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5th International Conference on Computer Science and Application Engineering, CSAE 2021

作者： Li, Chao Zhao, Ran Zhao, Ran State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781450389853

Computational fluid dynamic simulations with moving boundaries are widely involved in high performance computing applications. For problems with large-displacement or large-deformation boundaries, mesh cells near the boundaries are often excessively stretched or compressed, thus it's hard to maintain a high-quality mesh. To deal with the distorted cells, this paper adopts the mesh refinement method based on the open source software OpenFOAM. In order to achieve the desired effects of localization and adaptation, we propose an adaptive length scale estimation algorithm based on the specified growth factor and current edge lengths. Considering the inconsistency problems for the original implementation of parallelization, an optimized multi-threaded master/worker model is developed for the process of edge checking. Experiments show that our adaptive length scale estimation algorithm works well for moving boundary problems. Compared to the original mesh deformation, using the adaptive mesh refinement could greatly improve the mesh quality. In parallel testing, all the results are consistent and a maximum speedup of 3.8 is achieved on a computing node of 24 cores.. © 2021 Association for computing Machinery. All rights reserved.

关键词： Open source software

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Promoting ion adsorption and desolvation kinetics enables high capacity and rate capability of stibium anode for advanced alkaline battery

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Journal of Materials Science & technology 2022年第36期131卷 60-67页

作者： Peng Zhang Jinhao Xie Fan Yang Xin Shi Yanxia Yu Xihong Lu College of Materials Science and Engineering Guangdong Provincial Engineering Technology Research Center of Key Material for High Performance Copper Clad LaminateDongguan Cleaner Production CenterDongguan University of TechnologyDongguan 523808China MOE of the Key Laboratory of Bioinorganic and Synthetic Chemistry The Key Lab of Low-carbon Chem and Energy Conservation of Guangdong ProvinceSchool of ChemistrySun Yat-Sen UniversityGuangzhou 510275China School of Applied Physics and Materials Wuyi UniversityJiangmen 529020China

Stibium(Sb)metal with high theoretic capacity,suitable negative working window and inexpensive nature are promising anode material for advanced aqueous alkaline batteries(AABs).However,the further development of Sb anode is severely hindered by the low capacity and poor rate capability which is originated from deficient adsorption of[Sb(OH)_(4)]^(-)and its sluggish desolvation ***,a nitrogen doped carbon cages(NCCs)substrate is constructed as high capacity and rate capability anode by promoting the adsorption and following desolvation process of[Sb(OH)_(4)]^(-)via the enhanced attraction toward(OH)-in the solvated[Sb(OH)_(4)]^(-).Consequently,the designed Sb/NCCs anode delivers a high capacity of 627 m Ah g^(-1)with an average 95%Sb utilization,an outstanding coulombic efficiency(CE)of 95%and an impressive lifespan(>110 h).Meanwhile,the Ni_(3)Se_(2)//Sb/NCCs batteries show great capacity retention of 86.7%after 2000 cycles with an areal capacity of 0.52 m Ah cm^(-2).Implementation of the designed anode allows for the construction of Sb-based AABs with enhanced rate capability,energy density and cycling performance.

关键词： Aqueous alkaline batteries Stibium anode N-doped carbon Ion adsorption Desolvation kinetics

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Simultaneously Learning Syntactic Dependency and Semantics Reasonability for Relation Extraction

Simultaneously Learning Syntactic Dependency and Semantics R...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Wang, Xin Yin, Nan Zhang, Xiang Bai, Xinyi Luo, Zhigang College of Computer National University of Defense Technology Changsha China Institute for Quantum and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789811669620

Relation extraction as an important Natural Language Processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to capture syntactic features and achieved attractive performance. However, most existing dependency-based approaches ignore the positive influence of the words outside the dependency trees, sometimes conveying rich and useful information on relation extraction. In this paper, we propose a novel model, Entity-aware Self-attention Contextualized GCN (ESC-GCN), which efficiently incorporates syntactic structure of input sentences and semantic context of sequences. To be specific, relative position self-attention obtains the overall semantic pairwise correlation related to word position, and contextualized graph convolutional networks capture rich intra-sentence dependencies between words by adequately pruning operations. In this way, our proposed model not only reduces the noisy impact from dependency trees but also obtains easily-ignored entity-related semantic representation. Extensive experiments demonstrate that our model achieves encouraging performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Yanlong Wang, Siqi Yang, Xi Qiu, Xinyuan Wu, Chengkun Cui, Yingbo Yang, Canqun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science Hunan Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Hunan Changsha410073 China National University of Defense Technology Department of Biology and Chemistry College of Science Hunan Changsha410073 China

ISBN: (纸本)9798350337488

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词： Drug-Protein Interaction Knowledge Graph Embedding Triple Embedding Weakly Supervised Learning

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Extended Milstein Approximation to the Stochastic Allen-Cahn Equation with Random Diffusion Coefficient Field and Multiplicative Noise

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数学研究（英文） 2023年第4期56卷 366-391页

作者： Xiao Qi School of Mathematical Sciences Xiamen UniversityXiamen 361005China Fujian Provincial Key Laboratory of Mathematical Modeling and High Performance Scientific Computing Xiamen UniversityXiamen 361005China

This paper studies the stochastic Allen-Cahn equation driven by a random diffu-sion coefficient field and multiplicative force noise.A new time-stepping scheme based on a stabilized approach and Milstein scheme is proposed and *** proposed method is unconditionally stable in the sense that a discrete energy is dissipative when the multi-plicative noise is *** strong convergence rate of a spatio-temporal fully discrete scheme is *** experiments are finally reported to confirm the theoretical result and show that the new scheme is much more robust than the classical semi-implicit Euler-Maruyama scheme,especially when the interface width parameter is small.

关键词： extended Mil-stein scheme multiplicative noise stability Stochastic Allen-Cahn equation strong convergence

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Abnormal Thermal Conductivity Increase in β-Ga2O3 by an Unconventional Bonding Mechanism Using Machine-Learning Potential

SSRN

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SSRN 2024年

作者： Zhou, Wu-Xing Wu, Cheng-Wei Cao, Hao-Ran Zeng, Yu-Jia Xie, Guofeng Zhang, Gang School of Materials Science and Engineering Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion Hunan University of Science and Technology Xiangtan411201 China Institute of High Performance Computing A*STAR Singapore138632 Singapore

β-Ga2O3, with its ultrawide band gap (~4.9 eV) and high critical electric field, holds potential in power electronics but is limited by low thermal conductivity, which is critical to the performance and reliability because the high level of heat flux density induced by the extremely high levels of power density. Combining first-principles calculations, machine-learning potentials, and solving the phonon Boltzmann transport equation, we found that substituting octahedral-coordinated Ga with Al significantly enhances thermal conductivity from 100K to 800K. At room temperature, Al-substituted β-Ga2O3 achieves 38.91 W/mK, more than 2-fold that of pristine β-Ga2O3 (17.10 W/mK) and even higher than β-Al2O3 (34.77 W/mK). This enhancement, unusual due to the heavier atomic mass and mixed mass distribution, is rooted in suppressed anharmonic characteristics caused by reduced bonding strength inhomogeneity. Our results may inspire the rational design of materials with tailored thermal properties through chemical bonding mechanisms. © 2024, The Authors. All rights reserved.

关键词： Thermal conductivity

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Interplay of Crosslinking Structures and Segmental Dynamics in Solid-Liquid Elastomers

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Chinese Journal of Polymer Science 2022年第10期40卷 1297-1306,I0012页

作者： Hao Wang Wan-Su Peng Qi Wu Yan Zhao Shi-Tai Wang Yi Yang Jin-Rong Wu Xiao-Liang Wang Rong-Chun Zhang South China Advanced Institute for Soft Matter Science and Technology(AISMST) School of Emergent Soft MatterSouth China University of TechnologyGuangzhou 510640China Key Laboratory of High-Performance Polymer Materials and Technology of Ministry of Education Department of Polymer Science and EngineeringSchool of Chemistry and Chemical EngineeringNanjing UniversityNanjing 210093China State Key Laboratory of Polymer Materials Engineering College of Polymer Science and EngineeringSichuan UniversityChengdu 610065China Suzhou Niumag Analytical Instrument Corporation Chengdu 610065China Guangdong Provincial Key Laboratory of Functional and Intelligent Hybrid Materials and Devices South China University of TechnologyGuangzhou 510640China

There have been significant interests in recent years for incorporating dynamic bonds into polymer materials for achieving multiple functionalities,such as self-healing,recycling,stimuli-responsiveness,and so ***,the impact of dynamic bonds on the polymer dynamics is actually less *** this study,we investigate a self-healing solid-liquid elastomer(SLE),which is a dual-crosslinked network made by coupling a permanently crosslinked polydimethylsiloxane(PDMS)network with polyborosiloxane(PBS)via abundant dynamic boron/oxygen dative *** double-quantum(DQ)NMR reveals that the crosslinking degree is reduced while the structural heterogeneity of network is enhanced with increasing PBS content,i.e.,increasing the content of dynamic boron/oxygen dative *** experiments clearly reveal two chain relaxation modes in the SLE samples with a characteristic relaxation time of around 2.1 s and 11.8 s,corresponding to the relaxation of coupled PBS and PDMS chains,*** master curves obtained from variable-temperature frequency-dependent rheological experiments also reveal enhanced heterogeneity of chain relaxation with increasing PBS ***,the impact of boron/oxygen dative bonds on the Rouse dynamics is further revealed by fast-field-cycling(FFC)NMR experiments,where the spinlattice relaxation rate(R_(1))of all SLE samples follows the same power law of R_(1)(ω)∝ω^(-0.33).Nevertheless,the incorporation of PBS did slightly increase the energy barrier of Rouse *** study well demonstrates a combined use of rheology and solid-state NMR spectroscopy can provide piercing insights into the interplay of crosslinking structures and dynamics of polymer materials.

关键词： Fast-field-cycling NMR Multiple-quantum NMR Rheology Dynamics Dynamic bonds

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Structure design and property of multiple-basis-element(MBE)alloys flexible films

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Nano Research 2022年第6期15卷 4837-4844页

作者： Hao Huang Peter K.Liaw Yong Zhang Qinghai Provincial Engineering Research Center of High Performance Light Metal Alloys and Forming Qinghai UniversityXining 810016China State Key Laboratory of Advanced Metals and Materials University of Science and Technology BeijingBeijing 100083China Department of Materials Science and Engineering The University of TennesseeKnoxvilleTN 37996-2100USA Shunde Graduate School University of Science and Technology BeijingFoshan 528399China

A controlled wrinkled structure is a simple and effective approach to achieve unique properties and has been widely used in flexible *** this study,we reported a substrate prestrain method for fabricating wrinkle-structured Zr52Ti34Nb14 multiplebasis-element(MBE)alloy films as biocompatible *** in the film thickness and substrate prestraining enabled a precise control of the amplitude and wavelength of the wrinkled structures,ranging from micrometers to ***,owing to the flexibility of the wrinkled structures,the wrinkle-structure pattern could be adjusted by simply relaxing or further stretching of the substrate,leading to dynamically tunable transmittance and wetting *** result not only reveals Zr52Ti34Nb14 MBE alloy films as a potential flexible material,but also provides a new structural design approach for other MBE alloy systems.

关键词： multiple-basis-element alloy thin films microstructures optical transmission wetting behavior

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Neural-Network-Augmented Empirical Interpolation Method for Field Reconstruction with Noise and Vibration Tolerance

SSRN

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SSRN 2024年

作者： Li, Han Gong, Helin Xu, Chuanju Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High Performance Scientific Computing Xiamen University Xiamen361005 China

Field reconstruction methods based on machine learning often face challenges when handling unstructured data and designing complex model structures. On the other hand, Reduced Order Models (ROM) struggle with robustness in the presence of observation noise and sensor vibrations. Striking a balance between lightweight design, the ability to handle unstructured data, and robustness presents a significant challenge. In this paper, we introduce the EIM-NN algorithm, which leverages neural networks to determine the coefficients of the subspace found by the EIM algorithm. Furthermore, we present the EIM-TNN algorithm, which enhances robustness by designing a loss function incorporating Tikhonov regularization. Our neural network consists of only two fully connected layers, allowing it to handle unstructured data while maintaining a lightweight profile. Experimental results demonstrate the algorithm's ability to significantly enhance robustness against noise and sensor vibrations without compromising its accuracy in fitting the original data. Additionally, the algorithm's lightweight nature ensures that the added training time and memory requirements over EIM remain acceptable, making it adaptable to a range of industrial applications. © 2024, The Authors. All rights reserved.

关键词： Model structures

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A New Method for Evaluating Core Developers In Open Source Software 13

A New Method for Evaluating Core Developers In Open Source S...

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13th IEEE International Conference on Software Engineering and Service Science, ICSESS 2022

作者： Song, Yulin Wang, Tao Shen, Yang Chang, Junsheng National University of Defense Technology National Laboratory for Parallel and Distributed Processing Hunan Province Changsha China National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Hunan Province Changsha China

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

The high-quality development of Open Source Software(OSS) benefits from the contributions of developers. An important research question is how to truly assess the extent of contribution of all developers in open source software and effectively identify the core developers to avoid their loss. In this paper, we use the undirected weighted network model to build a Developer Collaboration Network(DCN) based on developers and their contributions to the GitHub community's open source software Rails, analyse the characteristics of developer partnerships through the Developer Collaboration Network, and propose a Developer Personal Impact Factor(DPIF) to evaluate the importance of developer, select the top 10 core developers in Rails, focus on their participation in the open source software process, and predicting the changes of their contributions. The experimental results show that core developers with extensive partnerships are more likely to associate with developers with fewer partnerships. The average degree of developer contribution does not increase with more developers. Developer partnerships are characterized by close localization and loose overall relationships. The Developer Personal Impact Factor identifies the core developers in open source software well. The Developer Personal Impact Factor gives a ranking in which the core team members rank in the top 1.17% on average. © 2022 IEEE.

关键词： Open source software

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