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Integrated Computational Materials Engineering for the Development and Design of High Modulus Al Alloys

Journal of Beijing institute of technology 2023年第4期32卷 443-462页

作者： Chengpeng Xue Xinghai Yang Shuo Wang Junsheng Wang School of Materials Science and Engineering Beijing Institute of TechnologyBeijing 100081China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China

Integrated computational materials engineering(ICME)is to integrate multi-scale computational simulations and key experimental methods such as macroscopic,mesoscopic,and microscopic into the whole process of Al alloys design and development,which enables the design and development of Al alloys to upgrade from traditional empirical to the integration of compositionprocess-structure-mechanical property,thus greatly accelerating its development speed and reducing its development *** study combines calculation of phase diagram(CALPHAD),Finite element calculations,first principle calculations,and microstructure characterization methods to predict and regulate the formation and structure of composite precipitates from the design of highmodulus Al alloy compositions and optimize the casting process parameters to inhibit the formation of micropore defects in the casting process,and the final tensile strength of Al alloys reaches420 MPa and Young's modulus reaches more than 88 GPa,which achieves the design goal of the high strength and modulus Al alloys,and establishes a new mode of the design and development of the strength/modulus Al alloys.

关键词： integrated computational materials engineering(ICME) high modulus Al alloys

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Prediction in Autism by Partial Differential Diffusion and Frobenius Convolutional Light Gated Recurrent in Toddlers

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SN Computer science 2024年第6期5卷 675页

作者： Sreevidya, S. Nayak, Lipsa Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India

World is discerned emergence of innumerable sicknesses which cannot perceived medically, identified, like Autism Spectrum Disorder (ASD). It takes holds of numerous behavioral spheres, as well as social and linguistic expertise and conventionalized actions. This ASD disability is a significant neuro-developmental disorder. Owing to the reason that several additional mental sicknesses is spectacularly homogeneous indicators to individuals of ASD, diagnosing ASD can laborious as well as time consuming. Also premature ASD diagnosis depend on distinct physiological features appears realistic through increasing employing of DL based methods in forecasting numerous human diseases. A novel ASD prediction method called, Partial Differential Diffusion and Frobenius Convolutional Light Gated Recurrent (PDD-FCLGR) in toddlers is introduced. PDD-FCLGR technique is divide as preprocessing and ASD prediction. The ASD toddler data experiment is subjected to preprocessing using Partial Differential Diffusion-based Preprocessing model to obtain noise reduced and dimensionality reduced data. Here, partial differential function is applied to remove noise whereas Discrete Gaussian Diffusion map is applied to produce dimensionality reduced data. Second, the Frobenius Convolutional Light Gated Recurrent Unit (FCLiGRU) ASD prediction model is designed by combining Frobenius Convolutional Neural Network and Light Gated Recurrent Unit. Extensive simulation is proposed to be done using Python high level general purpose programming language and detailed comparison is proposed to be made with conventional methods in terms of RMSE, ASD prediction accuracy, ASD prediction time and specificity. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Autism spectrum disorder Diffusion map Frobenius convolutional neural network Light gated recurrent unit Partial differential

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Seeing through Materials:X-Ray Imaging Using Computed Tomography

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Journal of Beijing institute of technology 2023年第4期32卷 494-516页

作者： Yisheng Miao Chengpeng Xue Xuelong Wu Zhongyao Li Junsheng Wang School of Materials Science and Engineering Beijing Institute of TechnologyBeijing 100081China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China

X-ray computed tomography(XCT)has recently emerged as a powerful tool for characterizing the evolution of microstructure during phase transformation in three dimensional(3D)such as dendritic solidification of *** paper briefly reviews the recent advances in the in-situ observation of aluminium alloys,magnesium alloys and nickel-based superalloys during solidification using laboratory XCT and synchrotron X-ray *** focus is on the growth kinetics of dendrites,porosity and secondary *** addition,in-situ characterization during the loading and corrosion process is also discussed.

关键词： X-ray computed tomography(XCT) synchrotron dendrite porosity alloys

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Real-time Intravital Imaging of Cellular and Vascular Dynamics in Mouse Cerebral Microinfarction

Real-time Intravital Imaging of Cellular and Vascular Dynami...

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2024 Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024

作者： Choi, Jieun Kim, Pilhan Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of KI for Health Science and Technology Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Graduate School of Nanoscience and Technology Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

We employs real-time intravital confocal microscopy to investigate real-time blood flow and cellular responses in cerebral microinfarction induced by photo-thrombosis. In vivo longitudinal observations of vascular leakage, reduced flow, and brain edema were achieved, providing insights into cerebrovascular dysfunction. © 2024 The Author(s)

关键词： Macroinvertebrates

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Nuclei Segmentation in Histopathology Images Using Structure-Preserving Color Normalization Based Ensemble Deep Learning Frameworks

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Computers, Materials & Continua 2023年第12期77卷 3077-3094页

作者： Manas Ranjan Prusty Rishi Dinesh Hariket Sukesh Kumar Sheth Alapati Lakshmi Viswanath Sandeep Kumar Satapathy Centre for Cyber-Physical Systems Vellore Institute of TechnologyChennai600127India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Centre for Advanced Data Science Vellore Institute of TechnologyChennai600127India

This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology *** purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols,as well as the segmentation of variable-sized and overlapping *** this extent,the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks(CNN)architectures as encoder backbones,along with stain normalization and test time augmentation,to improve segmentation ***,this paper employs a Structure-Preserving Color Normalization(SPCN)technique as a preprocessing step for stain *** proposed model was trained and tested on both single-organ and multi-organ datasets,yielding an F1 score of 84.11%,mean Intersection over Union(IoU)of 81.67%,dice score of 84.11%,accuracy of 92.58%and precision of 83.78%on the multi-organ dataset,and an F1 score of 87.04%,mean IoU of 86.66%,dice score of 87.04%,accuracy of 96.69%and precision of 87.57%on the single-organ *** findings demonstrate that the proposed model ensemble coupled with the right pre-processing and post-processing techniques enhances nuclei segmentation capabilities.

关键词： Nuclei segmentation image segmentation ensemble U-Net deep learning histopathology image convolutional neural networks

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Fabrication of porous TiZrNbTa high-entropy alloys/Ti composite with high strength and low Young’s modulus using a novel MgO space holder

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Journal of Materials science & technology 2023年第36期167卷 59-73页

作者： Tao Xiang Jie Chen Weizong Bao Shuyan Zhong Peng Du Guoqiang Xie School of Materials Science and Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

Stress shielding is caused by the mismatch of stiffness between bone and implant materials,which may give rise to bone resorption and loosening,thereby causing implantation *** is a huge gap between Young’s modulus of human bone and low Young’s modulusβTi alloys.A porous structure design can achieve the target of low Young’s modulus,and thus achieve the matching between human bone and implant ***,a suitable space holder(SH)that can be applied at high temperatures and sintering pressure has not been *** this study,the TiZrNbTa/Ti titanium matrix composite(TMC)with high strength and large ductility was used as scaffold materials and combined the SH technique with the spark plasma sintering(SPS)technique to obtain a porous structure.A novel space holder,i.e.,MgO particles was adopted,which can withstand high-temperature sintering accompanied by a sintering *** porous TiZrNbTa/Ti with 40 vol.%MgO added exhibits a maximum strength of 345.9±10.4 MPa and Young’s modulus of 24.72±0.20 GPa,*** possesses higher strength compared with human bone and matches Young’s modulus of human bone,which exhibits great potential for clinical application.

关键词： TiZrNbTa/Ti titanium matrix composite MgO space holder Low Young’s modulus High strength

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Facial Depression Estimation via Multi-Cue Contrastive Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6007-6020页

作者： Wang, Xinke Xu, Jingyuan Sun, Xiao Li, Mingzheng Hu, Bin Qian, Wei Guo, Dan Wang, Meng Anhui University School of Artificial Intelligence Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230031 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China University of Science and Technology of China Institute of Advanced Technology Hefei230094 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

Vision-based depression estimation is an emerging yet impactful task, whose challenge lies in predicting the severity of depression from facial videos lasting at least several minutes. Existing methods primarily focus on fusing frame-level features to create comprehensive representations. However, they often overlook two crucial aspects: 1) inter- and intra-cue correlations, and 2) variations among samples. Hence, simply characterizing sample embeddings while ignoring to mine the relation among multiple cues leads to limitations. To address this problem, we propose a novel Multi-Cue Contrastive Learning (MCCL) framework to mine the relation among multiple cues for discriminative representation. Specifically, we first introduce a novel cross-characteristic attentive interaction module to model the relationship among multiple cues from four facial features (e.g., 3D landmarks, head poses, gazes, FAUs). Then, we propose a temporal segment attentive interaction module to capture the temporal relationships within each facial feature over time intervals. Moreover, we integrate contrastive learning to leverage the variations among samples by regarding the embeddings of inter-cue and intra-cue as positive pairs while considering embeddings from other samples as negative. In this way, the proposed MCCL framework leverages the relationships among the facial features and the variations among samples to enhance the process of multi-cue mining, thereby achieving more accurate facial depression estimation. Extensive experiments on public datasets, DAIC-WOZ, CMDC, and E-DAIC, demonstrate that our model not only outperforms the advanced depression methods but that the discriminative representations of facial behaviors provide potential insights about depression. Our code is available at:https://***/xkwangcn/***. © 1991-2012 IEEE.

关键词： Face recognition

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Optimization of mechanical properties and corrosion resistance of Zn-0.4Mn-0.8Li alloy using the hot rolling process

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Journal of Materials science & technology 2023年第14期145卷 136-147页

作者： Xinxin Yang Peng Du Kun Li Weizong Bao Tao Xiang Jie Chen Xingjun Liu Guoqiang Xie School of Materials Science and Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

Zinc-based degradable metals are considered one of the most promising biodegradable materials due to their moderate corrosion rate,excellent mechanical properties,and good *** this work,biodegradable Zn-0.4Mn-0.8Li alloy was fabricated and rolled in multiple passes at different tem *** the hot rolling temperature increases,the grain size of Zn-0.4Mn-0.8Li alloy was found to increase ***,a multi-scale structure with the coexistence of coarse grains and fine grains was *** results demonstrated that the mechanical strength and corrosion resistance were improved by increasing the rolled *** was observed that Zn-0.4Mn-0.8Li alloy with a total reduction of 90%after hot rolling at 325℃ exhibited excellent mechanical and corrosion *** cooperation of multi-scale microstructure and twinning was found to improve the strength and guarantee the duc-tility of Zn-0.4Mn-0.8Li alloy significantly so that the 325℃ hot-rolled Zn-0.4Mn-0.8Li alloy has optimal comprehensive ***,yield strength,ultimate tensile strength,and elongation were found to be 449.7±5.3 MPa,505.1±6.5 MPa,and 40.5%±7.5%,***,Zn-0.4Mn-0.8Li al-loy via 325℃ hot-rolled processes also exhibited excellent corrosion *** corrosion current density and corrosion potential were found to be 8.8×10-5 mA cm^(-2)and−0.929 V,*** preliminary study indicates that Zn-0.4Mn-0.8Li alloy is a promising candidate material for medical de-vice applications.

关键词： Zn-based degradable metal Hot rolling Mechanical property Corrosion behavior

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Age-of-Information-Aware Federated Learning

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Journal of Computer science & technology 2024年第3期39卷 637-653页

作者：徐殷肖明军吴晨吴杰周津锐孙贺 Student Member CCF 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China School of Data Science University of Science and Technology of ChinaHefei 230026China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122U.S.A. CCF IEEE

Federated learning(FL)is an emerging privacy-preserving distributed computing paradigm,enabling numerous clients to collaboratively train machine learning models without the necessity of transmitting clients’private datasets to the central *** most existing research where the local datasets of clients are assumed to be unchanged over time throughout the whole FL process,our study addresses such scenarios in this paper where clients’datasets need to be updated periodically,and the server can incentivize clients to employ as fresh as possible datasets for local model *** primary objective is to design a client selection strategy to minimize the loss of the global model for FL loss within a constrained *** this end,we introduce the concept of“Age of Information”(AoI)to quantitatively assess the freshness of local datasets and conduct a theoretical analysis of the convergence bound in our AoI-aware FL *** on the convergence bound,we further formulate our problem as a restless multi-armed bandit(RMAB)***,we relax the RMAB problem and apply the Lagrangian Dual approach to decouple it into multiple ***,we propose a Whittle’s Index Based Client Selection(WICS)algorithm to determine the set of selected *** addition,comprehensive simulations substantiate that the proposed algorithm can effectively reduce training loss and enhance the learning accuracy compared with some state-of-the-art methods.

关键词： federated learning Age of Information restless multi-armed bandit Whittle’s index

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Arc plasma-deposited Co single-atom catalysts supported on an aligned carbon nanofiber for hydrogen peroxide electrosynthesis and an electro-Fenton process

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Carbon Energy 2024年第11期6卷 234-255页

作者： Chang‐Kyu Hwang Sooyeon Kim Ki Ro Yoon Thao Thi Le Chinh V.Hoang Jae Won Choi Wenjun Zhang Sae Yane Paek Chung Hyeon Lee Ji Hyun Lee Keun Hwa Chae Sohee Jeong Seung Yong Lee Byeong-Kwon Ju Sang Hoon Kim Sang Soo Han Jong Min Kim Materials Architecturing Research Center Korea Institute of Science and Technology (KIST))seculSecrngbuk-guRepublic of Korea Department of Micro/Nano Systems Korea UniversitySeoulSeongbuk-guRepublic of Korea Computational Science Research Center Korea Institute of Science and Technology (KIST)SeculSecrngbuk-guRepublic of Korea Advanced Textile R&D Department Korea Institute of Industrial Technology (KITECH)AmsanGyeonggi-doRepublic of Korea Extreme Materials Research Center Korea Institute of Science and Technology (KIST)SeoulSeongbuk:guRepubic of Korea Nanoscience and Technology KIST School University of Science and TechnologySeoulRepublic of Korea Advanced Analysis and Data Center Korea Institute of Science and TechnologySeoulSeongbuk-guRepublic of Korea Display and Nanosensor Laboratory School of Electrical Engineering Korea UniversitySeoulSeongbuk guRepublic of Korea KHU-KIST Department of Converging Science and Technology Kyung Hee UniversitySeoulSeongbuk-guRepubic of Korea

Atomically dispersed single-atom catalysts(SACs)on carbon supports show great promise for H_(2)O_(2) electrosynthesis,but conventional wet chemistry methods using particulate carbon blacks in powder form have limited their potential as two-electron(2^(e−))oxygen reduction reaction(ORR)***,we demonstrate high-performance Co SACs supported on a free-standing aligned carbon nanofiber(CNF)using electrospinning and arc plasma deposition(APD).Based on the surface oxidation treatment of aligned CNF and precise control of the deposition amount in a dry-based APD process,we successfully form densely populated Co SACs on aligned *** experimental analyses and density functional theory calculations,we reveal that Co SAC has a Co–N_(2)–O_(2) moiety with one epoxy group,leading to excellent 2^(e−)ORR ***,the aligned CNF significantly improves mass transfer in flow cells compared to randomly oriented CNF,showing an overpotential reduction of 30 mV and a 1.3-fold improvement(84.5%)in Faradaic efficiency,and finally achieves an outstanding production rate of 15.75 mol gcat^(−1) h^(−1) at 300 mA cm^(−2).The high-performance Co SAC supported on well-aligned CNF is also applied in an electro-Fenton process,demonstrating rapid removal of methylene blue and bisphenol F due to its exceptional 2e^(−)ORR activity.

关键词： arc plasma deposition electro-Fenton flow reactor hydrogen peroxide production oxygen reduction reaction single-atom catalysts

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