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Boosting methanol electro-oxidation to formate by trace iron induced suppression of cobalt(Ⅳ)formation

Nano Research 2025年第5期18卷 230-239页

作者： Jialong Lin Xinlin Wang Bingxue Cheng Ruiheng Zhou Yuhang Li Tamao Ishida Guangli Xiu Toru Murayama Mingyue Lin State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process Shanghai Environmental Protection Key Laboratory on Environmental Standard and Risk Management of Chemical PollutantsSchool of Resources and Environmental EngineeringEast China University of Science and TechnologyShanghai 200237China School of Materials Science and Engineering East China University of Science and TechnologyShanghai 200237China Department of Applied Chemistry for Environment Graduate School of Urban Environmental SciencesTokyo Metropolitan UniversityTokyo 192-0397Japan Institute for Catalysis Hokkaido UniversityHokkaido 001-0021Japan Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

Converting methanol to high-value formate through electrochemical methods can significantly reduce the energy consumption associated with conventional production *** this study,we directly synthesized iron-doped cobalt phosphate(Fe-CoPO)on nickel foam(NF)to achieve excellent activity for the methanol electro-oxidation reaction(MOR).Our results demonstrated that Fe-CoPO produced a current density of 100 mA·cm^(−2)at a significantly low operating potential of 1.436 V(*** hydrogen electrode(RHE))and operated steadily for 16 h at this current density with a Faradaic efficiency(FE)of 97%.Furthermore,Fe-CoPO maintained a high FE of 100%even at an extremely high current density of 300 mA·cm^(−2)for 8 *** found that the high MOR activity of Fe-CoPO results from electrochemical reconstruction to generate the Co^(2+/3+)-O *** heterogeneous interface between Fe and Co inhibits the formation of Co^(4+),which significantly enhances the MOR ***,this work not only provides insights into the mechanism of MOR over Co-based catalysts but also offers a novel direction for developing highly active MOR catalysts.

关键词： cobalt phosphate methanol electro-oxidation reaction formate Fe doping electrocatalysis

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Biodiesel production from used palm oil using magnetic solid base catalyst 21

Biodiesel production from used palm oil using magnetic solid...

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21st International Union of Materials Research Societies International Conference in Asia, IUMRS-ICA 2021

作者： Singhanatkaisi, Natthakit Kandiah, Mathi Zheng, Alvin Lim Teik Ando, Yoshito Boonyuen, Supakorn Department of Chemistry Faculty of Science and Technology Thammasat University Pathumthani12121 Thailand School of Science BMS 591 Galle Road Colombo06 Sri Lanka Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Fukuoka808-0196 Japan Oral and Maxillofacial Surgery Division Faculty of Dentistry Thammasat University Pathumthani Thailand

In this research the biodiesel from palm oil were synthesized by using magnetic solid base catalyst. The magnetic solid base catalyst was prepared by using co-precipitation method and followed by modified Stober's method and wet impregnation method. CaO-ferrite nanometre magnetic solid base catalysts (CaO@Fe3O4/SiO2) were synthesized by loading CaO on the surface of Fe3O4 core-shell using the polymeric binder, TEOS. Under the optimized condition reactions, the catalysts were synthesized by co-precipitation and incipient wetness impregnation followed by calcination at 700 °C for 5 h. The calcined catalyst was fully characterized. The basic strength of the catalysts (H_) was first assessed using Hammett indicators, which are in range 9.8-15.0. The calcined Ca/ferrite-TEOS showed homogenous spherical shape with an apparent thickness of 92 nm. The FT-IR of CaO observed at 520 cm-1 is attributed to the Ca-O vibrations. The EDX analysis confirmed the presence of Fe, Si and Ca on the catalyst. The optimization conditions for biodiesel production have been measured. The catalyst was used 5.0 % wt. The methanol to oil ratio is 5:1, reaction time is 180 minutes and reaction was produced at 80°C. The catalyst achieved a biodiesel conversion of used palm oil (acid value 1.2) above 92 %. The catalytic activity and recovery rate of CaO@Fe3O4/SiO2 are better as compared to CaO catalyst. The properties of synthesized biodiesel have been studied and meet standard the department of Energy Business. © Published under licence by IOP Publishing Ltd.

关键词： Biodiesel

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Distributed Sequential Receding Horizon control of Multi-Agent systems under Recurring Signal Temporal Logic

arXiv

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arXiv 2023年

作者： Vlahakis, Eleftherios E. Lindemann, Lars Dimarogonas, Dimos V. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Thomas Lord Department of Computer Science Viterbi School of Engineering University of Southern California Los AngelesCA90089 United States

We consider the synthesis problem of a multi-agent system under signal temporal logic (STL) specifications representing bounded-time tasks that need to be satisfied recurrently over an infinite horizon. Motivated by the limited approaches to handling recurring STL systematically, we tackle the infinite-horizon control problem with a receding horizon scheme equipped with additional STL constraints that introduce minimal complexity and a backward-reachability-based terminal condition that is straightforward to construct and ensures recursive feasibility. Subsequently, we decompose the global receding horizon optimization problem into agent-level programs the objectives of which are to minimize local cost functions subject to local and joint STL constraints. We propose a scheduling policy that allows individual agents to sequentially optimize their control actions while maintaining recursive feasibility. This results in a distributed strategy that can operate online as a model predictive controller. Last, we illustrate the effectiveness of our method via a multi-agent system example assigned a surveillance task. © 2023, CC BY.

关键词： Temporal logic

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The Future of Beyond 5G Sensing: Transforming Activity Recognition with Reconfigurable Intelligent Surfaces

The Future of Beyond 5G Sensing: Transforming Activity Recog...

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Activity and Behavior Computing (ABC), International Conference on

作者： Sherief Hashima Hamada Rizk Hirozumi Yamaguchi Computational Learning Theory Team RIKEN-AIP Fukuoka Japan Engineering Dept Egyptian Atomic Energy Authority Cairo Egypt Graduate School of Information Science and Technology Osaka University Suita Japan Computer and Control Engineering Department Tanta University Tanta Egypt

ISBN: (数字)9798350375503

ISBN: (纸本)9798350375510

The advent of Beyond 5G (B5G) and the anticipated arrival of 6G have spurred a remarkable impact on various aspects of human life. Next-generation Human Activity Recognition (HAR) systems are poised to advance healthcare, create smart environments, and enhance overall well-being. The imperative for next-gen HAR systems lies in their capability to be intelligent, privacy-preserving, and deeply accurate. These systems, leveraging the cutting-edge capabilities of B5G and 6G, such as Re-configurable Intelligent Surface (RIS), aim to revolutionize the monitoring process and intelligently discern various human activities. Hence, this paper introduces BSgActiv, a smart RIS-enhanced HAR system. BSgActiv utilizes fractional wavelet transform to effectively highlight time and frequency features of activities from the measured channel state information (CSI) reflected from RIS. Afterward, these features are utilized to train a recurrent neural network that records the temporal characteristics of the input, hence promoting activity recognition. Moreover, BSgActiv integrates diverse modules that enhance the deep model's overall observation and ability to withstand and perform well in the presence of noise. BSgActiv evaluations contain two different realistic scenarios, including both non-line-of-sight and multi-floor setups, proofing its efficacy. Particularly, BSgActiv overcomes benchmark techniques and delivers an activity recognition accuracy of 89.7%.

关键词： 6G mobile communication Wavelet transforms Time-frequency analysis Accuracy 5G mobile communication Reconfigurable intelligent surfaces Feature extraction

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Carbon-Aware Computing in a Network of Data Centers: A Hierarchical Game-Theoretic Approach

arXiv

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arXiv 2024年

作者： Breukelman, Enno Hall, Sophie Belgioioso, Giuseppe Dörfler, Florian The KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Division of Decision and Control Systems Malvinas väg 10 StockholmSE-100 44 Sweden The Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zürich8092 Switzerland

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach successfully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling. © 2024, CC BY-NC-ND.

关键词： Carbon

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Field-free spin-orbit torque switching and large dampinglike spin-orbit torque efficiency in synthetic antiferromagnetic systems using interfacial Dzyaloshinskii-Moriya interaction

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Physical Review B 2023年第2期108卷 024419-024419页

作者： Yoshiaki Saito Shoji Ikeda Nobuki Tezuka Hirofumi Inoue Tetsuo Endoh Center for Innovative Integrated Electronic Systems Tohoku University Sendai 980-0845 Japan Center for Spintronics Research Network Tohoku University Sendai 980-8577 Japan Center for Science and Innovation in Spintronics Tohoku University Sendai 980-8577 Japan Research Institute of Electrical Communication Tohoku University Sendai 980-8577 Japan Department of Materials Science Graduate School of Engineering Tohoku University Sendai 980-8579 Japan Department of Electrical Engineering Graduate School of Engineering Tohoku University Sendai 980-8579 Japan

Deterministic switching of perpendicularly magnetized systems using spin-orbit torque (SOT) usually requires an in-plane auxiliary magnetic field, which limits its practical applications. Here, an interfacial Dzyaloshinskii-Moriya interaction is introduced into perpendicularly magnetized synthetic antiferromagnetic (AFM) systems by changing the type of capping or underlayer in the [capping layer]/Co/[Pt/Ir/Pt spacer layer]/Co/[underlayer] synthetic AFM system. Field-free switching in the synthetic AFM system is observed in the cases of W, Ta, Ir22Mn78 capping layers or Ir22Mn78 underlayer. Among the synthetic AFM systems with field-free switching, the synthetic AFM system with Ir22Mn78 capping layer shows the largest dampinglike SOT efficiency, ξDL=28.1%. We also found observed large ξDL is related to the enhancement of spin absorption in antiferromagnetic Co magnetization configuration in synthetic AFM system compared to that in ferromagnetic Co magnetization configuration and the degree of enhancement increases with increasing the magnitude of interlayer exchange coupling between Co layers in synthetic AFM system. These results suggest that the synthetic AFM system is a promising building block for future nonvolatile high-speed memories and logic circuits using the spin Hall effect.

关键词： Electrical properties Magnetism Spintronics

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Intelligent Autonomous-Robot control for Medical Applications

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Computers, Materials & Continua 2021年第8期68卷 2189-2203页

作者： Rihem Farkh Haykel Marouani Khaled Al Jaloud Saad Alhuwaimel Mohammad Tabrez Quasim Yasser Fouad College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsDepartment of Electrical EngineeringNational Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Riyadh12354Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a *** has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods(including medicines)that is needed to prevent infection and treatment for infected *** COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a *** artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways,particularly in the control of autonomous medic *** officials aim to curb the spread of COVID-19 among medical,nursing staff and patients by using intelligent *** propose an advanced controller for a service robot to be used in *** type of robot is deployed to deliver food and dispense medications to individual *** autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its *** criteria were met by using two controllers simultaneously:a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative(PID)controller for automatic steering and speed control.

关键词： Autonomous medic robots PID control neural network control system real-time implementation navigation environment differential drive system

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Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning

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Neural Computing and Applications 2024年第30期36卷 18841-18862页

作者： Mamalakis, Michail Banerjee, Abhirup Ray, Surajit Wilkie, Craig Clayton, Richard H. Swift, Andrew J. Panoutsos, George Vorselaars, Bart Department of Psychiatry University of Cambridge Herchel Smith Building Robinson Way CambridgeCB2 0SZ United Kingdom Department of Computer Science University of Sheffield 211 Portobello SheffieldS1 4DP United Kingdom Insigneo Institute for in silico Medicine University of Sheffield The Pam Liversidge Building SheffieldS1 3JD United Kingdom School of Mathematics and Statistics University of Glasgow GlasgowG12 8QQ United Kingdom Department of Infection Immunity and Cardiovascular Disease University of Sheffield SheffieldS10 2HQ United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom School of Mathematics and Physics University of Lincoln Brayford Pool LincolnLN6 7TS United Kingdom Department of Engineering Science Institute of Biomedical Engineering University of Oxford OxfordOX3 7DQ United Kingdom Division of Cardiovascular Medicine Radcliffe Department of Medicine University of Oxford OxfordOX3 9DU United Kingdom

The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and demanding datasets. In our study, we have developed and evaluated a new training methodology named deep multi-metric training (DMMT) for enhanced training performance. The DMMT delivers a state of robust learning for deep networks using a new important criterion of multi-metric performance evaluation. We have tested the DMMT methodology in multi-class (three, four, and ten), multi-vendors (different X-ray imaging devices), and multi-size (large, medium, and small) datasets. The validity of the DMMT methodology has been tested in three different classification problems: (i) medical disease classification, (ii) environmental classification, and (iii) ecological classification. For disease classification, we have used two large COVID-19 chest X-rays datasets, namely the BIMCV COVID-19+ and Sheffield hospital datasets. The environmental application is related to the classification of weather images in cloudy, rainy, shine or sunrise conditions. The ecological classification task involves a classification of three animal species (cat, dog, wild) and a classification of ten animals and transportation vehicles categories (CIFAR-10). We have used state-of-the-art networks of DenseNet-121, ResNet-50, VGG-16, VGG-19, and DenResCov-19 (DenRes-131) to verify that our novel methodology is applicable in a variety of different deep learning networks. To the best of our knowledge, this is the first work that proposes a training methodology to deliver robust learning, over a variety of deep learning networks and multi-field classification problems. © Crown 2024.

关键词： COVID-19

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Non-proteolytic ubiquitination of HBx controls HBV replication

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Virologica Sinica 2024年第2期39卷 338-342页

作者： Xiangpeng Sheng Yi Yang Min Zhu Linlin Zhou Fang Zhu Yuanfei Zhu Siying Dong Hui Kong Honghua Wang Ji Jiang Mingyue Wan Mingyang Feng Qiang Deng Yumin Xu Qing You Ronggui Hu State Key Laboratory for Animal Disease Control and Prevention Harbin Veterinary Research InstituteChinese Academy of Agricultural SciencesHarbin 150069China State Key Laboratory of Molecular Biology Shanghai Institute of Biochemistry and Cell BiologyCenter for Excellence in Molecular Cell ScienceChinese Academy of SciencesShanghai 200031China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Department of Thoracic Surgery Ruijin HospitalShanghai Jiaotong University School of MedicineShanghai 200025China Shanghai Key Laboratory of Plant Molecular Sciences College of Life SciencesShanghai Normal UniversityShanghai 200234China Department of Pathogenic Biology West China School of Basic Medical Sciences&Forensic MedicineSichuan UniversityChengdu 610041China School of Medicine Guizhou UniversityGuiyang 550025China Key Laboratory of Medical Molecular Virology(MOE&MOH) School of Basic Medical SciencesFudan UniversityShanghai 200032China Department of Hospital Infection Management Department of Infectious DiseasesRuijin HospitalShanghai Jiao Tong University School of MedicineShanghai 200025China School of Biomedical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor,Although effective vaccines and antiviral therapies are available,hepatitis B virus(HBV)infection is still a serious global health *** HBV infection remains the principal cause of liver cirrhosis and hepatocellular carcinoma(HCC).HBV is a small DNA virus,owning a~3.2 kb genome that encodes several proteins:viral DNA polymerase,core antigen(HBcAg),E antigen(HBeAg),three surface antigens(PreS1/PreS2/HBsAg),and a regulatory X protein(HBx)(Lamontagne et al.,2016).X protein,named for its lack of homology with any known proteins,is a 154 aa protein that plays an essential role in HBV biology and regulates the development of HCC(Yang et al.,2022).Although previous studies have strongly expanded our understanding of HBx,the regulation of HBx is not completely elucidated.

关键词： infection cirrhosis

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RISense: 6G-Enhanced Human Activity Recognition System with RIS and Deep LDA

RISense: 6G-Enhanced Human Activity Recognition System with ...

引用

International Conference on Mobile Data Management

作者： Hamada Rizk Sherief Hashima Graduate School of Information Science and Technology Osaka University Suita Japan Computer and Control Engineering Department Tanta University Tanta Egypt Computational Learning Theory Team RIKEN-AIP Fukuoka Japan Engineering Dept Egyptian Atomic Energy Authority Cairo Egypt

ISBN: (数字)9798350374551

ISBN: (纸本)9798350374568

Human Activity Recognition (HAR) systems hold great potential in aiding disabled and elderly individuals to live independently. Various approaches have been suggested for identifying human activities, including sensors, cameras, wearables, and contactless microwave sensing. This latter method is gaining significant attention due to its ability to address privacy concerns arising from cameras and alleviate discomfort caused by wearables. However, current microwave sensing techniques have a key limitation, requiring controlled and ideal conditions to achieve accurate activity detection. In this paper, we propose RISense, a deep learning-aided system for HAR using Re-configurable Intelligent Surface (RIS). RISense introduces novel modules designed to generate a human activity representational space that ensures separability between activity classes, even in the presence of noisy and distorted Channel State Information (CSI) measurements. These representations are then fed into a Recurrent Neural Network (RNN), which learns the sequential changes in features to accurately estimate the user activity. The evaluation of RISense across two realistic settings, encompassing both non-line-of-sight and multi-floor scenarios, showcases its efficacy. Specifically, RISense achieves an activity recognition accuracy of 98.7%. This performance overcomes the accuracy of state-of-the-art systems by over 15%. These findings highlight the superior efficacy of the proposed methodologies, including integrating RIS and advanced learning techniques.

关键词： 6G mobile communication Accuracy Recurrent neural networks Microwave theory and techniques Feature extraction Cameras Human activity recognition

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