Frontiers in Big Data最新文献

{"title":"Big data approaches to bovine bioacoustics: a FAIR-compliant dataset and scalable ML framework for precision livestock welfare.","authors":"Mayuri Kate, Suresh Neethirajan","doi":"10.3389/fdata.2025.1723155","DOIUrl":"10.3389/fdata.2025.1723155","url":null,"abstract":"<p><p>The convergence of IoT sensing, edge computing, and machine learning is revolutionizing precision livestock farming. Yet bioacoustic data streams remain underexploited due to computational-complexity and ecological-validity challenges. We present one of the most comprehensive bovine vocalization datasets to date-569 expertly curated clips spanning 48 behavioral classes, recorded across three commercial dairy farms using multi-microphone arrays and expanded to 2,900 samples through domain-informed data augmentation. This FAIR-compliant resource addresses key Big Data challenges: volume (90 h of raw recordings, 65.6 GB), variety (multi-farm, multi-zone acoustic environments), velocity (real-time processing requirements), and veracity (noise-robust feature-extraction pipelines). A modular data-processing workflow combines denoising implemented both in iZotope RX 11 for quality control and an equivalent open-source Python pipeline using noisereduce, multi-modal synchronization (audio-video alignment), and standardized feature engineering (24 acoustic descriptors via Praat, librosa, and openSMILE) to enable scalable welfare monitoring. Preliminary machine-learning benchmarks reveal distinct class-wise acoustic signatures across estrus detection, distress classification, and maternal-communication recognition. The dataset's ecological realism-embracing authentic barn acoustics rather than controlled conditions-ensures deployment-ready model development. This work establishes the foundation for animal-centered AI, where bioacoustic streams enable continuous, non-invasive welfare assessment at industrial scale. By releasing a Zenodo-hosted, FAIR-compliant dataset (restricted access) and an open-source preprocessing pipeline on GitHub, together with comprehensive metadata schemas, we advance reproducible research at the intersection of Big Data analytics, sustainable agriculture, and precision livestock management. The framework directly supports UN SDG 9, demonstrating how data science can transform traditional farming into intelligent, welfare-optimized production systems capable of meeting global food demands while maintaining ethical animal-care standards.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1723155"},"PeriodicalIF":2.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12855049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108455","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":"Deep learning-enabled hybrid systems for accurate recognition of text in seal images.","authors":"Keke Zhang, Mingyu Guan, Chao Wu, Yutong Li, Qingguo Lü, Yi Liu, Yi Wang, Wei Wang, Wei Zhang","doi":"10.3389/fdata.2025.1753871","DOIUrl":"10.3389/fdata.2025.1753871","url":null,"abstract":"<p><p>Chinese seals are widely used in various fields within Chinese society as a tool for certifying legal documents. However, recognizing text on these seals presents challenges due to background text, high noise levels, and minimalistic image features. This paper introduces a hybrid model to address these difficulties in Chinese seal text recognition. Our model integrates preprocessing techniques tailored for real seals, a deep learning-based position correction model, a circular text unwrapping model, and OCR text recognition. First, we apply a color-based method to effectively remove the black background text on seals, eliminating redundant information while retaining crucial features for further analysis. Next, we introduce an innovative image denoising algorithm to significantly improve the system's robustness in processing noisy seal images. Additionally, we develop a deep learning-based angle prediction network and create synthetic datasets that mimic real seal scenes, enabling optimal seal image positioning for enhanced text flattening and recognition, thus boosting overall system performance. Finally, polar coordinate transformation is employed to convert the circular seal into a rectangular image for more efficient text recognition. Experimental results indicate that our proposed methods effectively enhance the accuracy of seal text recognition.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1753871"},"PeriodicalIF":2.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12847014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146088010","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":"Dynamic patterns of healthy lifestyle awareness after COVID-19: a study using Google Trends and joinpoint regression.","authors":"Zahroh Shaluhiyah, Shabrina Arifia Qatrannada, Roshan Kumar Mahato, Farid Agushybana, Sri Handayani, Dzul Fahmi Afriyanto, Usha Rani, Dewie Sulistyorini","doi":"10.3389/fdata.2025.1717592","DOIUrl":"https://doi.org/10.3389/fdata.2025.1717592","url":null,"abstract":"<p><strong>Introduction: </strong>The COVID-19 pandemic has significantly influenced public interest in health-related behaviors, as reflected in online search trends. Analyzing these trends provides insights into shifting health concerns and informing future public health strategies. This study examined Google Trends data to assess the changes in public interest in mental health, healthy diet, sleep, screen time, physical activity, and tobacco smoking before, during, and after the COVID-19 pandemic.</p><p><strong>Methods: </strong>Google Trends data (2019-2023) were analyzed using joinpoint regression to identify statistically significant shifts in relative search volume (RSV) over time. Additionally, the Mann-Whitney U test was conducted to examine differences in mean RSV across time period.</p><p><strong>Results: </strong>Awareness that consistently increased during and after the pandemic was observed in mental health, particularly anxiety, and sleep patterns. These topics showed significant positive trends in joinpoint regression and higher mean RSVs, with statistically significant differences across time periods (<i>p</i> < 0.05). In contrast, some behaviors such as physical activity and screen time saw increased awareness only during the pandemic but did not sustain afterward. Whilst, dietary behavior and smoking either remained stagnant or declined, indicating limited or declining public interest despite their relevance to health outcomes.</p><p><strong>Conclusion: </strong>Digital interest in health behaviors varied during and after COVID-19, with only mental health and sleep showing sustained concern. However, spikes in awareness often reflected personally relevant issues, highlighting Google Trends' potential as an early signal for health promotion efforts.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1717592"},"PeriodicalIF":2.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12834755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146094887","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":"Improving early liver metastasis detection in colorectal cancer using a weighted ensemble of ResNet50 and swin transformer: a KHCC study.","authors":"Ahmad Nasayreh, Hasan Gharaibeh, Rula Al-Qawabah, Azza Gharaibeh, Bayan Altalla, Iyad Sultan","doi":"10.3389/fdata.2025.1700292","DOIUrl":"10.3389/fdata.2025.1700292","url":null,"abstract":"<p><p>Colorectal cancer represents the third most diagnosed malignancy globally, with liver metastasis occurring in approximately 50-60% of patients following initial treatment. Current surveillance strategies utilizing carcinoembryonic antigen monitoring and interval cross-sectional imaging demonstrate significant limitations in early hepatic recurrence detection, often identifying disease at advanced, unresectable stages. This study addresses the critical research gap in AI-driven surveillance frameworks by developing a novel ensemble deep learning model for early liver metastasis prediction in colorectal cancer patients. The methodology employed six state-of-the-art architectures including ResNet50, MobileNetV2, DenseNet121, CNN-LSTM, and Swin Transformer as feature extractors through transfer learning, followed by weighted soft voting ensemble learning combining the top-performing models. The framework was evaluated on a comprehensive dataset of 1,628 medical images from colorectal cancer patients, with rigorous statistical validation using Friedman and Wilcoxon signed-rank tests. Results demonstrated that the ensemble model combining ResNet50 and Swin Transformer achieved superior performance with 75.48% accuracy, 79.0% sensitivity, 73.6% specificity, and 0.8115 AUC, representing statistically significant improvements over all individual architectures. The ensemble approach successfully addressed the challenging nature of the dataset where multiple state-of-the-art models achieved near-random performance, demonstrating the effectiveness of architectural diversity in medical image analysis. The clinical impact of this work extends to enhancing early detection capabilities that could increase patient eligibility for curative interventions, with balanced diagnostic performance suitable for surveillance applications. The computationally efficient framework requires only 0.39 s per image inference time, making it feasible for integration into existing clinical workflows and potentially improving outcomes for colorectal cancer patients through earlier identification of hepatic recurrence.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1700292"},"PeriodicalIF":2.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12832282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146068527","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":"Explainable attrition risk scoring for managerial retention decisions in human resource analytics.","authors":"M S Pavithran, S M Vadivel","doi":"10.3389/fdata.2025.1699561","DOIUrl":"10.3389/fdata.2025.1699561","url":null,"abstract":"<p><strong>Introduction: </strong>Employee turnover remains a significant challenge for organizations as it becomes difficult for them to retain the same employees and continue with their operations efficiently. With the assistance of predictive analytics, HR managers will be able to foresee and lower the potential turnover. Conventional research has focused on the effectiveness of technical models, yet there is a lack of studies investigating the interpretability and reliability of managerial forecasts.</p><p><strong>Methods: </strong>This research used the Employee Attrition dataset and applied various pre-processing methods, including label encoding, feature scaling, and SMOTE for class balancing. Machine learning models were trained and optimized using grid search with stratified cross-validation. The best-performing model was calibrated using the sigmoid method to ensure the accuracy of the predicted probabilities. LIME enabled local interpretability, thus providing practical insights into individual employee attrition-related risks. Permutation feature importance analysis and SHAP summary plots helped in better understanding the model by showing the individual features that contributed to the attrition probability.</p><p><strong>Results: </strong>The Random Forest classifier achieved the highest AUC-ROC score of 97.37%. Risk distribution visualizations highlight employees with the highest attrition probability, and calibration is the main reason for the Brier Score reduction from 0.03873 to 0.03480.</p><p><strong>Discussion: </strong>The study concludes that by prioritizing interventions and increasing the accuracy of retention strategies, a calibrated, interpretable, and risk-stratified model can enhance HR decision-making. This framework aids HR leaders in transitioning from reactive to proactive workforce management by leveraging data-driven insights.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1699561"},"PeriodicalIF":2.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12832383/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146068532","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":"Decoding deception: state-of-the-art approaches to deep fake detection.","authors":"Tarak Hussain, B Tirapathi Reddy, Kondaveti Phanindra, Sailaja Terumalasetti, Ghufran Ahmad Khan","doi":"10.3389/fdata.2025.1670833","DOIUrl":"10.3389/fdata.2025.1670833","url":null,"abstract":"<p><p>Deepfake technology evolves at an alarming pace, threatening information integrity and social trust. We present new multimodal deepfake detection framework exploiting cross-domain inconsistencies, utilizing audio-visual consistency. Its core is the Synchronization-Aware Feature Fusion (SAFF) architecture combined with Cross-Modal Graph Attention Networks (CM-GAN), both addressing the temporal misalignments explicitly for improved detection accuracy. Across eight models and five benchmark datasets with 93,750 test samples, the framework obtains 98.76% accuracy and significant robustness against multiple compression levels. Synchronized audio-visual inconsistencies are thus highly discriminative according to statistical analysis (Cohen's <i>d</i> = 1.87). With contributions centering around a cross-modal feature extraction pipeline, a graph-based attention mechanism for inter-modal reasoning and an extensive number of ablation studies validating the fusion strategy, the paper also provides statistically sound insights to guide future pursuit in this area. With a 17.85% generalization advantage over unimodal methods, the framework represents a new state of the art and introduces a self-supervised pre-training strategy that leverages labeled data 65% less.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1670833"},"PeriodicalIF":2.4,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12827133/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146054880","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":"Bias in AI systems: integrating formal and socio-technical approaches.","authors":"Amar Ahmad, Yvonne Vallès, Youssef Idaghdour","doi":"10.3389/fdata.2025.1686452","DOIUrl":"10.3389/fdata.2025.1686452","url":null,"abstract":"<p><p>Artificial Intelligence (AI) systems are increasingly embedded in high-stakes decision-making across domains such as healthcare, finance, criminal justice, and employment. Evidence has been accumulated showing that these systems can reproduce and amplify structural inequities, leading to ethical, social, and technical concerns. In this review, formal mathematical definitions of bias are integrated with socio-technical perspectives to examine its origins, manifestations, and impacts. Bias is categorized into four interrelated families: historical/representational, selection/measurement, algorithmic/optimization, and feedback/emergent, and its operation is illustrated through case studies in facial recognition, large language models, credit scoring, healthcare, employment, and criminal justice. Current mitigation strategies are critically evaluated, including dataset diversification, fairness-aware modeling, post-deployment auditing, regulatory frameworks, and participatory design. An integrated framework is proposed in which statistical diagnostics are coupled with governance mechanisms to enable bias mitigation across the entire AI lifecycle. By bridging technical precision with sociological insight, guidance is offered for the development of AI systems that are equitable, accountable, and responsive to the needs of diverse populations.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1686452"},"PeriodicalIF":2.4,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146054901","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":"Hybrid deep learning models for fake news detection: case study on Arabic and English languages.","authors":"Baqer M Merzah, Jafar Razmara, Zolfaghar Salmanian","doi":"10.3389/fdata.2025.1683786","DOIUrl":"10.3389/fdata.2025.1683786","url":null,"abstract":"<p><strong>Introduction: </strong>Fake news has become a significant threat to public discourse due to the swift spread of online content and the difficulty of detecting and distinguishing it from real news. This challenge is further amplified by society's increasing dependence on online social networks. Many researchers have developed machine learning and deep learning models to combat the spread of misinformation and identify fake news. However, the studies focused on a single language, and the performance analysis achieved a low accuracy, especially for Arabic, which faces challenges due to resource constraints and linguistic intricacies.</p><p><strong>Methods: </strong>This paper introduces an effective deep-learning technique for fake news detection (FND) in Arabic and English. The proposed model integrates a multi-channel Convolutional Neural Network (CNN) and dual Bidirectional Long Short-Term Memory (BiLSTM), parallelly capturing semantic and local textual features embedded by a pre-trained FastText model. Subsequently, a global max-pooling layer was added to reduce dimensionality and extract salient features from the sequential output. Finally, the model classifies news as fake or real. Moreover, the model is trained and evaluated on three benchmark datasets, AFND and ANS, Arabic datasets, and WELFake, an English dataset.</p><p><strong>Results: </strong>Experimental results highlight the model's effectiveness and performance superiority over state-of-the-art (SOTA) approaches, with (94.43 ± 0.19) %, (71.63 ± 1.45) %, and (98.85 ± 0.03) %, accuracy on AFND, ANS and WELFake, respectively.</p><p><strong>Discussion: </strong>This work provides a robust approach to combating misinformation, offering practical applications in enhancing the reliability of information on social networks.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1683786"},"PeriodicalIF":2.4,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12815712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020637","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":"Adaptive model for rate of penetration prediction based on the dynamic correlation of influencing factors.","authors":"Yonggang Deng, Xiaojing Zhou, Zixuan Feng, Xin Li, Hui Li","doi":"10.3389/fdata.2025.1676054","DOIUrl":"10.3389/fdata.2025.1676054","url":null,"abstract":"<p><strong>Introduction: </strong>Accurately predicting the rate of penetration (ROP) is a critical benchmark for evaluating operational efficiency in drilling operations, and it is necessary to optimize the drilling parameters and construct an accurate ROP prediction model. At present, the correlations between drilling operation parameters and the ROP are commonly evaluated using a static assessment, which overlooks dynamic changes in parameter correlations during drilling processes.</p><p><strong>Method: </strong>An adaptive ROP prediction model that incorporates depth-varying correlations of influential parameters is constructed. This model can automatically identify the dynamic correlations of the modeling parameters at different depths of well sections, and the optimal modeling parameters for adaptive training are selected based on the ranking of the correlation coefficients.</p><p><strong>Results: </strong>An analysis of 33 drilling parameters across 4,837 datasets collected from 4 wellbores in Sichuan. The comparison analysis revealed that at different well sections, the dynamic correlation coefficient of each parameter deviates significantly from the overall correlation coefficient. According to the proposed model, it can dynamically select key parameters and achieve self-update based on real-time data streams, avoiding the defect of traditional fixed-parameter models that ignore the dynamic changes of well sections.</p><p><strong>Discussion: </strong>Modeling comparison analysis revealed that in multiple rounds of prediction based on dynamic correlations, the prediction accuracy in 93% of the prediction rounds exceeded that of the overall correlation, indicating that the adaptive ROP prediction model with dynamic correlations has high application value.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1676054"},"PeriodicalIF":2.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12812677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146012440","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":"Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics.","authors":"Miguel Angel Valles-Coral, Lloy Pinedo, Ciro Rodríguez, Diego Rodríguez, Keller Sánchez-Dávila, Lolita Arévalo-Fasanando, Nelly Reátegui-Lozano","doi":"10.3389/fdata.2025.1678863","DOIUrl":"10.3389/fdata.2025.1678863","url":null,"abstract":"<p><strong>Introduction: </strong>The use of artificial intelligence (AI) in cervical cytology has increased substantially due to the need for automated tools that support the early detection of precancerous lesions.</p><p><strong>Methods: </strong>This systematic review examined deep learning models applied to cervical cytology images, focusing on the architectures used, the datasets employed, and the performance metrics reported. Articles published between 2022 and 2025 were retrieved from Scopus using PRISMA methodology. After applying inclusion criteria and full-text screening, 77 studies were included for RQ1 (models), 75 for RQ2 (datasets), and 71 for RQ3 (metrics).</p><p><strong>Results: </strong>Hybrid models were the most prevalent (56%), followed by convolutional neural networks (CNNs) and a growing number of Vision Transformer (ViT)-based approaches. SIPaKMeD and Herlev were the most frequently used datasets, although the use of private datasets is increasing. Accuracy was the most commonly reported metric (mean 87.76%), followed by precision, recall, and F1-score. Several hybrid and ViT-based models exceeded 92% accuracy. Identified limitations included limited cross-validation, reduced clinical representativeness of datasets, and inconsistent diagnostic criteria.</p><p><strong>Discussion: </strong>This review synthesizes current trends in AI-based cervical cytology, highlights common methodological limitations, and proposes directions for future research to enhance clinical applicability and standardization.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1678863"},"PeriodicalIF":2.4,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12807953/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999624","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}