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Prospects on mangrove knowledge services in the smart era: From plant atlas to knowledge graphs

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Science China Earth Sciences 2025年第1期68卷 111-127页

作者： Zhi-Wei HOU Wenlong JING Cheng-Zhi QIN Ji YANG Qing XIA Xiaoling YIN Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical information system Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Southern Marine Science and engineering Guangdong Laboratory (Guangzhou) State Key Laboratory of Resources and Environmental information system Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences College of Resources and Environment University of Chinese Academy of Sciences School of Traffic & Transportation engineering Changsha University of Science and Technology

Mangroves are crucial to the ecological security of the Earth and human well-being. Their management, conservation, and restoration are of great importance and necessitate the support of spatio-temporal information and multidisciplinary knowledge such as biology and ecology. Traditional knowledge services such as plant atlas provide illustrated textual knowledge of mangroves. However, this kind of service is oriented to information retrieval and is inc.pable of effectively mining and utilizing fragmented knowledge from multi-source heterogeneous data, facing the problem of “massive data, rare knowledge”. Knowledge graphs are capable of extracting, organizing, and fusing the knowledge contained in massive data into semantic networks that can be understood and computed by computers. They provide a solution for the realization of intelligent knowledge services. Focusing on the urgent need for mangrove knowledge acquisition, formal representation, and intelligent services, this paper proposes a research prospect on mangrove knowledge graphs and knowledge services. We first analyze the similarities and differences between various domain-specific concepts of Tupu. On this basis, we define the mangrove knowledge graph as a large-scale knowledge base that integrates multi-disciplinary knowledge and spatio-temporal information with mangrove ecosystems as the core. Then, we propose a research framework for mangrove knowledge services that can realize the transformation from multi-modal data to intelligent knowledge services, inc.uding multiple research levels such as ubiquitous data sensing and aggregation, knowledge organization and graph construction, and intelligent mangrove knowledge services. Subsequently, the methods and workflow for constructing mangrove knowledge graphs are introduced. Finally, we discuss the challenges and possible future directions of mangrove knowledge services in the smart era, inc.uding the construction of a mangrove knowledge system that integra

关键词： Mangrove Tupu Knowledge graph Multi-modal Knowledge service

An Improved Practical Byzantine Fault-Tolerant Algorithm Based on XGBoost Grouping for Consortium Chains

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Computers, Materials & Continua 2025年第1期82卷 1295-1311页

作者： Xiaowei Wang Haiyang Zhang Jiasheng Zhang Yingkai Ge Kexin Cui Zifu Peng Zhengyi Li Lihua Wang College of Electronic and information engineering Shandong University of Science and TechnologyQingdao266590China

In response to the challenges presented by the unreliable identity of the master node,high communication overhead,and limited network support size within the Practical Byzantine Fault-Tolerant(PBFT)algorithm for consortium chains,we propose an improved PBFT algorithm based on XGBoost grouping called XG-PBFT in this ***-PBFT constructs a dataset by training important parameters that affect node performance,which are used as classification indexes for *** XGBoost algorithm then is employed to train the dataset,and nodes joining the system will be grouped according to the trained grouping *** them,the nodes with higher parameter indexes will be assigned to the consensus group to participate in the consensus,and the rest of the nodes will be assigned to the general group to receive the consensus *** order to reduce the resource waste of the system,XG-PBFT optimizes the consensus protocol for the problem of high complexity of PBFT ***,we evaluate the performance of *** experimental results show that XG-PBFT can significantly improve the performance of throughput,consensus delay and communication complexity compared to the original PBFT algorithm,and the performance enhancement is significant compared to other algorithms in the case of a larger number of *** results demonstrate that the XG-PBFT algorithm is more suitable for large-scale consortium chains.

关键词： Consortium chain PBFT grouping reputation XGBoost

Fractal autoencoder with redundancy regularization for unsupervised feature selection

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Science China(information Sciences) 2025年

作者： Meiting SUN Fangyu LI Honggui HAN School of information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent system engineering Research Center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence(Beijing)

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Sinc. manual labeling is expensive, unsupervised feature selection has received inc.easing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder (RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

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Multi-party quantum private comparison of size relationship based on one-direction quantum walks on a circle

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Science China(information Sciences) 2025年

作者： Jintao WANG Jiangyuan LIAN Tianyu YE College of information & Electronic engineering Zhejiang Gongshang University

Quantum walks (QW) are the quantum counterpart of classical random walks, and portray a natural stochastic process when the walker randomly wanders around. In a discretetime QW system on a circle, when the walker particle generally steps towards two directions, i.e., clockwise and anticlockwise, this kind of QW is called two-direction quantum walks on a circle (TDQWC); and the walker particle always steps towards one direction or stays stationary, this kind of QW is called as one-direction quantum walks on a circle (ODQWC).

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Experimental demonstration of D-band signals wireless transmission over 30 km

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Science China Technological Sciences 2025年

作者： Yi WEI Xiongwei YANG Jianjun YU Xianming ZHAO Mingxu WANG Jingwen TAN Weiping LI Feng ZHAO Chengzhen BIAN Hansong MA Qinyi ZHANG Qiutong ZHANG Bing ZHANG Wen ZHOU Kaihui WANG Sicong XU Jianguo YU State Key Laboratory of ASIC and system Key Laboratory for Information Science of Electromagnetic Waves (MoE)and School of Information Science and Technology Fudan University Harbin Institute of Technology School of Automation Xi'an University of Posts and Telecommunications School of Electronic engineering Beijing University of Posts and Telecommunications

Due to the rise of innovative technologies like artificial intelligence(AI),the Internet of Things(IoT), and telemedicine, the demand for internet traffic is rapidly inc.easing. In order to achieve ultra-high-speed wireless communication, many researchers are directing their efforts towards the Dband. The D-band, spanning the frequency band 110–170 GHz, has abundant spectrum resources and license-free characteristics [1,2].

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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Science China(information Sciences) 2025年

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National engineering Research Center for Big Data Technology and system Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

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Distributed fiber optic sensing system for vibration monitoring of 3D printed bridges

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Optoelectronics Letters 2025年第1期21卷 28-34页

作者： ZHAO Shuai ZHOU Ran LUO Mingming LIU Jianfei LIU Xiongfei ZHOU Tao School of Electronic information engineering Hebei University of Technology Tianjin Key Laboratory of Electronic Materials and Devices School of Civil engineering and Transportation Hebei University of Technology Tianjin Jinhang Institute of Technical Physics

The fiber optic sensing technology provides data support in structural health monitoring of the macro facilities, inc.uding design, construction, and maintenance of bridges, tunnels, ports and other infrastructures. In this paper, a distributed vibration sensing system is proved to be responsive to a single touch over a 1.8-m-long equivalent fiber segment, covering a vibration frequency from 5 Hz to 25 kHz. The sensing fiber was arranged as an S type layout on the bridge to recognize the standing state, windblown disturbance, and walking vibration. Moreover, the knocking and climbing events are recognized fiber laying spinning lines and hanging on the fences, respectively. The demonstration shows an accurate positioning and sensitive vibration monitoring applied on the automated three-dimensional(3D) printed bridge, which is applicable to all kinds of 3D printed facilities as intelligent sensory neuro-networks.

关键词： Spinning (fibers)

A coordinated planning strategy of energy storage allocation and line upgrading to inc.ease photovoltaic consumption with lowest planning cost

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Science China Technological Sciences 2025年

作者： Bo ZHANG Yuqi JI Lu ZHANG Wei TANG Xiaohui ZHANG Liang WANG Haoran DING Hanchen MAO College of information and Electrical engineering China Agricultural University State Grid Heilongjiang Electric Power Research Institute State Grid Heilongjiang Electric Power Company Limited School of Electrical and information engineering Zhengzhou University of Light Industry

Random integration of massive distributed photovoltaic (PV) generation poses serious challenges to distribution networks. Voltage violations, line overloads, inc.eased peak–valley differences, and power-flow reversals can occur at different locations, times, and severities. Traditional planning methods such as energy storage (ES) allocation and upgrading of lines may result in poor economics and low equipment utilization. This study proposes a distribution-network planning strategy that coordinates three planning mechanisms:ES allocation to substations and to feeders, and line upgrading. The proposed strategy achieved the lowest annual cost, considering the distribution network problems at different locations, times, and severities. A net load curve description method was first proposed considering economic planning and the uncertain distribution of loads and PVs. Second, a multistage optimization model for ES allocation and line upgrading was established based on the net load curves. Finally, an improved IEEE33 node system was used in the case study. The planning results show that the proposed coordinated planning strategy can address the problems caused by the random integration of massive distributed PVs, improve equipment utilization, and obtain the lowest total annual planning cost.

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Improving the Position Accuracy and Computational Efficiency of UAV Terrain Aided Navigation Using a Two-Stage Hybrid Fuzzy Particle Filtering Method

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Computers, Materials & Continua 2025年第1期82卷 1193-1210页

作者： Sofia Yousuf Muhammad Bilal Kadri College of engineering Karachi Institute of Economics&TechnologyKarachi75190Pakistan College of Computer and information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia

Terrain Aided Navigation(TAN)technology has become inc.easingly important due to its effectiveness in environments where Global Positioning system(GPS)is *** recent years,TAN systems have been extensively researched for both aerial and underwater navigation ***,many TAN systems that rely on recursive Unmanned Aerial Vehicle(UAV)position estimation methods,such as Extended Kalman Filters(EKF),often face challenges with divergence and instability,particularly in highly non-linear *** address these issues,this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle *** enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states,a Fuzzy Particle Filter(FPF)is employed in the first *** approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in *** design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time *** the second stage,an Error State Kalman Filter(ESKF)is implemented to estimate the UAV’s *** ESKF is chosen over the conventional EKF method because it is more suitable for non-linear *** results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage.

关键词： Sensor fusion fuzzy logic particle filter composite feature terrain aided navigation