bluuebunny/crossref_metadata_2025_split huggingface dataset:
The anomalous temperature dependent low energy electron diffraction intensity at epitaxial Sr3Ir2O7 thin film surfaces
Authors: Adegbite Peace Ikeoluwa , Subedi Arjun , Zhang Yuanyuan , Hong Xia , Komesu Takashi , Dowben Peter A 
Published: February 2025

Abstract
We report on the temperature dependent low energy electron diffraction (LEED) studies of 12 nm epitaxial Sr3Ir2O7(001) thin films. The Debye temperature has been extracted from the temperature-dependence of LEED intensity at different electron kinetic energies. For the most surface sensitive LEED obtained with lowest electron kinetic energies, for temperatures above 410 K, the extracted surface Debye temperature is 270±22 K, which is much lower than the 488±40 K Debye temperature obtained using higher electron kinetic energies. Surprisingly, the LEED diffraction intensity with lowest electron kinetic energies increase, rather than decrease, with increasing sample temperatures up to about 440 K. This anomalous behavior has been attributed to the reduction of the quasi-harmonic lattice vibrational amplitudes along the surface normal. This damping of the normal mode vibrations, with increasing temperature, results from the enhanced electronic screening via thermally activated carriers. This scenario is corroborated by the transport measurement, showing Sr3Ir2O7 is a narrow band Mott insulator with a band gap of about 32 meV. We have identified criteria for finding anomalous scattering behavior in other transition metal oxide systems. The LEED data indicates that the surface symmetry of the epitaxial Sr3Ir2O7(001) thin films is C4v.&#xD;
A 3D Optical Microscopic Imaging System Based on PSO-BPNN-PID Control and Image Pyramid Transform Fusion with Experimental Verification
Authors: Wu Jim-Wei , Chen Bo-Hao , Li Jia-Cheng , Yu Jen-te 
Published: February 2025

Abstract
The limited depth of field of optical microscopes typically results in the blurring of all but a small portion of the image, making it difficult to assess the height, shape, and surface profile of samples. This issue can be partly resolved by capturing a series of images at various sample distances by adjusting the piezoelectric stage; however, scanning performance is prone to perturbations resulting from nonlinear piezoelectric response characteristics. This paper presents a novel three-dimensional optical microscopic imaging system that uses a particle swarm optimization control scheme in parallel with a back-propagation neural network to achieve high-precision trajectory tracking. The system also features an algorithm to perform image pyramid transform fusion, image stitching, and 2D-to-3D conversion to enable the capture of 3D surface profiles of high accuracy. In experiments, the proposed scheme significantly outperformed existing methods in terms of accuracy and the level of detail.&#xD;
A novel tacholess order tracking method for planetary gearbox fault detection under variable rotational speed conditions
Authors: Wang Chaoge , Wang Ran , Huang Yuanyuan , Wang Lijun , Li Hongkun 
Published: February 2025

Abstract
Planetary gearbox generally operates under variable speed conditions in response to actual industrial production requirements, which is prone to cause single or compound fault. The planetary gearbox fault signal exhibits time-varying, weak, and complex characteristics under varying conditions, and common diagnostic approaches are difficult to effectively extract and reveal fault-related features. In addition, the computed order tracking requires speed signal as auxiliary calculation. Unfortunately, due to economic and installation space limitations, encoders and tachometers are not always available, which poses a huge challenge for extracting variable speed planetary gearbox fault. To tackle these challenges, a novel tacholess order tracking method based on improved adaptive chirp mode decomposition (IACMD) and adaptive mximum second-order cyclostationarity blind deconvolution (CYCBD) is proposed for diagnosing planetary gearbox single and composite fault in this contribution. Firstly, the IACMD algorithm is utilized to adaptively decompose the fault signal, which constructs a composite index (CI)-based signal mode selection and recombination scheme to perform optimal modal decomposition. Secondly, the instantaneous dominant meshing multiply (IDMM) trend line with obvious amplitude advantage is extracted from the time-frequency representation of the original fault signal, and then converted to the reference shaft rotation frequency to perform angle domain resampling on sensitive model with the highest CI value. Thirdly, the adaptive CYCBD is adopted to deconvolute the angular domain sensitive modal to heighten the weak fault signatures. Finally, envelope spectrum of improved signal is utilized to identify prominent fault characteristic orders and ascertain failure type. Both numerical simulations and practical engineering data in different planetary gearbox failure cases have been thoroughly examined to demonstrate the correctness and feasibility of the proposed approach. Moreover, compared with some existing technologies demonstrates the superiority of the proposed approach.&#xD;
Multi-Scale Geometric Transformer for Tire Fragment Stitching with Small Feature Differences
Authors: Jiao Wenhua , wang zhaoyi , Yifei You , Zhang Tong , Liu Xiaofei , Liu Zhenfei , Zuo Mingcheng 
Published: February 2025

Abstract
Tire is a vital component of any vehicle, and the surface characteristics have a considerable impact on overall performance. Given the limitations of the sensor's scanning angle, it is imperative that the fragments of the t ire be stitched together to reconstruct a complete tire model. Notably, the smoothness of the tire surface's geometric structure and the subtle differences in small-scale features have resulted in inaccurate stitching and mismatches. This paper proposes an approach based on a Multi-Scale Geometric Transformer (MSGT) for high accuracy and robustness. Firstly, the multiscale geometric structure embedding module extracts the details and overall information of the tire surface at different scales. A gating mechanism is introduced to fuse the distance and angle features to enhance the sensitivity and expressiveness of MSGT to small-scale features. Then, we employ a Global Attention Mechanism (GAM) that combines channel and spatial information to enhance feature differentiation, causing the model to select effective features in various regions of the tire. To further tackle mismatch and uneven distribution existing in the tire point cloud data, we propose an adaptive dynamic thresholding network that dynamically adjusts thresholds based on the distribution characteristics in tread and non-tread areas, effectively filtering out low-confidence matched point pairs. The experiments are conducted on the 3DMatch and self built tire datasets,-with the results demonstrating that MSGT significantly outperforms the other four mainstream deep learning methods in terms of stitching accuracy and robustness. Consequently, the superior performance of MSGT in tire stitching scenarios is validated.&#xD;
Self-supervised U-transformer network with mask reconstruction for metal artifact reduction
Authors: Kong Fanning , Shi Zaifeng , Cao Huaisheng , Hao Yudong , Cao Qingjie 
Published: February 2025

Abstract
Objective. Metal artifacts severely damaged human tissue information from the computed tomography (CT) image, posing significant challenges to disease diagnosis. Deep learning (DL) has been widely explored for the metal artifact reduction (MAR) task. Nevertheless, paired metal artifact CT datasets suitable for training do not exist in reality. Although the synthetic CT image dataset provides additional training data, the trained networks still generalize poorly to real metal artifact data.&#xD;Approach. A self-supervised U-shaped Transformer network (SUTransNet) is proposed to focus on model generalizability enhancement in MAR tasks. This framework consists of a self-supervised mask reconstruction pre-text task and a down-stream task. In the pre-text task, the CT images are randomly corrupted by masks. They are recovered with themselves as the label, aiming at acquiring the artifacts and tissue structure of the actual physical situation. Down-stream task fine-tunes MAR target through labeled images. Utilizing the multi-layer long-range feature extraction capabilities of the Transformer efficiently captures features of metal artifacts. The incorporation of the MAR bottleneck allows for the distinction of metal artifact features through cross-channel self-attention.&#xD;Main result. Experiments demonstrate that the framework maintains strong generalization ability in the MAR task, effectively preserving tissue details while suppressing metal artifacts. The results achieved a peak signal-to-noise ratio (PSNR) of 43.86 dB and a structural similarity index (SSIM) of 0.9863 while ensuring the efficiency of the model inference. In addition, the Dice coefficient and Mean Intersection over Union (MIoU) are improved by 11.70% and 9.51% in the segmentation of the MAR image, respectively.&#xD;Significance. The combination of unlabeled real-artifact CT images and labeled synthetic-artifact CT images facilitates a self-supervised learning process that positively contributes to model generalizability.
Intelligent risk stratification of hypertension based on ambulatory blood pressure monitoring and machine learning algorithms
Authors: Deng Muqing , Guo Junsheng , Li Boyan , Yang Jingfen , Zhang Xiaobo , Liang Dandan , Wang Yanjiao , Huang Xiaoyu 
Published: February 2025

Abstract
Risk stratification of hypertension plays a crucial role in the treatment decisions&#xD;and medication guidance during clinical practices. Although fruitful achievements have been&#xD;reported on risk stratification of hypertension, the potential use of ambulatory blood pressure&#xD;monitoring data is not well investigated. Different from single measuring blood pressure&#xD;data, long-term blood pressure monitoring data can provide more comprehensive dynamical&#xD;blood pressure information. Therefore, this paper proposes an intelligent hypertension risk&#xD;stratification method based on ambulatory blood pressure monitoring data and improved&#xD;machine learning algorithms. A total of 262 patients with hypertension are enrolled at People’s&#xD;Hospital of Yangjiang, in which 93 subjects are with simple hypertension and 169 subjects&#xD;have hypertension with complication. Time-domain features, frequency-domain features,&#xD;nonlinear dynamics features and correlation features underlying time-varying ambulatory&#xD;blood pressure monitoring data are extracted to obtain discriminative feature representations.&#xD;Synthetic minority over-sampling (SMOTE) algorithm is applied to solve the problem of data&#xD;balancing. The particle swarm optimization (PSO) combined with kernel extreme learning&#xD;machine (KELM) is employed for feature fusion and optimization. The proposed method can&#xD;yield a diagnostic accuracy of 93.7%, 97.8%, and 98.4% under two-, five- and ten-fold crossvalidation, which demonstrates hypertension risk stratification in an intuitive, quantizable&#xD;manner using multi-dimensional feature representation and learning. The proposed method&#xD;is expected to provide early warning for latent serious cardiovascular diseases before obvious&#xD;symptoms are present.
Control of axial direction electron temperature distribution by gradient DC magnetic field in inductively coupled plasma
Authors: Jiang Yilang , He You , Seo Beom-Jun , Lee Myoung-Jae , Kim Ju Ho , Chung Chin-Wook 
Published: February 2025

Abstract
A gradient magnetic field was applied in the axial direction of an inductively coupled plasma (ICP) by controlling the currents of the upper and lower coils of the Helmholtz coil, respectively. This control changed the magnetic field conditions of electron cyclotron resonance (ECR) along the axial position of the chamber. The experiment was conducted under a low-pressure (0.67 Pa) argon discharge with a driving frequency of 60 MHz and radio frequency (RF) power of 300 W. The axial distributions of the electron temperature and ion density in the chamber were measured using a floating harmonic probe (FHP). Due to the ECR effect, the electron temperature along the axial direction was controlled by the Helmholtz coil currents. Considering the direction of the planar antenna towards the chamber bias electrode as the positive direction, a higher ion density was observed at a positive gradient magnetic field. The distribution of ion flux on the surface of the bias electrode was measured using a two-dimensional (2D) FHP. Although the gradient magnetic field increased the ion flux, the 2D distribution profile remained unchanged. The variations in electron temperature and ion density under these magnetic field conditions were analyzed using plasma simulations based on the fluid approximation method. The study demonstrates how adjusting magnetic field conditions can enhance ion bombardment energy and etching rates in plasma processing.
Sensorless calibration of piezoelectric shunts using capacitance measurements
Authors: Richardt Jens Damholt , Lossouarn Boris , Høgsberg Jan , Deü Jean-François 
Published: February 2025

Abstract
In this work, a new experimental shunt tuning procedure is proposed for piezoelectric shunts targeting a single vibration mode of a flexible structure. The procedure is based on measurements of the dynamic capacitance of the piezoelectric transducer, eliminating the need for additional sensors or actuators. It is demonstrated that all required electromechanical parameters for shunt calibration can be determined from two extremum points on the dynamic capacitance curve. Corrections for the influence of residual modes are included to enable the consistent application of existing tuning expressions for various shunt topologies. The hardware of a digital shunt is used for the measurement of the dynamic capacitance allowing for low-cost implementation. The proposed method is validated numerically and experimentally through the calibration of a series RL shunt using both analog and digital components.
Process parametric and performance characteristics study of WED machined Ni-Ti-Hf high-temperature shape memory alloys: An experimental and artificial intelligence approach
Authors: V Balaji , Sahu Ranjeet Kumar , S Narendranath 
Published: February 2025

Abstract
In recent years, to meet the shortcomings of the conventional machining of Ni-Ti-Hf shape memory alloy (SMA), Wire Electric Discharge Machining (WEDM) as one of the unconventional machining methods, has emerged as the preferred method for processing SMAs. Therefore, in this study, according to the RSM-based Central Composite Design (CCD) layout, WED machining of Ni-Ti-Hf SMA is carried out using the control parameters like spark time (SON), spark pause time (SOFF), gap voltage (Vg), and dielectric flow rate (FDL). A General Regression Neural Network (GRNN) model was used to predict the critical WEDM responses: material removal rate (MRR), surface roughness (Ra), and kerf width (KW). The GRNN model closely agrees with the experimental WEDM responses, resulting in a Mean Absolute Percentage Error (MAPE) below 2%. Field Emission Scanning Electron Microscopy (FESEM) result revealed a recast layer thickness of 10.64 ± 2.06 µm and 38.19 ± 9.55 µm for the samples with the lowest surface roughness (Ra) and highest material removal rate (MRR), respectively. The shape recovery test result shows a less than 4% reduction in recovery ratio post-WEDM. Further, electrochemical corrosion studies revealed that owing to these surface defects, the corrosion rate increased with higher discharge energy. The corrosion rate of the base material, low Ra sample, and high MRR sample were 0.0013mm/year, 0.0128mm/year, and 0.0334mm/year, respectively.
A full-wave high-Tc superconducting transformer-rectifier flux pump activated with two electromagnets
Authors: Lin Yi , Hu Changhao , Yang Zaixing , Tan Yunfei , Geng Jianzhao 
Published: February 2025

Abstract
High-Tc superconducting (HTS) transformer-rectifier flux pumps (TRFPs) normally have higher efficiency and better controllability than their travelling-wave based counterparts. However, the majority of existing TRFPs require separate activation of the transformer and switch(es), which may increase the footprint and power demand of a system. In this paper, we propose a novel full-wave TRFP circuit topology that integrates induction and switching. The TRFP consists of an HTS loop and two electromagnets. Each electromagnet induces current in the HTS loop as well as switches a portion of the HTS loop in half ac duty cycle, thus achieving full-wave rectification. We developed a finite element simulation model to demonstrate its working principle in detail. In the meantime, a compact experimental prototype was fabricated and successfully supplied 300 A current to an HTS coil. The work effectively reduces the footprint and power demand of TRFPs. Moreover, the proposed TRFP can be considered as a simplified linear travelling-wave flux pump, and may help understand the fundamental interconnection between a travelling-wave flux pump and a TRFP.
Tunable diode effect in a superconducting tunnel junction with biharmonic drive
Authors: Scheer David , Seoane Souto Rubén , Hassler Fabian , Danon Jeroen 
Published: February 2025

Abstract
A Josephson diode is a superconducting circuit element that enables non-reciprocal transport, allowing a dissipationless supercurrent to preferentially flow in a single direction.&#xD;Existing methods for achieving the required symmetry breaking mostly rely on specifically-designed materials or carefully-engineered circuits composed of multiple Josephson junctions.&#xD;Here, we investigate the diode effect induced by applying a biharmonic drive to a conventional superconducting tunnel-junction. In the slow-driving regime, the effect is straightforward to understand in a simple adiabatic picture, providing insight in the tunability of the magnitude and directionality of the diode effect through the drive parameters. We then focus on the fast-driving regime, where we show how the more complex physics underlying the dynamics of the junction can be approximated as a cascaded two-tone mixing process. We derive analytic expressions for the diode efficiency as a function of drive parameters in the limit of small driving amplitudes.
Nonequilibrium Thermodynamics of a Superconducting Szilard Engine
Authors: Tang Kuen Wai , Ray Kyle J , Crutchfield James 
Published: February 2025

Abstract
We implement a Szilard engine using a 2-bit logical unit consisting of inductively coupled quantum flux parametrons (QFPs)—Josephson-junction superconducting circuits with applications in both the classical and quantum information processing regimes. Detailed simulations show that it is highly thermodynamically efficient while functioning as a Maxwell demon—converting heat to work. The physically-calibrated design is targeted to direct experimental exploration. However, in realizing experiments variations in Josephson junction fabrication introduce asymmetries that result in energy inefficiency and low operational fidelity. We provide a design solution that mitigates these practical challenges. The resulting platform is ideally suited to probe the thermodynamic foundations of information processing devices far from equilibrium.
Alpha particles registration by the single-side ultraviolet protected Makrofol® TP 244
Authors: Al-Sayed Abdallah , El Ghazaly Mahmoud , El-Naggar Hala I. , Abdel-Fattah Essam Mohammed , Abdeen Mostafa , Ghanim E.H. 
Published: February 2025

Abstract
The registration of 0.5 to 5.5 MeV alpha particles by the single-side UV-protected Makrofol® TP 244 was investigated. The alpha-particles irradiated Makrofol® TP 244 samples were chemically etched; the bulk etch rate amounts to 3.15 ± 0.24 μm/h. The visible light transmittance through Makrofol® TP 244 goes up from 58.0% at 400 nm to 83.8% at 700 nm. The etch-pits contrast was evaluated for the RGB color channels, the green channel has a maximum contrast of (45.0 ± 10.0) % compared with other color channels and RGB images. That the alpha particle etch-pits' contrast (C%) decreases with the increase in etching time as well as etch-pits' diameter. The etch pits revelation time of 0.5 to 5.5 MeV alpha particle was less than 0.5 hour at the applied etching conditions. The standard deviation of etch-pits' diameters from 0.5 to 3 MeV is less than 5%. For 4 to 5.5 MeV alpha particles, the standard deviation of etch pits goes up till etching time of 4 h, but near the end of the alpha particle's energy range, it decreases sharply. Accordingly, the Makrofol® TP 244 can detect 4 - 5.5 MeV alpha particles only when the removed-layer thickness is equal to or longer than the range of alpha particles. This minimizes the etch pit diameter uncertainty that gives rise to increase the energy resolution. The measurement of the etch pit diameters as a function of alpha particle energy shows the dynamic range limitations, between 0.5 to 3 MeV, the of Makrofol® TP 244 detector as alpha particles spectrometer.
Arbitrary quantum states preparation aided by deep reinforcement learning
Authors: Wang Zhao-Wei , Wang Zhao-Ming 
Published: February 2025

Abstract
The preparation of quantum states is essential in the realm of quantum information processing, and the development of efficient methodologies can significantly alleviate the strain on quantum resources. Under the framework of deep reinforcement learning (DRL), we integrate the initial and the target state information within the state preparation task together, so as to realize the control trajectory design between two arbitrary quantum states. Taking a semiconductor double quantum dots (DQDs) model as an example, our results demonstrate that the resulting control trajectories can effectively achieve arbitrary quantum state preparation (AQSP) for both single-qubit and two-qubit systems, with average fidelities of 0.9868 and 0.9556 for the test sets, respectively. For the DQDs, charge and nuclear noises will exist, thus decreases the average fidelity of AQSP. We have also incorporated noise amplitude as an input feature in the training process, leading to the development of noise-aware models. The control trajectories designed by these models can effectively suppress the influence of noise. Our research validates the effectiveness of DRL in quantum state preparation and offers solutions for multi-initial and multi-objective quantum control tasks, with potential for broader application in quantum control problems