bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-canonical Higgs inflation
Authors: Pareek Pooja , Nautiyal Akhilesh 
Published: January 2024

AbstractThe large value of non-minimal coupling constantξrequired to satisfy cosmic microwave background observations in Higgs inflation violates unitarity. In this work we study Higgs-inflation with non-canonical kinetic term of DBI form to find whetherξcan be reduced. To study the inflationary dynamics, we transform the action to the Einstein frame, in which the Higgs is minimally coupled to gravity with a non-canonical kinetic term and modified potential. We choose the Higgs self coupling constantλ = 0.14 for our analysis. We find that the value ofξcan be reduced from 103–104toO(10)to satisfy Planck constraints on amplitude of scalar power spectrum. However, this model produces a larger tensor-to-scalar ratior, in comparison to the Higgs inflation with canonical kinetic term. We also find that, to satisfy joint constraints on scalar spectral indexnsand tensor-to-scalar ratiorfrom Planck-2018 and bounds onrfrom Planck and BICEP3, the value ofξshould be of the order of 104. Thus, the issue of unitarity violation remains even after considering Higgs inflation with non-canonical kinetic term.
Absorption cross section in gravity’s rainbow from confluent Heun equation
Authors: Amado Julián Barragán , Kwon Kihong , Gwak Bogeun 
Published: January 2024

AbstractWe investigate the scattering of a massless scalar field by a charged, non-rotating black hole in the presence of gravity’s rainbow. Using the connection coefficients of the confluent Heun equation expressed in terms of the semi-classical confluent conformal blocks and the instanton part of the Nekrasov–Shatashvili free energy, we obtain an asymptotic expansion for the low-energy absorption cross section. Furthermore, we consider the resummation of the instanton contributions to the absorption cross section, which gives the area of the black hole up to a factor of(4/3)2.
Indium defect complexes in (In
x
Ga1−x
)2O3: a combined experimental and hybrid density functional theory study
Authors: Welch Eric , Talukder Md Abdul Ahad , Martins Nathan Rabelo , Borges Pablo Damasceno , Droopad Ravi , Scolfaro Luisa 
Published: January 2024

Abstract
Indium defects in small concentration (In
x
Ga1−x
)2O3 were studied using a combination of spectroscopic and magnetic measurements on thin films varying the indium concentration, coupled with hybrid density functional theory simulations using the supercell method. X-ray diffraction spectra along with Tauc plots and density of states plots reveal a decrease (increase) in the electronic band gap (interlayer lattice spacing) due to the inclusion of indium in monoclinic Ga2O3, while room-temperature Hall measurements show an increase in n-type conductivity. Formation energy calculations reveal that the defect complex of substitutional indium at the octahedrally coordinated cation site (InGa) coupled with an indium interstitial (Ini) in the largest Ga–O cavity in the bulk (ia
), where the two impurities are a maximal distance away in the unit cell, results in the lowest formation energy across much of the electronic band gap; near the conduction band edge the single InGa defect becomes the lowest energy defect, though. These calculations help shed light on the impurity band enhanced, n-type conductivity increase due to small concentration indium doping in Ga2O3 as seen in the spectroscopic/magnetic measurements.
Terajet-assisted time-domain super-resolution imaging
Authors: Paddubskaya A G , Valynets N I , Novitsky A V , Minin O V , Minin I V 
Published: January 2024

Abstract
Large-scale applications of real-time terahertz (THz) imaging are often limited by the spatial resolution of a THz microscope. One of the simplest approaches to circumvent this problem in practice and achieve resolution below the diffraction limit consists in placing a dielectric particle with a size comparable to the radiation wavelength in the vicinity of the focal plane of the THz imaging system. Here, we theoretically consider the transmittance of a THz Gaussian beam through a Teflon sphere to further analyze experimental results on THz superresolution imaging. We address the resolution beyond the diffraction limit (the experimentally determined value is 0.38λ at 0.46 THz) through the superlens effect of the terajet generated by the mesoscale sphere owing to propagating and evanescent partial waves in the terajet. Due to the coupling of the dielectric sphere and time-domain THz spectrometer, we can detect a 10 µm thick PTFE film, which is almost transparent (98% transmittance) in the THz frequency range. The correlation between the transmittance spectrum of the dielectric sphere and the quality of THz images of thin, nonconducting films collected at different frequencies has been demonstrated experimentally and considered analytically. Our study is relevant for hyperspectral imaging applications and further development of the sphere-coupled time-domain THz imaging technique.
Inter-turn short circuit and demagnetization fault diagnosis of ship PMSM based on multiscale residual dilated CNN and BiLSTM
Authors: Yan Guohua , Hu Yihuai 
Published: January 2024

Abstract
Inter-turn short circuit (ITSC) and demagnetization of permanent magnet synchronous motors (PMSMs) can lead to serious ship accidents, timely and accurate fault diagnosis of these faults is very important. A multi-signal fusion fault diagnosis method (MD-CNN-BiLSTM) is proposed based on multi-scale residual dilated convolutional neural network (D-CNN) and bidirectional long and short-term memory (BiLSTM) for PMSM fault diagnosis. This method first takes three-phase current and vibration signals as input; uses a three-column parallel CNN structure with different scales to extract both global signal and local feature. A residual connection in the expanded CNN is then used to eliminate the problems of gradient disappearance or explosion; and finally, BiLSTM is used to further extract features and identify the fault. A 2.2 kW permanent magnet synchronous motor was used to build a fault simulation test rig. The motor stator was rewound to simulate the ITSC fault, and different sizes of permanent magnets were replaced to simulate demagnetization fault. ITSC, demagnetization and their coupled faults were simulated under 10 specific motor speeds and loads respectively. The test proved that the diagnostic accuracy of the proposed method was 4.2% higher than that of ordinary CNN and 29.06% higher than that of BiLSTM. It also had the best diagnostic effect under the noise interference of different intensities. It was verified that the proposed method has good noise interference and strong classification ability.
Comparison of aerosol spectrometers : accounting for evaporation and sampling losses
Authors: Lefebvre Xavier , Succar Antonella , Bédard Emilie , Prévost Michele , Robert Etienne 
Published: January 2024

Abstract
Measuring aerosol size distribution with precision is critical to understand the transmission of pathogens causing respiratory illnesses and to identify risk mitigation strategies. It is however a challenging task as the size of pathogen-carrying particles evolves over time due to evaporation. Although measurement techniques well established in the field of aerosol science are often used to characterize bioaerosols, their performance is seldom assessed with respect to evaporation and deposition in sampling lines. Four instruments providing aerosol size distribution were compared using oil and water-based particles. They each rely on different measurement principles: phase doppler anemometry, light scattering, electrical mobility and aerodynamic impaction. Size distributions of oil-based particles showed consistency across different measurement instruments, but significant discrepancies arose for water-based particles undergoing evaporation. These larger differences result from both evaporation and particle deposition in transit between the sampling point and the measurement inside the instrument. Phase doppler anemometry was best suited for precise size distribution measurement, as it eliminates the need for a sampling line, thereby preventing particle loss or evaporation during transit. With this instrument as a reference, empirical correction factors for evaporation and deposition were derived from dimensionless numbers and experimental data, enabling quantitative assessment of bioaerosol size distribution using different instruments. To obtain the size distribution at the source of the aerosol generation, complete drying of a salt solution was performed. Using the complete drying technique and accounting for losses, sampling instruments can reliably provide this critical information and allow for thorough risk assessment in the context of airborne transmission.
Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm
Authors: Zhou Wenjun , Xiao Xiaoping , Li Zisheng , Zhang Kai , He Ruide 
Published: January 2024

Abstract
Accurate tool wear monitoring is crucial for increasing tool life and machining productivity. Although many prediction models can achieve high prediction accuracy, there are problems such as poor stability in the face of different working conditions or tool signals. A tool wear prediction method based on improved deep extreme learning machines (DELMs) was proposed as a solution to this issue; it uses the sparrow search algorithm (SSA) to upgrade the input weight of DELM to improve the model, and then extracts the time-domain, frequency-domain, and time-frequency domain characteristics from multi-sensor signals to construct and test the improved model SSA-DELM. The verification results show that the proposed model accurately reflects the tool wear. Meanwhile, the RMSE of the proposed model decreased by 53.39%, 19.95%, 43.86%, 23.80%, 24.80%, and 3.72%, respectively, and the MAE decreased by 67.81%, 17.87%, 32.70%, 29.90%, 30.30%, and 6.78%, respectively, compared to the with unimproved DELM, particle swarm optimization-least squares support vector machine, long short-term memory, stacked sparse autoencoder, recurrent neural network, and dung beetle optimizer-DELM.
Enhanced curve-based segmentation method for point clouds of curved and irregular structures
Authors: Song Limei , Zhang Zongyang , Xu Chongdi , Yang Yangang , Zhu Xinjun 
Published: January 2024

Abstract
This paper proposes an improved method for model-based segmentation (MS) of curved and irregular mounded structures in 3D measurements. The proposed method divides the point cloud data into several levels according to the reasonable width calculated from the density of points. Then, it fits a curve model with 2D points for each level separately. The classification results of specific types are merged to obtain specific structural measurement data in 3D space. We use MS method, difference of normals based segmentation, region growing algorithm, constrained planar cuts, and locally convex connected patches as a control group. The results show that the proposed method achieves higher accuracy with a mean intersection merge ratio of more than 0.8238, at least 37.92% higher than other methods. The method proposed in this paper requires less time to process than other methods. Therefore, the proposed method effectively and efficiently segments the measurement data of curved and irregular mounded structures in 3D measurements. The method proposed in this paper has also been applied in the practical robotic grinding task. The root mean square error of the grinding amount is less than 2 mm, and good grinding results are achieved.
Incipient fault detection based on ensemble learning and distribution dissimilarity analysis in multi-feature processes
Authors: Liu Meizhi , Kong Xiangyu , Luo Jiayu , Yang Lei 
Published: January 2024

Abstract
Timely and accurate detection of incipient faults has attracted considerable attention and research interest in recent years, due to its potential for the prevention of serious safety incidents and for supporting preventive maintenance. However, most existing methods use single detection model, neglecting the coexistence of multiple features and the local data distribution information found in industrial scenes. To overcome this problem, an incipient fault detection method named multiple model ensemble and distribution dissimilarity analysis (MME-DISSIM) is proposed. First, various multivariate statistical analysis methods are employed as basic detectors to comprehensively capture the feature information hidden in the process data. Second, DISSIM analysis is performed to evaluate the dissimilarity between the current sliding window and each training subset. This evaluation allows for the calculation of weighting factors for each basic detector, which helps to preserve the local distribution information of the current sliding window. Third, ensemble learning is utilized to integrate the statistics from all basic detectors into two detection indices to determine the operation status of the system. In addition, two measurement metrics are defined to quantitatively analyze the fault level of incipient faults. Finally, several experiments on a numerical case, Tennessee Eastman process, and actual PROcess NeTwork Optimization are presented to verify the efficacy and superiority of the proposed method.
Adaptive graph-guided joint soft clustering and distribution alignment for cross-load and cross-device rotating machinery fault transfer diagnosis
Authors: Xu Huoyao , Peng Xiangyu , Wang Junlang , Liu Jie , He Chaoming 
Published: January 2024

AbstractDomain adaptation (DA) is an effective solution for addressing the domain shift problem. However, existing DA techniques usually directly match the distributions of the data in the original feature space, where some of the features may be distorted by a large domain shift. Besides, geometric and clustering structures of the data, which play a significant role in revealing hidden failure patterns, are not considered in traditional DA methods. To tackle the above issues, a new joint soft clustering and distribution alignment with graph embedding (JSCDA-GE) method is proposed. Specifically, weighted subspace alignment is proposed to align bases of source and target subspaces by combining instance reweighting and subspace alignment strategies. Then, JSCDA-GE formulates an objective function by incorporating dynamic distribution alignment, soft large margin clustering, and GE in a unified structural risk minimization framework. Ultimately, JSCDA-GE aims to learn a generalization classifier for fault diagnosis. Its effectiveness and superiority have been confirmed through 36 tasks on 2 bearing databases.
Nitriding layer depth detection based on mixing frequency nonlinear ultrasonic parameters
Authors: Li Xinxin , Bi Yiwen , Tang Weili , Mao Hanling , Huang Zhenfeng 
Published: January 2024

Abstract
Nitriding treatment can improve the surface properties of workpieces, thus increasing the service life of the workpiece. The depth of nitriding layer is not only one of the important indexes for evaluating the nitriding effect, but also an important factor affecting the end-use performance of the workpiece. While the existing hardness and metallographic methods cannot meet the needs for non-destructive testing of nitriding layer depth in shaft parts. Therefore, a method using non-linear ultrasonic testing technology is proposed for non-destructive evaluation of nitriding layer depth. In this study, 1045 steel shaft specimens with different nitriding layer depths were prepared by a liquid salt bath nitriding method. The total depth of the nitriding layer was measured using a microhardness tester, and metallographic microscopy was applied to observe microstructure changes before and after nitriding treatment. With the proposed non-destructive method, the longitudinal critically refracted (LCR) wave mixing detection model was established and the ultrasonic nonlinear coefficients were used for characterizing the nitrided layer depths. Experimental results show that the LCR wave sum frequency (LCRWSF) detection model better characterizes the nitriding layer depth of 1045 steel and has higher sensitivity. As a result, the LCRWSF model is more suitable to efficiently estimate the nitrided layer depth.
Design and dynamic analysis of a highly sensitive MEMS gyroscope based on mode localization
Authors: Hou W , Zhang Q C , Hao S Y , Zhang K P 
Published: January 2024

Abstract
Micro-electromechanical systems (MEMS) gyroscopes have important applications in various fields, including aviation, spaceflight, weaponry and automatic driving. To improve the robustness and sensitivity, we design a novel dual-mass MEMS gyroscope based on mode localization in this paper. The gyroscope structure comprises a pair of perturbation systems connected with weakly coupled resonator systems, which eliminates the mode matching and achieves mode localization effects. The dynamic behavior of the MEMS gyroscope is developed using the complex exponential method. The detection characteristics of the amplitude ratio (AR) and amplitude difference (AD) are compared. By combining numerical simulation, we analyzed the influence of critical parameters. It is indicated that the sensitivity can reach up to 40 036.9 ppm/°/s through the AR output, which is two magnitudes higher than the traditional MEMS gyroscope. For the detection of the micro-angular rate, the AD output has advantages in sensitivity, and the AR output has a smaller nonlinearity error. In addition, structural parameters, especially the voltage of the perturbation parallel plates, have a significant impact on the system’s sensitivity. If the breakdown voltage requirement is satisfied, the sensitivity can be enhanced more than ten times by amplifying the voltage, which further broadens the application field of the MEMS gyroscope.
High-performance plasmonic mid-infrared bandpass filters by inverse design
Authors: Zhang Jiarui , Chen Zeji , Su Zhijuan , Dan Yaping 
Published: January 2024

Abstract
Plasmonic spectral filters composed of periodic nanostructured metal films offer novel opportunities for the development of multispectral imaging technologies in the mid-infrared region. However, traditional plasmonic filters, which typically feature simplistic structures such as nanoholes or nanorings, are constrained by a narrow bandpass and significant crosstalk, leading to limited practical performance. Filters designed using inverse techniques allow a substantial degree of freedom in creating intricate structures that align with desired spectral characteristics, including a quasi-square spectral profile, high transmission, wide full width at half maximum, and reduced crosstalk. In this study, we have utilized an inverse design algorithm to engineer high-performance bandpass filters for the mid-infrared range, achieving an average transmittance exceeding 80% within the bandpass window and below 10% in the stop band, which is comparable to that of commercial multilayer Bragg filters. Nanofabrication processes were employed to transfer the designed pattern into the gold film on ZnS substrate that is transparent in the mid-infrared range. The resulting filters exhibit spectral performance analogous to that of the inversely designed models, making them suitable for direct integration with mid-infrared photodetector arrays in multispectral imaging systems.
Determination of output factor for CyberKnife using scintillation dosimetry and deep learning
Authors: Ocampo Jeremy , Heyes Geoff , Dehghani Hamid , Scanlon Tim , Jolly Simon , Gibson Adam 
Published: January 2024

Abstract

Objective. Small-field dosimetry is an ongoing challenge in radiotherapy quality assurance (QA) especially for radiosurgery systems such as CyberKnifeTM. The objective of this work is to demonstrate the use of a plastic scintillator imaged with a commercial camera to measure the output factor of a CyberKnife system. The output factor describes the dose on the central axis as a function of collimator size, and is a fundamental part of CyberKnife QA and integral to the data used in the treatment planning system. Approach. A self-contained device consisting of a solid plastic scintillator and a camera was build in a portable Pelicase. Photographs were analysed using classical methods and with convolutional neural networks (CNN) to predict beam parameters which were then compared to measurements. Main results. Initial results using classical image processing to determine standard QA parameters such as percentage depth dose (PDD) were unsuccessful, with 34% of points failing to meet the Gamma criterion (which measures the distance between corresponding points and the relative difference in dose) of 2 mm/2%. However, when images were processed using a CNN trained on simulated data and a green scintillator sheet, 92% of PDD curves agreed with measurements with a microdiamond detector to within 2 mm/2% and 78% to 1%/1 mm. The mean difference between the output factors measured using this system and a microdiamond detector was 1.1%. Confidence in the results was enhanced by using the algorithm to predict the known collimator sizes from the photographs which it was able to do with an accuracy of less than 1 mm. Significance. With refinement, a full output factor curve could be measured in less than an hour, offering a new approach for rapid, convenient small-field dosimetry.
A multi-resolution TOF-DOI detector for human brain dedicated PET scanner
Authors: He Wen , Zhao Yangyang , Huang Wenjie , Zhao Xin , Niu Ming , Yang Hang , Zhang Lei , Ren Qiushi , Gu Zheng 
Published: January 2024

Abstract

Objective. We propose a single-ended readout, multi-resolution detector design that can achieve high spatial, depth-of-interaction (DOI), and time-of-flight (TOF) resolutions, as well as high sensitivity for human brain-dedicated positron emission tomography (PET) scanners. Approach. The detector comprised two layers of LYSO crystal arrays and a lightguide in between. The top (gamma ray entrance) layer consisted of a 16 × 16 array of 1.53 × 1.53 × 6 mm3 LYSO crystals for providing high spatial resolution. The bottom layer consisted of an 8 × 8 array of 3.0 × 3.0 × 15 mm3 LYSO crystals that were one-to-one coupled to an 8 × 8 multipixel photon counter (MPPC) array for providing high TOF resolution. The 2 mm thick lightguide introduces inter-crystal light sharing that causes variations of the light distribution patterns for high DOI resolution. The detector was read out by a PETsys TOFPET2 application-specific integrated circuit. Main result. The top and bottom layers were distinguished by a convolutional neural network with 97% accuracy. All crystals in the top and bottom layers were resolved. The inter-crystal scatter (ICS) events in the bottom layer were identified, and the measured average DOI resolution of the bottom layer was 4.1 mm. The coincidence time resolution (CTR) for the top–top, top-bottom, and bottom–bottom coincidences was 476 ps, 405 ps, and 298 ps, respectively. When ICS events were excluded from the bottom layer, the CTR of the bottom–bottom coincidence was 277 ps. Significance. The top layer of the proposed two-layer detector achieved a high spatial resolution and the bottom layer achieved a high TOF resolution. Together with its high DOI resolution and detection efficiency, the proposed detector is well suited for next-generation high-performance brain-dedicated PET scanners.