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Principal components of nuclear mass models

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Science China(Physics,Mechanics & Astronomy) 2024年第7期67卷 65-71页

作者： xin-Hui Wu Pengwei zhao Department of Physics Fuzhou UniversityFuzhou 350108China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China

Principal component analysis(PCA)is employed to extract the principal components(PCs)present in nuclear mass models for the first *** effects from different nuclear mass models are reintegrated and reorganized in the extracted *** PCs are recombined to build new mass models,which achieve better accuracy than the original theoretical mass *** comparison indicates that using the PCA approach,the effects contained in different mass models can be collaborated to improve nuclear mass predictions.

关键词： nuclear mass principal component analysis nuclear models statistical methods

A sub-grid scale model for Burgers turbulence based on the artificial neural network method

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Theoretical & Applied Mechanics letters 2024年第3期14卷 162-165页

作者： xin zhao Kaiyi Yin Department of Mechanics Beijing Institute of TechnologyBeijing 100081China

The present study proposes a sub-grid scale model for the one-dimensional Burgers turbulence based on the neuralnetwork and deep learning *** filtered data of the direct numerical simulation is used to establish thetraining data set,the validation data set,and the test data *** artificial neural network(ANN)methodand Back Propagation method are employed to train parameters in the *** developed ANN is applied toconstruct the sub-grid scale model for the large eddy simulation of the Burgers turbulence in the *** proposed model well predicts the time correlation and the space correlation of the Burgers turbulence.

关键词： Artificial neural network Back propagation method Burgers turbulence Large eddy simulation Sub-grid scale model

Quantum self-attention neural networks for text classification

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Science China(Information Sciences) 2024年第4期67卷 301-313页

作者： Guangxi LI Xuanqiang zhao xin WANG Institute for Quantum Computing Baidu Research Centre for Quantum Software and Information University of Technology Sydney QICI Quantum Information and Computation Initiative Department of Computer ScienceThe University of Hong Kong Thrust of Artificial Intelligence Information HubHong Kong University of Science and Technology(Guangzhou)

An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing(NLP). Although some efforts based on syntactic analysis have opened the door to research in quantum NLP(QNLP), limitations such as heavy syntactic preprocessing and syntax-dependent network architecture make them impracticable on larger and real-world data sets. In this paper, we propose a new simple network architecture, called the quantum selfattention neural network(QSANN), which can compensate for these limitations. Specifically, we introduce the self-attention mechanism into quantum neural networks and then utilize a Gaussian projected quantum self-attention serving as a sensible quantum version of self-attention. As a result, QSANN is effective and scalable on larger data sets and has the desirable property of being implementable on near-term quantum devices. In particular, our QSANN outperforms the best existing QNLP model based on syntactic analysis as well as a simple classical self-attention neural network in numerical experiments of text classification tasks on public data sets. We further show that our method exhibits robustness to low-level quantum noises and showcases resilience to quantum neural network architectures.

关键词： quantum neural networks self-attention natural language processing text classification parameterized quantum circuits

Effect of Artificial Cooling Extrusion on Microstructure and Mechanical Properties of Mg–Zn–Y Alloys

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Acta Metallurgica Sinica(English letters) 2024年第7期37卷 1115-1127页

作者： Qi-Yu Liao Da-Zhi zhao Qi-Chi le Wen-xin Hu Yan-Chao Jiang Wei-Yang Zhou Liang Ren Dan-Dan Li zhao-Yang Yin School of Materials Science and Engineering Northeastern UniversityShenyang110819China Key Laboratory of Electromagnetic Processing of Materials Ministry of EducationNortheastern UniversityShenyang110819China Key Laboratory of Lightweight Structural Materials Liaoning ProvinceNortheastern UniversityShenyang110819China Baotou Research Institute of Rare Earths Baotou014000China

Microstructure and mechanical properties of Mg–Zn–Y alloys with different Zn/Y atomic ratios with or without artificial cooling (AC) extrusion were systematically investigated in this work. The results show that bimodal microstructure consisting of submicron dynamic recrystallized (DRXed) grains with high fraction of low-angle grain boundaries (LAGBs) and elongated unDRXed grains was formed in Mg_(98.7)Zn_(1)Y_(0.3) alloy with AC extrusion. The AC process effectively limits the growth of precipitated phases, and large amount of nanoscale precipitates were dynamically precipitated during the extrusion process. AC extrusion could effectually refine the lamellar 14H LPSO phases and inhibit the transition from stacking faults to LSPO phases in Mg_(98)Zn_(1)Y_(1) alloy and the narrow LPSO phase in Mg_(98)Zn_(1)Y_(1)-AC alloy which could promote the nucleation of DRXed grains. The AC extrusion significantly improves the strength of Mg–Zn–Y alloys. Owing to AC extrusion, the strength improvement of Mg_(98.7)Zn_(1)Y_(0.3) alloy is mainly attributed to fine grain strengthening, dislocation strengthening, and nano-phases precipitation strengthening. After AC process, more fine grains and nano-phases jointly strengthen the Mg_(98)Zn_(1)Y_(1) alloy. The Mg_(98)Zn_(1)Y_(1) alloy obtains optimal mechanical properties after extrusion at 623 K, with ultimate tensile strength (UTS) of 406 MPa, yield strength (YS) of 388 MPa, and elongation (EL) of 5.6%.

关键词： Mg-Zn-Y Artificial cooling Extrusion Microstructure Mechanical properties

Intracellular RNA Labeling Technologies for the Analysis of RNA Biology

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Chinese Journal of Chemistry 2024年第7期42卷 790-801页

作者： Ruiqi zhao xin Fang Xiaocheng Weng Department of Clinical Laboratory Center for Gene DiagnosisDepartment of NeurosurgeryZhongnan Hospital of Wuhan UniversityWuhanHubei 430071China College of Chemistryand Molecular Sciences Key Laboratory of Biomedical Polymers-Ministry of EducationWuhan UniversityWuhan Hubei 430072China

As a cornerstone of the central dogma of molecular biology,RNA plays vital roles in living *** the past few decades,many RNA labeling technologies have been developed to elucidate the biological function of *** technologies have signifi-cantly advanced our understanding of RNA secondary structure,localization,and ***,taking advantage of these innovative RNA labeling approaches,plenty of tool kits have been devised for the regulation of RNA-related biological process,such as gene expression and gene *** this review,we primarily focus on an array of intracellular RNA labeling methods,encom-passing chemical probes-based labeling,metabolic labeling,and proximity-dependent *** also provide a brief overview of their applications in the research of RNA ***,the perspectives of RNA labeling are also discussed.

关键词： RNA Chemical probes Metabolic labeling Proximity-dependent labeling RNA structures Gene sequencing RNA recognition

Photoredox catalytic alkylarylation of alkynes with arylsulfonylacetate as bifunctional reagent

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Science China Chemistry 2024年第6期67卷 2022-2028页

作者： Chonglong He Min Wang Yulong Wang Lirong zhao Youkang Zhou Keyuan Zhang Shenyu Shen Yaqiong Su xin-Hua Duan le Liu School of Chemistry Engineering Research Center of Energy Storage Materials and DevicesMinistry of EducationXi’an Key Laboratory of Sustainable Energy Material ChemistryXi’an Jiaotong UniversityXi’an710049China

Difunctionalization of alkynes represents a powerful and straightforward approach to the synthesis of complex ***,the radical difunctionalization of alkynes mediated by bifunctional reagents remains challenging and underexplored,despite significant progress having been made in alkene ***,we report a novel arylsulfonylacetate skeleton in which aryl rings are attached to acetates through SO_(2),serving as a powerful bifunctional reagent for the alkylarylation of alkynes via vinyl-radical intermediate under photoredox *** modular bifunctional reagent enables the simultaneous incorporation of a wide range of functional groups,including(hetero)aryl ring and alkyl carboxylate into alkynes,resulting in synthetically valuable all-carbon tetrasubstituted alkene *** transformation is distinguished by its redox-neutral nature,readily accessible starting materials,compatibility with diverse functional groups and its capacity to facilitate convergent *** utility of this approach was further demonstrated by the late-stage functionalization of complex molecules and the preparation of fluorescent molecules and anti-cancer drugs.

关键词： bifunctional reagent photoredox catalysis Smiles rearrangement alkylarylation tetrasubstituted acyclic all-carbon olefins

Probabilistic seismic inversion based on physics-guided deep mixture density network

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Petroleum Science 2024年第3期21卷 1611-1631页

作者： Qian-Hao Sun zhao-Yun Zong xin Li National Key Laboratory of Deep Oil and Gas China University of Petroleum(East China)Qingdao266580ShandongChina Laoshan Laboratory Qingdao266580ShandongChina CNOOC Research Institute Ltd. Beijing100028China

Deterministic inversion based on deep learning has been widely utilized in model parameters *** by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model ***,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the *** conventional inversion method based on deep learning can only establish the deterministic relationship between seismic data and parameters,and cannot quantify the uncertainty of *** order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency *** addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion *** illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion *** synthetic data examples of Marmousi II model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed *** addition,the robustness and generality of the proposed method are verified by the field seismic data.

关键词： Deep learning Probabilistic inversion Physics-guided Deep mixture density network

Polyp-Canal Reconstruction Reveals Evolution Toward Complexity in Corals

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Research 2024年第1期2023卷 417-428页

作者： Yixin Li xin Liao xin Wang Yuanchao Li Hongwei zhao Yunpeng zhao Junyuan Chen Chunpeng He Zuhong Lu State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China State Key Laboratory of Coastal and Offshore Engineering Dalian University of TechnologyDalian 116024China Guangxi Key Lab of Mangrove Conservation and Utilization Guangxi Mangrove Research CenterGuangxi Academy of SciencesBeihai 536000China Hainan Academy of Ocean and Fishery Sciences Haikou 571126China State Key Laboratory of Marine Resources Utilization in South China Sea Hainan UniversityHaikou 570228China Nanjing Institute of Geology and Palaeontology Chinese Academy of Sciences39 East Beijing RoadNanjing 210008China

Modern scleractinian corals are classified into robust,complex,and basal clades through comparative molecular ***,only few morphological or biological criteria can systematically determine the evolutionary trajectories of these major scleractinian coral ***,we obtained the structural information of 21 scleractinian coral species representing robust and complex clades:High-resolution micro-computed tomography was used to reconstruct the polyp-canal systems in their colonies and to visualize the dynamic polyp growth *** found that the emergence of mesh-like canals may distinguish representatives of complex and robust *** differences in polyp-canal connections suggest distinct evolutionary trajectories among coral species:The formation of the canal network promoted the development of more complex coral structures,and coral polyps within this network formed calices of very similar volume,following precise axial growth *** influence of individual polyps on the coral colony becomes less significant as coral structures become more complex,and coral species with more complicated polyp-canal systems occupied niches more *** work supplements current evolutionary studies on reef-building corals,providing insight for further studies on coral growth patterns.

关键词： Coral representing directions

Adaptive 5G-and-beyond network-enabled interpretable federated learning enhanced by neuroevolution

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Science China(Information Sciences) 2024年第7期67卷 107-132页

作者： Bin CAO Jianwei zhao xin LIU Yun LI State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology School of Artificial Intelligence Hebei University of Technology School of Economics and Management Hebei University of Technology Industrial Artificial Intelligence Centre Shenzhen Institute for Advanced StudyUniversity of Electronic Science and Technology of China

Mobile telemedicine systems based on the next-generation communication will significantly enhance deep fusion of network automation and federated learning(FL), but data privacy is a paramount issue in sectors like healthcare. This work hence considers FL augments 5G-and-beyond networks by training deep learning(DL) models without the need to exchange raw data. The substantial communication loads imposed on by extensive parameters involved in DL models are managed through adaptive scheduling mechanisms effectively. To address the opaque nature of DL models and to improve the interpretability of FL models, we introduce a convolutional fuzzy rough neural network specifically designed for medical image processing. We also develop a multiobjective memetic evolutionary algorithm to streamline and optimize the neural network architectures. Our comprehensive FL framework integrates smart scheduling, interpretable fuzzy rough logic,and neuroevolution. This framework is shown to improve communication efficiency, increase interpretability of diagnosis with protected privacy, and generate low-complexity neural architectures.

关键词： 5G-and-beyond network interpretable federated learning mobile telemedicine system fuzzy rough set theory neuroevolution