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Potassium ion doped manganese oxide nanoscrolls enhanced the performance of aqueous zinc-ion batteries

Chinese Chemical Letters 2025年第1期36卷 549-555页

作者： Yang Li Xiaoxu Liu Tianyi Ji Man Zhang Xueru Yan Mengjie Yao Dawei Sheng Shaodong Li Peipei Ren Zexiang Shen Shaanxi Key Laboratory of Green Preparation and Functionalization for Inorganic Materials School of Material Science and EngineeringShaanxi University of Science and TechnologyXi’an 710021China Division of Physics and Applied Physics School of Physical and Mathematical SciencesNanyang Technological UniversitySingapore 637371Singapore

α-MnO_(2) is a potential positive electrode material for aqueous zinc-ion batteries,but its electrochemical performance of zinc storage requires further *** this paper,potassium ion-doped manganese dioxide nanoscrolls(K-MnO_(2))with oxygen vacancy were synthesized by a one-step hydrothermal *** was observed that the electrochemical specific capacity was 250.9 m Ah/g at a current density of 0.2 C,which was better than the existing commercialα-MnO_(2).At a high current of 1 C,these batteries demonstrate improved cycle *** radiation and other experiments as well as DFT theoretical calculations provided additional evidence that K doping was efficient in regulating the metal bond type and the mean charge regulation of covalent bonds with oxygen atoms in MnO_(2).When Mn-O and Mn-K bonds are present,K-MnO_(2) showed outstanding adsorption of Zn~(2+)and further enhanced the Zn^(2+)embedding ***,oxygen defects caused by doping boosted the development of the nanoscroll structure,leading to an increase in active sites available for electrochemical reactions and subsequently enhancing the electrical conductivity ofα-MnO_(2).This study exhibits the potential of optimizing materials based on manganese with the introduction of a potassium doping strategy,resulting in improved performance for aquatic zinc-ion batteries,and presents novel perspectives for related research.

关键词： Manganese dioxide K^(+) doping Nanoscrolls XAFS Aqueous zinc-ion batteries

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SMS Spam Detection using Advance Naive-Bayes Approach

SMS Spam Detection using Advance Naive-Bayes Approach

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arora, Yojna Gupta, Neha Rathore, Yogesh Singh Bansal, Nidhi Mehta, Aina Chadha, Shikha Kumar, Ashwani Sharda University Department of Computer Science and Engineering Greater Noida India Symbiosis University of Applied Sciences School of Computer Science & Information Technology Madhya Pradesh Indore India Sharda University School of Engineering and Technology Department of Computer Science and Applications Uttar Pradesh Greater Noida India School of Engineering and Technology Department of Computer Science & Engineering Haryana Faridabad India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331508685

Recently, with increased use of mobile phones, it has transformed into a multibillion-dollar Short Message Service or SMS. However, the drop in the cost of messaging services has led to an increased number of unsolicited commercial messages, or spam, being delivered to mobile phones. Several reasons exist for which traditional algorithms used in email filtering may not efficiently classify the spam in SMS such as the absence of strong SMS spam databases, with the short length and features of the message, the informal language used, etc. This paper uses a real SMS spam database After preprocessing the data and extraction of relevant features, Naïve Bayes machine learning techniques is applied to get the results. Then it is analyzed using confusion matrix, accuracy, precision and recall values. A learning curve with clearly defined relationship between the training and testing data set and overall error at the end. © 2025 IEEE.

关键词： Adversarial machine learning

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Zero-dynamics attack detection based on data association in feedback pathway

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Cognitive Robotics 2025年 5卷 126-139页

作者： Zhang, Zeyu Li, Hongran Todo, Yuki Division of Electrical Engineering and Computer Science Kanazawa University Ishikawa Kanazawa9201192 Japan School of Computer Engineering Jiangsu Ocean University Jiangsu Lianyungang222005 China Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa9201192 Japan

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results. © 2025

关键词： Cyber attacks

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Flexible Thin-Film Transistor and Integration Strategies for Future Intelligent Displays

IEEE Open Journal on Immersive Displays

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IEEE Open Journal on Immersive Displays 2024年 1卷 187-203页

作者： Zhang, Yuqing Wang, Xu Zhang, Sixin Wang, Chunxiu Zhang, Min Peking University School of Electronic and Computer Engineering Shenzhen518055 China Hong Kong Applied Science and Technology Research Institute Hong Kong

Future flexible intelligent displays are driving demands for greater flexibility, higher resolution, faster refresh rates, and enhanced functionality. This review mainly focuses on recent advances and ongoing challenges in flexible thin-film transistors (TFTs) based on low-temperature polycrystalline silicon (LTPS), amorphous oxide semiconductor (AOS), and low-temperature polysilicon and oxide (LTPO) for flexible backplane technologies. Furthermore, the developments of process and function integration techniques, including device scaling, device stacking, as well as functional circuits and applications, are discussed from the perspective of innovation and significance. Besides, the integration of intelligent systems is briefly introduced, emphasizing the role of synaptic transistors, including metal oxide and carbon nanotube-based ones, in real-time data processing and sensing functions. By providing insights into the status and future directions, this review provides a comprehensive perspective and discussion on the development of next-generation flexible intelligent display technologies. © 2024 IEEE.

关键词： Thin film transistors

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An Adaptive on Demand Modified Ant Colony Optimization Routing for VANETs 23th

An Adaptive on Demand Modified Ant Colony Optimization Routi...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Kumari, J. Jesy Janet Thangam, S. Raja, A. Saleem Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering University of Technology and Applied Science-Shinas Shinas Oman

ISBN: (纸本)9783031648496

Around the world, vehicle accidents claim many priceless lives. To tackle this problem, the Vehicular ad hoc networks (VANETs) are the most effective solution. Effective communication between vehicular nodes in VANETs is extremely important because of the rapid mobility of vehicles and different network topology. Currently, VANET mostly serves the public security, stability, information for voyageurs, and traffic Management, control of congestion, guidance, etc. Chaos on roadways can occur at any moment, anyplace. It is a situation in which the flow of traffic regulations might cease to function. Collisions, maintaining roads, violation of legal rules, etc., so to evade the congestion in the surrounding region. VANETs represent a dynamic and challenging communication environment characterized by high mobility, intermittent connectivity, and rapid changes in network topology. Efficient and reliable routing is crucial for enabling diverse applications such as traffic management, safety communication, and infotainment services in VANETs. In this research, propose a multi-faceted approach to improve routing performance in VANETs. An adaptive routing algorithm that considers vehicle mobility patterns and dynamically adjusts communication routes based on the anticipated movement of neighboring vehicles such as Modified Ant Colony Optimization (MACO) technique. This adaptability enhances the robustness of the network against frequent topology changes and improves the overall packet delivery ratio, end-to-end delay, and network reliability compared to traditional routing protocols. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Vehicular ad hoc networks

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Clisp: A Robust Interactive Segmentation Framework for Pathological Images 46

Clisp: A Robust Interactive Segmentation Framework for Patho...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Hannan Shi, Jun Zhao, Minfan Qiao, Liang Wang, Zhaohui Dai, Yue Ma, Yuchen An, Hong University of Science and Technology of China School of Computer Science and Technology Hefei China University of Virginia School of Engineering & Applied Science Charlottesville United States

ISBN: (纸本)9798350371499

Recently, automatic segmentation algorithms based on deep learning have achieved promising results on various pathological image segmentation tasks. However, the inherent data-hungry nature of these methods and the high cost of annotating pathological images make them difficult to apply to practical clinical tasks. To address this problem, many studies have explored interactive segmentation methods to reduce the cost of annotating pathological images, which replace pixel-by-pixel annotation operations with clicks or other user instructions. Despite their effectiveness, most of these methods only utilize small-scale data resulting in poor generalization ability for different tasks or segmentation objects. In this paper, we propose a robust Click-based Interactive Segmentation framework for low-cost and interactive annotation of Pathological images, named Clisp. To improve the generalization ability, we construct a multi-source pathological image annotation dataset including around 79,000 images for supervised training of the proposed method. Besides, we adopt a heavy-parameter vision transformer as the image encoder to learn feature representations for large-scale data. We evaluate the proposed method on 7 open-source datasets, and the experimental results show that Clisp is superior to the baseline method in segmentation performance on most datasets and has outstanding generalization ability. © 2024 IEEE.

关键词： Image segmentation

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Multi-strategy Hybrid Coati Optimizer:A Case Study of Prediction of Average Daily Electricity Consumption in China

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Journal of Bionic engineering 2024年第5期21卷 2540-2568页

作者： Gang Hu Sa Wang Essam H.Houssein Department of Applied Mathematics Xi’an University of TechnologyXi’an710054China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China Faculty of Computers and Information Minia UniversityMinia61519Egypt

The power sector is an important factor in ensuring the development of the national *** simulation and prediction of power consumption help achieve the balance between power generation and power *** this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity ***,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning *** improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the ***,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test ***,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting *** experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.

关键词： Forecast of average daily electricity consumption in China Coati optimization algorithm Three-parameter combination optimization model Circle chaotic mapping

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Resource Allocation in Edge Computing Environment Using Deterministic Policy Gradient Algorithm 10

Resource Allocation in Edge Computing Environment Using Dete...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Shabariram, C.P. Shanthi, N. Ponnuswamy, P. Priya Subramaniyaswamy, V. Sathana, V. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India School of Computing Sastra University Department of Computer Science and Engineering Thanjavur India

ISBN: (纸本)9798350384369

In recent years, the edge computing paradigm enables the movement of processing units and storage nearer to the data available locations. The mechanism completes the computation in a short span of time in minimum bandwidth. Edge ecosystem is a type of distributed computing that is sensitive to topology and geography;the Internet of Things is a prime instance of this. Rather than referring to a single technology, Edge computing refers to an architecture. This paper proposes a resource allocation methodology that will enliven the situation between users and edge servers. By creating continuous control at the edge servers to determine resource allocation, edge computing improves reaction time, provides high security with decreased risk, scalability, lowers transmission costs, and versatility (offload targets, migration bandwidth and computing resources). The Deterministic Policy Gradient, Deep learning and Quality Network concepts are combined in the proposed system. The continuous action space is achieved by a deterministic policy gradient. The experience relay includes a quality network. In the proposed system, the actor-critic network produces a single continuous action instead of resulting probability based actions. The critic-part uses Q-value from a quality network based on current status and activity. The goal of the proposed system is to develop a Deep Deterministic Policy Gradient methodology to allocate servers for mobile users with the help of the Edge computing while taking computation resources, offloading goals and migration bandwidth into consideration. The simulation result indicates that deterministic policy gradients integrated deep learning models improve the system performance compared with Game theory. © 2024 IEEE.

关键词： Mobile edge computing

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Athletic signature: predicting the next game lineup in collegiate basketball

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Neural Computing and Applications 2024年第34期36卷 21761-21780页

作者： Sharma, Srishti Divakaran, Srikrishnan Kaya, Tolga Raval, Mehul School of Engineering and Applied Sciences Ahmedabad University Gujarat Ahmedabad India School of Interwoven Arts and Sciences Krea University Andhra Pradesh Sri City India School of Computer Science and Engineering Sacred Heart University FairfieldCT United States

The advances in machine learning (ML) tools and techniques have enabled the non-intrusive collection and rapid analysis of massive amounts of data involving athletes in competitive collegiate sports. It has facilitated the development of services that a coach can employ in analyzing these data into actionable insights in designing training schedules and effective strategies for maximizing an athlete’s performance, while minimizing injury risk. Collegiate sports utilize data to get a competitive advantage. While game statistics are publicly available, relying on more than one form of data can help reveal a pattern. We developed a framework that considers various modalities and creates an athletic signature to predict their future performance. Our research involves the study of 42 distinct features that quantify various internal/external stressors the athletes face to characterize and estimate their athletic readiness (in the form of reactive strength index modified—RSImod) using ML algorithms. Our study, conducted over 26 weeks with 17 collegiate women’s basketball athletes, developed a framework that first performed sensitivity analysis using a hybrid approach combining the strengths of various filter-based, wrapper-based, and embedded feature importance techniques to identify the features most significantly impacting athlete readiness. These features were then categorized into four moderating variables (MVs, i.e. factors): sleep, cardiac rhythm, training strain, and travel schedule. Further, we used factor analysis to enhance interpretability and reduce computational complexity. A hybrid boosted-decision-trees-based model designed based on athlete clusters predicted future athletic readiness based on MVs with a mean squared error (MSE) of 0.0102. Partial dependence plots (PDPs) helped qualitatively learn the relationship between the moderating variables and the RSImod score. Athletic signatures, uniquely defining athlete-specific MV patterns, account for intra-indi

关键词： Mean square error

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Dynamic Quantum Federated Learning for UAV-Based Autonomous Surveillance

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8158-8170页

作者： Park, Soohyun Son, Seok Bin Jung, Soyi Kim, Joongheon Sookmyung Women's University Division of Computer Science Seoul04310 Korea Republic of Korea University School of Electrical Engineering Seoul02841 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

In recent years, unmanned aerial vehicles (UAVs) have proven their effectiveness in surveillance due to their superior mobility. By utilizing multiple UAVs with collaborated learning, surveillance of a huge area while consuming minimum resources is possible. However, the performance of UAV collaborative learning systems is still severely limited because the number of parameters in a classical NN is generally very large, which is unsuitable for unstable wireless communication. To address this issue, this paper uses a quantum neural network (QNN), which has large computational capabilities and uses fewer parameters to overcome the problems caused by many parameters. However, it is still difficult to send all the parameters of the QNN to the server under poor channel conditions. Therefore, this paper proposes dynamic quantum federated learning (DQFL), a novel framework designed for UAVs employing quantum computing (QC) and federated learning (FL). The proposed DQFL uses a dynamic quantum neural network (DQNN) with a multi-depth circuit and employs dynamic control of the circuit layer to improve the efficiency of local parameter transmission between UAVs in unstable wireless communication environments. Extensive simulations conducted under real-world autonomous surveillance conditions demonstrate the robustness of DQFL to non-iidness, varying signal-to-noise ratios (SNRs), and poor communication channel conditions in the UAV environment. © 1967-2012 IEEE.

关键词： Federated learning

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