bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hybrid multi‐scale residual network for high‐voltage circuit breakers fault diagnosis
Authors: Shao Hongping , Jiang Yizhe , Zhao Jianeng , Yang Mingkun , Wang Xinyu , Yang Hao 
Published: January 2025

AbstractIn this letter, a novel Hybrid Multi‐Scale Residual Network (HMSR‐Net) is proposed to improve the accuracy of fault diagnosis in the spring operating mechanisms of High‐Voltage Circuit Breakers (HVCBs). To facilitate enhanced fault classification across diverse operational conditions, five distinct fault states are simulated in addition to a baseline healthy condition, thereby capturing the nuanced and multi‐scale features of vibration signals within varying working environments. The vibration signals generated during spring energy release are collected utilizing a test platform specifically for HVCBs. These one‐dimensional time‐series signals are transformed into two‐dimensional time‐frequency images by wavelet transform, thereby offering a precise and multi‐scale representation of local signal characteristics. Subsequently, the generated time‐frequency images are processed by the proposed HMSR‐Net, extracting deep and discriminative features that are further classified with a Multi‐Layer Perceptron (MLP) classifier. Experimental evaluations on field‐measured fault vibration data reveal that HMSR‐Net achieves a fault recognition accuracy of 98.2%, which notably outperforms other machine learning and deep learning approaches.
Flat sub-Lorentzian structures on Martinet distribution
Authors: Sachkov Yuri 
Published: January 2025

Two flat sub-Lorentzian problems on the Martinet distribution are studied. For the first one, the attainable set has a nontrivial intersection with the Martinet plane, but for the second one it does not. Attainable sets, optimal trajectories, sub-Lorentzian distances and spheres are described.
When performance studies meet discourse theory
Authors: Aiolfi Théo 
Published: January 2025

Abstract
Despite its versatility as a methodological tool, discourse analysis suffers from a logocentric bias, a tendency
to primarily focus on text while ignoring the non-textual components of discourse. Although poststructural approaches to discourse
analysis like Discourse Theory (DT) have developed an understanding of discourse going beyond language, there are few practical
methodological tools for scholars from these traditions to engage with these non-textual elements. This article fills that gap by
providing an original qualitative methodological tool, the Political Performance Analysis Protocol (PPAP) which adapts one of the
signature methods of Performance Studies to political performances. It does so by describing the four constitutive elements of
political performances — background symbols and foreground scripts, actor, audience and mise-en-scène — and empirically
illustrating them through the case of Greta Thunberg’s “How dare you?” performance at a United Nations to show the PPAP’s
relevance as a tool complementing other forms of discourse analysis.
Google Translate versus DeepL in Spanish to English translation of Don Quixote
Authors: Ibáñez Moreno Ana , Esther Domínguez Mora María 
Published: January 2025

Abstract
This paper analyses the effectiveness of neural machine translation when applied to literary translation and, more specifically, to the translation of collocations, one of the most difficult aspects in machine translation (Corpas-Pastor 2015; Shraiden and Mahadin 2015). Literary translation continues to constitute one of the biggest challenges for machine translation (Toral and Way 2018), where cohesion errors are amongst the most frequent (Voigt and Jurafsky 2012). A comparative analysis of the translation of the first chapter of the world literature masterpiece El ingenioso hidalgo don Quijote de la Mancha — known as Don Quixote in English — was carried out, paying close attention to collocations. The human translation done by Tom Lathrop (Don Quixote)1 was compared to the target texts obtained with the two biggest neural machine translation systems today, Google Translate and DeepL, to see which provided more accurate results. The results confirm that neural machine translation offers highly reliable results. On a quantitative level the margins are very narrow when determining which system, DeepL or Google Translate, is better. DeepL scored better in terms of accuracy and recall, but in the BLEU metrics Google Translate scored 28.10 and DeepL 26.63. On a qualitative level and from a subjective point of view, we found DeepL’s translation to be somewhat more fluid and natural than Google Translate’s.
Applying neural machine translation and ChatGPT in the teaching of business English writing
Authors: Xu Jun , Wang Qingran 
Published: January 2025

Abstract
As language teaching becomes more complex and diverse, there has been a rapid increase in the demand for advanced technology, driving the widespread adoption of neural machine translation (NMT) and ChatGPT in the field. This study contributes to the literature on the use of technology in language teaching by evaluating the application of NMT technology and ChatGPT in teaching English as a foreign language (EFL) writing in three business fields: finance, economics, and business administration. By building six comparable corpora consisting of students’ direct-writing and post-edited writing based on machine-translated texts, we examined whether NMT can help improve students’ performance in business English writing classes, and whether ChatGPT can complement NMT. Our statistical analyses show that in general, NMT can enhance the proficiency of students’ academic writing, but its improvement effect works on different dimensions for those students studying in different majors. Specifically, for finance students, NMT can improve their academic writing at the word and syntax levels and mechanics, while it harms the organizational dimension. For students in economics, the improvement effect of NMT mainly focuses on enhancing the dimensions of syntax, cohesion, and mechanics, whereas for students in business administration, NMT works primarily on the dimensions of content, cohesion, and mechanics. As for the dimensions where NMT performs poorly, our analysis of students’ essay writing shows that ChatGPT can complement NMT by making improvements and providing feedback to students. Our paper adds value to existing research on the use of technology in language teaching by investigating the application of NMT and ChatGPT in teaching EFL writing, and by proposing potential directions for their use in the teaching of writing business English.
Using neural networks: a guidance with application in inverse heat conduction problem
Authors: Shang Yuanbin , Tan Chaofa , Yu Xueling , Hu Xiaoyu , Jiang Hongquan , Ma Wenjiang , Liu Donghuan 
Published: January 2025

Abstract
In recent years, significant progress has been made in the development and application of artificial intelligence, spanning various fields and presenting powerful potential applications. The investigation of how to integrate the achievements of artificial intelligence with traditional physical problems has become a crucial task. Neural networks are considered as a pivotal technology of artificial intelligence, showcasing their distinctive ability to comprehend intricate nonlinear relationships through simulation and learning, and has showed exceptional performance in handling large-scale data and addressing inverse problems. This article begins by introducing the fundamental principles of neural networks, providing a detailed explanation of the process involved in constructing classical neural networks. A neural network was constructed on the MATLAB platform, and the practical application process was illustrated through lively demonstrations using simple examples. Tailored primarily for physics education, this article is designed to be suitable for undergraduate-level educational purposes. Through the integration of neural networks with practical inverse heat conduction problem, we not only provide a more interactive and dynamic learning experience but also enrich undergraduate students’ comprehension and interest in relevant issues within higher education. This integrated teaching approach has the potential to propel interdisciplinary collaboration, nurturing innovation and advancement within the field of physics education.
2D hybrid simulation of a dual-frequency capacitively coupled nitrogen discharge: effects of gas pressure and low-frequency voltage on generation and distribution of species
Authors: Zhou Fang-Jie , Zhang Yu-Ru , Wen De-Qi , Liu Yong-Xin , Wang You-Nian 
Published: January 2025

Abstract
The effect of the gas pressure and the low frequency voltage,

V

LF

, on the spatial distributions of electron energy distribution function (EEDF), electron and neutral densities in a dual-frequency (DF 2/60 MHz) driven capacitive discharge in nitrogen (N2) is investigated by employing a two-dimensional fluid model with electron collision processes treated by Monte Carlo model. It is found that by increasing the pressure the region of strong ionization shrinks immensely towards the sheath edge of the powered electrode and the ionization maximum occurs below the edge of the powered electrode, leading to edge-high density profiles of the electron and excited-state nitrogen molecules at a higher pressure, while the nitrogen atom density remains uniform in space, due to higher transport coefficient. By increasing

V

LF

, the discharge becomes more symmetrical in axial direction, the ionization rate changes from a radial uniform to an edge-high profile, leading to an edge-high profile of electron density. The EEDF is found to exhibit a dip at

ε

= 2.35 eV, due to depletion of electron via the electron-impact vibrational excitation of nitrogen molecule. This dip of the EEDF is most significant at the discharge center, and become less pronounced towards the sheath edge and the sidewall, due to low-energy electrons being heated by local strong electric field. Besides, at a higher

V

LF

, the dip becomes less significant, due to enhanced electron heating.
Characterization of a single emitter passively fed electrospray ion source: part I - luminescence imaging analysis
Authors: Kerber Thomas V , Allwine Nathaniel J , Lemmer Kristina M , Taylor Nicholas R 
Published: January 2025

Abstract
Luminescence at the face of ionic liquid ion sources and nearby facility surfaces is a commonly reported radiative phenomenon that requires thorough examination. In this study, we present magnified images of a single emitter porous-media ionic liquid electrospray in profile, which provides spatial information on the origin of the luminescence. To determine what role facility interactions play in luminescence at the electrospray face, we varied the distance and material of a downstream beam target on which the ion plume terminated. The effect of applied emitter voltage on luminescence was also examined. Analysis of luminescence images and corresponding telemetry data demonstrate that the formation of luminescence near the emitter tip is not only a function of the surrounding test facility, but also dependent on the electrospray device, itself. The data also indicate the majority of luminescence observed within our experimental system is formed primarily from high-energy collisions between emitted propellant ions and propellant accumulated on the orifice of the extractor electrode.
Characterization of a single emitter passively fed electrospray ion source: part II-luminescence spectral analysis
Authors: Kerber Thomas V , Allwine Nathaniel J , Lemmer Kristina M , Taylor Nicholas R 
Published: January 2025

Abstract
Luminescence spectroscopy was used to examine the dynamics of propellant dissociation near the emitter tip of a single-emitter porous electrospray thruster loaded with the ionic liquid EMI-BF4. Luminescence spectra from CH, C2, CN, NH, BH, H
α
, and H
β
were observed and confirmed by comparison with simulated spectra. Analysis of the CH (A 2Δ, v′ = 0) spectra yielded a rotational temperature 3082 ± 30 K while the C2 (d 3Πg− a 3Πu) Swan system yielded rotational temperatures 6252 ± 92 K and 5914 ± 75 K for Δv = 0 and Δv = +1, respectively. Examination of the integrated spectral signals from acquired CH (A 2Δ), BH (A 1Π), and H
α
spectra showed a strong correlation with measured extractor current in both positive and negative polarity mode. The evidence suggests the formation of these electronically excited species is due to dissociative excitation induced by high-energy collisions between emitted ions and propellant accumulated on the extractor orifice. A weak broadband signal was also observed and is likely due to dissociative excitation of the anion, BF4
−, leading to the formation of electronically excited BF2. Analysis of the neutral gas within the test chamber with a mass spectrometer confirmed the presence of BF2, providing strong evidence the observed broadband signal is the result of BF2.
A density functional theory perspective on the structural, elastic, thermal, and electronic properties of copper nitride doped with transition metal (Fe, Co, and Ni)
Authors: Sahoo Guruprasad , Jena Ajit , Patro L N , Behera Gangadhar 
Published: January 2025

Abstract
The present study demonstrates the structural, elastic, thermal, and electronic properties of copper nitride (Cu3N) doped with transition metal M (M: Fe, Co, and Ni) using pseudopotential-based density functional calculations as implemented in the Quantum ESPRESSO simulation code. The exchange-correlation is approximated by Perdew-Burke-Ernzerhof functional within the framework of generalized gradient approximation. The doped matrices, represented as Cu3NM, are verified to be stable structures, both thermodynamically and mechanically. Tailoring of elastic properties and their anisotropy due to M doping is successfully demonstrated through a comprehensive analysis of the computed elastic stiffness coefficients, elastic moduli, elastic anisotropy factors, and spatial variation of the elastic moduli, which have not been explored yet. An increase in bulk modulus due to M doping ensures enhanced mechanical stability under isotropic stress. Conversely, while doping of Co and Ni enhances the shear resistance of the host material, Fe doping slightly reduces it. The superior ductile nature of all the studied systems predicts their suitability for applications in flexible electronics. It is evident that doping of M substantially reduces the elastic anisotropy of Cu3N. Using the calculated elastic moduli, the velocity of acoustic waves and its anisotropy for Cu3N and Cu3NM are also predicted. The anisotropy in the acoustic velocity of the studied materials recommends their potential application in acoustic devices with directional selectivity. It is also noticed that, while the average acoustic velocity is reduced due to Fe doping, it increases for Co and Ni doping. Furthermore, analysis of the computed Debye temperature and minimum thermal conductivity forecasts their employability as thermal barrier coatings. Finally, the calculations reveal the ferromagnetic nature of Cu3NFe and Cu3NCo with respective induced magnetic moments of 2.71 and 1.47 μ
B/cell, recommending their potential applications in spintronics. It is also proved that the M-d–Cu-d coupling stabilizes the ferromagnetic ordering in such magnetic systems. On the other hand, Cu3NNi is observed to be non-magnetic.
Multi-parameter study of a diamond magnetometer
Authors: Jani Mona , Lazda Reinis , Gāhbauer Florian , Asare Antra , Mrózek Mariusz , Wojciechowski Adam M , Gawlik Wojciech , Auziņš Mārcis 
Published: January 2025

Abstract
Negatively charged nitrogen-vacancy (NV) diamond magnetometers are promising candidates for room-temperature magnetic field sensing. However, systematic studies of magnetic field sensors that evaluate the sensitivity of NV-diamond magnetometers as a function of multiple parameters are sparse. By comparing optically detected magnetic resonance (ODMR) dips for four single-crystal diamonds, we explored how the photon shot-noise-limited sensitivity and experimental magnetic noise floor were influenced by the density of the NV centers and methods of their formation. The lowest shot-noise-limited sensitivity was 380 pT √Hz−1; it was significantly affected by ODMR fluorescence, contrast, and linewidth. Using dual-resonance modulation in ODMR, the experimental lowest magnetic noise floor based on power spectral density was 1.14 nT √Hz−1. These values can be improved with an increase in laser power. This study will facilitate the selection of appropriate NV diamonds when designing magnetometers for various applications.
Discussion on modified Sigmund sputtering formula
Authors: Shang Shengfei , Yang Xiaoning , Yang Yong , Cai Guobiao , Wang Weizong , He Bijiao , Zhang Baiyi 
Published: January 2025

Abstract
The modified Sigmund sputtering formula offers high computational accuracy for calculating sputtering yields and the energy distribution of sputtered atoms. This formula introduces a parameter called ‘relative threshold energy’ (E
z), which, like the sputtering threshold energy (E
th), serves as a reference for the minimum energy required for sputtering. However, E
z is significantly larger than E
th, rendering the modified Sigmund model inaccurate for sputtering calculations when E < E
z. Additionally, our previous research provided an empirical expression for E
z, but it was complex and lacked theoretical justification. To develop a more concise, theoretically grounded expression, we explored the theoretical form of E
z and introduced E
th as a parameter to address the modified Sigmund formula’s failure to account for the sputtering threshold. Furthermore, we proposed an explicit calculation method for the model when E < E
z. The results, including sputtering yields and energy distributions, demonstrate that the new method for calculating E
z and the improvements to the modified Sigmund formula under E < E
z align well with experimental data.
Degradation of 4-nitrophenol using Ar/air thermal plasma in submerged conditions: plasma characteristics, degradation chemical kinetics and degradation pathways
Authors: Nandy Nanditta , Pasupathi Amarnath , Subramaniam Yugeswaran 
Published: January 2025

Abstract
This study explores the application of thermal plasma in submerged conditions for the efficient degradation of 4-nitrophenol (4-NP), a high-priority toxic pollutant. A customized 10 kW DC non-transferred arc thermal plasma torch was employed to generate the plasma using argon (Ar) and air as plasma-forming gases. The plasma jet was characterized through arc voltage fluctuations, optical emission spectroscopy, and submerged jet dynamics, offering detailed insights into the physical and chemical properties of the Ar/air plasma. The plasma treatment of an initial 50 mg l−1 concentration of 4-NP in an aqueous medium resulted in 94% degradation and 58% mineralization within 30 min at the plasma input power of 6.2 kW. The removal process followed a pseudo-first-order kinetic model with a reaction rate constant of 0.0684 min–1. The possible degradation intermediates and products were identified via Liquid chromatography-mass spectroscopy and Ion chromatography. The submerged thermal plasma technique demonstrated enhanced compound removal efficiency, making it a promising method for the treatment of toxic dissolved organic compounds in wastewater.
Nanoparticle measurement system based on laser self-mixing technique and FPGA
Authors: Wang Huarui , Wu Shangge 
Published: January 2025

Abstract
This paper introduces a laser self-mixing nanoparticle sizer based on field-programmable gate array (FPGA). This system combines the compactness of laser self-mixing technique with the advantages of FPGA’s parallel processing, achieving rapid and accurate measurement of nanoparticle sizes in solutions. Through the parallel operation of analog-to-digital conversion, autocorrelation processing, and wireless transmission modules within the FPGA, significant improvements in signal processing speed and data throughput are achieved. Experimental results demonstrate that the system can effectively extract the autocorrelation curves of nanoparticles and derive the particle size distribution accordingly. Measurements of standard polystyrene microspheres with nominal sizes of 30 nm, 60 nm, 100 nm, and 200 nm yield particle size results close to actual values, verifying the feasibility and potential of the system. However, there is still some dispersion in the measurement results, particularly for larger particles, indicating that further optimization of the system is needed to improve measurement accuracy and stability in the future.
A wavelet packet transform-based domain adaptation method for rolling bearings fault diagnosis with fusion of local and global time–frequency features
Authors: Ren Chen , Li Xiangyu , Wang Weijie , Xiong Xiaoyan , Wu Bing 
Published: January 2025

Abstract
Fault diagnosis transfer learning models commonly employ deep neural networks (DNNs) to analyze time–frequency features. However, excessively DNNs can result in diminished generalization capabilities, leading to subpar performance of the model across various working conditions. Furthermore, inappropriate domain adaptation (DA) strategies significantly constrain the accuracy of the model. To address this issue, a robustly optimized residual-network and vision transformer (ViT) domain adaptation model is proposed in this article, combining wavelet packet transform (WPT), residual networks, and self-attention mechanisms. Firstly, the WPT is utilized to construct multi-band wavelet coefficient matrix (MWCM) and corresponding multi-band wavelet coefficient time–frequency feature matrix (MWSM) with small size and feature aggregation. Subsequently, a shallow robustly optimized residual network is designed to effectively extract features from MWCM, considering the spatial distance dependencies of features. Additionally, ViT is employed for time–frequency global feature extraction from MWSM. Furthermore, domain adversarial neural network and multi-kernel maximum mean discrepancy are employed to extract domain-invariant features from signals of different operating conditions and fault types. At last, three fault diagnosis experiments are conducted in multi-condition scenarios of bearings. The experimental results illustrate the superiority and effectiveness of the proposed model.
Intelligent fault diagnosis based on multi-sensor data fusion and multi-scale dual attention enhanced network
Authors: Zhang Weizhong , Yan Xiaoan , Ye Maoyou , Hua Xing , Jiang Dong 
Published: January 2025

Abstract
As a significant component of rotating machinery, the health of rolling bearings and gears directly impact the normal operation of rotating machinery. Traditional single-sensor fault diagnosis approaches often fail to extract sufficient fault information, resulting in low diagnostic accuracy in practical engineering applications. Additionally, conventional multi-sensor fusion diagnosis methods exhibit the low robustness in noisy environments. To tackle these challenges, this paper presents a novel fault diagnosis approach derived from multi-sensor data fusion and a multi-scale dual attention enhanced network (MSDF-MSDAENet). Initially, a MSDF strategy is presented. This method reduces the dimensionality of high-dimensional data from multiple sensors to extract embedded low-dimensional effective information, which is then fused into a three-dimensional pixel matrix image. Subsequently, a MSDAENet is constructed, incorporating both time–frequency information feature extraction modules and position information extraction modules. These enhancements significantly enhance the model’s capacity to extract fault features. By dividing the RGB fused images into a well-organized dataset and inputting it into the network for fault diagnosis, the method achieves intelligent diagnosis of samples with different fault types. Tests were carried out on three datasets, and the presented method was compared with various existing methods. The ultimate experimental results demonstrate the efficacy and superiority of the presented method.