bluuebunny/crossref_metadata_2025_split huggingface dataset:
Toward more accurate surface properties of ceria using many-body perturbation theory
Authors: Wei Ziyang , Yan George , Sautet Philippe 
Published: August 2023

Despite the wide applications, the ab initio modeling of the ceria based catalyst is challenging. The partial occupation in the 4f orbitals creates a fundamental challenge for commonly used density functional theory (DFT) methods, including semilocal functionals with Hubbard U correction to force localization and hybrid functionals. In this work, we benchmark the random phase approximation (RPA) for ceria surface properties, including surface energy and hydrogenation energy, compared to the results utilizing the DFT + U approach or hybrid functionals. We show that, for the latter approaches, different surface properties require opposite directions of parameter tuning. This forms a dilemma for the parameter based DFT methods, as the improvement of a certain property by tuning parameters will inevitably lead to the worsening of other properties. Our results suggest that the parameter-free many-body perturbation theory methods exemplified by RPA are a promising strategy to escape the dilemma and provide highly accurate descriptions, which will allow us to better understand the catalytic reactions in ceria related systems.
Patients resist, doctors manage
Authors: Mao Yansheng , Wei Shuang , Wang Xiaojiang 
Published: August 2023

Abstract
Although there are abundant studies on the management of patient resistance in traditional face-to-face clinical settings, scant attention has been paid to that in the online context. This study thus delves into the types of patient resistance and the strategies Chinese doctors employ to manage such resistance in Online Medical Consultation (OMC). A dataset of 148 online doctor-patient conversations was collected from Chunyu Doctor. Three types of patient resistance – expertise-oriented, experience-oriented, and emotion-oriented – are identified and analyzed in Chinese OMC. Accordingly, Chinese doctors generally adopt linguistic strategies such as interpretives to justify decisions, supportives to facilitate relational work, and imperatives in the event of persistent resistance. Furthermore, the management of patient resistance is discussed as a joint goal-centered activity in Chinese OMC, aiming to contribute to a comprehensive understanding of the shared decision-making process in Chinese health communication.
Role of site–site interaction on the phase equilibria of multiple-site associating fluids in a functionalized slit pore
Authors: Mandal Sashanka Sekhar , Singh Sudhir Kumar , Khan Sandip 
Published: August 2023

Abstract
Vapor–liquid phase equilibria for multiple sites associating fluids with different associating strengths are investigated in a slit pore using grand-canonical transition matrix Monte Carlo method. The increase of critical temperature from two-site to four-site associating fluids at constant site strength is quite significant as compared to that of the one-site to two-site associating fluids, which is more pronounced at higher associating strength (ϵ* = 6). Monomer fraction and cluster size distribution are used to investigate the association of fluid particles in coexistence phases. The monomer fraction for both phases decreases with increased associating sites on the fluid particles due to more site–site interaction with neighboring fluid particles and forming a larger cluster. Therefore, the number of associating sites and their distribution play a vital role in the association of fluid particles. Moreover, the saturation chemical potential changes with the arrangement of the sites. For two-site associating fluids, we observe early vapor–liquid transition when the sites are oppositely placed, and when the sites are placed at 90°, the vapor–liquid transition is observed at the higher chemical potential. Moreover, four-site associating fluids with a square arrangement show early vapor–liquid phase transition, mainly because these arrangements of sites effectively interact with surface sites and the molecules in the next layer.
Roadmap on electronic structure codes in the exascale era
Authors: Gavini Vikram , Baroni Stefano , Blum Volker , Bowler David R , Buccheri Alexander , Chelikowsky James R , Das Sambit , Dawson William , Delugas Pietro , Dogan Mehmet , Draxl Claudia , Galli Giulia , Genovese Luigi , Giannozzi Paolo , Giantomassi Matteo , Gonze Xavier , Govoni Marco , Gygi François , Gulans Andris , Herbert John M , Kokott Sebastian , Kühne Thomas D , Liou Kai-Hsin , Miyazaki Tsuyoshi , Motamarri Phani , Nakata Ayako , Pask John E , Plessl Christian , Ratcliff Laura E , Richard Ryan M , Rossi Mariana , Schade Robert , Scheffler Matthias , Schütt Ole , Suryanarayana Phanish , Torrent Marc , Truflandier Lionel , Windus Theresa L , Xu Qimen , Yu Victor W-Z , Perez D 
Published: August 2023

Abstract
Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry, and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing.
CT2X-IRA: CT to x-ray image registration agent using domain-cross multi-scale-stride deep reinforcement learning
Authors: Geng Haixiao , Xiao Deqiang , Yang Shuo , Fan Jingfan , Fu Tianyu , Lin Yucong , Bai Yanhua , Ai Danni , Song Hong , Wang Yongtian , Duan Feng , Yang Jian 
Published: August 2023

Abstract

Objective. In computer-assisted minimally invasive surgery, the intraoperative x-ray image is enhanced by overlapping it with a preoperative CT volume to improve visualization of vital anatomical structures. Therefore, accurate and robust 3D/2D registration of CT volume and x-ray image is highly desired in clinical practices. However, previous registration methods were prone to initial misalignments and struggled with local minima, leading to issues of low accuracy and vulnerability. Approach. To improve registration performance, we propose a novel CT/x-ray image registration agent (CT2X-IRA) within a task-driven deep reinforcement learning framework, which contains three key strategies: (1) a multi-scale-stride learning mechanism provides multi-scale feature representation and flexible action step size, establishing fast and globally optimal convergence of the registration task. (2) A domain adaptation module reduces the domain gap between the x-ray image and digitally reconstructed radiograph projected from the CT volume, decreasing the sensitivity and uncertainty of the similarity measurement. (3) A weighted reward function facilitates CT2X-IRA in searching for the optimal transformation parameters, improving the estimation accuracy of out-of-plane transformation parameters under large initial misalignments. Main results. We evaluate the proposed CT2X-IRA on both the public and private clinical datasets, achieving target registration errors of 2.13 mm and 2.33 mm with the computation time of 1.5 s and 1.1 s, respectively, showing an accurate and fast workflow for CT/x-ray image rigid registration. Significance. The proposed CT2X-IRA obtains the accurate and robust 3D/2D registration of CT and x-ray images, suggesting its potential significance in clinical applications.
A capacitive method and experiment for measurement of sedimentation of magnetorheological fluid
Authors: Xie Lei , Li Pei , Yang Xunyong , Yin Jianfei , Lu Chuan , Shao Xiangyang , Zhang Xiang , Wang Pengsai , Liao Changrong 
Published: August 2023

Abstract
Sedimentation is one of the key issues in the practical engineering applications of magnetorheological fluids (MRFs). Testing and evaluation of the suspension stability of MRFs should be a prerequisite procedure before their applications. There are a few reported methods of measurement of sedimentation of MRFs. The opaque zone below the mudline, especially the sediment zone at the bottom in the MRF column, is the true key factor that affects the applications of MRF due to the possible irreversibility caused by the caking of the sediment zone. This study proposes a novel capacitive method on the basis of the change in dielectric constant resulting from the change in concentration of the magnetic metal particles of MRF upon sedimentation. First, an analytic model was established to present a positive correlation between the dielectric constant and volume fraction of particles throughout the entire MRF column and the theoretical expression of the dielectric constant of MRF in a test tube was obtained. Second, a capacitive sensor and the relevant experimental set-up were designed and fabricated. Especially, the effect of eccentricity error of the capacitor sensor structure on the measurement was analyzed. Then, a sedimentation experiment for prepared MRF samples was carried out, and the testing results were discussed and verified by a visual mudline observation of the supernatant zone, indicating the feasibility of testing and evaluating the suspension stability of MRF by the capacitive method.
Initial-condition effects on ReLU-type hyper-jerk system and its application in image encryption
Authors: Zhang Yunzhen , Peng Daao , Chen Chengjie , Zhao Guangzhe , Zhang Xiangqun 
Published: August 2023

Abstract
The memristive hyper-jerk system described in this study has a non-smooth Rectified Linear Unit (ReLU) memductance function. A non-smooth memductance function of this kind might cause the system to have three line equilibria within it, bring about the arrival of extreme multistability with infinitely many coexisting attractors. Analyzing the three line equilibria’s stabilities and researching the intricate dynamical effects triggered by memristor and non-memristor initial conditions are related topic. Phase portraits, bifurcation diagrams, Lyapunov exponent spectra, and the basins of attraction all show the dynamical tendencies. According to the results, the novel system’s dynamical behaviors depend on both the memristor initial condition and the non-memristor initial conditions. Moreover, PSIM circuit simulations and MCU-based hardware measurements are run to confirm the coexisting infinitely many attractors. The ReLU-type hyper-jerk system is then utilized to encrypt images. Experimental findings support its exceptional robustness against a range of potential attacks.
Predicting temperature field in keyhole-mode selective laser melting with combined heat sources: a rapid model
Authors: Li Bing , Li Changyou , Chunlei Hua 
Published: August 2023

Abstract
Predicting the temperature field during selective laser melting (SLM) is crucial for improving the performance of printed parts. However, there is still a lack of an efficient and accurate model for predicting the temperature field of keyhole-mode melting in SLM. Based on the physical phenomena of keyhole-mode melting observed in experiments and simulations, this study proposes an analytical model for rapidly predicting the temperature distribution during SLM keyhole-mode melting. The model considers vapor depression in the molten pool and the interaction between the laser and molten pool during keyhole-mode melting. The model was validated using numerical simulations and experimental data. The variation trend of the laser energy distribution and molten pool size with respect to the laser energy density was revealed. As the laser energy density increased, the depth of the molten pool and the vapor depression increased linearly, and the molten pool width increased to a peak and then remained constant. The process parameter window to avoid a lack-of-fusion was also investigated. With a computation time of 15 s and a prediction error of less than 10%, this model is an effective way to simulate SLM processes and guide the optimization of process parameters.
Determination of the angular momentum of the Kerr black hole from equatorial geodesic motion
Authors: Villegas Laura O. , Ramírez-Codiz Eduardo , Jaramillo Víctor , Degollado Juan Carlos , Moreno Claudia , Núñez Darío , Romero-Cruz Fernando J. 
Published: August 2023

Abstract
We present a method to determine the angular momentum of a black hole based on observations of the trajectories of the bodies in the Kerr spacetime. We use the Hamilton equations to describe the dynamics of a particle and present results for equatorial trajectories, obtaining an algebraic equation for the magnitude of the black hole's angular momentum with coefficients given by observable quantities. We tailor a numerical code to solve the dynamical equations and use it to generate synthetic data. We apply the method in some representative examples, obtaining the parameters of the trajectories as well as the black hole's angular momentum in good agreement with the input data.
General black-hole metric mimicking Schwarzschild spacetime
Authors: Konoplya R.A. , Zhidenko A. 
Published: August 2023

Abstract
Using the general parametrization of spherically symmetric and asymptotically flat black holes in arbitrary metric theories of gravity and implying that: a) the post-Newtonian constraints are taken into account and b) basic astrophysically relevant characteristics (such as, dominant quasinormal modes, frequency at the innermost stable circular orbit, binding energy, radius of the shadow etc.) are indistinguishable from their Schwarzschild values, we propose a simple metric which depends on three independent parameters (coefficients of the parametrization). Variation of these three parameters can, nevertheless, lead to the two distinctive features. The first is the black-hole temperature, and consequently the Hawking radiation, which can differ a lot from its Schwarzschild limit. The second is the outburst of overtones which become extremely sensitive to small changes of the parameters.
Investigation on the scaling of magneto-Rayleigh–Taylor instability to the current rise time of Z-pinch plasmas
Authors: Xiaoguang Wang , Xiaodong Ren , Shijian Meng , Chongyang Mao , Delong Xiao , Qiang Yi , Shaotong Zhou , Xianbin Huang , Xiuwen Zhou , Wenqian Weng , Zhanchang Huang , Chuang Xue , Guanqiong Wang , Shunkai Sun , Xiaojian Shu 
Published: August 2023

Abstract
Understanding how the magneto-Rayleigh–Taylor instability (MRTI) scales to the current rise time is vital for Z-pinch dynamic hohlraum driven inertial confinement fusion. Wang et al discovered in prior theoretical work that the perturbation amplitude of MRTI before stagnation increases linearly with the current rise time when the implosion velocity of Z-pinch plasma is held constant. In the present work, three types of wire-array experiments with similar implosion dynamics and constant implosion velocity are performed on an 8 MA pulse power generator to investigate the scaling of MRTI to the rise time. It is successfully accomplished for the first time to obtain the similar wire-array Z-pinch implosions in which the current rise time is scaled up to three times on the generator by controlling the trigger time of its 24 modules. Both the experimental results, which include x-ray radiation pulses and x-ray images of imploding plasmas, and the related numerical analysis have shown that the MRTI before stagnation grows linearly with the rise time, as predicted by the theoretical model.
Identifying influential nodes in complex networks based on network embedding and local structure entropy
Authors: Lu Pengli , Yang Junxia , Zhang Teng 
Published: August 2023

Abstract
The identification of influential nodes in complex networks remains a crucial research direction, as it paves the way for analyzing and controlling information diffusion. The currently presented network embedding algorithms are capable of representing high-dimensional and sparse networks with low-dimensional and dense vector spaces, which not only keeps the network structure but also has high accuracy. In this work, a novel centrality approach based on network embedding and local structure entropy, called the ELSEC, is proposed for capturing richer information to evaluate the importance of nodes from the view of local and global perspectives. In short, firstly, the local structure entropy is used to measure the self importance of nodes. Secondly, the network is mapped to a vector space to calculate the Manhattan distance between nodes by using the Node2vec network embedding algorithm, and the global importance of nodes is defined by combining the correlation coefficients. To reveal the effectiveness of the ELSEC, we select three types of algorithms for identifying key nodes as contrast approaches, including methods based on node centrality, optimal decycling based algorithms and graph partition based methods, and conduct experiments on ten real networks for correlation, ranking monotonicity, accuracy of high ranking nodes and the size of the giant connected component. Experimental results show that the ELSEC algorithm has excellent ability to identify influential nodes.
Tradeoff of generalization error in unsupervised learning
Authors: Kim Gilhan , Lee Hojun , Jo Junghyo , Baek Yongjoo 
Published: August 2023

Abstract
Finding the optimal model complexity that minimizes the generalization error (GE) is a key issue of machine learning. For the conventional supervised learning, this task typically involves the bias-variance tradeoff: lowering the bias by making the model more complex entails an increase in the variance. Meanwhile, little has been studied about whether the same tradeoff exists for unsupervised learning. In this study, we propose that unsupervised learning generally exhibits a two-component tradeoff of the GE, namely the model error (ME) and the data error (DE)—using a more complex model reduces the ME at the cost of the DE, with the DE playing a more significant role for a smaller training dataset. This is corroborated by training the restricted Boltzmann machine to generate the configurations of the two-dimensional Ising model at a given temperature and the totally asymmetric simple exclusion process with given entry and exit rates. Our results also indicate that the optimal model tends to be more complex when the data to be learned are more complex.
Large deviations for the Pearson family of ergodic diffusion processes involving a quadratic diffusion coefficient and a linear force
Authors: Monthus Cécile 
Published: August 2023

AbstractThe Pearson family of ergodic diffusions with a quadratic diffusion coefficient and a linear force is characterized by explicit dynamics of their integer moments and by explicit relaxation of spectral properties towards their steady state. Besides the Ornstein–Uhlenbeck process with a Gaussian steady state, other representative examples of the Pearson family are the square root or the Cox–Ingersoll–Ross process converging towards the gamma distribution, the Jacobi process converging towards the beta distribution, the reciprocal gamma process (corresponding to an exponential functional of the Brownian motion) that converges towards the inverse-gamma distribution, the Fisher–Snedecor process and the Student process. The last three steady states display heavy tails. The goal of the present paper is to analyze the large deviation properties of these various diffusion processes in a unified framework. We first consider level 1 concerning time-averaged observables over a large time windowT. We write the first rescaled cumulants for generic observables and identify specific observables whose large deviations can be explicitly computed from the dominant eigenvalue of the appropriate deformed generator. The explicit large deviations at level 2 concerning the time-averaged density are then used to analyze the statistical inference of model parameters from data on a very long stochastic trajectory in order to obtain the explicit rate function for the two inferred parameters of the Pearson linear force.
Exploring outputs from concatenated stochastic heat engines
Authors: Kumari Aradhana , Marathe Rahul , Lahiri Sourabh 
Published: August 2023

Abstract
Recent work on the concatenation of two simple heat engines has shown that it may lead to non-monotonic variations in the efficiency and power with parameters like driving amplitudes and asymmetries in cycle periods. Motivated by this study, we investigate the effect of the concatenation between two stochastic heat engines where colloidal particles have been trapped in harmonic potentials. The stiffness parameters of each engine are varied cyclically, but with different cycle periods, with a common thermal bath that acts as a sink for the first engine but as a source for the second. We consider two types of protocol, the first where the trap strength undergoes sudden jumps, and the second where it varies linearly with time. In both we find several non-trivial effects, like the non-monotonic functional dependence of the engine outputs on several parameters used in the setup. For the protocol that varies linearly with time, the concatenation leads to enhanced output power as compared to a single effective engine, in a suitable range of parameters. It has been shown that the output from the combined system shows a peak with respect to the asymmetry in the cycle times of the engines that have been concatenated. A general relation for the efficiency of an arbitrary number of concatenated engines driven quasistatically has been provided.
Computationally studying H- extraction and beamlet formation: the impact of the plasma model
Authors: den Harder Niek , Lindqvist Max , Mimo Alessandro , Minea Tiberiu , Mochalskyy Serhiy , Nocentini Riccardo , Revel Adrien , Wünderlich Dirk , Fantz Ursel 
Published: August 2023

Abstract
The ITER NBI requires H-/D- beamlets with a low divergence, to maximize the
transmitted power through the beamline. Ion extraction and beamlet formation are typically
studied with gun-type codes such as IBSimu, which do not treat the plasma explicitly. These codes
neglect many physical processes, such as the surface production of negative ions, particle drifts
in the plasma as a result of magnetic fields, and sheath formation at surfaces. 3D PIC
simulations show that the Debye sheath, which forms between the plasma and the plasma grid, has an
impact on the optics of negative ions extracted from the plasma. In this paper, a repelling
potential around the plasma grid is implemented ad-hoc in the IBSimu plasma model. The impact of
the sheath is investigated for the BUG-MLE grid system for negative ions that come from the
plasma. An electron repelling sheath increases the H- extraction probability since fewer
particles are lost on the plasma grid, however the extracted current density at the edge of the
aperture is reduced. The sheath increases the perpendicular energy distribution of the extracted
H-, because particles are reflected towards the extraction region. In the underperveant
regime, the sheath decreases the divergence, since it prevents H- from being extracted at the
edge of the aperture, and these particles are highly divergent in the underperveant regime. The
divergence optimum is shifted towards lower extracted current density with the sheath. The sheath
leads to a higher minimum divergence due to the increased perpendicular energy distribution in the
extraction region and the changed current density distribution.