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An Innovative Fast-Start Aerobic Anode Microbial Fuel Cell Biosensor for Copper Ion Detection

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SSRN 2024年

作者： Wang, Jie Dong, Bin Shen, Zhiqiang Zhou, Yuexi School of Environmental Science and Engineering Tongji University Shanghai200092 China State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environment Sciences Beijing100012 China Research Center of Environmental Pollution Control Engineering Technology Chinese Research Academy of Environmental Sciences Beijing100012 China

The potential of microbial fuel cells (MFCs) based biosensors for water monitoring and early warning is widely recognized. However, the electrochemically active biofilms (EABs) are typically obtained under anaerobic conditions and the formation requires a lengthy time span, which critically limits the application and advancement of the MFCs based monitoring technology. In this study, the aerobic sludge was employed as an inoculation source to construct the MFCs sensing system in order to address the challenges above. It was revealed that biofilms with stable electrochemical properties could be promptly formed within 13 hours, representing a more than 90% reduction in start-up time compared to previous studies. Moreover, toxicity tests for copper (Cu2+) ions at concentrations of 1 mg/L, 10 mg/L, and 50 mg/L demonstrated outstanding performance with the maximum inhibition rates of 19.99%, 46.65%, and 53.02% respectively. Remarkably, the response time was found to decrease significantly with the increase of copper ion concentrations. Our results thus open up a new avenue for achieving rapid start-up of MFCs in aerobic conditions which finally facilitates the applicability of MFCs based biosensing technology. © 2024, The Authors. All rights reserved.

关键词： Anodes

Robust Modification Optimization for Helical Gear Mounting Errors Based on Contact Ratio

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SSRN 2024年

作者： Yang, Ji-Xuan Chen, Zhi-Yong Shi, Wen-Ku Yang, Hong-Bo Liu, Jian Yuan, Ren-Fei Zhao, Yan-Yan Dong, Ya-Hui State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130025 China School of Automotive and Traffic Engineering Jiangsu University Zhenjiang212000 China Jinan250100 China Institute of Automotive Engineering R&D Shaanxi Heavy Duty Automobile Co. Ltd Xi’an710200 China

Mounting errors are a disruptive factor in gear utilization. This paper investigates the contribution of mounting error components in helical gears to the helix slope deviation using the involute equations. A model of the helical gear transmission system has been established, by which the impact of mounting errors on the loaded transmission error and contact ratio of the bevel gear transmission was studied using the Loaded Tooth Contact Analysis method. A bench test was then casted as a verification of this model. A methodology was proposed for optimizing the micro modification of helical gear flanks based on contact ratio control. Robust optimization of the mounting error was achieved by reducing the contact ratio under commonly used torques compared to the contact ratio at the local minimum of the loaded peak-to-peak transmission error. This approach has been demonstrated to enhance the robustness of gear transmission against mounting errors and torque fluctuations. © 2024, The Authors. All rights reserved.

关键词： Errors

Multi-Domain Graph Meta Learning Network for Few-Shot Prediction in Industrial Processes

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Kong, Liyuan Liu, Zhe Yan, Feng Yan, Duojin Yang, Chunjie Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

In process industries, the scarcity of data highlights the necessity of few-shot learning for accurate industrial predictions. Recently, model agnostic meta learning (MAML) has become an effective solution to this challenge. However, the existing MAML in industrial prediction still faces two key issues: i) training tasks originate from multiple domains, and ii) the inter-variable coupling relationships are not fully exploited to their potential in cases with limited information. Therefore, we propose a multi-domain graph meta learning network (MDGML) to enable knowledge transfer and enhance information utilization through domain generalization and graph-based meta learning. First, an empirical domain adaptation module is developed to map each source domain to a shared domain, ensuring consistent distributions across learning tasks. Next, robust hyperbolic graphs are devised to learn the spatial coupling relationships among process variables and offer additional insights through a graph-based perspective. Then, domain-specific prototypes are computed and designed to modulate the prediction results, thereby enhancing domain-dependent prediction accuracy. Furthermore, we also propose task-adaptive optimization for MAML inner loops under the regulation of the node information density in graphs. It can accommodate differences between learning tasks. Finally, the experimental results from four real-world blast furnace datasets validate the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Network theory (graphs)

Neural-Network-Based Filtering with Encoding Mechanisms: Applications to Multiple Unmanned Aerial Vehicles

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Neural-Network-Based Filtering with Encoding Mechanisms: App...

Chinese Automation Congress (CAC)

作者： Kaiqun Zhu Xinpeng Xu Zhiru Cao Ran Sun Ya Yang State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Shanghai Electro-Mechanical Engineering Institute Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai China

In this paper, the neural-network-based filtering problem is investigated for multi-sensor systems with dynamic encoding mechanisms. The sensor nodes and the remote filter are connected through bandwidth-constrained communication networks. To alleviate the communication burden, a novel dynamic encoding-based data compression-decompression mechanism is proposed so as to encode the data into a limited number of bits. Then, with the aid of the neural network learning method, a neural-network-based set-membership filter is developed for estimating the system states. Sufficient conditions are obtained to ensure that the filtering error remains within the bounded ellipsoidal set. In addition, the neural network tuning parameters and the filter gains are calculated by solving constrained optimization problems. Finally, the effectiveness of the proposed filtering algorithm is verified through a scenario of maneuvering target tracking using multiple unmanned aerial vehicles.

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Design of Palygorskite-Based Aerogels for Organic Dye Waste-Water Treatment

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SSRN 2023年

作者： Jin, Huiran Zhou, Xinyu Liu, Yingtong Sun, Weijie Song, Shuchao Yun, Shan Guan, Guofeng Chen, Jing National & Local Joint Engineering Research Center for Mineral Salt Deep Utilization Key Laboratory for Palygorskite Science and Applied Technology of Jiangsu Province School of Chemical Engineering Huaiyin Institute of Technology Huaian223003 China State Key Laboratory of Materials-Oriented Chemical Engineering College of Chemical Engineering Jiangsu National Synergetic Innovation Center for Advanced Materials Nanjing Tech University Nanjing210009 China Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse School of Environmental and Biological Engineering Nanjing University of Science and Technology Nanjing210094 China

It is a challenge to develop high-efficiency, easy-recyclable adsorbents for the removal of dyes from environmental waters. Herein, we report the construction of two types of palygorskite (Pal)-polymer aerogels with distinctive hierarchically meso−microporous structure, which different polymer species—sodium alginate (SA) and chitosan (CS)—through the sol-gel method, cross-linking, and freeze-drying, for the removal of dye pollutants from water. A hierarchical porous structure of aerogel accelerates the diffusion of dye water. Notably, this developed strategy enables selective dye adsorption by tuning the polymer and component contents in the Pal-polymer aerogel. Indeed, the maximum adsorption capacity of the Pal-SA composite aerogel for malachite green (cationic) dye reached 2008 mg/g and that of the Pal-CS(Fe3+) composite aerogel for sunset yellow (anionic) reached 976 mg/g. In addition, the composite aerogels can be regenerated many times without significant loss of effectiveness. This work provides a new concept and strategy for dye waste-water treatment. © 2023, The Authors. All rights reserved.

关键词： Aerogels

Emerging and legacy perfluoroalkyl and polyfluoroalkyl substances (PFAS) in surface water around three international airports in China

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Chemosphere 2023年 344卷 140360页

作者： Liu, Jing Zhao, Zhen Li, Jie Hua, Xia Zhang, Boxuan Tang, Caijun An, Xinyi Lin, Tian College of Marine Ecology and Environment Shanghai Ocean University Shanghai201306 China Handan Ecology and Environment Bureau Hebei 056008 China State Key Laboratory of Pollution Control and Resources Reuse College of Environmental Science and Engineering Tongji University Shanghai200092 China

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are a large category of crucial environmental contaminants of global concerns. There are limited data on PFAS in surface water around international airports in China. The present study investigated the concentrations, distributions, and sources of emerging and legacy PFAS in surface waters around Beijing Capital International Airport (BC), Shanghai Pudong International Airport (SP), and Guangzhou Baiyun International Airport (GB) in China. Twenty-seven target compounds were quantified. The Σ27PFAS concentrations ranged from 19.0 to 62.8 ng/L (mean 36.1 ng/L) in BC, 25.6–342 ng/L (mean 76.0 ng/L) in SP, 7.35–72.7 ng/L (mean 21.6 ng/L) in GB. The dominant compound was perfluorooctanoic acid (PFOA), which accounted for an average of 27% (5%–65%) of the Σ27PFAS concentrations. The alternatives with –C6F12- group had detection frequencies ranging from 72% to 100%. The partition coefficient results indicate that the longer chain PFAS (C > 8) tend to be more distributed in the particle phase. Fifty suspect and nontarget PFAS were identified. In GB, 44 PFAS were identified, more than SP of 39 and BC of 38. An ultra short-chain (C = 2) precursor, N-methylperfluoroethanesulfonamido acetic acid (MeFEtSAA), was identified and semi-quantified. Domestic wastewater discharges might be the main sources around BC, while industrial and aviation activities might be the main sources around SP and GB. © 2023 Elsevier Ltd

关键词： Surface waters

Development of a Duplex Recombinase Polymerase Amplification-Lateral Flow Strip Assay With 1 Min of DNA Extraction for Simultaneous Identification of Pork and Chicken Ingredients

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SSRN 2024年

作者： Feng, Jinchao Lan, Hangzhen Pan, Daodong Xu, Xiahong Wu, Yichun State Key Laboratory for Quality and Safety of Agro-products Zhejiang-Malaysia Joint Research Laboratory for Agricultural Product Processing and Nutrition College of Food Science and Engineering Ningbo University Ningbo315800 China Zhejiang Academy of Agricultural Sciences Hangzhou310021 China Zhoushan Institute for Food and Drug Control Zhoushan316012 China

Meat adulteration is a challenge faced by the global food industry. A simple, rapid, and affordable duplex recombinase polymerase amplification-lateral flow strip (dRPA-LFS) assay was developed for the identification of pork and chicken ingredients. Targeted genes, the pork (Porcine) mitochondrial ND2 gene and chicken (Gallus gallus) cytochrome B gene, were designed with primers and probes. Six difference-sized gold nanoparticles (AuNPs) were synthesized, and they were functionalized by streptavidin (SA), and the most suitable size (41.3±7.8 nm) of the AuNPs-SA was selected as visual labels. The AuNPs and AuNPs-SA complex were characterized by the transmission electron microscopy, ultraviolet-visible spectroscopy, and Fourier Transform Infrared spectroscopy. The whole dRPA-LFS detection procedure can be finished within 18.5 min (including 1 min of rapid DNA extraction, 15 min of dRPA amplification, and 2.5 min of LFS detection), and the result is quantitative and qualitative. No positive amplification was observed in beef, lamb, duck, rabbit, goose, ostrich, and horse DNA. This assay is sensitive and low to 0.1% (wt%) of pork and chicken in simulated adulterated meat mixtures can be quantified. This assay was successfully applied to the authentication identification of 14 commercial beef and lamb products. © 2024, The Authors. All rights reserved.

关键词： Meats

A robust temporal multivariable fault detection method for blast furnace: Robust temporal convolution detection network

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Information Sciences 2025年 716卷

作者： Zhu, Xiongzhuo Yang, Chunjie Lou, Siwei Yang, Yuelin State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

As a complex industrial process, the blast furnace ironmaking process (BFIP) often suffers from faults due to changes in raw materials, variables’ setpoints, reaction conditions, etc. However, because of the nonlinearity, dynamics, and mixture of normal and abnormal outliers in BFIP, the existing methods always meet difficulties in practical application. Therefore, this paper proposes a robust temporal convolution detection network (RTCDN) for BFIP fault detection. The basic TCDN network is constructed by stacking the residual blocks of the temporal convolution network (TCN). The residual block consists of the 1-D dilated causal convolution, which is performed on the time scale of variables, giving the network the ability to extract time-scale information. Since TCDN consists of convolutional networks, it balances the extraction of temporal information and speed compared to RNN-type methods. Furthermore, a robust solution is proposed to overcome the interference of abnormal outliers. The robust solution exploits that TCDN can't reconstruct abnormal outliers and decomposes the training dataset into the clean part and abnormal outliers. In this work, the proximal approach to optimizing the sparse outlier matrix is also improved to achieve complete separation of abnormal outliers. Finally, TCDN is applied to three BF fault datasets and performs better than the conventional temporal detection methods. RTCDN has also been proven to have good outlier separation capability, maintaining satisfactory detection performance when the proportion of abnormal outliers is less than 20%. © 2025 Elsevier Inc.

关键词： Time measurement

Simulating unsteady fluid flows on a superconducting quantum processor

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arXiv

arXiv 2024年

作者： Meng, Zhaoyuan Zhong, Jiarun Xu, Shibo Wang, Ke Chen, Jiachen Jin, Feitong Zhu, Xuhao Gao, Yu Wu, Yaozu Zhang, Chuanyu Wang, Ning Zou, Yiren Zhang, Aosai Cui, Zhengyi Shen, Fanhao Bao, Zehang Zhu, Zitian Tan, Ziqi Li, Tingting Zhang, Pengfei Xiong, Shiying Li, Hekang Guo, Qiujiang Wang, Zhen Song, Chao Wang, H. Yang, Yue State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China School of Physics ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control Zhejiang University Hangzhou310027 China Department of Engineering Mechanics School of Aeronautics and Astronautics Zhejiang University Hangzhou310027 China HEDPS-CAPT Peking University Beijing100871 China

Recent advancements of intermediate-scale quantum processors have triggered tremendous interest in the exploration of practical quantum advantage. The simulation of fluid dynamics, a highly challenging problem in classical physics but vital for practical applications, emerges as a good candidate for showing quantum utility. Here, we report an experiment on the digital simulation of unsteady flows, which consists of quantum encoding, evolution, and detection of flow states, with a superconducting quantum processor. The quantum algorithm is based on the Hamiltonian simulation using the hydrodynamic formulation of the Schrödinger equation. With the median fidelities of 99.97% and 99.67% for parallel single- and two-qubit gates respectively, we simulate the dynamics of a two-dimensional (2D) compressible diverging flow and a 2D decaying vortex with ten qubits. The experimental results well capture the temporal evolution of averaged density and momentum profiles, and qualitatively reproduce spatial flow fields with moderate noises. This work demonstrates the potential of quantum computing in simulating more complex flows, such as turbulence, for practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Flow of fluids