Journal of King Saud University-Computer and Information Sciences最新文献

{"title":"Visually meaningful image encryption for secure and authenticated data transmission using chaotic maps","authors":"Deep Singh ,&nbsp;Sandeep Kumar ,&nbsp;Chaman Verma ,&nbsp;Zoltán Illés ,&nbsp;Neerendra Kumar","doi":"10.1016/j.jksuci.2024.102235","DOIUrl":"10.1016/j.jksuci.2024.102235","url":null,"abstract":"<div><div>Image ciphering techniques usually transform a given plain image data into a cipher image data resembling noise, serving as an indicator of the presence of secret image data. However, the transmission of such noise-like images could draw attention, thereby attracting the attackers and may face several possible attacks. This paper presents an approach for generating a visually meaningful image encryption (VMIE) scheme that combines three layers of security protection: encryption, digital signature, and steganography. The present scheme is dedicated to achieving a balanced performance in robustness, security and operational efficiency. First, the original image is partially encrypted by using the RSA cryptosystem and modified Hénon map (MHM). In the second stage, a digital signature is generated for the partially encrypted image by employing a hash function and the RSA cryptosystem. The obtained digital signature is appended to the partially encrypted image produced after implementing the zigzag confusion in the above partially encrypted image. Further, to achieve better confusion and diffusion, the partially encrypted image containing a digital signature undergoes through the application of 3D Arnold cat map (<span><math><mrow><mi>A</mi><msub><mrow><mi>R</mi></mrow><mrow><mi>n</mi><mi>o</mi></mrow></msub></mrow></math></span> times), to produce the secret encrypted image <span><math><mrow><mo>(</mo><msub><mrow><mi>S</mi></mrow><mrow><mi>r</mi><mn>5</mn></mrow></msub><mo>)</mo></mrow></math></span>. To ensure the security and robustness of the proposed technique against various classical attacks, the hash value obtained from the SHA-256 hash function and carrier images is utilized to generate the initial conditions <span><math><mrow><mi>M</mi><msub><mrow><mi>h</mi></mrow><mrow><mn>10</mn></mrow></msub></mrow></math></span> and <span><math><mrow><mi>M</mi><msub><mrow><mi>h</mi></mrow><mrow><mn>20</mn></mrow></msub></mrow></math></span> for modified Hénon map, and initial position <span><math><mrow><msub><mrow><mi>Z</mi></mrow><mrow><mi>i</mi><mi>p</mi></mrow></msub><mo>=</mo><mrow><mo>(</mo><msub><mrow><mi>z</mi></mrow><mrow><mi>r</mi><mi>o</mi><mi>w</mi></mrow></msub><mo>,</mo><msub><mrow><mi>z</mi></mrow><mrow><mi>c</mi><mi>o</mi><mi>l</mi></mrow></msub><mo>)</mo></mrow></mrow></math></span> for zigzag confusion. In the proposed algorithm, the digital signature is utilized for both purposes to verify the sender’s authenticity and to enhance the encryption quality. The carrier image undergoes lifting wavelet transformation, and its high-frequency components are utilized in the embedding process through a permuted pattern of MHM, resulting in a visually meaningful encrypted image. The proposed scheme achieves efficient visual encryption with minimal distortion and ensures lossless image quality upon decryption (infinite PSNR), balancing high level of security along with a good computational efficiency.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102235"},"PeriodicalIF":5.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leukocyte segmentation based on DenseREU-Net","authors":"Jie Meng ,&nbsp;Yingqi Lu ,&nbsp;Wangjiao He ,&nbsp;Xiangsuo Fan ,&nbsp;Gechen Zhou ,&nbsp;Hongjian Wei","doi":"10.1016/j.jksuci.2024.102236","DOIUrl":"10.1016/j.jksuci.2024.102236","url":null,"abstract":"<div><div>The detection of white blood cells provides important information in cellular research regarding infections, inflammation, immune function, and blood pathologies. Effective segmentation of WBCs in blood microscopic images not only aids pathologists in making more accurate diagnoses and early detections but is also crucial for identifying the types of lesions. Due to significant differences among various types of pathological WBCs and the complexities associated with cellular imaging and staining techniques, accurately recognizing and segmenting these different types of WBCs remains challenging. To address these challenges, this paper proposes a WBC segmentation technique based on DenseREU-Net, which enhances feature exchange and reuse by employing Dense Blocks and residual units. Additionally, it introduces mixed pooling and skip multi-scale fusion modules to improve the recognition and segmentation accuracy of different types of pathological WBCs. This study was validated on two datasets provided by DML-LZWH (Liuzhou Workers’ Hospital Medical Laboratory). Experimental results indicate that on the multi-class dataset, DenseREU-Net achieves an average IoU of 73.05% and a Dice coefficient of 80.25%. For the binary classification blind sample dataset, the average IoU and Dice coefficient are 83.98% and 90.41%, respectively. In both datasets, the proposed model significantly outperforms other advanced medical image segmentation algorithms. Overall, DenseREU-Net effectively analyzes blood microscopic images and accurately recognizes and segments different types of WBCs, providing robust support for the diagnosis of blood-related diseases.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102236"},"PeriodicalIF":5.2,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge-embedded multi-layer collaborative adaptive fusion network: Addressing challenges in foggy conditions and complex imaging","authors":"Zhu Chen,&nbsp;Fan Li,&nbsp;Yueqin Diao,&nbsp;Wanlong Zhao,&nbsp;Puyin Fan","doi":"10.1016/j.jksuci.2024.102230","DOIUrl":"10.1016/j.jksuci.2024.102230","url":null,"abstract":"<div><div>Infrared and visible image fusion aims at generating high-quality images that serve both human and machine visual perception under extreme imaging conditions. However, current fusion methods primarily rely on datasets comprising infrared and visible images captured under clear weather conditions. When applied to real-world scenarios, image fusion tasks inevitably encounter challenges posed by adverse weather conditions such as heavy fog, resulting in difficulties in obtaining effective information and inferior visual perception. To address these challenges, this paper proposes a Mean Teacher-based Self-supervised Image Restoration and multimodal Image Fusion joint learning network (SIRIFN), which enhances the robustness of the fusion network in adverse weather conditions by employing deep supervision from a guiding network to the learning network. Furthermore, to enhance the network’s information extraction and integration capabilities, our Multi-level Feature Collaborative adaptive Reconstruction Network (MFCRNet) is introduced, which adopts a multi-branch, multi-scale design, with differentiated processing strategies for different features. This approach preserves rich texture information while maintaining semantic consistency from the source images. Extensive experiments demonstrate that SIRIFN outperforms current state-of-the-art algorithms in both visual quality and quantitative evaluation. Specifically, the joint implementation of image restoration and multimodal fusion provides more effective information for visual tasks under extreme weather conditions, thereby facilitating downstream visual tasks.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102230"},"PeriodicalIF":5.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature-fused residual network for time series classification","authors":"Yanxuan Wei ,&nbsp;Mingsen Du ,&nbsp;Teng Li ,&nbsp;Xiangwei Zheng ,&nbsp;Cun Ji","doi":"10.1016/j.jksuci.2024.102227","DOIUrl":"10.1016/j.jksuci.2024.102227","url":null,"abstract":"<div><div>In various fields such as healthcare and transportation, accurately classifying time series data can provide important support for decision-making. To further improve the accuracy of time series classification, we propose a Feature-fused Residual Network based on Multi-scale Signed Recurrence Plot (MSRP-FFRN). This method transforms one-dimensional time series into two-dimensional images, representing the temporal correlation of time series in a two-dimensional space and revealing hidden details within the data. To enhance these details further, we extract multi-scale features by setting receptive fields of different sizes and using adaptive network depths, which improves image quality. To evaluate the performance of this method, we conducted experiments on 43 UCR datasets and compared it with nine state-of-the-art baseline methods. The experimental results show that MSRP-FFRN ranks first on critical difference diagram, achieving the highest accuracy on 18 datasets with an average accuracy of 89.9%, making it the best-performing method overall. Additionally, the effectiveness of this method is further validated through metrics such as Precision, Recall, and F1 score. Results from ablation experiments also highlight the efficacy of the improvements made by MSRP-FFRN.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102227"},"PeriodicalIF":5.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-light image enhancement: A comprehensive review on methods, datasets and evaluation metrics","authors":"Zhan Jingchun ,&nbsp;Goh Eg Su ,&nbsp;Mohd Shahrizal Sunar","doi":"10.1016/j.jksuci.2024.102234","DOIUrl":"10.1016/j.jksuci.2024.102234","url":null,"abstract":"<div><div>Enhancing low-light images in computer vision is a significant challenge that requires innovative methods to improve its robustness. Low-light image enhancement (LLIE) enhances the quality of images affected by poor lighting conditions by implementing various loss functions such as reconstruction, perceptual, smoothness, adversarial, and exposure. This review analyses and compares different methods, ranging from traditional to cutting-edge deep learning methods, showcasing the significant advancements in the field. Although similar reviews have been studied on LLIE, this paper not only updates the knowledge but also focuses on recent deep learning methods from various perspectives or interpretations. The methodology used in this paper compares different methods from the literature and identifies the potential research gaps. This paper highlights the recent advancements in the field by classifying them into three classes, demonstrated by the continuous enhancements in LLIE methods. These improved methods use different loss functions showing higher efficacy through metrics such as Peak Signal-to-Noise Ratio, Structural Similarity Index Measure, and Naturalness Image Quality Evaluator. The research emphasizes the significance of advanced deep learning techniques and comprehensively compares different LLIE methods on various benchmark image datasets. This research is a foundation for scientists to illustrate potential future research directions.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102234"},"PeriodicalIF":5.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Binocular camera-based visual localization with optimized keypoint selection and multi-epipolar constraints","authors":"Guanyuan Feng,&nbsp;Yu Liu,&nbsp;Weili Shi,&nbsp;Yu Miao","doi":"10.1016/j.jksuci.2024.102228","DOIUrl":"10.1016/j.jksuci.2024.102228","url":null,"abstract":"<div><div>In recent years, visual localization has gained significant attention as a key technology for indoor navigation due to its outstanding accuracy and low deployment costs. However, it still encounters two primary challenges: the requirement for multiple database images to match the query image and the potential degradation of localization precision resulting from the keypoints clustering and mismatches. In this research, a novel visual localization framework based on a binocular camera is proposed to estimate the absolute positions of the query camera. The framework integrates three core methods: the multi-epipolar constraints-based localization (MELoc) method, the Optimal keypoint selection (OKS) method, and a robust measurement method. MELoc constructs multiple geometric constraints to enable absolute position estimation with only a single database image, while OKS and the robust measurement method further enhance localization accuracy by refining the precision of these geometric constraints. Experimental results demonstrate that the proposed system consistently outperforms existing visual localization systems across various scene scales, database sampling intervals, and lighting conditions</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102228"},"PeriodicalIF":5.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time semantic segmentation for autonomous driving: A review of CNNs, Transformers, and Beyond","authors":"Mohammed A.M. Elhassan ,&nbsp;Changjun Zhou ,&nbsp;Ali Khan ,&nbsp;Amina Benabid ,&nbsp;Abuzar B.M. Adam ,&nbsp;Atif Mehmood ,&nbsp;Naftaly Wambugu","doi":"10.1016/j.jksuci.2024.102226","DOIUrl":"10.1016/j.jksuci.2024.102226","url":null,"abstract":"<div><div>Real-time semantic segmentation is a crucial component of autonomous driving systems, where accurate and efficient scene interpretation is essential to ensure both safety and operational reliability. This review provides an in-depth analysis of state-of-the-art approaches in real-time semantic segmentation, with a particular focus on Convolutional Neural Networks (CNNs), Transformers, and hybrid models. We systematically evaluate these methods and benchmark their performance in terms of frames per second (FPS), memory consumption, and CPU runtime. Our analysis encompasses a wide range of architectures, highlighting their novel features and the inherent trade-offs between accuracy and computational efficiency. Additionally, we identify emerging trends, and propose future directions to advance the field. This work aims to serve as a valuable resource for both researchers and practitioners in autonomous driving, providing a clear roadmap for future developments in real-time semantic segmentation. More resources and updates can be found at our GitHub repository: <span><span>https://github.com/mohamedac29/Real-time-Semantic-Segmentation-Survey</span><svg><path></path></svg></span></div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102226"},"PeriodicalIF":5.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TFDNet: A triple focus diffusion network for object detection in urban congestion with accurate multi-scale feature fusion and real-time capability","authors":"Caoyu Gu ,&nbsp;Xiaodong Miao ,&nbsp;Chaojie Zuo","doi":"10.1016/j.jksuci.2024.102223","DOIUrl":"10.1016/j.jksuci.2024.102223","url":null,"abstract":"<div><div>Vehicle detection in congested urban scenes is essential for traffic control and safety management. However, the dense arrangement and occlusion of multi-scale vehicles in such environments present considerable challenges for detection systems. To tackle these challenges, this paper introduces a novel object detection method, dubbed the triple focus diffusion network (TFDNet). Firstly, the gradient convolution is introduced to construct the C2f-EIRM module, replacing the original C2f module, thereby enhancing the network’s capacity to extract edge information. Secondly, by leveraging the concept of the Asymptotic Feature Pyramid Network on the foundation of the Path Aggregation Network, the triple focus diffusion module structure is proposed to improve the network’s ability to fuse multi-scale features. Finally, the SPPF-ELA module employs an Efficient Local Attention mechanism to integrate multi-scale information, thereby significantly reducing the impact of background noise on detection accuracy. Experiments on the VisDrone 2021 dataset reveal that the average detection accuracy of the TFDNet algorithm reached 38.4%, which represents a 6.5% improvement over the original algorithm; similarly, its mAP50:90 performance has increased by 3.7%. Furthermore, on the UAVDT dataset, the TFDNet achieved a 3.3% enhancement in performance compared to the original algorithm. TFDNet, with a processing speed of 55.4 FPS, satisfies the real-time requirements for vehicle detection.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102223"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DNE-YOLO: A method for apple fruit detection in Diverse Natural Environments","authors":"Haitao Wu ,&nbsp;Xiaotian Mo ,&nbsp;Sijian Wen ,&nbsp;Kanglei Wu ,&nbsp;Yu Ye ,&nbsp;Yongmei Wang ,&nbsp;Youhua Zhang","doi":"10.1016/j.jksuci.2024.102220","DOIUrl":"10.1016/j.jksuci.2024.102220","url":null,"abstract":"<div><div>The apple industry, recognized as a pivotal sector in agriculture, increasingly emphasizes the mechanization and intelligent advancement of picking technology. This study innovatively applies a mist simulation algorithm to apple image generation, constructing a dataset of apple images under mixed sunny, cloudy, drizzling and foggy weather conditions called DNE-APPLE. It introduces a lightweight and efficient target detection network called DNE-YOLO. Building upon the YOLOv8 base model, DNE-YOLO incorporates the CBAM attention mechanism and CARAFE up-sampling operator to enhance the focus on apples. Additionally, it utilizes GSConv and the dynamic non-monotonic focusing mechanism loss function WIOU to reduce model parameters and decrease reliance on dataset quality. Extensive experimental results underscore the efficacy of the DNE-YOLO model, which achieves a detection accuracy (precision) of 90.7%, a recall of 88.9%, a mean accuracy (mAP50) of 94.3%, a computational complexity (GFLOPs) of 25.4G, and a parameter count of 10.46M across various environmentally diverse datasets. Compared to YOLOv8, it exhibits superior detection accuracy and robustness in sunny, drizzly, cloudy, and misty environments, making it especially suitable for practical applications such as apple picking for agricultural robots. The code for this model is open source at <span><span>https://github.com/wuhaitao2178827/DNE-YOLO</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102220"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient resource allocation for UAV-aided full-duplex OFDMA wireless powered IoT communication networks","authors":"Tong Wang","doi":"10.1016/j.jksuci.2024.102225","DOIUrl":"10.1016/j.jksuci.2024.102225","url":null,"abstract":"<div><div>The rapid development of wireless-powered Internet of Things (IoT) networks, supported by multiple unmanned aerial vehicles (UAVs) and full-duplex technologies, has opened new avenues for simultaneous data transmission and energy harvesting. In this context, optimizing energy efficiency (EE) is crucial for ensuring sustainable and efficient network operation. This paper proposes a novel approach to EE optimization in multi-UAV-aided wireless-powered IoT networks, focusing on balancing the uplink data transmission rates and total system energy consumption within an orthogonal frequency-division multiple access (OFDMA) framework. This involves formulating the EE optimization problem as a Multi-Objective Optimization Problem (MOOP), consisting of the maximization of the uplink total rate and the minimization of the total system energy consumption, which is then transformed into a Single-Objective Optimization Problem (SOOP) using the Tchebycheff method. To address the non-convex nature of the resulting SOOP, characterized by combinatorial variables and coupled constraints, we developed an iterative algorithm that combines Block Coordinate Descent (BCD) with Successive Convex Approximation (SCA). This algorithm decouples the subcarrier assignment and power control subproblems, incorporates a penalty term to relax integer constraints, and alternates between solving each subproblem until convergence is reached. Simulation results demonstrate that our proposed method outperforms baseline approaches in key performance metrics, highlighting the practical applicability and robustness of our framework for enhancing the efficiency and sustainability of real-world UAV-assisted wireless networks. Our findings provide insights for future research on extending the proposed framework to scenarios involving dynamic UAV mobility, multi-hop communication, and enhanced energy management, thereby supporting the development of next-generation sustainable communication systems.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102225"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}