International Journal of Intelligent Networks最新文献

{"title":"NVS-GAN: Benefit of generative adversarial network on novel view synthesis","authors":"H.S. Shrisha ,&nbsp;V. Anupama","doi":"10.1016/j.ijin.2024.04.002","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.04.002","url":null,"abstract":"<div><p>The methodology to generate new views for an object from provided input object view is called Novel View Synthesis (NVS). Humans imagine novel views through prior knowledge gathered through their lifetime. NVS-GAN predicts the novel views through computation. Literature survey reveals that there are limited NVS models with low Trainable Parameter Count (TPC) and low model size. Also, a study on the effect of different loss functions on NVS models was lacking. Lowering the TPC indicates less computational steps for the model to predict the output, therefore desirable. Combined with a low model size, the proposed model will become more suitable for deployment in diverse devices having limited resources for computation. Application of right combination of loss functions yield better accuracy. To address these research gaps, NVS-GAN is proposed. NVS-GAN is a Generative Adversarial Network (GAN) approach which yields NVS-Generator which performs NVS. NVS-Generator incorporates identity skip connections, bilinear sampling module, Depthwise Separable Convolution (DSC) as design features and results in low TPC, model size. In addition to discriminator loss, NVS-GAN is trained with different combinations of loss functions i.e. Mean Absolute Error (MAE) loss, Structural Similarity Index Measure (SSIM) loss, Huber loss on chair and car objects of ShapeNet dataset. The performance of NVS-Generator on test set measured in terms of MAE and SSIM is tabulated and analysed. The performance is compared with existing NVS models. The proposed NVS-GAN experiment recorded reduction in NVS-Generator TPC in 37 %–54.6 % range and reduction in model size between 37.2 % and 47.6 % range. NVS-Generator reduced MAE upto 55 % and improved SSIM upto 4 % than existing models. Summarily, NVS-GAN increased model performance and made the model “lightweight”.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 184-195"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000186/pdfft?md5=1c1cfb2444eb7781ad1ce312521adfae&pid=1-s2.0-S2666603024000186-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820113","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":"Variable rate power-controlled batch-based channel assignment for enhanced throughput in cognitive radio networks","authors":"Ahmed Musa ,&nbsp;Haythem Bany Salameh ,&nbsp;Rami Halloush ,&nbsp;Renad Bataineh ,&nbsp;Mahmoud M. Qasaymeh","doi":"10.1016/j.ijin.2024.04.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.04.001","url":null,"abstract":"<div><p>The number of users in wireless networks, such as mobile and Internet-of-Things networks, is witnessing a tremendous increase, turning the available frequency spectrum into a scarce resource that needs to be efficiently utilized. Cognitive radio (CR) is a key technology for achieving spectrum efficiency by continuously sensing and detecting frequency bands that are not used by licensed primary users (PU) and allowing unlicensed secondary users (SUs) to use them. One of the main challenges in CR is the design of a medium access control (MAC) protocol that ensures efficient spectrum sharing by SUs without disrupting the connectivity of PUs. To achieve that, many of the existing MAC protocols in the literature allow multiple SU transmissions to proceed simultaneously by performing batch-based power control decisions to limit mutual interference between them. Interestingly, the majority of such protocols are demand-rate unaware; i.e., all SUs are granted the same data rate, regardless of their data rate demand. In this paper, we highlight the severe drawbacks of demand-rate unawareness and propose the rate-aware power-controlled channel assignment (RPCCA) MAC protocol, which performs batch-based simultaneous channel assignment decisions to competing SUs along with power control to limit mutual interference, while taking into account the variable demand-rate across SUs. Simulation experiments have demonstrated that the RPCCA protocol offers substantial performance improvements over existing demand-rate unaware CR-based MAC protocols.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 175-183"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000174/pdfft?md5=838c656859212eb1731f3eca27b3befe&pid=1-s2.0-S2666603024000174-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140644364","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":"Revolutionizing prostate cancer diagnosis: Unleashing the potential of an optimized deep belief network for accurate Gleason grading in histological images","authors":"S. Angel Latha Mary ,&nbsp;S. Siva Subramanian ,&nbsp;G. Priyanka ,&nbsp;T. Vijayakumar ,&nbsp;Suganthi Alagumalai","doi":"10.1016/j.ijin.2024.05.004","DOIUrl":"10.1016/j.ijin.2024.05.004","url":null,"abstract":"<div><p>PC (Prostate Cancer) is the second highest cause of death due to cancer in men globally. Proper detection and treatment are critical for halting or controlling the growth and spread of cancer cells within the human organism. However, evaluating these sorts of images is difficult and time-consuming, requiring histopathological image recognition as the most reliable method for treating PC because of its distinct visual characteristics. Risk evaluation and treatment planning rely heavily on histological image-based Gleason grading of prostate tumors. This work introduces an innovative approach to histological image analysis for prostate cancer diagnosis and Gleason grading. The Elephant Herding Optimization-based Hyper-parameter Convolutional Deep Belief Network (CDBN-EHO) is presented alongside a grading network head-optimized deep belief network technique for multi-task prediction. Leveraging an effective Bayesian inference method, fully linked Conditional Random Field (CRF) techniques are utilized for segmentation, with pairwise boundary capacities determined by a linear mixture of Gaussian kernels. The multi-task approach aims to enhance performance by incorporating contextual information, leading to breakthrough results in the identification of epithelial cells and the grading of Gleason scores. The objective of this study is to demonstrate the effectiveness of the optimized deep belief network technique in improving diagnostic accuracy and efficiency for prostate cancer diagnosis and Gleason grading in histological images.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 241-254"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266660302400023X/pdfft?md5=eff597db63cf46fb94cc2cd30ac2bade&pid=1-s2.0-S266660302400023X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141145505","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":"Security experimental framework of trajectory planning for autonomous vehicles","authors":"Sujoud Al-sheyab ,&nbsp;Zakarea Al-shara ,&nbsp;Osama Al-khaleel","doi":"10.1016/j.ijin.2024.08.003","DOIUrl":"10.1016/j.ijin.2024.08.003","url":null,"abstract":"<div><p>In the contemporary landscape, autonomous vehicles (AVs) have emerged as a prominent technological advancement globally. Despite their widespread adoption, significant hurdles remain, with security standing out as a critical concern. The potential for attacks within AV networks, exemplified by the Trajectory Privacy Attack on Autonomous Driving (T-PAAD), underscores the urgency for robust security measures. Unfortunately, existing simulations for preemptively assessing the T-PAAD attack's impact are scarce. This paper introduces the Security Experimental Framework for Autonomous Vehicles (SEFAV), designed to address this gap by providing a versatile platform for simulating security scenarios in AV environments. SEFAV is cross-platform and compatible with different operating systems such as Windows and Linux, enhancing accessibility for researchers and practitioners. Our primary focus lies in showcasing the T-PAAD attack within our framework, highlighting its efficacy in evaluating and fortifying AV security.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 315-324"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000307/pdfft?md5=892f01ae9891afc0fe2026f438b5a155&pid=1-s2.0-S2666603024000307-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151317","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":"Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks","authors":"Rizwana Ahmad ,&nbsp;Dil Nashin Anwar ,&nbsp;Haythem Bany Salameh ,&nbsp;Hany Elgala ,&nbsp;Moussa Ayyash ,&nbsp;Sufyan Almajali ,&nbsp;Reyad El-Khazali","doi":"10.1016/j.ijin.2024.05.008","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.05.008","url":null,"abstract":"<div><p>Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 255-266"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000277/pdfft?md5=15fe93a13ef7559169ab6920f8be3686&pid=1-s2.0-S2666603024000277-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241222","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 method of vehicle networking environment information sharing based on distributed fountain code","authors":"Jianhang Liu ,&nbsp;Xinyao Wang ,&nbsp;Haibin Zhai ,&nbsp;Shibao Li ,&nbsp;Xuerong Cui ,&nbsp;Qian Zhang","doi":"10.1016/j.ijin.2024.01.001","DOIUrl":"10.1016/j.ijin.2024.01.001","url":null,"abstract":"<div><p>The exchange of perceptual information between autonomous vehicles could significantly improve driving safety. In general, obtaining more information means driving more safely. However, Frequent information sharing consumes a significant amount of channel bandwidth resources, which will reduce transmission efficiency and increase delay, especially in crowded cities. This paper presents a novel method of motion prediction compensation to solve this problem. Firstly, we propose a distributed fountain coding scheme to improve transmission efficiency and reduce vehicles’ delay in acquiring peripheral information. Secondly, we design a mobile prediction model and information transmission control algorithm to reduce traffic while ensuring information reliability. The simulation results show that the prediction accuracy of this method is above 94 %, the information transmission is reduced by more than 50 %, and the vehicle perception rate is increased by 34 %.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 19-29"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000010/pdfft?md5=34d2d987154140687cc34de68df3a69b&pid=1-s2.0-S2666603024000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139538922","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":"Research on the application of network security defence in database security services based on deep learning integrated with big data analytics","authors":"Feilu Hang,&nbsp;Linjiang Xie,&nbsp;Zhenhong Zhang,&nbsp;Wei Guo,&nbsp;Hanruo Li","doi":"10.1016/j.ijin.2024.02.006","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.006","url":null,"abstract":"<div><p>Every day, more people use the internet to send and receive sensitive information. A lot of confidential data is being transmitted electronically between people and businesses. Cyber-attacks, which are the inevitable result of our growing reliance on digital technology, are a reality that we must face today. This paper aims to investigate the impact of Big Data Analytics (BDA) on information security and vice versa. Additionally, an Artificial Neural Network (ANN)-based Deep Learning (DL) method for Anomaly Detection (AD) is presented in this work. To improve AD, the proposed method uses a DL-based detection method, which is used to parse through many collected security events to develop individual event profiles. The paper also investigated how BDA can be used to address Information Security (IS) issues and how existing Big Data technologies can be adapted to improve BDA's security. This study developed a DL-based Security Information System (DL-SIS) using a combination of event identification for data preprocessing and different Artificial Neural Network (ANN) methods. The feasibility and impact of implementing a Big Data Analytics (BDA) system for AD are investigated and addressed in this study. From this study, we learn that BDA systems are highly effective in securing Critical Information Setup from several discrete cyberattacks and that they are currently the best method available. By analyzing the False Positive Rate (FPR), the system facilitates quick action by security analysts in response to cyber threats. DL-SIS had the highest AD accuracy of 99.40% but performed poorly in the high-dimensional dataset.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 101-109"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000125/pdfft?md5=4ddc5ac4e95d6b926a7cf8f85af0a69e&pid=1-s2.0-S2666603024000125-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139748973","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":"Personal internet of things networks: An overview of 3GPP architecture, applications, key technologies, and future trends","authors":"Fariha Eusufzai,&nbsp;Aldrin Nippon Bobby,&nbsp;Farzana Shabnam,&nbsp;Saifur Rahman Sabuj","doi":"10.1016/j.ijin.2024.02.001","DOIUrl":"10.1016/j.ijin.2024.02.001","url":null,"abstract":"<div><p>The use of the personal Internet of Things (PIoT) is rapidly expanding across a range of application areas. It is needed for real-time monitoring, wireless coverage, remote sensing, delivery, security, surveillance, and civil infrastructure inspection. Smart PIoT devices provide new opportunities in wearable, public, and home automation, making them the next significant development in PIoT technology. The study provides a comprehensive overview of PIoT systems, including their architecture, applications, technology, and future developments. This paper begins with a comprehensive overview of prior research about the PIoT network. Then, we present an overview of PIoT classification and a comprehensive explanation of the PIoT architecture. We also investigate the requirements and technology of the PIoT network. Additionally, we provide a thorough examination of several applications for PIoT networks. Moreover, we emphasize some significant challenges and ongoing problems confronting the PIoT network. Finally, we address recent research trends and outline potential future study directions.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 77-91"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000071/pdfft?md5=ba2ba9fd4da955d707d4394c141822cc&pid=1-s2.0-S2666603024000071-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139819072","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":"Interference-assisted energy harvesting short packet communications with hardware impairments","authors":"Dechuan Chen ,&nbsp;Jin Li ,&nbsp;Jianwei Hu ,&nbsp;Xingang Zhang ,&nbsp;Shuai Zhang ,&nbsp;Dong Wang","doi":"10.1016/j.ijin.2024.05.005","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.05.005","url":null,"abstract":"<div><p>Radio frequency energy harvesting offers a promising solution to provide low power Internet of Things (IoT) devices with convenient and perpetual energy supply. In this work, we investigate the reliable performance of an energy-constrained transmitter communicating with a receiver over Nakagami-<em>m</em> channel, where the effects of transceiver hardware impairments and finite blocklength coding are jointly considered. Specifically, the communication link between the transmitter and receiver operates within the coverage of an existing wireless system, with radio frequency signal from the existing system serving as an energy signal for the transmitter while acting as an interference signal for the receiver. By utilizing the finite-blocklength information theory, we first derive average block error rate (BLER) and asymptotic average BLER in closed-form expressions, which enable us to quantify the extent of reliability loss. Then, we analyze effective throughput of the system, and determine the optimal blocklength that maximizes the effective throughput. Computer simulations are employed to validate the accuracy of our analytical findings, demonstrating the presence of an outage threshold solely due to hardware impairments. Furthermore, if transmission rate exceeds the outage threshold defined by the level of hardware impairments, reliable communication within the system under consideration cannot be achieved, regardless of the transmit signal-to-noise ratio (SNR).</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 231-240"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000241/pdfft?md5=e1b2b3a06d3734c687b06973e9a5a4cf&pid=1-s2.0-S2666603024000241-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141068732","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 swarm intelligence and deep learning strategy for wind power and energy storage scheduling in smart grid","authors":"Lin Geng ,&nbsp;Lei Zhang ,&nbsp;Fangming Niu ,&nbsp;Yang Li ,&nbsp;Feng Liu","doi":"10.1016/j.ijin.2024.08.001","DOIUrl":"10.1016/j.ijin.2024.08.001","url":null,"abstract":"<div><p>In today's world, rising energy demands are a significant challenge, and the smart grid emerges as a solution for sustainable energy management. An essential view of advancing the Smart Grid (SG) capabilities is the collaborative scheduling of Wind Power Generation (WPG) and energy storage. It plays a significant role in elevating SG efficiency, reliability, and environmental sustainability. This kind of strategic planning is essential to increase coordination between WPG and flexible deployment of Energy Storage Systems (ESS). Efficient SG functions will be maintained, and energy sources can be regulated with demand variations. Putting an emphasis on assumptions and empirical data is vital in conventional techniques. When it comes to the continuously shifting environment of SG and RE resources, traditional approaches aren't highly reliable or adaptable. The present article uses a hybrid model that integrates Deep Reinforcement Learning (DRL) and Particle Swarm Optimization (PSO) to address those drawbacks. The primary purpose of it is to help with the joint scheduling of WP and ESS. This technique is what permits DRL to reach selections rapidly in convoluted, ever-changing environments. The proposed approach, when combined with PSO's effectiveness for variable optimization, will result in improved scheduling findings. The framework additionally exploits the finest use of ESS, but it also effectively addresses the challenging task of integrating dynamic WP with the SG. Reliable and cost-effective supply is ensured by the system's design. The accuracy, stability, and versatility of the suggested approach to the dynamic features of Wind Energy (WE) and storage management are incomparable to traditional approaches. The findings indicate the method's actual validity and its significance for improving SG functions. Applying <em>state-of-the-art</em> statistical techniques for holistic optimization of RE resources and storage systems is emphasized by the framework. Owing to minimizing Energy Consumption (EC) and lowering greenhouse gas emissions, this study provides a significant step towards achieving the goal of effective and eco-friendly SG functions.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 302-314"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000289/pdfft?md5=563c206b94274c5d11f0a8178d5d3291&pid=1-s2.0-S2666603024000289-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130068","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}