International Journal of Cognitive Computing in Engineering最新文献

{"title":"MpoxNet: Leveraging hybrid deep learning for enhanced monkeypox diagnosis and risk identification","authors":"Tushar Deb Nath,&nbsp;Md. Golam Moazzam","doi":"10.1016/j.ijcce.2025.09.001","DOIUrl":"10.1016/j.ijcce.2025.09.001","url":null,"abstract":"<div><div>Monkeypox is a rare viral disease historically documented in Central and West Africa, though its recent emergence in global outbreaks has raised significant public health concerns. Accurate and timely diagnosis is crucial for effective containment. While existing research predominantly focuses on image-based diagnostics, the potential of tabular data—which is vital for symptom-driven screening and epidemiological analysis—remains largely underexplored. Furthermore, developing robust diagnostic models from tabular data is challenged by limited sample sizes and high data heterogeneity. To address this, we propose MpoxNet, a novel hybrid deep learning model that integrates Long Short-Term Memory (LSTM) networks with Multi-Layer Perceptrons (MLPs) to classify monkeypox cases and identify associated risk factors, particularly among HIV-positive individuals. We preprocessed publicly available Kaggle datasets by applying resampling techniques to mitigate class imbalance and conducted symptom correlation analysis to improve feature representation. Our experimental results demonstrate that MpoxNet achieved an accuracy of 65.35%, a precision of 65.04%, and a recall of 65.68% on Dataset D1, and an accuracy of 87.50%, a precision of 73.33%, and a recall of 100% on Dataset D2. For comparison, we evaluated traditional ensemble models, including AdaBoost, XGBoost, and Random Forest, which consistently outperformed baseline classifiers. These findings highlight the significant diagnostic value of tabular data and establish a foundation for deploying AI-driven, symptom-based tools to augment clinical decision-making and enhance public health surveillance strategies for monkeypox.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 86-94"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating artificial intelligence and quantum computing: A systematic literature review of features and applications","authors":"Vanessa García Pineda ,&nbsp;Alejandro Valencia-Arias ,&nbsp;Francisco Eugenio López Giraldo ,&nbsp;Edison Andrés Zapata-Ochoa","doi":"10.1016/j.ijcce.2025.08.002","DOIUrl":"10.1016/j.ijcce.2025.08.002","url":null,"abstract":"<div><div>Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 26-39"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"XGBoost-LR: A method for network traffic anomaly detection","authors":"Tianyu Liu ,&nbsp;Tonghong Chong ,&nbsp;Guangquan Xu ,&nbsp;Xiao Wang ,&nbsp;Peng Peng ,&nbsp;JingLi Ma","doi":"10.1016/j.ijcce.2025.11.007","DOIUrl":"10.1016/j.ijcce.2025.11.007","url":null,"abstract":"<div><div>With the rapid evolution of internet technology, individuals are facing increasingly severe network security challenges. Confronted with diverse and sophisticated network attacks, traditional methods for network attack detection often struggle to keep pace with these evolving threats, leading to problems such as low anomaly detection accuracy and high false alarm rates. After thoroughly reviewing domestic and international advances in network traffic anomaly detection, this paper introduces a machine learning-based approach, namely the extreme gradient boosting (XGBoost-LR) anomaly traffic detection method. Building on the XGBoost model, this method is designed to address the prevailing issue of low accuracy in current network traffic anomaly detection. Unlike conventional XGBoost+LR approaches that simply stack a single XGBoost model with LR, our method introduces three key innovations: (1) parallel training of multiple diversified XGBoost base classifiers on strategically partitioned data subsets to capture complementary feature representations; (2) a novel two-phase feature interaction mechanism, where base classifiers first learn high-level nonlinear patterns before LR performs fine-grained probabilistic calibration; and (3) dynamic sample re-weighting during ensemble to emphasize hard-to-detect attack categories. This architecture enables more comprehensive feature learning than single-model XGBoost+LR while maintaining computational efficiency. Through strategic dataset segmentation and sub-model parallelism, it effectively identifies malicious traffic, reducing training time and enhancing detection accuracy. Experimental results demonstrate that the proposed method successfully detects and recognizes network traffic anomalies. When compared with several conventional machine learning models using the F1 score, it achieves a modest but consistent improvement in anomaly traffic detection performance.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 325-333"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-label emotion and sentiment detection from Bengali text using hybrid deep learning model","authors":"Rownuk Ara Rumy ,&nbsp;Abdullah Al Maruf ,&nbsp;Zeyar Aung","doi":"10.1016/j.ijcce.2025.10.005","DOIUrl":"10.1016/j.ijcce.2025.10.005","url":null,"abstract":"<div><div>Emotion and sentiment play a significant role in many applications, such as e-commerce, public mental health, and online media security. Several works have been conducted on emotion and sentiment detection, both individually and in combination, using English texts. However, very few works are found in low-resource languages such as Bengali (endonym Bangla). Furthermore, most of these works still need improvements in accuracy, robustness, and data availability. In this research, we worked on multi-label emotion and sentiment detection using Bengali text. First, we gathered a large annotated dataset comprising five sentiment and seven emotion classes, which helped alleviate data scarcity in this area. Then, we employed a hybrid deep learning (DL) model that combines long short-term memory (LSTM) and convolutional neural network (CNN). Several experiments were conducted through two feature engineering methods (traditional and customized BERT) and seven optimizers (SGD, Adam, RMSprop, Adagrad, Adadelta, Adamax, and Nadam). The best combination turned out to be the customized BERT with the Adam optimizer. The proposed model achieved 99.35% and 99.85% accuracy for sentiment and emotion classifications, respectively. Furthermore, the proposed model achieved very high precision, recall, F-score, kappa, Matthews correlation coefficient (MCC), and area under the curve (AUC) values for both the sentiment and emotion detections.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 213-234"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Traditional Chinese medicine text segmentation model with multi-metadata embedding based on Bidirectional LSTM","authors":"Dangguo Shao,&nbsp;Nuoyun Duan,&nbsp;Lei Ma,&nbsp;Sanli Yi","doi":"10.1016/j.ijcce.2025.10.006","DOIUrl":"10.1016/j.ijcce.2025.10.006","url":null,"abstract":"<div><div>In recent years, natural language processing (NLP) has gained increasing attention in traditional Chinese medicine (TCM). Chinese word segmentation (CWS) is a fundamental task in NLP, focusing on accurately segmenting words based on context. However, TCM texts often exhibit incompleteness, ambiguity, and structural challenges, leading to suboptimal segmentation results. The limited size of available datasets and the inadequacy of existing models to capture the unique characteristics of TCM texts further exacerbate this issue. This paper proposes a multi-metadata embedding TCM text segmentation model (MBMC) based on a Bidirectional Long Short-Term Memory Network (BiLSTM) combined with a Multi-Head Self-Attention Mechanism (MHA). The MBMC model first constructs a comprehensive corpus of traditional Chinese medicine (TCM2023) explicitly designed for CWS tasks. This corpus fills a significant gap in the existing literature, as there were previously no dedicated datasets for TCM word segmentation. It employs Word2Vec and CNN networks to extract features from Chinese characters and their structural information, and uses them as inputs to the neural network to improve contextual feature extraction. Additionally, Conditional Random Fields (CRF) are utilized to refine segmentation outputs. On the TCM2023 dataset, the MBMC model achieved an F1-score of 95.64%, providing a reliable foundation for downstream NLP tasks in the TCM domain. Furthermore, the MBMC model obtained F1-scores of 98.93% and 97.50% on the MSRA and PKU datasets, respectively. It outperformed several pre-trained models, including BERT and Glyce-BERT, while requiring less time and memory.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 189-198"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A DistilBERT-based hierarchical text classification for traffic analysis","authors":"Quang Tran Minh,&nbsp;Do Thanh Thai","doi":"10.1016/j.ijcce.2025.10.008","DOIUrl":"10.1016/j.ijcce.2025.10.008","url":null,"abstract":"<div><div>Hierarchical multilabel text classification (HMTC) is constrained by the relationships between labels, often represented by nontrivial data structures such as directed acyclic graphs (DAGs). HMTC is widely utilized in real-world applications such as e-commerce and traffic condition classification for the intelligent transportation systems (ITS). However, the scarcity of training datasets and the strict constraints between labels make the HMTC one of the most challenging text classification problems. Recent advances in language modeling and transfer learning have enabled breakthroughs such as the pretraining of large-scale transformer models, vastly improving inference performance while lowering training requirements for task-specific models. This paper proposes a novel model, namely the DistilBERT branching hierarchical classification network (DB-BHCN), to maximize the utilization of advanced language comprehension capabilities in the DistilBERT. We also propose a combination with the adjacency wrapping matrix (AWX) layer, DB-BHCN+AWX, which is capable of full hierarchical compliance. We perform a comprehensive experimental analysis and find that both the proposed methods outperform the state-of-the-art Distil-BERT-based models. These results reveal the effectiveness and efficiency of the proposed mechanisms, showing their possibility to be applied in real-world applications, specifically in the ITS.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 145-154"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on cooperative relationship prediction based on transformer dynamic graph modeling and comparative learning","authors":"Wei Yu ,&nbsp;Ao Sun ,&nbsp;Hongyu Wang ,&nbsp;Dandan Chen ,&nbsp;Long Zhang ,&nbsp;Yanxia Zhao","doi":"10.1016/j.ijcce.2025.10.002","DOIUrl":"10.1016/j.ijcce.2025.10.002","url":null,"abstract":"<div><div>The goal of this study is to develop an accurate and efficient framework for predicting scientific research cooperation relationships in dynamic academic networks. Existing temporal graph learning models often struggle with sparse and heterogeneous collaboration data, limiting their applicability in real-world academic settings. To address this challenge, we propose the transformer-based contrastive learning (TCL) model, which integrates a topology-aware transformer with a dual-stream encoder to capture structural and temporal dependencies in evolving collaboration graphs jointly. We construct dynamic network snapshots from two large-scale datasets: National Key R&amp;D Project (NKPD) collaboration networks and citation network data (CND). Comparative experiments against state-of-the-art baselines, including DySAT, TGAT, TGN, and CLDG, demonstrate that TCL achieves the highest accuracy-up to 95.3%area under the ROC curve (AUC)—while reducing training time by 21%. Furthermore, our analysis reveals that network sparsity and time-slice granularity significantly influence prediction performance, with annual slices outperforming half-year slices by 35.8% in AUC. The impact of this work lies in providing a robust and scalable tool for forecasting research partnerships, enabling funding agencies, institutions, and policymakers to identify high-potential collaborations earlier. This contributes to accelerating interdisciplinary innovation and enhancing the strategic allocation of research resources.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 118-127"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing ML classifiers for superior intrusion detection in resource-constrained smart homes","authors":"Rong Xu","doi":"10.1016/j.ijcce.2025.08.003","DOIUrl":"10.1016/j.ijcce.2025.08.003","url":null,"abstract":"<div><div>Machine learning (ML) has indeed become essential to the enhancement of intrusion detection systems in different scenarios. It has become a critical barrier against various sophisticated cyber threats. Security vulnerabilities pose special challenges to smart homes, given that devices, sensors, and network connections make the ecosystem highly connected. Such systems improve convenience and efficiency but are generally based on hardware with limited processing power and storage capacity. Therefore, these are prone to a variety of potential attacks. To do so, an effective IDS would have to identify known and evolving threats at all the various vulnerable points, starting from network interfaces down to individual devices. This work tackles these challenges by designing and optimizing ML models that offer reliable intrusion detection tailored for resource-constrained smart home environments. This work argues for intrusion prediction in smart homes using the Extra Tree Classification (ETC) and Linear Discriminant Analysis Classification (LDAC). To strengthen these base models' predictive capability, this paper considered the use of 2 optimization algorithms: the Rider Optimization Algorithm (ROA) and the Aquila Optimizer (AO). The optimizers were integrated strategically with the base models for improved accuracy, thus giving rise to new hybrid models. The combination of ETC with AO provides the ETAO model, while ETC with ROA gives the ETRO model. In equal measure, the LDAC model combined with ROA gives the LDRO model, while that of the LDAC model combined with AO gives the LDAO model. Basically, these hybrid models aim to ensure better performance from a prediction perspective. In the test section, the ETAO model was head and shoulders above the others in this metric, with an excellent value of 0.984, while for the ETRO model, the second-best performing model achieved 0.975. Later on, looking at the entire section, the precision metric again scored highest with the ETAO model at 0.987, while the weakest performance was from the LDAO model, which had a value of 0.888.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 58-73"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel hybrid Res2Net-UNet model for accurate brain tumor segmentation in MRI","authors":"Chelli N. Devi ,&nbsp;Thomas Samraj Lawrence ,&nbsp;Prabhakar Rontala Subramaniam","doi":"10.1016/j.ijcce.2025.11.005","DOIUrl":"10.1016/j.ijcce.2025.11.005","url":null,"abstract":"<div><div>The automated segmentation and analysis of brain tumor from magnetic resonance images (MRI) is clinically significant and has immense social potential given the high incidence and mortality rate of brain tumors. Many atlas-based and deep learning techniques in the recent literature achieve good segmentation of the whole tumor. However, the segmentation of other tumor regions (like core and enhancing tumor) is less accurate. To address this issue, the paper proposes a novel hybrid Res2Net-UNet model for brain tumor segmentation. This consists of a modified Res2Net network in the encoder side of the U-Net. Likewise, a sequential channel and spatial attention mechanism is included in the decoder side. The segmentation results are evaluated both qualitatively and quantitatively. The following metrics are used for quantitative evaluation: Dice ratio, Jaccard index, sensitivity, specificity and precision. The proposed model obtained Dice values of 0.9630, 0.9513 and 0.9240 for the whole, core and enhancing tumor regions, respectively. To the best of our knowledge, this is the first combined Res2Net-UNet model for brain tumor segmentation. The segmentation results by the proposed method are higher than other traditional networks like U-Net, ResNet, DenseNet and EfficientNet. The results are superior to recent works in the state-of-the-literature, especially for the core and enhancing tumor regions. Thus, the model has potential for accurate diagnosis of brain tumors from clinical MR images.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 310-324"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a feature vector for accurate breast cancer detection in mammographic images","authors":"Aisulu Ismailova ,&nbsp;Gulzira Abdikerimova ,&nbsp;Nurgul Uzakkyzy ,&nbsp;Raikhan Muratkhan ,&nbsp;Murat Aitimov ,&nbsp;Aliya Tergeusizova ,&nbsp;Aliya Beissegul","doi":"10.1016/j.ijcce.2025.08.001","DOIUrl":"10.1016/j.ijcce.2025.08.001","url":null,"abstract":"<div><div>Breast cancer remains one of the leading causes of mortality among women, making early and accurate detection crucial for effective treatment. Despite the extensive use of deep learning models in mammographic image classification, existing approaches often lack interpretability. They are prone to diagnostic errors due to image heterogeneity, noise, and the limited availability of annotated datasets. This study addresses these challenges by proposing a novel hybrid model that integrates handcrafted texture and geometric features—such as entropy, eccentricity, mean intensity, and GLCM descriptors—directly into a modified Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture. The primary objective is to improve both diagnostic accuracy and transparency in mammogram classification. Experiments were conducted on the publicly available VinDr-Mammo dataset, which includes 2136 annotated DICOM images with BI-RADS labels. The hybrid model demonstrated superior performance, achieving a 30% reduction in Total Loss, higher sensitivity (0.96), specificity (0.97), and ROC-AUC (0.96), compared to the baseline model without additional features. The integration of clinically interpretable descriptors enhances not only detection accuracy but also the explainability of the results, offering valuable insights for radiologists. These findings contribute to the development of AI-assisted diagnostic tools that are both robust and transparent, particularly in low-resource clinical environments.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"7 ","pages":"Pages 12-25"},"PeriodicalIF":0.0,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}