bluuebunny/crossref_metadata_2025_split huggingface dataset:
Resonance-state selective photodissociation dynamics of OCS + hv → CS(X1Σ+) + O(3Pj=2,1,0) via the 21Σ+ state
Authors: Liu Qian , Li Zheng , Liu Peng , Yang Xueming , Yu Shengrui 
Published: May 2023

Understanding vacuum ultraviolet photodissociation dynamics of Carbonyl sulfide (OCS) is of considerable importance in the study of atmospheric chemistry. Yet, photodissociation dynamics of the CS(X1Σ+) + O(3Pj=2,1,0) channels following excitation to the 21Σ+(ν1′,1,0) state has not been clearly understood so far. Here, we investigate the O(3Pj=2,1,0) elimination dissociation processes in the resonance-state selective photodissociation of OCS between 147.24 and 156.48 nm by using the time-sliced velocity-mapped ion imaging technique. The total kinetic energy release spectra are found to exhibit highly structured profiles, indicative of the formation of a broad range of vibrational states of CS(1Σ+). The fitted CS(1Σ+) vibrational state distributions differ for the three 3Pj spin–orbit states, but a general trend of the inverted characteristics is observed. Additionally, the wavelength-dependent behaviors are also observed in the vibrational populations for CS(1Σ+, v). The CS(X1Σ+, v = 0) has a significantly strong population at several shorter wavelengths, and the most populated CS(X1Σ+, v) is gradually transferred to a higher vibrational state with the decrease in the photolysis wavelength. The measured overall β-values for the three 3Pj spin–orbit channels slightly increase and then abruptly decrease as the photolysis wavelength increases, while the vibrational dependences of β-values show an irregularly decreasing trend with increasing CS(1Σ+) vibrational excitation at all studied photolysis wavelengths. The comparison of the experimental observations for this titled channel and the S(3Pj) channel reveals that two different intersystem crossing mechanisms may be involved in the formation of the CS(X1Σ+) + O(3Pj=2,1,0) photoproducts via the 21Σ+ state.
Vapor etching to avoid micro-masking by gas-bubbles in wet release of MEMS
Authors: Plaza Alejandro , Maspero Federico , Cuccurullo Simone , Pavese Giulia , Avila Miguel Angel Badillo , Bertacco Riccardo 
Published: May 2023

Abstract
We report on the formation of gas bubbles during the release of MEMS devices using buffered oxide etch. Several approaches to mitigate the problem are proposed and tested together with a qualitative study of the phenomenon. The chemical reaction behind such phenomenon and the influence of defects and topography is discussed. Finally, a comparison with the HF-vapor release technique is shown.
Quantifying and correcting tilt-related positioning errors in microcantilever-based microelectromechanical systems probes
Authors: Arscott Steve 
Published: May 2023

Abstract
The impact of tilt-related errors on the positioning of microcantilever-based microelectromechanical systems (MEMS) on-wafer electrical probes, having multiple contact pads, is quantified and investigated here. A tilt error associated with probe roll results in the probe contact pads not being parallel to the approaching surface as a downward overtravel is imposed—this leads to one probe pad making contact with the surface before the others. In a MEMS-based probe, the analysis of the impact of roll error angle must consider both the bending and the torsion of the flexible cantilever as the overtravel is increased—something which eventually results in all pads being in contact with the surface, but not with the same contact force. An original mathematical description of the problem is presented. By making some assumptions, the analytical modeling enables the derivation of elegant equations relating the roll error angle and the cantilever deflection to achieve planarity of the cantilever apex with the underlying surface. The modeling predicts probe tip planarity for rectangular and trapezoidal shaped probes. The predictions of the modeling are tested by using macroscopic cantilevers—excellent agreement between modeling and experiment is demonstrated. The macroscopic experimental setup reveals interesting behavior concerning a bending/twisting, tilted cantilever in contact with—and skating across—an underlying surface. The experimental findings also indicate the pertinence of the modeling for the potential use with understanding the behavior of microscopic cantilevers—such as MEMS-based probes—similarly in contact with a surface. A flexible microcantilever enables a torsional compensation of the roll error angle. It also enables a protocol where the roll error angle can be corrected. The design geometry of the probe tip will determine which approach is best suited. In principle, the modeling is scalable to MEMS probes composed of silicon-based cantilevers.
Spectroscopic analysis improvement using convolutional neural networks
Authors: Saura N , Garrido D , Benkadda S , Ibano K , Ueda Y , Hamaguchi S 
Published: May 2023

Abstract
Removing noisy components of signals coming from edge tokamak plasmas, astrochemical organic matter or astronomical objects is one of the promising path to improve the underlying elements identification. Methods such as the penalized semi-supervised non negative matrix factorization (PSNMF) used to extract such prominent elements perform well on complex signals. However, it is results’ confidence decreases as the noise increases. In this context, we have tried to address this limitation by removing part of the undesired noise in atomic spectra using artificial intelligence (AI) method based on convolutional neural networks (CNNs). More specifically, we have tested different architectures of CNN classically used in denoising task: residual CNNs and auto-encoders, to benchmark their respective denoising capacity. The dataset used is made of high resolution atom and ion spectra extracted from the NIST Atomic Spectra Database. In the case of ions, we have considered several degrees of ionization. The synthetic added noise is generated from a typical experimental noise profile randomly modified for each signal. Performance of each AI-based noise remover is measured by analyzing the increase of the element identification precision obtained by the PSNMF. Auto-encoders and residual CNN are both suitable to remove the undesired noise and increase the PSNMF efficiency even for noise-prevailing signals. In this particular case, the auto-encoder architecture seems to be globally more accurate and should be selected when considering noisy multi-element atomic spectra.
Periodic solutions of the non-chiral intermediate Heisenberg ferromagnet equation described by elliptic spin Calogero-Moser dynamics
Authors: Berntson Bjorn K , Klabbers Rob 
Published: May 2023

Abstract
We present a class of periodic solutions of the non-chiral intermediate Heisenberg ferromagnet equation, which was recently introduced by the authors together with Langmann as a classical, continuum limit of an Inozemtsev-type spin chain. These exact analytic solutions are constructed via a spin-pole ansatz written in terms of certain elliptic functions. The dynamical parameters in our solutions solve an elliptic spin Calogero-Moser (CM) system subject to certain constraints. In the course of our construction, we establish a novel Bäcklund transformation for this constrained elliptic spin CM system.
DMCT-Net: dual modules convolution transformer network for head and neck tumor segmentation in PET/CT
Authors: Wang Jiao , Peng Yanjun , Guo Yanfei 
Published: May 2023

Abstract

Objective. Accurate segmentation of head and neck (H&N) tumors is critical in radiotherapy. However, the existing methods lack effective strategies to integrate local and global information, strong semantic information and context information, and spatial and channel features, which are effective clues to improve the accuracy of tumor segmentation. In this paper, we propose a novel method called dual modules convolution transformer network (DMCT-Net) for H&N tumor segmentation in the fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) images. Approach. The DMCT-Net consists of the convolution transformer block (CTB), the squeeze and excitation (SE) pool module, and the multi-attention fusion (MAF) module. First, the CTB is designed to capture the remote dependency and local multi-scale receptive field information by using the standard convolution, the dilated convolution, and the transformer operation. Second, to extract feature information from different angles, we construct the SE pool module, which not only extracts strong semantic features and context features simultaneously but also uses the SE normalization to adaptively fuse features and adjust feature distribution. Third, the MAF module is proposed to combine the global context information, channel information, and voxel-wise local spatial information. Besides, we adopt the up-sampling auxiliary paths to supplement the multi-scale information. Main results. The experimental results show that the method has better or more competitive segmentation performance than several advanced methods on three datasets. The best segmentation metric scores are as follows: DSC of 0.781, HD95 of 3.044, precision of 0.798, and sensitivity of 0.857. Comparative experiments based on bimodal and single modal indicate that bimodal input provides more sufficient and effective information for improving tumor segmentation performance. Ablation experiments verify the effectiveness and significance of each module. Significance. We propose a new network for 3D H&N tumor segmentation in FDG-PET/CT images, which achieves high accuracy.
A novel bonded-type 3-degree-of-freedom ultrasonic motor: design, simulation, and experimental investigation
Authors: Pan Zhiyong , Wang Liang , Yang Ying , Jin Jiamei , Qiu Jianmin 
Published: May 2023

Abstract
Most of 3-degree-of-freedom (3-DOF) ultrasonic motors generally have the problems of low positioning accuracy, difficulty to apply preload, and excessive longitudinal dimension, leading to the limitation in industrial application. To address these issues, a novel bonded-type 3-DOF ultrasonic motor, consists of a convex cylindrical stator, a pre-tightening force structure, and a hemispherical rotor, is proposed in this study. A quadratically polarized toroidal piezoelectric ceramic is bonded to the upper surface of the stator. Three vibration modes of the stator are excited by applying different excitation signals on the piezoelectric ceramic. By coupling any two of the three vibration modes, the elliptical motion of the driving feet of the convex cylindrical stator around the x, y, and z axes are excited, respectively. A novel pre-tightening force application method is proposed to adjust the contact between the stator and rotor. Under the action of friction, the elliptical motion of the driving feet in three directions drives the rotor to rotate around three axes, respectively. The vibration modes of the stator are first simulated by using the finite element method. In addition, the optimal structural dimensions of the stator and the elliptical motion trajectories of the driving feet are calculated. Finally, a prototype of the proposed 3-DOF ultrasonic motor is fabricated and experimentally investigated. The experimental results show that the no-load rotary velocities of the motor prototype in the three rotation directions are 25 r min−1, 22 r min−1, and 380 r min−1, respectively, its start/stop response time are 4.1 ms/3.9 ms, 6.3 ms/7.5 ms, and 6.1 ms/7.9 ms, respectively, and its angular displacement resolutions are 4.3 μrad, 5.1 μrad, and 10.2 μrad, respectively, when the excitation voltage is 250 VPP. The proposed motor holds the advantages of compact structure, adjustable preload, and high positioning accuracy, and presents a broad application prospect in the fields of robot joint technology, optical tracking system, and laser communication.
Investigation of a magnetorheological elastomer metamaterial sandwich beam with tunable graded stiffness for broadband vibration attenuation
Authors: Wang Yuhuai , Yang Jian , Chen Zexin , Lin Yu , Gong Liping , Zhang Shiwu , Li Weihua , Sun Shuaishuai 
Published: May 2023

Abstract
Metamaterials with local resonance show promising application prospects in low-frequency vibration attenuation. However, with the drawback of narrow band gap, such potential is greatly limited. In order to broaden the local resonant band gap, a semi-active graded magnetorheological elastomer (MRE) metamaterial sandwich beam (GMREMSB) with real-time tunable graded stiffness was proposed and investigated in this study. For theoretical calculation, a mass-spring model was established for the GMREMSB. Then the calculated band gap and transmissibility using Timoshenko beam theory and spectral element method were compared. An experimental test was also conducted for verification. The results show that the bandwidth of the proposed GMREMSB can be widened by the graded stiffness arranged in ascending order. The experimental band gap of the GMREMSB under the graded current of 0.0–0.5–1.0 A is 6.4 Hz wider than the band gap of the periodic structure with the single current of 0.0 A and is 5.0 Hz wider than that of 1.0 A. The growth rate reaches 15.06% and 11.39%, respectively.
Synergistic effects of adaptive reward and reinforcement learning rules on cooperation
Authors: Wang Lu , Fan Litong , Zhang Long , Zou Rongcheng , Wang Zhen 
Published: May 2023

Abstract
Cooperative behavior in multi-agent systems has been a focal point of research, particularly in the context of pairwise interaction games. While previous studies have successfully used reinforcement learning rules to explain and predict the behavior of agents in two-agent interactions, multi-agent interactions are more complex, and the impact of reward mechanisms on agent behavior is often overlooked. To address this gap, we propose a framework that combines the public goods game (PGG) with reinforcement learning and adaptive reward mechanisms to better capture decision-making behavior in multi-agent interactions. In that, PGG is adopted to reflect the decision-making behavior of multi-agent interactions, self-regarding Q-learning emphasizes an experience-based strategy update, and adaptive reward focuses on the adaptability. We are mainly concentrating on the synergistic effects of them. The simulations demonstrate that while self-regarding Q-learning fails to prevent the collapse of cooperation in the traditional PGG, the fraction of cooperation increases significantly when the adaptive reward strategy is included. Meanwhile, the theoretical analyses aligned with our simulation results, which revealed that there is a specific reward cost required to maximize the fraction of cooperation. Overall, this study provides a novel perspective on establishing cooperative reward mechanisms in social dilemmas and highlights the importance of considering adaptive reward mechanisms in multi-agent interactions.
Demonstration of a photonic router via quantum walks
Authors: Gao Huixia , Wang Kunkun , Qu Dengke , Lin Quan , Xue Peng 
Published: May 2023

Abstract
Motivated by the need for quantum computers to communicate between multiple, well separated qubits, we introduce the task of routing the quantum state from one input mode to a superposition of several output modes coherently. We report an experimental demonstration of a deterministic photonic routing protocol applied to an entangled state. We show in a quantum walk architecture, quantum networks perfectly route entangled states from an initial input mode to an arbitrary output mode coherently and deterministically. Our results demonstrate the key principle of a perfect router, opening a route toward data routing and transferring for quantum computing systems. The routing algorithm in our work can be applied to a wide range of physical systems, which provides a way for effective design of efficient routing protocols on practical quantum networks.
A real-time dynamic simulation platform for 6R manipulators based on an explicit modeling method
Authors: Liu Xiaohan , Ju Hehua , Chen Feifei 
Published: May 2023

Abstract
Dynamic simulation is critical for robotic design and control. In this study, a modular simulation platform is developed using a novel explicit dynamic modeling method. Combined with a PD controller and a globally linearized compensator, the dynamic modeling method presents an explicit and canonical form for tree-chain and closed-chain multi-body systems. Paired with the new approach, the platform makes it convenient for modeling, and it performs well in real-time with a high update frequency. The platform also offers a thorough parameter setting and simulation scene presentation interface through the construction of several modules. A simulation experiment using a 6-DOF manipulator demonstrates the platform’s capacity for real-time, stable, and practical simulations, which is also applicable for high-DOF multi-body systems of future uses.
Study on IMC Growth Law of Solder Joints in Aerospace Electronic Products and Its Influence on Reliability
Authors: Li Zhan , Yang Yiqing , Huang Wei , Gong Yubing , Pan Kailin 
Published: May 2023

Abstract
In order to better understand the influence degree of temperature curve parameters on the growth characteristics of intermetallic compounds (IMC) in solder joints during reflux welding, the influence of different temperature curve parameters on the thickness of IMC in Sn63Pb37 solder joints was explored based on the orthogonal test method. A multiple regression model was established for each parameter and the thickness of IMC. In addition, the results show that: (1) The influence degree of temperature curve parameters on IMC thickness in descending order is the highest temperature, liquidus time, cooling time and cooling rate. (2) The coefficient of the established multiple regression model is R2=0.70391. In this model, the IMC thickness increases with the increase of the maximum reflow temperature and liquidus time and decreases with the increase of the cooling time. (3) When the thickness of IMC is 1 μm- 4 μm, the thermal shock reliability of solder joints decreases with the increase of IMC thickness.
Numerical simulation of the temperature field of T2 copper/304 steel dissimilar metal electron beam welding
Authors: Yuan Shengbo , Liu Hailang , Yuan Shengbo , Ji Chuanshang , Wen Zhuochao 
Published: May 2023

Abstract
ANSYS workbench finite element analysis software was adopted to simulate and study the thermal process of electron beam welding of T2 copper and 304 steel heterogeneous materials in this paper. A solid model was established according to the size of the specimens, and a double ellipsoidal moving heat source model of the temperature field of the specimens of the three-dimensional dynamic simulation was imposed. Copper and steel welding transient temperature field distribution pattern and molten pool morphology were studied, and the temperature field welding thermal cycle curve was analyzed. The results show that the peak temperature of the steel side is higher than the copper side, the morphology of the welding pool is basically biased toward the steel side, and the highest temperature during the welding process appears in the center of the weld on the steel side about 0.5 mm.
Navigational water resistance analysis and optimization of the gondola of unmanned boat retractable device
Authors: Zhang Ke , Zhao Yapeng , Tai Jinghua 
Published: May 2023

Abstract
Combining the characteristics of the existing transfer slideway and double-point hanging type two retractable devices, this paper proposes a multiple-point hoisting, fast transfer, and retractable device. According to the viscous flow theory, one-way fluid-structure interaction simulation is used for the gondola structure of the unmanned boat retractable device. Considering the influencing factors of the width of the gondola, the void ratio, the shape and distribution of the void, and the deployment angle of the guardrail, the navigation water resistance of each model is compared and analyzed, and the optimal design of the gondola device is discussed. The results show that the increase of the top width and the decrease of the front-end void ratio will lead to an increase in the water resistance of the gondola; under the same porosity, the rectangular shape performs better than the circular shape, and the smaller interval between the rectangles is more conducive to reducing the water resistance of the gondola; the increase in the angle of the side door guardrail of the gondola will lead to an increase in the navigation water resistance; the final optimized design of the gondola device meets the strength requirements under extreme conditions of high sailing speed.
Study on optimizing transition layer thickness to reduce tire sidewall delamination
Authors: Zhang Shuang , Han Shanling , Zhang Kai , Chen Long , Li Yong 
Published: May 2023

Abstract
This paper begins with a common disease of tires. The analysis of the disease of the sidewall area reveals that the thickness of the transition layer is closely related to the tire side delamination. This paper combines the phenomenon of sidewall thinning with the actual working process. The shape optimization method is used to optimize the design of the tire side transition layer while maintaining the thickness of the tire’s inner liner layer. Finally, finite element analysis was used for simulation verification. The findings demonstrated that the optimum thickness of the transition layer structure enhanced the inner von Mises stress of the tire carcass chord by 8.04% and the shear stress by 8.79%-15.08%, hence boosting the sidewall resistance to thickness decrease by 12.98%-22.48%.
Optimal voltage compensation method for focal plane avalanche detector based on improved random forest
Authors: Chen Mengqiang , Yang Jiazhi , Yu Guangwang , Shen Jie 
Published: May 2023

Abstract
Working distance and background radiation greatly affect the signal-to-noise ratio of avalanche photodiode (APD) in the lidar detection system. The traditional method cannot adapt to a complex environment by offline compensation or pre-compensation according to the influence factors of the external environment. In this paper, an avalanche photodiode voltage compensation method based on the improved random forest is designed. Firstly, the distance image data is de-noised. Then the weight of each decision tree in the random forest was changed to improve the classification performance. The particle swarm optimization (PSO) algorithm was used to search for the optimal combination of parameters affecting classification accuracy and performance. Finally, the improved random forest algorithm is used to judge the current working state of APD at different distances, compensate for the bias voltage, and make APD work in the optimal state. The proposed method is compared with the k-nearest neighbor, support vector machine, and other commonly used classification algorithms, and the results verify the effectiveness of the proposed method.