Neuromorphic artificial synaptic devices exhibit significant application in the fields of deep learning and edge computing. In this study, we proposed a PbS film-based artificial synaptic device that features a simple structure and manufacturing process, and is easy to integrate. Under the stimulation of optical signals, the device can simulate the functions of human neural synapses, such as short-term plasticity, excitatory postsynaptic currents (EPSC), and paired pulse facilitation, exhibiting memory capabilities when triggered by consecutive light pulses. We used it for detection of radicals in standard Chinese characters, constructing an automatically controlled recognition system that uses a field-programmable gate array (FPGA) combined with a convolutional neural network (CNN) to accomplish the detection of radicals. A dataset of 1000 samples was established and extended using expansion techniques to prevent overfitting. Using FPGA and ARM combined with a CNN network, we have achieved an accuracy of 96% in detecting radical components. This study suggests that the present nanofilm photodetector with processing performance may find promising application in future pattern recognition.
{"title":"An Artificial Synaptic Devices Based on PbS Nanofilm Photodetectors for Radical Recognition System Application","authors":"Zhi-Guo Zhu;Jia Liu;Yang Wang;Sheng-Hui Luo;Can Fu;Meng-Fei Liang;Lin-Bao Luo;Feng-Xia Liang","doi":"10.1109/LSENS.2024.3497148","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3497148","url":null,"abstract":"Neuromorphic artificial synaptic devices exhibit significant application in the fields of deep learning and edge computing. In this study, we proposed a PbS film-based artificial synaptic device that features a simple structure and manufacturing process, and is easy to integrate. Under the stimulation of optical signals, the device can simulate the functions of human neural synapses, such as short-term plasticity, excitatory postsynaptic currents (EPSC), and paired pulse facilitation, exhibiting memory capabilities when triggered by consecutive light pulses. We used it for detection of radicals in standard Chinese characters, constructing an automatically controlled recognition system that uses a field-programmable gate array (FPGA) combined with a convolutional neural network (CNN) to accomplish the detection of radicals. A dataset of 1000 samples was established and extended using expansion techniques to prevent overfitting. Using FPGA and ARM combined with a CNN network, we have achieved an accuracy of 96% in detecting radical components. This study suggests that the present nanofilm photodetector with processing performance may find promising application in future pattern recognition.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777806","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}
Pub Date : 2024-11-13DOI: 10.1109/LSENS.2024.3497924
Lazar Milić;Željko Popović;Ivana Mišić;Alessandro Luzio;Mario Caironi;Goran M. Stojanović
Edible electronics present a blossoming path to a greener and eco-friendly future for electronics while being biocompatible with living beings. With this characteristic, edible electronics have been recently proposed for the design and fabrication of edible and digestible sensors. More precisely, it has become a strong and sustainable candidate for continuous and in vivo monitoring and diagnosis of patients. Yet, the field is in constant search for new functional materials satisfying the stringent and contrasting requirements of safe edibility and performing electronics. With this in mind, a novel edible substrate, based entirely on cookie dough, is presented in this letter. An extensive mechanical and electrical characterization of the edible substrate is provided, aside from a clear step-by-step guide for its fabrication. In addition, to prove the use of the cookie dough substrate for food-based electronics, we demonstrate a voltage divider and a resonant circuit fabricated on it. Tests have been conducted in dry and wet conditions, simulating intraoral environment. Sensing capabilities have been also investigated, with variations of temperature and pH. These findings push the boundaries of edible electronics, enabling a growing community of researchers to utilize the proposed substrate and circuits in a broad range of sensor technologies and applications.
{"title":"Electrical Circuits Developed on Cookie Dough-Based Substrate and Their Sensing Applications","authors":"Lazar Milić;Željko Popović;Ivana Mišić;Alessandro Luzio;Mario Caironi;Goran M. Stojanović","doi":"10.1109/LSENS.2024.3497924","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3497924","url":null,"abstract":"Edible electronics present a blossoming path to a greener and eco-friendly future for electronics while being biocompatible with living beings. With this characteristic, edible electronics have been recently proposed for the design and fabrication of edible and digestible sensors. More precisely, it has become a strong and sustainable candidate for continuous and in vivo monitoring and diagnosis of patients. Yet, the field is in constant search for new functional materials satisfying the stringent and contrasting requirements of safe edibility and performing electronics. With this in mind, a novel edible substrate, based entirely on cookie dough, is presented in this letter. An extensive mechanical and electrical characterization of the edible substrate is provided, aside from a clear step-by-step guide for its fabrication. In addition, to prove the use of the cookie dough substrate for food-based electronics, we demonstrate a voltage divider and a resonant circuit fabricated on it. Tests have been conducted in dry and wet conditions, simulating intraoral environment. Sensing capabilities have been also investigated, with variations of temperature and pH. These findings push the boundaries of edible electronics, enabling a growing community of researchers to utilize the proposed substrate and circuits in a broad range of sensor technologies and applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10752833","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757856","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}
Accurate detection of gram-positive bacterial colonies is essential for managing chronic wounds and overcoming delays in healing, as these bacteria can worsen wound conditions and impede recovery. This study introduces a cost-effective electrochemical sensing platform designed to support healthcare professionals in making timely, targeted treatment decisions. We developed the platform using chemically functionalized amine-terminated carbon surfaces combined with the TLR1/TLR2 heterodimer complex to detect gram-positive bacteria. The biosensors featuring these advanced carbon surfaces demonstrated superior performance due to their high surface area and efficient electron transfer capabilities. The TLR1/TLR2-based sensors accurately identified gram-positive bacteria, with a theoretical detection limit of 0.0413 CFU/mL. The sensors also exhibited high selectivity and sensitivity, with a response rate of 220.878 ((ΔR/R)/CFU/mL)/cm 2 for the amine-terminated carbon surfaces. This novel electrochemical sensing platform provides an effective solution for real-time detection and management of gram-positive bacterial infections in chronic wound care.
{"title":"Development of a TLR1/TLR2-Based Chemiresistive Biosensor for Ultra-Sensitive Gram-Positive Bacterial Detection Using Amine-Terminated Carbon Surfaces","authors":"Rahul Gangwar;Patta Supraja;Karri Trinadha Rao;Suryasnata Tripathy;Shiv Govind Singh;Siva Rama Krishna Vanjari","doi":"10.1109/LSENS.2024.3496995","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3496995","url":null,"abstract":"Accurate detection of gram-positive bacterial colonies is essential for managing chronic wounds and overcoming delays in healing, as these bacteria can worsen wound conditions and impede recovery. This study introduces a cost-effective electrochemical sensing platform designed to support healthcare professionals in making timely, targeted treatment decisions. We developed the platform using chemically functionalized amine-terminated carbon surfaces combined with the TLR1/TLR2 heterodimer complex to detect gram-positive bacteria. The biosensors featuring these advanced carbon surfaces demonstrated superior performance due to their high surface area and efficient electron transfer capabilities. The TLR1/TLR2-based sensors accurately identified gram-positive bacteria, with a theoretical detection limit of 0.0413 CFU/mL. The sensors also exhibited high selectivity and sensitivity, with a response rate of 220.878 ((ΔR/R)/CFU/mL)/cm\u0000<sup>2</sup>\u0000 for the amine-terminated carbon surfaces. This novel electrochemical sensing platform provides an effective solution for real-time detection and management of gram-positive bacterial infections in chronic wound care.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777890","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}
Pub Date : 2024-11-12DOI: 10.1109/LSENS.2024.3496540
Gandhimathinathan A;Ananthakrishnan C G;Lavanya R;R Jehadeesan;Pidapa Raghava Reddy
Industrial health monitoring is essential for managing and maintaining infrastructures in a process industry where the primary goals are reducing downtime, improving health, and ensuring safety performance. On the contrary, unplanned downtimes caused by regular maintenance often result in financial losses. This scenario calls for automated fault diagnosis that facilitates online health monitoring to predict faults before irreversible damage occurs. This letter proposes a deep learning approach based on long short-term memory denoising autoencoder (LSTM-DAE) combined with Isolation Forest (IF) for early detection of sensor anomalies in nuclear power plants. Residual signals from LSTM-DAE are fed to IF to generate anomaly scores for early fault detection. The proposed approach is validated using the dataset obtained from KALBR-SIM, a full scope operator training replica simulator, which replicates the Prototype Fast Breeder Reactor at Indira Gandhi Centre for Atomic Research. Results demonstrate that the proposed approach detects faults much earlier than the state-of-the-art approaches, with an accuracy of 98.2%.
{"title":"Sensor Anomaly Detection in Nuclear Power Plant Using Deep LSTM Denoising Autoencoder and Isolation Forest","authors":"Gandhimathinathan A;Ananthakrishnan C G;Lavanya R;R Jehadeesan;Pidapa Raghava Reddy","doi":"10.1109/LSENS.2024.3496540","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3496540","url":null,"abstract":"Industrial health monitoring is essential for managing and maintaining infrastructures in a process industry where the primary goals are reducing downtime, improving health, and ensuring safety performance. On the contrary, unplanned downtimes caused by regular maintenance often result in financial losses. This scenario calls for automated fault diagnosis that facilitates online health monitoring to predict faults before irreversible damage occurs. This letter proposes a deep learning approach based on long short-term memory denoising autoencoder (LSTM-DAE) combined with Isolation Forest (IF) for early detection of sensor anomalies in nuclear power plants. Residual signals from LSTM-DAE are fed to IF to generate anomaly scores for early fault detection. The proposed approach is validated using the dataset obtained from KALBR-SIM, a full scope operator training replica simulator, which replicates the Prototype Fast Breeder Reactor at Indira Gandhi Centre for Atomic Research. Results demonstrate that the proposed approach detects faults much earlier than the state-of-the-art approaches, with an accuracy of 98.2%.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736649","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}
Pub Date : 2024-11-11DOI: 10.1109/LSENS.2024.3495512
Bhavana Anchan;Suresh D. Kulkarni;Ajeetkumar Patil
In this letter, we present a sensitive approach for the qualitative and quantitative detection of vitamin B12 (VB-12). The method utilizes dual-capped CdTe quantum dots (QDs) as a fluorescent probe. These QDs were synthesized through a one-pot synthesis method in an aqueous medium. Various factors affecting the accuracy of VB-12 determination were systematically investigated to establish optimal conditions. The resulting calibration curve, spanning the concentration range of 10–300 ng/mL of VB-12, exhibited a linear relationship, with a limit of detection and a limit of quantification of 0.326 ng/mL (0.3 nM), and 1.22 ng/mL, respectively. Additionally, the interaction mechanism responsible for the fluorescence quenching of CdTe QDs by VB-12 was discussed. The proposed method holds promise for sensitive and accurate VB-12 detection and may have applications in analytical chemistry and medical diagnostics.
{"title":"Vitamin B12 Detection in Aqueous Media Using MPA-CdTe Quantum Dots","authors":"Bhavana Anchan;Suresh D. Kulkarni;Ajeetkumar Patil","doi":"10.1109/LSENS.2024.3495512","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3495512","url":null,"abstract":"In this letter, we present a sensitive approach for the qualitative and quantitative detection of vitamin B12 (VB-12). The method utilizes dual-capped CdTe quantum dots (QDs) as a fluorescent probe. These QDs were synthesized through a one-pot synthesis method in an aqueous medium. Various factors affecting the accuracy of VB-12 determination were systematically investigated to establish optimal conditions. The resulting calibration curve, spanning the concentration range of 10–300 ng/mL of VB-12, exhibited a linear relationship, with a limit of detection and a limit of quantification of 0.326 ng/mL (0.3 nM), and 1.22 ng/mL, respectively. Additionally, the interaction mechanism responsible for the fluorescence quenching of CdTe QDs by VB-12 was discussed. The proposed method holds promise for sensitive and accurate VB-12 detection and may have applications in analytical chemistry and medical diagnostics.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10748569","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713786","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}
Pub Date : 2024-11-08DOI: 10.1109/LSENS.2024.3494843
Bhaskar Pandey;Ajat Shatru Arora;Deepak Joshi
Pressure injuries cause discomfort and potential fatality, underscoring the importance of wound assessment. In the post-COVID era, remote monitoring of wounds, particularly through noncontact methods like infrared (IR) thermal imaging and deep learning, is imperative. This letter proposes a deep learning approach for dimension detection from thermal images, trained on data from 18 subjects. Instance segmentation achieved a maximum accuracy of 0.9542, with classification accuracy reaching 0.9922. The model exhibited a root mean square error (RMSE) of 0.1609 cm for measured dimensions, with superior accuracy in detecting wound length (RMSE: 0.1114 cm) compared to width (RMSE: 0.1506 cm).
{"title":"Noncontact Size Estimation of Pressure Ulcers Using IR Thermal Imaging","authors":"Bhaskar Pandey;Ajat Shatru Arora;Deepak Joshi","doi":"10.1109/LSENS.2024.3494843","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3494843","url":null,"abstract":"Pressure injuries cause discomfort and potential fatality, underscoring the importance of wound assessment. In the post-COVID era, remote monitoring of wounds, particularly through noncontact methods like infrared (IR) thermal imaging and deep learning, is imperative. This letter proposes a deep learning approach for dimension detection from thermal images, trained on data from 18 subjects. Instance segmentation achieved a maximum accuracy of 0.9542, with classification accuracy reaching 0.9922. The model exhibited a root mean square error (RMSE) of 0.1609 cm for measured dimensions, with superior accuracy in detecting wound length (RMSE: 0.1114 cm) compared to width (RMSE: 0.1506 cm).","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736346","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}
Electroencephalogram (EEG) signal classification is of utmost importance in brain-computer interface (BCI) systems. However, the inherent complex properties of EEG signals pose a challenge in their analysis and modeling. This letter proposes a novel approach of integrating wavelet scattering transform (WST) with convolutional neural network (CNN) for classifying motor imagery (MI) via EEG signals (referred as WST-CNN), capable of extracting distinctive characteristics in signals even when the data is limited. In this architecture, the first layer is nontrainable WST features with fixed initializations in WST-CNN. Furthermore, WSTs are robust to local perturbations in data, especially in the form of translation invariance, and resilient to deformations, thereby enhancing the network's reliability. The performance of the proposed idea is evaluated on the DBCIE dataset for three different scenarios: left-arm (LA) movement, right-arm (RA) movement, and simultaneous movement of both arms (BA). The BCI Competition IV-2a dataset was also employed to validate the proposed concept across four distinct MI tasks, such as movements in: left-hand (LH), right-hand (RH), feet (FT), and tongue (T). The classifications' performance was evaluated in terms of accuracy ( $eta$ ), sensitivity ( $S_{e}$ ), specificity ( $S_{p}$ ), and weighted F1-score, which reached up to 92.72%, 92.72%, 97.57%, and 92.75% for classifying LH, RH, FT, and T on the BCI Competition IV-2a dataset and 89.19%, 89.19%, 94.60%, and 89.33% for classifying LA, RA, and BA, on the DBCIE dataset, respectively.
{"title":"Classification of Motor Imagery Tasks Using EEG Based on Wavelet Scattering Transform and Convolutional Neural Network","authors":"Rantu Buragohain;Jejariya Ajaybhai;Karan Nathwani;Vinayak Abrol","doi":"10.1109/LSENS.2024.3488356","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3488356","url":null,"abstract":"Electroencephalogram (EEG) signal classification is of utmost importance in brain-computer interface (BCI) systems. However, the inherent complex properties of EEG signals pose a challenge in their analysis and modeling. This letter proposes a novel approach of integrating wavelet scattering transform (WST) with convolutional neural network (CNN) for classifying motor imagery (MI) via EEG signals (referred as WST-CNN), capable of extracting distinctive characteristics in signals even when the data is limited. In this architecture, the first layer is nontrainable WST features with fixed initializations in WST-CNN. Furthermore, WSTs are robust to local perturbations in data, especially in the form of translation invariance, and resilient to deformations, thereby enhancing the network's reliability. The performance of the proposed idea is evaluated on the DBCIE dataset for three different scenarios: left-arm (LA) movement, right-arm (RA) movement, and simultaneous movement of both arms (BA). The BCI Competition IV-2a dataset was also employed to validate the proposed concept across four distinct MI tasks, such as movements in: left-hand (LH), right-hand (RH), feet (FT), and tongue (T). The classifications' performance was evaluated in terms of accuracy (\u0000<inline-formula><tex-math>$eta$</tex-math></inline-formula>\u0000), sensitivity (\u0000<inline-formula><tex-math>$S_{e}$</tex-math></inline-formula>\u0000), specificity (\u0000<inline-formula><tex-math>$S_{p}$</tex-math></inline-formula>\u0000), and weighted F1-score, which reached up to 92.72%, 92.72%, 97.57%, and 92.75% for classifying LH, RH, FT, and T on the BCI Competition IV-2a dataset and 89.19%, 89.19%, 94.60%, and 89.33% for classifying LA, RA, and BA, on the DBCIE dataset, respectively.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636262","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}
Pub Date : 2024-11-08DOI: 10.1109/LSENS.2024.3494841
Manish Singh Negi;Sunil Mohan;Sunil K. Khijwania
The main objective of the present research is to develop an optical fiber relative humidity (RH) sensor having ultrahigh sensitivity, linear response over a wide dynamic range, as well as optimum response/recovery times while utilizing the simple optical fiber sensing configuration. The proposed sensor, developed to achieve these objectives, exploits the phenomena of intensity modulation via evanescent wave absorption in the sensing region, which is designed by employing TiO 2 –GO nanocomposite-doped silica sol–gel nanostructured thin sensing film onto a short centrally decladded region of a plastic-clad silica fiber. Detailed experimental investigations are carried out to analyze the response characteristics of the proposed sensor. The developed sensor is characterized by a significantly enhanced sensitivity of 0.0094 RH −1 while responding linearly over a large dynamic range of 9%−92% RH. In addition, the sensor exhibits a high degree of reversibility, repeatability, reliability, and fast response and recovery time.
{"title":"Novel TiO2–GO Nanocomposite-Based Ultrahigh Sensitive Optical Fiber Humidity Sensor","authors":"Manish Singh Negi;Sunil Mohan;Sunil K. Khijwania","doi":"10.1109/LSENS.2024.3494841","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3494841","url":null,"abstract":"The main objective of the present research is to develop an optical fiber relative humidity (RH) sensor having ultrahigh sensitivity, linear response over a wide dynamic range, as well as optimum response/recovery times while utilizing the simple optical fiber sensing configuration. The proposed sensor, developed to achieve these objectives, exploits the phenomena of intensity modulation via evanescent wave absorption in the sensing region, which is designed by employing TiO\u0000<sub>2</sub>\u0000–GO nanocomposite-doped silica sol–gel nanostructured thin sensing film onto a short centrally decladded region of a plastic-clad silica fiber. Detailed experimental investigations are carried out to analyze the response characteristics of the proposed sensor. The developed sensor is characterized by a significantly enhanced sensitivity of 0.0094 RH\u0000<sup>−1</sup>\u0000 while responding linearly over a large dynamic range of 9%−92% RH. In addition, the sensor exhibits a high degree of reversibility, repeatability, reliability, and fast response and recovery time.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777933","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}
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