bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gas volume fraction measurement based on ultrasonic array sensors in gas–liquid two-phase flow
Authors: Guo Jiachen , Yang Xuning , Liu Miaomiao , Xie Fei , Zhao Ning 
Published: November 2024

Abstract
The volume gas fraction is an important parameter in gas–liquid two-phase flow, which plays a significant role in the study of mass and heat transfer, pressure drop, and other aspects of gas–liquid two-phase flow. Accurate identification and characterization of gas volume fraction is an important prerequisite for scientific study and industrial process. In this paper, phased array ultrasonic technology was used to conduct flow parameter measurement experiments of three flow patterns (slug flow, plug flow, and stratified flow) in horizontal pipes at the high-precision gas–liquid two-phase flow testing device. Through an analysis of measurement results by the sector scan method, coupled with principal component analysis to mitigate noise and extract eigenvalues from the acquired 128 * 448 matrix data, a predictive model for gas volume fraction satisfying the three flow patterns is established by the random forest algorithm. The Laboratory results show that the average mean absolute percentage error is 10.98%. The model is adaptive to the three flow patterns, which provides a technical solution to the gas volume fraction detection of gas–liquid two-phase flow in industrial process.
Novel calibration method for fine soil electrical resistivity based on van der Pauw configuration
Authors: Ma Deliang , Zhang Chunqi , Li Teng , Feng Huaiping 
Published: November 2024

Abstract
Soil electrical resistivity (ER) serves as an important parameter for indirectly monitoring various physical properties. The Miller box, based on Ohm’s Law, is commonly employed to measure resistivity. However, its probes tend to insert destructively into the soil sample. Van der Pauw (vdP) method is a classical, non-destructive technique used for measuring the ER of thin samples. Applying the vdP method to soil, which is a non-flat sample, presents certain challenges. In this paper, a novel soil resistivity calibration method (vdP box) is developed based on the vdP method, with consideration given to thickness extension. The relative resistivity error between the VDP box and Miller box for fine soil is less than 1% with various diameters and moisture content, which verified the accuracy and stability of the new device. This device achieves simple, fast, and non-destructive testing of soil resistivity and provides a novel method to study other properties of soil indirectly through resistivity.
Fault diagnosis of rolling bearing based on parameter-adaptive re-constraint VMD optimized by SABO
Authors: Guo Jinxi , Zhang Tianyao , Xue Kunlin , Liu Jiehui , Wu Jie , Zhao Yadong 
Published: November 2024

Abstract
Variational mode decomposition (VMD) is widely used in fault-bearing vibration-signal processing. Nonetheless, VMD remains a challenging task because of the difficulty in finding the optimal combination of parameters and excessive fault information in the residual term. The optimal parameter combination plays a balancing role in the optimization process, controlling the error between the reconstructed signal and the original signal while suppressing interference between modes. To address these defects, a parameter-adaptive re-constrained VMD method based on a subtraction average-based optimizer (SABO) is proposed. In this method, exponential functions are first used to build filters to implement a re-constrained VMD. Focusing on the fault information and minimizing it in the residuals. Then, SABO was employed to find the best parameter combination for subsequent signal processing. Finally, the signal is decomposed, and envelope spectral analysis is performed on each component to extract the fault frequencies, thereby identifying the specific fault type. Numerical simulations and real experimental data were used to demonstrate the effectiveness of the proposed method. In addition, the generalization ability of the proposed method was tested using 40 sets of sample data, and the average accuracy of this method reached 97.5%. Compared with other commonly used signal decomposition methods, the superiority of this method in rolling bearing fault feature extraction is proved.
Automated treatment planning with deep reinforcement learning for head-and-neck (HN) cancer intensity modulated radiation therapy (IMRT)
Authors: Yang Dongrong , Wu Xin , Li Xinyi , Mansfield Ryan , Xie Yibo , Wu Qiuwen , Jackie Wu Q , Sheng Yang 
Published: November 2024

Abstract

Purpose. To develop a deep reinforcement learning (DRL) agent to self-interact with the treatment planning system to automatically generate intensity modulated radiation therapy (IMRT) treatment plans for head-and-neck (HN) cancer with consistent organ-at-risk (OAR) sparing performance. Methods. With IRB approval, one hundred and twenty HN patients receiving IMRT were included. The DRL agent was trained with 20 patients. During each inverse optimization process, the intermediate dosimetric endpoints’ values, dose volume constraints’ values and structure objective function losses were collected as the DRL states. By adjusting the objective constraints as actions, the agent learned to seek optimal rewards by balancing OAR sparing and planning target volume (PTV) coverage. Reward computed from current dosimetric endpoints and clinical objectives were sent back to the agent to update action policy during model training. The trained agent was evaluated with the rest 100 patients. Results. The DRL agent was able to generate a clinically acceptable IMRT plan within

12.4
±
3.1

min without human intervention. DRL plans showed lower PTV maximum dose (109.2%) compared to clinical plans (112.4%) (p < .05). Average median dose of left parotid, right parotid, oral cavity, larynx, pharynx of DRL plans were 15.6 Gy, 12.2 Gy, 25.7 Gy, 27.3 Gy and 32.1 Gy respectively, comparable to 17.1 Gy, 15.7 Gy, 24.4 Gy, 23.7 Gy and 35.5 Gy of corresponding clinical plans. The maximum dose of cord + 5 mm, brainstem and mandible were also comparable between the two groups. In addition, DRL plans demonstrated reduced variability, as evidenced by smaller 95% confidence intervals. The total MU of the DRL plans was 1611 vs 1870 (p < .05) of clinical plans. The results signaled the DRL’s consistent planning strategy compared to the planners’ occasional back-and-forth decision-making during planning. Conclusion. The proposed DRL agent is capable of efficiently generating HN IMRT plans with consistent quality.
Convolution spatial-temporal attention network for EEG emotion recognition
Authors: Cao Lei , Yu Binlong , Dong Yilin , Liu Tianyu , Li Jie 
Published: November 2024

Abstract
In recent years, emotion recognition using electroencephalogram (EEG) signals has garnered significant interest due to its non-invasive nature and high temporal resolution. We introduced a groundbreaking method that bypasses traditional manual feature engineering, emphasizing data preprocessing and leveraging the topological relationships between channels to transform EEG signals from two-dimensional time sequences into three-dimensional spatio-temporal representations. Maximizing the potential of deep learning, our approach provides a data-driven and robust method for identifying emotional states. Leveraging the synergy between convolutional neural network and attention mechanisms facilitated automatic feature extraction and dynamic learning of inter-channel dependencies. Our method showcased remarkable performance in emotion recognition tasks, confirming the effectiveness of our approach, achieving average accuracy of 98.62% for arousal and 98.47% for valence, surpassing previous state-of-the-art results of 95.76% and 95.15%. Furthermore, we conducted a series of pivotal experiments that broadened the scope of emotion recognition research, exploring further possibilities in the field of emotion recognition.
A skewed-Gaussian model for pulse decomposition analysis of photoplethysmography signals
Authors: Basso Giulio , Haakma Reinder , Vullings Rik 
Published: November 2024

Abstract

Objective. Pulse decomposition analysis (PDA) has been proposed to extract reliable information from photoplethysmography (PPG) morphology by decomposing the signal in its physiological sub-waves. The Gaussian model has been widely used in the literature, even though it often underperforms because it is limited to symmetric morphologies. More advanced asymmetric models, such as the Gamma model, have been proposed to achieve improved accuracy. However, the physiological interpretation of the Gamma model is less effective than the Gaussian model, challenging the assessment of the clinical relevance of the outcomes. This paper aims to design an asymmetric PDA model with improved accuracy and effective physiological interpretability. Approach. We implemented a novel PDA model called the skewed-Gaussian model and tested it on 8000 PPG pulses from the MIMIC-III Waveform Database. The performances were compared with the reference Gamma-Gaussian model. Models’ accuracies were assessed using the residual sum of squares, while Bland–Altman plots were used to evaluate biases. Lastly, the sensitivity and robustness of the models to the initial values’ choice were evaluated using random initial values. Main results. Our model achieved significantly higher accuracy than the reference model. The analysis with random initial values suggested that the model was less sensitive and consistently more robust. Finally, we highlighted the physiological interpretation of the model. Significance. The proposed model may help to establish a link between alterations in cardiovascular functions and variations detectable in the PPG signal, as well as opening up new avenues for PPG-based remote patient monitoring.
openBF: an open-source finite volume 1D blood flow solver
Authors: Benemerito I , Melis A , Wehenkel A , Marzo A 
Published: November 2024

Abstract
Computational simulations are widely adopted in cardiovascular biomechanics because of their capability of producing physiological data otherwise impossible to measure with non-invasive modalities. Objective. This study presents openBF, a computational library for simulating the blood dynamics in the cardiovascular system. Approach. openBF adopts a one-dimensional viscoelastic representation of the arterial system, and is coupled with zero-dimensional windkessel models at the outlets. Equations are solved by means of the finite-volume method and the code is written in Julia. We assess its predictions by performing a multiscale validation study on several domains available from the literature. Main results. At all scales, which range from individual arteries to a population of virtual subjects, openBF’s solution show excellent agreement with the solutions from existing software. For reported simulations, openBF requires low computational times. Significance. openBF is easy to install, use, and deploy on multiple platforms and architectures, and gives accurate prediction of blood dynamics in short time-frames. It is actively maintained and available open-source on GitHub, which favours contributions from the biomechanical community.
A ferromagnetic notched soft guidewire for enhanced flexibility
Authors: Shen Yang , Cui Chaoyu , Liang Shenglong , Zhang Hongchuan , Zhang Xianmin , Lu Yihua , Li Hai , Zhu Benliang 
Published: November 2024

Abstract
Ferromagnetic soft guidewires (FSGs) capable of remote active steering and navigation offer substantial potential for biomedical applications. However, in practical applications, such as tasks requiring navigation through large-angle bends in narrow spaces and carrying loads, the performance of FSG is limited. This paper presents a design framework for ferromagnetic notched soft guidewires (FNSGs) that significantly enhances steering performance while maintaining sufficient stiffness. Then, an modified segmented Cosserat rod model is proposed and validated through finite element analysis and experiments, providing an effective tool for the analysis of FNSGs. Moreover, analysis of the workspace and the bending stiffness is conducted on FNSG design with different notch geometric parameters, revealing that the notches have a significant impact on the deflection of FNSG along different directions. Finally, the superior performance of the FNSG in navigation, maneuvering through large-angle bends, and bearing heavy loads was demonstrated through an integrated magnetic field system.
Microstructural characterisation studies on hot corrosion behaviour of YSZ/Gd2Zr2O7 based plasma spray coatings on SS316
Authors: S Maharajan , F Michael Thomas Rex 
Published: November 2024

Abstract
The present study uses microstructural analysis to examine the impact of integrating the rare earth oxide Gadolinium Zirconate (Gd2Zr2O7) into the primary YSZ powder during plasma spray coating on an SS316 substrate. The ceramic coatings are formulated with two distinct concentrations: 5 wt. % Gd2Zr2O7, designated as 5GDZ, and 15 wt. % Gd2Zr2O7, designated as 15GDZ. The coating thickness was consistently maintained at a bond coating of 50 μm and a top coating of 200 μm across all coated samples by controlling relevant process parameters, including current, powder feed rate, and standoff distance. Hot corrosion tests were conducted on the samples using 50 mg/cm2 of molten salt comprising 60 wt. % V2O5 and Na2SO4 at a temperature of 700 °C for 12 h. The results indicated that YSZ and 15GDZ effectively prevented corrosion in the hot molten salt environment. The corrosive products containing YVO4 and m-ZrO2 in the YSZ and 15GDZ coating act as a passivation layer to inhibit corrosion to a certain extent. Compared to YSZ, 5GDZ shows a weight gain of 162.5 mg/cm2, 58.54% higher. However, there is no noticeable improvement in hot corrosion resistance. The 5GDZ coating exhibited the formation of thin, corrosive products. More spallation, cracks, and fractures are evident in the 5GDZ coating. The weight gain of 15GDZ is quantified at 115.32 mg/cm2, representing a 40.9% reduction compared to the 5GDZ coating. Hence, further increases in the weight of Gd2Zr2O7 were added with YSZ beyond 5 wt. % demonstrate an enhancement in hot corrosion resistance. The penetration of molten salt into the bond coating interface and substrate is completely inhibited in all three coatings, as evidenced by the SEM and EDAX analysis.
Fusing thermal and visual sensory data for enhanced VIO localization in low-light conditions
Authors: Si Pengju , Liu Yuxuan , Zhao Li , Cui Wei , Fu Zhumu , Tao Fazhan , Gao Song 
Published: November 2024

Abstract
Visual inertial odometry (VIO) is a crucial technique for autonomous driving, but it encounters challenges in dark or shadowy environments, where the accuracy of the positioning system may be degraded or lost. Moreover, different kinds of sensor noise can affect the positioning accuracy. In this paper, we present a novel VIO localization algorithm that fuses data from a thermal camera, a visual camera, and an inertial measurement unit (IMU) in a tightly coupled manner. To handle non-Gaussian noise in dark environments and improve positioning accuracy and stability, we employ an iterative extended Kalman filter (IEKF) with the maximum correntropy criterion and adaptive noise, enhancing the robustness. We first combine the thermal camera with the conventional VIO method to form a thermal VIO framework. Then, an adaptive maximum correntropy criterion IEKF (AMCCIEKF) algorithm is proposed to refine the accuracy and stability. We implement and experimentally evaluate our proposed localization method using an open dataset. In a dark and shaking environment, our method reduces errors by approximately 56% compared to IEKF. Moreover, our method achieves a 41% reduction in median and 65% in standard deviation (S.D.).
Nanoindentation study of PbMoO4 single crystals: mechanical properties and implications for applications
Authors: Isik M , Gasanly N M 
Published: November 2024

Abstract
Nanomechanical properties of lead molybdate (PbMoO4) single crystal were investigated using nanoindentation measurements. The force-dependent Young’s modulus and hardness of PbMoO4 along the [100] direction was determined using the Oliver–Pharr method. As the applied force increased, hardness and young modulus values decreased. This behavior was referred to the indentation size effect (ISE). The force-dependent plots were analyzed using proportional specimen resistance model and true hardness value was determined as 1.84 GPa. As a result of increasing the applied force from 5 to 100 mN, the Young modulus decreased from 81.7 to 60.2 GPa. The dependencies of plastic and elastic deformation components were also reported in the present study. It was seen that plastic deformation is the dominant component. The findings suggest that PbMoO4 is relatively soft material and can be considered as a promising material for mechanical and optoelectronics applications that require revealed hardness and Young’s modulus values.
Optical field modulation-enhanced cantilever sensor-based photoacoustic spectroscopy gas detection system
Authors: Cheng Hongtu , Shen Yang , Feng Jie , Fang Zhi 
Published: November 2024

Abstract
This study presents an approach to enhance the sensitivity of cantilever sensor-based photoacoustic (PA) spectroscopy gas detection systems through optical field modulation. Theoretical investigations into the mechanism and method of optical field modulation are followed by experimental validation. Acetylene is selected as the target molecule, with a near-infrared DFB LD laser serving as the light source. A dual-lens configuration was constructed to yield a better optical field distribution compared to the single-lens configuration. In the near-field of the laser, the setup of the dual-lens configuration has a better sensitivity than that of the single-lens setup, with a limit of detection reaching 0.52 μl l−1. In the far-field of the laser, however, the PA signals are significantly lower than that of near-field measurements, indicating that far-field measurement is not preferred. The results can provide references to the sensitivity improvement of the PA spectroscopy-based gas sensing system.
Thermal kinetics and photodegradation performance towards methylene blue of melt-quenched Se70Te30 glass
Authors: Mahmoud A Z , Abd-Elnaiem Alaa M , Shamekh A M A 
Published: November 2024

Abstract
This paper reports the structure, basic characteristics, photocatalytic performance, and crystallization kinetics of the Se70Te30 glass. The Se70Te30 glass was synthesized using the melt-quenching method. The crystallization kinetics were investigated under non-isothermal condition via the differential scanning calorimetry (DSC) technique. For example, the glass transition activation energy, the thermal stability, and the Avrami index have been determined and discussed. It was found that thermal stability and glass-forming ability influence the crystallization rate. Furthermore, the activation energy needed for the amorphous-crystalline transition of the Se70Te30 glass was obtained using conventional methods. The thermal kinetic parameters were analyzed using various conventional approaches and were revealed to be influenced by the heating rate (β). The photocatalytic activity of the Se70Te30 glass towards methylene blue (MB) was examined and compared to the literature. The UV-visible irradiation time affects the photodegradation of MB and reaches 72.13% under the UV-visible irradiation for 70 min. A hypothesized mechanism for the degradation of the MB dye by Se70Te30 catalysts is described. The study employed Langmuir–Hinshelwood kinetics to ascertain the rate constant of 2.1 × 10–2 min–1 towards MB dye degradation and to assess the photo-reactivity of the Se70Te30 glass under investigation using quantitative analysis. The synthesized Se70Te30 glass could be helpful for purifying wastewater and degrading other organic dyes, in addition to its benefits for phase change memory applications.
Combining deep reinforcement learning with heuristics to solve the traveling salesman problem
Authors: Hong 洪 Li 莉 , Liu 刘 Yu 宇 , Xu 徐 Mengqiao 梦俏 , Deng 邓 Wenhui 文慧 
Published: November 2024

Abstract
Recent studies employing deep learning to solve the traveling salesman problem (TSP) have mainly focused on learning construction heuristics. Such methods can improve TSP solutions, but still depend on additional programs. However, methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient. Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements. This paper proposes a novel framework for learning improvement heuristics, which automatically discovers better improvement policies for heuristics to iteratively solve the TSP. Our framework first designs a new architecture based on a transformer model to make the policy network parameterized, which introduces an action-dropout layer to prevent action selection from overfitting. It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism (named RL-SA) to learn the pairwise selected policy, aiming to improve the 2-opt algorithm’s performance. The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning. The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods, and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets. Moreover, our pre-trained model M can be applied to guide the SA algorithm (named M-SA (ours)), which performs better than existing deep models in small-, medium-, and large-scale TSPLIB datasets. Additionally, the M-SA (ours) achieves excellent generalization performance in a real-world dataset on global liner shipping routes, with the optimization percentages in distance reduction ranging from 3.52% to 17.99%.
HiFAST: An H i Data Calibration and Imaging Pipeline for FAST. III. Standing Wave Removal
Authors: Xu Chen , Wang Jie , Jing Yingjie , Li Fujia , Gan Hengqian , Liu Ziming , Liang Tiantian , Chen Qingze , Liu Zerui , Hou Zhipeng , Hu Hao , Hu Huijie , Huang Shijie , Jiang Peng , Zhang Chuan-Peng , Zhu Yan 
Published: November 2024

Abstract
The standing waves existing in radio telescope data are primarily due to reflections among the instruments, which significantly impact the spectral quality of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Eliminating these standing waves for FAST is challenging given the constant changes in their phases and amplitudes. Over a ten-second period, the phases shift by 18° while the amplitudes fluctuate by 6 mK. Thus, we developed the fast Fourier transform (FFT) filter method to eliminate these standing waves for every individual spectrum. The FFT filter can decrease the rms from 3.2 to 1.15 times the theoretical estimate. Compared to other methods such as sine fitting and running median, the FFT filter achieves a median rms of approximately 1.2 times the theoretical expectation and the smallest scatter at 12%. Additionally, the FFT filter method avoids the flux loss issue encountered with some other methods. The FFT is also efficient in detecting harmonic radio frequency interference (RFI). In the FAST data, we identified three distinct types of harmonic RFI, each with amplitudes exceeding 100 mK and intrinsic frequency periods of 8.1, 0.5, and 0.37 MHz, respectively. The FFT filter, proven as the most effective method, is integrated into the H i data calibration and imaging pipeline for FAST (HiFAST, https://hifast.readthedocs.io).