IEEE Journal of Selected Areas in Sensors最新文献

{"title":"Large Language Models for Sensor Placement in Smart Spaces","authors":"Yangyang Wang;Naser Hossein Motlagh;Agustin Zuniga;Huber Flores;Petteri Nurmi;Sasu Tarkoma","doi":"10.1109/JSAS.2026.3729163","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3729163","url":null,"abstract":"Optimal sensor placement is vital for smart space monitoring, requiring a balance between data fidelity and signal stability. This study introduces an embodied layout reasoning framework, evaluating the zero-shot deployment capabilities of six state-of-the-art large language models (LMs) (e.g., ChatGPT-4o, Claude-Sonnet-4.5) acting as “virtual field engineers” within a university office. To quantify spatial hallucination and decision-making risks, each model generated placements 100 times. While Claude-Sonnet-4.5 demonstrated the highest intramodel consistency (59.7%), Gemini’s 9.9% reliability highlighted significant challenges in autonomous spatial grounding. Empirically, LLM-driven strategies achieved numerically stronger signal stability (mean received signal strength indicator: <inline-formula><tex-math>$-$</tex-math></inline-formula>49.04 dBm) compared with expert-designed strategies (<inline-formula><tex-math>$-$</tex-math></inline-formula>52.01 dBm), although this difference did not reach statistical significance. Furthermore, we establish a cross-modality spatial reasoning robustness framework, revealing that models like ChatGPT-4o and Grok can reconstruct geometric topologies from abstracted text or floor plans, enabling high-performance, privacy-preserving deployments. In addition, Doubao offers a favorable tradeoff between reasoning performance and operational cost. Finally, we propose a human–AI hybrid Intelligence paradigm; by transitioning from unconstrained inference to a two-stage workflow using a vetted candidate set, model consistency improved about 69.6%. Overall, our results suggest that combining LLM-based geometric exploration with human feasibility constraints yields robust, deployable sensor placement solutions for real-world Internet of Things environments.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"439-454"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11673158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133794","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":"IP-UNet: Intensity Projection UNet Architecture for IoMT-Enabled 3-D Medical Volume Segmentation","authors":"Nyothiri Aung;Mohand Tahar Kechadi;Liming Chen;Sahraoui Dhelim","doi":"10.1109/JSAS.2026.3679493","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3679493","url":null,"abstract":"The proliferation of high-resolution medical imaging data, increasingly generated and transmitted via Internet of Medical Things (IoMT) infrastructure, presents significant computational challenges. Limited memory capacity remains a common drawback when processing these high-resolution 3-D volumetric datasets, as direct cropping or down-sizing often results in a loss of critical diagnostic detail, exacerbates class imbalance, and degrades segmentation performance. 3-D volumes are usually cropped or downsized first before processing, which can result in a loss of resolution, increase class imbalance, and affect the performance of the segmentation algorithms. In this article, we propose an end-to-end deep learning approach called IP-UNet. IP-UNet is a UNet-based model that performs multiclass segmentation on intensity projection (IP) of 3-D volumetric data instead of the memory-consuming 3-D volumes. IP-UNet uses limited memory capability for training without losing the original 3-D image resolution. We compare the performance of three models in terms of segmentation accuracy and computational cost. 1) Slice-by-slice 2-D segmentation of the CT scan images using a conventional 2-D UNet model. 2) IP-UNet that operates on data obtained by merging the extracted maximum intensity projection (MIP), closest vessel projection (CVP), and average intensity projection (AvgIP) representations of the source 3-D volumes, then applying the UNet model on the output IP images. 3) 3-D-UNet model directly reads the 3-D volumes constructed from a series of CT scan images and outputs the 3-D volume of the predicted segmentation. We test the performance of these methods on 3-D volumetric images. Experimental results show that IP-UNet can achieve similar segmentation accuracy with 3-D-UNet but with much better performance. It reduces the training time by 70% and memory consumption by 92%.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"191-198"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11458901","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796176","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 Unified Federated Framework for Trust Management and Privacy-Preserving Anomaly Detection in Zero Trust Edge Networks","authors":"Shengjie Xu;Yi Qian","doi":"10.1109/JSAS.2026.3709766","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3709766","url":null,"abstract":"Edge computing and Internet of Things (IoT) have expanded the attack surface of modern networks. Security designs often tradeoff detection quality and privacy: centralized trust creates single points of failure, while distributed approaches may sacrifice accuracy or formal privacy guarantees. This article presents a federated trust modeling framework that integrates multimodal anomaly detection, Byzantine-resilient federated learning with <inline-formula><tex-math>$(epsilon,delta)$</tex-math></inline-formula>-differential privacy (DP), and context-aware zero trust architecture decision-making. The three-layer architecture comprises device-level trust learning with enhanced variational autoencoder, Isolation Forest, long short-term memory, and statistical process control modalities; a federated aggregation layer for robust aggregation with DP; and a trust scoring and decision layer that maps evidence to continuous trust and access levels using a subjective logic-inspired formulation. We provide detailed algorithmic implementations with pseudocode for each layer. Comprehensive evaluation demonstrates exceptional performance: high precision with low false positive rate, near-linear scalability achieving high efficiency, high accuracy with precision detecting most of attacks with zero false alarms, and sublinear time complexity. Privacy preservation is maintained through DP guarantees without accuracy degradation. The results support deployability studies for large-scale IoT and Artificial Intelligence of Things settings, with generalization to real telemetry left to future work.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"277-290"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11594408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626907","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":"Agentic AI-Powered Sensors-Based Privacy-Preserving Postquantum Secure Authentication for IoT-Enabled Medical Cyber-Physical Environment","authors":"Snehal Jain;Abhishek Kumar Pandey;Ashok Kumar Das;Mohammad Wazid;Biplab Sikdar","doi":"10.1109/JSAS.2026.3728328","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3728328","url":null,"abstract":"Healthcare technology is continuously evolving and has undergone significant transformation with the integration of artificial intelligence (AI) into the medical domain. AI has facilitated various tasks, such as patient monitoring, clinical decision support, and automated report generation. However, it has also introduced new security and reliability concerns. Medical data is highly sensitive and confidential, and its exposure to malicious parties may pose serious risks both in the present and future. Furthermore, quantum computing presents a significant challenge to classical public-key cryptographic algorithms, such as Rivest-Shamir-Adleman (RSA) and elliptic curve cryptography, which enable adversaries to collect encrypted data today with the intention of decrypting it in the future (harvest-now-decrypt-later model). To address these concerns, an agentic and quantum-safe patient monitoring system has been suggested in this article. The proposed framework achieves security through a lattice-based protocol while incorporating agentic reasoning and verification mechanisms to mitigate hallucinations during patient report generation before final doctor verification. Experimental evaluation and detailed security analysis demonstrate that the proposed framework effectively protects patient data against quantum-era adversaries while enhancing the AI-assisted trustworthiness of clinical alerts. The integration of postquantum security with agentic reasoning and verification provides a practical foundation for next-generation intelligent healthcare systems supporting remote patient monitoring and clinical decision support.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"362-378"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11668650","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033372","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":"Integrated Noninvasive Brain Imaging Advancements: A Multimodal Approach","authors":"Sivarenjani Balasubramanian;Pragati Patel;Srujana Kathi;Reemi Joseph;Ramesh Naidu Annavarapu","doi":"10.1109/JSAS.2026.3685662","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3685662","url":null,"abstract":"Neuroimaging has been an incredibly “lifesaving” scientific development for visualizing, observing, and studying the anatomical and functional properties of neural tissue, specifically the brain. Several cutting-edge neuroimaging methods are constantly being developed and refined for clinical and scientific research. Multimodal fusion in neuroimaging is a prominent and highly needed advancement. It helps address the limitations of individual modalities by integrating data from multiple modalities. In this article, we have explored different approaches to multimodal neuroimaging methods, including an interim analysis of the utility of combining modalities for brain imaging. It explains how multimodal neuroimaging methods are advantageous compared with conventional methods. It mentions their boundaries, thereby providing insight into developments in predominantly noninvasive brain imaging techniques. Each part describes a different multimodal imaging method, along with its advantages and limitations.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"218-229"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11488398","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171677","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 Context-Aware Service Recommendation System for the Social Internet of Things","authors":"Amar Khelloufi;Huansheng Ning;Guan Mingxiang;Muhammad Sadiq;Sahraoui Dhelim","doi":"10.1109/JSAS.2026.3666177","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3666177","url":null,"abstract":"The Social Internet of Things (SIoT) refers to the interconnection of Internet of Things (IoT) devices with the addition of a social layer, enabling them to mimic human-like behavior. This technology facet facilitates data and service sharing among devices, creating a scalable network of socially interconnected devices. However, this rapid increase in device numbers and generated data has led to the challenge of data explosion. To address this challenge, service recommendation systems play a crucial role in improving network navigability and service discovery within the SIoT context. However, existing research often overlooks important aspects that could make service selection more pertinent and relevant. Specifically, current service recommendation approaches focus on extracting social relationships between devices while neglecting the contextual presentation of service reviews. In this research, we introduce a service recommendation framework for SIoT context that integrates service-review aggregation and feature learning processes by utilizing the factorization machines to capture complex feature interactions unique to each SIoT device–service pair. Empirical results across three benchmark datasets demonstrate that the proposed Context-Aware Service Recommendation (CASR-SIoT) framework consistently outperforms state-of-the-art baselines, achieving up to 14% improvement in Recall, 8% in Precision, and 15% reduction in RMSE, thereby validating its effectiveness in delivering accurate and context-aware service recommendations in SIoT environments.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"139-151"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11399623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557812","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":"Active Inference-Enhanced DRL for UAV and Integrated Sensing, Communication, and Computing Enabled Metaverse Systems","authors":"Jianbo Du;Huifang Fang;Zhengqin Pei;Chong Wang;Suzhi Cao;Dongming Wang;Bo Ai","doi":"10.1109/JSAS.2026.3729164","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3729164","url":null,"abstract":"In this article, we investigate the problem of computational task offloading and unmanned aerial vehicle (UAV) path planning within an edge computing-enabled space–ground integrated metaverse system. Specifically, we conceptualize the UAV as a “Communication – Perception – Computing” integrated device that provides real-time services within the metaverse system. The ground base station (GBS) augments UAV processing capabilities, while the edge computing platform only provides assistance for GBS, forming a two-level offloading architecture. To satisfy both user and operator requirements simultaneously, our objective is to minimize energy consumption and delay to meet user quality of service demands, and also to minimize economic costs for virtual service providers (VSPs), by jointly optimizing the sensing duration, the trajectory and the flying time of the UAV, and the data offloading ratio of the UAV and the VSP. Given the dynamic nature of the metaverse system, we reformulate the problem as a Markov decision process and employ a deep reinforcement learning algorithm based on active inference to dynamically learn the optimal joint solution. Simulation results demonstrate that our approach achieves rapid convergence and performs effectively in terms of maximizing cumulative rewards, minimizing VSP costs, reducing system delays, and optimizing UAV energy consumption.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"411-424"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11673219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133412","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":"Autoencoder-Based Compressive Sensing for Adaptive IoT Sensor Networks","authors":"Igor Bisio;Chiara Garibotto;Fabio Lavagetto;Andrea Sciarrone;Matteo Zerbino","doi":"10.1109/JSAS.2026.3728768","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3728768","url":null,"abstract":"Recently, energy efficient signal processing has emerged as a critical area of research, driven by the increasing demand for cost-effective solutions in modern communication systems, particularly in Internet of Things (IoT) sensor networks applications. At the same time, artificial intelligence (AI)-based approaches have gained significant popularity, extending their potential far beyond user-driven content creation. In this work, we explore the intersection of these emerging technologies, aiming to enable faster processing, lower power consumption, and reduced hardware complexity. We apply the combined potential of both frameworks to compressive sensing (CS) for structural health monitoring (SHM) applications. Specifically, we propose a conceptual sensing and monitoring framework incorporating an adaptive data compression strategy that jointly leverages traditional CS and deep-learning-based signal compression. This work represents a feasibility study toward the development of a low-power, low-rate, and cost-effective IoT-based sensor network for SHM capable of adapting to changing channel conditions. To support this framework, the study provides a comprehensive comparison between traditional CS methods and AI-based compression, focusing on compression efficiency and reconstruction capabilities when processing data streams from SHM systems.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"353-361"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11672719","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033371","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":"IoT-Enabled Indoor Air Quality Index Prediction Based on Thermal Comfort Metrics","authors":"Yingbo Zhu;Muhammad Zeeshan Shakir","doi":"10.1109/JSAS.2026.3729946","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3729946","url":null,"abstract":"This research integrates Internet of Things (IoT) technology into the evaluation of indoor air quality index (IAQI) and predicted mean vote (PMV), aiming to create healthier indoor environments, which are known to support occupant well-being and productivity. Traditionally, measuring PMV requires specific sensors or instruments, posing challenges in its widespread application. Using temperature, humidity, and Carbon dioxide (CO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula>) concentration data collected from IoT sensors, the study introduces two approaches for PMV prediction: an empirical model and a machine learning algorithm, with the latter achieving accuracy 74.2% within a <inline-formula><tex-math>$pm 0.5$</tex-math></inline-formula> error margin. The adopted random forest (RF) models were benchmarked against extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), support vector regression, and neural network baselines under identical data partitions, delivering comparable accuracy while providing the interpretable feature importances required for the subsequent weight derivation. Furthermore, this article presents an innovative technique for estimating air changes per hour using CO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> concentration over time. In the dataset, the PMV measured using two methods indicates that temperature is a critical factor, with both methods yielding a PMV correlation coefficient of 0.89. Another major contribution is the refinement of the IAQI design through a time fraction method, where machine learning is used to derive weighting parameters. Using a time-feature-based RF model, the BME680 air quality sensor's IAQI was predicted with 89.3% accuracy within a 10% margin of error, from which the weighting parameters were obtained. This IAQI is compared to the BME680’s IAQI, and demonstrated a stronger correlation with PMV. The average correlation between PMV obtained from both methods and the time fraction IAQI is 0.65, which is higher than the correlation <inline-formula><tex-math>$-$</tex-math></inline-formula>0.13 with the BME680 IAQI. These findings indicate that the proposed IAQI offers a more accurate reflection of the considerations of thermal comfort. It also contributes to Sustainable Development Goal 3 (Good Health and Well-being) by aiming to improve the productivity and health of the residents through the integration of IoT technology.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"455-467"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11676221","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133698","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":"IUCA: A Model-Agnostic In Situ Uncertainty Calibration Framework for High-Dimensional State Estimation in AIoT Robotic Systems","authors":"Yongshuai Wu;Jian Zhang;Shaoen Wu;Shiwen Mao","doi":"10.1109/JSAS.2026.3731931","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3731931","url":null,"abstract":"Autonomous robots increasingly serve as mobile Artificial Intelligence of Things (AIoT) sensing nodes, where reliable edge perception and state estimation are critical to safe and efficient system operation. In practical deployment, uncertainties arising from perception and decision-making processes (e.g., localization algorithms) often distort the system state and misalign with real-world conditions, leading to overconfident decisions and potential system failures. This article presents the In-situ uncertainty calibration approach (IUCA), a model-agnostic uncertainty calibration framework for AIoT-enabled robotic systems based on high-dimensional conformal prediction (CP). Unlike conventional uncertainty metrics that rely on model assumptions or heuristic confidence measures, the proposed framework provides distribution-free, finite-sample guarantees, ensuring that reported confidence regions reflect actual deployment-time reliability. This framework calibrates joint and correlated state uncertainties without modifying the underlying estimator or requiring retraining, making it suitable for embedded and edge-deployed AIoT systems. As a representative case study, we integrate the framework with adaptive Monte Carlo localization, a widely deployed localization module. The calibrated system constructs conformal confidence ellipses with provable coverage guarantees in the robot pose space, statistically bounding estimation error and mitigating catastrophic failures, such as the Robot Kidnapping Problem. Extensive validation in simulated environments, open-source datasets, and real-world warehouse deployments demonstrates that the proposed framework consistently achieves target coverage while preserving or improving localization accuracy. These results establish IUCA as a foundational reliability layer for trustworthy, robust, and scalable AIoT-enabled robotic sensing systems.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"535-553"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11683368","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149249632","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}