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Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network

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Journal of Oceanology and Limnology 2024年第3期42卷 728-743页

作者： ning zhang Junfang YANG Shanwei LIU Yi MA Jie zhang College of Oceanography and Space Informatics China University of PetroleumQingdao 266580China First Institute of Oceanography Ministry of Natural ResourcesQingdao 266061China Technology Innovation Center for Ocean Telemetry Ministry of Natural ResourcesQingdao 266061China

The accurate identification of marine oil spills and their emulsions is of great significance for emergency response to oil spill pollution.The selection of characteristic bands with strong separability helps to realize the rapid calculation of data on aircraft or in orbit,which will improve the timeliness of oil spill emergency monitoring.At the same time,the combination of spectral and spatial features can improve the accuracy of oil spill monitoring.Two ground-based experiments were designed to collect measured airborne hyperspectral data of crude oil and its emulsions,for which the multiscale superpixel level group clustering framework(MSGCF)was used to select spectral feature bands with strong separability.In addition,the double-branch dual-attention(DBDA)model was applied to identify crude oil and its emulsions.Compared with the recognition results based on original hyperspectral images,using the feature bands determined by MSGCF improved the recognition accuracy,and greatly shortened the running time.Moreover,the characteristic bands for quantifying the volume concentration of water-in-oil emulsions were determined,and a quantitative inversion model was constructed and applied to the AVIRIS image of the deepwater horizon oil spill event in 2010.This study verified the effectiveness of feature bands in identifying oil spill pollution types and quantifying concentration,laying foundation for rapid identification and quantification of marine oil spills and their emulsions on aircraft or in orbit.

关键词： hyperspectral image spectral analysis dimensionality reduction multiscale superpixel level group clustering framework(MSGCF) double-branch dual-attention(DBDA)

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Formation and properties of a Zr-based amorphous coating by laser cladding

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Rare Metals 2024年第4期43卷 1809-1814页

作者： ning zhang Ying Liu Wei Yang Shu-Jie Pang Key Laboratory of Aerospace Materials and Performance (Ministry of Education) School of Materials Science and EngineeringBeihang University

(Zr0.53Al0.1Ni0.05Cu0.3Ti0.02)99Y1（at%） coating with amorphous layer of about 180 μm thick was prepared on a steel substrate by using laser cladding method.The coating is compact and shows good metallurgical bonding with substrate.The microstructure,microhardness and corrosion behavior along the depth from the coating surface to the substrate were investigated.It is found that a gradient structure consisted of amorphous surface layer,amorphous-crystalline transitional layer and substrate is formed after the laser cladding.The microhardness and corrosion behavior exhibit variation with the microstructural evolution at different depths from the coating surface.The microhardness and corrosion resistance in 3.5 wt%NaCl solution of the amorphous surface layer are comparable to those of the as-cast Zr-based BMG with the same composition,and higher than those of the steel substrate.

关键词： Laser cladding Amorphous coating Microstructure Hardness Corrosion resistance

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A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete

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Frontiers of Structural and Civil Engineering 2024年第1期18卷 30-50页

作者： Bin XI ning zhang Enming LI Jiabin LI Jian ZHOU Pablo SEGARRA Department of Civil and Environmental Engineering Politecnico Di MilanoMilano 20133Italy Leibniz Institute of Ecological Urban and Regional Development(IOER) Dresden 01217Germany Universidad Politécnica de Madrid-ETSI Minas y Energía Madrid 28003Spain Department of Civil Engineering KU Leuven CampusBrugge 8200Belgium School of Resources and Safety Engineering Central South UniversityChangsha 410083China

The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic advantages.However,RA concrete is plagued with considerable durability concerns,particularly carbonation.To advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is needed.On the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to monitor.On the other hand,carbonation is influenced by many factors and is hard to predict.Regarding this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA concrete.Three types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive tools.It was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer(XGB-MVO)with R^(2) value of 0.9949 and 0.9398 for training and testing sets,respectively.XGB-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were investigated.It also showed better generalization capabilities when compared with different models in the literature.Overall,this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.

关键词： recycled aggregate concrete carbonation depth nature-inspired optimization algorithms extreme gradient boosting technique parametric analysis

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Communication Resources Allocation for Time Delay Reduction of Frequency Regulation Service in High Renewable Penetrated Power System

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 468-480页

作者： Hongjie He ning zhang Chongqing Kang Song Ci Fei Teng Goran Strbac State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Electrical and Electronic Engineering Imperial College LondonSW72BULondonU.K

The high renewable penetrated power system has severe frequency regulation problems.Distributed resources can provide frequency regulation services but are limited by com-munication time delay.This paper proposes a communication resources allocation model to reduce communication time delay in frequency regulation service.Communication device resources and wireless spectrum resources are allocated to distributed resources when they participate in frequency regulation.We reveal impact of communication resources allocation on time delay reduction and frequency regulation performance.Besides,we study communication resources allocation solution in high renewable energy penetrated power systems.We provide a case study based on the HRP-38 system.Results show communication time delay decreases distributed resources'ability to provide frequency regulation service.On the other hand,allocating more communication resources to distributed resources'communica-tion services improves their frequency regulation performance.For power systems with renewable energy penetration above 70%,required communications resources are about five times as many as 30%renewable energy penetrated power systems to keep frequency performance the same.Index Terms-Communication resources allocation,commun-ication time delay,distributed resource,frequency regulation,high renewable energy penetrated power system.

关键词： Communication resources allocation communication time delay distributed resource frequency regulation high renewable energy penetrated power system

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Alternative relay regulates the adenosine triphosphatase activity of Locusta migratoria striated muscle myosin

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Insect Science 2024年第2期31卷 435-447页

作者： Jie Hao Chang Liu ning zhang Jing Li Tong Ni Mingbo Qu Xiang-dong Li State Key Laboratory oflntegrated Management of lnsect Pests and Rodents Insttute of ZoologyChinese Academy of SciencesBejingChina University of Chinese Academy of Sciences BejingChina School of Bioengeering Dalian University of TechnologyDalianChina

Locust(Locusta migratoria)has a single striated muscle myosin heavy chain(Mhc)gene,which contains 5 clusters of alternative exclusive exons and 1 differently included penultimate exon.The alternative exons of Mhc gene encode 4 distinct regions in the myosin motor domain,that is,the N-terminal SH3-like domain,one lip of the nucleotide-binding pocket,the relay,and the converter.Here,we investigated the role of the alternative regions on the motor function of locust muscle myosin.Using Sf9-baculovirus protein expression system,we expressed and purified 5 isoforms of the locust muscle myosin heavy meromyosin(HMM),including the major isoform in the thorax dorsal longitudinal flight muscle(FL1)and 4 isoforms expressed in the abdominal intersegmental muscle(AB1 to AB4).Among these 5 HMMs,FL1-HMM displayed the highest level of actin-activated adenosine triphosphatase(ATPase)activity(hereafter referred as ATPase activity).To identify the alternative region(s)responsible for the elevated ATPase activity of FL1-HMM,we produced a number of chimeras of FL1-HMM and AB4-HMM.Substitution with the relay of AB4-HMM(encoded by exon-14c)substantially decreased the ATPase activity of FL1-HMM,and conversely,the relay of FL1-HMM(encoded by exon-14a)enhanced the ATPase activity of AB4-HMM.Mutagenesis showed that the exon-14a-encoded residues Gly474 and Asn509 are responsible for the elevated ATPase activity of FL1-HMM.Those results indicate that the alternative relay encoded by exon-14a/c play a key role in regulating the ATPase activity of FL1-HMM and AB4-HMM.

关键词： alternative splicing ATPase Locusta migratoria molecular motor muscle myosin

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Synergetic optimization operation method for distribution network based on SOP and PV

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Global Energy Interconnection 2024年第2期7卷 130-141页

作者： Lei Chen ning zhang Xingfang Yang Wei Pei Zhenxing Zhao Yinan Zhu Hao Xiao State Grid Shijiazhuang Electric Power Company Shijiazhuang 130100P.R.China Institute of Electrical Engineering Chinese Academy of SciencesBeijing 100190P.R.China IEEE Power and Energy Society New YorkUSA

The integration of distributed generation brings in new challenges for the operation of distribution networks,including out-of-limit voltage and power flow control.Soft open points(SOP)are new power electronic devices that can flexibly control active and reactive power flows.With the exception of active power output,photovoltaic(PV)devices can provide reactive power compensation through an inverter.Thus,a synergetic optimization operation method for SOP and PV in a distribution network is proposed.A synergetic optimization model was developed.The voltage deviation,network loss,and ratio of photovoltaic abandonment were selected as the objective functions.The PV model was improved by considering the three reactive power output modes of the PV inverter.Both the load fluctuation and loss of the SOP were considered.Three multi-objective optimization algorithms were used,and a compromise optimal solution was calculated.Case studies were conducted using an IEEE 33-node system.The simulation results indicated that the SOP and PVs complemented each other in terms of active power transmission and reactive power compensation.Synergetic optimization improves power control capability and flexibility,providing better power quality and PV consumption rate.

关键词： Synergetic optimization Soft open point(SOP) Photovoltaic(PV) Distribution network

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Predicting Credit Bond Default with Deep Learning: Evidence from China

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Journal of Social Computing 2024年第1期5卷 36-45页

作者： ning zhang Wenhe Li Haoxiang Chen Binshu Jia Pei Deng School of Information Central University of Finance and EconomicsBeijing 100081China SDIC Taikang Trust Co. Ltd.Beijing 100034China

China’s credit bond market has rapidly expanded in recent years.However,since 2014,the number of credit bond defaults has been increasing rapidly,posing enormous potential risks to the stability of the financial market.This study proposed a deep learning approach to predict credit bond defaults in the Chinese market.A convolutional neural network(CNN)was selected as the classification model and to reduce the extreme imbalance between defaulted and non-defaulted bonds,and a generative adversarial network(GAN)was used as the oversampling model.Based on 31 financial and 20 non-financial indicators,we collected Wind data on all credit bonds issued and matured or defaulted from 2014 to 2021.The experimental results showed that our GAN+CNN approach had superior predictive performance with an area under the curve(AUC)of 0.9157 and precision of 0.8871 compared to previous research and other commonly used classification models-including the logistic regression,support vector machine,and fully connected neural network models-and oversampling techniques-including the synthetic minority oversampling technique(SMOTE)and Borderline SMOTE model.For one-year predictions,indicators of solvency,capital structure,and fundamental properties of bonds are proved to be the most important indicators.

关键词： credit bond default prediction convolutional neural network imbalanced data processing generative adversarial network

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Recent advances in multifunctional metal-organic frameworks for lithium metal batteries

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Science China Chemistry 2024年第3期67卷 759-773页

作者： Jiaqi Li Zheng Weng Zuosu Qin Ying zhang ning zhang Xiaohe Liu Chuankun Jia Gen Chen Jiang Zhou School of Materials Science and Engineering Key Laboratory of Electronic Packaging and Advanced Functional Materials of Hunan ProvinceCentral South University Zhongyuan Critical Metals Laboratory Zhengzhou University College of Materials Science and Engineering Changsha University of Science&Technology

In view of novel materials in the field of lithium metal batteries(LMBs), metal-organic frameworks(MOFs) have attracted extensive research interest owing to their controllable pore size, unsaturated metal sites and multifunctional organic groups. A variety of MOFs have been elaborately calculated and synthesized to be applied as separator coating, electrolyte modulators and solid-state electrolyte fillers in LMBs. In this mini-review, we summarize the mechanism of MOFs to limit the migration of anions, improve the Li-ion transference number and prolong the lifespan of LMBs. Suitable pore structure of MOFs can physically restrict the movement of Li^(+). Unsaturated metal sites can adsorb anions by electrostatic interaction. In addition,multifunctional organic functional groups that limit the migration of anions are discussed. Finally, the key challenges and perspectives in the development direction of MOFs-based separators and electrolytes are further elaborated.

关键词： metal-organic frameworks lithium metal battery separator electrolyte

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Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

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Engineering 2024年第2期33卷 178-189页

作者： Xiaoming Yuan Jiahui Chen ning zhang Qiang(John)Ye Changle Li Chunsheng Zhu Xuemin Sherman Shen Qinhuangdao Branch Campus Northeastern UniversityQinhuangdao 066004China State Key Laboratory of Integrated Services Networks&the Research Institute of Smart Transportation Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering University of WindsorWindsorON N9B 3P4Canada Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada SUSTech Institute of Future Networks Southern University of Science and TechnologyShenzhen 518055China Department of Electrical and Computer Engineering University of WaterlooWaterlooON N2L 3G1Canada

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

关键词： Knowledge sharing Internet of Vehicles Federated learning Broad learning Reconfigurable intelligent surfaces Resource allocation

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Advanced nano drug delivery systems for neuroprotection against ischemic stroke

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Chinese Chemical Letters 2024年第2期35卷 151-162页

作者： Chenchen Xie Jun Liao ning zhang Yuhan Sun Yi Li Liyan Xiong Yingnan zhang Xiaoyan Liu Wei Su Heming Chen Ping Zeng Xiaoqing zhang Ying Lu Tingfang Wang Chuan zhang Shanghai Engineering Research Center of Organ Repair School of MedicineShanghai UniversityShanghai 200444China Department of Pharmaceutical Science School of PharmacyNaval Medical UniversityShanghai 200433China Department of pharmacy the International Peace Maternity and Child Health HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai 200030China School of Pharmacy Shanghai Jiao Tong UniversityShanghai 200040China

Ischemic stroke(IS)represents a significant threat to brain health due to its elevated mortality and disability rates.The efficacy of small-molecule neuroprotective agents has been impeded by challenges associated with traversing the blood-brain barrier(BBB)and limited bioavailability.Conversely,advanced nano drug delivery systems hold promise for overcoming these obstacles by facilitating efficient transportation across the BBB and maintaining optimal drug concentrations.This review aims to explore advanced neuroprotective nano drug delivery systems as a means of effectively administering neuroprotective agents to the brain using pharmaceutical approaches in the treatment of IS.By examining these systems,researchers and clinicians can gain valuable insights and innovative concepts,illuminating the potential of advanced neuroprotective nano drug delivery systems.Leveraging these advancements can drive the progress of pioneering and efficacious therapeutic interventions for IS.

关键词： Target Blood-brain barrier Ischemic stroke Nano drug delivery Neuroprotection

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