bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sexual Violence in Higher Education: A Critical Discourse Analysis of University Policies
Authors: Ali Farah 
Published: November 2024

AbstractAs sexual violence has become increasingly visible in public discourse, scholarly research across disciplines has begun to critically examine how such cases are handled, ranging from the investigation processes, media reporting, and how the accused and accusing parties are impacted. While much of the existing linguistic research makes an important contribution to our understanding of the role of language in interpreting and evaluating sexual violence by focusing on the narrations of specific cases, one area of discourse that remains unexamined is that which is produced by those responsible for defining sexual violence and the protocols for investigating such cases. This study examines discourse on sexual violence found in US university policies and specifically focuses on contrasts between different discourse participants (i.e., faculty and students). Findings reveal that university policies frequently produce disparate discourse for students and employees that rarely acknowledge the hierarchical nature of faculty–student relationships. Given the increasing visibility of public discourse on sexual aggression and the power that such discourse has in influencing audiences, analyzing the linguistic strategies used to construct victims and perpetrators is crucial to our understanding of the various ways that sexual violence perpetuates injustice.
Response of a 4-nitrothiophenol monolayer to rapid heating studied by
vibrational sum frequency spectroscopy
Authors: Linke Matthias , Multhaup Joshua , Hasselbrink Eckart 
Published: November 2024

A monolayer of 4-nitrothiophenol adsorbed on an Au substrate was heated by illuminating the substrate with a 19 ps laser pulse of 532 nm wavelength. Within 91 ps, the temperature of the sample increased from room temperature by 113 K. Vibrational sum frequency spectroscopy was used to characterize the adsorption geometry of the molecules in the ordered domains in the monolayer film. Upon heating, the initially ordered monolayer largely lost its structure. While the molecules are initially tilted by about 50° with respect to the surface normal, the analysis indicates that the mean tilt angle increased to 80° with a spread for individual molecules of up to a tilt angle of 40° upon heating. The evolution of this loss of order lagged about 100 ps behind the temperature rise of the substrate.
Investigation and insights on the on-demand generation of monodispersed emulsion droplets from a floating capillary-based open microfluidic device
Authors: Deng Yueming , Xie Shuting , Li Zhengguang , Yang Ruizhi , Liu Zhenping , Yao Jiyuan , Zhang Haopeng , Jin Mingliang , Shui Lingling 
Published: November 2024

Simple and stable generation of monodispersed droplets with volume from picolitre to nanoliter is one of the key factors in high-throughput quantitative microreactors for chemical and biomedical applications. In this work, an efficient method that could realize simple manipulating microflow with a broad operation window for preparing monodispersed droplets with controllable diameter is developed. The microfluidic device is constructed by inserting a capillary with an oblique angle (α) into the continuous phase, named a floating capillary-based open microfluidic device (FCOMD). The transition of droplet-generating mode between dripping and jetting can be achieved by changing capillary number and α. A computational model based on the volume-of-fluid/continuum-surface-force method to explain the controllability of α on the droplet formation regime and droplet breakage, verifying the synergistic effect of ΔP and Fb, facilitates the droplet pinching. A descending order of Pn of capillary with different α is that 45° &gt; 30° &gt; 15° &gt; 60° &gt; 75°, leading to the same order of generated droplet’s D. When compared with the traditional capillary co-flow device, the generating throughput of the integrated FCOMD obtained by integrating different numbers of capillaries is at least ten times. Moreover, water in oil, oil in water double-emulsion, colloidal dispersed droplets, and liquid crystal droplets with diameters ranging from 25 to 800 μm are prepared on-demand by the FCOMD, indicating the universality of the microfluidic device. Thus, the FCOMD shows the features of simplicity, practicability, and flexibility, offering valuable guidance for generating controllable droplets with wide size change and showing a great potential application in material science, foods, pharmaceuticals, and cosmetics.
Automated potential energy surface development and quasi-classical dynamics for the F− + SiH3I system
Authors: Molnár Balázs J. , Dékány Attila Á. , Czakó Gábor 
Published: November 2024

We report a potential energy surface (PES) development for the F− + SiH3I system to study its gas-phase reactions through quasi-classical dynamics simulations. The PES is represented by a full-dimensional permutationally invariant polynomial fitted to composite coupled cluster energy points obtained at the ManyHF-[CCSD-F12b + BCCD(T) − BCCD]/aug-cc-pVTZ(-PP) level of theory. The development was automated by Robosurfer, which samples the configurational space, manages ab initio calculations, and iteratively extends the fitting set. When selecting the ab initio method, we address two types of electronic structure calculation issues: first, the gold standard CCSD(T)-F12b is prone to occasional breakdown due to the perturbative (T) contribution, whereas CCSD-F12b + BCCD(T) − BCCD, with the Brueckner (T) term, is more robust; second, the underlying Hartree–Fock calculation may not always converge to the global minimum, resulting in highly erroneous energies. To mitigate this, we employed ManyHF, configuring the Hartree–Fock calculations with multiple initial guess orbitals and selecting the solution with the lowest energy. According to the simulations, the title system exhibits exceptionally high and diverse reactivity. We observe two dominant product formations: SN2 and proton abstraction. Moreover, SiH2F− + HI, SiHFI− + H2, SiH2FI + H−, SiH2 + FHI−, SiH2 + HF + I−, and SiHF + H2 + I− formations are found at lower probabilities. We differentiated inversion and retention for SN2, both being significant throughout the entire collision energy range. Opacity- and excitation functions are reported, and the details of the atomistic dynamics are visually examined via trajectory animations.
An explicitly correlated potential energy surface for N2–OCS complex: Out-of-plane motion and tunneling dynamics
Authors: Zheng Rui , Cheng Tong , Liu Tongyu , Tian Yanshan 
Published: November 2024

A four-dimensional potential energy surface (4D-PES) has been constructed for the N2–OCS complex. The PES is achieved by applying the explicitly correlated coupled cluster method, which incorporates single, double, and perturbative triple excitations [CCSD(T)-F12a], along with the augmented correlation consistent triple zeta (aug-cc-pVTZ) basis set. The rovibrational levels are precisely determined and assigned through bound state calculations and wavefunction analysis. The calculated transition frequencies reproduce the experimental observations accurately, achieving an RMSE of 0.0005 cm−1 for the 23 rotational transitions (J ≤ 6, Ka ≤ 2). The R-φ contour plot of the wave function clearly demonstrates the unambiguous delocalization of the dihedral angle, and the averaged geometry of the ground vibrational state is determined to be non-planar with φ = 90°. To obtain a quantitative analysis of this phenomenon, we expanded the 3H-solution model [Guo et al., J. Quant. Spectrosc. Radiat. Transfer 309 (2023) 108711] from a three-dimensional system (Ar–AgF) to a nine-dimensional system (N2–OCS). Based on this model, the tunneling splitting was calculated to be 0.0822 cm−1, which excellently matches the experimental result of 0.0817 cm−1. The excellent agreement between the theoretical and experimental results suggests that the wavefunction delocalization and out-of-plane motion can be attributed to the tunneling effects in the ground vibrational state.
Demystifying the English bias in science
Authors: Thomas Enlli Môn , Parry Nia Mererid , Caulfield Gareth , Siôn Cadi Gwen 
Published: November 2024

Abstract
Whilst the prevalence of English in STEM-related fields is well established, less is understood about the
opportunities to study these subjects in languages other than English. This study examined pupils’ likelihood of studying
STEM-related subjects in Welsh-medium secondary schools in Wales, their linguistic choices around these subjects and their
attitudes towards learning such subjects in English and/or in Welsh. The results revealed a general impression among pupils of the
importance of English for STEM, which, mediated by their own linguistic backgrounds, abilities and experiences, influenced pupils’
linguistic choices and desires. Since the propensity towards monolingual engagement with these subjects — in Welsh or in English —
may limit the scope for scaffolding their learning across languages and the benefits that incur, we propose alternative bilingual
approaches to STEM-related subject teaching.
Exponential decay of the critical points in a discrete model of polyacetylene
Authors: Gontier David , Kouande Adechola E K , Séré Éric 
Published: November 2024

Abstract
In this paper we consider stationary states of the SSH model for infinite polyacetylene chains that are homoclinic or heteroclinic connections between two-periodic dimerized states. We prove that such connections converge exponentially fast to the corresponding asymptotic periodic states.
Relaxation model for a homogeneous plasmas with spherically symmetric velocity space
Authors: Wang 王 Yanpeng 彦鹏 , Xiao 肖 Jianyuan 建元 , Rao 饶 Xianhao 贤昊 , Zhang 张 Pengfei 鹏飞 , Adil 阿 Yolbarsop 迪里 , Zhuang 庄 Ge 革 
Published: November 2024

Abstract
We derive the transport equations from the Vlasov–Fokker–Planck equation when the velocity space is spherically symmetric. The Shkarofsky’s form of Fokker–Planck–Rosenbluth collision operator is employed in the Vlasov–Fokker–Planck equation. A closed-form relaxation model for homogeneous plasmas could be presented in terms of Gauss hypergeometric 2F1 functions. This has been accomplished based on the Maxwellian mixture model. Furthermore, we demonstrate that classic models such as two-temperature thermal equilibrium model and thermodynamic equilibrium model are special cases of our relaxation model and the zeroth-order Braginskii heat transfer model can also be derived. The present relaxation model is a nonequilibrium model based on the hypothesis that the plasmas system possesses finitely distinguishable independent features, without relying on the conventional near-equilibrium assumption.
A Stochastic Inertial Limited Memory BFGS Algorithm Based on the Mathematical Model of Rural Pipeline Network and its Application in Machine Learning
Authors: Deng Xi , Yuan Gonglin , Yang Yuehan 
Published: November 2024

Abstract
Stochastic algorithms are critical in addressing complex rural pipe networks and non-convex stochastic optimization problems. With the development of artificial intelligence, large-scale optimization problems that cannot be solved effectively by traditional optimization methods have emerged. Therefore, stochastic optimization algorithms are rapidly developing in the field of optimization. This paper introduces an inertial extrapolation stochastic BFGS (IESBFGS) algorithm, an innovative amalgamation of the inertial extrapolation technique and the finite memory quasi-Newton algorithm to solve nonconvex stochastic optimization problems. Firstly, the inertial extrapolation technique is employed to track the iteration point to the optimal x-value. Second, it is combined with a finite-memory proposed Newton algorithm thereby increasing the convergence speed. Then, the superiority of IESBFGS is verified by comparing the experimental performance with other better algorithms on machine learning SVM model and ERM model. Finally, it is shown that the algorithm offers good prospects for solving nonconvex problems.
Global Error Bounds for the Extended Vertical LCP of DZ matrices and DZ-B maxtrices
Authors: Gan Mengting , Yan Gaoyan 
Published: November 2024

Abstract
The linear complementarity problem is a mathematical problem with important theoretical value and wide practical applications. Research on linear complementarity problems mainly focuses on algorithm improvement and innovation; Combining with fields such as machine learning and artificial intelligence to expand the application scope of linear complementarity problems; Further research on the properties of linear complementarity problems, the existence and uniqueness of solutions, and other theoretical issues. In this paper, by virtue of the properties of DZ-matrices and DZ-B matrices, error bound estimation formulas for the extended vertical linear complementarity problem are given respectively for block matrices composed of DZ-matrices and DZ-B matrices under the condition of satisfying the row W-property. At the same time, the feasibility and superiority of the results are illustrated by numerical examples.
Thermal-mechanical Coupling Model of Angular Contact Ball Bearing and the Influence of its Surface Roughness
Authors: Xin Jiajia , Xing Shunmei , Xu Hao , Wang Jiayu , Wen Baogang 
Published: November 2024

Abstract
A model that integrates thermal and mechanical aspects for an angular contact ball bearing (ACBB) has been introduced, considering its surface roughness. Subsequently, the analysis of friction torque, clearance, contact force, and temperature within the bearing is conducted under varying levels of surface roughness based on the established model. Additionally, the characteristics of thermal-mechanical coupling are examined under different conditions. The results indicate a significant interdependence between temperature and contact forces in the bearing system. It is observed that both friction torque and temperature increase with increasing surface roughness. This analysis provides valuable insights into the thermal-mechanical coupling characteristics of bearings.
Crowd Abnormal Event Detection Based on Crowd Density
Authors: Liu Xuelin , Fei Fengchang 
Published: November 2024

Abstract
This paper proposes a crowd abnormal event detection algorithm based on the change of crowd density. This method does not use the optical flow of the traditional model, thus the algorithm is fast. The algorithm first estimates the crowd density in the scene, then uses the change of crowd density as the feature representation of the crowd and constructs 3D feature blocks by adding time axis attributes. Finally, a single classifier is used to classify the 3D feature blocks for detecting crowd abnormal events in videos. Because the algorithm uses the change of crowd density in the scene as the feature representation of the crowd, the algorithm will not be disturbed by the motion of vehicles and other objects in the scene. The experimental results show that the proposed algorithm runs faster and has higher accuracy than the optical flow algorithm.
A Data Augmentation Method and the Embedding Mechanism for Detection of Pulmonary Nodules on Small Samples
Authors: Liu Y , Hou Y J , Qin C X , Li X H , Li S J , Wang B , Zhou C C 
Published: November 2024

Abstract
Lung Computed Tomography (CT) screening for pulmonary nodules provides an effective method for early diagnosis. The deep-learning-based computer-aided detection (CAD) system effectively identifies and precisely localizes suspicious pulmonary nodules in CT images, thereby significantly enhancing the accuracy and efficiency of CT diagnosis. In the medical field, the availability of medical data is limited, and research using small samples is of practical significance. By studying the data augmentation technology based on the generative model under the condition of small samples, and refining the model structure through the embedding mechanism, the accuracy and robustness of the deep learning model are explored. A 3D pixel-level statistical algorithm is proposed for the generation of pulmonary nodules. By combining simulated pulmonary nodules with healthy lung tissue, we can generate new samples of pulmonary nodules. The embedding mechanism is designed to enhance the comprehension of pixel meanings in pulmonary nodule samples by introducing latent variables. The results of the 3DVNET model with the augmentation method for pulmonary nodule detection under small sample conditions demonstrate that the proposed data augmentation method outperforms the method based on a generative adversarial network (GAN) framework, and the embedding mechanism for pulmonary nodules detection shows a significant improvement in accuracy. Conclusion: the proposed data augmentation method and embedding mechanism demonstrate significant potential in enhancing the accuracy and robustness of the model, thereby facilitating their application to various common imaging diagnostic tasks, and research using small samples is of practical significance.
Evaluation of Distributed Power and Electric Vehicle Grid-connected Carrying Capacity Based on Genetic Algorithm Optimisation Neural Network
Authors: Yan Guifu , Ruan Wenjun , Xu Liang , Miao Huiyu , Liao Chenglin , Wang Lifang , Wang Liye 
Published: November 2024

Abstract
The integration of new types of loads, such as large electric vehicles (EVs) and distributed energy sources, and the conventional structure and operation of the power distribution network have been significantly transformed. This presents a significant challenge in maintaining the network’s capacity to handle the load. To address this challenge, a carrying capacity assessment method based on a machine learning model optimized by a bionic optimization algorithm is proposed. Firstly, the assessment model’s index system is established, and the distribution network data are collected and preprocessed. Secondly, considering the complexity of the distribution network carrying capacity assessment, a backpropagation (BP) neural network is constructed within the machine learning model. The genetic algorithm, inspired by biological optimization, is used to fine-tune the machine learning model’s parameters. Subsequently, the model’s accuracy and generalization capability are evaluated, and the optimized machine learning model is used to assess the carrying capacity of EVs and distributed power sources. The results of the carrying capacity assessment can provide an effective basis for subsequent power distribution network planning. This method combines the benefits of machine learning with bionic optimization algorithms to tackle the challenges associated with incorporating new load types into the power distribution network. The model’s ability to accurately assess the carrying capacity can support effective planning and management of the evolving distribution network.
Numerical Realization of Minimum Distance Estimation for Poisson Processes
Authors: Yang L , Zhou L 
Published: November 2024

Abstract
The statistical inference for minimum distance parametric estimation has been well developed in recent years, concerning the asymptotic behaviors of the minimum L
1-distance estimator, minimum L
2-distance estimator and minimum Skorohod-distance estimator. The question is how to realize the numerical solution of the minimum Skorohod-distance estimator, due to its complicated analytic structure. This paper aims to discuss the minimum distance estimation problem for Poisson processes. The main contribution includes the consistency and the approximate numerical solution of the minimum Skorohod-distance estimator, accompanied with the study of the minimum L
1-distance estimator and minimum L
2-distance estimator. In the numerical examples, three estimators exhibit the consistency trend. The mean square error of the minimum L
1-distance estimator and minimum L
2-distance estimator are comparable to, or superior to the minimum Skorohod-distance estimator in the regular cases, while the results are reversed in the change-point case. The bias of the minimum Skorohod-distance estimator is either superior or comparable to the others in the regular cases.
Research on the Motion of Few Tooth Magnetic Gears with Given Initial Angular Velocity Based on Euler Method
Authors: Chen Jiyun , Gao Feng , Li Yangyang , Wang Jun , Li Changyue , Li Shensi , Li Zhihan , Sun Mengwei 
Published: November 2024

Abstract
This paper aims to investigate the motion patterns of magnetically interacting gears with a few teeth and given initial angular velocities. Based on Ampere’s molecular current hypothesis, an equivalent current model is utilized to solve the magnetic field generated by cylindrical permanent magnets. Comsol software is employed to simulate the distribution of the magnetic field, demonstrating that the magnetic field calculated by the equivalent current model is in good agreement with the actual situation. Vector analysis, field theory, and the symmetry of the magnetic field distribution are leveraged to simplify the calculations, thereby reducing the runtime of the program. The Euler method is adopted to numerically simulate the motion of the gears. Finally, a comparison with experiments shows that the motion patterns in the experiments are essentially consistent with those in the numerical simulations. It is concluded that the ratio of magnetic field intensity to resistance torque, initial magnetic potential energy, and initial angular velocity positively correlate with the motion duration; the ratio of magnetic field intensity to resistance torque and initial magnetic potential energy positively correlate with the intensity of motion; when the initial angular velocity is excessively high, the other gear exhibits slight vibrations in its original position, and only when the initial velocity decreases to a certain value do the two gears interact significantly.
Numerical Solution of Backward Problem of Distributed-order Time-space Fractional Diffusion Equation
Authors: Wang H M , Li Y S 
Published: November 2024

Abstract
This paper aims to determine the initial value problem in the distributed-order time-space fractional diffusion equation (DTSFDE) with Neumann boundary conditions using final data. Initially, the direct problem of the DTSFDE is computed using the Finite Difference Method (FDM), followed by applying the Tikhonov regularization method to minimize a function and regularize the inverse problem. By employing the novel Optimal Perturbation Algorithm (OPA) for the inversion of initial values, the efficiency and accuracy of solving the inverse problem are enhanced. Ultimately, the effectiveness and practicality of this approach in addressing the initial value problem in the DTSFDE are demonstrated through numerical examples in one and two dimensions.