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Changes in physiology, antioxidant system, and gene expression in Microcystis aeruginosa under fenoxaprop-p-ethyl stress

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Environmental Science and Pollution Research 2024年第19期31卷 28754-28763页

作者： Liu, Sijia Ni, Jiawei Guan, Ying Tao, Jianwei Wu, Liang Hou, Meifang Wu, Shichao Xu, Wenwu Zhang, Chu Ye, Jing School of Chemical and Environmental Engineering Shanghai Institute of Technology Shanghai201418 China Los Angeles Regional Water Quality Control Board Los AngelesCA90013 United States School of Ecological Technology and Engineering Shanghai Institute of Technology Shanghai201418 China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

Fenoxaprop-p-ethyl (FE) is one of the typical aryloxyphenoxypropionate herbicides. FE has been widely applied in agriculture in recent years. Human health and aquatic ecosystems are threatened by the cyanobacteria blooms caused by Microcystis aeruginosa, which is one of the most common cyanobacteria responsible for freshwater blooming. Few studies have been reported on the physiological effects of FE on M. aeruginosa. This study analyzed the growth curves, the contents of chlorophyll a and protein, the oxidative stress, and the microcystin-LR (MC-LR) levels of M. aeruginosa exposed to various FE concentrations (i.e., 0, 0.5, 1, 2, and 5 mg/L). FE was observed to stimulate the cell density, chlorophyll a content, and protein content of M. aeruginosa at 0.5- and 1-mg/L FE concentrations but inhibit them at 2 and 5 mg/L FE concentrations. The superoxide dismutase and catalase activities were enhanced and the malondialdehyde concentration was increased by FE. The intracellular (intra-) and extracellular (extra-) MC-LR contents were also affected by FE. The expression levels of photosynthesis-related genes psbD1, psaB, and rbcL varied in response to FE exposure. Moreover, the expressions of microcystin synthase-related genes mcyA and mcyD and microcystin transportation-related gene mcyH were significantly inhibited by the treatment with 2 and 5 mg/L FE concentrations. These results might be helpful in evaluating the ecotoxicity of FE and guiding the rational application of herbicides in modern agriculture. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Gene expression

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A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 462-473页

作者： Bingyun Li Qinmin Yang Innocent Kamwa IEEE the College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Huzhou Institute of Zhejiang University China the Department of Electrical and Computer Engineering Laval UniversityQuebec CityQCG1V 0A6Canada

Demand response(DR)using shared energy storage systems(ESSs)is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU)price.A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this *** this scheme,the interactions between selfish users and an operator are characterized as a Stackelberg *** holds a large-scale ESS that is shared among users in the form of energy *** sells energy to users and sets the selling price *** maximizes its profit through optimal pricing and ESS *** purchase some energy from operator for the reduction of their demand charges after operator's selling price is *** game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization *** upper-level optimization maximizes operator's profit and the lower-level optimization minimizes users'*** bi-level model is transformed and linearized into a mixed-integer linear programming(MILP)model using the mathematical programming with equilibrium constraints(MPEC)method and model linearizing *** studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.

关键词： Bi-level optimization demand charge energy storage system(ESS)sharing energy transaction mathematical program with equilibrium constraints(MPEC) stackelberg game

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GK-Means SOM Algorithm Used to Plan the Paths for Multiple Agents Exploring Multiple Target Points 1st

GK-Means SOM Algorithm Used to Plan the Paths for Multiple A...

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1st International Conference on Cognitive Computation and Systems, ICCCS 2022

作者： Yang, Hexing Zhao, Qingjie Wang, Lei Liu, Wangwang Chong, Ling School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9789819927883

Multi-agent technology is widely used in many fields such as intelligent manufacturing, logistics and environment exploration. In this paper, we propose a greedy K-means self-organizing map algorithm to balance the tasks and plan the paths for multiple agents exploring multiple target points, where greedy k-means adopts greedy strategy to ensure the tasks allocated for agents tending equally, and the self-organizing map networks are used for parallel path planning to speed up the problem solved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Self organizing maps

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Efficient Tool Condition Monitoring Using SOM-Hebb Classifier on FPGA 6

Efficient Tool Condition Monitoring Using SOM-Hebb Classifie...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Akanksha, A. Somani Kokate, R.D. Abhilasha, Mishra Maharashtra Institute of Technology Department of Electronics & Computer Engineering Aurangabad India College of Engineering Department of Instrumentation and Control Engineering Pune India

ISBN: (纸本)9798350304480

Tool condition monitoring is vital for enhancing productivity, reducing costs, and improving product quality in manufacturing industries. Existing approaches, such as threshold-based methods, sensor-based methods, and machine learning-based techniques, have limitations in terms of accuracy, adaptability, interpretability, and computational costs. This research proposes a tool condition monitoring system based on the Self-Organizing Map (SOM) algorithm and the Hebbian learning algorithm. The system is implemented on a Field-Programmable Gate Array (FPGA) platform for real-time processing in manufacturing environments. A dataset is acquired using sensors, pre-processed, and divided into training and testing sets. The SOM-Hebb classifier is designed with appropriate parameters, and the tool condition monitoring algorithm involves projection histograms, discrete Fourier transform (DFT), and the SOM-Hebb classifier. The FPGA implementation achieves high-speed processing and meets timing constraints. Simulation results demonstrate the system's functionality and accuracy, with a classification accuracy of 99.22%. The system exhibits a processing time of 35 clock cycles. This research provides a promising approach for efficient and accurate tool condition monitoring in manufacturing industries. © 2023 IEEE.

关键词： Field-Programmable Gate Array (FPGA) Hebbian learning algorithm Real-time processing Self-Organizing Map (SOM) Tool condition monitoring

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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Fusion information refresh rate improvement based on adaptive visual tracking in vehicle augmented reality sightseeing interactive system

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Journal of the Society for Information Display 2024年第8期32卷 545-554页

作者： Tsai, Yu-Hsiang Dai, Hong-Ming Yan, Yung-Jhe Ou-Yang, Mang Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taipei Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Taipei Taiwan Div. of Display & System Integration Electronic and Optoelectronic System Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan

This paper proposes a dynamic information fusion interactive system with a transparent display applied in the vehicle. The dynamic information fusion interactive system integrates three leading technologies: relative position acquisition, fusion information mapping, and visual field tracking adaptive information generation. For visual field tracking adaptive information generation, the position of fusion information changes as the observer's field of view varies to help observers use this system more comfortably and intuitively. The frame rate of the dynamic virtual information display can be increased from 10 to 20 Hz without increasing the AI computing power and providing observers with a better observer experience. Integrating those technologies could achieve an AR-based interactive system on a direct-view transparent display. Such a system can provide passengers with location-based interactive virtual and real integration information in the mobile field. © 2024 Society for Information Display.

关键词： Augmented reality

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A Real-Time Semantic Segmentation Method Based on Transformer for Autonomous Driving

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computers, Materials & Continua 2024年第12期81卷 4419-4433页

作者： Weiyu Hao Jingyi Wang Huimin Lu Donald Bren School of Information and Computer Sciences University of CaliforniaIrvineCA 92612USA Department of Control Engineering Kyushu Institute of TechnologyKitakyushu804-8550Japan School of Automation Southeast UniversityNanjing210096China

While traditional Convolutional Neural Network(CNN)-based semantic segmentation methods have proven effective,they often encounter significant computational challenges due to the requirement for dense pixel-level predictions,which complicates real-time *** address this,we introduce an advanced real-time semantic segmentation strategy specifically designed for autonomous driving,utilizing the capabilities of Visual *** leveraging the self-attention mechanism inherent in Visual Transformers,our method enhances global contextual awareness,refining the representation of each pixel in relation to the overall *** enhancement is critical for quickly and accurately interpreting the complex elements within driving sce-narios—a fundamental need for autonomous *** experiments conducted on the DriveSeg autonomous driving dataset indicate that our model surpasses traditional segmentation methods,achieving a significant 4.5%improvement in Mean Intersection over Union(mIoU)while maintaining real-time *** paper not only underscores the potential for optimized semantic segmentation but also establishes a promising direction for real-time processing in autonomous navigation *** work will focus on integrating this technique with other perception modules in autonomous driving to further improve the robustness and efficiency of self-driving perception frameworks,thereby opening new pathways for research and practical applications in scenarios requiring rapid and precise decision-making *** experimentation and adaptation of this model could lead to broader implications for the fields of machine learning and computer vision,particularly in enhancing the interaction between automated systems and their dynamic environments.

关键词： Visual transformer semantic segmentation autonomous driving

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AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

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Digital Communications and Networks 2024年第2期10卷 292-303页

作者： Yuteng Xiao Kaijian Xia Hongsheng Yin Yu-Dong Zhang Zhenjiang Qian Zhaoyang Liu Yuehan Liang Xiaodan Li School of Information and Control Engineering China University of Mining and TechnologyXuzhou221116China Department of Informatics University of LeicesterLeicesterLE17RHUK The Affiliated Changshu Hospital of Soochow University Changshu215500China School of Computer Science and Engineering Changshu Institute of TechnologySuzhou215500China

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other ***,it is important to construct a digital twin ***,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted *** this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)***,we fuse the spatial-temporal graph based on the interrelationship of spatial ***,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding ***,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)*** module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like *** dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted *** on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

关键词： Adaptive adjacency matrix Digital twin Graph convolutional network Multivariate time series prediction Spatial-temporal graph

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Biomedically Informed ECG Synthesis: Customizing Cardiac Cycle Phases with Diffusion Model

Biomedically Informed ECG Synthesis: Customizing Cardiac Cyc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Yuxin Ma, Jing Wang, Wei Wu, Zhihao Dong, Suyu Luo, Gongning Wang, Kuanquan Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology Faculty of Computing Harbin China

ISBN: (纸本)9798350386226

Cardiovascular diseases are a major global health challenge, with electrocardiography (ECG) being critical for diagnosis and monitoring. As artificial intelligence and automated ECG diagnostic technologies rapidly advance, the demand for large-scale ECG databases continues to grow. Generative ECG has become a mainstream method to enhance database size and diversity. However, existing methods typically generate ECG randomly or focus on limited physiological categories, lacking the ability to synthesize ECG with varying physiological features and cardiac cycles, which is crucial for various practical applications. In response to this need, we propose a novel approach introducing a diffusion model called DIFF-ECG to generate precisely customized ECG that accurately reflect diverse cardiac conditions. Segmentation-based quality assessments confirmed that the synthesized ECG accurately followed the specified cardiac cycle information, with our model significantly outperforming baseline diffusion and GAN-based methods. Therefore, our approach addresses the critical need for generating clinically relevant and customizable ECG, contributing significantly to the field of automated cardiac disease diagnosis. By enabling fine-tuning of cardiac cycle phases, our method significantly expands the application range of generative ECG, potentially improving the diagnostic accuracy for rare diseases and advancing personalized medicine. © 2024 IEEE.

关键词： Electrocardiography

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MPC-based Optimization Design for 3D Collision Avoidance of a Mobile Manipulator Based-on Obstacle Velocity Estimation

MPC-based Optimization Design for 3D Collision Avoidance of ...

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2024 International Automatic control Conference, CACS 2024

作者： Song, Kai-Tai Lin, Chih-Hsuan Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University 1001 Ta Hsueh Road Hsinchu Taiwan

ISBN: (纸本)9798350354904

This paper presents an optimization design of 3D dynamic obstacle avoidance for a mobile manipulator based on model predictive control (MPC). The design enables a mobile manipulator to achieve optimized 3D collision avoidance motion with shorter avoidance path and faster avoidance time. A 3D LiDAR is installed onboard the robot to acquire environmental point cloud and estimate obstacle velocity. The MPC is designed to track an initial 3D path of the mobile manipulator and avoid any static and dynamic obstacles in real time. Experimental results show that the proposed method can simultaneously avoid static and dynamic obstacles in 3D space. Compared with baseline algorithms without velocity estimation, the proposed method reduces the avoidance path length by 8.27% and path execution time by 13.79%. © 2024 IEEE.

关键词： Predictive control systems

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