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Stackelberg Game for Wireless Powered and Backscattering Enabled Sensor networks

China Communications 2024年第3期21卷 189-204页

作者： Lyu Bin Cao Yi Wang Shuai Guo Haiyan Hao Chengyao Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research center of Novel Optical Fiber Technology and Communication network Suzhou 215006China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.

关键词： backscatter communication energy interaction stackelberg game wireless powered sensor network

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Protein Arginine Methyltransferase 1 Ablation in Motor Neurons Causes Mitochondrial Dysfunction Leading to Age-related Motor Neuron Degeneration with Muscle Loss

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Research 2024年第1期2023卷 637-649页

作者： Hyun-Kyung So Hyebeen Kim Jinwoo Lee Chang-Lim You Chae-Eun Yun Hyeon-Ju Jeong Eun-Ju Jin Yunju Jo Dongryeol Ryu Gyu-Un Bae Jong-Sun Kang Department of Molecular Cell Biology Sungkyunkwan University School of MedicineSuwonKorea Single Cell network Research center Sungkyunkwan University School of MedicineSuwonKorea Research Institute of Aging-Related Diseases AniMusCureInc.SuwonKorea Drug Information Research Institute Muscle Physiome Research CenterCollege of PharmacySookmyung Women’s UniversitySeoulKorea

Neuromuscular dysfunction is tightly associated with muscle wasting that occurs with age or due to degenerative ***,the molecular mechanisms underlying neuromuscular dysfunction are currently *** studies have proposed important roles of Protein arginine methyltransferase 1(Prmt1)in muscle stem cell function and muscle *** the current study,we set out to determine the role of Prmt1 in neuromuscular function by generating mice with motor neuron-specific ablation of Prmt1(mnKO)using *** exhibited age-related motor neuron degeneration and neuromuscular dysfunction leading to premature muscle loss and ***1 deficiency also impaired motor function recovery and muscle reinnervation after sciatic nerve *** transcriptome analysis of aged mnKO lumbar spinal cords revealed alterations in genes related to inflammation,cell death,oxidative stress,and ***,mnKO lumbar spinal cords of sciatic nerve injury model or aged mice exhibited elevated cellular stress response in motor ***,Prmt1 inhibition in motor neurons elicited mitochondrial *** findings demonstrate that Prmt1 ablation in motor neurons causes agerelated motor neuron degeneration attributing to muscle ***,Prmt1 is a potential target for the prevention or intervention of sarcopenia and neuromuscular dysfunction related to aging.

关键词： degeneration impaired prevention

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An Efficient Long Short-TermMemory and Gated Recurrent Unit Based Smart Vessel Trajectory Prediction Using Automatic Identification System Data

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computers, Materials and Continua 2024年第1期81卷 1789-1808页

作者： Zaman U. Khan J. Lee E. Hussain S. Balobaid A.S. Aburasain R.Y. Kim K. Department of computer Engineering Chungnam National University Daejeon 34134 South Korea Department of Environmental IT Engineering Chungnam National University Daejeon 34134 South Korea Autonomous Ship Research center Samsung Heavy Industries Daejeon 34051 South Korea ICAROS center (PRISMA Lab) University of Naples Federico II Naples 80131 Italy Department of computer Science College of Computer Science and Information Technology Jazan University Jazan 45142 Saudi Arabia

Maritime transportation, a cornerstone of global trade, faces increasing safety challenges due to growing sea traffic volumes. This study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System (AIS) data and advanced deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (DBLSTM), Simple Recurrent Neural network (SimpleRNN), and Kalman Filtering. The research implemented rigorous AIS data preprocessing, encompassing record deduplication, noise elimination, stationary simplification, and removal of insignificant trajectories. Models were trained using key navigational parameters: latitude, longitude, speed, and heading. Spatiotemporal aware processing through trajectory segmentation and topological data analysis (TDA)was employed to capture dynamic *** using a three-month AIS dataset demonstrated significant improvements in prediction accuracy. The GRU model exhibited superior performance, achieving training losses of 0.0020 (Mean Squared Error, MSE) and 0.0334 (Mean Absolute Error, MAE), with validation losses of 0.0708 (MSE) and 0.1720 (MAE). The LSTM model showed comparable efficacy, with training losses of 0.0011 (MSE) and 0.0258 (MAE), and validation losses of 0.2290 (MSE) and 0.2652 (MAE). Both models demonstrated reductions in training and validation losses, measured by MAE, MSE, Average Displacement Error (ADE), and Final Displacement Error (FDE). This research underscores the potential of advanced deep learning models in enhancing maritime safety through more accurate trajectory predictions, contributing significantly to the development of robust, intelligent navigation systems for the maritime industry. © 2024 The Authors. Published by Tech Science Press.

关键词： AIS data deep learning GRU LSTM smart vessel Trajectory prediction

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980 nm Near-Infrared Light-Emitting Diode Using All-Inorganic Perovskite Nanocrystals Doped with Ytterbium Ions

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Tsinghua Science and Technology 2024年第1期29卷 207-215页

作者： Zhenglan Ye Taoran Liu Dan Chen Yazhou Yang Jiayi Li Yaqing Pang Xiangquan Liu Yuhua Zuo Jun Zheng Zhi Liu Buwen Cheng State Key Laboratory on Integrated Optoelectronics Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Intelligent network Research Institute Zhejiang LabHangzhou 311100China

All-inorganic perovskite(CsPbX3)nanocrystals(NCs)have recently been widely investigated as versatile solution-processable light-emitting *** to its wide-bandgap nature,the all-inorganic perovskite NC Light-Emitting Diode(LED)is limited to the visible region(400-700 nm).A particularly difficult challenge lies in the practical application of perovskite NCs in the infrared-spectrum *** this work,a 980 nm NIR all-inorganic perovskite NC LED is demonstrated,which is based on an efficient energy transfer from wide-bandgap materials(CsPbCl3 NCs)to ytterbium ions(Yb3+)as an NIR emitter doped in perovskite *** optimized CsPbCl3 NC with 15 mol%Yb3+doping concentration has the strongest 980 nm photoluminescence(PL)peak,with a PL quantum yield of 63%.An inverted perovskite NC LED is fabricated with the structure of ITO/PEDOT:PSS/poly-TPD/CsPbCl3:15 mol%Yb3+NCs/TPBi/LiF/*** LED has an External Quantum Efficiency(EQE)of 0.2%,a Full Width at Half Maximum(FWHM)of 47 nm,and a maximum luminescence of 182 cd/m?.The introduction of Yb3+doping in perovskite NCs makes it possible to expand its working wavelength to near-infrared band for next-generation light sources and shows potential applications for optoelectronic integration.

关键词： perovskite nanocrystals rare-earth doping Light-Emitting Diode(LED) near-infrared optical interconnection

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Span-based joint entity and relation extraction augmented with sequence tagging mechanism

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Science China(Information Sciences) 2024年第5期67卷 84-98页

作者： Bin JI Shasha LI Hao XU Jie YU Jun MA Huijun LIU Jing YANG College of computer National University of Defense Technology

Span-based joint extraction simultaneously conducts named entity recognition(NER) and relation extraction(RE) in a text span form. However, since previous span-based models rely on span-level classifications, they cannot benefit from token-level label information, which has been proven advantageous for the task. In this paper, we propose a sequence tagging augmented span-based network(STSN), a spanbased joint model that can make use of token-level label information. In STSN, we construct a core neural architecture by deep stacking multiple attention layers, each of which consists of three basic attention *** the one hand, the core architecture enables our model to learn token-level label information via the sequence tagging mechanism and then uses the information in the span-based joint extraction; on the other hand, it establishes a bi-directional information interaction between NER and RE. Experimental results on three benchmark datasets show that STSN consistently outperforms the strongest baselines in terms of F1,creating new state-of-the-art results.

关键词： joint extraction named entity recognition relation extraction span sequence tagging mechanism

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Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

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computers, Materials & Continua 2024年第4期79卷 735-759页

作者： Wael M.S.Yafooz Abdullah Alsaeedi Department of computer Science College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user ***, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

关键词： Mobile apps issues play store user comments deep learning LSTM bidirectional LSTM

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A Task Scheduling Algorithm Based on Clustering Pre-processing in Space-Based Information network

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Chinese Journal of Electronics 2024年第1期33卷 217-230页

作者： Yufei WANG Jun LIU Shengnan ZHANG Sai XU Jingyi WANG School of computer Science and Engineering Northeastern University

With the diversification of space-based information network task requirements and the dramatic increase in demand, the efficient scheduling of various tasks in space-based information network becomes a new challenge. To address the problems of a limited number of resources and resource heterogeneity in the space-based information network, we propose a bilateral pre-processing model for tasks and resources in the scheduling pre-processing stage. We use an improved fuzzy clustering method to cluster tasks and resources and design coding rules and matching methods to match similar categories to improve the clustering effect. We propose a space-based information network task scheduling strategy based on an ant colony simulated annealing algorithm for the problems of high latency of space-based information network communication and high resource dynamics. The strategy can efficiently complete the task and resource matching and improve the task scheduling performance. The experimental results show that our proposed task scheduling strategy has less task execution time and higher resource utilization than other algorithms under the same experimental conditions. It has significantly improved scheduling performance.

关键词： Scheduling algorithms Heuristic algorithms Clustering methods Clustering algorithms Simulated annealing Dynamic scheduling Search problems

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DDI-Transform:A neural network for predicting drug-drug interaction events

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Quantitative Biology 2024年第2期12卷 155-163页

作者： Jiaming Su Ying Qian School of computer Science and Technology Shanghai Frontiers Science Center of Molecule Intelligent SynthesesEast China Normal UniversityShanghaiChina

Drug-drug interaction(DDI)event prediction is a challenging problem,and accurate prediction of DDI events is critical to patient health and new drug ***,many machine learning-based techniques have been proposed for predicting DDI ***,most of the existing methods do not effectively integrate the multidimensional features of drugs and provide poor mitigation of noise to get effective feature *** address these limitations,we propose a DDI-Transform neural network framework for DDI event *** DDI-Transform,we design a drug structure information feature extraction module and a drug bind-protein feature extraction module to obtain multidimensional feature information.A stack of DDI-Transform layers in the DDI-Transform network module are then used for adaptive learning,thus adaptively selecting the effective feature information for *** results show that DDI-Transform can accurately predict DDI events and outperform the state-of-the-art *** on different scale datasets confirm the robustness of the method.

关键词： adaptive learning graph convolutional networks interaction prediction meta-path

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Topological defects on solutions of the non-relativistic equation for extended double ring-shaped potential

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Communications in Theoretical Physics 2024年第8期76卷 29-43页

作者： Badredine Boudjedaa Faizuddin Ahmed Natural Sciences and Materials Laboratory University Center Abdelhafid BoussoufMilaAlgeria Department of Mathematics Institute of Mathematics and Computer ScienceUniversity Center Abdelhafid BoussoufMilaAlgeria Department of Physics University of Science&Technology MeghalayaRi-Bhoi793101India

In this study, we focus into the non-relativistic wave equation described by the Schrodinger equation, specifically considering angular-dependent potentials within the context of a topological defect background generated by a cosmic string. Our primary goal is to explore quasi-exactly solvable problems by introducing an extended ring-shaped potential. We utilize the Bethe ansatz method to determine the angular solutions, while the radial solutions are obtained using special functions. Our findings demonstrate that the eigenvalue solutions of quantum particles are intricately influenced by the presence of the topological defect of the cosmic string,resulting in significant modifications compared to those in a flat space background. The existence of the topological defect induces alterations in the energy spectra, disrupting ***, we extend our analysis to study the same problem in the presence of a ring-shaped potential against the background of another topological defect geometry known as a point-like global monopole. Following a similar procedure, we obtain the eigenvalue solutions and analyze the results. Remarkably, we observe that the presence of a global monopole leads to a decrease in the energy levels compared to the flat space results. In both cases, we conduct a thorough numerical analysis to validate our findings.

关键词： non-relativistic wave-equation:Schrödinger equation topological defect:cosmic string point-like global monopole potential:extended ring-shaped potentials Bethe ansatz method

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Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective

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Journal of computer Science & Technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

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