Frontiers in Big Data最新文献

{"title":"Enhancing IoT botnet detection with explainable ensemble learning.","authors":"Linda Joseph, Sambath M, Vivekanandan M","doi":"10.3389/fdata.2026.1811967","DOIUrl":"https://doi.org/10.3389/fdata.2026.1811967","url":null,"abstract":"<p><strong>Introduction: </strong>Internet of Things (IoT) botnet detection faces significant challenges due to the growing intricacy and decreased transparency of Machine Learning (ML) models.</p><p><strong>Methods: </strong>In this work, we provide an ensemble-based detection system that makes use of a voting classifier made up of a Boosted Decision Tree and a Bagged Random Forest. Using manual feature extraction, the model is trained and assessed using the N-BaIoT dataset. The popular Explainable AI (XAI) method, SHapley Additive exPlanations (SHAP), is used to analyze feature contributions across models while taking important factors like consistency, sensitivity, and monotonicity into account in order to improve the interpretability of the features. SHAP eases the black-box characteristic of sophisticated machine learning models by quantifying the influence of specific features, hence facilitating transparent model interpretation.</p><p><strong>Results: </strong>According to experimental data, the ensemble model is more sensitive than individual classifiers. Additionally, dynamic changes in SHAP values are shown by the weight adjustments made within the voting classifier, highlighting the impact of weight tuning on feature importance.</p><p><strong>Discussion: </strong>This study highlights the effectiveness of integrating SHAP-based XAI into ensembled models, enhancing the transparency, interpretability, and reliability of IoT botnet detection systems.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1811967"},"PeriodicalIF":3.8,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13506276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148834555","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}

{"title":"Differentiated memory and scientific cognition in AI research agents.","authors":"Diego F Cuadros, Abdoul-Aziz Maiga, Sid Thatham, Alvaro Ortiz, Margaret Powers-Fletcher, Ming Tang","doi":"10.3389/fdata.2026.1916523","DOIUrl":"https://doi.org/10.3389/fdata.2026.1916523","url":null,"abstract":"<p><p>AI research agents increasingly support ideation, literature search, coding, experimental execution, analysis, and manuscript drafting across the scientific workflow. This progress advances automated discovery, but workflow automation is not scientific cognition. Scientific reasoning is cumulative and path-dependent: it depends on what a researcher has written, read, learned from critique, absorbed through experience, and used as habitual standards for judging novelty, rigor, feasibility, and significance. We propose Mnemo as a framework for modeling scientific cognition in AI research agents. First, scientific cognition may require differentiated memory, organized into distinct spaces for authored work, external reference, critique, experience, and judgment. Second, provenance should be treated not as passive metadata but as memory routing, because source origin helps determine cognitive function in reasoning. Third, new ideas may be better modeled as controlled collisions across memory spaces, filtered by judgment, rejection, and epistemic calibration, than as generic recombination from model priors. Mnemo motivates a research agenda for AI in science centered on routing fidelity, critique use, judgment alignment, rejection quality, and epistemic calibration.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1916523"},"PeriodicalIF":3.8,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13485561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148801298","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}

{"title":"A computational analysis of human rights discourse in news media using institutional theory.","authors":"Joel Andrew B Cruz, Ma Rowena R Caguiat","doi":"10.3389/fdata.2026.1899143","DOIUrl":"https://doi.org/10.3389/fdata.2026.1899143","url":null,"abstract":"<p><strong>Introduction: </strong>News coverage of politically sensitive events involves framing choices that accumulate across thousands of headlines, years, and media institutions. Likewise, no single reader or journalist can track these patterns as they happen. This study examines how English-language news media framed Rodrigo Duterte's anti-drug campaign in the Philippines from June 2016 to June 2025.</p><p><strong>Methods: </strong>The study applies BERTopic topic modeling and VADER sentiment analysis to 1,133 headlines collected through Google News's RSS endpoint. Institutional Theory organizes the results around three pressures: normative, coercive, and mimetic.</p><p><strong>Results: </strong>Ten topic clusters emerged from the corpus, centered on human rights violations, International Criminal Court (ICC) legal proceedings, and post-presidency accountability narratives. Domestic Philippine outlets covered human rights themes in a larger share of headlines than international outlets during the campaign's early years, but that pattern reversed after Duterte left office in 2022. By 2024 and 2025, international outlets covered human rights themes in roughly 64 percent of headlines, compared with about half for domestic outlets. Sentiment analysis classified 807 headlines as negative, 203 as neutral, and 123 as positive. Negative coverage rose sharply in early 2025, around Duterte's arrest on an ICC warrant. Headline language also clustered around three institutional events: the 2018 ICC filing, the 2020 United Nations (UN) Human Rights Report, and the 2021 ICC investigation authorization, each marked by distinct word patterns across positive, negative, and neutral coverage.</p><p><strong>Discussion: </strong>Normative pressure explains why domestic outlets led on human rights framing before any international body had acted. Coercive pressure from the ICC and UN corresponds to the sentiment spikes around each event. Mimetic pressure explains how framing conventions moved between domestic and international outlets over time, first from domestic to international sources and later in the other direction. The study also considers how Google News's own curation choices shape which outlets and frames appear in the corpus. These findings suggest computational analysis is both a research method and a way to hold a media record open to scrutiny after the news cycle moves on.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1899143"},"PeriodicalIF":3.8,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13472784/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148766614","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}

{"title":"Shifting supply chain interdependencies among global automakers.","authors":"Hiromitsu Goto, Wataru Souma","doi":"10.3389/fdata.2026.1886902","DOIUrl":"10.3389/fdata.2026.1886902","url":null,"abstract":"<p><p>Against the backdrop of electrification and supply chain resilience, the global automotive industry is in an era of great transformation. Using component supply information provided by MarkLines, this study investigated the impact on inter-firm dependencies among major global automakers from 2018 to 2024. Specifically, it analyzed changes in the community structure of the interdependency network between manufacturers, based on common suppliers for each model year and component category. The results revealed the impact of electrification: a reduction of roughly two-thirds (about 64%) in transactions for internal combustion engine (ICE) powertrains and an approximately eight-fold increase in e-powertrain transactions. Furthermore, the results of community detection also revealed a structural reorganization: while geographical clustering among manufacturers intensified for ICE components, new cross-border interdependencies formed for e-powertrain components, leading to the fragmentation of the traditionally integrated Japan-US-Europe bloc and the emergence of a China-centric ecosystem. This study provides new empirical evidence on the structural realignment of the global automotive value chain, offering important implications for management strategy and industrial policy in an era of great technological and geopolitical change.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1886902"},"PeriodicalIF":3.8,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13441850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148686284","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}

{"title":"AI applied to Saudi Arabia higher education: systematic literature review-SLR with PRISMA and VOSviewer.","authors":"Jehad Alqurni","doi":"10.3389/fdata.2026.1846964","DOIUrl":"10.3389/fdata.2026.1846964","url":null,"abstract":"<p><p>This study explored the role of artificial intelligence in higher education in Saudi Arabia. It aims to identify trends, key contributors, influential papers, collaborations, and important research areas from January 2022 to February 2026 to guide future research. The researchers used bibliometric and content analyses. This combined quantitative descriptive methods and network analysis with qualitative content analysis of the most-cited articles. They extracted data from Scopus and the Web of Science, resulting in 66 documents after removing duplicates, editorials, and notes. The analytical techniques included the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), co-word analysis, citation analysis, co-authorship analysis, and bibliographic coupling. VOSviewer supported the visualization. The key findings show that King Abdulaziz University, Qassim University, and King Saud University are the top contributors. A total of 52 papers have been published in journals indexed by WOS and Scopus, while 14 papers are indexed only by Scopus. Additionally, five major groups revealed significant correlations among various word pairs: High-education, Artificial intelligence, AI Chatbot, learning systems, and ChatGPT. The leading journals include Sustainability, Acta Psychological, and Systems, with notable authors such as Al-Harbi. Content analysis highlights the potential of AI to improve learning, boost administrative efficiency, and drive innovation in Saudi Arabia. This study offers practical recommendations for students, universities, and policy makers. This study has several limitations that should be addressed in future studies.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1846964"},"PeriodicalIF":3.8,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13437471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681016","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}

{"title":"A cost-sensitive random forest framework for ARP spoofing detection in Internet of Medical Things networks.","authors":"Siddhartha Singhal, Kakelli Anil Kumar","doi":"10.3389/fdata.2026.1878242","DOIUrl":"10.3389/fdata.2026.1878242","url":null,"abstract":"<p><strong>Introduction: </strong>ARP spoofing poses a major security threat to Internet of Medical Things (IoMT) networks by enabling man-in-the-middle attacks that compromise the integrity of life-critical communications. Existing intrusion detection methods fail to simultaneously address temporal attack dynamics, unequal medical safety requirements, and explicit control of false negative rates.</p><p><strong>Methods: </strong>This study proposes the Self-Healing IoT-Optimized Random Forest (SH-IORF) framework, which integrates temporal behavioral feature engineering, validation-guided cost-sensitive learning, and medical safety-constrained threshold optimization. To ensure methodological rigor and prevent information leakage, a stratified three-way partitioning strategy consisting of training, validation, and completely held-out testing datasets was employed. Class penalty weights and operating thresholds were determined exclusively from the validation dataset.</p><p><strong>Results: </strong>Experimental evaluation on the CICIoMT2024 benchmark demonstrated that the proposed SH-IORF framework achieved 99.90% accuracy, 99.83% recall, 99.95% precision, a 0.9989 F1-score, and an AUC-ROC of 0.9996. The framework limited the false negative rate to 0.17%, satisfying the predefined medical safety constraint (FNR ≤ 0.5%), corresponding to 40 missed detections among 23,390 attack samples and 12 false alarms across 28,768 benign traffic instances.</p><p><strong>Discussion: </strong>The results demonstrate that the proposed framework provides stable and safety-oriented intrusion detection capability under heterogeneous IoMT deployment conditions while maintaining strict testing independence and robust performance under rigorous evaluation settings.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1878242"},"PeriodicalIF":3.8,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13437320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680878","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}

{"title":"Machine learning approach for predicting the severity risk of obstructive sleep apnea syndrome.","authors":"Qi Wang, Xiaoyu Yang, Shuran Xu, Haohao Wu, Guixuan Wang, Huixian Liu, Ronghua Chen, Fengming Xu, Cheng Wang, Kang Du","doi":"10.3389/fdata.2026.1832790","DOIUrl":"10.3389/fdata.2026.1832790","url":null,"abstract":"<p><strong>Background: </strong>Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) has a high global prevalence and is prone to causing various serious complications. Our objective is to develop severity stratification of OSAHS by integrating multiple commonly available clinical features based on machine learning (ML).</p><p><strong>Materials and methods: </strong>This study collected data from 432 cases at Qujing Central Hospital in Yunnan Province, integrating 25 clinical feature variables. The cases were randomly split into training (70%) and validation (30%) sets. The importance of the 25 features was analyzed.</p><p><strong>Results: </strong>It showed that the HCY, TBIL, BMI, GGT, and Age made significant contributions to OSAHS severity. We established five machine learning models-Multilayer Perceptron (MLP), Random Forest, XGBoost, LightGBM, and Support Vector Machine (SVM)-by integrating 25 clinical features. Through cross-validation and continuous adjustment of model parameters, the optimal predictive model was determined. By calculating model accuracy and <i>F</i>1-score, XGBoost was identified as the best-performing model, achieving an area under the curve (AUC) of 0.63, an accuracy of 75% and an <i>F</i>1-score of 65.60.</p><p><strong>Conclusion: </strong>In this study, we established a predictive model for the severity stratification of OSAHS based on machine learning algorithms. The XGBoost model demonstrated superior predictive performance.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1832790"},"PeriodicalIF":3.8,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13402873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148609904","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}

{"title":"A dataset-centric review of IoT and IIoT intrusion detection: realism, evaluation biases, and future research directions.","authors":"Dwarsala Sreedhar Reddy, Kakelli Anil Kumar","doi":"10.3389/fdata.2026.1878260","DOIUrl":"10.3389/fdata.2026.1878260","url":null,"abstract":"<p><p>The rapid growth of IoT and IIoT expands the cyber-attack surface of interconnected and safety-critical systems, and, as such, IDSs have become a fundamental security mechanism. Although very impressive results have been reported for machine learning and deep learning-based IDS in benchmark datasets, these gains often do not generalize to real-world deployments owing to dataset design limitations, realism deficits, and evaluation biases, rather than inherent flaws in detection algorithms, which can lead to significant vulnerabilities in actual operational environments. This study presents a dataset-centric review of widely used intrusion detection datasets from the IIoT, IoT, and traditional network domains. A unified taxonomy differentiates datasets based on the domain context, traffic representation, protocol semantics, and attack modeling assumptions. Based on a common analytical framework, each dataset was reviewed regarding its realism, coverage of the threats, class imbalance, temporal continuity, and modern ML/DL-based evaluation of the IDS. The cross-dataset analysis conducted in this study shows that, in addition to the fact that model architecture and feature engineering play a major role, several studies indicate that the simplicity of the datasets, the class imbalance, and the repetitive attack patterns as well as the evaluation methods can affect accuracy of the IDS. This work further underlines the remaining gaps, such as zero-day and adaptive attacks, limited encrypted traffic, weak temporal evolution, poor support for federated learning, and sparse annotations for explainable IDSs. Finally, this study presents future directions for dataset design aligned with the requirements of next-generation IDSs by highlighting digital twin-based IIoT environments, edge-cloud collaborative data generation, sequential traffic modeling, and explainability-oriented annotations that can ensure robust, trustworthy, and deployment-ready IDS solutions.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1878260"},"PeriodicalIF":3.8,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13395705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148585982","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}

{"title":"A prognostic tool for pulmonary collapse: nomogram-based prediction of 28-day mortality.","authors":"Xinming He, Wenchong Yu, Yuling Li, Ao Ma, Zhichao Meng, Jiehao Zhu, Minghui Tan, Xiaodong Zhao, Mu Chen","doi":"10.3389/fdata.2026.1728498","DOIUrl":"10.3389/fdata.2026.1728498","url":null,"abstract":"<p><strong>Background: </strong>Pulmonary collapse is a common and serious respiratory condition, but there is no dedicated bedside tool to estimate prognosis. This study aimed to develop a nomogram to predict 28-day mortality in patients with pulmonary collapse.</p><p><strong>Methods: </strong>We extracted data for patients with pulmonary collapse from MIMIC-III, identified predictors using regression analyses, and used MIMIC-IV for temporal validation. We then built a nomogram based on the selected predictors. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), AUC comparisons using the DeLong test, reclassification (NRI and IDI), calibration (calibration curves, calibration slope, and Brier score), and decision curve analysis (DCA).</p><p><strong>Results: </strong>A total of 4,088 patients with pulmonary collapse were included in the study. Logistic regression analysis identified twelve independent predictive factors associated with 28-day mortality: age (OR = 1.01, <i>P</i> =0.040), married status (OR =0 .63, <i>P</i> =0 .040), Glasgow Coma Scale score (OR = 0.94, <i>P</i> = 0.02), creatinine (OR = 0.82, <i>P</i> = 0.03), chloride ions (OR = 0.85, <i>P</i> = 0.03), sodium ions (OR = 1.19, <i>P</i> = 0.02), blood urea nitrogen (OR = 1.02, <i>P</i> < 0.001), white blood cell count (OR = 1.04, <i>P</i> < 0.001), heart rate (OR = 1.02, <i>P</i> = 0.02), respiratory rate (OR = 1.05, <i>P</i> = 0.03), temperature (OR = 0.54, <i>P</i> < 0.001), and metastatic cancer (OR = 6.66, <i>P</i> < 0.001). The nomogram showed moderate discrimination and consistently higher AUC than Age+Gender, SOFA, and SAPSII across cohorts.</p><p><strong>Conclusion: </strong>This study identified factors associated with 28-day mortality in patients with pulmonary collapse and developed a nomogram for early risk stratification.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1728498"},"PeriodicalIF":3.8,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148581160","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}

{"title":"Noise-robust temporal-spectral fusion transformers for EEG-based cognitive state classification in aviation environments.","authors":"Quynh Anh Nguyen, Nam Anh Dao, Long Nguyen","doi":"10.3389/fdata.2026.1837706","DOIUrl":"10.3389/fdata.2026.1837706","url":null,"abstract":"<p><p>Attention-related Pilot Performance Decrements (APPD) contribute substantially to aviation incidents, yet existing electroencephalography (EEG)-based monitoring methods often lack generalization, robustness to noise, and effective temporal-spectral integration. We propose a temporal-spectral fusion transformer (TF-T) combining multi-scale preprocessing, dual-stream temporal and spectral feature extraction, and transformer-based fusion with enhanced temporal-spectral integration and multi-resolution feature processing for multiclass cognitive state recognition. Three variants (TF-T1-TF-T3) are evaluated on controlled and ecologically realistic EEG datasets under clean and noise-augmented (Gaussian, Uniform, COMBO) conditions, using chronological partitioning to avoid temporal leakage. TF-T2 achieves the highest clean-data accuracy (99.2%), while TF-T3 offers superior robustness, improving Macro-F1 by ~4.5-4.7 points across all noise types and outperforming state-of-the-art baselines by up to +8 Macro-F1 under COMBO noise, supporting its deployment in perturbation-prone aviation environments.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1837706"},"PeriodicalIF":3.8,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13392987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148581212","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}