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IEEE Transactions on Computational Biology and Bioinformatics 2025年第1期22卷 151-159页

作者： Shifei Ding Chuangui Cao Fan Sun Xuan Li Xindong Wu School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of Ministry of Education Xuzhou China Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China

Since the regulatory roles of microRNAs in complex human diseases have been gradually demonstrated, more and more enlightening models were developed for predicting microRNA-disease associations. These models can inform bio-logical studies on the differential expression of microRNAs. In this paper, a graph convolutional network-based model using multi-view feature fusion and matrix completion, FMCGCN for brevity, is proposed. First of all, graph autoencoders are used to learn multiple embeddings from different networks. For convenience, attention mechanisms are used to fuse them. As a result, multi-view features constructed from different perspectives are aggregated. Then, the fused embedding is fed into the graph convolutional network to aggregate local information. This fused embedding is thought to facilitate feature extraction for graph convolutional networks. Finally, feature and nuclear norm minimization, a method for matrix completion, is used to obtain the prediction matrix. Additionally, evaluation results under 5-fold cross-validation prove that FMCGCN outperforms current models in several metrics. Furthermore, case studies for esophageal neoplasms and pancreatic neoplasms also demonstrate the validity of our model.

关键词： Diseases Semantics Predictive models Kernel Topology Graph convolutional networks Feature extraction Learning systems Biological system modeling Reliability

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Interpretable Machine Learning Method for Compressive Strength Prediction and Analysis of Pure Fly Ash-based Geopolymer Concrete

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Journal of Wuhan University of Technology(Materials Science) 2025年第1期40卷 65-78页

作者： SHI Yuqiong LI Jingyi ZHANG Yang LI Li Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education Northwest A&F Uni-versityYangling 712100China College of Water Resources and Architectural Engineering Northwest A&F UniversityYangling 712100China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing 401420China Chongqing College of Mobile Communication Chongqing 401520China Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina National Rail Transit Electrification and Automation Engineering Technology Research Center(Hong Kong Branch) The Hong Kong Polytechnic UniversityKowloonHung HomHong KongChina

In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive *** this study,505 groups of data were collected,and a new database of compressive strength of PFGC was *** order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative *** five machine learning models all showed good compressive strength prediction performance on *** them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,*** R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,*** two models have high prediction accuracy and outstanding generalization *** order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 *** 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide *** temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement *** importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.

关键词： machine learning pure fly ash geopolymer compressive strength feature perception

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Advances in intelligent computing approaches for solving problems related to photonic crystal fibers: Advances in Intelligent..: D. Yang et al.

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Optical and Quantum Electronics 2025年第1期57卷 1-31页

作者： Yang, Dan Zhao, Yuyu Liu, Hong Li, Yijin Tang, Chang Li, Shijun Lingye, Zhenglin College of Information Science & Engineering Northeastern University Shenyang110819 Thailand National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 Thailand

Photonic crystal fiber (PCF) has shown a promising application in various fields of modern optics due to its excellent transmission characteristics. However, conventional numerical solution methods have limitations such as high computational effort and low efficiency in designing PCF structures and characterizing pulse propagation in PCFs. With the advancement of artificial intelligence technology, various types of intelligent computing approaches, especially intelligent algorithm and machine learning algorithm (IML), provide new perspectives to solve the above difficulties. Therefore, an increasing number of researchers are committed to using IML to break through the limitations of conventional methods and accordingly open up a new interdisciplinary research direction. This paper begins with a brief overview of common optical properties and typical algorithms. Subsequently, it reviews specific areas where PCF and IML are combined, including the prediction and optimization of optical properties, the design of polarization devices, fiber lasers and amplifiers, as well as the control and prediction of Supercontinuum generation. Finally, this paper offers insights into the future developments and challenges in this emerging interdisciplinary field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Supercontinuum generation

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Solidification modes and delta-ferrite of two types of 316L stainless steels:a combination of as-cast microstructure and HT-CLSM research

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Journal of Iron and Steel Research International 2025年第2期32卷 426-436页

作者： Yang Wang Chao Chen Xiao-yu Yang Zheng-rui Zhang Jian Wang Zhou Li Lei Chen Wang-zhong Mu College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan030024ShanxiChina Department of Materials Science and Engineering KTH Royal Institute of TechnologyStockholm10044Sweden College of Artificial Intelligence and Big Data for Medical Sciences Shandong First Medical University and Shandong Academy of Medical SciencesJinan250117ShandongChina College of Mechanical Engineering Taiyuan University of TechnologyTaiyuan030024ShanxiChina Key Laboratory of Electromagnetic Processing of Materials(Ministry of Education) Northeastern UniversityShenyang110819LiaoningChina

In 316L austenitic stainless steel,the presence of ferrite phase severely affects the non-magnetic properties.316L austenitic stainless steel with low-alloy type(L-316L)and high-alloy type(H-316L)has been *** microstructure and solidification kinetics of the two as-cast grades were in situ observed by high temperature confocal laser scanning microscopy(HT-CLSM).There are significant differences in the as-cast microstructures of the two 316L stainless steel *** L-316L steel,ferrite morphology appears as the short rods with a ferrite content of 6.98%,forming a dual-phase microstructure consisting of austenite and ***,in H-316L steel,the ferrite appears as discontinuous network structures with a content of 4.41%,forming a microstructure composed of austenite and sigma(σ)*** alloying elements in H-316L steel exhibit a complex distribution,with Ni and Mo enriching at the austenite grain ***-CLSM experiments provide the real-time observation of the solidification processes of both 316L specimens and reveal distinct solidification modes:L-316L steel solidifies in an FA mode,whereas H-316L steel solidifies in an AF *** differences result in ferrite and austenite predominantly serving as the nucleation and growth phases,*** solidification mode observed by experiments is similar to the thermodynamic calculation *** L-316L steel solidified in the FA mode and showed minimal element segregation,which lead to a direct transformation of ferrite to austenite phase(δ→γ)during phase transformation after ***,the H-316L steel solidified in the AF mode and showed severe element segregation,which lead to Mo enrichment at grain boundaries and transformation of ferrite into sigma and austenite phases through the eutectoid reaction(δ→σ+γ).

关键词： 316L austenitic stainless steel As-cast microstructure High-temperature confocal laser scanning microscopy Solidification mode Ferrite Characterization

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How to Mitigate Information Loss in knowledge Graphs for GraphRAG: Leveraging Triple Context Restoration and Query-Driven Feedback

arXiv

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arXiv 2025年

作者： Huang, Manzong Bu, Chenyang He, Yi Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data [Hefei University of Technology Ministry of Education China School of Data Science William & Mary WilliamsburgVA United States

knowledge Graph (KG)-augmented Large Language Models (LLMs) have recently propelled significant advances in complex reasoning tasks, thanks to their broad domain knowledge and contextual awareness. Unfortunately, current methods often assume KGs to be complete, which is impractical given the inherent limitations of KG construction and the potential loss of contextual cues when converting unstructured text into entity-relation triples. In response, this paper proposes the Triple Context Restoration and Query-driven Feedback (TCR-QF) framework, which reconstructs the textual context underlying each triple to mitigate information loss, while dynamically refining the KG structure by iteratively incorporating query-relevant missing knowledge. Experiments on five benchmark question-answering datasets substantiate the effectiveness of TCR-QF in KG and LLM integration, where it achieves a 29.1% improvement in Exact Match and a 15.5% improvement in F1 over its state-of-the-art GraphRAG competitors. © 2025, CC BY.

关键词： knowledge graph

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Effect of Mn alloying on the hydrogen-assisted cracking behavior in multiphase/duplex stainless steel

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材料科学技术（英文版） 2025年第4期207卷 126-141页

作者： Menghao Liu Cuiwei Du Xiaogang Li Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China Key Laboratory for Corrosion and Protection of the Ministry of Education Institute of Advanced Materials & TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China

There remains debate on whether Mn is beneficial or detrimental to hydrogen embrittlement in stainless *** this work,a series of stainless steels were designed to study the change of hydrogen embrit-tlement sensitivity,crack propagation,and hydrogen trapping behaviors upon Mn *** results suggest that adding 4 wt.％Mn increased hydrogen embrittlement susceptibility,whereas adding 8 wt.％Mn decreased hydrogen embrittlement *** banded α'-martensite through austenitic grain is the main reason for the increased hydrogen embrittlement sensitivity when adding 4 wt.％Mn,by ad-sorbing hydrogen,promoting crack initiation,and accelerating crack propagation.

关键词： Hydrogen-assisted cracking Multiphase stainless steel Manganese

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Distributed predefined-time robust adaptive control design for attitude consensus of multiple spacecraft

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Advances in Space Research 2025年第10期75卷 7473-7486页

作者： Yao, Qijia Li, Qing Xie, Shumin Jahanshahi, Hadi School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education University of Science and Technology Beijing Beijing100083 China School of Mechanics and Civil Engineering and Architecture Northwestern Polytechnical University Xi'an710072 China Institute of Electrical and Electronics Engineers TorontoM5V3T9 Canada

This article investigates the distributed predefined-time attitude consensus of multiple rigid spacecraft under inertia uncertainties and external perturbations over directed communication topology. First, a distributed predefined-time observer is constructed for each follower spacecraft to estimate the virtual leader's attitude, velocity, and acceleration. Then, based on the recovered information, a predefined-time nonsingular terminal sliding mode (NTSM) controller is developed for each follower spacecraft to fulfill the local attitude and velocity tracking. The predefined-time NTSM controller is strongly robust against uncertainties and perturbations but owns the undesirable chattering phenomena. To overcome such problem, an adaptive version of the predefined-time NTSM controller is carried out by incorporating with the parametric adaptation mechanism to identify the total disturbance. In this manner, the predefined-time adaptive NTSM controller has no chattering phenomenon and can maintain the high tracking accuracy simultaneously. Lastly, comparative simulations verify and highlight the main results. © 2025 COSPAR

关键词： Sliding mode control

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ETFIDS: An Entropy-Driven, Time-Frequency Analysis Framework for In-Vehicle CAN Signal Intrusion Detection

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IEEE Internet of Things Journal 2025年

作者： Liu, Wanning Qin, Guihe Liang, Yanhua Song, Jiaru Liu, Qingxin Zhou, Xue Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Changchun130012 China

In recent years, cyberattacks against automobiles have exposed significant security threats to in-vehicle networks. The vulnerability of communication signals to malicious interference and manipulation can lead to serious system failures or abnormal behavior. The existing in-vehicle network intrusion detection methods do not fully exploit the time-frequency characteristics of Controller Area Network (CAN) signals. This limitation reduces their effectiveness in capturing subtle changes and signal complexity. Based on the above motivation, from the perspective of signal perception, we propose an entropy-driven, time-frequency analysis framework for in-vehicle network intrusion detection. The framework integrates a signal sampler, a frequency-domain detector, and a time-domain detector. The signal sampler, as the system's front-end module, extracts real-time physical signal data streams from CAN messages. The frequency-domain detector identifies frequency components, detecting high-frequency disturbances and cyclic variations. Meanwhile, the time-domain detector captures instantaneous changes and sudden anomalies. It analyzes signal complexity and anomalies through both stream and block detection. Experimental results demonstrate that the proposed method performs well under various attack scenarios, offering superior detection and real-time performance. It effectively senses multiple signal anomalies, providing a robust intrusion detection solution for modern in-vehicle networks. © 2025 IEEE.

关键词： Network intrusion

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Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction 31

Unified Grid Tagging Scheme for Aspect Sentiment Quad Predic...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Su, Guixin Zhang, Yongcheng Wang, Tongguan Wu, Mingmin Sha, Ying Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China College of Informatics Huazhong Agricultural University Wuhan China

ISBN: (纸本)9798891761964

Aspect Sentiment Quad Prediction (ASQP) aims to extract all sentiment elements in quads for a given review to explain the reason for the sentiment. Previous table-filling based methods have achieved promising results by modeling word-pair relations. However, these methods decompose the ASQP task into several subtasks without considering the association between sentiment elements. Most importantly, they fail to tackle the situation where a sentence contains multiple implicit expressions. To address these limitations, we propose a simple yet effective Unified Grid Tagging Scheme (UGTS) to extract sentiment quadruplets in one shot, with two additional special tokens from pre-trained models to represent potential implicit aspect and opinion terms. Based on this, we first introduce the adaptive graph diffusion convolution network to construct the direct connection between explicit and implicit sentiment elements from syntactic and semantic views. Next, we utilize conditional layer normalization to refine the mutual indication effect between words for matching valid aspect-opinion pairs. Finally, we employ the triaffine mechanism to integrate heterogeneous word-pair relations to capture higher-order interactions between sentiment elements. Experimental results on four benchmark datasets show the effectiveness and robustness of our model, which achieves state-of-the-art performance. © 2025 Association for Computational Linguistics.

关键词： Semantics

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Joint Double Auction-Based Channel Selection in Wireless Monitoring Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Xia, Na Chen, Lei Li, Meng Yin, Yutao Zhang, Ke Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China State Grid Electric Power Research Institute Co. Ltd Hefei230088 China

In wireless networks, utilizing sniffers for fault analysis, traffic traceback, and resource optimization is a crucial task. However, existing centralized algorithms cannot be applied to high-density wireless networks. Therefore, distributed optimization of channel selection to maximize the monitoring rate of sensors in Wireless Monitoring Networks (WMNs) is a challenge. This paper proposes a joint double auction-based distributed channel selection algorithm (J2A-CS) to maximize overall quality of monitoring (QoM). First, sniffers are redundantly deployed in WMNs, and an initial channel allocation strategy is formulated. Subsequently, sniffers collectively act as buyers and sellers at different stages. Finally, buyers bid asynchronously, and sellers settle synchronously to maximize the seller's marginal revenue and update the channel selection scheme. As a distributed channel selection algorithm, J2A-CS addresses the highest overall QoM issue in WMNs, demonstrating high scalability and fault tolerance. Simulation results show that J2A-CS significantly improves QoM compared to existing distributed algorithms and outperforms centralized algorithms in high-density scenarios. © 2004-2012 IEEE.

关键词： Sales

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