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Distribution of fatty acids in the alpine grassland soils of the Qinghai-Tibetan Plateau

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Science China Earth Sciences 2016年第7期59卷 1329-1338页

作者： DAI Guo Hua ZHU Shan Shan liU Zong Guang chen li tong HE Jin Sheng FENG Xiao Juan State Key Laboratory of Vegetation and Environmental Change Institute of Botany Chinese Academy of Sciences Key Laboratory of Adaptation and Evolution of Plateau Biota Northwest Institute of Plateau Biology Chinese Academy of Sciences Department of Ecology College of Urban and Environmental Sciences Peking University

As an important biomarker, fatty acids(FAs) have been extensively used to trace the origin of organic matter in sediments and soils. However, studies of the distribution and abundance of FAs in alpine grassland soils are still rare, especially on the Qinghai-Tibetan Plateau(QTP), the highest plateau in the world, which contributes sediments to many large rivers in Asia. This study investigates the composition, distribution and source of FAs with increasing soil depths from 17 typical alpine grassland sites in the QTP. The most abundant FAs included the ubiquitous C16 FA and even-numbered long-chain FAs(C20–C30), indicating mixed inputs from microbial and higher plant sources. Source apportionment showed that higher plants were the dominant contributor of FAs(approximately 40%) in QTP soils. The abundance of FAs decreased with soil depth, with the highest value(1.08±0.09 mg/g C) at a 0–10 cm depth and the lowest value(0.46±0.12 mg/g C) at a 50–70 cm depth, due to much lower plant inputs into the deeper horizons. The total concentration of FAs was negatively correlated to the mean annual temperature(MAT; Plitates the accumulation of fresh FAs in QTP soils. Other environmental parameters, such as the soil mineral content(aluminum and iron oxide), microbial community composition as well as litter quality and quantity, may also exert a strong control on the preservation of FAs in QTP soils and warrant further research to better understand the mechanisms responsible for the preservation of FAs in QTP soils.

关键词： Qinghai-Tibetan Plateau Soil organic matter Biomarker Fatty acids Distribution sources

Neutron irradiation influence on high-power thyristor device under fusion environment

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Nuclear Science and Techniques 2024年第4期35卷 65-81页

作者： Wei tong Hua li Meng Xu Zhi-Quan Song Bo chen School of Electrical Engineering and Automation Hefei University of TechnologyHefei 230009China Institute of Plasma Physics Hefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China University of Science and Technology of China Hefei 230026China

Because of their economy and applicability,high-power thyristor devices are widely used in the power supply systems for large fusion *** high-dose neutrons produced by deuterium–tritium(D–T)fusion reactions are irradiated on a thyristor device for a long time,the electrical characteristics of the device change,which may eventually cause irreversible *** this study,with the thyristor switch of the commutation circuit in the quench protection system(QPS)of a fusion device as the study object,the relationship between the internal physical structure and external electrical parameters of the irradiated thyristor is ***,a series of targeted thyristor physical simulations and neutron irradiation experiments are conducted to verify the accuracy of the theoretical *** addition,the effect of irradiated thyristor electrical characteristic changes on the entire QPS is studied by accurate simulation,providing valuable guidelines for the maintenance and renovation of the QPS.

关键词： Fusion device Neutron irradiation effects Thyristor Quench protection

Analytical evaluation of steady-state solute distribution in through- diffusion and membrane behavior test under non-perfectly flushing boundary conditions

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第1期16卷 258-267页

作者： Guannian chen Yuchao li Kristin MSample-Lord Shan tong School of Civil&Environmental Engineering and Geography Science Ningbo UniversityNingbo315211China MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou310058China Ningbo ZCONE High-tech Company limited Ningbo315000China Department of Civil and Environmental Engineering Villanova UniversityVillanovaPAUSA

The through-diffusion and membrane behavior testing procedure using a closed-system apparatus has been widely used for concurrent measurement of diffusion and membrane efficiency coefficients of low-permeability clay-based barrier ***,the common assumption of perfectly flushing conditions at the specimen boundaries could induce errors in analyses of the diffusion coefficients and membrane *** this study,an innovative pseudo three-dimensional(3D)analytical method was proposed to evaluate solute distribution along the boundary surfaces of the soil-porous disks system,considering the non-perfectly flushing *** results were consistent with numerical models under two scenarios considering different inflow/outflow *** proposed model has been demonstrated to be an accurate and reliable method to estimate solute distributions along the *** calculated membrane efficiency coefficient and diffusion coefficient based on the proposed analytical method are more accurate,resulting in up to 50%less relative error than the traditional approach that adopts the arithmetic mean value of the influent and effluent *** retar-dation factor of the clay specimen also can be calculated with a revised cumulative mass ***,the simulated transient solute transport matched with experimental data from a multi-stage through-diffusion and membrane behavior test,validating the accuracy of the proposed method.

关键词： Diffusion testing Membrane behavior Coupled transport Clay barrier Transport modeling

Effect of Three-High Rotary Piercing Process on Microstructure,Texture and Mechanical Properties of Magnesium Alloy Seamless Tube

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Acta Metallurgica Sinica(English Letters) 2024年第6期37卷 953-968页

作者： Xiaofeng Ding Zehao Wu tong li Jianxun chen Yuanhua Shuang Baosheng liu Heavy Machinery Engineering Research Center of Education Ministry Taiyuan University of Science and TechnologyTaiyuan030024China College of Materials Science and Engineering Taiyuan University of Science and TechnologyTaiyuan030024China

Mg alloy seamless tubes(MASTs)were prepared through three-high rotary piercing process,effect of billet temperature,feed angle and plug advance on microstructure,texture and mechanical properties of tubes were *** effect on the deformation mechanism and improving mechanical properties mechanism of this process for MASTs were *** results show that the grain size could be refined to 11.3-31.1%of the initial grain size and the microstructure was more uniform due to the accumulation of *** formation of high strain gradient at the grain boundary activated the non-basal *** piercing process could change the grain orientation of as-extruded billet and eliminate the initial basal texture to produce new favorable *** the process could accelerate the continuous dynamic recrystallization *** piercing,yield strength of pierced tubes decreased by 6.7%,ultimate tensile strength(UTS)and elongation increased by 32.4 and 45%,respectively,at optimal *** plate-shapedβ_(1)-Mg_(17)Al_(12) orientation transformed from basal plates to prismatic plates,facilitating the increase in UTS and *** decrease size of nanoscale precipitates could reduce the cracking *** critical resolved shear stress ratios of pyramidal(10−11)slip and(11−22)slip to basal slip for the sample including prismatic plates both decreased compared to that including basal *** could enhance the ductility of tube ***,grain boundary sliding could contribute to a better ductility via coordinating deformation and reducing stress concentration during piercing process.

关键词： Mg alloy tube Rotary piercing Microstructure Texture Mechanical properties

Predicting fatigue life of automotive adhesive bonded joints:a data-driven approach using combined experimental and numerical datasets

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Advances in Manufacturing 2024年第3期12卷 522-537页

作者： chen-Di Wei Qiu-Ren chen Min chen li Huang Zhong-Jie Yue Si-Geng li Jian Wang li chen Chao tong Qing liu School of Advanced Technology Xi’an Jiaotong-Liverpool UniversitySuzhou215123People’s Republic of China Key Laboratory for light-weight Materials Nanjing Tech UniversityNanjing210009People’s Republic of China Material Academy JITRISuzhou215100People’s Republic of China Nanjing University of Science and Technology Nanjing210094People’s Republic of China

The majority of vehicle structural failures originate from joint *** loading is one of the primary factors in joint failures,making the fatigue performance of joints a critical consideration in vehicle structure *** use of traditional fatigue analysis methods is constrained by the absence of adhesive life data and the wide variety of joint ***,there is a pressing need for an accurate fatigue life estimation method for the joints in the automotive *** this work,we proposed a data-driven approach embedding physical knowledge-guided parameters based on experimental data and finite element analysis(FEA)*** machine learning(ML)algorithms are adopted to investigate the fatigue life of three typical adhesive joints,namely lap shear,coach peel and KSII *** the feature engineering and tuned process of the ML models,the preferable model using the Gaussian process regression algorithm is established,fed with eight input parameters,namely thicknesses of the substrates,line forces and bending moments of the adhesive bonded joints obtained from *** proposed method is validated with the test data set and part-level physical tests with complex loading states for an unbiased *** demonstrates that for life prediction of adhesive joints,the data-driven solutions can constitute an improvement over conventional solutions.

关键词： Fatigue life Adhesive bonded joints Finite element analysis(FEA) Machine learning(ML)

Deep learning for joint channel estimation and feedback in massive MIMO systems

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Digital Communications and Networks 2024年第1期10卷 83-93页

作者： Jiajia Guo tong chen Shi Jin Geoffrey Ye li Xin Wang Xiaolin Hou National Mobile Communications Research Laboratory Southeast UniversityNanjing210096China Department of Electrical and Electronic Engineering Imperial College LondonLondonSW72AZUK DOCOMO Beijing Communications Laboratories Co. LtdBeijingChina

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base ***,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive *** this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO *** networks are constructed to perform estimation and feedback explicitly and *** explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network *** module is also designed to generate data-bearing *** results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

关键词： Channel estimation CSI feedback Deep learning Massive MIMO FDD

Machine learning optimization strategy of shaped charge liner structure based on jet penetration efficiency

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Defence Technology（防务技术） 2024年第9期39卷 23-41页

作者： Ziqi Zhao tong li Donglin Sheng Jian chen Amin Yan Yan chen Haiying Wang Xiaowei chen Lanhong Dai State Key Laboratory of Nonlinear Mechanics Institute of MechanicsChinese Academy of SciencesBeijing 100190China School of Future Technology University of Chinese Academy of SciencesBeijing 100049 China School of Engineering Science University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing 100081China

Shaped charge liner(SCL)has been extensively applied in oil recovery and defense *** superior penetration capability through optimizing SCL structures presents a substantial challenge due to intricate rate-dependent processes involving detonation-driven liner collapse,high-speed jet stretching,and *** study introduces an innovative optimization strategy for SCL structures that employs jet penetration efficiency as the primary objective *** strategy combines experimentally validated finite element method with machine learning(FEM-ML).We propose a novel jet penetration efficiency index derived from enhanced cutoff velocity and shape characteristics of the jet via machine *** index effectively evaluates the jet penetration ***,a multi-model fusion based on a machine learning optimization method,called XGBOOST-MFO,is put forward to optimize SCL structure over a large input *** strategy's feasibility is demonstrated through the optimization of copper SCL implemented via the FEM-ML ***,this strategy is extended to optimize the structure of the recently emerging CrMnFeCoNi high-entropy alloy conical liners and hemispherical copper ***,the strategy can provide helpful guidance for the engineering design of SCL.

关键词： Jet penetration efficiency Shaped charge liner FEM-ML XGBOOST MFO High-entropy alloy

Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-imagefree phase retrieval from single-shot hologram

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Opto-Electronic Advances 2024年第9期7卷 22-38页

作者： Xuan Tian Runze li tong Peng Yuge Xue Junwei Min Xing li chen Bai Baoli Yao State Key Laboratory of Transient Optics and Photonics Xi’an Institute of Optics and Precision MechanicsChinese Academy of SciencesXi’an 710119China University of Chinese Academy of Sciences Beijing 100049China.

Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact ***,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel ***,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed *** deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these ***,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ***,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is *** encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural *** effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval *** the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.

关键词： optical microscopy quantitative phase imaging digital holographic microscopy deep learning super-resolution

Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms

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Advances in Manufacturing 2024年第3期12卷 447-464页

作者： Si-Geng li Qiu-Ren chen li Huang Min chen chen-Di Wei Zhong-Jie Yue Ru-Xue liu Chao tong Qing liu School of Advanced Technology Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuPeople’s Republic of China Key Laboratory for light-weight Materials Nanjing Tech UniversityNanjing210009People’s Republic of China Materials Bigdata and Application Division Material Academy JitriSuzhou215131JiangsuPeople’s Republic of China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China

The stress-life curve(S–N)and low-cycle strain-life curve(E–N)are the two primary representations used to characterize the fatigue behavior of a *** material fatigue curves are essential for structural fatigue ***,conducting material fatigue tests is expensive and *** address the challenge of data limitations on ferrous metal materials,we propose a novel method that utilizes the Random Forest Algorithm and transfer learning to predict the S–N and E–N curves of ferrous *** addition,a data-augmentation framework is introduced using a conditional generative adversarial network(cGAN)to overcome data *** incorporating the cGAN-generated data,the accuracy(R2)of the Random Forest Algorithm-trained model is improved by 0.3–*** is proven that the cGAN can significantly enhance the prediction accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.

关键词： Fatigue life curve Machine learning Transfer learning Conditional generative adversarial network(cGAN)