bluuebunny/crossref_metadata_2025_split huggingface dataset:
State-to-state dynamics and machine learning predictions of inelastic and reactive O(3P) + CO(1∑+) collisions relevant to hypersonic flows
Authors: Huang Xia , Cheng Xinlu 
Published: May 2024

The state-to-state (STS) inelastic energy transfer and O-atom exchange reaction between O and CO(v), as two fundamental processes in non-equilibrium air flow around spacecraft entering Mars’ atmosphere, yield the same products and both make significant contributions to the O + CO(v) → O + CO(v′) collisions. The inelastic energy transfer competes with the O-atom exchange reaction. The detailed reaction mechanisms of these two elementary processes and their specific contributions to the CO relaxation process are still unclear. To address these concerns, we performed systematic investigations on the 3A′ and 3A″ potential energy surfaces (PESs) of CO2 using quasi-classical trajectory (QCT) calculations. Analysis of the collision mechanisms reveals that inelastic collisions have an apparent PES preference (i.e., they tend to occur on the 3A′ PES), while reactive collisions do not. Reactive rates decrease significantly when the total collision energy approaches dissociation energy, which differs from the inelastic process. Inelastic rates are generally lower than the reactive rates below ∼10 000 K, except for single quantum jumps, whereas the reverse is observed above ∼10 000 K. In addition, by combining QCT with convolutional neural networks, we have established neural network (NN)-STS1 (inelastic) and NN-STS2 (reactive) models to generate all possible STS cross sections. The NN-based models accurately reproduce the results calculated from QCT calculations. In this study, all calculations have been focused on analyzing collisions at the ground rotational level.
A MASH simulation of the photoexcited dynamics of cyclobutanone
Authors: Lawrence Joseph E. , Ansari Imaad M. , Mannouch Jonathan R. , Manae Meghna A. , Asnaashari Kasra , Kelly Aaron , Richardson Jeremy O. 
Published: May 2024

In response to a community prediction challenge, we simulate the nonadiabatic dynamics of cyclobutanone using the mapping approach to surface hopping (MASH). We consider the first 500 fs of relaxation following photoexcitation to the S2 state and predict the corresponding time-resolved electron-diffraction signal that will be measured by the planned experiment. 397 ab initio trajectories were obtained on the fly with state-averaged complete active space self-consistent field using a (12,11) active space. To obtain an estimate of the potential systematic error, 198 of the trajectories were calculated using an aug-cc-pVDZ basis set and 199 with a 6-31+G* basis set. MASH is a recently proposed independent trajectory method for simulating nonadiabatic dynamics, originally derived for two-state problems. As there are three relevant electronic states in this system, we used a newly developed multi-state generalization of MASH for the simulation: the uncoupled spheres multi-state MASH method (unSMASH). This study, therefore, serves both as an investigation of the photodissociation dynamics of cyclobutanone, and also as a demonstration of the applicability of unSMASH to ab initio simulations. In line with previous experimental studies, we observe that the simulated dynamics is dominated by three sets of dissociation products, C3H6 + CO, C2H4 + C2H2O, and C2H4 + CH2 + CO, and we interpret our predicted electron-diffraction signal in terms of the key features of the associated dissociation pathways.
Structural, morphological, optical, electrical, and magnetic properties of aluminum-doped CoxCa(0.90−x)Ni0.10Fe2O4 flexible substrate for visible to NIR spectra applications
Authors: Billa Md. Bakey , Islam Mohammad Tariqul , Alam Touhidul , Islam Md. Shabiul , Moubark Asraf Mohamed , Alsaif Haitham , Albadran Saleh , Alzamil Ahmed , Alshammari Ahmed S. 
Published: May 2024

This paper presents a conductive component tailored to a flexible substrate using Al-doped CoxCa(0.90−x)Ni0.10Fe2O4 (x = 0.25, 0.50, and 0.75) for visible to near-infrared (NIR) spectra in magneto-optical applications. The developed nanoparticles show uniformity, nanosized grains, and capillary nanopore fusion characteristics, which are confirmed by x-ray diffraction (XRD), field emission scanning electron microscopy, and energy-dispersive x-ray spectroscopy analyses, respectively. The XRD analysis revealed crystallite sizes of 33.36, 37.08, and 44.25 nm and particle sizes of 45.6, 34.6, and 31.5 nm for the compositions x = 0.25, 0.50, and 0.75, respectively. The Al-doped nanoparticles are converted to a flexible solid substrate utilizing a polyvinyl alcohol matrix, facilitating conformality to build complex shapes and broadening their application scope. The structure shows higher absorption across 450–720 nm, 480–720 nm, and 200–850 nm spectra for x = 0.25, 0.50, and 0.75, respectively. The distinctive magnetic and electrical properties are also evaluated through magnetic force microscopy and conductive atomic force microscopy, culminating in a substrate with exceptional control over light–matter interactions with smooth surfaces with lower surface roughness. The vibrating sample magnetometer analysis of the substrate shows how varying cobalt content affects magnetic properties relevant for visible to near-infrared (NIR) applications, offering insights into coercivity, magnetization, and retentivity changes at different x values. The perceptible novelties of this work are advancements in material sciences aimed at enhancing light manipulation and flexibility for electronic devices.
Photoluminescence study of anatase TiO2 photocatalysts at the pico- and nanosecond timescales
Authors: Katoh Ryuzi , Seki Kazuhiko 
Published: May 2024

We studied the photoluminescence decay kinetics of three nanosized anatase TiO2 photocatalysts (particle diameter: 7, 25, or 200 nm) at the pico- and nanosecond timescales for elucidating the origin of the luminescence. Luminescence spectra from these photocatalysts obtained under steady-state excitation conditions comprised green luminescence that decayed on the picosecond timescale and red luminescence that persisted at the nanosecond timescale. Among the photocatalysts with different sizes, there were marked differences in the rate of luminescence decay at the picosecond timescale (&lt;600 ps), although the spectral shapes were comparable. The similarity in the spectral shape indicated that self-trapped excitons (STEs) directly populated in the bulk of the particle by light excitation emit the luminescence in a picosecond timescale, and the difference in the rate of luminescence decay originated from the quenching at the particle surface. Furthermore, we theoretically considered excitation light intensity dependence on the quantum yield of the luminescence and found that the quenching reaction was not limited by the diffusion of the STEs but by the reaction at the particle surface. Both the spectral shape and time-evolution of the red luminescence from the deep trapped excitons in the nanosecond timescale varied among the photocatalysts, suggesting that the trap sites in different photocatalysts have different characteristics with respect to luminescence. Therefore, the relation between trap states and photocatalytic activity will be elucidated from the red luminescence study.
State-selective dissociative double ionization of CH3I and CH2I2 via I 4d core-hole states studied by multi-electron–ion coincidence spectroscopy
Authors: Fushitani Mizuho , Hikosaka Yasumasa , Tashiro Motomichi , Hishikawa Akiyoshi 
Published: May 2024

The dissociative double ionization of CH3I and CH2I2 irradiated with extreme ultraviolet light at hv = 100 eV is investigated by multi-electron–ion coincidence spectroscopy using a magnetic bottle type electron spectrometer. The spin–orbit state-resolved Auger electron spectra for the I 4d core-hole states, (I 4d3/2)−1 and (I 4d5/2)−1, provide clear identifications of electronic states of CH3I2+ and CH2I22+. The dominant ion species produced after the double ionization correlate with the Auger electron energy, showing that different fragmentation pathways are open depending on the electronic states populated by the Auger decay. Theoretical calculations are performed to understand the fragmentation from the doubly charged states and the observed spin–orbit specificity in the Auger decay.
Border-making as illiberal politics
Authors: Scott James Wesley 
Published: May 2024

Abstract
Radical Right-wing populism frequently involves ‘divide and rule’ strategies as a means to attain and consolidate
political power. In the cases of Viktor Orban’s political regime in Hungary and Donald Trump’s four-year presidency (and its
aftermath), we find a pronounced attempt to create narrative hegemony of a sense of nation built upon Christian civilization and
foundationalist understandings of national identity. Moreover, both cases reveal processes of social border-making that are
reflected in norming and the creation of distinctions based on degrees of national authenticity. Applying an ontological security
and critical borders studies approach, this paper will specifically focus on the different ways in which border-making processes,
or bordering, are implicated in the exercise of illiberal political power in the Hungarian and US cases. Despite many
similarities, the actual mobilization of popular support reflects local conditions and has resulted in rather different political
outcomes.
The extended transmission of cold atmospheric plasma-induced anticancer effects between cells
Authors: Zhang Jishen , Wu Tong , Jing Xixi , Xu Shengduo , Wang Zifeng , Lin Jiao , Guo Li , Liu Dingxin , Zhang Hao , Xu Yujing , Wang Xiaohua , Rong Mingzhe 
Published: May 2024

Abstract
The application of cold atmospheric plasma (CAP) in cancer therapy has recently received extensive attention. However, researchers have mainly studied the effects of CAP on plasma-irradiated cancer cells and have not determined whether CAP affects surrounding or distant non-irradiated cancer cells. Here, the viability and intracellular reactive oxygen species levels of plasma-irradiated and non-irradiated cancer cells were monitored in vitro. Our results showed that CAP may induce plasma-irradiated cancer cells (A549 cells, A375 cells, J82 cells and HT29 cells) to secrete soluble factors into the cell culture medium, which can transmit plasma-induced anticancer effects from plasma-irradiated cancer cells to non-irradiated cancer cells. Our research confirmed that plasma-induced anticancer effects can be transmitted between cancer cells.
Trends in hot carrier distribution for disordered noble-transition metal alloys
Authors: Thareja Eklavya , McPeak Kevin M , Sprunger Phillip T , Vekhter Ilya , Shelton William A 
Published: May 2024

Abstract
We developed and tested an approach for predicting trends for efficient hot carrier generation among disordered metal alloys. We provide a simple argument for the importance of indirect transitions in the presence of disorder, thus justifying the use of joint density of states (JDOS)-like quantities for exploring these trends. We introduce a new JDOS-like quantity, JDOSK
, which heuristically accounts for longer lifetimes of quasiparticles close to the Fermi energy. To demonstrate the efficacy of this new quantity, we apply it to the study of Cu50X50 where X = Ag, Au, Pd and Y50Pd50 where Y = Au, Ni. We predict that Ni50Pd50 produces the most hot carriers among the alloys considered. The improvement in the density of excited photocarriers over the base alloy used, Cu50Ag50, is 3.4 times for 800 nm and 19 times for 1550 nm light. This boost in hot-carrier generation is consequence of the ferromagnetic nature of the Ni alloy. We argue that our method allows efficient material-specific predictions for low bias photoconductivity of alloys.
Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network
Authors: Yu Chongchong , Li Mengxiong , Wu Zongning , Gao Kuo , Wang Fei 
Published: May 2024

Abstract
Feature selection is a crucial step in fault diagnosis. When rolling bearings are susceptible to compound faults, causal relationships are hidden within the signal features. Complex network analysis methods provide a tool for causal relationship modeling and feature importance assessment. Existing studies mainly focus on unweighted networks, overlooking the impact of the strength of causal relationships on feature selection. To address this issue, we propose a compound fault feature selection method based on the causal feature weighted network. First, we construct a weighted network using the incremental association Markov blanket discovery algorithm and Pearson correlation coefficient. Then, we quantify the importance of features by treating node strength as a centrality index and rank them to partition the feature subset. Finally, the optimal feature subset is obtained through a neural network with the accuracy of compound fault diagnosis as the threshold. Analysis of public datasets and comparative experiments demonstrate the advantages of our method. Compared to existing research, our method not only effectively reduces the number of optimal feature subsets to 11 but also improves the accuracy of compound fault diagnosis to 95.2%. Furthermore, we employ the SHapley Additive exPlanations to interpret the contribution of each feature in the optimal subset to the accuracy of compound fault diagnosis. This provides reference from both physical and network perspectives to feature selection and compound fault diagnosis in rolling bearings in practical working conditions.
Performance degradation assessment of rolling bearings based on the comprehensive characteristic index and improved SVDD
Authors: Du Yongzhi , Cao Yu , Wang Haochen , Li Guohua 
Published: May 2024

Abstract
Rolling bearing is one of the most critical parts of mechanical equipment, so the performance degradation assessment of rolling bearing is vital to ensure the normal operation of the whole mechanical equipment. Aiming at the problems that a single degradation characteristic can only contain limited performance degradation information of rolling bearings, a large number of redundant characteristics exist in the high-dimension characteristic set resulting in the inability to effectively mine the characteristic information of rolling bearings, and that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings, a performance degradation assessment method of rolling bearings based on the comprehensive characteristic index and improved support vector data description (SVDD) is proposed in this paper. Firstly, to solve the parameter selection problem of variational mode decomposition (VMD), a parameter-adaptive VMD method based on salp swarm algorithm based on mixed strategy (MSSSA) is proposed. Secondly, to extract the performance degradation information of rolling bearings more comprehensively and fully, the comprehensive characteristic index is proposed. Then, a kernel locality preserving projection orthogonal kernel principal component analysis (KLPPOKPCA) method is proposed to reduce the dimensionality of the extracted multi-domain characteristic set of the rolling bearing. Finally, a support vector data description with negative samples (NSSVDD) is proposed and optimized by MSSSA to solve the problem that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings and improve the detection performance of abnormal data. The experimental results show that the proposed method can accurately divide the performance degradation process of the rolling bearing. Moreover, the comparison with other methods further highlights the superiority of the proposed method in determining the point in time of early fault of the rolling bearing.
Simulation study of compressed ultrafast 3D imaging based on interferometry
Authors: Long Jiale , Du Zihao , Zhang Jianmin , Xi Jiangtao , Ma Zhao , Peng Yuyang 
Published: May 2024

Abstract
Observing the 3D (three-dimensional) surface vibration in 100 MHz is challenging due to low speed of CCD/CMOS camera. In order to obtain the ultrafast dynamic 3D shape information, a compressed ultrafast 3D imaging framework based on interferometry is proposed in this paper. Based on this imaging framework, an reconstruction method combining plug and play-fast flexible denoising network is developed to recover high resolution interference fringe patterns and the corresponding 3D information. The simulation results show that the proposed method can observe the dynamic processes of the anti-phase vibration and the in-phase vibration of the crystal oscillator, therefore the dynamic 3D model is established. The proposed method outperforms other reconstruction methods in terms of average peak signal to noise ratio and structural similarity in recovery the ultrafast dynamic 3D surface.
Simultaneous measurement of thickness and group refractive index in birefringent crystals
Authors: Yangmei Zhang , Qiukun Zhang 
Published: May 2024

Abstract
In order to measure the group refractive index and thickness of birefringent crystals simultaneously, a method based on spectral-domain interferometry is proposed. The optical path of each reflection layer of birefringent crystal is converted into spectral interference fringe of corresponding frequency by the principle of spectral-domain interferometry, and the accurate optical path calculation is realized by fast Fourier transform and Hanning-windowed energy centrobaric method. A two-step measurement method was designed to measure the crystal thickness and group refractive index of ordinary (

o

) and extraordinary (

e

) light synchronously by comparing the changes of the optical path before and after the insertion of the sample. The comparison between experimental and theoretical results of LiNbO3 crystals shows that the accuracy of the thickness measured by this system is better than 1 μm and the relative error of the group refractive index is better than 0.15%. The measurement method is simple to implement and has high precision, which is of practical significance for realizing the rapid and high-precision measurement of optical parameters of birefringent crystals.
Fault feature extraction method based on maximum envelope spectrum PFGI2 and empirical wavelet transform
Authors: Tian Yinchu , Tang Guiji , Liu Zichen , Tian Tian , Xiaolong Wang 
Published: May 2024

Abstract
Fast kurtogram (FK) is an efficient method for processing non-stationary signals, widely recognized by scholars as a rapid and effective approach for fault diagnosis. However, it has limitations in distinguishing between periodic pulse and random interference pulses due to the drawbacks in its frequency band segmentation methods and the inherent shortcomings of the kurtosis index itself. To address this, this paper proposes a fault feature extraction method based on the maximum envelope spectrum power function-based Gini indices (PFGI2) and empirical wavelet transform. This method, inspired by the concept of FK, constructs a series of band-pass filters following the principles of empirical wavelet decomposition. It applies envelope spectrum analysis to a series of sub-bands and calculates the PFGI2 value for each, to identify the optimal sub-band. The effectiveness of the proposed method is validated through simulations of vibration signals and experimental data.
Asymptotic stability of rarefaction wave for compressible Euler system with velocity alignment
Authors: Bai Xiang , Li Lin-An , Xu Xiaojing 
Published: May 2024

Abstract
In this paper, we study the asymptotic stability of the rarefaction wave for the one-dimensional compressible Euler system with nonlocal velocity alignment. Namely, for the initial data approaching to rarefaction wave, we prove the corresponding solution converges toward the rarefaction wave. Moreover, we obtain this system has weak alignment behavior. We develop some promoted estimates for the smooth approximate rarefaction wave and new a priori estimates by Fourier analysis tools. Moreover, we introduce the weighted energy method and Besov spaces to obtain the key high-order derivative estimates, in which we overcome the difficulties caused by the nonlocal velocity alignment. It is worth mentioning that this is the first stability result of rarefaction wave for compressible Euler system with velocity alignment.
Deep learning based linear energy transfer calculation for proton therapy
Authors: Tang Xueyan , Wan Chan Tseung Hok , Moseley Douglas , Zverovitch Alexei , Hughes Cian O , George Jon , Johnson Jedediah E , Breen William G , Qian Jing 
Published: May 2024

Abstract

Objective. This study aims to address the limitations of traditional methods for calculating linear energy transfer (LET), a critical component in assessing relative biological effectiveness (RBE). Currently, Monte Carlo (MC) simulation, the gold-standard for accuracy, is resource-intensive and slow for dose optimization, while the speedier analytical approximation has compromised accuracy. Our objective was to prototype a deep-learning-based model for calculating dose-averaged LET (LETd) using patient anatomy and dose-to-water (DW) data, facilitating real-time biological dose evaluation and LET optimization within proton treatment planning systems. Approach. 275 4-field prostate proton Stereotactic Body Radiotherapy plans were analyzed, rendering a total of 1100 fields. Those were randomly split into 880, 110, and 110 fields for training, validation, and testing. A 3D Cascaded UNet model, along with data processing and inference pipelines, was developed to generate patient-specific LETd distributions from CT images and DW. The accuracy of the LETd of the test dataset was evaluated against MC-generated ground truth through voxel-based mean absolute error (MAE) and gamma analysis. Main results. The proposed model accurately inferred LETd distributions for each proton field in the test dataset. A single-field LETd calculation took around 100 ms with trained models running on a NVidia A100 GPU. The selected model yielded an average MAE of 0.94 ± 0.14 MeV cm−1 and a gamma passing rate of 97.4% ± 1.3% when applied to the test dataset, with the largest discrepancy at the edge of fields where the dose gradient was the largest and counting statistics was the lowest. Significance. This study demonstrates that deep-learning-based models can efficiently calculate LETd with high accuracy as a fast-forward approach. The model shows great potential to be utilized for optimizing the RBE of proton treatment plans. Future efforts will focus on enhancing the model’s performance and evaluating its adaptability to different clinical scenarios.