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On the Performance of Active RIS-Assisted Mixed RF-THz Relaying Systems

IEEE Internet of Things Journal 2025年第10期12卷 14938-14951页

作者： Yin, Yiyang Yang, Liang Li, Xingwang Liu, Hongwu Guo, Kefeng Li, Yingsong Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Jishou University School of Communication and Electronic Engineering Jishou416000 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Shandong Jiaotong University School of Information Science and Electrical Engineering Jinan250357 China Space Engineering University School of Space Information Beijing101407 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

We investigate the performance of an active reconfigurable intelligent surface (RIS)-assisted mixed radio frequency (RF)-terahertz (THz) relaying system, where the RF signal reaches the relay through the active RIS and is then transmitted to the user via the THz channel. Under this scenario, we analyze the system performance with the relay employing amplify-and-forward (AF) and decode-and-forward (DF) protocols. More specifically, we derive the exact expressions for the cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) for both relaying protocols. Based on this, we obtain the exact expressions for the outage probability (OP), average bit error rate (ABER), and average channel capacity (ACC). Furthermore, to gain deeper insights, we derive the asymptotic expressions at high SNRs and obtain diversity order (DO) of the system. Moreover, we extend the analysis to the variable gain relaying scheme. The findings reveal that the DOs for both relaying protocols are determined by the THz channel parameters, and the DO under the AF relaying protocol is twice that of the DF relaying protocol. Finally, we validate that active RIS (A-RIS) can more effectively assist the performance of the mixed RF-THz relaying system compared to passive RIS. © 2014 IEEE.

关键词： Distribution functions

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Secrecy Rate Maximization for STAR-RIS-Assisted MIMO Cognitive Radio Networks

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IEEE Wireless Communications Letters 2025年第5期14卷 1281-1285页

作者： Huang, Zihao Lei, Xianfu Shen, Meng Wu, Mingjiang Li, Xingwang Karagiannidis, George K. Southwest Jiaotong University School of Information Science and Technology Chengdu610031 China Chengdu Technological University School of Network and Communication Engineering Chengdu611730 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki541 24 Greece

In this letter, we study secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multiple-input multiple-output cognitive radio network, where a secondary transmitter (ST) sends confidential information to a secondary user while sharing the same frequency band with a primary user in the presence of an eavesdropper. We aim to maximize the secrecy rate of the secondary system by jointly optimizing the transmit precoding matrices at the ST, along with the transmission and reflection coefficients of the STAR-RIS. An efficient alternating optimization algorithm based on the penalty method and the generalized S-procedure is proposed to solve the formulated non-convex problem. Simulation results show that the proposed scheme significantly outperforms the conventional RIS in improving the secrecy rate. © 2012 IEEE.

关键词： MIMO Optimization Interference Reflection Eavesdropping Cognitive radio Vectors Security Radio transmitters Precoding

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Monte Carlo modelling of Raman signal enhancement using power-efficient Apple processors 15

Monte Carlo modelling of Raman signal enhancement using powe...

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Biomedical Light Scattering XV 2025

作者： Vladyko, Ilya Yakovlev, Vladislav Doronin, Alexander School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Department of Biomedical Engineering Department of Electrical and Computer Engineering Department of Physics and Astronomy Institute for Quantum Science and Engineering Texas A&M University College StationTX77843 United States

ISBN: (纸本)9781510683884

Modern optical technologies encompass classical light phenomena and non-linear effects, crucial for biomedical imaging and therapies. Despite substantial interest and many experimental studies, non-linear optical effects in inhomogeneous scattering media require extensive computer modeling to understand light-material interactions. This report focuses on Monte Carlo simulations of Raman scattering in turbid media, such as biological tissues, essential for clinical applications. We developed a novel, open-source, power-efficient Monte Carlo algorithm optimized for Apple s M-series chips. By incorporating single-photon tracking, the algorithm accurately models time-resolved light-Tissue interactions. Validated against experiments, it matches traditional methods accuracy while offering superior efficiency, advancing Raman functional imaging. © 2025 SPIE.

关键词： Tissue

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MoreGCN: Distributed IoT Service Recommendation Considering Temporal User Interest Dynamics

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IEEE Internet of Things Journal 2025年第10期12卷 13657-13670页

作者： Zhou, Yu Hu, Chunqiang Liu, Zewei Xing, Xiaoshuang Li, Xingwang Chongqing University School of Big Data and Software Engineering Chongqing401331 China China Southern Power Grid Joint Laboratory on Cyberspace Security Guangdong510663 China Changshu Institute of Technology School of Computer Science and Engineering Suzhou215556 China Henan Polytechnic University College of Physics and Electronic Information Engineering Jiaozuo454000 China

With the continuous development of Internet of Things (IoT), significant value has been generated, but numerous challenges remain. Recommender systems, as an effective tool to optimize IoT services, can significantly enhance user experience. However, the IoT’s demands for low latency and high-computational load make it difficult for traditional recommender systems to adapt. Moreover, traditional approaches often overlook the dynamic nature of user preferences, which are crucial for determining user satisfaction with IoT services. To address these issues, we propose a novel method called MoreGCN, which quantifies the temporal evolution of user preferences and integrates user interest modeling to accurately match similar users. This approach guides the learning of convolutional networks during the recommendation process. Deployed within a distributed computing framework and combined with meta computing, MoreGCN significantly improves computational efficiency and recommendation accuracy. Experimental results demonstrate that, across three benchmark datasets, MoreGCN consistently outperforms several existing state-of-the-art methods in terms of performance. © 2014 IEEE.

关键词： Internet of Things Recommender systems Computational modeling Accuracy Load modeling Edge computing Feature extraction Collaborative filtering Vectors Servers

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Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

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computers, Materials & Continua 2025年第2期82卷 2495-2520页

作者： Jeng-Shyang Pan Na Yu Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan College of Electronics Communication and PhysicsShandong University of Science and TechnologyQingdao266590China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.

关键词： Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy

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A High-Precision Generality Method for Chinese Nested Named Entity Recognition 18th

A High-Precision Generality Method for Chinese Nested Named...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Ji, Xiayan Chen, Lina Gao, Hong Shen, Fangyao Guo, Hongjie College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua China School of Computer Science and Technology Zhejiang Normal University Jinhua China

ISBN: (纸本)9783031714696

Chinese Named Entity Recognition (CNNER) faces numerous challenges, including the diversity of the Chinese language, the complex representation of mixed Chinese and English characters and symbols in texts, the complexity of the Chinese language itself with long sentences containing multiple entities, and the uneven distribution of named entity classes in actual Chinese scenarios. To address these challenges, we propose a method called CPMFA (Character Pair-based method with Multi-feature representation and Attention mechanism). The CPMFA method predicts predetermined relations between character pairs, facilitating the identification of nested named entities based on these relations. Firstly, the method leverages the pre-trained language model LERT (Linguistically-motivated Bidirectional Encoder Representation from Transformer) and BiLSTM (Bidirectional Long Short-Term Memory) to generate comprehensive and accurate character embeddings. Secondly, it incorporates multi-feature representation to capture complex semantic information and introduces the Pyramid Squeeze Attention (PSA) module to emphasize key features. Finally, the PolyLoss function is integrated into the model training process to tackle the challenge of an imbalanced distribution of entity classes. We employed the DiaKG, Yidu-S4K and Weibo datasets to validate and evaluate the efficacy and adaptability of our method. The F1 obtained by the CPMFA on these three datasets is 83.79%, 72.03%, and 70.39%, in that order. The experimental results illustrate the outstanding performance of the proposed CPMFA method in both general knowledge and Chinese medical domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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An electron-transfer-tuning strategy at the graphene/metal interface for improving acidic water electrolysis in proton exchange membrane electrolyzers

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Journal of Energy Chemistry 2025年第4期103卷 344-352页

作者： Yue Xu Shuaidong Li Yingjian He Fumiya Shiokawa Samuel Jeong Aimi Asilah Haji Tajuddin Zeyu Xi Yoshikazu Ito Jingzi Zhang Zeyun Cai Xi Lin Kailong Hu School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Special Environments Physical Sciences Harbin Institute of TechnologyShenzhen 518055GuangdongChina School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Applied Physics Graduate School of Pure and Applied SciencesUniversity of TsukubaTsukuba 305-8573Japan

Graphene encapsulation has been shown to be an effective technique for improving the corrosion resistance of non-noble metal catalysts for the acidic water *** key challenge lies in enhancing the electrocatalytic activity of graphene-encapsulated metals while maintaining their durability in acidic ***,an electron-transfer-tuning strategy is investigated at the graphene/NiMo interface,aiming to improve the hydrogen evolution reaction(HER)performance of graphene-encapsulated NiMo *** doping of Ti,a low electronegativity element,into NiMo substrate was confirmed to increase electron transfer from the metal core toward the *** electron-rich state on graphene facilitates the adsorption of positively charged protons on graphene,thereby enabling a Pt/C-comparable performance in 0.5 M H_(2)SO_(4),with only a 3.8%degradation in performance over a 120-h continuous *** proton exchange membrane(PEM)water electrolyzer assembled by the N-doped grapheneencapsulated Ti-doped NiMo exhibits a smaller cell voltage to achieve a current density of 2.0 A cm^(-2),in comparison to the Pt/C based *** study proposes a novel electron-transfer-tuning strategy to improve the HER activity of graphene-encapsulated non-noble metal catalysts without sacrificing durability in acidic electrolytes.

关键词： Hydrogen evolution Acidic media Graphene encapsulation Interface Proton exchange membrane

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Deep learning-based compressed sampling reconstruction algorithm for digitizing intensive neutron ToF signals

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Nuclear science and Techniques 2025年第7期36卷 1-13页

作者： Deng, Chao Wang, Shu-Jun Hu, Qin Tang, Ying-Hong Li, Peng-Cheng Xie, Bo Yang, Jian-Bo Tuo, Xian-Guo Wang, Qi-Biao School of Physics and Electronic Engineering Sichuan University of Science and Engineering Zigong643000 China School of Computer Science and Engineering Sichuan University of Science and Engineering Zigong643000 China

Neutron time-of-flight (ToF) measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity, but requires precise acquisition of the neutron signal arrival time. However, the high hardware costs and data burden associated with the acquisition of neutron ToF signals pose significant challenges. Higher sampling rates increase the data volume, data processing, and storage hardware costs. Compressed sampling can address these challenges, but it faces issues regarding optimal sampling efficiency and high-quality reconstructed signals. This paper proposes a revolutionary deep learning-based compressed sampling (DL-CS) algorithm for reconstructing neutron ToF signals that outperform traditional compressed sampling methods. This approach comprises four modules: random projection, rising dimensions, initial reconstruction, and final reconstruction. Initially, the technique adaptively compresses neutron ToF signals sequentially using three convolutional layers, replacing random measurement matrices in traditional compressed sampling theory. Subsequently, the signals are reconstructed using a modified inception module, long short-term memory, and self-attention. The performance of this deep compressed sampling method was quantified using the percentage root-mean-square difference, correlation coefficient, and reconstruction time. Experimental results showed that our proposed DL-CS approach can significantly enhance signal quality compared with other compressed sampling methods. This is evidenced by a percentage root-mean-square difference, correlation coefficient, and reconstruction time results of 5%, 0.9988, and 0.0108 s, respectively, obtained for sampling rates below 10% for the neutron ToF signal generated using an electron-beam-driven photoneutron source. The results showed that the proposed DL-CS approach significantly improves the signal quality compared with other compressed sampling methods, exhibiting excellent recons

关键词： Kinetic energy

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Experimental investigation on thermal and mechanical performance of alkali-activated coal gangue foam materials doped with phosphogypsum

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Archives of Civil and Mechanical engineering 2025年第3期25卷 1-27页

作者： Cheng, Guowei Zhang, Zujing Mao, Ruiyong Wu, Hongwei Liang, Xing Zhou, Jiri College of Civil Engineering Guizhou Provincial Key Laboratory of Rock and Soil Mechanics and Engineering Safety Guizhou University Guiyang550025 China School of Physics Engineering and Computer Science University of Hertfordshire HatfieldAL10 9 AB United Kingdom School of Computer Science and Mathematics Kingston University London Kingston upon ThamesKT1 2EE United Kingdom China MCC5 Group CORP.LTD. Chengdu China

Using low-grade solid wastes to prepare building foam insulation materials can effectively reduce environmental pollution and building energy consumption. However, their poor gelling activity results in poor mechanical and thermal insulation properties. In this study, alkali activated foam material was successfully prepared using coal gangue (CG) as the main component, with phosphogypsum (PG) added to improve activation and pore quality. An orthogonal experiment with six factors and five levels was designed to comprehensively explore the influence mechanism and optimal dosage of polypropylene fibers (PPF), expandable polystyrene (EPS) particles, and alkali activators. The results show that: (I) An appropriate amount of PG can effectively promote the reaction and assist in the formation of pores in the foam, which is attributed to the fact that PG can preferentially and rapidly dissolve and participate in the reaction to generate ettringite under strong alkaline environment. (II) An alkali activator with a modulus of 1.1 and a Na₂O content ranging from 10 to 12% optimizes both the mechanical and thermal insulation properties. It enables foam materials with a density of 536 kg/m3 to achieve a compressive strength of 3.68 MPa while maintaining a thermal conductivity of 0.1033 W/(m·K). (III) EPS particles and PPF further enhance compressive strength and thermal insulation by stabilizing the foam and maximizing PPF tensile properties utilization. These results indicate that the combination of EPS particles with PPF and PG can effectively improve the performance of alkali-activated solid waste foam materials. © Crown 2025.

关键词： Thermal insulation

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Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

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Soft Computing 2025年第3期29卷 1845-1877页

作者： Balaha, Hossam Magdy Hassan, Asmaa El-Sayed Ahmed, Rawan Ayman Balaha, Magdy Hassan Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computer Science and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Obstetrics and Gynecology Faculty of Medicine Tanta University Tanta Egypt

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder affecting millions worldwide. Early detection is vital for effective management, yet remains challenging. In this study, we investigated four distinct datasets for PD detection. Through comprehensive experimentation employing ensemble methods and feature selection, we achieved high classification accuracies across the datasets. For the Oxford Parkinson’s Disease Detection Dataset, an accuracy of 95.67%, precision of 97.59%, recall of 84.5%, specificity of 99.32%, and F1-score of 90.57% were achieved. For the Alzheimer Parkinson Diseases 3 Class Dataset, the "Stacking" approach surpasses individual models, reaching an accuracy of 99.85%, precision of 99.81%, recall of 99.81%, specificity of 99.86%, and F1 of 99.81%. For the NewHandPD dataset, Regarding the Spiral category, The "Base-P32-384" model surpasses others with an accuracy of 97.35%, precision of 96.50%, recall of 98.57%, and F1-score of 97.53%. The collective "Stacking" approach proves highly effective regarding the Circle category, achieving 100% across all performance metrics. Regarding the Meander category, the "Base-P16-224" model achieves an accuracy of 97.35%, precision of 99.26%, recall of 95.71%, specificity of 99.19%, and F1 of 97.45%. The Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset contains two datasets. Regarding the "SpontaneousDialogue" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 94.03%, 92.83%, 90.78%, 100.0%, and 85.67%, respectively. Regarding the "ReadText" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 91.89%, 90.62%, 87.5%, 100.0%, and 81.25%, respectively. Our findings highlight the efficacy of leveraging diverse data sources and advanced machine learning techniques to enhance PD detection accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germa

关键词： Neurodegenerative diseases

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