Egyptian Journal of Remote Sensing and Space Sciences最新文献

{"title":"GIS-based species distribution modeling of invasive Mnemiopsis leidyi in the southern caspian sea using satellite imageries","authors":"Mahdieh Abadijoo ,&nbsp;Mehdi Gholamalifard ,&nbsp;Mehdi Mokhtarzade ,&nbsp;Parviz Jokar ,&nbsp;Tiit Kutser ,&nbsp;Andrey G. Kostianoy ,&nbsp;Aleksander V. Semenov","doi":"10.1016/j.ejrs.2024.12.002","DOIUrl":"10.1016/j.ejrs.2024.12.002","url":null,"abstract":"<div><div>Due to its unique variety of species, the Caspian Sea has great ecological-economic values and the people living on its coasts use this environmental asset as a source of income. <em>Mnemiopsis leidyi</em>’s invasion of this ecosystem in 1999, however, has led to instability of the ecosystem and decreased access to the services provided by it causing a decline in the population of Kilka fish and in the fishing industry. Accordingly, the present study attempted to carry out spatial modeling of <em>M. leidyi</em> using Multi-Criteria Evaluation (MCE) and Maximum Entropy (MaxEnt) models in summer and autumn. The main goal of comparing these two models was to find the best distribution sites of <em>M. leidyi</em> as an inhibiting species for the ecosystem services. The modelling was based on the following variables including: chlorophyll <em>a</em> concentration, photosynthetic active radiation (PAR), water temperature, turbidity, concentration of nitrogen, phosphorus, oxygen, salinity, sea level anomaly, depth, distance from the coast and bottom slope on Mazandaran coasts of Iran in the southern basin of the Caspian Sea. The findings indicated that the most favorable distribution of <em>M. leidyi</em> was in summer near the central (sub-region N 6563 in Behshahr and 6463 in Babolsar (and eastern coasts (sub-region N 6663 in Noshahr), and the least favorable distribution was near the western coast (sub-region N 6163 in Ramsar). In autumn, however, all the coastal zones had a high level of favorability for the viability of <em>Mnemiopsis leidyi</em>. ‘Distance from the coast’ and ‘depth’ were identified as the most important variables explaining the variation in the distribution of <em>M. leidyi</em> and helping to identify the favorable areas for the viability of <em>M. leidyi</em> in summer and autumn. The obtained results can help to understand better the behavior of <em>M. leidyi</em>, its spatial and temporal distribution, as well as to improve the ecosystem services in the southern basin of the Caspian Sea including in location of cage aquaculture sites.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 63-76"},"PeriodicalIF":3.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison and accuracy assessment of unmanned aerial vehicle and terrestrial measurement in base map production","authors":"Veysel Yildiz,&nbsp;Aydan Yaman","doi":"10.1016/j.ejrs.2024.12.003","DOIUrl":"10.1016/j.ejrs.2024.12.003","url":null,"abstract":"<div><div>In the present era, unmanned aerial vehicles (UAVs) have become a prevalent tool for data and map production in the domain of remote sensing and photogrammetry, driven by advancements in technology. The production of base maps has become more straightforward, precise, economical, and time-efficient in recent years, largely due to the advent of UAVs and the subsequent development of new techniques. The base maps of the area were produced using two methods: Terrestrial measurement and UAV data. The squared mean errors were calculated and found to be my = ±1.49 cm, mx= ±1.58 cm and m_z = ±2.52 cm for ground control points, m_y = ±1.54 cm, m_x= ±1.65 cm and m_z = ±2.55 cm for check points and my = ±2.41 cm, mx= ±2.66 cm and m_z= ±3.47 cm for detail points. The results were found to fall within the specified limit values. It was therefore concluded that UAVs provide the anticipated accuracy for the production of base maps, which are required to be continually updated and form the basis for a range of projects and can be readily employed in this regard. This study demonstrates that base maps produced with UAV data meet the requisite scientific and academic standards, including accuracy and precision. Additionally, it illuminates the advantages of UAV data in base map production, particularly in terms of time, accuracy, and cost.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 53-62"},"PeriodicalIF":3.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced lithological mapping via remote sensing: Employing SVM, random trees, ANN, with MNF and PCA transformations","authors":"Mohamed Ali El-Omairi,&nbsp;Manal El Garouani,&nbsp;Abdelkader El Garouani","doi":"10.1016/j.ejrs.2024.12.001","DOIUrl":"10.1016/j.ejrs.2024.12.001","url":null,"abstract":"<div><div>This study examines the performance of three classification algorithms—Support Vector Machines (SVM), Random Trees (RT), and Artificial Neural Networks (ANN)—applied to Landsat 9 and Sentinel-2 spectral data for lithological mapping. The study area, located in the Central Anti-Atlas, is covered by the 1:50,000 geological map of Aït Semgane, featuring diverse geological formations, ideal for testing advanced remote sensing techniques. Results show that SVM, particularly with Minimum Noise Fraction (MNF) transformation, offers the best performance. For Sentinel-2 images, SVM with MNF achieves high user and producer accuracies and well-defined lithological boundaries. While RT and ANN also show good performance, they are slightly inferior to SVM, with RT achieving a Kappa index of 0.84 for raw Landsat 9 bands and ANN obtaining a maximum of 0.75 for Sentinel-2 data transformed with MNF. The MNF transformation generally improves SVM and ANN performance, whereas Principal Component Analysis (PCA) often produces inferior results. The robustness of SVM for high-dimensional data and its resistance to overfitting make it a promising tool for accurate lithological classification. This research has practical implications for geology and Earth sciences. The use of dimensionality reduction, particularly MNF, can greatly enhance classification quality for multispectral and hyperspectral data. These results are not only valuable for improving geological mapping, mineral exploration, and natural resource management at local and regional scales but also have significant potential for large-scale terrain analysis in diverse global contexts. The findings could support global efforts in geological hazard assessments, resource management, and environmental monitoring, particularly in regions with challenging geological settings. The study also proposes future research directions, such as exploring new dimensionality reduction techniques, evaluating classification methods with different remote sensing datasets, and integrating geophysical or geochemical data to further improve accuracy</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 34-52"},"PeriodicalIF":3.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral–Spatial Adaptive Weighted Fusion and Residual Dense Network for hyperspectral image classification","authors":"Junding Sun ,&nbsp;Hongyuan Zhang ,&nbsp;Xiaoxiao Ma ,&nbsp;Ruinan Wang ,&nbsp;Haifeng Sima ,&nbsp;Jianlong Wang","doi":"10.1016/j.ejrs.2024.11.001","DOIUrl":"10.1016/j.ejrs.2024.11.001","url":null,"abstract":"<div><div>The dense and nearly continuous spectral bands in hyperspectral images result in strong inter-band correlations, which can diminish performance of the model in classification tasks. Moreover, most convolutional neural network-based methods for hyperspectral image classification typically depend on a fixed scale to extract spectral–spatial features, which ignore the detail features of some objects. To address the above issues, a novelty Spectral Spatial Adaptive Weighted Fusion and Residual Dense Network (S<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>AWF-RDN) is proposed for Hyperspectral image classification. Specifically, the proposed S<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>AWF-RDN consists of spectral–spatial adaptive weighted fusion module, multi-channel feature concatenation residual dense module, and spatial feature fusion module. Firstly, the spectral information optimization branch is developed to adjust the weights assigned to various spectral channels. Similarly, the spatial information optimization branch is developed to adjust the weights for different spatial regions. Secondly, to obtain rich spectral spatial information from different levels, multi-channel feature concatenation residual dense module has been proposed. In addition, a multi-channel feature concatenation block is designed guiding the model to extract spectral spatial information at different scales. Finally, spatial feature fusion module is introduced to retain more spatial information. The experimental outcomes illustrate that the proposed network model exhibits superior classification performance on three renowned hyperspectral image datasets. Furthermore, the efficacy of the proposed network model is further corroborated through comparative and ablation studies.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 21-33"},"PeriodicalIF":3.7,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New radio-seismic indicator for ELF seismic precursors detectability","authors":"Andrea Mariscotti ,&nbsp;Renato Romero","doi":"10.1016/j.ejrs.2024.10.003","DOIUrl":"10.1016/j.ejrs.2024.10.003","url":null,"abstract":"<div><div>This work considers the effectiveness of earthquakes (EQs) radio precursors mainly in the Extremely Low Frequency (ELF) range and below, and carries out an analysis based on a comprehensive set of EQ events documented in past publications and provided by the Opera 2015 project (six stations located in Italy). A new Radio-Seismic Indicator (RSI) is proposed, with the magnitude-distance relationship physically justified by path-loss expressions of the transverse magnetic mode. Classification performances of past and proposed RSIs are assessed calculating confusion matrices and on those the balanced accuracy and Matthews’ coefficient: the RSI performs significantly better reducing fall-outs and increasing precision for both classes, positive and negative precursors. Performance improvement is inherently limited by the overlap of the classes.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 12-20"},"PeriodicalIF":3.7,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of above ground biomass of mangrove forest plot using terrestrial laser scanner","authors":"Yeshwanth Kumar Adimoolam ,&nbsp;Nithin D. Pillai ,&nbsp;Gnanappazham Lakshmanan ,&nbsp;Deepak Mishra ,&nbsp;Vinay Kumar Dadhwal","doi":"10.1016/j.ejrs.2024.11.002","DOIUrl":"10.1016/j.ejrs.2024.11.002","url":null,"abstract":"<div><div>Above-Ground Biomass (AGB) is an important parameter in the conservation of mangrove ecosystem owing to their ecological and economic benefits. LiDAR technologies in forest studies have become popular, due to its highly accurate 3D spatial data acquisition. In this study, we propose an end-to-end framework for estimating AGB of mangroves from Terrestrial Laser Scanner (TLS) point clouds. The framework includes pre-processing of data, segmenting the wood and foliage at tree level using Weighted Random Forest (WRF) classifier and constructing Quantitative Structure Model (QSM) of the wooden components to estimate its biomass. The flow was extended to AGB estimation of 33 x 33 m plot by integrating tree level framework. The study also finds a unique solution to estimate the contribution of pneumatophores in the AGB. Segmentation of wood/foliage of tree point cloud using WRF yielded better results with an increment of 15.27 % in Balanced accuracy, 0.2 of Cohen’s Kappa coefficient, and 7.45 % in F1score than RF classifier. AGB estimation of mangroves using our approach using TLS data is 47.54 T/ha which has a mean bias of 0.0044 T/ha and RMS variation of 0.026 T/ ha when compared with the allometric methods. A Breadth-first graph-search segmentation approach was used to count the pneumatophores, aerial roots seen in few mangrove species (R² = 0.94 with manual counting) and estimate its contribution to AGB of mangroves which is first of its kind using TLS point cloud. This outcome could also aid future studies in modeling the underlying root network and estimating the below-ground biomass.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 1-11"},"PeriodicalIF":3.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient bundle optimization for accurate camera pose estimation in mobile augmented reality systems","authors":"Shanglin Li ,&nbsp;Yalan Li ,&nbsp;Yulin Lan ,&nbsp;Anping Lin","doi":"10.1016/j.ejrs.2024.10.006","DOIUrl":"10.1016/j.ejrs.2024.10.006","url":null,"abstract":"<div><div>Augmented reality has a long research history in computer vision and computer graphics communities. It aims to enhance the user experience for real scenes via overlapping virtual objects. Nowadays, mobile augmented reality has attracted much attention from researchers and developers due to the development of hardware techniques. Modern mobile devices such as mobile phones have a powerful computational ability for augmented reality applications. As a result, many researchers have paid attention to mobile augmented reality. From the technical viewpoint of augmented reality, mobile augmented reality largely depends on camera pose estimation. However, existing methods make it difficult to achieve the best balance between accuracy and efficiency, according to our investigation, and this may handicap the performance of mobile augmented reality systems. To overcome the problem, in this paper, we propose a novel approach to camera pose estimation based on bundle optimization. Our proposed method is evaluated on real-world datasets and is also tested in the mobile augmented reality system. Both experiments demonstrate that our proposed method has fast speed and high accuracy.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 743-752"},"PeriodicalIF":3.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing Potential Mineralization Zones Utilizing Landsat-9, ASTER and Airborne Radiometric Data at Elkharaza-Dara Area, North Eastern Desert, Egypt","authors":"Mahmoud Abd El-Rahman Hegab,&nbsp;Islam Abou El Magd,&nbsp;Kareem Hamed Abd El Wahid","doi":"10.1016/j.ejrs.2024.10.005","DOIUrl":"10.1016/j.ejrs.2024.10.005","url":null,"abstract":"<div><div>The present work enhances mineral exploration in Egypt’s Eastern Desert by mapping lithological units and identifying hydrothermal alteration zones, potentially leading to the discovery of economically viable mineral deposits. This study employs a comprehensive approach of integrating multispectral bands from Landsat-9 and ASTER images with airborne radiometric data. Various image enhancement techniques such as False Color Composite (FCC), Minimum Noise Fraction (MNF), and Principal Component Analysis (PCA) are utilized to map enhanced lithological units. Additionally, image classification techniques, including Spectral Angle Mapper (SAM) and Crosta Principal Component (CROSTA PC), are applied to emphasize hydrothermal alteration minerals like alunite, calcite, hematite, illite, chlorite, epidote, kaolinite, montmorillonite, and sericite. Furthermore, radioelement ratios (eU/eTh, eU/K, eTh/K, and eU-(eTh/3.5)) and the F-parameter (K*(eU/eTh)) are utilized. Mineral percentages are determined using Scanning Electron Microscope (SEM), allowing for the observation of ore minerals from the Elkharaza-Dara area deposits, which exhibit varying compositions. Maximum values are recorded for specific elements: aluminum (10.48 wt% Al), silicon (65.38 wt% Si), silver (0.32 wt% Ag), copper (2.65 wt% Cu), gold (5.25 wt% Au), potassium (4.32 wt% K), hafnium (3.84 wt% Hf), calcium (26.94 wt% Ca), carbon (56.92 wt% C), and oxygen (53.71 wt% O). These findings offer valuable insights into the elemental composition of the mineralized deposits in the study area. The multi-algorithm integration approach has been confirmed through various methods, including comparison with existing geological maps, fieldwork, and microscopic analysis of selected samples from alteration zones across the study area.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 716-733"},"PeriodicalIF":3.7,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential of temporal satellite data analysis for detection of weed infestation in rice crop","authors":"Manju Tiwari ,&nbsp;Prasun Kumar Gupta ,&nbsp;Nitish Tiwari ,&nbsp;Shrikant Chitale","doi":"10.1016/j.ejrs.2024.10.002","DOIUrl":"10.1016/j.ejrs.2024.10.002","url":null,"abstract":"<div><div>Weeds are unwanted vegetation that compete with main crops for essential resources like light, water, and nutrients, leading to significant reductions in food crop yield and economic losses. Addressing this issue is crucial, particularly during the Kharif cropping season when cloud cover interferes with remote sensing capabilities. This study is an attempt to investigate the potential of satellite-based temporal analysis in weed detection from agricultural fields. The research focused on rice cultivation at the Research cum Instructional farms of Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh. The study explored the utility of satellite imagery for assessing crop health, demonstrating how weed infestation influences vegetative indices. The study utilized satellite images from PlanetScope and Sentinel-2 to examine the temporal variation in vegetation indices across two treatments: pure rice and rice with weeds. NDVI analysis revealed a significant decline in treatments affected by weeds (upto 41% less), suggesting that time-series satellite data can serve as an early indicator of weed infestation in standing rice crops. These findings were further verified by backscatter values from the Sentinel-1 dataset, which indicated a reduction in backscatter (upto 18% less) due to the suboptimal growth conditions in weed-infested treatments compared to weed-free rice. While the technology has shown efficacy at a preliminary stage, there is significant potential for its broader application and scalability in operational contexts.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 734-742"},"PeriodicalIF":3.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualization of humpback whale tracking on edge device using space-borne remote sensing data for Indian Ocean","authors":"S. Vasavi,&nbsp;Vasanthi Sripathi,&nbsp;Chandra Mouli Simma","doi":"10.1016/j.ejrs.2024.10.004","DOIUrl":"10.1016/j.ejrs.2024.10.004","url":null,"abstract":"<div><div>The conservation of humpback whale populations faces ongoing challenges, including human-induced mortality, despite the ban on commercial whaling. Recent advancements in high-resolution satellite imagery offer promise for estimating whale populations, particularly in remote and inaccessible regions. However, significant research gaps persist, necessitating innovative approaches for effective monitoring and conservation efforts. This paper presents a novel methodology that integrates high- resolution satellite imagery with state-of-the-art deep learning techniques to monitor and conserve humpback whale populations, with a focus on the Indian Ocean region. Specifically, application of cutting-edge deep learning models such as YOLO for object detection and EfficientNet for classification to automate the detection, classification, and tracking of humpback whales in satellite images is explored. By leveraging deep convolutional neural networks (CNNs), the proposed ensemble system offers a robust and generalizable approach for automatically detecting, classifying, and tracking whales in space-borne satellite imagery, thereby addressing the challenge of uncertain whale populations in the world’s oceans. The results demonstrate promising accuracy and performance metrics: the Segment Anything Model(SAM) achieves an accuracy of 89.2%, YOLO achieves an accuracy of 99.2%, EfficientNet achieves an accuracy of 99% across various tasks.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 705-715"},"PeriodicalIF":3.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}