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BrailleSegNet: A novel methodology for Braille dataset generation and character segmentation BrailleSegNet：一种新的盲文数据集生成和字符分割方法

IF 3.7 2区工程技术

Displays Pub Date : 2025-07-08 DOI: 10.1016/j.displa.2025.103145

Shana Sherin M., Shyna A., Jini Raju, Reena Mary George

{"title":"BrailleSegNet: A novel methodology for Braille dataset generation and character segmentation","authors":"Shana Sherin M., Shyna A., Jini Raju, Reena Mary George","doi":"10.1016/j.displa.2025.103145","DOIUrl":"10.1016/j.displa.2025.103145","url":null,"abstract":"<div><div>Recent research in the field of Braille learning has highlighted vital role of accurately segmenting Braille letters from Braille documents to improve accessibility and educational opportunities for visually impaired children. A novel methodology, BrailleSegNet, is proposed for Braille Dataset Generation and Braille character segmentation, structured into six distinct phases: Image Acquisition, Image Preprocessing, Fixed-Sized Square Conversion, Rows Extraction, Zonal Operations, and Braille Character Extraction. The initial phase involves acquiring images from the Braille-TextStory dataset, followed by preprocessing steps including grayscale conversion, binary conversion, Gaussian filtering for noise removal, and image inversion. Subsequently, the method standardizes the varying sizes and shapes of Braille dots into fixed-sized squares, extracts rows containing Braille characters, and performs zonal operations such as vertical dilation, zone identification, full zone conversion, and space zone addition to accurately segment and recognize Braille characters. The final phase extracts Braille characters from the designated full zones. Addressing the scarcity of datasets with appropriate ground truth reflecting real-world Braille document scenarios, a new dataset, Braille-TextStory, was created as part of this work. This dataset includes short stories in English, generated using the Braille-PageMap algorithm for evaluating Braille character segmentation techniques. The Braille-TextStory dataset maps English letters to their corresponding Braille images, accurately placing them on plain pages with proper management of parameters such as letter spacing, word spacing, and line spacing to preserve the integrity and readability of Braille documents. The proposed segmentation methodology was tested using this dataset, demonstrating a high level of effectiveness and accuracy compared to state-of-the-art methods.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103145"},"PeriodicalIF":3.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Underwater image enhancement by jointly exploiting RGB and polarization modalities 联合利用RGB和偏振模式的水下图像增强

IF 3.7 2区工程技术

Displays Pub Date : 2025-07-07 DOI: 10.1016/j.displa.2025.103143

Yushan Wang, Jiqing Zhang, Zixuan Wan, Xinbo Zhang, Yafei Wang, Xianping Fu

{"title":"Underwater image enhancement by jointly exploiting RGB and polarization modalities","authors":"Yushan Wang, Jiqing Zhang, Zixuan Wan, Xinbo Zhang, Yafei Wang, Xianping Fu","doi":"10.1016/j.displa.2025.103143","DOIUrl":"10.1016/j.displa.2025.103143","url":null,"abstract":"<div><div>Underwater images often suffer from severe quality degradation due to light absorption and scattering in the water medium. Most existing underwater image enhancement (UIE) methods rely solely on RGB inputs, which lack the capability to distinguish between scattered and reflected light, thus limiting their performance. In contrast, polarization imaging offers the potential to disentangle physical components and preserve surface structures by capturing the polarization state of light. This paper thus proposes a novel RGB-polarization multimodal fusion framework for UIE tasks. Specifically, we first present a Polarization Feature Extractor (PFE) to capture direction-dependent polarization responses via multi-dimensional interaction modeling. In addition, a cross-modal fusion module is introduced to effectively and adaptively combine meaningful cues from both RGB and polarization domains. The effectiveness is enforced by the channel attention mechanism and the spatial attention mechanism to improve feature representation; the adaptiveness is facilitated by a specifically designed weighting scheme that balances the contributions of the two domains. Extensive experiments show that the proposed approach outperforms state-of-the-art underwater image enhancement methods in terms of both full-reference and non-reference metrics. Furthermore, the contribution of each key component is validated through comprehensive ablation study.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103143"},"PeriodicalIF":3.7,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving usability and safety: Exploring the impact of information hierarchical structure design in HUDs system 提高可用性和安全性：探讨信息分层结构设计对hud系统的影响

IF 3.7 2区工程技术

Displays Pub Date : 2025-07-05 DOI: 10.1016/j.displa.2025.103148

Ronghua Li, Rui Li, Haibo Yin

引用次数: 0

Semi-supervised echocardiography segmentation via cross-center invariant prior 基于交叉中心不变先验的半监督超声心动图分割

IF 3.7 2区工程技术

Displays Pub Date : 2025-07-05 DOI: 10.1016/j.displa.2025.103152

Lixin Xu , Yiman Liu , Deng Chen , Xiaoxiang Han , Tongtong Liang , Jianke Xia

{"title":"Semi-supervised echocardiography segmentation via cross-center invariant prior","authors":"Lixin Xu , Yiman Liu , Deng Chen , Xiaoxiang Han , Tongtong Liang , Jianke Xia","doi":"10.1016/j.displa.2025.103152","DOIUrl":"10.1016/j.displa.2025.103152","url":null,"abstract":"<div><div>Echocardiography is a clinically significant diagnostic tool, and segmenting heart chambers from echocardiograms holds great clinical importance. Semi-supervised learning can effectively reduce the amount of annotated data required for deep learning segmentation models. However, most existing methods tend to overfit on single-center training data, struggling with cross-center generalization. To alleviate this, we propose a novel semi-supervised framework crafted to comprehensively leverage cross-center transferable image prior that each image can be decomposed into complementary low-frequency content details and high-frequency structural characteristics. Specifically, we decompose each image into high-frequency and low-frequency components, input them parallelly into Mamba-UNet, and enforce consistency between their outputs and the output of the original image input into U-Net. This can be regarded as image-level and network-level perturbations. Additionally, we introduce evidential deep learning to further enhance the robustness of the model. More importantly, our consistency regularization promotes consistency in evidence for predictions between the original image and its decomposed frequency components, aiding in learning image feature embeddings and uncertainty that generalize across centers. Experimental results demonstrate the competitiveness of our proposed method.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103152"},"PeriodicalIF":3.7,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimized, color-adaptive blue light filtering approach using a novel color space transformation 利用一种新颖的色彩空间变换，提出了一种优化的自适应蓝光滤波方法

IF 3.7 2区工程技术

Displays Pub Date : 2025-07-04 DOI: 10.1016/j.displa.2025.103124

Juan Bayón, Joaquín Recas, María Guijarro

{"title":"An optimized, color-adaptive blue light filtering approach using a novel color space transformation","authors":"Juan Bayón, Joaquín Recas, María Guijarro","doi":"10.1016/j.displa.2025.103124","DOIUrl":"10.1016/j.displa.2025.103124","url":null,"abstract":"<div><div>Wearable screens are part of everyday life, but the blue light they emit can affect the human body. Known as the Blue Hazard, high-energy blue light has been linked to circadian rhythm disruption, reduced focus, cognitive functions, and Computer Vision Syndrome. As screens move closer to the eyes, especially in users with pre-existing eye conditions, effective filtering becomes increasingly important.</div><div>This work presents a blue light filter that processes images in a novel color space, selectively reducing high-energy pixels while preserving most colors. After filtering, both contrast and image quality remain virtually unchanged, according to several widely used metrics.</div><div>Physical measurements showed that blue light absorption exceeded theoretical expectations. Spectrophotometric tests across various screens demonstrated consistent performance—typically reducing 30%–40% of blue light for a color difference (<span><math><mrow><mi>Δ</mi><mi>E</mi></mrow></math></span>) of 10, with absorption reaching up to 100%. Compared to f.lux and Night Shift, our filter reduces blue emissions by 17% and 34% more, respectively. With an average processing time of 0.012 s per image using basic parallelization (up to 85 Hz), it is well-suited for modern wearable and electronic devices.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103124"},"PeriodicalIF":3.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on HIFU diagnosis and treatment assistance system: Prediction and optimization of desmoid tumor treatment based on dynamic-static feature interaction parallel network HIFU诊疗辅助系统研究：基于动-静态特征交互并行网络的硬纤维瘤治疗预测与优化

IF 3.7 2区工程技术

Displays Pub Date : 2025-07-03 DOI: 10.1016/j.displa.2025.103149

Wenjing Liu , Peng Zhao , Zuozhou Pan , Lingying Wang , Yiming Ma , Xiyu Pan , Yuebing Wang , Xiaoye Hu , Hong Shen , Junsheng Jiao

{"title":"Research on HIFU diagnosis and treatment assistance system: Prediction and optimization of desmoid tumor treatment based on dynamic-static feature interaction parallel network","authors":"Wenjing Liu , Peng Zhao , Zuozhou Pan , Lingying Wang , Yiming Ma , Xiyu Pan , Yuebing Wang , Xiaoye Hu , Hong Shen , Junsheng Jiao","doi":"10.1016/j.displa.2025.103149","DOIUrl":"10.1016/j.displa.2025.103149","url":null,"abstract":"<div><div>High-intensity focused ultrasound (HIFU) ablation is recognized as an effective treatment for desmoid tumors. However, the treatment process is heavily reliant on the clinical expertise of physicians. Moreover, limitations caused by the scarcity and heterogeneity of medical data restrict the effectiveness of feature extraction and accurate modeling. To address these challenges, a medical data-driven intelligent assistance system is proposed for HIFU treatment. Initially, an improved stacked weighted random forest is employed for data augmentation, where weighting strategies, enhanced feature matrix methods, and multi-model stacking techniques are utilized to strengthen critical features. Then, the enhanced HIFU treatment data are extracted utilizing a parallel static and dynamic feature-fusing network equipped with an enhanced module. Within this network, the static branche promotes the accurate capturing of complex feature relationships and the efficient integration of key information through feature space transformation and a global context modeling mechanism for discrete patient-related features. The dynamic branch combined with a learnable activation function accurately extracts complex nonlinear relationships related to the treatment process. Finally, a cross-attention mechanism is introduced to realize the positive and negative simultaneous enhancement between static and dynamic features, fully revealing the spatio-temporal correlation between dynamic and static features, and improving the comprehensive performance of the model. Finally, the effectiveness and superiority of the proposed method are verified through simulation and experiment. The proposed method is integrated into the HIFU assisted diagnostic and treatment framework to enhance decision-making in HIFU treatment modeling.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103149"},"PeriodicalIF":3.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial-Temporal Transformer for point cloud registration in digital modeling of complex environments 复杂环境数字建模中点云配准的时空转换器

IF 3.7 2区工程技术

Displays Pub Date : 2025-07-03 DOI: 10.1016/j.displa.2025.103139

Li An , Pengbo Zhou , Mingquan Zhou , Yong Wang , Guohua Geng , Wuyang Shui , Wen Tang

{"title":"Spatial-Temporal Transformer for point cloud registration in digital modeling of complex environments","authors":"Li An , Pengbo Zhou , Mingquan Zhou , Yong Wang , Guohua Geng , Wuyang Shui , Wen Tang","doi":"10.1016/j.displa.2025.103139","DOIUrl":"10.1016/j.displa.2025.103139","url":null,"abstract":"<div><div>Building sustainable cities and societies requires precise spatial data to support high-accuracy digital modeling and environmental analysis. Terrestrial Laser Scanning (TLS) provides detailed 3D point cloud data, but these data are often segmented into multiple local datasets due to measurement range and environmental limitations, making point cloud registration a critical step for achieving comprehensive environmental representation. However, point cloud registration faces challenges in low-overlap, large-scale, and cross-dataset scenarios. To address these issues, this paper proposes a Spatial-Temporal Transformer-based point cloud registration method (TransPCR), designed specifically for multi-temporal data fusion in complex urban environments. The key innovation of this method is the use of dual-branch position encoding and a Spatial-Temporal Transformer for multi-level point cloud information interaction. The dual-branch position encoding combines local features and coordinates, enhancing the model’s ability to represent complex spatial structures and improving accuracy in low-overlap scenarios. The core Spatial-Temporal Transformer module further facilitates interaction between local positions and features, enabling the model to meet large-scale registration requirements. Additionally, the Temporal Transformer module achieves local-to-global fusion, promoting the learning and extraction of internal point cloud features. Tested on the 3DMatch and KITTI datasets and validated on WHU-TLS and ETH datasets, including complex scenes like urban areas, rivers, and forests. TransPCR demonstrates outstanding registration accuracy, indicating its potential in multi-source data integration and applications within complex environments.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103139"},"PeriodicalIF":3.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MEFSCFFormer: Multiscale edge-aware fusion block with stereo cross Fourier transformer for stereo image super-resolution and diffusion-based image enhancement MEFSCFFormer：多尺度边缘感知融合块与立体交叉傅立叶变压器用于立体图像超分辨率和基于扩散的图像增强

IF 3.7 2区工程技术

Displays Pub Date : 2025-07-03 DOI: 10.1016/j.displa.2025.103153

Zihao Zhou, Yongfang Wang, Zhihui Gao

{"title":"MEFSCFFormer: Multiscale edge-aware fusion block with stereo cross Fourier transformer for stereo image super-resolution and diffusion-based image enhancement","authors":"Zihao Zhou, Yongfang Wang, Zhihui Gao","doi":"10.1016/j.displa.2025.103153","DOIUrl":"10.1016/j.displa.2025.103153","url":null,"abstract":"<div><div>Current stereo super-resolution (SR) methods present significant challenges in the effective exploitation of intra-view and inter-view features, especially in how to simultaneously maintain structural coherence and high-frequency detail recovery. To address these challenges, we propose Multiscale Edge-Aware Fusion Block with Stereo Cross Fourier Transformer(MEFSCFFormer) to better utilize intra-view and inter-view information for feature extraction, alignment and fusion. Proposed Multiscale Edge-Aware Fusion Block(MEFB) integrates the Multiscale Edge-Enhanced Mobile Convolution Block Module(MEMB) and the Multi-level Decentralized Mixed Pooled Spatial Attention Module(MDMPSA) to achieve efficient fusion of global and local features, which also combines edge information to better capture structural details that are consistent across viewpoints. To further enhance inter-view information, we design a Stereo Cross Fourier Transformer Module(SCFFormer) that adaptively selects and enhances cross-view-consistent frequency components in stereo images that contribute to the recovery. Besides the MEFSCFFormer can access the Diffusion model and fine-tune the supervised fine-tuning layer to further improve SR subjective quality. This approach overcomes the shortcomings of existing stereo image processing methods in viewpoint-consistent processing and significantly improves the accuracy and detail fidelity of stereo image restoration. We have conducted extensive experiments on several public datasets (Flickr1024 [<span><span>1</span></span>], KITTI2012 [<span><span>2</span></span>], KITTI2015 [<span><span>3</span></span>] and Middlebury [<span><span>4</span></span>]). The experimental results show that our method excels in several evaluation metrics compared to other state-of-the-art methods, especially in maintaining a new level of detail accuracy and structural consistency.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103153"},"PeriodicalIF":3.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Instance-level feature representation calibration for visual object detection 用于视觉目标检测的实例级特征表示校准

IF 3.7 2区工程技术

Displays Pub Date : 2025-07-03 DOI: 10.1016/j.displa.2025.103130

Hua Zhang , Jingzhi Li , Wenqi Ren , Chaopeng Li , Xiaochun Cao

{"title":"Instance-level feature representation calibration for visual object detection","authors":"Hua Zhang , Jingzhi Li , Wenqi Ren , Chaopeng Li , Xiaochun Cao","doi":"10.1016/j.displa.2025.103130","DOIUrl":"10.1016/j.displa.2025.103130","url":null,"abstract":"<div><div>Few-shot object detection has gained significant attention due to the scarcity of training samples in real-world applications. Most existing methods attempt to transfer knowledge learned from abundant base classes to novel class detection, typically following a two-stage process: base training and fine-tuning. While these detectors excel at object localization, they often struggle with classification due to biased feature representations of instances. In this paper, we propose a novel framework for feature representation learning in few-shot object detection, aimed at refining the instance representation using a prototype-based supervised contrastive learning approach. Specifically, we design a prototype representation bank that serves as a template for supervised contrastive learning and introduce a positive example learning strategy to obtain generalized and discriminative object features. Additionally, we introduce a balanced cross-entropy loss that dynamically adjusts the weightings of gradients from positive and negative samples, thereby enhancing the confidence in object recognition. Extensive experiments on the Pascal VOC and MS-COCO benchmarks show that our method achieves state-of-the-art performance, with significant improvements across most splits and shot settings.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103130"},"PeriodicalIF":3.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SFP-Net: Scatter-feature-perception network for underwater image enhancement 用于水下图像增强的散射特征感知网络

IF 3.7 2区工程技术

Displays Pub Date : 2025-07-03 DOI: 10.1016/j.displa.2025.103132

Zhengjie Wang , Jiaying Guo , Junchen Zhang , Guokai Zhang , Tianrun Yu , Shangzixin Zhao , Dandan Zhu

{"title":"SFP-Net: Scatter-feature-perception network for underwater image enhancement","authors":"Zhengjie Wang , Jiaying Guo , Junchen Zhang , Guokai Zhang , Tianrun Yu , Shangzixin Zhao , Dandan Zhu","doi":"10.1016/j.displa.2025.103132","DOIUrl":"10.1016/j.displa.2025.103132","url":null,"abstract":"<div><div>Inspired by atmospheric scattering light models, substantial progresses have been achieved in deep learning-based methods for underwater image enhancement. However, these methods suffer from a deficiency in accurately modeling scattering information, which can incur some quality issues of visual perception. Moreover, insufficient attention to the key scene features leads to enhanced images that lack fine-grained information. To alleviate these challenges, we propose an efficient scatter-feature-perception network(SFP-Net). It consists of two core ideas: firstly, the dark channel map is synergistically combined with the K-value map to precisely perceive scattering light features within the scene. Subsequently, multi-scale cross-space learning is used to capture the inter-dependencies between channels and spatial positions, facilitating the perception of scene feature information. Besides, the adaptive scatter feature loss is formulated on the basis of the atmospheric scattering model, which evaluates the impact of scattered light. Extensive experimental results demonstrate that our model effectively mitigates the influence of underwater environmental factors, circumvents interference caused by image depth of field, and exhibits superior performance in terms of adaptability and reliability. Notably, our model achieves maximum values of 29.76 and 0.91 on the PSNR and SSIM metrics, which indicates superior enhancement effects compared to existing methods. Meanwhile, the UCIQE and UIQM metrics also reached 0.431 and 2.763 respectively, which are more in line with human visual preferences.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"90 ","pages":"Article 103132"},"PeriodicalIF":3.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0