OES), while spectroscopic ellipsometry was used to characterize the optical properties of the thin films and deposition rates. In this work, the plasma generated under different deposition parameters is analyzed by monitoring the emission lines of different species (Si I, Ar I, O2, and N2) and these results are then correlated with the corresponding target poisoning process and the optical properties of the grown thin films. According to the spectral analysis, the emission lines proved to be sensitive to the synthesis process as it was possible to differentiate deposition variations when other parameters remained unchanged. Furthermore, similar plasma conditions determined via the emission of different species were found to produce consistent thin film optical properties and vice versa. OES showed to be an effective method of monitoring and controlling the deposition of SiO
x
N
y
thin films with specific compositions.
Light-assisted thermochemical reduction of CuFe2O4 within a photo-thermogravimetric analyser for solar fuel production
Authors: Gai Zhongrui , Tang Sanli , Yang Tianlong , Zhang Jinrui , Rao Qiong , Li Yang , Li Peng , Pan Ying , Jin Hongguang 
Published: April 2024

Abstract
The solar thermochemical cycle has emerged as a promising clean energy technology that enables the splitting of water for solar fuel production. However, conventional two-step thermochemical cycles using single-metal oxides require high operating temperatures above 1000 °C, especially for the reduction step. Typical solar thermal systems struggle to meet such high temperature requirements, making it vital to reduce the operating temperature. To find a solution enabling lower temperature requirements, we propose a photo-thermochemical reduction (PTR) strategy, which employs light illumination as assistance, combining both thermally induced and photo-induced effects for more generation of oxygen vacancies (VOs), within the oxygen carrier copper ferrite (CuFe2O4). Experimental studies were performed in a specially-designed photo-thermogravimetric analyser (photo-TGA) that directly measures the weight change of solid reactants under direct light illumination. The results indicate that the PTR achieves a decrease of nearly 40 °C in temperature requirements, giving a higher oxygen release of 21% compared to that driven by pure thermal heating at 800 °C. We also measured an increase of 0.09 in the non-stoichiometry parameter δ in the photo-TGA. Additionally, we observed that oxygen release increases distinctly with the light intensity of incident illumination. From the viewpoint of spectral ranges, ultraviolet and visible light illumination give the primary boost to the generation of photo-induced VOs. These results demonstrate the effective assistance of concentrated solar energy to enhance the two-step thermochemical cycle for solar fuel production at lower temperatures.
Bearing fault diagnosis based on transfer learning with dual-flow manifold ResNet and improved CapsNet
Authors: Yao Linhu , Wang Hongwei , Tao Lei , Fang Zhiwen , Wang Haoran , Liu Yu , Wang Hongli 
Published: April 2024

Abstract
In intelligent fault diagnosis for rolling bearings, the variable operating conditions of rotating machinery lead to inconsistent distributions between training and testing data, inevitably decreasing fault diagnosis accuracy. To overcome this issue, we propose a bearing fault diagnosis method based on transfer learning with a dual-flow manifold residual network and improved capsule network (DMRCN). Firstly, we introduce a dual-flow manifold residual network structure consisting of continuous wide-kernel convolution, continuous narrow-kernel convolution, and manifold learning spatial information description blocks. This architecture is utilized to extract low-level features from the data. Then, we present a novel architecture of the capsule network with self-attention output. We incorporate a self-attention mechanism into the dynamic routing algorithm to transfer richer high-level features from the low-level capsule layer to the higher-level capsule layer. During training, we propose a loss function known as global-local distribution discrepancy loss to enhance the model’s generalization ability and accelerate model convergence. Finally, we validate the performance of DMRCN in cross-condition transfer fault diagnosis on both the Case Western Reserve University public dataset and a laboratory-built bearing experimental rig dataset. Experimental results demonstrate that DMRCN outperforms other commonly used deep transfer learning methods regarding fault diagnosis across different operating conditions.
Measurement method for large aperture angle incomplete spherical surface stitching based on confocal focusing
Authors: Wang Yun , Yang Zheng , Zhao Jiahao , Cui Jian , Qiu Lirong , Li Zhigang , Yang Shuai 
Published: April 2024

Abstract
In traditional stitching measurements, the central sub-aperture is usually used as the reference and is not suitable for incomplete spherical surfaces without central sub-aperture. The measurement of each sub-aperture requires manual readjusting the position and attitude of the measured component, resulting in low measurement accuracy and low measurement efficiency. In response to this issue, this study proposed a method based on confocal focusing for large aperture angle incomplete spherical surface stitching measurement. Automatically and accurately determine the position of the common focus through confocal focusing technology, the positioning accuracy of the measured component is improved. A sub-aperture stitching model was built, coordinate mapping rotation algorithm and overlapping area error compensation algorithm were applied to the surface shape of each sub-aperture, achieving stitching measurement of large aperture angle incomplete spherical surfaces. Finally, the confocal interference stitching measurement system was built to carry out stitching experiments of sphere and large aperture angle incomplete spherical surface. The stitching experimental results indicated that the method increases the measurement accuracy of PV by 2.2 times, increases the measurement accuracy of RMS by 2.3 times, and increases the measurement efficiency by 1.6 times. Therefore, this method provides a high-precision measurement method for detecting large aperture angle spherical surface shape.
Gas-insulated switch-gear mechanical fault detection based on acoustic feature analysis using a multi-state pre-trained neural network
Authors: Wang Zhihua , Zhang Zipeng , Shao Yuying , Qian Kun , Liu Houguang , Hu Bin , Schuller Björn W 
Published: April 2024

Abstract
The acoustic-based approach is a prevalent way for non-contact fault diagnosis on gas-insulated switch-gear (GIS). GIS always works under different voltages causing great diversity in acoustic frequency. However, based on the frequency principle, neural networks always focus on a specific frequency, which challenges robust fault detection on GIS. This paper introduces a novel multi-stage training method to improve the robustness of fault detection on GIS. The proposed method consists of three components: a multi-channel based frequency regressor (MCBFR), an audio spectrogram transformer auto-encoder (AST-AE), and a feature interaction module (FIM). MCBFR and AST-AE are optimised to extract specific features from acoustics during the pre-training stage. The FIM fuses components extracted by MCBFR and AST when training the model that can indicate the final result. Also, we apply a multi-stage training strategy during the training stage to reduce the cost of potential model retraining. The efficacy of the proposed method was validated using experimental data from a real GIS, and it shows competitive performance in fault detection compared to existing methods.
Cathodoluminescence emission and electron energy loss absorption from a 2D transition metal dichalcogenide in van der Waals heterostructures
Authors: Bonnet Noémie , Baaboura Jassem , Castioni Florian , Woo Steffi Y , Ho Ching-Hwa , Watanabe Kenji , Taniguchi Takashi , Tizei Luiz H G , Coenen Toon 
Published: April 2024

Abstract
Nanoscale variations of optical properties in transition metal dichalcogenide (TMD) monolayers can be explored with cathodoluminescence (CL) and electron energy loss spectroscopy (EELS) using electron microscopes. To increase the CL emission intensity from TMD monolayers, the MoSe2 flakes are encapsulated in hexagonal boron nitride (hBN), creating van der Waals (VdW) heterostructures. Until now, the studies have been exclusively focused on scanning transmission electron microscopy (STEM-CL) or scanning electron microscopy (SEM-CL), separately. Here, we present results, using both techniques on the same sample, thereby exploring a large acceleration voltage range. We correlate the CL measurements with STEM-EELS measurements acquired with different energy dispersions, to access both the low-loss region at ultra-high spectral resolution, and the core-loss region. This provides information about the weight of the various absorption phenomena including the direct TMD absorption, the hBN interband transitions, the hBN bulk plasmon, and the core losses of the atoms present in the heterostructure. The S(T)EM-CL measurements from the TMD monolayer only show emission from the A exciton. Combining the STEM-EELS and S(T)EM-CL measurements, we can reconstruct different decay pathways leading to the A exciton CL emission. The comparison with SEM-CL shows that this is also a good technique for TMD heterostructure characterization, where the reduced demands on sample preparation are appealing. To demonstrate the capabilities of SEM-CL imaging, we also measured on a SiO2/Si substrate, quintessential in the sample preparation of two-dimensional materials, which is electron-opaque and can only be measured in SEM-CL. The CL-emitting defects of SiO2 make this substrate challenging to use, but we demonstrate that this background can be suppressed by using lower electron energy.
Temperature-dependent excitonic emission characteristics of highly crystallized carbon nitride nanosheets
Authors: Wang Yue , Zhang Guodi , Zhao Min , Qi Hongbo , Gao Tianqi , An Limin , Sun Jianhui 
Published: April 2024

Abstract
Highly-crystallized carbon nitride (HCCN) nanosheets exhibit significant potential for advancements in the field of photoelectric conversion. However, to fully exploit their potential, a thorough understanding of the fundamental excitonic photophysical processes is crucial. Here, the temperature-dependent excitonic photoluminescence (PL) of HCCN nanosheets and amorphous polymeric carbon nitride (PCN) is investigated using steady-state and time-resolved PL spectroscopy. The exciton binding energy of HCCN is determined to be 109.26 meV, lower than that of PCN (207.39 meV), which is attributed to the ordered stacking structure of HCCN with a weaker Coulomb interaction between electrons and holes. As the temperature increases, a noticeable reduction in PL lifetime is observed on both the HCCN and PCN, which is ascribed to the thermal activation of carrier trapping by the enhanced electron–phonon coupling effect. The thermal activation energy of HCCN is determined to be 102.9 meV, close to the value of PCN, due to their same band structures. Through wavelength-dependent PL dynamics analysis, we have identified the PL emission of HCCN as deriving from the transitions: σ*–LP, π*–π, and π*–LP, where the π*–LP transition dominants the emission because of the high excited state density of the LP state. These results demonstrate the impact of high-crystallinity on the excitonic emission of HCCN materials, thereby expanding their potential applications in the field of photoelectric conversion.
Well-posedness for the surface quasi-geostrophic front equation
Authors: Ai Albert , Avadanei Ovidiu-Neculai 
Published: April 2024

Abstract
We consider the well-posedness of the surface quasi-geostrophic (SQG) front equation. Hunter–Shu–Zhang (2021 Pure Appl. Anal.
3 403–72) established well-posedness under a small data condition as well as a convergence condition on an expansion of the equation’s nonlinearity. In the present article, we establish unconditional large data local well-posedness of the SQG front equation, while also improving the low regularity threshold for the initial data. In addition, we establish global well-posedness theory in the rough data regime by using the testing by wave packet approach of Ifrim–Tataru.
Image denoising and model-independent parameterization for IVIM MRI
Authors: Sample Caleb , Wu Jonn , Clark Haley 
Published: April 2024

Abstract

Objective. To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versus b-value curve. Approach. IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method's ability to recover artificial ‘pseudokernels’. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis. Main results. Image denoising improved blind image quality metrics, smoothed the signal versus b-value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising. AUC parameters were more correlated with dose and had higher relative importance than exponential model parameters. Significance. IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versus b-value curve with model-independent parameters like the AUC and preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.
DFUSNN: zero-shot dual-domain fusion unsupervised neural network for parallel MRI reconstruction
Authors: Chen Shengyi , Duan Jizhong , Ren Xinmin , Wang Junfeng , Liu Yu 
Published: April 2024

Abstract

Objective. Recently, deep learning models have been used to reconstruct parallel magnetic resonance (MR) images from undersampled k-space data. However, most existing approaches depend on large databases of fully sampled MR data for training, which can be challenging or sometimes infeasible to acquire in certain scenarios. The goal is to develop an effective alternative for improved reconstruction quality that does not rely on external training datasets. Approach. We introduce a novel zero-shot dual-domain fusion unsupervised neural network (DFUSNN) for parallel MR imaging reconstruction without any external training datasets. We employ the Noise2Noise (N2N) network for the reconstruction in the k-space domain, integrate phase and coil sensitivity smoothness priors into the k-space N2N network, and use an early stopping criterion to prevent overfitting. Additionally, we propose a dual-domain fusion method based on Bayesian optimization to enhance reconstruction quality efficiently. Results. Simulation experiments conducted on three datasets with different undersampling patterns showed that the DFUSNN outperforms all other competing unsupervised methods and the one-shot Hankel-k-space generative model (HKGM). The DFUSNN also achieves comparable results to the supervised Deep-SLR method. Significance. The novel DFUSNN model offers a viable solution for reconstructing high-quality MR images without the need for external training datasets, thereby overcoming a major hurdle in scenarios where acquiring fully sampled MR data is difficult.
Deep learning for high-resolution dose prediction in high dose rate brachytherapy for breast cancer treatment
Authors: Quetin Sébastien , Bahoric Boris , Maleki Farhad , Enger Shirin A 
Published: April 2024

Abstract

Objective. Monte Carlo (MC) simulations are the benchmark for accurate radiotherapy dose calculations, notably in patient-specific high dose rate brachytherapy (HDR BT), in cases where considering tissue heterogeneities is critical. However, the lengthy computational time limits the practical application of MC simulations. Prior research used deep learning (DL) for dose prediction as an alternative to MC simulations. While accurate dose predictions akin to MC were attained, graphics processing unit limitations constrained these predictions to large voxels of 3 mm × 3 mm × 3 mm. This study aimed to enable dose predictions as accurate as MC simulations in 1 mm × 1 mm × 1 mm voxels within a clinically acceptable timeframe. Approach. Computed tomography scans of 98 breast cancer patients treated with Iridium-192-based HDR BT were used: 70 for training, 14 for validation, and 14 for testing. A new cropping strategy based on the distance to the seed was devised to reduce the volume size, enabling efficient training of 3D DL models using 1 mm × 1 mm × 1 mm dose grids. Additionally, novel DL architecture with layer-level fusion were proposed to predict MC simulated dose to medium-in-medium (D

m,m
). These architectures fuse information from TG-43 dose to water-in-water (D

w,w
) with patient tissue composition at the layer-level. Different inputs describing patient body composition were investigated. Main results. The proposed approach demonstrated state-of-the-art performance, on par with the MC D

m,m
maps, but 300 times faster. The mean absolute percent error for dosimetric indices between the MC and DL-predicted complete treatment plans was 0.17% ± 0.15% for the planning target volume V
100, 0.30% ± 0.32% for the skin D
2cc
, 0.82% ± 0.79% for the lung D
2cc
, 0.34% ± 0.29% for the chest wall D
2cc
and 1.08% ± 0.98% for the heart D
2cc
. Significance. Unlike the time-consuming MC simulations, the proposed novel strategy efficiently converts TG-43 D

w,w
maps into precise D

m,m
maps at high resolution, enabling clinical integration.
Effects of low-frequency voltage on nonlinear standing wave excitation, plasma uniformity, and ion dynamics in dual-frequency asymmetric capacitive discharges
Authors: Zhou Fang-Jie , Wen De-Qi , Liu Jian-Kai , Su Zi-Xuan , Zhao Kai , Zhang Yu-Ru , Wang You-Nian 
Published: April 2024

Abstract
It is known that in very-high-frequency (VHF) capacitively coupled plasmas, the higher harmonics generated by nonlinear sheath motion can enhance the standing wave effect (SWE), which can lead to center-peaked plasma density profiles. In this work, an improved nonlinear electromagnetic model incorporating a transmission line model, an electron momentum balance model, a bulk plasma model, a collisionless nonlinear numerical sheath model, and an ion Monte-Carlo collision (MCC) model is developed to study the effects of low-frequency (LF) voltage

V

L

on the nonlinear standing wave excitation, plasma uniformity, and ion energy and angular distribution functions (IEDFs and IADFs) in dual-frequency (DF) asymmetric capacitive argon discharges at relatively low pressure of 3 Pa. The plasma diffusion in the radial direction and ion dynamics within the LF oscillating sheath are self-consistently considered. The LF voltage

V

L

at 2 MHz varies from 0 to 700 V while the HF voltage

V

H

at 60 MHz is fixed at 100 V. Simulation results indicate that without the addition of an LF source (i.e.

V

L

=

0 V), there are a considerable number of high-order harmonics with short wavelengths, leading to significant SWE and central peak in the radial plasma density profile. Nevertheless, the high-order harmonic excitations tend to be weakened and merely occur around the phase of the full LF sheath collapse due to a shorter characteristic damping time of the surface waves as

V

L

increases. This, combined with increased surface wavelengths of both the driving frequency and the higher harmonics at a higher

V

L

, leads to suppressed standing waves and improved plasma uniformity. Meanwhile, the simulations show that both the low and the high energy peaks of IEDF move towards higher energies, and the energy peak separation width ΔE becomes wider with the increase of

V

L

. The IEDF at the radial center of the powered electrode exhibits a broader ΔE than that at the edge. For the IADF, an increased

V

L

results in more ions incident on the electrode with a smaller deflection angle. Because of a thinner sheath and a higher sheath voltage at the electrode center, the peak value of IADF at the electrode center is greater than that at the edge.
Biexcitons and quadrons in self-assembled quantum dots
Authors: Hong Quang Nguyen , Thi Kim Thanh Nguyen , Que Huong Nguyen 
Published: April 2024

Abstract
We theoretically study biexcitons and quadrons in quantum dots with parabolic confinement and give a complete comparison between the two excitations. The calculation of quadron and biexciton binding energies as functions of electron-to-hole confinement potentials and mass ratios, using the unrestricted Hartree–Fock method, shows the essential differences between biexcitons and quadrons. The crossover between the negative and positive binding energies is indicated. The effect of an external magnetic field on the quadron and biexciton binding energies has also been investigated. In addition, the crossover between anti-binding and binding of both excited quadron and biexciton states in a certain range of the electron-to-hole oscillator length ratios has been found.