IEEE Open Journal of the Computer Society最新文献

{"title":"Neurological Disorder Recognition via Comprehensive Feature Fusion by Integrating Deep Learning and Texture Analysis","authors":"Najmul Hassan;Abu Saleh Musa Miah;Yuichi Okuyama;Jungpil Shin","doi":"10.1109/OJCS.2025.3594701","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3594701","url":null,"abstract":"Neurological disorders, including Brain Tumors (BTs), Alzheimer’s Disease (AD), and Parkinson’s Disease (PD), pose significant global health challenges. Early and accurate diagnosis is crucial for effective treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is a key diagnostic tool, but traditional Machine Learning (ML) approaches often rely on labor-intensive handcrafted features, leading to inconsistent performance. Recent advancements in Deep Learning (DL) enable automated feature extraction, which offers improved robustness and scalability. However, many existing methods face challenges in fully exploiting the complementary strengths of DL and handcrafted features across multiple disease types. This study proposes a novel hybrid DL model that integrates automated deep features with statistical textural descriptors for the classification of BTs, AD, and PD. The model employs a dual-stream architecture: (1) a modified VGG16 convolutional neural network (CNN), chosen for its favorable trade-off between performance and computational efficiency in medical imaging, to extract deep features from MRI slices, and (2) a sequential one dimensional (1D) CNN to process six gray-level co-occurrenc matrix (GLCM)derived handcrafted features, empirically validated for their superior discriminative power in neuroanatomical texture analysis. By integrating these complementary feature sets, the model leverages global patterns and fine-grained textural details, resulting in a robust and comprehensive representation for accurate and reliable medical image classification. Gradient-weighted class activation mapping (Grad-CAM) is incorporated to enhance interpretability by localizing diagnostically relevant brain regions. The fused features are passed through a fully connected layer for final classification. The proposed model was evaluated on four publicly available MRI datasets, achieving accuracies of 98.86%, 99.50%, 98.52%, and 99.13% on the CE-MRI (multi-class BT), Br35H (binary BT), AD, and PD datasets, respectively. The model achieved an average classification accuracy of 99.05% across the three neurological disorders. Our method outperforms recent state-of-the-art (SOTA) methods, which shows the effectiveness of the proposed model integrating DL and handcrafted features to develop interpretable, robust, and generalizable AI-driven diagnostic systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1366-1377"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11106739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998024","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 Comprehensive AI-Based Digital Twin Model for Residential Hydrogen-Based Energy Systems","authors":"Laura Rodríguez de Lope;Victor M. Maestre;Luis Diez;Alfredo Ortiz;Ramón Agüero;Inmaculada Ortiz","doi":"10.1109/OJCS.2025.3594439","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3594439","url":null,"abstract":"As the urgency to mitigate climate change intensifies, the residential sector, a significant contributor to greenhouse gas emissions, calls for innovative solutions to foster decarbonization efforts. The integration of renewable energy sources and hydrogen-based technologies offers a promising pathway to achieve energy independence and so reduce reliance on traditional power grids. In this sense, digital twins, powered by artificial intelligence techniques, offer significant potential to enhance the performance of these systems, fostering energy self-sufficiency. This article presents a comprehensive architecture for a digital twin of residential hydrogen-based energy systems. We discuss the implementation of the digital replica based on both logical behavior and machine learning techniques. The resulting models are validated using real data collected from an electrically self-sufficient social housing in Spain, located in the town of Novales (Cantabria). The results evince that the behavior of the proposed solution accurately mimics the one shown by the physical counterpart, suggesting its utility as a valuable instrument for enhancing the efficiency of renewable hydrogen-based energy systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1317-1328"},"PeriodicalIF":0.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11106257","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880518","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 New Cryptographic Frontier: Key-Independent Security and Post-Quantum Hardness Assumptions","authors":"Abdelkader Laouid;Mostefa Kara;Mohammad Hammoudeh","doi":"10.1109/OJCS.2025.3592218","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3592218","url":null,"abstract":"With the rapid advancement of quantum computing, many classical encryption schemes are becoming increasingly vulnerable to quantum attacks, highlighting the urgent need for post-quantum cryptographic solutions that can withstand this emerging threat. In this context, this article introduces the Q-Problem, a novel post-quantum hardness assumption specifically designed to resist quantum adversaries by presenting them with a vast and computationally infeasible preimage space. Building on this foundation, we propose Q-KIE (a post-quantum key-independent encryption scheme), which replaces persistent cryptographic keys with ephemeral, message-bound secret holders. Q-KIE features dynamic complexity tuning, offering flexible security levels and maintaining efficient performance across both classical and quantum computing environments. Detailed analysis and comprehensive evaluations demonstrate the scheme’s strong potential in preserving confidentiality, integrity, and computational practicality, positioning it as a promising candidate for hybrid and post-quantum cryptographic frameworks.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1306-1316"},"PeriodicalIF":0.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11095641","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880508","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":"Few-Shot Segmentation Using Multi-Similarity and Attention Guidance","authors":"Ehtesham Iqbal;Sirojbek Safarov;Seongdeok Bang;Sajid Javed;Yahya Zweiri;Yusra Abdulrahman","doi":"10.1109/OJCS.2025.3592291","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3592291","url":null,"abstract":"Few-shot segmentation (FSS) methods aim to segment objects of novel classes with relatively few annotated samples. Prototype learning, a popular approach in FSS, employs prototype vectors to transfer information from known classes (support images) to novel classes(query images) for segmentation. However, using only prototype vectors may not be sufficient to represent all features of the support image. To extract abundant features and make more precise predictions, we propose a <bold>M</b>ulti-<bold>S</b>imilarity and <bold>A</b>ttention <bold>N</b>etwork (MSANet) including two novel modules, a multi-similarity module and an attention module. The multi-similarity module exploits multiple feature-map of support images and query images to estimate accurate semantic relationships. The attention module instructs the MSANet to concentrate on class-relevant information. We evaluated the proposed network on standard FSS datasets, PASCAL-<inline-formula><tex-math>$5^{i}$</tex-math></inline-formula> 1-shot, PASCAL-<inline-formula><tex-math>$5^{i}$</tex-math></inline-formula> 5-shot, COCO-<inline-formula><tex-math>$20^{i}$</tex-math></inline-formula> 1-shot, and COCO-<inline-formula><tex-math>$20^{i}$</tex-math></inline-formula> 5-shot. An MSANet model with a ResNet101 backbone achieved state-of-the-art performance for all four benchmark datasets with mean intersection over union (mIoU) values of 69.13%, 73.99%, 51.09%, and 56.80%, respectively. The code used is available at <uri>https://github.com/AIVResearch/MSANet</uri>.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1271-1282"},"PeriodicalIF":0.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11095423","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852699","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":"Towards seL4 for Enhanced System Isolation and Security on Embedded Devices","authors":"Everton de Matos;George Lawton;Conor Lennon","doi":"10.1109/OJCS.2025.3592377","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3592377","url":null,"abstract":"Embedded systems increasingly face security threats due to limited isolation and hardware constraints, creating a demand for robust solutions. The seL4 microkernel, recognized for its minimal footprint and strong security guarantees, is particularly promising for embedded applications requiring secure isolation. This article explores seL4’s capabilities, specifically focusing on its use as a hypervisor on ARM platforms and as a Trusted Execution Environment (TEE) on RISC-V hardware. We describe our implementation of these approaches, highlighting key challenges and presenting methods to simplify their development and deployment. Performance evaluations indicate that seL4 effectively delivers strong isolation with minimal impact on resource usage and overall system performance. In particular, our results demonstrate low overheads for CPU utilization, memory consumption, and network throughput, even under intensive workloads. Finally, the article discusses challenges and recommendations towards the adoption of seL4-based solutions, providing a valuable reference for researchers and practitioners working towards enhancing security in embedded and Internet of Things systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1329-1340"},"PeriodicalIF":0.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11095653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926879","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":"XPolypNet: A U-Net-Based Model for Semantic Segmentation of Gastrointestinal Polyps With Explainable AI","authors":"Arjun Kumar Bose Arnob;Muhammad Mostafa Monowar;Md. Abdul Hamid;M. F. Mridha","doi":"10.1109/OJCS.2025.3592204","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3592204","url":null,"abstract":"Automated segmentation of gastrointestinal polyps is a critical step in the early detection and prevention of colorectal cancer (CRC), which is one of the most common causes of cancer-related deaths worldwide. This article presents a U-Net-based model enhanced with Attention Mechanisms and Atrous Spatial Pyramid Pooling (ASPP) for accurate polyp segmentation. To address the challenges of varying polyp sizes, indistinct boundaries, and complex textures, the model used a combined loss function (Binary Cross-Entropy and Dice Loss). Additionally, Gradient-Weighted Class Activation Mapping (Grad-CAM) was integrated to provide visual explanations of the model’s decisions to increase trust and interpretability by clinical practitioners. The presented model was evaluated on five benchmark datasets, achieving a Dice Coefficient of 0.8378 and a Mean Intersection over Union (mIoU) of 0.8427. The comparative analysis highlighted its superiority when compared to state-of-the-art contemporary approaches, with a precision and accuracy of 97%. Qualitative analyses also underline the ability to accurately delineate polyps, even in difficult situations. Although the model exhibited satisfactory performance, it still faced challenges regarding boundary misclassification and reduced efficacy in datasets with high variability. The next steps of this research will focus on domain adaptation and integration of additional modalities to enhance generalizability. This study provides a step toward automated polyp detection and demonstrates the potential of explainable artificial intelligence (XAI) to change the accuracy of diagnosis and healthcare for patients.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1283-1293"},"PeriodicalIF":0.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11095343","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852698","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 Probabilistic Method for Hierarchical Multisubject Classification of Documents Based on Multilingual Subject Term Vocabularies","authors":"Nikolaos Makris;Stamatina K. Koutsileou;Nikolaos Mitrou","doi":"10.1109/OJCS.2025.3592254","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3592254","url":null,"abstract":"Hierarchical Multilabel Classification (HMC) is a challenging task in information retrieval, especially within scientific textbooks, where the objective is to allocate multiple labels adhering to a hierarchical taxonomy. This research presents a new language neutral methodology for HMC to assess documents as normalised weighted distributions of well-defined subjects across hierarchical levels, based on a hierarchical subject term vocabulary. The proposed approach utilizes Bayesian formulas, in contrast to typical methods that depend on machine learning models, thereby obviating the necessity for resource-intensive training processes at various hierarchical levels. The method integrates refined pre-processing techniques, such as natural language processing (NLP) and filtering of non-distinctive terms, to enhance classification accuracy. It employs Bayesian inference along with real time and cached computations across all hierarchical levels, yielding an effective, time-efficient and interpretable classification method while ensuring scalability for large datasets. Experimental results demonstrate the potency of the algorithm to classify scientific textbooks across hierarchical subject tiers with significant precision and recall and retrieve semantically related scientific textbooks, thereby verifying its efficacy in tasks requiring hierarchical subject classification. This study presents a streamlined, interpretable alternative to model-dependent HMC approaches, rendering it particularly appropriate for real-world applications in educational and scientific fields. Furthermore, in the context of the present study, two public Web User Interfaces were published, the first is founded on Skosmos to illustrate the hierarchical structure of the subject term vocabulary, while the second one employs the HMC method to present in real-time the classification between subjects in English and Greek textual data.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1294-1305"},"PeriodicalIF":0.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11095338","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880509","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 Robust Cross-Channel Image Watermarking Technique for Tamper Detection and its Precise Localization","authors":"Muhammad Ashraf;Adnan Nadeem;Oussama Benrhouma;Muhammad Sarim;Kashif Rizwan;Amir Mehmood","doi":"10.1109/OJCS.2025.3589948","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3589948","url":null,"abstract":"Several watermarking techniques have been suggested to safeguard the integrity of transmitted images in public video surveillance applications. However, these techniques have a critical drawback in their embedding schemes: the watermark is limited to residing in a narrow traceable space to avoid fidelity issues. Such a protection layer can be evaluated or forcefully removed to breach data security. Once the protection layer (watermark) is removed, a watermarking algorithm cannot pinpoint the falsified regions in affected images and gives a binary answer. Consequently, attackers can present the falsification of visual elements as a non-malicious perturbation. Such a type of attack poses a serious security challenge. This study introduces a novel cross-channel image watermarking technique that randomly scatters the watermark pattern across a 24-bit image structure so that no emergence of embedding signatures and fidelity issues occurs after the process. Chaotic systems are employed to leverage their sensitivity to initial conditions and control parameters, resulting in high confusion and diffusion properties in the proposed scheme. The protection layer is completely intractable as it is randomly scattered in the entire RGB space, making it very hard to remove without leaving a clear footprint in affected images. This method creates a good balance between security and imperceptibility, it effectively detects and localizes falsified regions in tampered images, and maintains this ability until clear evidence of a removal attempt emerges in histograms. This property makes proposed algorithm a preferred choice for data integrity protection; it achieved an average F1-score of 0.97 for tamper detection.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1202-1213"},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11081476","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750810","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":"Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection","authors":"Safa Ben Atitallah;Maha Driss;Wadii Boulila;Anis Koubaa","doi":"10.1109/OJCS.2025.3587486","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3587486","url":null,"abstract":"The Industrial Internet of Things (IIoT) faces significant cybersecurity threats due to its ever-changing network structures, diverse data sources, and inherent uncertainties, making robust intrusion detection crucial. Conventional machine learning methods and typical Graph Neural Networks (GNNs) often struggle to capture the complexity and uncertainty in IIoT network traffic, which hampers their effectiveness in detecting intrusions. To address these limitations, we propose the Fuzzy Graph Attention Network (FGATN), a novel intrusion detection framework that fuses fuzzy logic, graph attention mechanisms, and GNNs to deliver high accuracy and robustness in IIoT environments. FGATN introduces three core innovations: (1) fuzzy membership functions to explicitly model uncertainty and imprecision in traffic features; (2) fuzzy similarity-based graph construction with adaptive edge pruning to build meaningful graph topologies that reflect real-world communication patterns; and (3) an attention-guided fuzzy graph convolution mechanism that dynamically prioritizes reliable and task-relevant neighbors during message passing. We evaluate FGATN on three public intrusion datasets, Edge-IIoTSet, WSN-DS, and CIC-Malmem-2022, achieving accuracies of 99.07%, 99.20%, and 99.05%, respectively. It consistently outperforms state-of-the-art GNN (GCN, GraphSAGE, FGCN) and deep learning models (DNN, GRU, RobustCBL). Ablation studies confirm the essential roles of both fuzzy logic and attention mechanisms in boosting detection accuracy. Furthermore, FGATN demonstrates strong scalability, maintaining high performance across a range of varying graph sizes. These results highlight FGATN as a robust and scalable solution for next-generation IIoT intrusion detection systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1065-1076"},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11075530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657392","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":"Survey and Evaluation of Converging Architecture in LLMs Based on Footsteps of Operations","authors":"Seongho Kim;Jihyun Moon;Juntaek Oh;Insu Choi;Joon-Sung Yang","doi":"10.1109/OJCS.2025.3587005","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3587005","url":null,"abstract":"Large language models (LLMs), which have emerged from advances in natural language processing (NLP), enable chatbots, virtual assistants, and numerous domain-specific applications. These models, often comprising billions of parameters, leverage the Transformer architecture and Attention mechanisms to process context effectively and address long-term dependencies more efficiently than earlier approaches, such as recurrent neural networks (RNNs). Notably, since the introduction of Llama, the architectural development of LLMs has significantly converged, predominantly settling on a Transformer-based decoder-only architecture. The evolution of LLMs has been driven by advances in high-bandwidth memory, specialized accelerators, and optimized architectures, enabling models to scale to billions of parameters. However, it also introduces new challenges: meeting compute and memory efficiency requirements across diverse deployment targets, ranging from data center servers to resource-constrained edge devices. To address these challenges, we survey the evolution of LLMs at two complementary levels: architectural trends and their underlying operational mechanisms. Furthermore, we quantify how hyperparameter settings influence inference latency by profiling kernel-level execution on a modern GPU architecture. Our findings reveal that identical models can exhibit varying performance based on hyperparameter configurations and deployment contexts, emphasizing the need for scalable and efficient solutions. The insights distilled from this analysis guide the optimization of performance and efficiency within these converged LLM architectures, thereby extending their applicability across a broader range of environments.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1214-1226"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072851","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144782051","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}