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Interfacial coordination bonds accelerate charge separation for unprecedented hydrogen evolution over S-scheme heterojunction

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Chinese Journal of Catalysis 2024年第10期65卷 174-184页

作者： Chunxue Li Hao Lu Guixiang Ding Tianyi Ma Shiyong Liu Li Zhang Guangfu Liao College of Ecological Environment and Urban Construction Fujian University of TechnologyFuzhou 350118FujianChina School of Materials Science and Engineering Suzhou University of Science and TechnologySuzhou 215009JiangsuChina College of Materials Engineering Fujian Agriculture and Forestry UniversityFuzhou 350002FujianChina School of Science RMIT UniversityMelbourneVIC 3000Australia Jiangxi Province Key Laboratory of Functional Crystalline Materials Chemistry Jiangxi University of Science and TechnologyGanzhou 341000JiangxiChina State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile UniversityWuhan 430200HubeiChina

Inspired by natural photosynthesis,fabricating high-performance S-scheme heterojunction is regarded as a successful tactic to address energy and environmental ***,NH_(2)-MIL-125(Ti)/Zn_(0.5)Cd_(0.5)S/NiS(NMT/ZCS/NiS)S-scheme heterojunction with interfacial coordination bonds is successfully synthesized through in-situ solvothermal ***,the optimal NMT/ZCS/NiS S-scheme heterojunction exhibits comparable photocatalytic H_(2)evolution(PHE)rate of about 14876.7μmol h^(−1)g^(−1)with apparent quantum yield of 24.2%at 420 nm,which is significantly higher than that of recently reported MOFs-based *** interfacial coordination bonds(Zn–N,Cd–N,and Ni–N bonds)accelerate the separation and transfer of photogenerated charges,and the NiS as cocatalyst can provide more catalytically active sites,which synergistically improve the photocatalytic ***,theoretical calculation results display that the construction of NMT/ZCS/NiS S-scheme heterojunction also optimize the binding energy of active site-adsorbed hydrogen atoms to enable fast adsorption and *** Kelvin probe force microscopy,in-situ irradiation X-ray photoelectron spectroscopy,femtosecond transient absorption spectroscopy,and theoretical calculations provide sufficient evidence of the S-scheme charge migration *** work offers unique viewpoints for simultaneously accelerating the charge dynamics and optimizing the binding strength between the active sites and hydrogen adsorbates over S-scheme heterojunction.

关键词： Interfacial coordination bond S-schemeheterojunction Photocatalytic H_(2)evolution Charge dynamics Free energy barrier

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Channel-Vocoder-Centric Modelling of Cochlear Implants: Strengths and Limitations 9th

Channel-Vocoder-Centric Modelling of Cochlear Implants: Stre...

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9th Conference on Sound and Music technology, CSMT 2021

作者： Kong, Fanhui Mo, Yefei Zhou, Huali Meng, Qinglin Zheng, Nengheng Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China Acoustic Laboratory School of Physics and Optoelectronics South China University of Technology Guangdong Guangzhou510610 China School of Medicine South China University of Technology Guangdong Guangzhou510006 China

ISBN: (纸本)9789811947025

Modern cochlear implants (CIs) generate electric current pulsatile stimuli from real-time incoming to stimulate residual auditory nerves of deaf ears. In this unique way, deaf people can (re)gain a sense of hearing and consequent speech communication abilities. The electric hearing mimics the normal acoustic hearing (NH), but with a different physical interface to the neural system, which limits the performance of CI devices. Simulating the electric hearing process of CI users through NH listeners is an important step in CI research and development. Many acoustic modelling methods have been developed for simulation purposes, e.g., to predict the performance of a novel sound coding strategy. Channel vocoders with noise or sine-wave carriers are mostly popular among the methods. The simulation works have accelerated the procedures of re-engineering and understanding of the electric hearing. This paper presents an overview of the literature on channel-vocoder simulation methods. Strengths, limitations, applications, and future works about acoustic vocoder simulation methods are introduced and discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cochlear implants

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Joint Moment Estimation for Hip Exoskeleton Control: A Generalized Moment Feature Generation Method

arXiv

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arXiv 2024年

作者： Zhang, Yuanwen Xiong, Jingfeng Xian, Haolan Chen, Chuheng Chen, Xinxing Fu, Chenglong Leng, Yuquan Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China The Key Laboratory of Image Processing and Intelligent Control The Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Hip joint moments during walking are the key foundation for hip exoskeleton assistance control. Most recent studies have shown estimating hip joint moments instantaneously offers a lot of advantages compared to generating assistive torque profiles based on gait estimation, such as simple sensor requirements and adaptability to variable walking speeds. However, existing joint moment estimation methods still suffer from a lack of personalization, leading to estimation accuracy degradation for new users. To address the challenges, this paper proposes a hip joint moment estimation method based on generalized moment features (GMF). A GMF generator is constructed to learn GMF of the joint moment which is invariant to individual variations while remaining decodable into joint moments through a dedicated decoder. Utilizing this well-featured representation, a GRU-based neural network is used to predict GMF with joint kinematics data, which can easily be acquired by hip exoskeleton encoders. The proposed estimation method achieves a root mean square error of 0.1180 ± 0.0021 Nm/kg under 28 walking speed conditions on a treadmill dataset, improved by 6.5% compared to the model without body parameter fusion, and by 8.3% for the conventional fusion model with body parameter. Furthermore, the proposed method was employed on a hip exoskeleton with only encoder sensors and achieved an average 20.5% metabolic reduction (p Copyright © 2024, The Authors. All rights reserved.

关键词： Exoskeleton (Robotics)

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Enhancing Security in Smart Homes with IoT Using Logit-Boosted Techniques 6

Enhancing Security in Smart Homes with IoT Using Logit-Boost...

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6th International Conference on Computer Information Science and Application technology, CISAT 2023

作者： Rahim, Asif Zhong, Yanru Ahmad, Tariq School of Computer and Information Security Guilin university of Electronic Technology Guilin54001 China Guangxi Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin541001 China School of Information and Communication Engineering Guilin University of Electronic Technology Guilin541001 China

ISBN: (纸本)9781510668546

The utilization and advancement of Internet of Things (IoT) gadgets have noticeably increased in recent times. Various sectors, including intelligent homes, healthcare, sports analysis, and different industries, use IoT devices. Traffic is a crucial aspect of IoT devices, and it differs from conventional device traffic in several ways. This investigation employed 41 Internet of Things (IoT) devices. A machine learning model was developed that can classify IoT devices into multiple categories based on 13 different network traffic features. The raw data that was gathered was preprocessed using techniques like standardization and scaling techniques of the dataset. Algorithms for feature selecting can be utilized to extract characteristics from textual data. Following stratification, The dataset contains 106,234 sets of features that were utilized to enhance the predictive model. This research employed various evaluation metrics to demonstrate the effectiveness of the logistic-boosted algorithms. In this research, collaborative machine learning methods were utilized as a starting point to develop an intrusion detection system (IDS) that can identify abnormal network traffic. The principal aim of this investigation is to identify attacks and abnormalities within a Smart Home IoT setting. We have introduced a new technique for constructing Logit-Boosted algorithm, namely Logi-CatBoost Classifier. Once the logitboosted algorithm was employed to classify the dataset, Logi-CBC performed the best, with an accuracy of 88.70%. We suggested Logi-CBC algorithm achieved the top precision among previously used Logit-Boosted algorithms in past studies on the same dataset. © 2023 SPIE.

关键词： Internet of things

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Dual-mode predictive control of a rotor suspension system

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Science China(Information Sciences) 2020年第1期63卷 178-189页

作者： Yue WU Gui-Ping REN Hai-Tao ZHANG Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology State Key Laboratory of Digital Manufacturing Equipments and Technology

Rotor active magnetic bearing(rotor-AMB) systems are frequently used to alleviate vibrations for various applications such as in national defense, manufacturing industries, IC production, and aerospace engineering. One obstacle to improve machining efficiency and quality is the open-loop instability of rotorAMB systems during the machining process. We built a closed-loop processing platform using a spindle rotor installed with AMBs and thereby developed a rotor-AMB suspension system embedded with a dual-mode predictive controller(DMPC). The performance of the system is thus substantially improved. In the proposed DMPC, both model-based prediction and receding horizon optimization are utilized to guarantee the closedloop stability of the rotor-AMB suspension systems with input constraints. Finally, the effectiveness and superiority of the proposed method are examined through substantial levitation experiments on a machining platform with installed AMBs.

关键词： dual-mode predictive control system identification optimization control model predictive control suspension control

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Spatio-Temporal Transformer-Based Reinforcement Learning for Robot Crowd Navigation

Spatio-Temporal Transformer-Based Reinforcement Learning for...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： He, Haodong Fu, Hao Wang, Qiang Zhou, Shuai Liu, Wei Chen, Yang Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430081 China Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China

ISBN: (纸本)9798350325706

Ensuring robots can move safely and adhere to social norms in dynamic human environments is a crucial step towards robot autonomous decision-making. In existing work, double serial separate modules are generally used to capture spatial and temporal interactions, respectively. However, such methods lead to extra difficulties in improving the utilization of spatio-temporal features and reducing the conservatism of navigation policy. In light of this, this paper proposes a spatiotemporal transformer-based policy optimization algorithm to more effectively preserve the human-robot interactions. Specifically, a gated embedding mechanism is introduced to effectively fuses the spatial and temporal representations by integrating both modalities at the feature level. Then Transformer is leveraged to encode the spatio-temporal semantic information, with the hope of finding the optimal navigation policy. Finally, a combination of spatio-temporal Transformer and self-adjusting policy entropy significantly reduce the conservatism of navigation policies. Experimental results demonstrate the priority of the proposed algorithm over the state-of-the-art methods. © 2023 IEEE.

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Software Defect Prediction Based on SMOTE-Tomek and XGBoost 16th

Software Defect Prediction Based on SMOTE-Tomek and XGBoost

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Yang, Haotian Li, Min School of Information Engineering Nanchang Institute of Technology Jiangxi330099 China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Jiangxi330099 China

ISBN: (纸本)9789811912528

The use of machine learning techniques to predict software defects has received extensive attention over the years. In practice, there are far fewer defective software samples than non-defective software samples, resulting in a high imbalance of sample categories. The problem of imbalanced data classification is a hotspot and difficulty in machine learning and data mining. In order to effectively solve the problem of software defect prediction, we introduce a classification model that combines SMOTE-Tomek sampling and ensemble learning algorithm XGBoost. Our initial experimental results on ten NASA software defect datasets indicate a clear superiority of the proposed techniques when compared to other hybrid model regarding various assessment metrics, such Accuracy, F-Measure, and AUC. © 2022, Springer Nature Singapore Pte Ltd.

关键词： NASA

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Drug-Protein Interaction Prediction by Fusion of Attention and Graph Neural Network

Drug-Protein Interaction Prediction by Fusion of Attention a...

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第42届中国控制会议

作者： Tianrui Chen Shouheng Tuo Zengyu Feng School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing

The identification of drug-protein interactions(DTIs) is a critical step in drug development and ***,detecting these interactions using scientific methods presents a formidable *** models often struggle to extract relevant information from drug-protein sequences,and they fail to consider pre-existing interactions between ***,we introduce a deep learning model,DPAG,based on attention mechanisms and graph neural networks,which overcomes these *** leverages information from both the drug and protein sequences,as well as the association between drug-protein pairs,to predict *** conducted multiple experiments to evaluate the performance of the model in benchmark datasets,and the results showed that DPAG achieved excellent performance in terms of AUC,AUPR,precision,and *** findings suggest that DPAG holds significant potential as a promising tool for predicting DTIs in drug development and *** is available at https://***/veres se/DPAG.

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Feature Selection Algorithm Based on Discernibility Matrix and Fruit Fly Optimization 16th

Feature Selection Algorithm Based on Discernibility Matrix a...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Ao, Jiaxin Ye, Jun Ji, Yuxuan Yang, Zhenyu College of Information Engineering Nanchang Institute of Technology Nanchang330000 China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang330000 China

ISBN: (纸本)9789811912559

Feature selection methods that combine swarm intelligence optimization algorithms with rough sets have been widely used. Normally, this type of method can quickly search for feature subsets, effectively reducing the computational workload;However, in some decision tables of this type of method, there were also problems such as the searched feature subset containing redundant features, which led to a decrease in accuracy. Aiming at such problems in the current feature selection algorithms based on rough sets and fruit fly optimization, improvements have been made from two aspects. Firstly, a method for measuring the importance of discernibility matrix attributes was proposed and used as a heuristic factor to constructed a new fitness function;Secondly, guided by the fitness function, a fruit fly optimized feature selection method was designed, which can avoid redundant attributes in the feature subset. Examples and experimental results on the UCI datasets show that the new feature selection method can effectively search for the smallest feature subset and improve the stability. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Fruits

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Boosting Piezoelectric Output Performance of F@Bt/Pvdf Flexible Piezoelectric Nanogenerators Via Incorporation of Mxene

SSRN

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SSRN 2025年

作者： Wang, Shiwen Wu, Minghua Feng, Jie Feng, Mao Zheng, Zhirong Sun, Li Xu, Chenglong Textile college Zhejiang Fashion Institute of Technology Zhejiang Ningbo315211 China Ningbo Key Laboratory of Textile and Garment Intelligent Manufacturing Technology Textile college Zhejiang Fashion Institute of Technology Zhejiang Ningbo315211 China Key Laboratory of Advanced Textile Materials and Manufacturing Technology Ministry of Education College of Textile Science and Engineering Zhejiang Sci-Tech University Zhejiang Hangzhou310018 China Ningbo Runhe High-Tech Materials Co. Ltd. Zhejiang Ningbo315211 China

High output efficiency of wearable piezoelectric nanogenerators (PENG) can be achieved by improving the piezoelectric performance of composite nanofiber films. In this paper, a 2D conductive carbon material, MXene, was added to the F@BT/PVDF nanofiber-based film to obtain MXene/F@BT/PVDF PENG. MXene possess good electrical conductivity that can enhance the local electric field of F@BT/PVDF composite nanofiber film during the electrospinning process. This leads to higher polarization of the nanofiber films and enhanced piezoelectric properties of the MXene/F@BT/PVDF composite nanofiber film. The β phase content and the d33 value of the prepared MXene/F@BT/PVDF composite nanofiber film reached up to 95% and 48.7 ± 3 pc/N, respectively. In addition, the output voltage of the MXene/F@BT/PVDF PENG (MXene with 1.5 wt%) was measured to be 4.9 V, which was about 2 times higher than that of the F@BT/PVDF PENG. The study confirmed that the improvement was closely related to the content of MXene. Furthermore, the 2D MXene was compared with conductive carbon materials of other dimensions (0D carbon black and 1D CNTs), which revealed that the MXene/F@BT/PVDF PENG demonstrated the highest piezoelectric output performance. These results indicated that the addition of MXene improved the piezoelectric performance of the MXene/F@BT/PVDF PENG. Thus, a PENG exhibiting high piezoelectric output has been developed in this work through the synergistic enhancement effect of F@BT nanoparticles and 2D MXene. The findings of this study can prove to be extremely useful in various applications, such as wrist bending, respiratory vibration and knee, shoe at the stamp. © 2025, The Authors. All rights reserved.

关键词： Piezoelectricity

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