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Multi-Material Topology Optimization of 2D Structures Using Convolutional Neural Networks

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1919-1947页

作者： Jiaxiang Luo Weien Zhou Bingxiao Du Daokui Li wen yao College of Aerospace Science and Engineering National University of Defense TechnologyChangsha410073China Defense Innovation Institute Chinese Academy of Military ScienceBeijing100071China Intelligent Game and Decision Laboratory Chinese Academy of Military ScienceBeijing100071China

In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural ***,these methods have primarily focused on solving binary TO problems,and effective solutions for multi-material topology optimization(MMTO)which requires a lot of computing resources are still ***,this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO *** framework employs convolutional neural network(CNN)to construct a surrogate model for solving MMTO,and the obtained surrogate model can rapidly generate multi-material structure topologies in negligible time without any *** performance evaluation results show that the proposed method not only outputs multi-material topologies with clear material boundary but also reduces the calculation cost with high prediction ***,in order to find a more reasonable modeling method for MMTO,this paper studies the characteristics of surrogate modeling as regression task and classification *** the training of 297 models,our findings show that the regression task yields slightly better results than the classification task in most ***,The results indicate that the prediction accuracy is primarily influenced by factors such as the TO problem,material category,and data ***,factors such as the domain size and the material property have minimal impact on the accuracy.

关键词： Multi-material topology optimization convolutional neural networks deep learning finite element analysis

Populational change of CD4^(+)CD25^(+)Treg cells is responsible for the synergistic effect of the combination of RAMP2 with baicalin in treating recurrent spontaneous abortion mouse models

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Traditional Medicine Research 2024年第8期9卷 59-66页

作者： Cong Chen Zhuo-Lan Li Jing-Tian Guo wen-yao Xue Wei Guo Department of Gynecology Jiangsu Province Hospital of Chinese MedicineAffiliated Hospital of Nanjing University of Chinese MedicineNanjing 210029China Jiangsu Key Laboratory of Druggability of Biopharmaceuticals State Key Laboratory of Natural MedicinesSchool of Life Science and TechnologyChina Pharmaceutical UniversityNanjing 211198China

Background: The absence of a safe and effective therapy for recurrent spontaneous abortion due to a maternofetal failure in immunological tolerance remains an intractable clinical obstacle for surgeons. Recently, traditional Chinese medicine has become a feasible alternative for certain diseases, including recurrent spontaneous abortion. However, because of the complex composition of the traditional Chinese medicine formula, its action mechanism remains unclear. Methods: We selected two isolated active ingredients (RAMP and baicalin) from the traditional Chinese medicine formula and used an abortion-prone CBA/J × DBA/2 model to simulate human RSA and compared the changes in fetal resorption rate, Treg cell percentage, and relevant cytokines before and after combination therapy. In addition, The mechanisms were preliminarily discussed using in vitro differentiation models. Results: In CBA/J × DBA/2 abortion-prone mice, the combination therapy resulted in a lower embryo resorption rate compared to that obtained with individual delivery of either RAMP or baicalin, thereby playing an embryo-protective role through the increase in Treg cells for the maintenance of maternal-fetal immune tolerance. In in vitro primary cell differentiation experiments, the concentration of Treg cells significantly increased from 11% to 17.9% after the combination therapy compared to that of the single administration group. Conclusion: the synergistic effects of RAMP and baicalin were responsible for Treg differentiation. The present study provides a solid basis for improving the applicability of traditional Chinese herbs in the treatment of recurrent spontaneous abortion.

关键词： recurrent spontaneous abortion Atractylodes macrocephala Koidz. Scutellaria baicalensis Georgi CBA/J×DBA/2 regulatory T cells

A new two-step variational model for multiplicative noise removal with applications to texture images

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Applied Mathematics:A Journal of Chinese Universities 2024年第3期 486-501页

作者： ZHANG Long-hui yao wen-juan SHI Sheng-zhu GUO Zhi-chang ZHANG Da-zhi School of Mathematics Harbin Institute of Technology

Multiplicative noise removal problems have attracted much attention in recent *** additive noise, multiplicative noise destroys almost all information of the original image,especially for texture images. Motivated by the TV-Stokes model, we propose a new two-step variational model to denoise the texture images corrupted by multiplicative noise with a good geometry explanation in this paper. In the first step, we convert the multiplicative denoising problem into an additive one by the logarithm transform and propagate the isophote directions in the tangential field smoothing. Once the isophote directions are constructed, an image is restored to fit the constructed directions in the second step. The existence and uniqueness of the solution to the variational problems are proved. In these two steps, we use the gradient descent method and construct finite difference schemes to solve the problems. Especially, the augmented Lagrangian method and the fast Fourier transform are adopted to accelerate the calculation. Experimental results show that the proposed model can remove the multiplicative noise efficiently and protect the texture well.

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FDX1 as a novel biomarker and treatment target for stomach adenocarcinoma

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World Journal of Gastrointestinal Surgery 2024年第6期16卷 1803-1824页

作者： Xian-Ze Xie Lei Zuo Wei Huang Qiao-Mei Fan Ya-Yun weng wen-Dong yao Jia-Li Jiang Jia-Qi Jin Department of Pharmacy The First Affiliated Hospital of Zhejiang Chinese Medical University(Zhejiang Provincial Hospital of Chinese Medicine)Hangzhou 310018Zhejiang ProvinceChina Anhui Province Huainan City Shou County Agricultural Machinery Affairs Management Center Huainan 232200Anhui ProvinceChina

BACKGROUND Stomach adenocarcinoma(STAD)is one of the main reasons for cancer-related deaths *** investigation aimed to define the connection between STAD and Cuproptosis-related genes(CRGs).Cuproptosis is a newly identified form of mitochondrial cell death triggered by *** To explore the identification of potential biomarkers for STAD disease based on *** A predictive model using Gene Ontology(GO),Least Absolute Shrinkage and Selection Operator(LASSO),Kyoto Encyclopedia of Genes and Genomes(KEGG),Gene Set Variation Analysis(GSVA),and Gene Set Enrichment Analysis analyzed gene interconnections,focusing on 3 copper-related genes and their expression in The Cancer Genome *** for mRNA-miRNA and mRNA-transcription factor interactions were *** prognostic significance of CRG scores was evaluated using time-receiver operating characteristic,Kaplan-Meier curves,and COX regression *** was conducted with datasets GSE26942,GSE54129,and *** of copper-related differ-entially expressed genes was also analyzed in various human tissues and gastric cancer subpopulations using the human protein *** Three significant genes(FDX1,LIAS,MTF1)were identified and selected via LASSO analysis to predict and classify individuals with STAD into high and low CRG score *** genes were down-regulated in both risk *** and KEGG analyses highlighted their involvement mainly in the electron transport *** validating their differential expression,FDX1 emerged as the most accurate diagnostic marker for gastric ***,the RCircos package localized FDX1 on chromosome *** Our study revealed that FDX1 could be a potential biomarker and treatment target for gastric malignancy,providing new ideas for further scientific research.

关键词： Stomach adenocarcinoma disease Copper death FDX1 prognostic biomarkers Cuproptosis-related genes

液晶分子优化给体材料组装制备高性能有机太阳能电池

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高分子学报 2024年第6期55卷 698-708页

作者：曾光张奔李耀文苏州大学苏州市新型半导体光电材料与器件重点实验室苏州215123

活性层形貌优化是实现高效、稳定的体异质结有机太阳能电池(OSC)的关键,添加剂工程被广泛用于优化OSC活性层的膜形貌.基于此,本文工作提出了一种利用具有简单结构的液晶分子4-氰基-4'-庚基联苯(7-CB)作为添加剂优化给体聚合物(PM6)在共... 详细信息

活性层形貌优化是实现高效、稳定的体异质结有机太阳能电池(OSC)的关键,添加剂工程被广泛用于优化OSC活性层的膜形貌.基于此,本文工作提出了一种利用具有简单结构的液晶分子4-氰基-4'-庚基联苯(7-CB)作为添加剂优化给体聚合物(PM6)在共混膜中聚集行为的策略.通过掠入射广角X射线散射、原位吸收光谱及分子动力学模拟等表征发现,7-CB通过与PM6的烷基侧链之间的范德华力和CH/π相互作用诱导PM6规整排列,增强了PM6之间的相互作用,提高了活性层薄膜的结晶度.得益于此,无任何后处理的活性层薄膜中电荷传输得到改善,电荷重组被大幅抑制.基于7-CB处理的PM6:L8-BO制备的硬质和柔性二元OSCs效率分别从15.41%和14.90%提高到18.01%和17.26%.

关键词：有机太阳能电池添加剂自组装

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Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications,sensing and computing

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Journal of Information and Intelligence 2024年第4期2卷 302-324页

作者： Ruijin Sun yao wen Nan Cheng Wei Wang Rong Chai Yilong Hui State Key Laboratory of ISN Xidian UniversityXi'an 710071China Chongqing Key Laboratory of Mobile Communication Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China

Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing ***,the overwhelming upload traffic may lead to unacceptable uploading *** tackle this issue,for tasks taking environmental data as input,the data perceived by roadside units(RSU)equipped with several sensors can be directly exploited for computation,resulting in a novel task offloading paradigm with integrated communications,sensing and computing(I-CSC).With this paradigm,vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the *** optimizing the computation mode and network resources,in this paper,we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each *** this nonconvex problem can be handled by the alternating minimization(AM)algorithm that alternatively minimizes the divided four sub-problems,it leads to high computational complexity and local optimal *** tackle this challenge,we propose a creative structural knowledge-driven meta-learning(SKDML)method,involving both the model-based AM algorithm and neural ***,borrowing the iterative structure of the AM algorithm,also referred to as structural knowledge,the proposed SKDML adopts long short-term memory(LSTM)networkbased meta-learning to learn an adaptive optimizer for updating variables in each sub-problem,instead of the handcrafted counterpart in the AM ***,to pull out the solution from the local optimum,our proposed SKDML updates parameters in LSTM with the global loss *** results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.

关键词： Knowledge-driven meta-learning Integration of communication Sensing and computing Task offloading Vehicular networks

Probing the peripheral self-generated magnetic field distribution in laser-plasma magnetic reconnection with Martin-Puplett interferometer polarimeter

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Chinese Physics B 2024年第4期33卷 129-134页

作者：张雅芃姚嘉文刘正东马作霖仲佳勇 Department of Astronomy Beijing Normal UniversityBeijing 100875China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China

Magnetic reconnection of the self-generated magnetic fields in laser-plasma interaction is an important laboratory method for modeling high-energy density astronomical and astrophysical *** use the Martin-Puplett interferometer(MPI)polarimeter to probe the peripheral magnetic fields generated in the common magnetic reconnection configuration,two separated coplanar plane targets,in laser-target *** introduce a new method that can obtain polarization information from the interference pattern instead of the sinusoidal function fitting of the intensity.A bidirectional magnetic field is observed from the side view,which is consistent with the magneto-hydro-dynamical(MHD)simulation results of self-generated magnetic field *** find that the cancellation of reverse magnetic fields after averaging and integration along the observing direction could reduce the magnetic field strength by one to two orders of *** indicates that imaging resolution can significantly affect the accuracy of measured magnetic field strength.

关键词： laser-plasma experiment polarimeter self-generated magnetic field magnetic reconnection

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深度学习赋能结构拓扑优化设计方法研究

力学进展 2024年第2期54卷 213-258页

作者：陈小前张泽雨李昱姚雯周炜恩军事科学院北京100195 军事科学院国防科技创新研究院北京100071 国防科技大学空天科学学院长沙410073 智能博弈与决策实验室北京100071

本文综合论述了近年来结构拓扑优化领域与深度学习技术交叉融合发展的相关研究进展.围绕结构拓扑优化设计的核心方法与关键环节,从深度学习赋能的角度系统性梳理了两大类赋能方法.研究指出,基于深度学习技术的结构优化设计全局代理模型... 详细信息

本文综合论述了近年来结构拓扑优化领域与深度学习技术交叉融合发展的相关研究进展.围绕结构拓扑优化设计的核心方法与关键环节,从深度学习赋能的角度系统性梳理了两大类赋能方法.研究指出,基于深度学习技术的结构优化设计全局代理模型构建方法作为一种直接映射式结构设计方法,因其简单而典型的设计思想目前已被广泛研究,然而全局代理模型在计算性和泛化性上的局限与不足也尤为明显;融合深度学习技术的结构优化设计局部子环节加速与替代方法是一种更加灵活与多样的局部赋能形式,具有较好的普适性和独特的优越性.文章对智能赋能结构优化未来的发展进行了展望,研究重点在于深度学习与结构设计的有机结合方式,以及数据和知识的混合驱动设计范式.

关键词：拓扑优化深度学习人工神经网络代理模型

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模板运算代码的自动生成与调优框架

计算机工程 2024年第6期50卷 35-47页

作者：刘金硕文尧武汉大学国家网络安全学院空天信息安全与可信计算教育部重点实验室湖北武汉430072

针对现有模板代码生成方法不支持多图形处理器(GPU)、调优不充分等问题,提出一种由领域专用语言(DSL)描述的模板代码的自动生成与调优框架。在代码自动生成阶段,该框架能够自动解析上层提供的描述语言,构建计算图进而生成模板运算的统... 详细信息

针对现有模板代码生成方法不支持多图形处理器(GPU)、调优不充分等问题,提出一种由领域专用语言(DSL)描述的模板代码的自动生成与调优框架。在代码自动生成阶段,该框架能够自动解析上层提供的描述语言,构建计算图进而生成模板运算的统一计算设备架构(CUDA)核函数,同时根据单GPU或多GPU环境生成不同的主机端代码。在代码调优阶段,根据不同的GPU型号确定候选参数范围,动态调用生成的CUDA核函数以确定最优参数。在多GPU的情况下,自动生成的主机端代码能够使用计算与通信重叠的方法进行边界数据交换。在4种不同的GPU与7、13、19、27点模板运算中,该框架能找到最优的参数配置。实验结果表明,对于Tesla V100-SXM2,以调优过的参数进行模板运算,该框架在单精度4种模板运算下的每秒万亿次浮点运算数(TFLOPs)分别为1.230、1.680、1.120、1.480,在双精度下分别为0.690、1.010、0.480、1.470,平均性能达到手工优化代码的98%,并且描述更简单,支持多GPU扩展。

关键词：模板运算统一计算设备架构计算图领域专用语言代码生成自动调优