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Pseudocapacitive Heteroatom-Doped Carbon Cathode for Aluminum-Ion Batteries with Ultrahigh Reversible Stability

Energy & Environmental Materials 2024年第5期7卷 150-159页

作者： Jiahui Li Jehad KEl-Demellawi Guan Sheng Jonas Björk Fanshuai Zeng Jie Zhou Xiaxia Liao Junwei Wu Johanna Rosen Xingjun Liu Husam N.Alshareef Shaobo Tu School of Physics and Materials Science Nanchang University999 Xuefu RoadHonggutan DistrictNanchang 330031China School of Materials Science and Engineering and Institute of Materials Genome and Big DataHarbin Institute of TechnologyShenzhen 518055China KAUST Upstream Research Center(KURC) EXPEC-ARCSaudi AramcoThuwal 23955Saudi Arabia Materials Design Division Department of PhysicsChemistry and Biology(IFM)Linköping UniversitySE-58183LinköpingSweden Physical Science and Engineering(PSE)Division King Abdullah University of Science and TechnologyThuwal 23955Saudi Arabia

Aluminum(Al)-ion batteries have emerged as a potential alternative to conventional ion batteries that rely on less abundant and costly materials like ***,given the nascent stage of advancement in Al-ion batteries(AIBs),attaining electrode materials that can leverage both intercalation capacity and structural stability remains ***,we demonstrate a C3N4-derived layered N,S heteroatom-doped carbon,obtained at different pyrolysis temperatures,as a cathode material for AIBs,encompassing the diffusion-controlled intercalation and surface-induced capacity with ultrahigh *** developed layered N,S-doped corbon(N,S-C)cathode,synthesized at 900℃,delivers a specific capacity of 330 mAhg^(-1)with a relatively high coulombic efficiency of~85%after 500 cycles under a current density of 0.5 A g^(-1).Owing to its reinforced adsorption capability and enlarged interlayer spacing by doping N and S heteroatoms,the N,S-C900 cathode demonstrates outstanding energy storage capacity with excellent rate performance(61 mAhg^(-1)at 20 A g^(-1))and ultrahigh reversibility(90 mAhg^(-1)at 5Ag^(-1)after 10000cycles).

关键词： 2D carbon adsorption energy heteroatoms-doping high capacity long cycling life

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Recent Advances and Future Scopes of Multi-Linear Polarization Reconfigurable Antennas 15

Recent Advances and Future Scopes of Multi-Linear Polarizati...

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15th International Conference on Microwave and Millimeter Wave technology, ICMMT 2023

作者： Chen, Shu-Lin Qin, Pei-Yuan Liu, Yanhui Lin, Wei University of Technology Sydney Global Big Data Technologies Centre Sydney Australia University of Electronic Science and Technology of China School of Electronic Science and Engineering China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong

ISBN: (纸本)9798350338874

Reconfigurable antennas are attractive for dynamic changing electromagnetic environments. Multi-linear polarization (multi-LP) antennas are particularly desired for polarization matching in LP wireless systems. In this work, we discussed our recent advances on the multi-LP antennas, including their ability to facilitate millimeter-wave (mm-wave) operation and enable flexible selection of LP number. In addition, we further discuss their bottlenecks and future scopes of the multi-LP antenna for beyond 5G and 6G systems. © 2023 IEEE.

关键词： Millimeter waves

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Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

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Computers, Materials & Continua 2022年第5期71卷 2599-2616页

作者： Muhammad Ibrahim Faisal Jamil YunJung Lee DoHyeun Kim Big Data Research Center Department of Computer Science and StatisticsJeju National UniversityKoreaJeju-si63243Korea Department of Computer Engineering(and Advanced Technology Research Institute) Jeju National UniversityJeju 63243Korea

In recent times,the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security *** IoT has shown wide adoption in various applications including smart cities,healthcare,trade,business,*** these applications,fitness applications have been widely considered for smart fitness *** users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness ***,scheduling such a huge number of requests for fitness exercise is a big ***,the user fitness data is critical thus securing the user fitness data from unauthorized access is also *** overcome these issues,this work proposed a blockchain-based load-balanced task scheduling approach.A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling *** proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users’requests and improve resource *** performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection *** obtained results confirm the efficiency of the proposed scheduling *** investigating the performance of the blockchain,various experiments are performed using the Hyperledger Caliper concerning latency,throughput,resource *** Solo approach has shown an improvement of 32%and 26%in throughput as compared to Raft and Solo-Raft approaches *** obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.

关键词： Load balancing resource scheduling task scheduling Internet of things blockchain fitness applications

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Comparing the Performance of Individual and Hybrid Classifiers for Medical datasets

Comparing the Performance of Individual and Hybrid Classifie...

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2022 International Conference on data Analytics for Business and Industry, ICDABI 2022

作者： Alkhayyat, Aysha Musaaed, Elham Alheddi, Isa Hewahi, Nabil University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Information Technology Department of Computer Science Bahrain

ISBN: (纸本)9781665490580

An accurate prediction of a certain medical condition is a crucial matter that is of great benefit to patients and healthcare systems. Machine Learning can be of a great help to the medical professionals in assisting them to precisely predict the occurrence of medical conditions. This research paper aims to enhance the prediction capabilities of machine learning models, so it become more adopted in the medical field. To fill in this gap, the capabilities of the ensemble hybrid model were explored in comparison to individual models. The machine learning algorithms were tested using five medical datasets pertaining to five different diseases. The Random Forest model was the best performing individual model. In terms of accuracy, the ensemble hybrid model performance was close to the individual models in all datasets, except for the Liver Disease dataset where it exceled. This research could be extended to experiment the integrated hybrid models along with the optimization techniques. © 2022 IEEE.

关键词： Logistic regression

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Cross Direction Attention Network for Facial Expression Recognition 11

Cross Direction Attention Network for Facial Expression Reco...

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11th International Conference on Information Systems and Computing technology, ISCTech 2023

作者： Lin, Likang Peng, Cheng Zhang, Bowen Zou, Kun Huang, Peijie South China Agricultural University College of Mathematics and Informatics GuangZhou China University of Electronic Science and Technology of China Zhongshan Institute School of Computer Science Zhongshan China Shenzhen Technology University College of Big Data and Internet Shenzhen China

ISBN: (纸本)9798350342406

Facial expression recognition is an area of growing interest in computer vision research. Achieving a balance between learning subtle facial features and reducing memory consumption presents a persistent challenge, resulting in hardly to deploy model on portable mobile devices restricted by hardware resources. To overcome this issue, we propose a spatial attention module on lightweight backbone, named the cross-direction attention network (CDAN). The proposed model achieves enhanced fine-grained feature learning and used less memory consumption through cross-learning in both vertical and horizontal directions without complicated artificial feature technology. This study proposes a novel spatial attention model, which contributes an innovative approach for future research in this field. Based on the experimental outcomes, it can be concluded that the proposed model achieves state-of-the-art performance on the AffectNet dataset. © 2023 IEEE.

关键词： Face recognition

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Towards sustainable adversarial training with successive perturbation generation

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Frontiers of Information technology & Electronic Engineering 2024年第4期25卷 527-539页

作者： Wei LIN Lichuan LIAO College of Computer Science and Mathematics Fujian University of TechnologyFuzhou 350118China College of Economics and Management Xi’an University of TechnologyXi’an 710048China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou 350118China

Adversarial training with online-generated adversarial examples has achieved promising performance in defending adversarial attacks and improving robustness of convolutional neural network ***,most existing adversarial training methods are dedicated to finding strong adversarial examples for forcing the model to learn the adversarial data distribution,which inevitably imposes a large computational overhead and results in a decrease in the generalization performance on clean *** this paper,we show that progressively enhancing the adversarial strength of adversarial examples across training epochs can effectively improve the model robustness,and appropriate model shifting can preserve the generalization performance of models in conjunction with negligible computational *** this end,we propose a successive perturbation generation scheme for adversarial training(SPGAT),which progressively strengthens the adversarial examples by adding the perturbations on adversarial examples transferred from the previous epoch and shifts models across the epochs to improve the efficiency of adversarial *** proposed SPGAT is both efficient and effective;e.g.,the computation time of our method is 900 min as against the 4100 min duration observed in the case of standard adversarial training,and the performance boost is more than 7%and 3%in terms of adversarial accuracy and clean accuracy,*** extensively evaluate the SPGAT on various datasets,including small-scale MNIST,middle-scale CIFAR-10,and large-scale *** experimental results show that our method is more efficient while performing favorably against state-of-the-art methods.

关键词： Adversarial training Adversarial attack Stochastic weight average Machine learning Model generalization

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Classification of Water Potability Using Machine Learning Algorithms

Classification of Water Potability Using Machine Learning Al...

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2022 International Conference on data Analytics for Business and Industry, ICDABI 2022

作者： Yusuf, Husain Alhaddad, Salman Yusuf, Salman Hewahi, Nabil University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Information Technology Department of Computer Science Bahrain

ISBN: (纸本)9781665490580

Water is the most important resource for sustaining life. Everyone has the right to have access to pollution-free water. The achievement of safe drinking water leads to tangible benefits to health. According to the World Health organization (WHO), the number of global deaths due to water diseases is about two and a half million deaths per year. Therefore, it is important to study new applications for analyzing and classifying safe water quality. In this research, the classification algorithms of K Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM) are used to classify the potability of the drinking water. RF and DT achieved the highest performance with an accuracy of 83.78% and 74.98% respectively. The lowest classifier accuracy is LR with 48.74%. In addition, the most important features of the RF model are pH, followed by Hardness, followed by Sulfate. On other hand, the least important feature is Turbidity. © 2022 IEEE.

关键词： Neural networks

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Navigating the Complexities of Municipal Waste Management: Enhancing Cost Prediction for Sustainable Urban Solutions 1st

Navigating the Complexities of Municipal Waste Management: E...

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1st International Research Conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Minu, M.S. Samal, Tushar Bisani, Khushi Tewari, Aishwarya Department of Computer Science and Engineering with Specialisation in Big Data Analytics SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (纸本)9783031823824

The escalating rates of municipal waste generation in urban areas worldwide present a critical challenge for effective waste management. This paper examines the complexities surrounding municipal waste management, emphasizing the significance of accurate cost prediction for sustainable decision-making. Urbanization, driven by population growth and rising standards of living, intensifies waste generation, while shifts in consumption patterns further complicate waste management efforts. Environmental concerns and regulatory pressures mandate innovative waste reduction strategies. Accurate cost prediction is paramount for budgetary planning and resource allocation, enabling proactive measures to optimize operational efficiency and mitigate financial risks. Emerging methodologies, including data analytics and predictive modeling, offer promising avenues for enhancing cost prediction accuracy by capturing the multifaceted drivers of cost variability. By leveraging these methodologies, decision-makers can forecast municipal waste management costs with precision, informing strategic initiatives to foster sustainable waste management practices in urban areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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How to Mitigate Information Loss in Knowledge Graphs for GraphRAG: Leveraging Triple Context Restoration and Query-Driven Feedback

arXiv

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arXiv 2025年

作者： Huang, Manzong Bu, Chenyang He, Yi Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data [Hefei University of Technology Ministry of Education China School of Data Science William & Mary WilliamsburgVA United States

Knowledge Graph (KG)-augmented Large Language Models (LLMs) have recently propelled significant advances in complex reasoning tasks, thanks to their broad domain knowledge and contextual awareness. Unfortunately, current methods often assume KGs to be complete, which is impractical given the inherent limitations of KG construction and the potential loss of contextual cues when converting unstructured text into entity-relation triples. In response, this paper proposes the Triple Context Restoration and Query-driven Feedback (TCR-QF) framework, which reconstructs the textual context underlying each triple to mitigate information loss, while dynamically refining the KG structure by iteratively incorporating query-relevant missing knowledge. Experiments on five benchmark question-answering datasets substantiate the effectiveness of TCR-QF in KG and LLM integration, where it achieves a 29.1% improvement in Exact Match and a 15.5% improvement in F1 over its state-of-the-art GraphRAG competitors. © 2025, CC BY.

关键词： Knowledge graph

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