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Spatio-temporal variability of surface chlorophyll a in the Yellow Sea and the East China Sea based on reconstructions of satellite data of 2001-2020

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Journal of Oceanology and Limnology 2024年第2期42卷 390-407页

作者： Weichen xie tao WANG Wensheng JIANG Key Laboratory of Marine Environment and Ecology Ocean University of ChinaQingdao 266100China College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao 266100China

Chlorophyll-a(Chl-a)concentration is a primary indicator for marine environmental monitoring.The spatio-temporal variations of sea surface Chl-a concentration in the Yellow Sea(YS)and the East China Sea(ECS)in 2001-2020 were investigated by reconstructing the MODIS Level 3 products with the data interpolation empirical orthogonal function(DINEOF)method.The reconstructed results by interpolating the combined MODIS daily+8-day datasets were found better than those merely by interpolating daily or 8-day data.Chl-a concentration in the YS and the ECS reached its maximum in spring,with blooms occurring,decreased in summer and autumn,and increased in late autumn and early winter.By performing empirical orthogonal function(EOF)decomposition of the reconstructed data fields and correlation analysis with several potential environmental factors,we found that the sea surface temperature(SST)plays a significant role in the seasonal variation of Chl a,especially during spring and summer.The increase of SST in spring and the upper-layer nutrients mixed up during the last winter might favor the occurrence of spring blooms.The high sea surface temperature(SST)throughout the summer would strengthen the vertical stratification and prevent nutrients supply from deep water,resulting in low surface Chl-a concentrations.The sea surface Chl-a concentration in the YS was found decreased significantly from 2012 to 2020,which was possibly related to the Pacific Decadal Oscillation(PDO).

关键词： chlorophyll a(Chl a) data interpolation empirical orthogonal function(DINEOF) empirical orthogonal function(EOF)analysis Yellow Sea East China Sea

Dual-channel mechano-phosphorescence:a combined locking effect with twisted molecular structures and robust interactions

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Light(Science & Applications) 2024年第4期13卷 738-746页

作者： Zongliang xie Zhu Mao Hailan Wang Yuxin Xiao Xiayu Zhang tao Yu Zhongfu An Wei Huang Frontiers Science Center for Flexible Electronics Xi’an Institute of Flexible Electronics&Xi’an Institute of Biomedical Materials and EngineeringNorthwestern Polytechnical University127 West Youyi RoadXi’an 710072China. Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518100China. Shenzhen Institutes of Advanced Electronic Materials Shenzhen 518100China. Key Laboratory of Flexible Electronics&Institute of Advanced Materials Nanjing Tech University30 South Puzhu RoadNanjing 211816China

Organic mechanoluminescence materials,featuring dual emission and ultralong phosphorescence characteristics,exhibit significant potential for applications in real-time stress sensing,pressure-sensitive lighting,advanced security marking techniques,and material breakage monitoring.However,due to immature molecular design strategies and unclear luminescence mechanisms,these materials remain rarely reported.In this study,we propose a valuable molecular design strategy to achieve dual-channel mechano-phosphorescence.By introducing the arylphosphine oxide group into a highly twisted molecular framework,enhanced intra-and intermolecular interactions could be achieved within rigid structures,leading to dual-channel mechanoluminescence with greatly promoted ultralong phosphorescence.Further investigations reveal the substantial boosting effect of intra-and intermolecular interactions on mechanoluminescence and ultralong phosphorescence properties by locking the highly twisted molecular skeleton.This work provides a concise and guiding route to develop novel smart responsive luminescence materials for widespread applications in material science.

关键词： channel luminescence locking

Adaptive detection of range-spread targets in homogeneous and partially homogeneous clutter plus subspace interference

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Journal of Systems Engineering and Electronics 2024年第1期35卷 43-54页

作者： JIAN tao HE Jia WANG Bencai LIU Yu XU Congan xie Zikeng Research Institute of Information Fusion Naval Aviation UniversityYantai 264001China Air Force Early Warning Academy Wuhan 430019China

Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.

关键词： adaptive detection subspace interference constant false alarm rate Gradient test partially homogeneous environment

Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 982-995页

作者： tao Wang Qiming Chen Xun Lang Lei xie Peng Li Hongye Su the Department of Electronic Engineering the School of InformationYunnan UniversityKunming 650091China DAMO Academy Alibaba GroupHangzhou 311100China the State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027China

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

关键词： Convolutional neural networks(CNNs) deep learning image processing oscillation detection process industries

Heralded entanglement between error-protected logical qubits for fault-tolerant distributed quantum computing

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Science China(Physics,Mechanics & Astronomy) 2024年第2期67卷 25-33页

作者： Jialun Li Zhihao xie Yachen Li Yansen Liang Zhenhua Li tao Li MIIT Key Laboratory of Semiconductor Microstructure and Quantum Sensing School of PhysicsNanjing University of Science and TechnologyNanjing 210094China National Laboratory of Solid State Microstructures Nanjing UniversityNanjing 210093China

Quantum error correction is essential for achieving reliable quantum information processing tasks,as it can mitigate the detrimental effects of noise by encoding single-qubit information into a larger quantum system.However,the generation of distributed entanglement between logical qubits located within two spatially separated nodes presents a significant resource-intensive challenge that has yet to be overcome.Here we present a heralded protocol for generating distributed entanglement between two nonlocal error-protected logical qubits.A high-dimensional single photon can evolve physical qubits into a logical qubit that entangles with the photon and then converts logical qubit-photon entanglement into entanglement between two logical qubits,when the photon state is properly tuned and an effective photon-spin interface between single photons and individual spins is exploited.Furthermore,the success of the entanglement generation is heralded by the detection of the photon,and the corresponding efficiency can,in principle,approach unity.These distinguished features make our protocol highly appealing for future large-scale quantum technologies.

关键词： quantum networks distributed entanglement error-protected logical qubits high-dimensional photons

Yolk-shell FeSe_(2)@CoSe_(2)/FeSe_(2) heterojunction as anode materials for sodium-ion batteries with high rate capability and stability

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Journal of Materials Science & Technology 2024年第5期172卷 185-195页

作者： Liuyang Zhang Bicheng Zhu Difa Xu Zibao Qian Ping xie tao Liu Jiaguo Yu Laboratory of Solar Fuel Faculty of Materials Science and ChemistryChina University of GeosciencesWuhan 430078China Hunan Key Laboratory of Applied Environmental Photocatalysis Changsha UniversityChangsha 410022China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China

Sodium-ion batteries are promising candidates for large-scale grid storage systems and other applications.Their foremost advantage derives from superior environmental credentials,enhanced safety as well as lower raw material costs than lithium-ion batteries.It is still challenging to explore desirable anode material.In this study,FeSe_(2)@CoSe_(2)/FeSe_(2),with a yolk-shell structure was prepared by ion exchange and selenisation.The FeSe_(2)@CoSe_(2)/FeSe_(2)prepared as anode material for sodiumion batteries exhibits excellent rate capability due to the synergistic effect of bimetallic selenides and the interfacial effect of the heterostructure.Moreover,it delivers high performance(510 mAh g^(-1)at 0.2 A g^(-1)),superior rate capa-bility(90%retention at 5 A g^(-1)),and good long-time cycling stability(78%capacity retention after 1800 cycles at a high current density of 2 A g^(-1)).The optimized sodiumion full cell with FeSe_(2)@CoSe_(2)/FeSe_(2)as the anode and Na 3 V 2(PO 4)3 as the cathode still demonstrates excellent performance.Namely,a ca-pacity of 272 mAh g^(-1)(at 1 A g^(-1))within the operating voltage from 1 to 3.8 V can be obtained.This work illustrates the potential of bimetallic selenides with heterostructures for performance enhancement of sodium-ion batteries.

关键词： Cobalt-iron selenide Heterojunctions Sodium storage Anode material Full battery

Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises

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Chinese Journal of Electronics 2024年第3期33卷 814-822页

作者： Junqi LIU tao WEN Guo xie Yuan CAO Clive Roberts Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology The School of Electronic and Information Engineering Beijing Jiaotong University Birmingham Centre for Railway Research and Education University of Birmingham

The fault diagnosis of railway point machines(RPMs) has attracted the attention of engineers and researchers.Seldom have studies considered diverse noises along the track.To fulfill this aspect,a multi-time-scale variational mode decomposition(MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple noises.MTSVMD decomposes condition monitoring signals after coarse-grained processing in varying degrees.In this manner,the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single scale.In the experimental validation,a random position,random type,random number,and random length(4R) noise-adding algorithm helps to verify the robustness of the approach.The adequate experimental results demoristrate the superiority of the proposed MTSVMD-based fault diagnosis.

关键词： Fault diagnosis Condition monitoring Noise Signal processing algorithms Aerospace electronics Rail transportation Robustness

Combining machine and deep transfer learning for mediastinal lymph node evaluation in patients with lung cancer

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虚拟现实与智能硬件（中英文） 2024年第3期6卷 226-238页

作者： Hui xie Jianfang ZHANG Lijuan DING tao TAN Qing LI Department of Radiation Oncology Affiliated Hospital(Clinical College)of Xiangnan UniversityChenzhou 423000China Faulty of Applied Sciences Macao Polytechnic UniversityMacao 999078China Department of Physical examination Beihu Centers for Disease Control and PreventionChenzhou 423000China Department of Radiation Oncology the Third People's Hospital of Chenzhou CityChenzhou 423000China School of Medical Imaging Laboratory Science and RehabilitationXiangnan UniversityChenzhou 423000China

Background The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis.Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis,thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and prognosis.Methods In total,623 eligible patients were recruited from two medical institutions.Seven deep learning models,namely Alex,GoogLeNet,Resnet18,Resnet101,Vgg16,Vgg19,and MobileNetv3(small),were utilized to extract deep image histological features.The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient(r≥0.9)and Least Absolute Shrinkage and Selection Operator.Eleven machine learning methods,namely Support Vector Machine,K-nearest neighbor,Random Forest,Extra Trees,XGBoost,LightGBM,Naive Bayes,AdaBoost,Gradient Boosting Decision Tree,Linear Regression,and Multilayer Perceptron,were employed to construct classification prediction models for the filtered final features.The diagnostic performances of the models were assessed using various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value.Calibration and decision-curve analyses were also performed.Results The present study demonstrated that using deep radiomic features extracted from Vgg16,in conjunction with a prediction model constructed via a linear regression algorithm,effectively distinguished the status of mediastinal lymph nodes in patients with lung cancer.The performance of the model was evaluated based on various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value,which yielded values of 0.808,0.834,0.851,0.745,0.829,and 0.776,respectively.The validation set of the model was assessed using clinical decision curv

关键词： Machine learning Deep transfer learning Evaluation Mediastinal lymph node lung cancer patie

Toward a Learnable Climate Model in the Artificial Intelligence Era

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Advances in Atmospheric Sciences 2024年第7期41卷 1281-1288页

作者： Gang HUANG Ya WANG Yoo-Geun HAM Bin MU Weichen tao Chaoyang xie State key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China University of Chinese Academy of Sciences Beijing 100049China Department of Environmental Planning Graduate School of Environmental StudiesSeoul National UniversitySeoul 08826South Korea School of Software Engineering Tongji UniversityShanghai 200092China

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

关键词： artificial intelligence deep learning learnable climate model