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Advanced application of polyacrylic acid in laser-induced graphene technology

Journal of Industrial and Engineering Chemistry 2025年 148卷 300-314页

作者： Qu, Yachuan Yin, Zhifu Chen, Shi Yang, Xue Sun, Linlin School of Mechanical and Aerospace Engineering Jilin University Changchun130025 China School of Architecture and Construction Jilin Jianzhu University Changchun130118 China Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Key Laboratory of Optoelectronic Chemical Materials and Devices of Ministry of Education Jianghan University Wuhan430056 China

Laser-induced graphene (LIG) has emerged as a revolutionary material in the graphene field, capitalizing on its remarkable electrical conductivity, excellent mechanical properties and exceptional chemical stability, underpinning its extensive applications in cutting-edge technologies. Polyacrylic acid (PAA), a soluble and film-forming PI precursor, allows for introducing various dopants into LIG, consequently significantly improving the electrical performances of the composite LIG. PAA heralds a paradigm shift in LIG synthesis. However, the existing reviews focused on the mainstream precursor materials like PES, PSU and PEEK. There are very limited reviews to summarize the synthesis, laser induction, and the application of PAA based LIG. Herein, we comprehensively investigate the diverse applications of PAA in LIG preparation and expound on its substantial advantages. PAA not only facilitates the uniform distribution of dopants but also significantly enhances the overall performance of LIG, unveiling unprecedented application potential in key areas such as energy storage, environmental protection and highly efficient sensors. The principal objective of this study is to systematically explore the unique role of PAA in LIG technology and examine its applications in energy storage, environmental protection and sensors, thereby providing novel insights and directions for further research and innovation in this burgeoning area. © 2025

关键词： Laser applications

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Deep Learning and Projection Neural Network With Finite-Time Convergence for Energy Management of Multi-Energy System

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IEEE Transactions on Smart Grid 2025年第3期16卷 2156-2168页

作者： Liu, Xueying He, Xing Li, Chaojie Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. Simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES. © 2010-2012 IEEE.

关键词： Cogeneration Heuristic algorithms Convergence Deep learning Prediction algorithms Optimization Heating systems Energy management Approximation algorithms Power system stability

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Impact of aerosol-radiation interaction and heterogeneous chemistry on the winter decreasing PM_(2.5)and increasing O_(3)in Eastern China 2014–2020

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Journal of Environmental sciences 2025年第5期151卷 469-483页

作者： Yasong Li Tijian Wang Qin’geng Wang Mengmeng Li Yawei Qu Hao Wu Min Xie School of the Environment Nanjing UniversityNanjing 210023China School of Atmospheric Sciences Nanjing UniversityNanjing 210023China College of Intelligent Science and Control Engineering Jinling Institute of TechnologyNanjing 211169China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric SciencesNanjing 210041China School of Environment Nanjing Normal UniversityNanjing 210023China

In the context of the prevalent winter air quality issues in China marked by declining PM_(2.5)and rising O_(3),this study employed amodified WRF-Chem model to examine the aerosol radiation interaction(ARI),heterogeneous chemistry(AHC),and their combined impact(ALL)on the variations in O_(3)and PM_(2.5)during the 2014–2020 in eastern *** analysis confirmed that ARI curtailed O_(3)while elevating PM_(2.5).AHC reduced O_(3)through heterogeneous absorption of NOx and hydroxideswhile notably fostering fine-grained sulfate,resulting in a PM_(2.5)*** reductions mitigated the inhibitory impact of ARI on meteorological fields and photolysis *** reduction individually without aerosol feedback led to a 5.43 ppb O_(3)increase and a 22.89μg/m^(3)PM_(2.5)*** and AHC amplified the emission-reduction-induced(ERI)O_(3)rise by 1.83 and 0.31 ppb,*** response of ARI to emission diminution brought about a modest PM_(2.5)increase of 0.31μg/m^(3).Conversely,AHC,acting as the primary contributor,caused a noteworthy PM_(2.5)decrease of 4.60μg/m^(3).As efforts concentrate on reducing PM_(2.5),the promotion of ARI on PM_(2.5)counterbalanced the efficacy of emission reduction and the AHC-induced strengthening of PM_(2.5)*** ALL magnified the ERI O_(3)increase by 38.9%and PM_(2.5)decrease by 18.7%.Sensitivity experiments with different degrees of emission reduction demonstrated a consistent linear relationship between the ALL-induced enhancement of O_(3)increase and PM_(2.5)decrease to the ERI PM_(2.5)*** investigation revealed the complex connection between emissions and aerosol feedback in influencing air quality.

关键词： Aerosol-radiation interaction Heterogeneous chemistry O_(3)increase PM_(2.5)decrease Emission reduction

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2,4,6-trinitrotoluene induces neurotoxicity by affecting the G protein pathways in Caenorhabditis elegans

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NeuroToxicology 2025年 108卷 328-337页

作者： Li, Ying Zhou, Yanping Wang, Chunyan Nie, Yaguang Zhu, Yongbing Zhao, Sanping Wu, Lijun Xu, An Center of Free Electron Laser & High Magnetic Field Anhui University Hefei 230601 China State Key Laboratory of NBC Protection for Civilian Beijing 102205 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Hefei 230601 China Anhui Province Key Laboratory of Environmental Toxicology and Pollution Control Technology High Magnetic Field Laboratory HFIPS Anhui Chinese Academy of Science Hefei 230031 China

2,4,6-trinitrotoluene (TNT) is a chemical widely used to make explosives, and its use of residues can lead to potential threats to both ecosystems and human health. A thorough understanding of the various toxic effects of TNT is essential for developing effective environmental protection and health safety measures. Thus, we employed Caenorhabditis elegans (C. elegans), a typical model organism, to explore the neurotoxic effects of TNT and the signaling pathways involved. The results showed that neurotoxicity induced by 10–100 ng/mL TNT was manifested in reduced behavioral capacity (head thrashes, body bends, and pharyngeal pumping rates), and inhibition of foraging behavior and ethanol avoidance behavior. Using fluorescence-labeled transgenic nematodes, it was found that TNT damaged dopaminergic and cholinergic neurons, which resulted in a significant decrease in the release of neurotransmitters and the expression of associated genes (dat-1 and unc-17). We studied the role of G protein signaling pathways and discovered that the related genes (egl-30, gsa-1, goa-1, and unc-13) were significantly down-regulated, resulting in reduced acetylcholine release, which in turn corresponded to the observed behavioral abnormality and damaged neurons in the worms. This study shed light on TNT's neurotoxic mechanisms and associated health risks. © 2025 Elsevier B.V.

关键词： Caenorhabditis elegans G protein pathways Neurotoxicity TNT

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Object Detection in Occluded Environments Based on Adaptive Optimal Label Assignment

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IEEE Sensors Journal 2025年

作者： Dong, Yongxiang Lian, Jie Wang, Dong Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian116024 China

The occluded environment leads to a low success rate of object detection, owing to label assignment errors and imbalances for occluded objects during neural network training process. To address this problem, an adaptive optimal label assignment algorithm is proposed, which employs a global optimal assignment strategy to minimize global loss by label assignment. It assigns an appropriate number of anchors to each ground-truth, to improve the detection success rate of occluded objects. Furthermore, a focal angle loss function is proposed based on the angle loss between anchors and ground-truth to achieve smooth convergence, and intersection over union loss weights are added to solve the problem of an uneven number of high-quality and low-quality anchors. Adaptive optimal label assignment and focal angle loss are integrated to YOLOv10-X, termed as YOLOv10-X-FAO. Experiments demonstrate that YOLOv10-X-FAO enhances performance over the baseline and other latest detection models. Furthermore, ablation experiments confirm the effectiveness of adaptive optimal label assignment and focal angle loss function in enhancing the detection performance of occluded targets for both anchor-based and anchor-free detectors. © 2001-2012 IEEE.

关键词： Deep learning

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Elastic–plastic crack-tip-opening-displacement-based description for surface, corner and embedded cracks tip stress field

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International Journal of Solids and Structures 2025年 317卷

作者： Zhang, Jianqiang Cui, Pengfei Guo, Wanlin State Key Laboratory of Mechanics and Control for Aerospace Structures & Institute for Frontier Science & Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education & College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China

Since part-through crack growth stages occupy most of crack growth life of engineering structures, it is essential to investigate the fracture parameters of part-through cracks. However, the complex three-dimensional (3D) stress states make it difficult to efficiently dominate the crack-tip fields. Here, the 3D elastic–plastic stress intensity factor Kδ-Tz is extended to dominate part-through cracks. Systematic 3D finite element (FE) analyses are conducted for typical part-through cracks (embedded, corner, and surface cracks) considering different elliptical ratios and hardening exponents. It is found that the predicted stress distributions by the δ-Tz solution agree well with 3D FE results. Additionally, the predictive performance of the δ-Tz solution improves with increasing hardening exponents. Across all experimental and numerical results, the variation of J-integral along the crack front line can reach 200%, while remaining within 21% for Kδ-Tz. These results demonstrate that Kδ-Tz can reduce geometric constraints effectively and be a more stable elastic–plastic fracture parameter for part-through cracks in engineering structures. © 2025 Elsevier Ltd

关键词： Crack tips

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Learning for predictive control: A Dual Gaussian Process approach

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Automatica 2025年 177卷

作者： Liu, Yuhan Wang, Pengyu Tóth, Roland Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian Process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based Model Predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learnt knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. A novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation without a "dictionary" update and re-computation of the Gram matrix at each time step. Effectiveness of the proposed strategy is demonstrated via numerical simulations. © 2025

关键词： Predictive control systems

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MASS FLOW DETECTION technology FOR SEED AND FERTILISER PARTICLES AND ITS APPLICATION IN UAV-BASED SPREADING

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INMATEH - Agricultural Engineering 2025年第1期75卷 584-599页

作者： Wang, Yanshuang Song, Cancan Gao, Yongqi Lan, Yubin Wang, Guobin Ming, Rui College of Agricultural Engineering and Food Science Shandong University of Technology Zibo China Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology Zibo China Research of Institute of Ecological Unmanned Farm Shandong University of Technology Zibo China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

The processes of sowing and fertiliser application represent a significant aspect of agricultural production. In order to achieve efficient and precise seeding and fertiliser application, mass flow detection of seed and fertiliser particles can facilitate real-time monitoring and precise decision-making for intelligent seeding and fertiliser application. However, the diversity of seed and fertiliser particle types and particle flow modes presents a challenge for existing detection methods, particularly in meeting the varying operational requirements, especially for particle mass flow detection in low-altitude and high-speed sowing operations of agricultural drones. In such cases, the overlap and flow rate will have a significant impact on the detection results due to the large displacement and the continuous high-throughput dense phase of the particle flow. This paper provides a summary of the existing seed and fertiliser particle mass flow detection techniques and their underlying working principles. It compares the direct detection based on mass with the indirect detection methods based on velocity and concentration, and analyses their respective advantages, disadvantages and applicability. It also explores the possibility of optimising the existing detection methods for the specific needs of agricultural UAVs and considers the potential introduction of cutting-edge science and technology in order to develop an efficient, accurate and convenient detection system to meet the growing market demand. © (2025), (INMA Bucharest). All Rights Reserved.

关键词： Precision agriculture

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Frequency-domain analysis for cooling load prediction: A Multi-Scale FourierGNN Crossformer approach

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International Journal of Refrigeration 2025年 176卷 268-283页

作者： Cen, Jian Zeng, Linzhe Liu, Xi Yang, Jianming Li, Xinyao Deng, Feiqi School of Automation Guangdong Polytechnic Normal University Guangzhou510665 China Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory GuangZhou510665 China School of Automation Science and Engineering South China University of Technology GuangZhou510640 China

With the rise in energy consumption in buildings, particularly for air conditioning systems, intelligent air conditioning control has become more important. Accurate prediction of central air conditioning system cooling load is crucial to optimizing energy efficiency. This paper proposes a Multi-Scale FourierGNN Crossformer (MFGformer) model based on Crossformer, which integrates Fourier Graph Neural Networks (FourierGNN) and Multi-scale Cross-axis Attention (MCA) mechanisms for multivariate cooling load time series forecasting of central air conditioning systems. The complexity of dynamic characteristics of multivariable time series data is often misunderstood by current forecasting models, which this model takes into account. The FourierGNN module maps time series data into the frequency domain through the Discrete Fourier Transform, effectively capturing periodic and trend features of the data. The MCA mechanism captures multi-scale characteristics and local detail information in time series data through dual cross-attention computation, enhancing the ability to capture dependencies between different variables. Validation on two real case datasets shows that the MFGformer model performs well in predicting the cooling loads of central air conditioning systems, especially when outputting 24-hour predictions at a 96-hour input time step, the model's Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Arctangent Absolute Percentage Error (MAAPE), and Coefficient of Variation of Root Mean Square Error (CV-RMSE) are 130.7765 kW, 322.2957 kW, 25.2611% and 56.6846%. Compared with eight other typical models, the MAE, RMSE, MAAPE and CV-RMSE of the MFGformer model were reduced by 22.8869-156.1751 kW, 59.6766-336.4538 kW, 0.9422-51.109% and 8.6393-32.6965%, respectively, for different output step sizes. These results demonstrate the model's ability to provide accurate predictions when dealing with data characterized by volatility and nonlinearity, while maintain

关键词： Bode diagrams

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Tissue P Systems with Look-Ahead Mode

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Chinese Journal of Electronics 2025年第1期23卷 81-86页

作者： Yun Jiang Tao Song Zheng Zhang School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Department of Control Science and Engineering Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China

Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.

关键词： Computers Computational modeling Medical services Parallel processing Polynomials Computational efficiency Registers Business

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