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Journal of Ocean University of China 2024年第2期23卷 447-454页

作者： ZHAO Xuning zhang zhiming ZHAO Yangguo MUPINDU Progress Shandong Provincial Key Laboratory of Marine Environment and Geological Engineering(MEGE) College of Environmental Science and EngineeringOcean University of ChinaQingdao 266100China Key Laboratory of Marine Environmental Science and Ecology(Ocean University of China) Ministry of EducationQingdao 266100China

The production of toxic sulfides is a common environmental problem in mariculture.Therefore,the effective inhibition of sulfidogens is the key to prevent sulfides production.In this study,the possibility and mechanism of nitrate(NO_(3)^(−))inhibiting the activity of the sulfate-reducing microbiota(SRM)from mariculture sediments was investigated.The results showed that 1,3,and 5 mmol L^(−1)NO_(3)^(−)continuously inhibited sulfide production for 1-3 d.As NO_(3)^(−) dosage increased to 7 mmol L^(−1),the duration of inhibition increased to 6 days.Denitrifying product NO_(2)^(−)heavily inhibited the activity of dissimilar sulfate reductase gene(dsrB)by 3 orders,which was the main reason that the sulfate-reducing activity was inhibited.The SRM structure changed significantly with the dosage of NO_(3)^(−),while the abundance of sulfidogens Desulfovibrio species increased due to their capability of detoxifying nitrite through nitrite reductase.Hence,sulfidogens Desulfovibrio species are more adaptable to a high nitrate/nitrite environment,and the traditional control strategies by dosing nitrate/nitrite should be paid more attention to.The findings will serve as helpful guidelines for sulfate-reducing microbiota in the habitat of mariculture to reduce their generation of poisonous sulfide.

关键词： sulfate-reducing microbiota sulfide nitrate Desulfovibrio mariculture

Spatiotemporal Characteristics of Typical Ecosystem Services and Their Spatial Responses to Driving Factors in Ecologically Fragile Areas in Upper Yellow River,China

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Chinese Geographical Science 2024年第4期34卷 674-688页

作者： LIANG Gui FANG Fengman LIN Yuesheng zhang zhiming School of Geography and Tourism Anhui Normal UniversityWuhu 241003China Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze Huaihe River Basin Anhui Normal UniversityWuhu 241003China

The identification of dominant driving factors for different ecosystem services(ESs)is crucial for ecological conservation and sustainable development.However,the spatial heterogeneity of the dominant driving factors affecting various ESs has not been adequately elucidated,particularly in ecologically fragile regions.This study employed the integrated valuation of ESs and trade-offs(InVEST)model to evaluate four ESs,namely,water yield(WY),soil conservation(SC),habitat quality(HQ),and carbon storage(CS),and then to identify the dominant driving factors of spatiotemporal differentiation of ES and further to characterize the spatial heterogeneity characteristics of the dominant driving factors in the eco-fragile areas of the upper Yellow River,China from 2000 to 2020.The results demonstrated that WY exhibited northeast-high and northwest-low patterns in the upper Yellow River region,while high values of SC and CS were distributed in central forested areas and a high value of HQ was distributed in vast grassland areas.The CS,WY,and SC exhibited decreasing trends over time.The most critical factors affecting WY,SC,HQ,and CS were the actual evapotranspiration,precipitation,slope,and normalized difference vegetation index,respectively.In addition,the effects of different factors on various ESs exhibited spatial heterogeneity.These results could provide spatial decision support for eco-protection and rehabilitation in ecologically fragile areas.

关键词： integrated valuation of ecosystem services and trade-offs(InVEST)model geographically weighted regression(GWR) natural factor spatial heterogeneity Lanxi urban agglomeration upper Yellow River China

Alleviating the anisotropic microstructural change and boosting the lithium ions diffusion by grain orientation regulation for Ni-rich cathode materials

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Journal of Energy Chemistry 2024年第1期88卷 213-222,I0005页

作者： Xinyou He Shilin Su Bao zhang zhiming Xiao Zibo zhang Xing Ou Engineering Research Center of the Ministry of Education for Advanced Battery Materials School of Metallurgy and EnvironmentCentral South UniversityChangsha 410083HunanChina Zhejiang Power New Energy Co.Ltd Zhuji 311899ZhejiangChina

Generally,layered Ni-rich cathode materials exhibit the morphology of polycrystalline secondary sphere composed of numerous primary particles.While the arrangement of primary particles plays a very important role in the properties of Ni-rich cathodes.The disordered particle arrangement is harmful to the cyclic performance and structural stability,yet the fundamental understanding of disordered structure on the structural degradation behavior is unclarified.Herein,we have designed three kinds of LiNi_(0.83)Co_(0.06)Mn_(0.11)O_(2) cathode materials with different primary particle orientations by regulating the precursor coprecipitation process.Combining finite element simulation and in-situ characterization,the Li^(+)transport and structure evolution behaviors of different materials are unraveled.Specifically,the smooth Li^(+)diffusion minimizes the reaction heterogeneity,homogenizes the phase transition within grains,and mitigates the anisotropic microstructural change,thereby modulating the crack evolution behavior.Meanwhile,the optimized structure evolution ensures radial tight junctions of the primary particles,enabling enhanced Li^(+)diffusion during dynamic processes.Closed-loop bidirectional enhancement mechanism becomes critical for grain orientation regulation to stabilize the cyclic performance.This precursor engineering with particle orientation regulation provides the useful guidance for the structural design and feature enhancement of Ni-rich layered cathodes.

关键词： Ni-rich cathode Grain orientation regulation Anisotropic microstructural change Precursor engineering Li~+-ions diffusion

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1331-1341页

作者： zhiming zhang Shangce Gao MengChu Zhou Mengtao Yan Shuyang Cao Faculty of Engineering University of ToyamaToyama 930-8555Japan Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Gaoling School of Artificial Intelligence Renmin University of ChinaBeijing 100872China College of Civil Engineering Tongji UniversityShanghai 200092China IEEE

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://***/zhangzm0128/MSU.

关键词： Convolutional neural network deep learning recurrent neural network turbulence prediction wind load predic-tion.

Attention Guided Multi Scale Feature Fusion Network for Automatic Prostate Segmentation

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Computers, Materials & Continua 2024年第2期78卷 1649-1668页

作者： Yuchun Li Mengxing Huang Yu zhang zhiming Bai School of Information and Communication Engineering Hainan UniversityHaikouChina College of Computer Science and Technology Hainan UniversityHaikouChina Urology Department Haikou Municipal People’s Hospital and Central South University Xiangya Medical College Affiliated HospitalHaikouChina School of Information Science and Technology Hainan Normal UniversityHaikouChina

The precise and automatic segmentation of prostate magnetic resonance imaging(MRI)images is vital for assisting doctors in diagnosing prostate diseases.In recent years,many advanced methods have been applied to prostate segmentation,but due to the variability caused by prostate diseases,automatic segmentation of the prostate presents significant challenges.In this paper,we propose an attention-guided multi-scale feature fusion network(AGMSF-Net)to segment prostate MRI images.We propose an attention mechanism for extracting multi-scale features,and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder.In the decoder stage,a feature fusion module is proposed to obtain global context information.We evaluate our model on MRI images of the prostate acquired from a local hospital.The relative volume difference(RVD)and dice similarity coefficient(DSC)between the results of automatic prostate segmentation and ground truth were 1.21%and 93.68%,respectively.To quantitatively evaluate prostate volume on MRI,which is of significant clinical significance,we propose a unique AGMSF-Net.The essential performance evaluation and validation experiments have demonstrated the effectiveness of our method in automatic prostate segmentation.

关键词： Prostate segmentation multi-scale attention 3D Transformer feature fusion MRI

Dendritic Learning-Incorporated Vision Transformer for Image Recognition

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 539-541页

作者： zhiming zhang Zhenyu Lei Masaaki Omura Hideyuki Hasegawa Shangce Gao Faculty of Engineering University of ToyamaToyama-shi 930-8555Japan IEEE

Dear Editor,This letter proposes to integrate dendritic learnable network architecture with Vision Transformer to improve the accuracy of image recognition.In this study,based on the theory of dendritic neurons in neuroscience,we design a network that is more practical for engineering to classify visual features.Based on this,we propose a dendritic learning-incorporated vision Transformer(DVT),which out-performs other state-of-the-art methods on three image recognition benchmarks.

关键词： Image network image

Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang zhiming Zhou Kaiwen Li Tao zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex problems.Given the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

Effect of Y_(2)O_(3) on microstructure and properties of CoCrFeNiTiNb high entropy alloy coating on Ti-6Al-4V surface by laser cladding

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Journal of Rare Earths 2024年第3期42卷 586-599,I0006页

作者： Zhen Li Wei Zhao Kedong Yu Ning Guo Guangchun Xiao zhiming Wang Hui zhang College of Mechanical Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan250353China Shandong Institute of Mechanical Design and Research Jinan250031China

The effects of Y_(2)O_(3) on the microstructure, microhardness, wear resistance, high-temperature oxidation resistance, hot corrosion resistance, and electrochemical corrosion behaviour of CoCrFeNiTiNb high entropy alloy coatings formed on Ti-6Al-4V alloy surfaces were studied. The results show that the addition of Y_(2)O_(3) changes the proportion of the phase but does not change its type. The average grain size is only 1/4.7 of that of the high entropy alloy(HEA) coating, and the fine-grained strengthening leads to increases in the microhardness and wear resistance of 21.8% and 26.9%, respectively. The addition of Y_(2)O_(3) enhances the denseness and bonding properties of the oxide and corrosion product layers, reducing the oxidation and hot corrosion rates by 60.3% and 40.3%, respectively. The addition of Y_(2)O_(3) doubles the corrosion resistance which is attributed to the refinement of the grains, the increased proportion of HCP and TiN, and the weakening of galvanic coupling corrosion.

关键词： Laser cladding High entropy alloy coatings Rare earths Wear properties High temperature properties Electrochemical corrosion

Understanding the failure mechanism towards developing high-voltage single-crystal Ni-rich Co-free cathodes

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Green Energy & Environment 2024年第6期9卷 1045-1057页

作者： Jixue Shen Bao zhang Changwang Hao Xiao Li zhiming Xiao Xinyou He Xing Ou Engineering Research Center of the Ministry of Education for Advanced Battery Materials School of Metallurgy and EnvironmentCentral South UniversityChangsha410083China Zhejiang Power New Energy Co. Ltd.Zhuji311899China Material Science and Chemistry Engineering College China University of GeosciencesWuhan430074China

Benefited from its high process feasibility and controllable costs,binary-metal layered structured LiNi_(0.8)Mn_(0.2)O_(2)(NM)can effectively alleviate the cobalt supply crisis under the surge of global electric vehicles(EVs)sales,which is considered as the most promising nextgeneration cathode material for lithium-ion batteries(LIBs).However,the lack of deep understanding on the failure mechanism of NM has seriously hindered its application,especially under the harsh condition of high-voltage without sacrifices of reversible capacity.Herein,singlecrystal LiNi_(0.8)Mn_(0.2)O_(2) is selected and compared with traditional LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)(NCM),mainly focusing on the failure mechanism of Cofree cathode and illuminating the significant effect of Co element on the Li/Ni antisite defect and dynamic characteristic.Specifically,the presence of high Li/Ni antisite defect in NM cathode easily results in the extremely dramatic H2/H3 phase transition,which exacerbates the distortion of the lattice,mechanical strain changes and exhibits poor electrochemical performance,especially under the high cutoff voltage.Furthermore,the reaction kinetic of NM is impaired due to the absence of Co element,especially at the single-crystal architecture.Whereas,the negative influence of Li/Ni antisite defect is controllable at low current densities,owing to the attenuated polarization.Notably,Co-free NM can exhibit better safety performance than that of NCM cathode.These findings are beneficial for understanding the fundamental reaction mechanism of single-crystal Ni-rich Co-free cathode materials,providing new insights and great encouragements to design and develop the next generation of LIBs with low-cost and high-safety performances.

关键词： Li/Ni antisite defect Dynamic characteristic High-voltage Single-crystal Ni-rich Co-free cathodes Lithium-ion batteries

Research progress of CO_(2) capture and mineralization based on natural minerals

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International Journal of Minerals,Metallurgy and Materials 2024年第6期31卷 1208-1227页

作者： Chenguang Qian Chunquan Li Peng Huang Jialin Liang Xin zhang Jifa Wang Jianbing Wang zhiming Sun School of Chemical and Environmental Engineering China University of Mining and Technology(Beijing)Beijing 100083China Shanghai Construction Building Material Technology Group Shanghai 200080China

Natural minerals,such as kaolinite,halloysite,montmorillonite,attapulgite,bentonite,sepiolite,forsterite,and wollastonite,have considerable potential for use in CO_(2) capture and mineralization due to their abundant reserves,low cost,excellent mechanical prop-erties,and chemical stability.Over the past decades,various methods,such as those involving heat,acid,alkali,organic amine,amino sil-ane,and ionic liquid,have been employed to enhance the CO_(2) capture performance of natural minerals to attain high specific surface area,a large number of pore structures,and rich active sites.Future research on CO_(2) capture by natural minerals will focus on the full utiliza-tion of the properties of natural minerals,adoption of suitable modification methods,and preparation of composite materials with high specific surface area and rich active sites.In addition,we provide a summary of the principle and technical route of direct and indirect mineralization of CO_(2) by natural minerals.This process uses minerals with high calcium and magnesium contents,such as forsterite(Mg_(2)SiO_(4)),serpentine[Mg_(3)Si_(2)O(OH)_(4)],and wollastonite(CaSiO_(3)).The research status of indirect mineralization of CO_(2) using hydro-chloric acid,acetic acid,molten salt,and ammonium salt as media is also introduced in detail.The recovery of additives and high-value-added products during the mineralization process to increase economic benefits is another focus of future research on CO_(2) mineralization by natural minerals.

关键词： natural mineral carbon dioxide capture modification composite material carbon dioxide mineralization