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Federated Prediction of Remaining Useful Life for Critical Components of Autonomous Vehicles in Multiple Ports
MA Chao;ZHANG Chengcheng;GUAN Zhibo;ZHANG Kaiqi;HUANG Hai;To address the challenge of remaining useful life prediction caused by data silos and privacy concerns among ports, a federated learning method based on long short-term memory(LSTM) was proposed. Firstly, autoencoders were locally deployed at each port to extract key features and constructed a federated LSTM model. Secondly, a feature similarity-driven adaptive client grouping and weighted aggregation strategy was developed. In this strategy, clients with similar degradation patterns were grouped, and model aggregation was performed within each group based on validation performance to enhance global performance with non-independent and identically distributed conditions. Experiments conducted on a real-world port battery dataset demonstrated that the proposed method effectively mitigated model drift and significantly improved remaining useful life prediction accuracy.
Community Detection Algorithm Based on Graph Attention Mechanism and Multi-distance Analysis
ZHANG Zhen;WU Guohao;ZHANG Hongxia;ZHOU Qi;To address the low accuracy of community detection in large-scale social networks, which was caused by complex node relationships and hidden community structures, a community detection algorithm based on graph attention mechanisms and multi-distance analysis was proposed. Firstly, a multilayer perceptron(MLP) module was introduced during the pre-training phase so that the accuracy of sample recognition could be improved. Secondly, a graph attention-based readout module was proposed, by which the contribution weights of nodes in graph representation learning were adaptively learned. The weighted aggregation was enabled, the discriminative power of graph-level representations was enhanced, and the accuracy and robustness of community detection were improved. Finally, a multi-distance analysis method was designed to strengthen the detection of small communities and to further increase overall detection accuracy. It was demonstrated by experimental results that superior performance was delivered by the improved model across multiple datasets. On the Amazon dataset, the F1 score of 90.22% was achieved, which represented improvements of 11.33 and 5.86 percentage points over the CLARE and ProCom models, respectively. For Jaccard similarity, a score of 83.46% was obtained, with improvements of 14.96 and 7.62 percentage points over CLARE and ProCom, respectively.
Block Adjustment Measure Recommendation Model Based on Multi-graph Embedded Dual-channel Graph Convolution
WANG Mei;YIN Chuanlong;SUN Hongguo;ZHU Xiaoli;The intelligent recommendation of oilfield block adjustment measures is an important part of the construction of intelligent oilfield. The existing methods was difficult to capture high-order nonlinear relationships in highly sparse oil field data, so a recommendation model based on multi-graph embedding dual-channel graph convolution was proposed. The latent relationship between blocks and their attributes was learnt through knowledge graph embedded to enhance the representation ability of block nodes. A dual-channel graph convolutional network was used to capture the high-order embedded representation and collaborative embedded representation of blocks and measures in heterogeneous interaction graphs and collaborative graphs respectively, and the two representations information were fused to achieve the final measure recommendation. The experimental results showed that the proposed model was significantly better than the mainstream baseline model in the recommended precision, recall and normalized discounted cumulative gain with the real oilfield development effect evaluation data set.
Research on Metaheuristic Algorithm Based on Dual Closed-loop PID
YU Chunming;ZHU Xiaodong;CHEN Ke;REN Chunxiao;A global optimization metaheuristic algorithm utilizing a dual closed-loop PID(proportional-integral-derivative) control mechanism(DCLPID) was proposed to improve global exploration capability and local exploitation efficiency for solving high-dimensional complex problems. Inspired by control theory, the algorithm incorporated a position-velocity dual closed-loop structure: the outer position loop enhanced global search, while the inner velocity loop enhanced local refinement. PID parameters were dynamically tuned to achieve an adaptive balance during optimization. Experiments using the CEC 2017 benchmark suite were conducted, showing that DCLPID surpassed eight state-of-the-art meta-heuristic algorithms in both convergence precision and rate; rank-sum tests confirmed significantce of superiority with significance. These findings confirmed the algorithm′s superior optimization performance and broad applicability, offering an effective approach for complex optimization tasks.
UAV Small Target Detection Algorithm Based on Improved YOLOv11n
YAO Yue;YANG Xiaohong;GAO Zhiwei;TANG Qiang;To address the challenges of target scale variation, dense occlusion, and background interference in small target detection from UAV perspectives, a detection algorithm based on improved YOLOv11n was proposed. Firstly, a multi-modal feature enhancement attention module was introduced to strengthen the model′s perception of key regions through integrating multi-dimensional feature information, thereby improving the completeness and discriminability of feature representation. Secondly, an MS＿C3K2 module was constructed, which utilized convolutional kernels with different receptive fields to process input features in parallel, to effectively enhance the ability to extract and fuse both local and global information. Finally, to further enhance the adaptability and dynamism of the feature fusion stage,an adaptive gated bi-directional feature pyramid network was proposed to dynamically fuse multi-scale features, to enhance the network′s attention and response capability to key information. Experimental results demonstrated that the mAP50 of the improved model reached 33.1%, mAP50-95 reached 18.9%, which was 3.4 and 2.3 percentage points higher than the baseline model, respectively, validating its excellent detection performance and robustness in complex UAV scenarios.
Fluorescence Immunochromatography Method of AFP-L3 and AFP Based on LCA Enrichment
CHEN Mengtian;WANG Xiaojun;CHENG Fan;WANG Xudong;ZHANG Heng;LI Fang;LI Min;ZHANG Yingfu;WANG Yunlong;Using Lens culinaris agglutinin(LCA) enrichment and time-resolved fluorescence immunoassay technology, a preliminary process optimization was achieved based on single-index detection. Then, specific capture lines were introduced to complete the joint detection process adjustment. Through performance evaluation and clinical assessment, a rapid and simultaneous quantitative detection method for AFPL3 and AFP was established. The results showed that the total detection time of the test strip was only 12 min. The minimum detection limits of AFP-L3 and AFP were 0. 291 ng/mL and 1. 105 ng/mL, respectively, with good linearity, no cross-reaction, and satisfactory stability and precision that met laboratory requirements. Tests on 150 clinical samples showed that the combined detection results were in good consistency with clinical diagnoses(Kappa was 0. 746,P<0. 01), and the area under the ROC curve was 0. 948 2, indicating that the diagnostic efficacy was obviously superior to that of single detection. This method exhibited high sensitivity, strong specificity, and was rapid and convenient, making it suitable for on-site screening in primary medical institutions, and provided an efficient and convenient auxiliary approach for the early identification and detection of hepatocellular carcinoma.
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Establishment of a Method for Expressing Influenza A H3N2 Hemagglutinin Protein in Nicotiana benthamiana
HOU Die;WANG Haili;GENG Shanshan;KONG Zhengjie;ZHANG Yifeng;ZHANG Gaiping;The hemagglutinin( HA) protein of influenza A virus H3N2 was expressed in Nicotiana benthamiana using an agrobacterium-mediated transient expression system. Plant expression vectors carrying His and Flag tags were constructed through codon optimization, and transient expression was performed by infiltrating tobacco leaves with Agrobacterium tumefaciens GV3101. SDS-PAGE analysis showed that a band of His-tagged protein was observed at approximately 70 kDa, and a band of Flagtagged protein was observed at approximately 100 kDa. In Western blot analysis, both proteins were specifically detected at approximately 100 kDa, indicating that the recombinant HA was successfully expressed and might have undergone post-translational modifications. Glycosylation modification of the protein suggested by nickel column affinity purification. A dual-tag expression system of H3N2 HA in tobacco was established, which initially proved its potential as a vaccine antigen and provided a basis for the production of influenza antigens using plant platforms and subsequent evaluations.
[Downloads: 16 ] [Citations: 0 ] [Reads: 11 ] HTML PDF Cite this article
In Vivo Secondary Structure Mapping of 18S rRNA in Physcomitrium patens Using DMS-MaPseq Technology
QU Tianyu;LI Shichang;FANG Shuqian;WANG Ning;CHEN Jun;LIAO Rongkui;WU Lifang;HOU Jinyan;To evaluate the accuracy and applicability of dimethyl sulfate mutational profiling with sequencing(DMS-MaPseq) technology for resolving RNA secondary structures in the lower plant Physcomitrium patens, the optimal in vivo reaction conditions were established as 2%(volume fraction) DMS for 5 min through concentration and time gradient experiments. A transcriptome-wide library of Physcomitrium patens was constructed using in vivo labeling and mutational profiling library preparation techniques. Analysis revealed that high consistency was maintained between biological replicates, with Pearson correlation coefficients exceeding 0. 9, and about 90% of the transcripts in the samples were covered. Under the optimized conditions, DMS reactivity profiles with high signal-to-noise ratios were obtained. Using the known structure of 18S rRNA as a reference, the overall true positive rate of the secondary structure map reached 87. 0%, which confirmed that the single-stranded RNA regions were specifically labeled by this method. These results demonstrated that the established DMS-MaPseq library construction method could accurately resolve in vivo RNA secondary structures in Physcomitrium patens, providing a methodological basis for applying this technique to plant-wide transcriptome-scale RNA structure studies and potential gene function analysis.
[Downloads: 24 ] [Citations: 0 ] [Reads: 58 ] HTML PDF Cite this article
Skeleton Action Recognition Method Based on Efficient Channel Attention
LUO Yong;ZHANG Pengyuan;Existing skeleton action recognition methods based on channel-wise topology refinement graph convolutional networks lacked effective screening mechanisms in joint feature and channel-wise topology modeling, resulting in insufficient prominence of key local features and easy attenuation of important topological information. To address this issue, a lightweight enhanced human skeleton action recognition method was proposed. Firstly, the original spatiotemporal graph convolution unit was improved by introducing an efficient channel attention mechanism to enhance its feature expression ability. Secondly, a Top-K salient response-guided feature aggregation strategy was designed to enhance local salient feature representations while preserving global topological information, thereby improving the model′s ability to capture discriminative action patterns. The experimental results on the NTU RGB+D 60 and NTU RGB+D 120 datasets showed that the proposed method could effectively improve the accuracy of action recognition, with almost no increase in the number of parameters and computational complexity, achieving a balance between accuracy and efficiency.
[Downloads: 38 ] [Citations: 0 ] [Reads: 61 ] HTML PDF Cite this article
Formal Analysis of PQXDH Protocol Based on Logic of Events
XIAO Meihua;CHEN Gaoxiang;YANG Ke;ZHOU Li;To address the difficulty in implementing formal analysis caused by the complex hybrid keyexchange structure of the post-quantum extended Diffie-Hellman(PQXDH) protocol and the difficulty of precise modeling, a formal modeling and verification method based on the logic of events theory(LoET) was proposed. Firstly, a formal model of the PQXDH protocol was constructed within the LoET framework, and key processes such as session establishment and key derivation were specified. Secondly, the logic was extended by introducing KEM-related events, axioms, and inference rules, and post-quantum characteristics were characterized more accurately. Finally, verification was conducted in combination with the ProVerif tool, and the satisfaction of authentication properties was proved. The results indicated that the extended LoET was effectively applied to the formal analysis of the PQXDH protocol, thereby promoting the application and development of post-quantum protocols.
[Downloads: 18 ] [Citations: 0 ] [Reads: 81 ] HTML PDF Cite this article
Second Development for Fore Treatment of ABAQUS Using Python Language
ZHONG Tong-sheng~1,WEI Feng~1,WANG Zhi~2,ZHI You-hai~1 (1.Department of Mechanical Engineering,Northwestern Polytechnical University, Xi'an 710072,China;2.Department of Civill Engineering and Architecture, Northwestern Polytechnical University,Xi'an 710072,China)The fore treatment modular of ABAQUS software is developed using Python language.The function and calling procedure of the scenarios ports and target models in the second treatment of ABAQUS are discussed.The model building and ploting grid course of ABAQUS are controlled by the Python scenarios program.Therefore,it improves the fore treatment efficiency and solves the fussy problem that apprears during the manual model building of the model which includes a lot of repeated members.
A New Extreme Learning Machine Optimized by PSO
WANG Jie,BI Hao-yang(School of Electrical Engineering,Zhengzhou University,Zhengzhou 450001,China)Extreme learning machine(ELM) was a new type of feedforward neural network.Compared with traditional single hidden layer feedforward neural networks,ELM possessed higher training speed and smaller error.Due to random input weights and hidden biases,ELM might need numerous hidden neurons to achieve a reasonable accuracy.A new ELM learning algorithm,which was optimized by the particle swarm optimization(PSO),was proposed.PSO algorithm was used to select the input weights and bias of hidden layer,then the output weights could be calculated.To test the validity of proposed method,two simulation experiments were drawn on the approximation curves of the Sinc function.Experimental results showed that the proposed algorithm achieved better performance with less hidden neurons than other similar methods.
[Downloads: 2,746 ] [Citations: 204 ] [Reads: 210 ] HTML PDF Cite this article
A Review on Image Mosaicing Techniques
PEI Hongxing;LIU Jinda;GE Jialong;ZHANG Bin;School of Physics and Engineering,Zhengzhou University;Image mosaicing was analyzed and summarized comprehensively. Based on the development of this technology the background and applications were described generally. The definition and steps of image mosaicing were indicated initially,and the algorithms of image registration were examined by classifying them into several groups. For each algorithm, especially the scale-invariant feature transform( SIFT),the basic theory and characteristics were demonstrated in detail. In the last part,the classifications of image fusion were illustrated and methods of this stage,specially the weighted average,were explained briefly. In addition,the challenges and tendencies of image mosaicing were also pointed out.
[Downloads: 4,343 ] [Citations: 129 ] [Reads: 201 ] HTML PDF Cite this article
Study on the Stability of Aqueous Ferrate(VI) Solution
Jia Handong Ma Ning Sun Hongbin Liu Tongliang Yang Yong (Department of Chemistry, Zhengzhou University, Zhenzhou 450052)The catalytic behaviour of Fe(OH) 3 for the hydrolysis reaction of ferrate(VI) is reported.The effect of temperature, concentration and alkalinity on stability of aqueous ferrate(VI) solutions was studied. It has been descovered that the stability of FeO 2- 4 decreased with increasing amount of Fe(Ⅲ) and with increasing concentration of FeO 2- 4, that the solubility of FeO 2- 4 was not affected by alkalinity at the concentration of OH - below 1.0 mol/L, and that dilute solution of FeO 2- 4 is stable at the normal temperature . These important laws have been used to the synthesis of potassium ferrate(VI) with satisfactory results.
Review on Infrared Dim and Small Target Detection Technology
REN Xiangyang;WANG Jie;MA Tianlei;ZHU Xiaodong;BAI Ke;WANG Jiaqi;With the continuous development of infrared detection technology, the requirements for detection distance were increasingly high. The infrared dim and small target detection technology became the focus of infrared detection. The background and significance of infrared dim and small target detection were reviewed at first. Then the current research status and latest progress of various typical methods in the field of infrared dim and small target detection were trained. After that, experimental comparisons of several different types of infrared dim and small target detection methods were presented. Finally, the research on infrared dim and small target detection technology was summarized and prospected.
Second Development for Fore Treatment of ABAQUS Using Python Language
ZHONG Tong-sheng~1,WEI Feng~1,WANG Zhi~2,ZHI You-hai~1 (1.Department of Mechanical Engineering,Northwestern Polytechnical University, Xi'an 710072,China;2.Department of Civill Engineering and Architecture, Northwestern Polytechnical University,Xi'an 710072,China)The fore treatment modular of ABAQUS software is developed using Python language.The function and calling procedure of the scenarios ports and target models in the second treatment of ABAQUS are discussed.The model building and ploting grid course of ABAQUS are controlled by the Python scenarios program.Therefore,it improves the fore treatment efficiency and solves the fussy problem that apprears during the manual model building of the model which includes a lot of repeated members.
A Review on Image Mosaicing Techniques
PEI Hongxing;LIU Jinda;GE Jialong;ZHANG Bin;School of Physics and Engineering,Zhengzhou University;Image mosaicing was analyzed and summarized comprehensively. Based on the development of this technology the background and applications were described generally. The definition and steps of image mosaicing were indicated initially,and the algorithms of image registration were examined by classifying them into several groups. For each algorithm, especially the scale-invariant feature transform( SIFT),the basic theory and characteristics were demonstrated in detail. In the last part,the classifications of image fusion were illustrated and methods of this stage,specially the weighted average,were explained briefly. In addition,the challenges and tendencies of image mosaicing were also pointed out.
[Downloads: 4,343 ] [Citations: 129 ] [Reads: 201 ] HTML PDF Cite this article
Research on Trusted Cloud Computing Technologies
ZHANG Liqiang;LYU Jianrong;YAN Fei;XIONG Yunfei;With the advantages of high performance, servitization, elastic scale and environmental-friendliness, as a new IT infrastructure, cloud computing has been widely used. Because of its feature of resource outsourcing and resource renting, security and privacy requirements were of great importance. Traditional security technologies were unable to meet the requirements of security in cloud computing. So in recent years, vast security improvements and innovations were proposed in academia and industry. These schemes were used to solve various security problems in cloud computing bottom-up, and built a trusted cloud system architecture in order to achieve a secure and reliable cloud computing. Based on the security threats to cloud computing, the implementations and key technologies of trusted cloud computing were discussed. The advantages and drawbacks of the related works were summarized, and the developing directions of trusted cloud computing were discussed.
A Survey on the Construction and Application of Knowledge Graphs Driven by Large Language Models
ZHANG Kunli;WANG Ying;FU Wenhui;ZHU Yongqi;ZHANG Yanli;ZAN Hongying;With the support of large language models, knowledge graphs, characterized by their structured and semantically rich features, enhanced data association and interpretability. This provided new research directions and application potential for complex knowledge reasoning and intelligent decision-making. Therefore, from the perspective of knowledge graphs, the latest progress in the construction and application of knowledge graphs driven by large language models was summarized. Firstly, the new methods for building knowledge graphs were explored from the perspectives of knowledge modeling, information extraction, knowledge integration, and knowledge graph completion. Secondly, the applications of knowledge graphs in augmenting large language models, improving retrieval capabilities, and achieving mutual enhancement with large language models were investigated. Finally, the future directions of combining large language models with knowledge graphs were discussed.
Synthesis and Characterization of Mesoporous Molecular Sieves SBA-15
ZHENG Xiu-cheng,YUAN Cheng-yuan,ZHAO Wen-ping,YANG Chun-yan,YE Wen-hao,WANG Xiang-yu(Department of Chemistry,Zhengzhou University,Zhengzhou 450001,China)Mesoporous molecular sieves SBA-15 are synthesized by conventional hydrothermal process using tetraethylorthosilicate(TEOS) as silicon source and triblock copolymer P123 as template.The synthesized samples are characterized by N2 adsorption-desorption,X-ray diffraction (XRD) and transmission electron microscopy(TEM).The results show that with SiO2/P123 ratio increasing,the pore volume and pore size of SBA-15 decrease.With the increasing of reaction solution acidity,the surface area does not change observably but the pore volume and pore size increase.The addition of co-solvents can increase the pore volume and pore size to a certain extent and PhMe3 is the best one.
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