Neurocomputing最新文献_第3页

Multi-scale trend decomposition mixture of experts and time series retrieval-augmented modeling for erythromycin fermentation process 红霉素发酵过程多尺度趋势分解混合专家和时间序列检索增强模型

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-10-01 DOI: 10.1016/j.neucom.2025.131701

Yifei Sun , Xuefeng Yan

{"title":"Multi-scale trend decomposition mixture of experts and time series retrieval-augmented modeling for erythromycin fermentation process","authors":"Yifei Sun , Xuefeng Yan","doi":"10.1016/j.neucom.2025.131701","DOIUrl":"10.1016/j.neucom.2025.131701","url":null,"abstract":"<div><div>Multivariate time series (MTS) is the primary modality for storing data in real-world and industrial applications. In the context of batch fermentation processes, such data exhibit periodicity and repetition between samples, while demonstrating stage-wise and trending patterns within samples. Effectively leveraging historical production samples to uncover stage-specific characteristics and dynamic distribution patterns is a crucial approach for improving predictive accuracy. This paper proposes an MTS modeling framework that combines Retrieval-Augmented Generation (RAG) and a Mixture of Experts (MoE) model, i.e., <strong>M</strong>ulti-scale <strong>A</strong>ugmented <strong>S</strong>eries <strong>T</strong>rend <strong>E</strong>xperts with <strong>R</strong>etrieval, referred to as MASTER. We designed a general temporal feature augmentation method (MTS-RAG) to enhance predictive accuracy by efficiently completing contextual information during the data loading stage using representative historical samples. Additionally, we developed a multi-scale trend decomposition model based on the Kolmogorov-Arnold Network, which enhances both interpretability and predictive performance by independently modeling trend and seasonal components. Inspired by the success of sparse MoE in large language models, we introduce a Time Stage Router that employs temporal position embeddings and sparse gating structures to assist the model in identifying the current fermentation phase, thereby improving its generalization and practicality in multi-stage tasks. On an industrial dataset of erythromycin fermentation processes, MASTER achieved state-of-the-art predictive performance, and ablation studies further validated the effectiveness of its components.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"657 ","pages":"Article 131701"},"PeriodicalIF":6.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DRIFT: DCT-based robust and intelligent federated learning with trusted privacy 漂移：基于dct的鲁棒和智能联邦学习，具有可信任的隐私

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-30 DOI: 10.1016/j.neucom.2025.131697

Qihao Dong , Yang Bai , Mang Su , Yansong Gao , Anmin Fu

{"title":"DRIFT: DCT-based robust and intelligent federated learning with trusted privacy","authors":"Qihao Dong , Yang Bai , Mang Su , Yansong Gao , Anmin Fu","doi":"10.1016/j.neucom.2025.131697","DOIUrl":"10.1016/j.neucom.2025.131697","url":null,"abstract":"<div><div>Federated Learning (FL) allows collaborative model training across decentralized clients without sharing private data. However, traditional FL frameworks face dual challenges: vulnerability to Byzantine attacks (where malicious clients submit adversarial model updates) and privacy breaches (where curious clients infer sensitive information from exchanged parameters), exacerbated by decentralized operations and unencrypted communications. While existing work addresses robustness or privacy individually, the interplay between defense mechanisms, particularly the trade-off between attack resilience and utility degradation caused by privacy safeguards, remains understudied. To bridge this gap, we propose <em>DRIFT</em>, a novel FL framework that simultaneously achieves Byzantine robustness and privacy preservation. Our approach uniquely combines spectral analysis with cryptographic protection: By transforming model parameters into the frequency domain through Discrete Cosine Transform, <em>DRIFT</em> identifies malicious updates via spectral clustering while inherently obscuring sensitive parameter patterns. This defense mechanism is further reinforced by a privacy-preserving aggregation protocol leveraging fully homomorphic encryption with floating-point computation. It encrypts client updates during transmission and aggregation without compromising their computational usability. Extensive evaluations on MNIST and PathMNIST demonstrate that <em>DRIFT</em> outperforms baseline methods in resisting state-of-the-art Byzantine attacks while maintaining model utility and providing provable privacy guarantees.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"658 ","pages":"Article 131697"},"PeriodicalIF":6.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DJIST: Decoupled joint image and sequence training framework for sequential visual place recognition 序列视觉位置识别的解耦联合图像和序列训练框架

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-30 DOI: 10.1016/j.neucom.2025.131622

Shanshan Wan , Lai Kang , Yingmei Wei , Tianrui Shen , Haixuan Wang , Chao Zuo

{"title":"DJIST: Decoupled joint image and sequence training framework for sequential visual place recognition","authors":"Shanshan Wan , Lai Kang , Yingmei Wei , Tianrui Shen , Haixuan Wang , Chao Zuo","doi":"10.1016/j.neucom.2025.131622","DOIUrl":"10.1016/j.neucom.2025.131622","url":null,"abstract":"<div><div>Traditional image-to-image (im2im) visual place recognition (VPR) involves matching a single query image to stored geo-tagged database images. In real-time robotic and autonomous applications, while a continuous stream of frames naturally leads to a simpler sequence-to-sequence (seq2seq) VPR problem, the challenges remain since labeled sequential data is much scarcer than labeled individual images. A recent work addressed this by using a unified network optimized for both seq2seq and im2im tasks, but the resulting sequential descriptors are heavily dependent on the individual descriptors trained on the im2im task. This paper proposes a decoupled joint image and sequence training (DJIST) framework, using a frozen backbone and two independent sequential branches, where one branch is supervised by both im2im and seq2seq losses and the other solely by the seq2seq loss. The feature reduction procedures for generating individual descriptors and sequential descriptors are further separated in the former branch. An attention separation loss is employed between the two branches, which forces them to focus on different parts of the images to produce more informative sequential descriptors. We retrain various existing seq2seq methods using the same backbone and two types of joint training strategies for a fair comparison. Extensive experimental results demonstrate that our proposed DJIST outperforms its original counterpart JIST by 3.9 % to 18.8 % across four benchmark test cases and achieves state-of-the-art Recall@1 scores against retrained baselines on three key benchmarks with robust cross-dataset generalization, negligible degradation under dimensionality reduction, and superior robustness against varying test-time sequence lengths. Code will be available at <span><span>https://github.com/shuimushan/DJIST</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"658 ","pages":"Article 131622"},"PeriodicalIF":6.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalizable 3D Gaussian splatting via multi-view stereo and consistency constraints 通过多视图立体和一致性约束的可推广的三维高斯飞溅

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-30 DOI: 10.1016/j.neucom.2025.131696

Yongjuan Yang , Jie Cao , Hong Zhao , Weijie Wang

{"title":"Generalizable 3D Gaussian splatting via multi-view stereo and consistency constraints","authors":"Yongjuan Yang , Jie Cao , Hong Zhao , Weijie Wang","doi":"10.1016/j.neucom.2025.131696","DOIUrl":"10.1016/j.neucom.2025.131696","url":null,"abstract":"<div><div>Recent neural rendering methods still struggle with fine-grained detail reconstruction and scene generalization, especially when handling complex geometries and low-texture regions. To address these challenges, we propose a 3D Gaussian Splatting (3DGS) framework enhanced by Multi-view Stereo (MVS), aiming to improve both rendering quality and cross-scene adaptability. Specifically, we first introduce an Adaptive Perception-aware Feature Aggregation (APFA) module, which effectively fuses 2D image features into 3D geometry-aware representations via a Local Feature Adaptive Collaboration (LFAC) mechanism and a global Attention-Aware Module (AAM), significantly improving reconstruction performance in challenging scenes. Subsequently, we propose a depth and normal supervision strategy based on multi-view geometric consistency, where aggregated point clouds are utilized for optimized initialization, enhancing stability and fine-grained detail fidelity. Finally, a Gaussian geometric consistency regularization module is introduced to further enforce the coherence between depth and normal predictions, leading to more refined rendering results. Extensive experiments on standard benchmarks including DTU, Real Forward-facing, NeRF Synthetic, and Tanks and Temples demonstrate that our method outperforms state-of-the-art approaches in terms of PSNR, SSIM, and LPIPS metrics. Particularly in real-world complex scenes, our approach achieves superior generalization ability and perceptual quality, validating the effectiveness of the proposed framework. The code for our method will be made available at <span><span>https://github.com/yangyongjuan/MVS-APFA-GS</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"658 ","pages":"Article 131696"},"PeriodicalIF":6.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SiTrEx: Siamese transformer for feedback and posture correction on workout exercises SiTrEx：暹罗变压器，用于锻炼时的反馈和姿势纠正

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-30 DOI: 10.1016/j.neucom.2025.131703

Abdellah Sellam, Dounya Kassimi, Abdelhadi Djebana, Sara Mokhtari

{"title":"SiTrEx: Siamese transformer for feedback and posture correction on workout exercises","authors":"Abdellah Sellam, Dounya Kassimi, Abdelhadi Djebana, Sara Mokhtari","doi":"10.1016/j.neucom.2025.131703","DOIUrl":"10.1016/j.neucom.2025.131703","url":null,"abstract":"<div><div>Applying Machine Learning and Deep Learning techniques to sequences of Human Pose Landmarks to recognize workout exercises and count repetitions is widely studied in the computer vision literature. However, existing approaches suffer from two major problems. The first issue is that they lack the ability to provide detailed feedback on the postures performed by the athletes or provide feedback for a limited range of exercises using hand-designed rules and algorithms. The second problem is that these approaches consider only a predefined set of exercises and do not generalize to exercises outside their training data, which limits their usability. In this paper, we aim to address these two shortcomings by proposing a one-shot learning approach that utilizes Siamese Transformers to provide detailed feedback on individual human joints and can generalize to new exercises that are not present in the used dataset. The proposed configuration of the Siamese Transformer model deviates from its standard use in that it outputs a vector of similarity indicators rather than a single similarity score. Additionally, an accompanying binary classification Transformer model is used to assess the usefulness of different parts of the human pose for the input exercise without prior knowledge of the exercise itself. These properties allow the proposed approach to be used in general-purpose fitness applications and coach/athlete training platforms. The proposed approach achieved a 5-fold cross-validation test accuracy of <span><math><mn>94.4</mn><mspace></mspace><mi>%</mi><mo>±</mo><mn>0.8</mn></math></span> on the collected dataset.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"658 ","pages":"Article 131703"},"PeriodicalIF":6.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-supervised multi-blind network for real image denoising via multivariate Gaussian-poisson noise 基于多变量高斯泊松噪声的自监督多盲网络去噪

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-30 DOI: 10.1016/j.neucom.2025.131557

Hang Zhao, Zitong Wang, Xiaoli Zhang, Zhaojun Liu

{"title":"Self-supervised multi-blind network for real image denoising via multivariate Gaussian-poisson noise","authors":"Hang Zhao, Zitong Wang, Xiaoli Zhang, Zhaojun Liu","doi":"10.1016/j.neucom.2025.131557","DOIUrl":"10.1016/j.neucom.2025.131557","url":null,"abstract":"<div><div>The noise in real images exhibits more complex distributions than the synthetic noise and distinguishes across different scenarios. Furthermore, the scarcity of \"clean-to-noisy\" paired image datasets makes the current models difficult to denoise successfully. To address these challenges, we propose MGP-MBF<span><math><msup><mtext>M</mtext><mn>2</mn></msup></math></span>ANet, a self-supervised multi-blind feature multi-modulation attention network based on multivariate Gaussian-Poisson noise prior for real image denoising. Firstly, we propose a multivariate Gaussian-Poisson distribution to construct noisy images that contain more complex pixel spatial positions and intensity correlations, which expand the training domain and improve the model’s ability to generalize across diverse real noisy images. Building on this, we implement a random sampling mechanism based on four-neighborhood similarity to construct \"noise-noise\" training pairs, effectively exploiting the statistical properties of local structures in noisy images, without relying on any clean reference image. During the network design phase, a multi-blind feature multi-modulation attention module successfully enhances the representation of local features, which introduces multi-masked strategy to force network to learn more information to address the challenge of feature identity mapping. Experimental results demonstrate that the proposed method effectively suppresses noise and recovers high-frequency details within an unsupervised learning paradigm, achieving superior performance in both objective evaluation metrics and subjective visual quality across multiple real-world datasets.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"658 ","pages":"Article 131557"},"PeriodicalIF":6.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Node classification via simplicial interaction with augmented maximal clique selection 基于增广最大团选择的简单交互节点分类

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-30 DOI: 10.1016/j.neucom.2025.131705

Eunho Koo , Tongseok Lim

{"title":"Node classification via simplicial interaction with augmented maximal clique selection","authors":"Eunho Koo , Tongseok Lim","doi":"10.1016/j.neucom.2025.131705","DOIUrl":"10.1016/j.neucom.2025.131705","url":null,"abstract":"<div><div>Considering higher-order interactions allows for a more comprehensive understanding of network structures beyond simple pairwise connections. While leveraging all cliques in a network to handle higher-order interactions is intuitive, it often leads to computational inefficiencies due to overlapping information between higher-order and lower-order cliques. To address this issue, we propose an augmented maximal clique strategy. Although using only maximal cliques can reduce unnecessary overlap and provide a concise representation of the network, certain nodes may still appear in multiple maximal cliques, resulting in imbalanced training data. Therefore, our augmented maximal clique approach selectively includes some non-maximal cliques to mitigate the overrepresentation of specific nodes and promote more balanced learning across the network. Comparative analyses on synthetic networks and real-world citation datasets demonstrate that our method outperforms approaches based on pairwise interactions, all cliques, or only maximal cliques. Finally, by integrating this strategy into GNN-based semi-supervised learning, we establish a link between maximal clique-based methods and GNNs, showing that incorporating higher-order structures improves predictive accuracy. As a result, the augmented maximal clique strategy offers a computationally efficient and effective solution for higher-order network learning.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"658 ","pages":"Article 131705"},"PeriodicalIF":6.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MPFBL: Modal pairing-based cross-fusion bootstrap learning for multimodal emotion recognition 基于模态配对的多模态情感识别交叉融合自举学习

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-30 DOI: 10.1016/j.neucom.2025.131577

Yong Zhang , Yongqing Liu , HongKai Li , Cheng Cheng , Ziyu Jia

{"title":"MPFBL: Modal pairing-based cross-fusion bootstrap learning for multimodal emotion recognition","authors":"Yong Zhang , Yongqing Liu , HongKai Li , Cheng Cheng , Ziyu Jia","doi":"10.1016/j.neucom.2025.131577","DOIUrl":"10.1016/j.neucom.2025.131577","url":null,"abstract":"<div><div>Multimodal emotion recognition (MER), a key technology in human-computer interaction, deciphers complex emotional states by integrating heterogeneous data sources such as text, audio, and video. However, previous works either retained only private information or focused solely on public information, resulting in a conflict between the strategies used in each approach. Existing methods often lose critical modality-specific attributes during feature extraction or struggle to align semantically divergent representations across modalities during fusion, resulting in incomplete emotional context modeling. To address these challenges, we propose the Modal Pairing-based Cross-Fusion Bootstrap Learning (MPFBL) framework, which integrates modal feature extraction, cross-modal bootstrap learning, and multi-modal cross-fusion into a unified approach. Firstly, the feature extraction module employs a Uni-Modal Transformer (UMT) and a Multi-Modal Transformer (MMT) to jointly capture modality-specific and modality-invariant information, addressing feature degradation in single-encoder paradigms, while alleviating inter-modal heterogeneity by explicitly distinguishing between modality-specific and shared representations. Subsequently, cross-modal bootstrap learning employs attention-guided optimization to align heterogeneous modalities and refine modality-specific representations, enhancing semantic consistency. Finally, a multi-modal cross-fusion network integrates convolutional mapping and adaptive attention to dynamically weight cross-modal dependencies, mitigating spatial-semantic misalignment induced by inter-modal heterogeneity in fusion processes. Extensive experimental results on CMU-MOSEI and CMU-MOSI demonstrate that MPFBL outperforms state-of-the-art methods, while ablation studies further confirm its effectiveness.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"658 ","pages":"Article 131577"},"PeriodicalIF":6.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prototype-based multi-domain self-distillation for unbiased scene graph generation 基于原型的多域自蒸馏无偏场景图生成

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-30 DOI: 10.1016/j.neucom.2025.131625

Yuan Gao , Yaochen Li , Yujie Zang , Jingze Liu , Yuehu Liu

{"title":"Prototype-based multi-domain self-distillation for unbiased scene graph generation","authors":"Yuan Gao , Yaochen Li , Yujie Zang , Jingze Liu , Yuehu Liu","doi":"10.1016/j.neucom.2025.131625","DOIUrl":"10.1016/j.neucom.2025.131625","url":null,"abstract":"<div><div>Scene Graph Generation (SGG) plays an important role in reinforcing visual image understanding. Existing methods often encounter difficulties in effectively representing implicit relationship features, which limits their capacity to distinguish between predicates. Meanwhile, these approaches are susceptible to imbalanced instance distributions, hindering the efficient training of fine-grained predicates. To address these problems, we propose a novel prototype-based multi-domain self-distillation training framework. Specifically, a Multi-Domain Fusion (MDF) module is introduced to improve predicate feature representation by integrating global contextual information and local spatial-frequency domain information. Then, a Prototype Generation Network (PGN) is designed for building the class prototypes, which consists of the design of different granularity predicates and loss functions. Furthermore, we design two different data balancing strategies under the guidance of class prototypes, which correspond to mining the in-distribution and out-of-distribution information of the original data, respectively. The experimental results demonstrate that the proposed method is superior to the existing methods on VG, GQA and Open Images V6 datasets, which makes it more applicable to generating unbiased scene graph models.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"658 ","pages":"Article 131625"},"PeriodicalIF":6.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A small-sample cross-domain bearing fault diagnosis method based on knowledge-enhanced domain adversarial learning 基于知识增强域对抗学习的小样本跨域轴承故障诊断方法

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-30 DOI: 10.1016/j.neucom.2025.131699

Peiming Shi , Yan Zhao , Xuefang Xu , Dongying Han

引用次数: 0