International Journal of Sensors, Wireless Communications and Control最新文献

{"title":"Crowd-Sourced AI based Indoor Localization using Support Vector Regression and Pedestrian Dead Reckoning","authors":"Thandu Nagaraju, R. Murugeswari","doi":"10.2174/2210327913666230523114125","DOIUrl":"https://doi.org/10.2174/2210327913666230523114125","url":null,"abstract":"\u0000\u0000Artificial intelligence (AI) is expanding in the market daily to assist humans in a variety of ways. However, as these models are expensive, there is still a gap in the availability of AI products to the common public with high component dependency.\u0000\u0000\u0000\u0000To address the issue of additional component dependency on AI products, we propose a model that can use available Smartphone resources to perceive real-world huddles and assist ordinary people with their daily needs. The proposed AI model is to predict the user’s indoor position (Node) at the computer science and engineering block of CMR Institute of Technology (CMRIT) by using Smartphone sensors and wireless signals. We used SVR to predict the regular walk steps needed between two Nodes and Pedestrian Dead Reckoning (PDR) to predict the walk steps needed while the signal was lost in the indoor environment.\u0000\u0000\u0000\u0000The Support vector regression (SVR) models make the locations to be available within the specified building boundaries for proper guidance. The PDR approach supports the user while signal loss between two Received Signal Strength Indicators (RSSI). The Pedestrian dead reckoning - Support Vector Regression (PD-SVR) results are showing 98% accuracy in NODE predictions with routing tables. The indoor positioning is 100% accurate with dynamic crowd-sourcing Node preparation.\u0000\u0000\u0000\u0000The results are compared with other indoor navigation models K-nearest neighbor (KNN) and DF-SVM are given 95% accurate NODE estimation with minimal need for network components.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81890413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time and space complexity reduction of KFDA-based LTE modulation classification","authors":"H. K. Bizaki, I. Kadoun","doi":"10.2174/2210327913666230519152820","DOIUrl":"https://doi.org/10.2174/2210327913666230519152820","url":null,"abstract":"\u0000\u0000Kernel Fisher discriminant analysis (KFDA) is a nonlinear discrimination technique for improving automatic modulation classification (AMC) accuracy. Our study showed that the higher-order cumulants (HOCs) of the Long-term evolution (LTE) modulation types are nonlinearly separable, so the KFDA technique is a good solution for its modulation classification problem. Still, research papers showed that the KFDA suffers from high time and space computational complexity. Some studies concentrated on reducing the KFDA time complexity while preserving the AMC performance accuracy by finding faster calculation techniques, but unfortunately, they couldn't reduce the space complexity.\u0000\u0000\u0000\u0000This study aims to reduce the time and space computational complexity of the KFDA algorithm while preserving the AMC performance accuracy.\u0000\u0000\u0000\u0000Two new time and space complexity reduction algorithms have been proposed. The first algorithm is the most discriminative dataset points (MDDP) algorithm, while the second is the k-nearest neighbors-based clustering (KNN-C) algorithm.\u0000\u0000\u0000\u0000The simulation results show that these algorithms could reduce the time and space complexities, but their complexity reduction is a function of signal-to-noise ratio (SNR) values. On the other hand, the KNN-C-based KFDA algorithm has less complexity than the MDDP-based KFDA algorithm.\u0000\u0000\u0000\u0000The time and space computation complexity of the KFDA could be effectively reduced using MDDP and KNN-C algorithms; as a result, its calculation became much faster and had less storage size.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90653387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BER Performance Of Co-Operative Relay NOMA-Assisted PS Protocol With Imperfect SIC And CSI","authors":"Faical Khennoufa, Khelil Abdellatif","doi":"10.2174/2210327913666230512102359","DOIUrl":"https://doi.org/10.2174/2210327913666230512102359","url":null,"abstract":"\u0000\u0000Wireless networks and devices are consuming a significant amount of energy as wireless communication is rapidly expanding, radio frequency (RF) energy harvesting has been envisioned as a feasible technology for powering low-power wireless systems. This paper investigates a bit error rate (BER) of the non-orthogonal multiple access with cooperative relay-assisted power splitting (CR-NOMA-PS) based energy harvesting (EH). We consider that the relay works in the decode-forward (DF) mode. For more practical scenarios, we consider the imperfect successive interference cancellation (SIC) and channel state information (CSI) are available. We obtain the end-to-end (e2e) BER expressions for the CR-NOMA-PS with imperfect CSI. Under different scenarios of PS, we evaluate and discuss the BER performance of the users with imperfect SIC and CSI. We validate the derivation of the BER expressions by simulation results. The results indicated that the high values of the PS factor reduce the users' performance. Furthermore, in the high signal-to-noise ratio (SNR), the CSI error degrade BER performance and produced an error floor.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"54 5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73604264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Antennas PAPR reduction for FBMC/OQAM system","authors":"Ammar Boudjelkha, H. Merah, A. Khelil","doi":"10.2174/2210327913666230512163935","DOIUrl":"https://doi.org/10.2174/2210327913666230512163935","url":null,"abstract":"\u0000\u0000The filter bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) is a promising future generation of wireless systems. However, like multicarrier modulations (MCM), FBMC/OQAM has a high peak-to-average power ratio (PAPR), which allows the FBMC/OQAM signal to pass through the nonlinear region of the high-power amplifier (HPA) in the time domain and causes in-band and out of band (OOB) distortion.\u0000\u0000\u0000\u0000In this paper, a new method to overcome this problem called multi-antennas PAPR (MAP) reduction is proposed. It consists of using I antennas in transmission and reception to transmit I FBMC/OQAM sub-signals with low PAPR. The complementary cumulative distribution function (CCDF), the bit error rate (BER), and the energy efficiency are used to evaluate the method's performance.\u0000\u0000\u0000\u0000The simulation results showed that the new technique can reduce the PAPR of the original signal by more than half, achieve BER comparable to that of the original signal without HPA, and when the input back-off (IBO) equals 3dB, the error vector magnitude (EVM) result can be reduced from 19% to 7%.\u0000\u0000\u0000\u0000The PAPR, BER, and EVM of MAP technique are much better than the original system.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"153 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79654303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT and AI-based Intelligent Agriculture framework for Crop Prediction","authors":"Pushpa Singh, M. Singh, Narendra Singh, A. Chakraverti","doi":"10.2174/2210327913666230509144225","DOIUrl":"https://doi.org/10.2174/2210327913666230509144225","url":null,"abstract":"\u0000\u0000Currently, Artificial Intelligence (AI) and the Internet of Things (IoT) have transformed the field of agriculture with the innovative idea of automation and intelligence. The agriculture field completely relies on the uncertainty parameter of soil, atmosphere, and water. Technological advancement in IoT and AI assist in resolving this uncertainty factor and recommend the best crops to the farmers so that they can also enhance the productivity of the crops and meet the world's large food demand smartly.\u0000\u0000\u0000\u0000In this paper, we have suggested an IoT and AI-based model which trained with 2200 records of the dataset and seven attributes in Python. The model suggests 22 different crops to farmers after collecting samples through different sensor data. We used soil, temperature, humidity, pH, and rainfall sensors. Soil sensors were used to measure the amount of N, P, and K in soil.\u0000\u0000\u0000\u0000The samples of of the SLE patients, Cell culture and treatment, Plasmid construction and transfection, Quantitative real-time PCR analysis, Enzyme-linked immunosorbent assay (ELISA), Cell viability analysis, Cell apoptosis analysis, Western blot were collected.\u0000\u0000\u0000\u0000In this research, we investigated the contribution of GAS5 in the pathogenesis of SLE. We confirmed that, compared to healthy people, the expression of GAS5 was significantly decreased in peripheral monocytes of SLE patients. Subsequently, we found that GAS5 can inhibit the proliferation and promote the apoptosis of monocytes by over-expressing or knocking down the expression of GAS5. Additionally, the expression of GAS5 was suppressed by LPS. Silencing GAS5 significantly increased the expression of a group of chemokines and cytokines, including IL-1β, IL-6 and THFα, which were induced by LPS. Furthermore, it was identified that the involvement of GAS5 in TLR4-mediated inflammatory process was through affecting the activation of the MAPK signaling pathway.\u0000\u0000\u0000\u0000In general, the decreased GAS5 expression may be a potential contributor to the elevated production of a great number of cytokines and chemokines in SLE patients. And our research suggests that GAS5 contributes a regulatory role in the pathogenesis of SLE, and may provide a potential target for therapeutic intervention.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83107249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A secure and energy-efficient framework for air quality prediction using smart sensors and ISHO-DCNN","authors":"Vineet Singh, K. Singh, Sarvpal H. Singh","doi":"10.2174/2210327913666230504122805","DOIUrl":"https://doi.org/10.2174/2210327913666230504122805","url":null,"abstract":"\u0000\u0000The World Health Organization (WHO) reported that Air pollution (AP) is prone to the highest environmental risk and has caused numerous deaths. Polluted air has many constituents where Particulate Matter (PM) is majorly reported as a global concern. Currently, the most crucial challenges faced by the globe are the identification and treatment of augmenting AP. The air pollution level was indicated by the Air Quality Index (AQI). It is affected by the concentrations of several pollutants in the air. Many pollutants in the air are harmful to human health. Thus, an efficient prediction system is required. Many security problems and lower classification accuracy are faced by them even though several prediction systems have been formed. A secure air quality prediction system (AQPS) centered upon the energy efficiency of smart sensing is proposed in this paper to overcome these issues. From disparate sensor nodes, the input data is initially amassed in the proposed work. The gathered data is stored in the temporary server. Next, the air-polluted data of the temporary server is offered to the AQPS, wherein preprocessing of the input data along with classification is executed.\u0000\u0000\u0000\u0000Utilizing the Improved Spotted Hyena Optimization-based Deep Convolution Neural Network (ISHO-DCNN) algorithm, the classification is executed. Utilizing the Repetitive Data Coding Based Huffman Encoding (RDC-HE) method, the polluted data attained from the classified output is compressed and encrypted by employing the American Standard Code for Information Interchange based Elliptical Curve Cryptography (ASCII-ECC) method.\u0000\u0000\u0000\u0000Afterward, the encrypted and compressed data is saved in the Cloud Server (CS). Finally, for notifying about the AP, the decrypted and decompressed data is offered to the Base Stations (BS).\u0000\u0000\u0000\u0000The proposed work is more effective when analogized to the prevailing methods as denoted by the experimental outcomes. Higher accuracy of 97.14% and precision of 91.44% were obtained by the proposed model. Further, lower Encryption Time (ET) and Decryption Time (DT) of 0.565584 sec and 0.005137 sec were obtained by the model.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87830893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CNN-RNN Algorithm-based Traffic Congestion Prediction System using Tri-Stage Attention","authors":"S. Asif, K. Kartheeban","doi":"10.2174/2210327913666230503105942","DOIUrl":"https://doi.org/10.2174/2210327913666230503105942","url":null,"abstract":"\u0000\u0000Most people consider traffic congestion to be a major issue since it increases noise, pollution, and time wastage. Traffic congestion is caused by dynamic traffic flow, which is a serious concern. The current normal traffic light system is not enough to handle the traffic congestion problems since it functions with a fixed-time length strategy.\u0000\u0000\u0000\u0000Despite the massive amount of traffic surveillance videos and images collected in daily monitoring, deep learning techniques for traffic intelligence management and control have been underutilized. Hence, in this paper, we propose a novel traffic congestion prediction system using a deep learning approach. Initially, the traffic data from the sensors is obtained and pre-processed using normalization. The features are extracted using Multi-Linear Discriminant Analysis (M-LDA). We propose Tri-stage Attention-based Convolutional Neural Network- Recurrent Neural Network (TA- CNN-RNN) for predicting traffic congestion.\u0000\u0000\u0000\u0000To evaluate the effectiveness of the proposed model, the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) were used as the evaluation metrics.\u0000\u0000\u0000\u0000The experimental trial could extend its successful application to the traffic surveillance system and has the potential to enhancement an intelligent transport system in the future.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"18 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78362015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MLCEL: Machine Learning and Cost-Effective Localization Algorithms for WSNs","authors":"Omkar Singh, Lalit Kumar","doi":"10.2174/2210327913666230502124733","DOIUrl":"https://doi.org/10.2174/2210327913666230502124733","url":null,"abstract":"\u0000\u0000Wireless communication systems provide an indispensable act in real-life scenarios and\u0000permit an extensive range of services based on the users' location.\u0000The forthcoming implementation of versatile localization networks and the formation of subsequent\u0000generation Wireless Sensor Network (WSN) will permit numerous applications.\u0000In this perspective, localization algorithms have converted into an essential tool to afford compact implementation\u0000for the location-based system to increase accuracy and reduce computational time, proposing\u0000a Machine Learning and Cost-Effective Localization (MLCEL) algorithm. MLCEL algorithm\u0000is assessed with considered localization algorithms called Support Vector Machine for Regression\u0000(SVR), Artificial Neural Network (ANN), and K-Nearest Neighbor (KNN). Numerous outcomes\u0000show that the MLCEL algorithm performs better than state art algorithms.\u0000The results are assessed on different parameters, and MLCEL achieves better results in localization\u0000error 13%-16%, cumulative probability 19%-21%, root mean square error 14%-18%, distance error\u000017%-20%, and computational time 22%-24% than SVR, ANN, and KNN.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88790192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An IoT Enabled Cost Effective Smart Healthcare for Real-Time COVID19 Patient Early Identification and Monitoring System Using Smartphone","authors":"Md. Tanvir Shahed, Abda Fariha Azim Meem, Md. Shazibul Habib, Goyur Prosad Sen, Md. Shamim Hossen, Md. Shamim Uddin","doi":"10.2174/2210327913666230426112047","DOIUrl":"https://doi.org/10.2174/2210327913666230426112047","url":null,"abstract":"\u0000\u0000The SARS-CoV-2 virus causes COVID-19, a highly contagious disease.\u0000Meetings between COVID-19 patients, their families, and medical professionals are no longer safe.\u0000To meet their patients, doctors and patients' families must take extreme precautions. Even with these\u0000stringent safety precautions, there is a chance that he or she will be affected by COVID-19. In this\u0000context, remote patient monitoring via IoT devices can be a highly effective system for today's\u0000healthcare system with no safety concerns.\u0000\u0000\u0000\u0000This paper describes an IoT-based system for remote monitoring of COVID-19 patients that\u0000uses measured values of the patient's heart rate, body temperature, and oxygen saturation, the most\u0000critical measures required for critical care. This device can monitor the observed body temperature,\u0000heart rate, and oxygen saturation level in real time and can be easily synchronized with a ThingSpeak\u0000IoT cloud platform channel for instant access through a smartphone. When the sensor value exceeds\u0000the system's safe threshold, the system will send an email alert to the system user. Some people may\u0000notice a decrease in oxygen saturation without any symptoms or respiratory problems. This system\u0000can be very useful for early COVID-19 identification in this case. The proposed IoT-based technique\u0000is based on an Arduino Uno system and has been tested and validated by a large number of human test\u0000participants. As an example, five sample results are shown in this paper.\u0000\u0000\u0000\u0000The system yielded promising results. When compared to other commercially available devices, the system's results were found to be accurate, with a maximum error rate of less than 5%,\u0000which is quite acceptable. The system's data can be saved in the ThingSpeak cloud server for further\u0000analysis. This system requires a unique email and password verification to maintain system security\u0000and user data privacy. This patient monitoring system has grown in popularity during this COVID-19\u0000pandemic due to its uniqueness and diverse medical applications. Many people's lives are impacted\u0000daily when illnesses are not identified in a timely and accurate manner, denying us the opportunity to\u0000provide medical care. To deal with such scenarios, this system will help to monitor a COVID-19 patient's specific parameters, predict the patient's status on a regular basis, and send an email alert to the\u0000system user if something abnormal occurs.\u0000\u0000\u0000\u0000As a result, this IoT-based smart healthcare solution could help save lives during the current COVID-19 pandemic. This technology is easy to use and reduces the need for human intervention.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77095476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploiting Predictability of Random Vector Functional Link Networks in Forecasting Quality of Service (QoS) parameters of IoT-Based Web Services Data","authors":"Sarat Chandra Nayak, Stitapragyan Lenka, Sateesh Kumar Pradhan, Samaleswari Prasad Nayak","doi":"10.2174/2210327913666230411125347","DOIUrl":"https://doi.org/10.2174/2210327913666230411125347","url":null,"abstract":"\u0000\u0000QoS parameters are volatile in nature and possess high nonlinearity, thus\u0000making the IoT-based service and recommendation process challenging.\u0000\u0000\u0000\u0000An efficient and accurate forecasting model is lacking in this area and needs to be explored.\u0000Though an artificial neural network is a prominent option for capturing such nonlinearities, its efficiency is limited by the structural complexity and iterative learning method. The random vector functional link network (RVFLN) significantly reduces the time complexity by randomly assigning input\u0000weights and biases without further modification. Only output layer weights are calculated iteratively\u0000by gradient methods or non-iteratively by least square methods. It is an efficient algorithm with low\u0000time complexity and can handle complex domain problems without compromising accuracy. Motivated by these characteristics, this article develops an RVFLN-based model for forecasting QoS parameter sequences.\u0000\u0000\u0000\u0000Two real-world IoT-enabled web service dataset series are used in developing and evaluating\u0000the effectiveness of RVFLN-based forecasts in terms of three performance metrics.\u0000\u0000\u0000\u0000Experimental results, comparative studies, and statistical tests are conducted to establish\u0000the superiority of the proposed approach over four other similar forecasting techniques.\u0000\u0000\u0000\u0000The comparative models included are MLR, ARIMA, MLP, and RBFNN. The experimental results revealed that the proposed RVFLN based QoS parameter forecasting gives amended prediction accuracy for majority of the QoS parameters over other forecasts. The superiority of RVFLN is also established through relative worth tests.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"135 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77773031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}