Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Balancing Security and Communication Performance in 5G-A Industrial IoT With CES-Based TD3","authors":"Naixing Wu,&nbsp;Zhishan Li,&nbsp;Li Zhang,&nbsp;Zhongxiang Zhou,&nbsp;Tong He,&nbsp;Jiamin He,&nbsp;Wei Leng,&nbsp;Zhongliang Deng,&nbsp;Wenliang Lin,&nbsp;Yao Jin","doi":"10.1002/ett.70492","DOIUrl":"https://doi.org/10.1002/ett.70492","url":null,"abstract":"<div>\u0000 \u0000 <p>5G-Advanced (5G-A) industrial Internet of Things (IIoT) requires security mechanisms and communication resources to be coordinated under stringent reliability and ultra-low-latency constraints. Existing security-oriented or communication-oriented optimization methods usually treat protection strength and resource allocation separately, making it difficult to capture the nonlinear coupling among authentication, encryption, deep packet inspection, access control, latency, residual risk, and quality of service under dynamic traffic and threat conditions. To address this problem, this paper proposes a security-aware CES-TD3 framework that integrates a unified latency-risk model, a constant elasticity of substitution (CES) utility function, and a twin delayed deep deterministic policy gradient (TD3) algorithm for continuous joint security–communication control. The framework explicitly quantifies security-induced transmission, processing, and queueing delays, penalizes weak security or communication dimensions through CES utility, and incorporates threat-guided exploration, violation-aware prioritized replay, twin-Critic learning, and feasible-action projection to improve safe adaptation. Experiments with 20 industrial terminals, mixed load and threat scenarios, and five independent random seeds show that the proposed method achieves the highest reward of <span></span><math></math> and the highest CES utility of <span></span><math></math>, improving utility by <span></span><math></math> and reward by <span></span><math></math> over the Risk-aware heuristic, outperforming DDPG, SAC, and PPO in joint utility, reducing packet loss by <span></span><math></math>, and maintaining a zero risk-violation rate. Ablation, Pareto-frontier, adaptive-behavior, and CES-parameter analyses further confirm that the proposed method provides an interpretable and adjustable route for balancing security protection and communication performance in 5G-A industrial private networks.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 9","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-Aware Microgrid Networks With Federated Data Analytics","authors":"Sen Liu,&nbsp;Weiguo Wu,&nbsp;Shaowei Wang,&nbsp;Jin Li","doi":"10.1002/ett.70490","DOIUrl":"https://doi.org/10.1002/ett.70490","url":null,"abstract":"<div>\u0000 \u0000 <p>Microgrid networks are crucial for enhancing energy resilience and efficiency. Effective operation and optimization of microgrids rely heavily on analyzing multidimensional data, including user consumption patterns, generation profiles, and grid status information. However, this sensitive data often originate from numerous independent participants (e.g., consumers, prosumers, and operators), posing significant privacy challenges. Federated data analytics is a suitable paradigm for collaborative analysis without centralizing raw data, while differential privacy (DP) can further provide formal data privacy guarantees. This work addresses the need for privacy-preserving multi-target data aggregation in federated microgrid analytics. We leverage the shuffle model of differential privacy, known for its favorable privacy-utility trade-offs in decentralized environments. We propose a protocol where each participant (e.g., smart meter or user device) simulates multiple virtual users to report target functions through distinct, anonymized messages. This approach improves utility for tested multi-target aggregation tasks compared to representative decentralized DP baselines, simplifies privacy amplification analysis through group privacy properties, and matches the central-DP error order only in the high-communication regime specified by our utility analysis. Empirical validation using synthetic energy consumption datasets demonstrates a 40%–80% reduction in aggregation error in the tested settings, relative to the single-message shuffle baseline.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 9","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Context-Aware Network Traffic Prediction and Resource Allocation: An AI-Enabled Network Slicing for Smart City","authors":"Monika Dubey,&nbsp;Mohammad S. Obaidat,&nbsp;Ashutosh Kumar Singh,&nbsp;Aditya Bhushan,&nbsp;Richa Mishra","doi":"10.1002/ett.70488","DOIUrl":"https://doi.org/10.1002/ett.70488","url":null,"abstract":"<div>\u0000 \u0000 <p>Smart cities represent a critical vertical use case that demands comprehensive attention in managing heterogeneous, temporal, and dynamically managed traffic demands. This dynamic management of smart city faces two primary challenges: (1) identification of AI-driven approaches capable of managing heterogeneous, complex, and scalable smart city data, and (2) enabling efficient management and reallocation of network resources during sudden fluctuations in traffic demands. To address these challenges, this work first proposed a hybrid model that combines LSTM and Q-learning for traffic prediction and resource optimization. Subsequently, LLM is incorporated to handle contextual information and dynamic resource reallocation in response to sudden fluctuations in traffic demand. Experimental results show that the proposed model achieves a prediction accuracy of 95%, a Normalized Mean Absolute Error (NMAE) of 0.032, and a Pearson's Correlation Coefficient (PCC) of 0.92, indicating a strong alignment between predicted and actual traffic loads. In addition, the latency variability is reduced by 15%, and the RL agent attains a cumulative reward of 250.5. The integrated LSTM-Q-learning-LLM framework achieves a classification accuracy of 97.3%, demonstrating superior performance in handling dynamic network traffic scenarios. Analysis reveals that the proposed model outperforms individual LSTM and Q-learning models, showing a 10% improvement in prediction accuracy, a 5% greater reduction in latency variability, and a higher cumulative reward during training. This work provides a solid foundation for the development of adaptive and intelligent network management systems in smart city environments.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 9","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Enhanced Convolution Transformer With Ladder Self-Attention for Breast Cancer Histopathological Image Classification and Recurrence Prediction","authors":"Shivani Agarwal,&nbsp;Sachi Gupta,&nbsp;Atul Kumar Rai,&nbsp;Gaurav Agarwal","doi":"10.1002/ett.70491","DOIUrl":"https://doi.org/10.1002/ett.70491","url":null,"abstract":"<div>\u0000 \u0000 <p>The most common cancer that kills women worldwide is breast cancer. Early detection of breast cancer and the prediction of recurrence is essential for diagnosis because metastasis and recurrence account for the majority of cancer-related deaths. Images from the BreakHis dataset are gathered because physicians frequently use histopathological images to classify breast cancer. It takes a pathologist and specialists to grade histopathological images, and the process is inaccurate and time-consuming. Therefore, using a score-based VGG-16 model, an improved convolution transformer with ladder self-attention was developed for the purpose of classifying breast cancer histopathological images and predicting recurrence. Initially, the images are pre-processed using a color deconvolution filter. Convolution transformers with Ladder Self-Attention units and attention-aware frequency domain transformers are used to extract spatial and frequency features from the pre-processed images. The adaptive fusion module combines the multi-scale features, which are then fed to the softmax classifier to classify images as benign or malignant. Using the greylag goose optimization (GGO) algorithm, the model's parameters are adjusted to produce better classification results. Next, by merging the cancerous images from The Cancer Genome Atlas (TCGA) dataset and the BreakHis dataset, recurrence is predicted. Recurrence is predicted using clinical and image features. Finally, it evaluated the performance of the proposed framework. The accuracy obtained by the proposed method is 99%, precision is 98.5%, recall is 98%, <i>f</i>-score is 98.2%, and MCC is 98%.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 9","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Enabled 6G Space-Air-Ground–Integrated Networks for Ultra-Reliable Low Latency Internet of Medical Things Healthcare","authors":"Tanvir H. Sardar,&nbsp;Gousia Thahniyath,&nbsp;Ahlam Almusharraf,&nbsp;T. R. Mahesh,&nbsp;Mohammad Attique Khan,&nbsp;Muhammad Asim Saleem,&nbsp;Mohammad Alhefdi","doi":"10.1002/ett.70480","DOIUrl":"https://doi.org/10.1002/ett.70480","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper explores an AI-assisted resource scheduling and cooperative learning model in a space–air–ground combined network (SAGIN) to possess ultra-reliable low-latency Internet of Medical Things (IoMT) applications. The generated healthcare data by the IoMT devices are processed by three levels in the considered scenario including the LEO satellites, the UAV swarms, and the ground MEC servers and adhere to strict latency, reliability, and privacy requirements. We aim at designing a multi-tier resource allocation policy and federated learning policy that coordinates end-to-end latency and energy consumption and at the same time is highly accurate in terms of the model given privacy constraints. In this direction, we come up with a multi agent—deep deterministic policy gradient (MA-DDPG) agent that allocates resources in a distributed manner and a hierarchical federated learning (HFL) system with delay-sensitive aggregation to train models privately. Extensive simulation findings indicate that the presented framework can achieve 4.2 ms latency, 99.92% reliability, and 96.2% federated (global) model accuracy and 67% minimization of communication overhead, all of which are superior to baseline and the state-of-the-art approaches in a variety of measures.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 9","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Blockchain and Deep Learning Model for Robust Internet of Things Security in Intelligent Transportation Systems","authors":"R. Anitha,&nbsp;M. Murugan","doi":"10.1002/ett.70486","DOIUrl":"https://doi.org/10.1002/ett.70486","url":null,"abstract":"<div>\u0000 \u0000 <p>Smart cities are digitally advanced urban environments that are equipped with sensor networks to gather, share, and analyze extensive data across interconnected systems. Among various smart city applications, the intelligent transportation system represents one of the most critical and security-sensitive domains. An intelligent transportation system relies heavily on continuous vehicular communication, a low-latency decision-making process, as well as real-time traffic monitoring. Existing Internet of Things security methods encounter significant computational overhead and limited scalability, making them unfit for real-time applications. To address these issues, this paper proposes a novel security model, named Deep Residual Stacked Bidirectional Network. The proposed system is integrated into a blockchain-supported hybrid system to ensure security and privacy for users and systems in smart cities. This enhanced Deep-Learning model combines the residual learning power with bidirectional long short-term memory layers. To effectively manage deeper networks, residual connections help mitigate the vanishing gradient problem, while bidirectional long short-term memory provides sequential dependencies in backward and forward directions. This allows the model to detect patterns in data, especially in security environments where data is highly dynamic and time-sensitive. Four Internet of Things-related datasets are used to evaluate the efficiency of the developed algorithm. These datasets offer various real-world network traffic and attack scenarios that allow comprehensive performance evaluation of the proposed approach in comparison with existing methods. The test outcomes revealed that the blockchain-supported proposed method outperforms traditional methods with an accuracy of 98.21%, specificity of 97.39%, and F1-score of 97.46%.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 9","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAFORI-IDS: Semantic-Aware Federated Intrusion Detection for IoT-Enabled 6G Metaverse Infrastructure","authors":"Shakila Basheer,&nbsp;Ghadah Aldehim,&nbsp;Ala Saleh Alluhaidan,&nbsp;Sapiah Sakri","doi":"10.1002/ett.70479","DOIUrl":"https://doi.org/10.1002/ett.70479","url":null,"abstract":"<div>\u0000 \u0000 <p>The proliferation of Internet of things (IoT) devices and the emerging 6G Metaverse introduces unprecedented opportunities, but also exposes massive attack surfaces risking severe privacy breaches and physical dangers. Conventional intrusion detection systems (IDS), whether rule-based or deep learning-based, fail to mitigate these critical threats under heterogeneous non-IID traffic, adversarial perturbations, and stringent resource constraints. To address these challenges, our original SAFORI-IDS framework uniquely merges semantic context and federated privacy to secure 6G-enabled Metaverse environments. The framework integrates multimodal feature extraction, semantic representation learning, and federated aggregation to ensure privacy-preserving and scalable detection across distributed clients. An explainability module based on SHAP enhances interpretability by attributing detection outcomes to meaningful features, improving trustworthiness for security administrators. Experimental evaluation on the CICIDS2017 dataset demonstrates that SAFORI-IDS achieves over 99% accuracy, precision, recall, and F1-score, significantly outperforming state-of-the-art baselines while maintaining lightweight computational efficiency suitable for IoT edge devices.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 9","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trust-Driven ECC With Random Ensemble and Bat Optimization for Advanced Healthcare Cybersecurity","authors":"Deepa Rajasekar,&nbsp;Karthikeyan Lakshmanan,&nbsp;Harinee Shanmuganathan,&nbsp;Surendran Subbaraj","doi":"10.1002/ett.70485","DOIUrl":"https://doi.org/10.1002/ett.70485","url":null,"abstract":"<div>\u0000 \u0000 <p>In healthcare, cybersecurity breaches have increased significantly, with recent reports indicating that over 78% of hospitals experienced at least one cyberattack in the past year, exposing millions of patient records. Existing cybersecurity models often pose drawbacks such as high computational cost, poor energy efficiency, and limited capability in detecting sophisticated anomalies in IoT-enabled healthcare environments. To tackle these issues, this study introduces a Trust-based Elliptical Curve Cryptography with Random Ensemble model that combines an energy-efficient anomaly detection mechanism based on the Pearson correlation coefficient with lightweight cryptographic security. The method includes Pearson Correlation Coefficient-based feature selection (reducing dimensionality by up to 35%), and class imbalance is tackled by Synthetic Minority Over-sampling Technique-based data augmentation. A Random Forest ensemble model integrating Gaussian Naïve Bayes and Decision Tree to identify anomalies is enhanced. Elliptical Curve Cryptography ensures efficient encryption and decryption, while a Trust Agreement Model and Body Area Network logic verify secure communication between healthcare servers and patients. In addition, the Bat Optimization Algorithm optimizes authentication parameters, and computational overhead is reduced. The Trust-based Elliptical Curve Cryptography with Random Ensemble model is validated on three datasets, including BoT-IoT, UNSW-NB15, and CSE-CIC-IDS2018, and demonstrates superior performance, attaining 98.89% accuracy, 98.60% precision, and 98.45% F1-score outperforming existing methods. The results demonstrate that the proposed method provides a more reliable, energy-efficient, and scalable cybersecurity solution for modern healthcare IoT systems.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 9","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Clustering Framework for Energy Optimization and Management in Electric Vehicle Propulsion System","authors":"R. Nagalakshmi,&nbsp;K. Rahimunnisa","doi":"10.1002/ett.70481","DOIUrl":"https://doi.org/10.1002/ett.70481","url":null,"abstract":"<div>\u0000 \u0000 <p>The propulsion systems of electric vehicles (EVs) need to have precise energy control and battery healthcare in dynamic operating environments. However, it is difficult for traditional equivalent circuit models to model the nonlinear electrochemical characteristics of batteries mainly when there is dynamic load-dependent behavior; thus, they provide inaccurate predictions. Thus, this work proposes an Adaptive clustering framework for optimized energy management and battery health monitoring. In this, an adaptive noise-filtered feature-driven fuzzy neural network (ANF-FNN) is introduced to improve the accuracy of battery performance and degradation estimation. Furthermore, a Quantum-inspired Thermal Enhanced Temporal Particle Clustering approach (QTEPC) is employed to enhance real-time estimation of State-of-Charge (SOC) and State-of-Health (SOH), which accounts for analyzing complex relationships with voltage, current, and temperature. Moreover, to improve regenerative braking energy, a novel Resilient Regenerative Dempster–Shafer Gain Scheduling (RRDGS) method is integrated for enabling dynamic energy distribution with minimal wastage. The experimental findings show that it works better with 99.2% accuracy, 0.85 RMSE rate, 0.18% SOC estimation error, lower prediction time and better regenerative energy use which proves its better performance in the automotive industry.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 9","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Kernel Polar Decoder Based on Semi-Parallel Architecture and 2-Bit Decoding Approach for 5G and Beyond Wireless Communication","authors":"D. Dinesh Kumar,&nbsp;R. Shantha Selvakumari","doi":"10.1002/ett.70484","DOIUrl":"https://doi.org/10.1002/ett.70484","url":null,"abstract":"<div>\u0000 \u0000 <p>Polar codes are the error-correcting codes adopted as the channel code for the control channel in fifth-generation new radio (5G NR) standards. The polar code constructed using <span></span><math></math> binary kernels is familiar in 5G and restricts the code length to powers of two. As 5G and beyond (B5G) applications demand higher flexibility, the multi-kernel polar codes are chosen as a viable candidate. This work proposes a multi-kernel (MK) polar decoder and analyzes the latency, utilization rate, and hardware utilization efficiency. The last stage of the architecture is replaced by a hard decision (HD) node that decodes two bits simultaneously. The proposed method uses a semi-parallel architecture with fewer processing units to minimize the hardware resources. The processing unit (PU) consists of a binary processing element (PE) and a mixed PE to adapt to the different binary or ternary kernel stages during decoding. The PEs and the HD logic in the last stage are optimized with MUX-based logic to reduce the critical path delay. The latency and utilization rate improved by 15%–26% compared to conventional architecture as code length varies from <span></span><math></math> to 3888 and achieved a lower bit-error rate (BER) of <span></span><math></math> at 3.5 dB for <span></span><math></math>. The decoder is synthesized and implemented on a Virtex-6 FPGA board for a code length of <span></span><math></math> and compared with other decoders. It is observed that the decoder has the advantage of lower look-up tables (LUTs) and flip-flops (FFs) with additional random-access memory (RAM) usage and higher hardware utilization efficiency (HUE) compared to other existing decoders.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 9","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}