Computer Physics Communications最新文献

{"title":"AutoEncoders latent space interpretability in the light of proper orthogonal decomposition: Machine learning of periodically forced fluid flows","authors":"Rémi Bousquet,&nbsp;Caroline Nore,&nbsp;Didier Lucor","doi":"10.1016/j.cpc.2025.109728","DOIUrl":"10.1016/j.cpc.2025.109728","url":null,"abstract":"<div><div>This work explores the learning and interpretability challenges of Autoencoders (AEs) and Variational Autoencoders (VAEs) when applied to the reconstruction of dynamic velocity fields governed by the Navier-Stokes equations. Throughout model training, the emphasis is on understanding how flow features are encoded into the latent space and how this impacts the interpretability and usability of the models. Based on a parametric study of forced flows, i.e. flows around an oscillating cylinder, as well as a von Kármán swirling flow, we first investigate the trade-offs between reconstruction accuracy and regularization in VAEs. We confirm that increasing the regularization parameter degrades reconstruction quality, which underscores a significant limitation of the Gaussian prior from this point of vue. A comparative analysis reveals that standard AEs exhibit quite robust training behaviour, while VAEs show a sharper transition between non-learning and learning regimes, depending on the amount of regularization. By leveraging Proper Orthogonal Decomposition (POD) to identify characteristic flow structures and frequencies, we establish connections between latent space organisations and POD modes. To address the interpretability challenge, we then perform a symmetry analysis of latent spaces, stating equivariance relations between latent and physical variables. Despite reduced reconstruction precision, VAEs show greater fidelity in preserving these relationships. Building on this, we propose a clustering-inspired method to interpret latent representations, identifying characteristic states from temporal POD time coefficients to provide deeper insights into latent space structure and untangling. This work highlights pathways for autoencoder's analysis methodological advancements, emphasizing the critical need to align latent space representations with physical interpretation for broader applicability in fluid dynamics.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109728"},"PeriodicalIF":7.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-performance data format for scientific data storage and analysis","authors":"Gagik Gavalian","doi":"10.1016/j.cpc.2025.109732","DOIUrl":"10.1016/j.cpc.2025.109732","url":null,"abstract":"<div><div>In this article, we present the High-Performance Output (HiPO) data format developed at Jefferson Laboratory for storing and analyzing data from Nuclear Physics experiments. The format was designed to efficiently store large amounts of experimental data, utilizing modern fast compression algorithms. The purpose of this development was to provide organized data in the output, facilitating access to relevant information within the large data files. The HiPO data format has features that are suited for storing raw detector data, reconstruction data, and the final physics analysis data efficiently, eliminating the need to do data conversions through the lifecycle of experimental data. The HiPO data format is implemented in C++ and JAVA, and provides bindings to FORTRAN, Python, and Julia, providing users with the choice of data analysis frameworks to use. In this paper, we will present the general design and functionalities of the HiPO library and compare the performance of the library with more established data formats used in data analysis in High Energy and Nuclear Physics (such as ROOT <span><span>[3]</span></span> and Parquete).</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109732"},"PeriodicalIF":7.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A numerical study of the symplectic FDTD(p,q) method combined with matrix exponential technique for anisotropic magnetized plasma","authors":"Guilin Hou ,&nbsp;Guoda Xie ,&nbsp;Wenjie Ding ,&nbsp;Yingsong Li ,&nbsp;Yang Hong ,&nbsp;Zhixiang Huang","doi":"10.1016/j.cpc.2025.109735","DOIUrl":"10.1016/j.cpc.2025.109735","url":null,"abstract":"<div><div>A novel algorithm has been developed to simulate the electromagnetic properties of anisotropic magnetized plasma media, integrating the matrix exponential (ME) approach with the symplectic finite-difference time-domain (ME-SFDTD⁽<em>^p</em>^,<em>^q</em>⁾) method. The SFDTD⁽<em>^p</em>^,<em>^q</em>⁾ method achieves <em>p</em>-th order accuracy in the temporal domain and <em>q</em>-th order accuracy in the spatial domain, providing a foundational numerical discretization of Maxwell's equations and the current density equation. Subsequently, the ME method is employed to accurately solve the matrix exponential coefficient terms that arise from the multi-stage symplectic integration of the governing equations. This leads to the successful establishment of a unified numerical framework for the ME-SFDTD⁽<em>^p</em>^,<em>^q</em>⁾ method, capable of computing the field components in anisotropic magnetized plasma regions. In parallel, an efficient sub-grid technique is introduced to manage the air-plasma interface when employing a high-order spatial difference approximation. Following this, a thorough analysis of the numerical characteristics of the proposed method, including dispersion, stability, and computational complexity, is conducted. Additionally, two numerical examples are utilized to examine the computational characteristics of the ME-SFDTD⁽<em>^p</em>^,<em>^q</em>⁾ method under different differential strategies. Finally, a comprehensive assessment of computational accuracy, efficiency, and memory usage observes that the ME-SFDTD^(4,4) method effectively reconciles the trade-off between these factors, establishing itself as a viable numerical solver for the accurate simulation of anisotropic magnetized plasmas.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109735"},"PeriodicalIF":7.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid EPIC–GOD: An energy–conserving hybrid particle–in–cell code for GPU acceleration using OpenACC","authors":"Sunjung Kim ,&nbsp;Dongsu Ryu ,&nbsp;G.S. Choe ,&nbsp;Sibaek Yi","doi":"10.1016/j.cpc.2025.109726","DOIUrl":"10.1016/j.cpc.2025.109726","url":null,"abstract":"<div><div>Hybrid simulations, which combine ion particles with an inertialess, charge-neutralizing electron fluid, offer a valuable bridge between fully kinetic particle–in–cell (PIC) and magnetohydrodynamic (MHD) approaches. In this paper, we present Hybrid EPIC–GOD, a new hybrid particle–in–cell code designed to ensure strict conservation of both local charge and total energy—two critical properties often neglected in conventional hybrid codes.</div><div>Hybrid EPIC–GOD solves the coupled equations governing ion particle dynamics and electromagnetic fields using an iterative scheme. The code exactly satisfies the charge continuity equation, while total energy conservation is guaranteed through the iterative convergence process. We describe the implementation in detail and validate the code's performance across a broad spectrum of plasma processes, including waves, instabilities, collisionless shocks, and magnetic reconnection.</div><div>The results show that Hybrid EPIC–GOD accurately reproduces analytical solutions and benchmark results, while maintaining rigorous charge and energy conservation. Moreover, the code is optimized for GPU acceleration using OpenACC, delivering significant performance gains when running on multiple GPUs compared to its CPU–based counterpart. With its combination of accuracy, conservation properties, and computational efficiency, Hybrid EPIC–GOD provides a powerful tool for studying collisionless plasma dynamics in both space and astrophysical environments.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109726"},"PeriodicalIF":7.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A neural-network-based Python package for performing large-scale atomic CI using pCI and other high-performance atomic codes","authors":"Pavlo Bilous ,&nbsp;Charles Cheung ,&nbsp;Marianna S. Safronova","doi":"10.1016/j.cpc.2025.109731","DOIUrl":"10.1016/j.cpc.2025.109731","url":null,"abstract":"<div><div>Modern atomic physics applications in science and technology pose ever higher demands on the precision of computations of properties of atoms and ions. Especially challenging is the modeling of electronic correlations within the configuration interaction (CI) framework, which often requires expansions of the atomic state in huge bases of Slater determinants or configuration state functions. This can easily render the problem intractable even for highly efficient atomic codes running on distributed supercomputer systems. Recently, we have successfully addressed this problem using a neural-network (NN) approach [1]. In this work, we present our Python code for performing NN-supported large-scale atomic CI using pCI [2] and other high-performance atomic codes.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> nn_for_pci</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/yy29nhwkbw.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/pavlobilous/nn_for_pci</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPLv3</div><div><em>Programming language:</em> Python</div><div><em>Nature of problem:</em> Exponential scaling of the basis size in the atomic CI approach</div><div><em>Solution method:</em> Iterative NN-based selection of the relevant basis elements out of a large CI basis</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div>P. Bilous, C. Cheung, M. Safronova, Phys. Rev. A 110 (2024) 042818.</div></span></li><li><span>[2]</span><span><div>C. Cheung, M.G. Kozlov, S.G. Porsev, M.S. Safronova, I.I. Tupitsyn, A.I. Bondarev, Comput. Phys. Commun. 308 (2025) 109463.</div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109731"},"PeriodicalIF":7.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient algorithm for computing entanglement entropy in systems with a restricted Hilbert space or U(1) symmetry","authors":"Yu Li,&nbsp;Zhiyuan Yao","doi":"10.1016/j.cpc.2025.109729","DOIUrl":"10.1016/j.cpc.2025.109729","url":null,"abstract":"<div><div>We present an efficient algorithm for computing entanglement entropies in systems with a restricted Hilbert space or <span><math><mi>U</mi><mo>(</mo><mn>1</mn><mo>)</mo></math></span> symmetry. For the case of a restricted Hilbert space, the algorithm is straightforward in that only a map table from physical states to indices of an intermediate matrix is needed. In systems with a <span><math><mi>U</mi><mo>(</mo><mn>1</mn><mo>)</mo></math></span> symmetry, the reduced density matrix can be put into a block-diagonal form by properly grouping matrix elements according to the total charge in the subsystem, leading to a significant boost in the efficiency of entanglement entropy calculation.</div></div><div><h3>Program summary</h3><div><em>Program title:</em> ResEE.jl</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/d8s5byx96r.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/top2group/ResEE.jl</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT License</div><div><em>Programming language:</em> Julia</div><div><em>Nature of problem:</em> Computing the entanglement entropy of quantum many-body systems with restricted Hilbert space and/or <span><math><mi>U</mi><mo>(</mo><mn>1</mn><mo>)</mo></math></span> symmetry.</div><div><em>Solution method:</em> The program constructs a map table to efficiently compute the reduced density matrix in systems with restricted Hilbert spaces. For <span><math><mi>U</mi><mo>(</mo><mn>1</mn><mo>)</mo></math></span> symmetric systems, it exploits charge conservation to put the reduced density matrix in a block-diagonal form, further improving the efficiency.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109729"},"PeriodicalIF":7.2,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Goodbye Christoffel symbols: A flexible and practical approach for solving physical problems in curved spaces","authors":"Miguel A. Herrada","doi":"10.1016/j.cpc.2025.109727","DOIUrl":"10.1016/j.cpc.2025.109727","url":null,"abstract":"<div><div>Traditional methods for solving physical equations in curved spaces, particularly in areas like fluid dynamics and continuum mechanics, often face significant complexity due to the necessity of incorporating Christoffel symbols to account for spatial curvature. These symbols complicate the formulation and numerical implementation. In this paper, we present a novel and flexible methodology that entirely obviates the need for Christoffel symbols by fundamentally changing the approach to problem formulation and solution. The method operates by formulating the physical problem directly within a Euclidean 3D Cartesian space, where differential operators are standard and well-defined. The core of our innovation lies in the combined and systematic application of symbolic calculus to perform both the necessary chain rule transformations between the physical curved space and the embedding Euclidean space, and the subsequent projection operations. This powerful symbolic framework allows us to effectively derive and solve the governing equations on the curved geometry without explicitly computing or using Christoffel symbols or specialized curved-space operators. We demonstrate the robustness, flexibility, and advantages of this approach through several examples, including the derivation of the Navier-Stokes equations in cylindrical coordinates, the modelling of complex flows in bent cylindrical tubes, and the simulation of the breakup of viscoelastic threads. These examples highlight the method's ability to simplify the mathematical formulation and provide a robust framework for complex or evolving geometries. The flexibility in choosing basis representations within the Euclidean space is also shown to offer potential benefits for numerical stability in certain applications.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109727"},"PeriodicalIF":7.2,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating Gaussian beam tracing method with dynamic parallelism on graphics processing units","authors":"Sheng Zhang ,&nbsp;Lishu Duan ,&nbsp;Hanbo Jiang","doi":"10.1016/j.cpc.2025.109722","DOIUrl":"10.1016/j.cpc.2025.109722","url":null,"abstract":"<div><div>This study presents an efficient implementation of the Gaussian beam tracing (GBT) method utilizing graphics processing units (GPUs) to overcome the performance limitations of traditional CPU-based acoustic simulations. The algorithm was implemented and optimized on an NVIDIA RTX A6000 GPU, significantly enhancing the Gaussian beam summation (GBS) performance. We addressed the challenge of irregular control flows inherent to GBT by leveraging CUDA's dynamic parallelism to effectively flatten and dispatch nested loops directly on the GPU. Additionally, a profiling-driven optimization workflow using NVIDIA Nsight Compute enabled targeted improvements, raising SM throughput from 22.27% to 33.32%, L1 cache throughput from 13.15% to 22.15%, and L2 cache throughput from 9.16% to 21.26%. Consequently, the GPU-accelerated GBS algorithm achieved up to an 817× speedup compared to the original single-threaded CPU implementation, while the full computational pipeline reached 112× acceleration in a city-environment scenario involving 16,384 rays. Furthermore, this study introduces innovative strategies for overcoming GPU memory limitations, enabling efficient processing of large-scale ray datasets beyond single-kernel constraints. Finally, we establish systematic performance evaluation methodologies critical for analyzing and tuning GPU-accelerated algorithms, laying a foundation for future enhancements and scalability improvements.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109722"},"PeriodicalIF":7.2,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"quTARANG: A high-performance computing Python package to study turbulence using the Gross-Pitaevskii equation","authors":"Sachin Singh Rawat ,&nbsp;Shawan Kumar Jha ,&nbsp;Mahendra Kumar Verma ,&nbsp;Pankaj Kumar Mishra","doi":"10.1016/j.cpc.2025.109725","DOIUrl":"10.1016/j.cpc.2025.109725","url":null,"abstract":"<div><div>We present <span>quTARANG</span>, a robust GPU-accelerated Python package developed for a comprehensive study of turbulence problems in Bose-Einstein condensates (BECs). It solves the mean-field Gross-Pitaevskii equation (GPE) using a Time-splitting pseudo-spectral (TSSP) scheme and ground state calculations are performed using a Backward Euler spectral (BESP) scheme. <span>quTARANG</span> also has post-processing tools that can compute different statistical properties of turbulent Bose-Einstein condensates, such as kinetic energy spectra, particle number spectrum and corresponding fluxes. This paper provides detailed descriptions of the code, along with specific examples for calculating the ground state and turbulent state of the condensate under different initial conditions for both 2-D and 3-D cases. We also present results on the dynamics of the GPE in 2-D and 3-D used to validate our code. Finally, we compare the performance of <span>quTARANG</span> on different GPUs to its performance on a CPU, demonstrating the speedup achieved on various GPU architectures.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> <strong>quTARANG</strong></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/s6xh86fkcm.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/sachinrawat2207/quTARANG</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT</div><div><em>Programming language:</em> Python</div><div><em>Nature of problem:</em> This software is designed to compute the ground state and dynamical evolution of the Gross-Pitaevskii equation for 2-D and 3-D cases with GPU acceleration.</div><div><em>Solution method:</em> We have used a Time-splitting pseudo-spectral (TSSP) scheme to compute the dynamics and Backward Euler Pseudo-spectral (BESP) scheme used to compute the ground state of the system by evolving the system in imaginary time.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109725"},"PeriodicalIF":7.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A feature-preserving parallel particle generation method for complex geometries","authors":"Xingyue Yang ,&nbsp;Zhenxiang Nie ,&nbsp;Yuxin Dai ,&nbsp;Zhe Ji","doi":"10.1016/j.cpc.2025.109723","DOIUrl":"10.1016/j.cpc.2025.109723","url":null,"abstract":"<div><div>In this paper, a Feature-preserving Particle Generation (FPPG) method for arbitrary complex geometry is proposed. Instead of basing on implicit geometries, such as level-set, FPPG employs an explicit geometric representation for the parallel and automatic generation of high-quality surface and volume particles, which enables the full preservation of geometric features, such as sharp edges, singularities and etc. Several new algorithms are proposed in this paper to achieve the aforementioned objectives. First, a particle mapping and feature line extraction algorithm is proposed to ensure the adequate representation of arbitrary complex geometry. An improved and efficient data structure is developed too to maximize the parallel efficiency and to optimize the memory footprint. Second, the physics-based particle relaxation procedure is tailored for the explicit geometric representation to achieve a uniform particle distribution. Third, in order to handle large-scale industrial models, the proposed FPPG method is entirely parallelized on shared memory systems and Boolean operations are allowed to tackle structures with multiple assemblies. Intensive numerical tests are carried out to demonstrate the capabilities of FPPG. The scalability tests show that a speedup of 10X is achieved through multi-threading parallelization with various models. Comparative studies with other particle generation methods show that FPPG achieves better performance in both runtime and accuracy. Last, two industrial cases of vehicle wading and gearbox oiling are studied to illustrate that FPPG is applicable to complex geometries.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109723"},"PeriodicalIF":7.2,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}