Engineering Applications of Artificial Intelligence最新文献_第10页

Harnessing high-quality pseudo-labels for robust few-shot nested named entity recognition 利用高质量的伪标签进行鲁棒的少镜头嵌套命名实体识别

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-05-21 DOI: 10.1016/j.engappai.2025.110992

Hong Ming , Jiaoyun Yang , Shuo Liu , Lili Jiang , Ning An

{"title":"Harnessing high-quality pseudo-labels for robust few-shot nested named entity recognition","authors":"Hong Ming , Jiaoyun Yang , Shuo Liu , Lili Jiang , Ning An","doi":"10.1016/j.engappai.2025.110992","DOIUrl":"10.1016/j.engappai.2025.110992","url":null,"abstract":"<div><div>Few-shot Named Entity Recognition (NER) methods have shown initial effectiveness in flat NER tasks. However, these methods often prioritize optimizing models with a small annotated support set, neglecting the high-quality data within the unlabeled query set. Furthermore, existing few-shot NER models struggle with nested entity challenges due to linguistic or structural complexities. In this study, we introduce <strong>R</strong>etrieving h<strong>i</strong>gh-quality pseudo-label <strong>T</strong>uning, RiTNER, a framework designed to address few-shot nested named entity recognition tasks by leveraging high-quality data from the query set. RiTNER comprises two main components: (1) contrastive span classification, which clusters entities into corresponding prototypes and generates high-quality pseudo-labels from the unlabeled data, and (2) masked pseudo-data tuning, which generates a masked pseudo dataset and then uses it to optimize the model and enhance span classification. We train RiTNER on an English dataset and evaluate it on both English nested datasets and cross-lingual nested datasets. The results show that RiTNER outperforms the top-performing baseline models by 1.67%, and 3.04% in the English 5-shot task, as well as the cross-lingual 5-shot tasks, respectively.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"156 ","pages":"Article 110992"},"PeriodicalIF":7.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108144","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

Critical success factors for implementing artificial intelligence in construction projects: A systematic review and social network analysis 在建设项目中实施人工智能的关键成功因素：系统回顾和社会网络分析

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-05-21 DOI: 10.1016/j.engappai.2025.111192

Ibrahim Yahaya Wuni

{"title":"Critical success factors for implementing artificial intelligence in construction projects: A systematic review and social network analysis","authors":"Ibrahim Yahaya Wuni","doi":"10.1016/j.engappai.2025.111192","DOIUrl":"10.1016/j.engappai.2025.111192","url":null,"abstract":"<div><div>Artificial intelligence (AI) is increasingly deployed to automate routine tasks, generate accurate insights from big data, and build predictive models to inform better decision-making in construction projects. However, AI deployment in construction projects constitutes a sociotechnical process, such that adopting solely a technical approach becomes inadequate. This study investigated the critical success factors for implementing AI in construction projects. It combined a systematic literature review, meta-analysis, and social network analysis to evaluate the scientific evidence on the critical success factors, and quantitatively reveal the underrepresented factors. The meta-analysis identified 38 critical success factors, ranked according to normalized scores and degree centralities. The study derived four dimensions of the critical success factors, including organizational, technological, stakeholder, and data success factors. The social network analysis quantitatively revealed the strengths and existing gaps in the reviewed studies and provide insights into factors that need further investigation.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"156 ","pages":"Article 111192"},"PeriodicalIF":7.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108147","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 hybrid architecture based on structured state space sequence model and convolutional neural network for real-time object detection 一种基于结构化状态空间序列模型和卷积神经网络的实时目标检测混合体系结构

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-05-21 DOI: 10.1016/j.engappai.2025.111067

Jie Chen, Meng Joo Er

{"title":"A hybrid architecture based on structured state space sequence model and convolutional neural network for real-time object detection","authors":"Jie Chen, Meng Joo Er","doi":"10.1016/j.engappai.2025.111067","DOIUrl":"10.1016/j.engappai.2025.111067","url":null,"abstract":"<div><div>Real-time performance is essential for practical deployment of object detection on edge devices, where high processing speed and low latency are paramount. This paper introduces a novel approach aimed at boosting real-time object detection while strictly adhering to computational constraints. A structured state space sequence model, Mamba, is strategically embedded in the early stages of the backbone network to capture long-range dependencies, thereby enhancing the model’s representation capability. Given the limitations of Mamba in directional perception, a lightweight spatial attention mechanism is introduced to integrate global context into each spatial location. Additionally, a computationally efficient module inspired by the Ghost module is developed to reduce resource demands. This dual-strategy approach optimizes both performance and efficiency in real-time object detection. Extensive experiments demonstrate the superiority of this proposed approach; on the Microsoft Common Objects in Context (MS COCO) dataset, it achieves a +1.6 AP (Average Precision) improvement over state-of-the-art methods, reaching 41.1 AP with minimal added model complexity on the nano scale. The effectiveness and efficiency of each component are further substantiated through ablation studies on the Pascal Visual Object Classes (Pascal VOC dataset). To verify the universality of the proposed method, this study selects underwater object detection, characterized by an extremely complex background environment, as the other validation scenario. Through the application of this proposed approach to underwater object detection, a state-of-the-art result of 69.5 AP was obtained on the Detecting Underwater Objects (DUO) dataset, exceeding that of You Only Look Once Detector version 11 (YOLO11) by +0.3 AP. Code: <span><span>https://github.com/chenjie04/Hybrid-YOLO</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"156 ","pages":"Article 111067"},"PeriodicalIF":7.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107909","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

Decimal place separable complex convolutional neural network for wideband beamforming 用于宽带波束形成的小数点可分复杂卷积神经网络

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-05-21 DOI: 10.1016/j.engappai.2025.111057

Hairui Zhu , Bi Wen , Cong Xue , Jie Luo , Jiali Li , Shurui Zhang

{"title":"Decimal place separable complex convolutional neural network for wideband beamforming","authors":"Hairui Zhu , Bi Wen , Cong Xue , Jie Luo , Jiali Li , Shurui Zhang","doi":"10.1016/j.engappai.2025.111057","DOIUrl":"10.1016/j.engappai.2025.111057","url":null,"abstract":"<div><div>With the rapid development of synthetic aperture radar and increasing demand for remote sensing, wideband beamforming technology has been a hot spot. Recently, deep learning from computer science has given a hint for the next generation of beamforming. Many neural network-based beamformers have been reported. However, those methods show disadvantages on wideband signals. Due to the precision limitations of current computing devices, neural networks may encounter precision errors when generating extremely small numerical values. However, the performance of beamforming is still highly sensitive to small numerical values. Existing neural network-based methods produce decreased performance due to those errors. To enhance the precision of generation and achieve a better trade-off between performance and efficiency, we propose a generation mechanism with high precision at the framework level. In this paper, the decimal place separable complex convolutional neural network (DSCCNN) is proposed for wideband beamforming. Firstly, we apply different networks to handle distinct decimal places contributing to a mixture-of-experts framework, which can increase the fitting precision. Then, multilayer perceptrons are used to enhance the learning capabilities of the proposed network’s backbone referring to current popular computer vision network architectures. Last, an improved attention module is proposed to better process the different parts of complex-valued feature maps based on the squeeze-and-excitation module. Simulation experiments show the proposed beamforming method has excellent performance in anti-jamming. The computational complexity of the proposed method is low, which is beneficial for potential engineering applications. In addition, the proposed network can be trained within a very short time.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"156 ","pages":"Article 111057"},"PeriodicalIF":7.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107983","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

Deep learning-based real-time estimation of transcranial focused ultrasound acoustic field 基于深度学习的经颅聚焦超声声场实时估计

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-05-20 DOI: 10.1016/j.engappai.2025.111157

Minyeong Jang , Minwook Choi , Insu Jeong , Seung-Schik Yoo , Kyungho Yoon , Gunwoo Noh

{"title":"Deep learning-based real-time estimation of transcranial focused ultrasound acoustic field","authors":"Minyeong Jang , Minwook Choi , Insu Jeong , Seung-Schik Yoo , Kyungho Yoon , Gunwoo Noh","doi":"10.1016/j.engappai.2025.111157","DOIUrl":"10.1016/j.engappai.2025.111157","url":null,"abstract":"<div><div>Transcranial focused ultrasound (tFUS) techniques have garnered considerable attention as a novel noninvasive brain stimulation modality due to their high spatial specificity and depth penetration. However, estimating the intensity, location, and shape of the ultrasound focus is challenging due to wave distortion through the inhomogeneous skull. Because conventional imaging methods cannot capture low-intensity acoustic foci, numerical simulations are required to estimate intracranial pressure fields. However, such simulations are computationally intensive, limiting real-time use. In this study, we introduce a deep learning-based surrogate model to enable real-time estimation of the intracranial acoustic field distribution of tFUS. The proposed model effectively captures skull computed tomography (CT) features via a pre-trained deep neural network and includes two modules: one predicts acoustic field distributions, and the other estimates peak pressure values to enhance overall accuracy. The model was trained using data from 13 cranial CT scans and validated against direct field measurements from three <em>ex vivo</em> calvaria. The proposed model demonstrated high accuracy in focal point estimation, achieving a peak pressure ratio error of 3.94 % and a focal position error of 2.46 mm, indicating precise localization of the ultrasound focus. For focal volume prediction, the model exhibited a maximum boundary error of 5.90 mm while maintaining a focal volume conformity of 81 %. Notably, the inference time was 16 ms, which is significantly faster than conventional numerical simulations, ensuring feasibility for real-time applications. This method facilitates precise intracranial targeting, significantly enhancing clinical viability of tFUS for therapeutic applications, including functional neuromodulation.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"156 ","pages":"Article 111157"},"PeriodicalIF":7.5,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090581","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 framework for super-resolution of side-scan sonar images: Combination of variational Bayes and regional feature selection 侧扫声纳图像的超分辨率框架：变分贝叶斯与区域特征选择的结合

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-05-20 DOI: 10.1016/j.engappai.2025.111007

Xin Wen , Chensheng Cheng , Lu Li , Feihu Zhang , Guang Pan

{"title":"A framework for super-resolution of side-scan sonar images: Combination of variational Bayes and regional feature selection","authors":"Xin Wen , Chensheng Cheng , Lu Li , Feihu Zhang , Guang Pan","doi":"10.1016/j.engappai.2025.111007","DOIUrl":"10.1016/j.engappai.2025.111007","url":null,"abstract":"<div><div>Side-scan sonar is widely used in ocean exploration due to its broad search range and strong identification capabilities. However, the inherent characteristics of acoustic images often result in poor image quality, negatively impacting subsequent downstream tasks’ accuracy. Image super-resolution (SR) technology based on deep learning technology is employed to address this issue. Despite this, existing SR models face two main challenges when applied to side-scan sonar images: (1) less data in side-scan sonar images causes the model overfitting problem; (2) less effective features in side-scan sonar images cause lower efficiency. To overcome these challenges, this paper proposes a deep learning framework that integrates a Bayesian structure with region-based feature selection. First, we introduce a rolling region selection method to extract key features of interest from side-scan sonar images, enhancing efficiency without compromising quality. Additionally, we replace traditional Convolutional Neural Networks (CNN) with Variational Bayes Convolutional Neural Networks (VB-CNN) to perform the SR task, improving generalization on small datasets and mitigating the risk of overfitting. Experiments conducted on the Side-Scan Sonar Visual Object Classes (SSS-VOC) dataset and other datasets demonstrate our proposed approach’s effectiveness through both qualitative and quantitative comparisons.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"155 ","pages":"Article 111007"},"PeriodicalIF":7.5,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089567","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

Weakly supervised temporal action localization via a multimodal feature map diffusion process 基于多模态特征映射扩散过程的弱监督时间动作定位

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-05-20 DOI: 10.1016/j.engappai.2025.111044

Yuanbing Zou , Qingjie Zhao , Shanshan Li

{"title":"Weakly supervised temporal action localization via a multimodal feature map diffusion process","authors":"Yuanbing Zou , Qingjie Zhao , Shanshan Li","doi":"10.1016/j.engappai.2025.111044","DOIUrl":"10.1016/j.engappai.2025.111044","url":null,"abstract":"<div><div>With the continuous growth of massive video data, understanding video content has become increasingly important. Weakly supervised temporal action localization (WTAL), as a critical task, has received significant attention. The goal of WTAL is to learn temporal class activation maps (TCAMs) using only video-level annotations and perform temporal action localization via post-processing steps. However, due to the lack of detailed behavioral information in video-level annotations, the separability between foreground and background in the learned TCAM is poor, leading to incomplete action predictions. To this end, we leverage the inherent advantages of the Contrastive Language-Image Pre-training (CLIP) model in generating high-semantic visual features. By integrating CLIP-based visual information, we further enhance the representational capability of action features. We propose a novel multimodal feature map generation method based on diffusion models to fully exploit the complementary relationships between modalities. Specifically, we design a hard masking strategy to generate hard masks, which are then used as frame-level pseudo-ground truth inputs for the diffusion model. These masks are used to convey human behavior knowledge, enhancing the model’s generative capacity. Subsequently, the concatenated multimodal feature maps are employed as conditional inputs to guide the generation of diffusion feature maps. This design enables the model to extract rich action cues from diverse modalities. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popular benchmarks. These results highlight the proposed method’s capability to achieve precise and efficient temporal action detection under weak supervision, making a significant contribution to the advancement in large-scale video data analysis.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"156 ","pages":"Article 111044"},"PeriodicalIF":7.5,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090583","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

Multi-class Agent Trajectory Prediction with Selective State Spaces for autonomous driving 基于选择状态空间的自动驾驶多类智能体轨迹预测

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-05-19 DOI: 10.1016/j.engappai.2025.111027

Jin Fan , Zhanwen Liu , Yong Fang , Zeyu Huang , Yang Liu , Shan Lin

{"title":"Multi-class Agent Trajectory Prediction with Selective State Spaces for autonomous driving","authors":"Jin Fan , Zhanwen Liu , Yong Fang , Zeyu Huang , Yang Liu , Shan Lin","doi":"10.1016/j.engappai.2025.111027","DOIUrl":"10.1016/j.engappai.2025.111027","url":null,"abstract":"<div><div>Understanding and predicting multi-class agents’ movement has become more critical and challenging in diverse applications such as autonomous driving and urban intelligent monitoring. The current research mainly focuses on the motion trajectory of single-class agents. However, due to real traffic scenarios’ complexity and interactive behaviors’ variability, the motion patterns displayed by various classes of agents show inherent randomness. In this paper, inspired by the linear-time sequence model Mamba, we propose a Multi-class Agent Trajectory Prediction with Selective State Spaces (MTPSS) to model the interaction between different agents and better predict the trajectory of an individual. Specifically, MTPSS includes modeling relationships in both temporal and spatial dimensions. When encoding the spatial correlation within the trajectory graph, we construct a category-based sorting approach, which puts large-size category nodes behind to enhance contextual access. Then the sorted nodes are bi-directionally scanned through Mamba blocks, which makes the model more robust to permutations. In terms of temporal, considering the highly dynamic nature of rapidly moving agents, we utilize Mamba’s remarkable performance on sequential data to effectively conduct temporal scans to capture long-range temporal dependencies. Finally, to compute physically feasible trajectories, MTPSS employs the Neural Ordinary Differential Equation to smooth the predicted trajectory of the agent. We conducted extensive experiments on two publicly available traffic datasets and compared our method with state-of-the-art methods. Quantitative experiments show that our performance metrics are superior to state-of-the-art methods, and qualitative experiments demonstrate that the predicted trajectories have good diversity, which shows its potential in real-world traffic scenarios.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"156 ","pages":"Article 111027"},"PeriodicalIF":7.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084069","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

Probabilistic intervals prediction based on adaptive regression with attention residual connections and covariance constraints 基于注意残差连接和协方差约束的自适应回归概率区间预测

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-05-19 DOI: 10.1016/j.engappai.2025.111013

Fan Zhang , Min Wang , Lin Li , Yepeng Liu , Hua Wang

引用次数: 0

An efficient technique for reversible data hiding using bidirectional histogram shifting and multistage embedding 一种利用双向直方图移位和多级嵌入的有效的可逆数据隐藏技术

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-05-19 DOI: 10.1016/j.engappai.2025.110986

Sanjay Kumar , Gurjit Singh Walia , Anjana Gupta

{"title":"An efficient technique for reversible data hiding using bidirectional histogram shifting and multistage embedding","authors":"Sanjay Kumar , Gurjit Singh Walia , Anjana Gupta","doi":"10.1016/j.engappai.2025.110986","DOIUrl":"10.1016/j.engappai.2025.110986","url":null,"abstract":"<div><div>Reversible Data Hiding has been extensively investigated due to its myriad applications in different fields such as defense, medical, cloud storage, and secure data communication over public networks. However, most of the techniques suffer due to concerns about data capacity, communication overhead, and data security. To solve these concerns, a novel technique has been proposed that ensures high security and optimum capacity, wherein no additional overhead is required to be transmitted through a separate channel. For this, bidirectional embedding of secret data has been proposed wherein both the left and right peaks from the main histogram peak have been exploited for data embedding, thereby achieving high embedding capacity. Data embedding is performed in multistage in both plain and encrypted domains to achieve not only optimum quality but also high security. The secret data embedding capacity is also tunable, wherein the number of bits per pixel can be determined from the size of the cover and the amount of secret data. In addition, block-wise embedding of secret data further enhances the data embedding capacity. The proposed technique is demonstrated to perform better in comparison to the state-of-the-art techniques while, experimental analysis over different types of cover by different performance metrics was done. On average, the PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) values of 43.22 dB and 0.9984 respectively, is obtained for one bit per peak pixel embedding.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"156 ","pages":"Article 110986"},"PeriodicalIF":7.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090580","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