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SSRN 2023年

作者： Padligur, Maria C. Linnartz, Christian J. Zimmer, Stephan Linkhorst, John Wessling, Matthias RWTH Aachen University Aachener Verfahrenstechnik-Chemical Process Engineering Forckenbeckstraße 51 Aachen52074 Germany Process Engineering of Electrochemical Systems Department of Mechanical Engineering Technical University of Darmstadt Otto-Berndt-Str. 2 Darmstadt64287 Germany DWI - Leibniz-Institute for Interactive Materials e.V. Forckenbeckstraße 50 Aachen52074 Germany

The industrial application of electrochemical processes is limited by a variety of challenges such as catalyst stability and selectivity. However, the question of how to efficiently exploit the entire reactor volume for the process is also essential for a viable implementation. Flow electrodes, which consist of conductive particles suspended in the electrolyte, are promising for increasing the active electrode area, without increasing reactor footprint. They are currently being used for different applications, such as energy storage or desalination. However, especially the charge transport mechanisms in flow electrodes remain unclear because the occurring phenomena are fast and highly *** this work, we introduce a methodology that combines electrical measurements with high-speed recording of particle movements in flow electrodes, simultaneously. Our algorithm evaluates the video sequences and calculates the detected particle area in the channel. This allows the correlation of the electrical current with the actual amount of particles in the channel. The results reveal that only an aqueous electrolyte can result in a substantial increase in conductivity because of the particles present in the fluid channel. Furthermore, the conductivity increases with the number of particles, regardless of the observed particle-electrode contact. When the particle flow was hydrodynamically forced toward the electrode, we found that the particle-electrode contact increased the conductivity even more strongly than that of isolated particle clusters in an unfocused flow. However, the greatest effect on the electrical conductivity relates to the formation of particle bridges resulting from the percolation networks between the electrodes. © 2023, The Authors. All rights reserved.

关键词： Electrodes

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Data-driven joint noise reduction strategy for flutter boundary prediction

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The European Physical Journal Special Topics 2025年 1-18页

作者： Yan, Haoxuan Xu, Yong Liu, Qi Wang, Xiaolong Kurths, Jürgen School of Mathematics and Statistics Northwestern Polytechnical University Xi’an China MOE Key Laboratory for Complexity Science in Aerospace Northwestern Polytechnical University Xi’an China Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan School of Mathematics and Statistics Shaanxi Normal University Xi’an China Potsdam Institute for Climate Impact Research Potsdam Germany Department of Physics Humboldt University Berlin Berlin Germany

Flutter test data processing is crucial for modal parameter identification, which facilitates flutter boundary prediction. However, the response signals acquired from real experiments have difficulties due to non-smoothness, multimodal mixing and low signal-to-noise ratio. A direct analysis and prediction will often lead to low accuracy on the predictions and seriously threaten flight safety. Therefore, this paper proposes a data-driven joint noise reduction strategy to improve the performance of flutter boundary prediction. Particularly, a variational mode decomposition is substantially improved by introducing an optimization algorithm. The decomposed effective signal components are reprocessed via a wavelet threshold denoising method with a soft-hard compromise threshold function. Then, based on the matrix pencil method, the modal parameters of original turbulence response signals are identified from the impulse responses generating by deep learning. The effectiveness of the presented method is verified by a comparative analysis with conventional methods.

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Sampled-Data Observer Design With Exponential Time-Varying Gains for Semilinear Parabolic PDE systems

Sampled-Data Observer Design With Exponential Time-Varying G...

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2021 China Automation Congress, CAC 2021

作者： Wang, Zi-Peng Wu, Huai-Ning Li, Han-Xiong Huang, Tingwen Shen, Tao The School of Electrical Engineering University of Jinan Jinan250022 China The Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Department of Systems Engineering and Engineering Management City University of Hong Kong Hong Kong The Science Program Texas A&M University at Qatar Doha Qatar The HIT Robot Group Co. Ltd China

ISBN: (纸本)9781665426473

For semilinear parabolic PDE systems, the sampled-data observer (SDO) design under spatially point measurements (SPMs) with exponential time-varying gains is concerned. On basis of a Lyapunov functional, a SDO with exponential time-varying gains under SPMs via linear matrix inequalities is presented to guarantee the exponential stability of estimation error PDE system. Lastly, two numerical examples are provided to support the proposed SDO design approach. © 2021 IEEE

关键词： Linear matrix inequalities

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Dynamics Loads of the Automated Electric Drive for Moving FKM-500 Filter-Press Plates

Dynamics Loads of the Automated Electric Drive for Moving FK...

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International Conference on Modern Electrical and Energy systems (MEES)

作者： Oksana Khrebtova Iurii Zachepa Nataliia Zachepa Andriy Nekrasov Kvitash Pavlo Oleksandr Hrytsai Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Electrical Engineering Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Department of Jurisprudence Sciences Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine

Operational loads have both static and dynamic characteristics. Dynamic loads occur during the operation of the considered mechanism, both during acceleration and deceleration periods, as well as during steady motion due to the movement of the moving element along an uneven path caused by the presence of the working environment on the elements over which the filter-press plates are moving. The dynamic loading is significant and affects the formation of plate-side displacements during their movement while performing the technological operation. Experimental studies of the load on the movement elements were conducted to determine optimal control modes for the filter-press plate movement mechanism, aiming to avoid overloading during stopping and filter jamming. To analyze the loading, discretization of random processes of the filter movement mechanism loading was performed. The sequence of calculating the statistical characteristics of the schematized dynamic process is presented. Based on experimental data, mathematical calculation of the results of schematization and the type of probability distribution law was carried out. To select the law that best reflects the experimental data distribution of load amplitudes, the Kolmogorov-Smirnov goodness-of-fit criterion was applied. According to the calculation results, it was found that the Weibull distribution law adequately reflects the distribution of amplitudes since its corresponding Kolmogorov-Smirnov criterion value is the smallest.

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FluxRETAP: A REaction TArget Prioritization Genome-Scale Modeling Technique for Selecting Genetic Targets

Research Square

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Research Square 2025年

作者： Czajka, Jeffrey J. Kim, Joonhoon Tang, Yinjie J. Pomraning, Kyle R. Mukhopadhyay, Aindrila Martin, Hector Garcia Energy and Environment Directorate Pacific Northwest National Laboratory RichlandWA United States US Department of Energy Agile BioFoundry EmeryvilleCA United States US Department of Energy Joint BioEnergy Institute EmeryvilleCA United States Biological Systems and Engineering Division Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Energy Environmental and Chemical Engineering Washington University St. LouisMO United States Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory BerkeleyCA United States

Metabolic engineering is evolving rapidly as a result of new advances in synthetic biology and automation, as well as the irruption of machine learning (ML). ML has been shown to provide the predictive power synthetic biology lacked and needed, and to be able to effectively guide the metabolic engineering process. However, current technical limitations prevent the independent application of ML approaches to metabolic engineering without the use of previous biological knowledge in the form of a prioritized list of desirable engineering targets. Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale metabolic models (GSMMs) for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing the production of a desired metabolite. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production in the literature, 50% of targets that experimentally improved taxadiene production in E. coli and ~60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida, while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets which can also be utilized in ML pipelines. © 2025, CC BY.

关键词： Metabolic engineering

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Refined H-Wo3 by Mo Doping: A Strategy for Enhancing Hydrogen Ion Storage Performance

SSRN

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SSRN 2024年

作者： Wang, Liushun Yi, Jinqiao Tang, Dongsheng Ren, Dahua Zhang, Teng Zhang, Yuan Cheng, Jiaxin Li, Dong Zhang, Chunyan College of Intelligent Systems Science and Engineering Hubei Minzu University Enshi445000 China Synergetic Innovation Center for Quantum Effects and Application Key Laboratory of Low-dimensional Quantum Structures and Quantum Control Ministry of Education School of Physics and Electronics Hunan Normal University Changsha410081 China Hunan Key Laboratory of Two-Dimensional Materials Advanced Catalytic Engineering Research Center The Ministry of Education College of Chemistry and Chemical Engineering Hunan University Changsha410082 China Hubei University for Nationalities China Hunan Normal University China

The crystal structure shear and the electronic structure modulation through ion doping are both kinetically relevant to develop high-performance electrode materials for electrochemical energy storage, yet they remain a significant challenge. This study proposes the preparation of ultrafine molybdenum-doped tungsten oxide nanowires as advanced materials for hydrogen ion (H+) storage. These materials exhibited high multiplicity performance (98 mAh/g at 20C;89 mAh/g at 100C, with 1C=100 mAh/g) at the optimal Mo doping concentration (MW-5, The molar ratio of Mo to W is equal to 5%, abbreviated as MW = 5). Additionally, the MW-5 nanowire electrode showed superior Na+ and NH4+ storage capacities as compared to h-WO3. The enhanced hydrogen ion storage capability of Mo-doped WO3 nanowires was ascribed to their larger specific surface area and to the presence of a substantial amount of W6+, W5+, Mo6+, Mo5+, and Mo4+ ions. The high specific surface area of ultrafine nanowires with abundant defect structures significantly shortened the charge carrier transport distance, increased hydrogen ionization ion storage sites, and accelerated interfacial electron motion, thereby improving ion storage kinetics. Our findings revealed the potential of interface-engineering strategies in advancing towards high-performance hydrogen ion storage electrode materials for efficient green energy conversion applications. © 2024, The Authors. All rights reserved.

关键词： Nanowires

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Erratum to: Data-driven soft sensors in blast furnace ironmaking: a survey (Frontiers of Information Technology & Electronic engineering, (2023), 24, 3, (327-354), 10.1631/FITEE.2200366)

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Frontiers of Information Technology and Electronic engineering 2023年 1-1页

作者： Luo, Yueyang Zhang, Xinmin Kano, Manabu Deng, Long Yang, Chunjie Song, Zhihuan State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Systems Science Kyoto University Kyoto606-8501 Japan Research Institute Baoshan Iron & Steel Co. Ltd. Shanghai201900 China

The article "Data-driven soft sensors in blast furnace ironmaking: a survey," written by Yueyang LUO, Xinmin ZHANG, Manabu KANO, Long DENG, Chunjie YANG, and Zhihuan SONG, was originally published electronically on the publisher’s Internet portal on Mar. 27, 2023 without open access. With the authors’ decision to opt for Open Choice the copyright of the article changed on Sept. 14, 2023 to © The Authors 2023, and the article is forthwith distributed under a Creative Commons Attribution: This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://***/licenses/by-nc/4.0/ © 2023 Zhejiang University Press.

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Towards Data-driven LQR with Koopmanizing Flows ⋆

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IFAC-PapersOnLine 2022年第15期55卷 13-18页

作者： Petar Bevanda Max Beier Shahab Heshmati-Alamdari Stefan Sosnowski Sandra Hirche Chair of Information-oriented Control (ITR) Department of Electrical and Computer Engineering Technical University of Munich D-80333 Munich Germany Department of Electronic Systems Aalborg University Fredrik Bajers Vej 7K 9220 Aalborg Denmark

We propose a novel framework for learning linear time-invariant (LTI) models for a class of continuous-time non-autonomous nonlinear dynamics based on a representation of Koopman operators. In general, the operator is infinite-dimensional but, crucially, linear. To utilize it for effcient LTI control design, we learn a finite representation of the Koopman operator that is linear in controls while concurrently learning meaningful lifting coordinates. For the latter, we rely on Koopmanizing Flows - a diffeomorphism-based representation of Koopman operators and extend it to systems with linear control entry. With such a learned model, we can replace the nonlinear optimal control problem with quadratic cost to that of a linear quadratic regulator (LQR), facilitating efficacious optimal control for nonlinear systems. The superior control performance of the proposed method is demonstrated on simulation examples.

关键词： Machine learning Koopman operators Learning for control Representation Learning Neural networks Learning systems

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Facile Fabrication of 2d/2d G-C3n4/Nh2-Mil-125(Ti) Mof Nanocomposite with Z-Scheme Heterojunction for Efficient Photocatalytic H2 Production and Co2 Reduction Under Visible Light

SSRN

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SSRN 2023年

作者： Ikreedeegh, Riyadh Ramadhan Tasleem, Sehar Hossen, Md. Arif School of Chemical and Energy Engineering Universiti Teknologi Malaysia Johor Johor Bahru81310 Malaysia Department of Analysis and Quality Control Sarir Oil Refinery Arabian Gulf Oil Company El Kish P.O. Box 263 Benghazi Libya Faculty of Civil Engineering Technology Universiti Malaysia Pahang Pahang Gambang26300 Malaysia Center for Environmental Science and Engineering Research Chittagong University of Engineering and Technology Chattogram4349 Bangladesh

A binary 2D/2D g-C3N4/NH2-MIL-125(Ti) MOF nanocomposite was fabricated through a facile sonochemical-assisted thermal approach for enhanced photocatalytic H2 production and CO2 reduction under visible light. Compared to pure g-C3N4, the g-C3N4/MOF photocatalyst showed enhanced visible light absorption with promoted charge carrier separation which increased the H2 production rate and the CO2 reduction into CH4 and CO. This enhancement was attributed to the successfully constructed Z-scheme heterojunction in addition to the visible-active, large surface area and highly CO2 adsorbable NH2-MIL-125(Ti) MOF. The highest H2 production of 480 µmol g−1 was exhibited over the g-C3N4/NH2-MIL-125(Ti) nanocomposite with 20 wt% MOF. Similarly, the highest CO production rate of 338 µmol g−1 was achieved with 20 wt% MOF composite. However, for the CH4 product gas, it was observed that the highest production rate was attained with pure g-C3N4 which reveals the NH2-MIL-125(Ti) MOF selectivity towards CO production instead of CH4. Among all the investigated sacrificial agents for H2 production, methanol was the best. The performance of CO2 reduction process was found to be increasing with the pressure increase. Furthermore, the stability investigations revealed continuous productions of H2, CO and CH4 over the C3N4/MOF photocatalyst in multiple cyclic runs without any significant photocatalyst deactivation. This study provides new ideas for the fabrication of cheap, efficient and easy-synthesized nanomaterials for energy production and environmental remediation applications. © 2023, The Authors. All rights reserved.

关键词： Solar fuels

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Predicting the effectiveness of chemotherapy treatment in lung cancer utilizing artificial intelligence-supported serum N-glycome analysis

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Computers in Biology and Medicine 2025年 186卷 109681-109681页

作者： Torok, Rebeka Meszaros, Brigitta Gombas, Veronika Vathy-Fogarassy, Agnes Szabo, Miklos Csanky, Eszter Jarvas, Gabor Guttman, Andras Research Institute of Biomolecular and Chemical Engineering University of Pannonia Veszprem Hungary Horváth Csaba Memorial Laboratory of Bioseparation Sciences Research Center for Molecular Medicine Doctoral School of Molecular Medicine Faculty of Medicine University of Debrecen Debrecen Hungary Department of Computer Science and Systems Technology University of Pannonia Veszprem Hungary Department of Pulmonology Borsod Academic County Hospital Miskolc Hungary

An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in lung cancer by monitoring the serum N-glycome of patients combined with artificial intelligence-based data analysis. The study involved thirty-three lung cancer patients undergoing chemotherapy treatments. Serum samples were taken before and after the treatment. The N-linked oligosaccharides were enzymatically released, fluorophore-labeled, and analyzed by capillary electrophoresis with laser-induced fluorescence detection. The resulting electropherograms were thoroughly processed and evaluated by artificial intelligence-based classifiers, i.e., utilizing a machine learning algorithm to categorize the data into two (binary) classes. The classifier analysis method revealed a strong association between the structural changes in the N-glycans and the outcomes of the chemotherapy treatments (ROC >0.9). This novel combination of bioanalytical and AI methods provided a precise and rapid tool for predicting the effectiveness of chemotherapy. © 2025 The Authors

关键词： Lung cancer

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