Frontiers in Big DataPub Date : 2025-07-09eCollection Date: 2025-01-01DOI: 10.3389/fdata.2025.1569147
Varad Bhandarkawthekar, T M Navamani, Rishabh Sharma, K Shyamala
{"title":"Design and development of an efficient RLNet prediction model for deepfake video detection.","authors":"Varad Bhandarkawthekar, T M Navamani, Rishabh Sharma, K Shyamala","doi":"10.3389/fdata.2025.1569147","DOIUrl":"10.3389/fdata.2025.1569147","url":null,"abstract":"<p><strong>Introduction: </strong>The widespread emergence of deepfake videos presents substantial challenges to the security and authenticity of digital content, necessitating robust detection methods. Deepfake detection remains challenging due to the increasing sophistication of forgery techniques. While existing methods often focus on spatial features, they may overlook crucial temporal information distinguishing real from fake content and need to investigate several other Convolutional Neural Network architectures on video-based deep fake datasets.</p><p><strong>Methods: </strong>This study introduces an RLNet deep learning framework that utilizes ResNet and Long Short Term Memory (LSTM) networks for high-precision deepfake video detection. The key objective is exploiting spatial and temporal features to discern manipulated content accurately. The proposed approach starts with preprocessing a diverse dataset with authentic and deepfake videos. The ResNet component captures intricate spatial anomalies at the frame level, identifying subtle manipulations. Concurrently, the LSTM network analyzes temporal inconsistencies across video sequences, detecting dynamic irregularities that signify deepfake content.</p><p><strong>Results and discussion: </strong>Experimental results demonstrate the effectiveness of the combined ResNet and LSTM approach, showing an accuracy of 95.2% and superior detection capabilities compared to existing methods like EfficientNet and Recurrent Neural Networks (RNN). The framework's ability to handle various deepfake techniques and compression levels highlights its versatility and robustness. This research significantly contributes to digital media forensics by providing an advanced tool for detecting deepfake videos, enhancing digital content's security and integrity. The efficacy and resilience of the proposed system are evidenced by deepfake detection, while our visualization-based interpretability provides insights into our model.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1569147"},"PeriodicalIF":2.4,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frontiers in Big DataPub Date : 2025-07-07eCollection Date: 2025-01-01DOI: 10.3389/fdata.2025.1594062
Afraa Attiah, Manal Kalkatawi
{"title":"AI-powered smart emergency services support for 9-1-1 call handlers using textual features and SVM model for digital health optimization.","authors":"Afraa Attiah, Manal Kalkatawi","doi":"10.3389/fdata.2025.1594062","DOIUrl":"10.3389/fdata.2025.1594062","url":null,"abstract":"<p><p>In emergency situations, 9-1-1 is considered the first point of contact, and their call handlers play a crucial role in managing the emergency response. Due to the large number of daily calls and the hectic routine, there are severe chances that the call handlers can make any mistake or human error during data taking in a high-pressure environment. These mistakes or errors impact 9-1-1 performance in emergencies. To address this problem, this research introduces an AI-powered digital health framework called Emergency Calls Assistant (ECA) that leverages artificial intelligence (AI) and natural language processing (NLP) techniques to assist call handlers during data collection. ECA is designed to predict the type of emergency, suggest relevant questions to collect deeper information, suggest pre-arrival instructions to emergency personnel, and generate incident reports that helps in data-driven decision making. The ECA framework works in two phases; the first phase is to convert the audio call into digital textual form, and the second phase is to analyze the textual information using NLP tools and mining techniques to retrieve contextual information. The second phase also deals with emergency categorization using a support vector machine (SVM) learning model to prioritize the emergency dealing with an accuracy of 92.7%. The key factors involved in categorization by ML models are the severity of injury and weapons involvement. The objective of ECA's development is to provide digital health-saving technology to 9-1-1 call handlers and save lives by making accurate decisions by providing real-time assistance. This research aligns with the advancement of digital health technologies by exhibiting how NLP-driven decision support systems can revolutionize emergency healthcare, improve patient outcomes through real-time AI integration, and reduce errors.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1594062"},"PeriodicalIF":2.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frontiers in Big DataPub Date : 2025-06-25eCollection Date: 2025-01-01DOI: 10.3389/fdata.2025.1505877
Hussam Ghanem, Christophe Cruz
{"title":"Fine-tuning or prompting on LLMs: evaluating knowledge graph construction task.","authors":"Hussam Ghanem, Christophe Cruz","doi":"10.3389/fdata.2025.1505877","DOIUrl":"10.3389/fdata.2025.1505877","url":null,"abstract":"<p><p>This paper explores Text-to-Knowledge Graph (T2KG) construction, assessing Zero-Shot Prompting, Few-Shot Prompting, and Fine-Tuning methods with Large Language Models. Through comprehensive experimentation with Llama2, Mistral, and Starling, we highlight the strengths of FT, emphasize dataset size's role, and introduce nuanced evaluation metrics. Promising perspectives include synonym-aware metric refinement, and data augmentation with Large Language Models. The study contributes valuable insights to KG construction methodologies, setting the stage for further advancements.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1505877"},"PeriodicalIF":2.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12237976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frontiers in Big DataPub Date : 2025-06-19eCollection Date: 2025-01-01DOI: 10.3389/fdata.2025.1596615
Nesrin Alkan, Deniz Ersan Yilmaz, Bilal Baris Alkan
{"title":"Conceptualization and scale development for big data-based learning organization capability.","authors":"Nesrin Alkan, Deniz Ersan Yilmaz, Bilal Baris Alkan","doi":"10.3389/fdata.2025.1596615","DOIUrl":"10.3389/fdata.2025.1596615","url":null,"abstract":"<p><strong>Introduction: </strong>In today's competitive business landscape, organizations must enhance learning and adaptability to gain a strategic edge. While big data significantly influences organizational learning, a comprehensive tool to measure this capability has been lacking in the literature. This study aims to develop a valid and reliable scale to assess big data-based learning organization capability.</p><p><strong>Methods: </strong>A two-phase research design was employed. In the first phase, Exploratory Factor Analysis (EFA) was conducted on data collected from 232 managers, identifying 22 items across three underlying factors. In the second phase, Confirmatory Factor Analysis (CFA) was applied to an independent sample (<i>n</i> = 128) to validate the scale's structure and its alignment with the theoretical model.</p><p><strong>Results: </strong>The EFA results revealed a clear three-factor structure, and the CFA confirmed the model's fit to the data, demonstrating good psychometric properties. The final BD-LOC scale shows high internal consistency and construct validity.</p><p><strong>Discussion: </strong>The BD-LOC scale provides organizations with a valuable tool to assess their big data-driven learning capabilities. It supports strategic decision-making, fosters innovation, and enhances operational efficiency. This study fills a significant gap in the literature and contributes to the effective implementation of digital transformation strategies in organizations.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1596615"},"PeriodicalIF":2.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12222110/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frontiers in Big DataPub Date : 2025-06-18eCollection Date: 2025-01-01DOI: 10.3389/fdata.2025.1539724
Sam Martin, Maya Janse Van Rensburg, Huong Thien Le, Charlie Firth, Abinaya Chandrasekar, Sigrún Eyrúnardóttir Clark, Samantha Vanderslott, Cecilia Vindrola-Padros, Norha Vera San Juan
{"title":"LISTEN: lived experiences of Long COVID: a social media analysis of mental health and supplement use.","authors":"Sam Martin, Maya Janse Van Rensburg, Huong Thien Le, Charlie Firth, Abinaya Chandrasekar, Sigrún Eyrúnardóttir Clark, Samantha Vanderslott, Cecilia Vindrola-Padros, Norha Vera San Juan","doi":"10.3389/fdata.2025.1539724","DOIUrl":"10.3389/fdata.2025.1539724","url":null,"abstract":"<p><strong>Introduction: </strong>Long COVID, or Post-Acute Sequelae of SARS-CoV-2 infection (PASC), is a complex condition characterized by a wide range of persistent symptoms that can significantly impact an individual's quality of life and mental health. This study explores public perspectives on the mental health impact of Long COVID and the use of dietary supplements for recovery, drawing on social media content. It uniquely addresses how individuals with Long COVID discuss supplement use in the absence of public health recommendations.</p><p><strong>Methods: </strong>The study employs the LISTEN method (\"Collaborative and Digital Analysis of Big Qual Data in Time Sensitive Contexts\"), an interdisciplinary approach that combines human insight and digital analysis software. Social media data related to Long COVID, mental health, and supplement use were collected using the Pulsar Platform. Data were analyzed using the free-text discourse analysis tool Infranodus and collaborative qualitative analysis methods.</p><p><strong>Results: </strong>The findings reveal key themes, including the impact of Long COVID on mental health, occupational health, and the use of food supplements. Analysis of attitudes toward supplement use highlights the prevalence of negative emotions and experiences among Long COVID patients. The study also identifies the need for evidence-based recommendations and patient education regarding supplement use.</p><p><strong>Discussion: </strong>The findings contribute to a better understanding of the complex nature of Long COVID and inform the development of comprehensive, patient-centered care strategies addressing both physical and mental health needs.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1539724"},"PeriodicalIF":2.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12224277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frontiers in Big DataPub Date : 2025-06-10eCollection Date: 2025-01-01DOI: 10.3389/fdata.2025.1461016
Lars Leyendecker, Anna Louisa Weltin, Florian Nienhaus, Michaela Matthey, Bastian Nießing, Daniela Wenzel, Robert H Schmitt
{"title":"Deep learning based automation of mean linear intercept quantification in COPD research.","authors":"Lars Leyendecker, Anna Louisa Weltin, Florian Nienhaus, Michaela Matthey, Bastian Nießing, Daniela Wenzel, Robert H Schmitt","doi":"10.3389/fdata.2025.1461016","DOIUrl":"10.3389/fdata.2025.1461016","url":null,"abstract":"<p><p>Chronic obstructive pulmonary disease (COPD), a major cause of global mortality, necessitates novel therapies targeting lung function and remodeling. Their effect on emphysema formation is initially investigated using mouse models by analyzing histological lung sections. The extent of airspace enlargement that is characteristic for emphysema is quantified by manual assessment of the mean linear intercept (MLI) across multiple histological microscopy images. Besides being tedious and cost intensive, this manual task lacks scientific comparability due to complexity and subjectivity. In order to continue with the well-established practice and to preserve the comparability of study results, we propose a deep learning-based approach for automating the determination of MLI in histological lung sections utilizing the AutoML software <i>AIxCell</i> which is specialized for the domain of semantic segmentation-based cell culture and tissue analysis. We develop and evaluate our image processing pipeline on stained histological microscope images that stem from a study including two groups of C57BL/6 mice where one group was exposed to cigarette smoke while the control group was not. The results indicate that the <i>AIxCell</i> segmentation algorithm achieves excellent performance, with IoU scores consistently exceeding 90%. Furthermore, the automated approach consistently yields higher MLI values compared to the manually generated values. However, the consistent nature of this discrepancy suggests that the automated approach can be reliably employed without any limitations. Moreover, it demonstrates statistical significance in distinguishing between smoker's and non-smoker's lungs.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1461016"},"PeriodicalIF":2.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186305/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144486930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frontiers in Big DataPub Date : 2025-06-09eCollection Date: 2025-01-01DOI: 10.3389/fdata.2025.1562557
Francisco Carlos Paletta, Audilio Gonzalez-Aguilar, Lise Verlaet
{"title":"Data visualization of complex research systems aligned with the sustainable development goals.","authors":"Francisco Carlos Paletta, Audilio Gonzalez-Aguilar, Lise Verlaet","doi":"10.3389/fdata.2025.1562557","DOIUrl":"10.3389/fdata.2025.1562557","url":null,"abstract":"<p><p>This study presents a methodological framework for visualizing the alignment between complex research systems and the Sustainable Development Goals (SDGs), using CIRAD as a case study. By leveraging advanced data visualization and bibliometric analysis, the research maps CIRAD's publications to the SDGs and explores thematic priorities and institutional collaborations. The findings underscore CIRAD's significant contributions to climate action, food security, biodiversity conservation, and rural development. The integration of complex systems theory and network analysis enhances understanding of SDG interlinkages and provides actionable insights for strategic decision-making in research governance.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1562557"},"PeriodicalIF":2.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12183659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144477802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frontiers in Big DataPub Date : 2025-06-06eCollection Date: 2025-01-01DOI: 10.3389/fdata.2025.1603106
Jiandong Si, Chang Liu, Jingxian Ye, Jianfeng Wu, Jianguo Wang, Kairui Hu, Chunhua Ju, Qianwen Cao
{"title":"Conceptual design of a decision knowledge service model integrating a multi-agent supply relationship diagram for electric power emergency equipment.","authors":"Jiandong Si, Chang Liu, Jingxian Ye, Jianfeng Wu, Jianguo Wang, Kairui Hu, Chunhua Ju, Qianwen Cao","doi":"10.3389/fdata.2025.1603106","DOIUrl":"10.3389/fdata.2025.1603106","url":null,"abstract":"<p><strong>Introduction: </strong>The decision regarding the supply of emergency equipments for power emergencies requires timeliness, efficiency, and accuracy. The multi-agent supply relationship graph, based on complex data fusion, enables the comprehensive exploration of interconnections among key entities in power emergency supplies.</p><p><strong>Methods: </strong>This approach enhances decision-making efficiency and quality by uncovering multiple relationships between main bodies involved. The present study focuses on the decision-making process for power emergency equipments supply and aims to enhance its professionalization. To achieve this goal, multi-modal data regarding power emergency equipments supply is collected from both internal and external power enterprises. Subsequently, a decision support knowledge base is established, along with a four-dimensional relationship graph that integrates events, time, equipments, and suppliers based on the knowledge graph. This enables the mining of multidimensional relationships pertaining to the main body. Finally, supported by the graph, the platform can offer intelligent assistance in decision-making, supplier recommendation, optimization of emergency equipment scheduling for electric power supply, and provides effective information and guidance for decision-making in electric power emergency equipment supply.</p><p><strong>Results: </strong>After conducting a comparative analysis, the decision support system based on the knowledge graph proposed in this study demonstrates superior effectiveness and precision. By integrating the four-dimensional relationship graph with data mining algorithms, precise decision support can be provided for power emergency response. After verification through case studies, the model developed in this study was utilized to recommend suppliers of power emergency equipment, and the recommendation results demonstrated a closer alignment with actual procurement outcomes.</p><p><strong>Conclusion and recommendation: </strong>This system proposed by this study delivers multidimensional knowledge guidance and optimized decision pathways for emergency supply management.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1603106"},"PeriodicalIF":2.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12179217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144477801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frontiers in Big DataPub Date : 2025-06-04eCollection Date: 2025-01-01DOI: 10.3389/fdata.2025.1600267
K Jyothi Upadhya, Ronan Lobo, Mini Shail Chhabra, Aman Paleja, B Dinesh Rao, Geetha M, Prachi Sisodia, Bolusani Akshita Reddy
{"title":"Sliding window based rare partial periodic pattern mining algorithms over temporal data streams.","authors":"K Jyothi Upadhya, Ronan Lobo, Mini Shail Chhabra, Aman Paleja, B Dinesh Rao, Geetha M, Prachi Sisodia, Bolusani Akshita Reddy","doi":"10.3389/fdata.2025.1600267","DOIUrl":"10.3389/fdata.2025.1600267","url":null,"abstract":"<p><p>Periodic pattern mining, a branch of data mining, is expanding to provide insight into the occurrence behavior of large volumes of data. Recently, a variety of industries, including fraud detection, telecommunications, retail marketing, research, and medical have found applications for rare association rule mining, which uncovers unusual or unexpected combinations. A limited amount of literature demonstrated how periodicity is essential in mining low-support rare patterns. In addition, attention must be placed on temporal datasets that analyze crucial information about the timing of pattern occurrences and stream datasets to manage high-speed streaming data. Several algorithms have been developed that effectively track the cyclic behavior of patterns and identify the patterns that display complete or partial periodic behavior in temporal datasets. Numerous frameworks have been created to examine the periodic behavior of streaming data. Nevertheless, such a method that focuses on the temporal information in the data stream and extracts rare partial periodic patterns has yet to be proposed. With a focus on identifying rare partial periodic patterns from temporal data streams, this paper proposes two novel sliding window-based single scan approaches called <i>R3PStreamSW-Growth</i> and <i>R3PStreamSW-BitVectorMiner</i>. The findings showed that when a dense dataset <i>Accidents</i> is considered, for different threshold variations <i>R3P-StreamSWBitVectorMiner</i> outperformed <i>R3PStreamSW-Growth</i> by about 93%. Similarly, when the sparse dataset <i>T10I4D100K</i> is taken into account, <i>R3P-StreamSWBitVectorMiner</i> exhibits a 90% boost in performance. This demonstrates that on a range of synthetic, real-world, sparse, and dense datasets for different thresholds, <i>R3P-StreamSWBitVectorMiner</i> is significantly faster than <i>R3PStreamSW-Growth</i>.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1600267"},"PeriodicalIF":2.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12175007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144327735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frontiers in Big DataPub Date : 2025-05-22eCollection Date: 2025-01-01DOI: 10.3389/fdata.2025.1605788
Paolo Parigi, Kinga Makovi
{"title":"Editorial: Applied computational social sciences.","authors":"Paolo Parigi, Kinga Makovi","doi":"10.3389/fdata.2025.1605788","DOIUrl":"https://doi.org/10.3389/fdata.2025.1605788","url":null,"abstract":"","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1605788"},"PeriodicalIF":2.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12137231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144235945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}