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Effect of Boundary Scattering on Magneto-Transport Performance in BN-Encapsulated Graphene

Chinese Physics Letters 2022年第9期39卷 57-61页

作者： Lijun Zhu Lin Li Xiaodong Fan Zhongniu Xie Changgan Zeng CAS Key Laboratory of Strongly Coupled Quantum Matter Physics and Department of PhysicsUniversity of Science and Technology of ChinaHefei 230026China International Center for Quantum Design of Functional Materials(ICQD) Hefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei 230026China Hefei National Laboratory University of Science and Technology of ChinaHefei 230088China

For conductors in the ballistic regime, electron-boundary scattering at the sample edge plays a dominant role in determining the transport performance, giving rise to many intriguing phenomena like low-field negative magnetoresistance effect. We systematically investigate the magneto-transport behaviors of BN-encapsulated graphene devices with narrow channel width W, wherein the bulk mean free path Lmfp can be very large and highly tunable. By comparing the magnetoresistance features and the amplitude of Lmfp in a large parameter space of temperature and carrier density, we reveal that the boundary-scattering-dominated negative magnetoresistance effect can still survive even when the ballistic ratio(Lmfp/W) is as low as 0.15. This striking value is much smaller than the expected value for achieving(quasi-) ballistic transport regime(Lmfp/W ≥ 1), and can be attributed to the ultra-low specularity of the sample edge of our graphene devices. These findings enrich our understanding of the effects of boundary scattering on channel transport, which is of vital importance for future designs of two-dimensional electronic devices with limited lateral sizes.

关键词： transport scattering boundary

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Realizing high thermoelectric performance via selective resonant doping in oxyselenide BiCuSeO

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Nano Research 2023年第1期16卷 1679-1687页

作者： Yue-Xing Chen Wenning Qin Adil Mansoor Adeel Abbas Fu Li Guang-xing Liang Ping Fan Muhammad Usman Muzaffar Bushra Jabar Zhen-hua Ge Zhuang-hao Zheng F1 Shenzhen Key Laboratory of Advanced Thin Films and Applications Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong ProvinceCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China Faculty of Materials and Manufacturing Beijing University of TechnologyBeijing 100124China International Center for Quantum Design of Functional Materials(ICQD) Hefei National Laboratory for Physical Sciences at Microscale(HFNL)and CAS Center for Excellence in Quantum Information and Quantum PhysicsUniversity of Science and Technology of ChinaHefei 230026China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming 650093China

Tuning the charge carrier concentration is imperative to optimize the thermoelectric(TE)performance of a *** BiCuSeO based oxyselenides,doping efforts have been limited to optimizing the carrier *** the present work,dual-doping of In and Pb at Bi site is introduced for p-type BiCuSeO to realize the electric transport channels with intricate band characteristics to improve the power factor(PF).Herein,the impurity resonant state is realized via doping of resonant dopant In over Pb,where Pb comes forward to optimize the Fermi energy in the dual-doped BiCuSeO system to divulge the significance of complex electronic *** manifold roles of dual-doping are used to adjust the elevation of the PF due to the significant enhancement in electrical ***,the combined experimental and theoretical study shows that the In/Pb dual doping at Bi sites gently reduces bandgap,introduces resonant doping states with shifting down the Fermi level into valence band(VB)with a larger density of state,and thus causes to increase the carrier concentration and effective mass(m*),which are favorable to enhance the electronic transport *** a result,both improved ZTmax=0.87(at 873 K)and high ZTave=0.5(at 300–873 K)are realized for InyBi(1−x)−yPbxCuSeO(where x=0.06 and y=0.04)*** obtained results successfully demonstrate the effectiveness of the selective dual doping with resonant dopant inducing band manipulation and carrier engineering that can unlock new prospects to develop high TE materials.

关键词： thermoelectric material BiCuSeO dual-doping power factor figure of merit

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Multi-Level Robotic Pouring Using Offline Reinforcement Learning

Multi-Level Robotic Pouring Using Offline Reinforcement Lear...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhang, Xiaoxiong Liu, Junwei Zhang, Wei School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Shenzhen518055 China

ISBN: (纸本)9781665481090

This paper presents a novel learning-based method for a robotic manipulator to achieve liquid pouring across different liquid levels using only visual sensors. Previous works have relied on either online reinforcement learning or imitation learning, which are limited by sim-to-real gaps or data efficiency. In this paper, we propose to combine supervised learning and offline reinforcement learning, utilizing a human demonstration dataset containing only successful pouring at the highest level. Specifically, our approach employs supervised learning for a visual classifier, transforming the visual input into a categorical distribution over liquid levels. The offline reinforcement learning method is applied to a binary-conditioned control policy, which takes a binary signal and the robot's proprioception as inputs to determine the action. The binary signal indicates whether the desired liquid level has been reached based on the target liquid level. Through experiments, our method demonstrates superior effectiveness compared to a state-of-the-art imitation learning algorithm. Moreover, tests in unseen scenarios and multiple liquid level commands verify the generalization and transferability of our approach. © 2024 IEEE.

关键词： Self-supervised learning

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MC-SORT: A Motion Correction-Based Framework for Long-Term Multiple Object Tracking 27

MC-SORT: A Motion Correction-Based Framework for Long-Term M...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Xiangyu Qin, Yunchuan Li, Ruihui Tan, Guanghua Tang, Zhuo Li, Kenli College of Computer Science and Electronic Engineering Hunan University China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University China

ISBN: (纸本)9781643685489

Long-term occlusion is one of the most formidable challenges in Multi-Object Tracking (MOT). The motion models of existing SORT-based trackers are unreliable in estimating the motion states of long-term occluded targets. This is mainly because as the occlusion period increases, the increases speed of estimation errors in the motion model increases faster. In practical applications, we believe that the estimation error of the tracker during long-term occlusion is mainly concentrated in the estimation error of the motion model on the velocity of the occluded target. In this work, we have demonstrated that in the long-term occlusion period, appropriately correcting the estimated values of the motion model on the target motion velocity and fully utilizing the temporal and attribute information of the target's historical trajectory as calculation indicators of correlation are beneficial for improving the robustness of the tracker in long-term occlusion. We refer to our proposed motion correction-based framework as MC-SORT, which mainly consists of a Momentum Compensation Module (MCM) and a Backtracking Re-association (BRA) module. The former can correct the estimated value of the target's motion state during long-term occlusion, the latter uses the temporal and attribute information of the target's historical trajectory during long-term occlusion as correlation indicators to measure the degree of correlation between the target and trajectory. Our proposed MC-SORT has the characteristics of simplicity, online, real-time, and plug-and-play, particularly improving the robustness of the tracker in long-term occlusion. The extensive experimental results on the MOT17 and MOT20 datasets demonstrate the robustness and superiority of our framework. © 2024 The Authors.

关键词： Trajectories

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Scaffold protein RACK1A positively regulates leaf senescence by coordinating the EIN3-miR164-ORE1 transcriptional cascade in Arabidopsis

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Journal of Integrative Plant Biology 2023年第7期65卷 1703-1716页

作者： Jan Masood Wei Zhu Yajuan Fu Zhiyong Li Yeling Zhou Dong Zhang Huihui Han Yan Yan Xing Wen Hongwei Guo Jiansheng Liang Key Laboratory of Molecular Design for Plant Cell Factory of Guangdong Higher Education Institutes Institute of Plant and Food SciencesDepartment of BiologySchool of Life SciencesSouthern University of Science and Technology518055ShenzhenChina

Plants have adopted versatile scaffold proteins to facilitate the crosstalk between multiple signaling pathways. Leaf senescence is a well-programmed developmental stage that is coordinated by various external and internal signals. However,the functions of plant scaffold proteins in response to senescence signals are not well understood. Here, we report that the scaffold protein RACK1A(RECEPTOR FOR ACTIVATED C KINASE1A) participates in leaf senescence mediated by ethylene signaling via the coordination of the EIN3-miR164-ORE1 transcriptional regulatory cascade. RACK1A is a novel positive regulator of ethylene-mediated leaf senescence. The rack1a mutant exhibits delayed leaf senescence, while transgenic lines overexpressing RACK1A display early leaf senescence. Moreover, RACK1A promotes EIN3(ETHYLENE INSENSITIVE 3) protein accumulation, and directly interacts with EIN3 to enhance its DNA-binding activity. Together, they then associate with the miR164 promoter to inhibit its transcription, leading to the release of the inhibition on downstream ORE1(ORESARA 1) transcription and the promotion of leaf *** study reveals a mechanistic framework by which RACK1A promotes leaf senescence via the EIN3-miR164-ORE1 transcriptional cascade, and provides a paradigm for how scaffold proteins finely tune phytohormone signaling to control plant development.

关键词： Arabidopsis ethylene signaling leaf senescence RACK1 scaffold protein transcriptional cascade

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Force Analysis of Water Jet Propulsion Grille 33rd

Force Analysis of Water Jet Propulsion Grille

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33rd International Ocean and Polar Engineering Conference, ISOPE 2023

作者： Guorui, Ji Liu, Xueqin Chen, Gang Lv, Ning Liu, Xiaochen Li, Heng Science and Technology on Water Jet Propulsion Laboratory Shanghai China Marine Design & Research Institute of China Shanghai China

ISBN: (纸本)9781880653807

In this paper, the grille of a certain water jet propulsion unit is taken as the research object. By changing the cross section shape of grille, the foece and deformation characteristics of grille bar are gotten by using numerical calculation method and finite element calculation method. And the paper gives the optimal way of the cross section and layout of the grille, which has important guiding significance for the design of the water jet propulsion grille. © 2023 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Numerical methods

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A Transformer-Based Time Series Prediction Model with Contrastive Learning for Nuclear Power System State Monitoring

A Transformer-Based Time Series Prediction Model with Contra...

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Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Pengfei Gou Daiwei Zheng Lei Tang Mengjuan Liu School of Information and Software Engineering University of Electronic Science and Technology of China UESTC Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China NPIC Chengdu China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

Prognostics and Health Management (PHM) is currently the mainstream solution for the condition monitoring and maintenance of industrial systems. In PHM, the system condition is monitored by monitoring the operational status parameters of the system to diagnose equipment faults, predict the remaining useful life, etc. Utilizing sequential operational status parameters to complete prediction and classification tasks is usually modeled as a time series processing task. Therefore, a high-performance time series prediction model is crucial for PHM. This paper focuses on the use of time series prediction models to achieve the prediction of key state parameters of the nuclear power system. However, there are still some challenges when directly applying existing time series prediction models to industrial systems. First, obtaining operational data on the nuclear power system is relatively difficult, resulting in a small amount of training data, which causes many models to face the risk of insufficient training or overfitting. Second, most of the existing time series prediction models are composed of high- and low-frequency decoupling modules, while the operational data of nuclear power systems often have fewer high-frequency components, making it difficult for many existing SOTA models to achieve good results. In order to address these challenges, we have developed a Transformer-based time series prediction model with contrastive learning for monitoring the status of nuclear power systems. We evaluated the new model on three nuclear power simulation datasets and four power transformer datasets. The experimental results show that the new model's performance is superior to baseline models. We also proved the effectiveness of our improvement through ablation experiments.

关键词： Training Time series analysis Training data Contrastive learning Predictive models Data models Power systems Power transformers Prognostics and health management Monitoring

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A lateral-immobilization zebrafish microfluidic chip-based system for in vivo real-time evaluation of antithrombotic agents

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Chinese Chemical Letters 2024年第3期35卷 312-315页

作者： Lijuan He Hongxia Du Yi Yang Zhihua Guan Jinjin Li Honglin Li Xudong Lin Lili Zhu Shanghai Key Laboratory of New Drug Design School of PharmacyEast China University of Science&TechnologyShanghai 200237China Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument School of Biomedical EngineeringShenzhen Campus of Sun Yat-sen UniversityShenzhen 518000China Innovation Center for AI and Drug Discovery East China Normal UniversityShanghai 200062China

Thrombosis remains a major global health concern mainly characterized by high rates of morbidity and *** models serve as an indispensable tool to understand the underlying pathogenesis of thrombosis and assess the efficacy of novel antithrombotic ***,zebrafish has emerged as a valuable model organism for thrombosis ***,the traditional method of studying zebrafish thrombosis requires a laborious and time-consuming procedure,including anesthesia and manual immobilization of *** this study,based on hydrodynamic force,a lateral-immobilization zebrafish microfluidic chip(LIZMC)was designed to evaluate the cardiovascular system of multiple larvae within a single microscope field of ***,coupling with microscope imaging,real-time monitoring of the peripheral blood circulation in the tail of phenylhydrazine(PHZ)-induced zebrafish thrombosis was ***,the reliability of LIZMC for in vivo evaluation of antithrombotic agents in zebrafish was verified using ***,this novel LIZMC-based system can be used for in vivo zebrafish thrombosis studies and rapid screening of antithrombotic agents.

关键词： Microfluidic chip Zebrafish Thrombosis In vivo evaluation Real-time monitor

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Online soft measurement for wastewater treatment system based on hybrid deep learning

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Frontiers of Environmental science & Engineering 2024年第2期18卷 77-89页

作者： Wenjie Mai Zhenguo Chen Xiaoyong Li Xiaohui Yi Yingzhong Zhao Xinzhong He Xiang Xu Mingzhi Huang SCNU Environmental Research Institute Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety&MOE Key Laboratory of Theoretical Chemistry of EnvironmentSchool of EnvironmentSouth China Normal UniversityGuangzhou 510006China Fujian Environmental Protection Design Institute Co.Ltd. Fuzhou 350000China Huashi(Fujian)Environment Technology Co.Ltd. Quanzhou362001China SCNU Qingyuan Institute of Science and Technology Innovation Co.Ltd. Qingyuan 511517China Econ Technology Co.Ltd. Yantai 265503China

The existing automated wastewater treatment control systems encounter challenges such as the utilization of specialized testing instruments, equipment repair complications, high operational costs, substantial operational errors, and low detection accuracy. An effective soft measure model offers a viable approach for real-time monitoring and the development of automated control in the wastewater treatment process. Consequently, a novel hybrid deep learning CNN-BNLSTM-Attention (CBNLSMA) model, which incorporates convolutional neural networks (CNN), bidirectional nested long and short-term memory neural networks (BNLSTM), attention mechanisms (AM), and Tree-structure Parzen Estimators (TPE), has been developed for monitoring effluent water quality during the wastewater treatment process. The CBNLSMA model is divided into four stages: the CNN module for feature extraction and data filtering to expedite operations;the BNLSTM module for temporal data’s temporal information extraction;the AM module for model weight reassignment;and the TPE optimization algorithm for the CBNLSMA model’s hyperparameter search optimization. In comparison with other models (TPE-CNN-BNLSTM, TPE-BNLSTM-AM, TPE-CNN-AM, PSO-CBNLSTMA), the CBNLSMA model reduced the RMSE for effluent COD prediction by 25.4%, decreased the MAPE by 32.9%, and enhanced the R2 by 14.9%. For the effluent SS prediction, the CBNLSMA model reduced the RMSE by 26.4%, the MAPE by 21.0%, and improved the R2 by 35.7% compared to other models. The simulation results demonstrate that the proposed CBNLSMA model holds significant potential for real-time effluent quality monitoring, indicating its high potential for automated control in wastewater treatment processes.

关键词： Prediction model Soft measurement CNN-BNLSTM-AM model TPE optimization algorithm

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