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Al_xCoCrCuFeNi (X = 0, 0.8) high entropy alloys fabricated by laser powder bed fusion

Heat Treatment and Surface engineering 2024年第1期6卷

作者： Feng, Zhengjie Gao, Jingbo Zhang, Deliang School of Materials Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry (Ministry of Education) Northeastern University Shenyang China

The AlxCoCrCuFeNi (x = 0, 0.8) high entropy alloys (HEAs) were prepared by laser powder bed fusion (LPBF), and the effects of volumetric energy density (VED) and heat treatment on microstructures and mechanical proper... 详细信息

The Al_xCoCrCuFeNi (x = 0, 0.8) high entropy alloys (HEAs) were prepared by laser powder bed fusion (LPBF), and the effects of volumetric energy density (VED) and heat treatment on microstructures and mechanical properties of HEAs were investigated. The results show that the CoCrCuFeNi HEA has a single FCC phase structure and exhibits good ductility. Its yield strength is relatively low, being 520 MPa. After the alloy composition was adjusted from CoCrCuFeNi to Al_0.8CoCrCuFeNi, the HEA changed from FCC single-phase to FCC + BCC dual-phase structure and the hardness and compressive properties were significantly improved. Among them, the Al_0.8CoCrCuFeNi HEA prepared with the VED of 167 J/mm³ has the best mechanical properties, with a compressive yield strength of 652 MPa and good plasticity. After annealing at 600°C and 900°C for 1 h, the mechanical properties of HEAs are significantly improved. In particular, the microhardness and compressive yield strength of the Al_0.8CoCrCuFeNi HEA annealed at 600°C for 1 h are 378 HV and 810 MPa, respectively. The coordinated deformation between FCC and BCC domains, back stress strengthening, solid solution strengthening and precipitation strengthening after heat treatment make the Al_0.8CoCrCuFeNi HEA achieve a balance of high strength and good ductility. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： heat treatment high entropy alloys Laser powder bed fusion mechanical properties microstructures

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Enhancing cell subpopulation discovery in cancer by integrating single-cell transcriptome and expressed variants

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Fundamental Research 2025年

作者： Wang, Tao Mai, Duoduo Shu, Han Hu, Jialu Wang, Yongtian Peng, Jiajie Chen, Jing Shang, Xuequn School of Computer Science Northwestern Polytechnical University Xian 710072 China Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology Northwestern Polytechnical University Xian 710072 China School of Computer Science and Engineering Xi'an University of Technology Xian 710048 China

The emergence of single-cell RNA sequencing (scRNA-seq) technology has revolutionized the study of cellular heterogeneity at the single-cell level. However, existing methods for identifying subpopulations of cells in scRNA-seq data mainly rely on gene expression features, neglecting the valuable genomic information present in the raw sequencing data. To address this limitation, we propose an end-to-end deep clustering model called scCluster, which integrates single-cell gene expression profiles and expressed variant features derived from the raw scRNA-seq data to stratify cell subpopulations in cancer tissues. scCluster employs a joint optimization strategy that combines a zero-inflated negative binomial model-based dual-modal autoencoder with deep embedding clustering in the pre-training phase. This allows both gene expression profiles and variant features to be encoded into the same latent embedding space. In the fine-tuning stage, scCluster further enhances the discriminability of the latent representations by integrating deep soft K-means clustering and cross-instance guided contrastive clustering techniques. Our extensive evaluations reveal that scCluster surpasses state-of-the-art methods in multiple real-world cancer scRNA-seq datasets. The results also indicate that incorporating the expressed variant features alongside gene expressions substantially enhances the stratification of cell subpopulations in cancer single-cell research. © 2025

关键词： Cancer Deep learning Expressed variants Multi-omics data integration Single-cell subpopulation

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Neural Collaborative Recommendation with knowledge Graph

Neural Collaborative Recommendation with Knowledge Graph

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IEEE International Conference on Big knowledge (ICBK)

作者： Lei Sang Lei Li Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem in recommender system. However, existing KG based recommendation methods mainly rely on handcrafted meta-path features or simple triple-level entity embedding, which cannot automatically capture entities' long-term relational dependencies for the recommendation. In this paper, a two-channel neural interaction method named knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network (KGNCF-RRN) is proposed, which leverages both long-term relational dependencies KG context and user-item interaction for recommendation. (1) For the KG context interaction channel, we propose a Residual Recurrent Network (RRN) to construct context-based path embedding, which incorporates residual learning into traditional recurrent neural networks (RNNs) to efficiently encode the long-term relational dependencies of KG. The self-attention network is then applied to the path embedding to capture the polysemy of various user interaction behaviours. (2) For the user-item interaction channel, the user and item embeddings are fed into a newly designed two-dimensional interaction map. (3) Finally, above the two-channel neural interaction matrix, we employ a convolutional neural network to learn complex correlations between user and item. Extensive experimental results on three benchmark datasets show that our proposed approach outperforms existing state-of-the-art approaches for knowledge graph based recommendation.

关键词： Collaboration Recurrent neural networks knowledge engineering Semantics Motion pictures Context modeling Filtering

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Label Informed Contrastive Pretraining for Node Importance Estimation on knowledge Graphs

arXiv

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arXiv 2024年

作者： Zhang, Tianyu Hou, Chengbin Jiang, Rui Zhang, Xuegong Zhou, Chenghu Tang, Ke Lv, Hairong The Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China The School of Computer Science and Engineering Fuyao Institute of Technology Fuzhou350109 China The State Key Laboratory of Resources and Environmental Information Systems Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Fuzhou Institute of Data Technology Fuzhou350200 China

Node Importance Estimation (NIE) is a task of inferring importance scores of the nodes in a graph. Due to the availability of richer data and knowledge, recent research interests of NIE have been dedicating to knowledge graphs for predicting future or missing node importance scores. Existing state-of-the-art NIE methods train the model by available labels, and they consider every interested node equally before training. However, the nodes with higher importance often require or receive more attention in real-world scenarios, e.g., people may care more about the movies or webpages with higher importance. To this end, we introduce Label Informed ContrAstive Pretraining (LICAP) to the NIE problem for being better aware of the nodes with high importance scores. Specifically, LICAP is a novel type of contrastive learning framework that aims to fully utilize the continuous labels to generate contrastive samples for pretraining embeddings. Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a non-top bin based on node importance scores, and then divide the nodes within top bin into several finer bins also based on the scores. The contrastive samples are generated from those bins, and are then used to pretrain node embeddings of knowledge graphs via a newly proposed Predicate-aware Graph Attention Networks (PreGAT), so as to better separate the top nodes from non-top nodes, and distinguish the top nodes within top bin by keeping the relative order among finer bins. Extensive experiments demonstrate that the LICAP pretrained embeddings can further boost the performance of existing NIE methods and achieve the new state-of-the-art performance regarding both regression and ranking metrics. The source code for reproducibility is available at https://***/zhangtia16/LICAP Copyright © 2024, The Authors. All rights reserved.

关键词： knowledge graph

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An External Denoising Framework for Magnetic Resonance Imaging: Leveraging Anatomical Similarities Across Subjects with Fast Searches

An External Denoising Framework for Magnetic Resonance Imagi...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Lifeng Mei Sixing Liu Chenhui Tang Jiaqia Cai Jingli Wang Yilong Liu Ed X. Wu Mengye Lyu College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen China Guangdong-Hongkong-Macau Institute of CNS Regeneration Key Laboratory of CNS Regeneration (Ministry of Education) Jinan University Guangzhou China Department of Electrical and Electronic Engineering Laboratory of Biomedical Imaging and Signal Processing The University of Hong Kong Hong Kong China

External denoising, also known as reference-based denoising, utilizes information from clean reference images, yielding more robust results than internal denoising, especially in situations with high noise levels. The effectiveness of reference-based denoising relies on the similarity between reference and noisy images. In this paper, we introduce a novel external denoising framework for magnetic resonance imaging (MRI) that takes advantage of the inherent anatomical structure similarities across subjects. Our framework employs noise-resistant neural networks to extract deep features, which facilitate searches for high-quality reference images from a large external database. This versatile framework is compatible with all reference-based denoising algorithms, obviating the need for acquiring additional reference images from the subject under examination.

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Deep Fusion of Shifted MLP and CNN for Medical Image Segmentation

Deep Fusion of Shifted MLP and CNN for Medical Image Segment...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chengyu Yuan Hao Xiong Guoqing Shangguan Hualei Shen Dong Liu Haojie Zhang Zhonghua Liu Kun Qian Bin Hu Björn W. Schuller Yoshiharu Yamamoto Shlomo Berkovsky College of Computer and Information Engineering Henan Normal University China Key Laboratory of Artificial Intelligence and Personalized Learning in Education Henan China Big Data Engineering Lab of Teaching Resources & Assessment of Education Quality Henan China Centre for Health Informatics Australian Institute of Health Innovation Macquarie University Australia Key Laboratory of Brain Health Intelligent Evaluation and Intervention (BIT) Ministry of Education China School of Medical Technology Beijing Institute of Technology China School of Information Engineering Zhejiang Ocean University China GLAM – Group on Language Audio & Music Imperial College London U.K. Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan

Medical image segmentation is an important task in modern analysis of medical images. Current methods tend to extract either local features with convolutions or global features with Transformers. However, few of them are able to effectively fuse global and local features to facilitate segmentation. In this work, we propose a novel hybrid network that involves three main branches: the Multi-Layer Perception (MLP) branch, the Convolutional Neural Network (CNN) branch, and a Fusion branch. The MLP and CNN branches aim to learn global and local features, respectively. To fuse these, the fusion branch introduces a novel hierarchical fusion that performs multi-layered fusions that generate high-level representations to enhance segmentation. Our evaluation with two datasets shows strong performance of the proposed method compared to state-of-the-art baselines.

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可持续发展情景下丝绸之路沿线国家土地利用及其对陆地碳库影响

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Science Bulletin 2023年第7期68卷 740-749页

作者：曹敏田雅吴凯陈旻陈玉胡雪孙中昶左丽君林键骆磊朱瑞许振赐 Temenoujka Bandrova Milan Konecny 袁文平郭华东林珲闾国年 Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China School of Geography and Environment Jiangxi Normal UniversityNanchang 330022China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China The Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Sierra Nevada Research Institute University of CaliforniaMerced CA 95348USA Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Geography the University of Hong KongHong Kong 999077China Laboratory on Cartography University of ArchitectureCivil Engineering and GeodesySofia 1164Bulgaria Laboratory on Geoinformatics and Cartography Institute of GeographyMasaryk UniversityBrno 60177Czech Republic School of Atmospheric Sciences Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Sun Yat-sen UniversityZhuhai 519082China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of EducationJiangxi Normal UniversityNanchang 330022China

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其... 详细信息

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其对陆地碳库的影响在不同情景下存在显著差异。在可持续环境情景下,林地得到了有效保护,相较于2020年中国林地碳储量增加了约0.60%;在可持续粮食情景下,耕地流失得到了缓解,尤其在南亚及东南亚地区的耕地面积呈现上升趋势:在可持续经济情景下,城市扩张速度加快,引起的碳损失量最多这项研究有助于推进丝绸之路沿线甚至全球可持续发展进程,为探寻可持续发展路径提供了科学依据。

关键词： Sustainable development goals(SDGs) Land-use projection Carbon pool Scenarios System dynamics model Cellular automata

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Securing Sdn/Nfv-Enabled Campus Networks with Software-Defined Perimeter-Based Zero-Trust Architecture

SSRN

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SSRN 2023年

作者： Ruambo, Francis A. Zou, Deqing Lopes, Ivandro O. Yuan, Bin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on Big Data Security Wuhan China National Engineering Research Center for Big Data Technology and System Wuhan China Services Computing Technology and System Lab Wuhan China Cluster and Grid Computing Lab Wuhan China Mbeya university of Science and Technology Mbeya131 Tanzania United Republic of Nucleo Operacional para a Sociedade de Informacao Cape Verde Songshan Laboratory Zhengzhou China

Network softwarization is a breakthrough in designing modern networks and providing numerous new network operations and services. This change is exemplified by Software Defined Networks (SDN) and Network Function Virtualization (NFV). As a result, the combination of SDN and NFV (SDN/NFV) frameworks and individual SDN and NFV frameworks have gained substantial traction in network security over the past decade, significantly supporting on-demand protection measures. However, the inherent security shortcomings of SDN, NFV, and SDN/NFV frameworks provide the fundamental issue in implementing security procedures via network softwarization. To solve this issue, we present a Software-Defined Perimeter (SDP)-powered Zero-Trust Architecture (ZTA) to improve security in the SDN/NFV-enabled campus network holistically. In addition to this, we also propose a threat model and quantitatively assess the suggested approach's performance and security. Furthermore, we highlight potential zero-trust network security function implementations in SDN/NFV-enabled infrastructure to increase protection for endpoint devices, network resources, and flows for detecting threats and responding effectively with minimal latency and overhead. © 2023, The Authors. All rights reserved.

关键词： Access control

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Optimizing large-scale biomedical ontology alignment through co-operative NSGA-II

Optimizing large-scale biomedical ontology alignment through...

引用

International Conference on Computational Intelligence and Security

作者： Xingsi Xue Wenbin Tan Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian China School of Electrical and Power Engineering Taiyuan University of Technology Taiyuan Shanxi China

Since extant biomedical ontologies may have different approaches to model biomedical knowledge, the problem of semantic heterogeneity arises. As a key technique to address semantic heterogeneity, ontology matching intends to discover correspondences between semantically related entities of biomedical ontologies. Since the optimal objectives of ontology matching problem, i.e. precision and recall, are often in conflict with each other, it is difficult to find one perfect solution for all the decision-makers. In this work, we further propose a Co-operative NSGA-II (C-NSGA-II) to deal with large-scale biomedical ontology matching problems. In particular, C-NSGA-II divides the original population into multiple subpopulations, and each subpopulation is individually evolved, which aims to increase the diversity of individuals in the population to reduce the probability of early convergence. In addition, C-NSGA-II uses the External Archive (ExA) to collect the dominant individuals for each subpopulation, and on this basis, an adaptive resource allocation is presented to dynamically adjust the subpopulation's scale, which can improve the algorithm's converging speed. The experiment uses the Ontology Alignment Evaluation Initiative (OAEI)'s benchmark and anatomy track to test C-NSGA-II's performance, and the experimental results show that it outperforms OAEI's participants.

关键词： Heuristic algorithms Biological system modeling Sociology Semantics Ontologies Security Resource management

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Self-Ensembling Semi-Supervised Model for Bone X-ray Images Landmark Detection

Self-Ensembling Semi-Supervised Model for Bone X-ray Images ...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Bai, Tian Liu, Shenyao Wang, Yuzhao Wang, Yu Dong, Dong Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China College of Software Jilin University Changchun China Department of Radiology First Hospital of Jilin University Changchun China

ISBN: (纸本)9781665401265

Bone image landmark detection plays a vital role in orthopedic diseases diagnosis and analysis. As a promising approach, the data-driven convolutional neural network has been widely applied in landmarks detection. However, the model's performance relies heavily on numerous high-quality labeled data, which are difficult to access. A practical solution for this issue in bone images landmark detection has not been provided, so we propose a framework of semi-supervised bone images landmark detection encouraged by the recent success of the self-ensembling method. The student and teacher networks share the same structure in our framework, where unlabeled data are utilized by enforcing the prediction consistency of two networks under different perturbations to enhance the network's performance. The two networks use a mutual learning approach to improve the efficiency of using unlabeled data. In addition, we also propose a transformative perturbation scheme to prevent model overfitting during training and enhance the generalization capability of the model on unseen data. Experimental results on an in-house hip dataset collected from the actual process of clinical diagnosis and a publicly available dataset demonstrate that our framework has excellent performance compared with other semi-supervised methods. © 2021 IEEE.

关键词： Convolutional neural networks

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