Based on characteristics of plastic head cover, the paper carried out 5 design concepts to improve its NVH property. It’s useful to improve NVH properties of product if follow the concept when design or optimize a head cover. In this paper, finite element and multi-body dynamics analysis methods were used to analyse a plastic head cover firstly. At second the head cover was optimized with the concept in the process. Finally, the plastic head cover met the development target. Test results proved the reliability of design concepts as well.
{"title":"Design and optimization of engine plastic cylinder head cover","authors":"Si Chen, Xianneng Luo, Dan Su, Yuxuan Liu","doi":"10.1117/12.2671847","DOIUrl":"https://doi.org/10.1117/12.2671847","url":null,"abstract":"Based on characteristics of plastic head cover, the paper carried out 5 design concepts to improve its NVH property. It’s useful to improve NVH properties of product if follow the concept when design or optimize a head cover. In this paper, finite element and multi-body dynamics analysis methods were used to analyse a plastic head cover firstly. At second the head cover was optimized with the concept in the process. Finally, the plastic head cover met the development target. Test results proved the reliability of design concepts as well.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128758505","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}
In this paper, a research was conducted to analyse and predict the impacts of COVID-19 on public transportation ridership in the U.S. and 5 most populous cities of the U.S. (New York City, Los Angeles, Chicago, Houston, Philadelphia). The paper aims to exploit the correlation between COVID-19 and public transportation ridership in the U.S. and make the reasonable prediction by machine learning models, including ARIMA and Prophet, to help the local governments improve the rationality of their policy implementation. After correlation analyses, high level of significant and negative correlations between monthly growth rate of COVID-19 infections and monthly growth rate of public transportation ridership are decidedly validated in the total U.S., and New York City, Los Angeles, Chicago, Philadelphia, except Houston. To analyse the errors of Houston, we consult the literature and made a discussion of Influencing factors. We find that the level of public transportation in quantity and utilization is terribly low in Houston. In addition, the factors, such as the lack of planning law and estimation of urban expressways, the high level of citizens’ dependence on private cars and pride of owning cars play a considerable roll in the errors. And the impacts can be predicted to a certain extent through two forecasting models (ARIMA and Prophet), although the precision of our models is not enough to make a precise forecast due to the limitations of model tuning and model design. According to the comparison of the two models, ARIMA models' forecasting accuracy is between 6% and 10%, and Prophet's forecasting accuracy is between 8%-12%, depending on the city. Since the insufficient stationarity, periodicity, seasonality of time series, the Prophet models are hard be more refined.
{"title":"U.S. public transportation ridership analysis and prediction based on COVID-19","authors":"Yuan Gao, Jiangfan Li, Jiani Wang, Zeming Yang","doi":"10.1117/12.2672649","DOIUrl":"https://doi.org/10.1117/12.2672649","url":null,"abstract":"In this paper, a research was conducted to analyse and predict the impacts of COVID-19 on public transportation ridership in the U.S. and 5 most populous cities of the U.S. (New York City, Los Angeles, Chicago, Houston, Philadelphia). The paper aims to exploit the correlation between COVID-19 and public transportation ridership in the U.S. and make the reasonable prediction by machine learning models, including ARIMA and Prophet, to help the local governments improve the rationality of their policy implementation. After correlation analyses, high level of significant and negative correlations between monthly growth rate of COVID-19 infections and monthly growth rate of public transportation ridership are decidedly validated in the total U.S., and New York City, Los Angeles, Chicago, Philadelphia, except Houston. To analyse the errors of Houston, we consult the literature and made a discussion of Influencing factors. We find that the level of public transportation in quantity and utilization is terribly low in Houston. In addition, the factors, such as the lack of planning law and estimation of urban expressways, the high level of citizens’ dependence on private cars and pride of owning cars play a considerable roll in the errors. And the impacts can be predicted to a certain extent through two forecasting models (ARIMA and Prophet), although the precision of our models is not enough to make a precise forecast due to the limitations of model tuning and model design. According to the comparison of the two models, ARIMA models' forecasting accuracy is between 6% and 10%, and Prophet's forecasting accuracy is between 8%-12%, depending on the city. Since the insufficient stationarity, periodicity, seasonality of time series, the Prophet models are hard be more refined.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121347966","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}
The repeated failure of a plug-in capacitor solder joint occurs in an equipment in the process of long-term use, which has certain effect on the function and performance of the equipment. This paper analyzes the cause of the failure, by SEM、 EDS and anatomy of the solder joint, it is found that the legs of the capacitor are oxidized, resulting in cracks during soldering. By oxide layer removing and tining, it can effectively improve wettability and reduce the risk of defects, achieve the purpose of improving product quality.
{"title":"Analysis and control of solder joints failure of plug-in capacitors","authors":"Yang Lu, Xu Chen, Cangbi Zhao, Zhiwei Cao","doi":"10.1117/12.2673084","DOIUrl":"https://doi.org/10.1117/12.2673084","url":null,"abstract":"The repeated failure of a plug-in capacitor solder joint occurs in an equipment in the process of long-term use, which has certain effect on the function and performance of the equipment. This paper analyzes the cause of the failure, by SEM、 EDS and anatomy of the solder joint, it is found that the legs of the capacitor are oxidized, resulting in cracks during soldering. By oxide layer removing and tining, it can effectively improve wettability and reduce the risk of defects, achieve the purpose of improving product quality.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115267781","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}
Yu Zhang, H. Yuan, Yong He, Jianan Yao, Lin Lu, Qi Tan, Tianhan Jiang, Haiao Tan, Cheng Dong, Yanchao Zeng
The current UAV inspection multi-link data congestion control method based on link capacity uses a cache queue model to regulate the data throughput at the sending end, which leads to low control performance due to the lack of monitoring of data sending nodes. In this regard, the transmission line UAV inspection multi-link data congestion control method is proposed. The state of the UAV network data nodes is sensed using an ant colony algorithm, data scheduling flows are selected according to the bandwidth load, and data congestion is alleviated through data allocation as well as route maintenance. In the experiments, the control performance of the proposed control method is verified. The analysis of the experimental results shows that the proposed method is used to construct a multi-link data congestion control technique with a low data congestion rate and its control performance is high.
{"title":"Transmission line UAV inspection multi-link data congestion control method","authors":"Yu Zhang, H. Yuan, Yong He, Jianan Yao, Lin Lu, Qi Tan, Tianhan Jiang, Haiao Tan, Cheng Dong, Yanchao Zeng","doi":"10.1117/12.2671863","DOIUrl":"https://doi.org/10.1117/12.2671863","url":null,"abstract":"The current UAV inspection multi-link data congestion control method based on link capacity uses a cache queue model to regulate the data throughput at the sending end, which leads to low control performance due to the lack of monitoring of data sending nodes. In this regard, the transmission line UAV inspection multi-link data congestion control method is proposed. The state of the UAV network data nodes is sensed using an ant colony algorithm, data scheduling flows are selected according to the bandwidth load, and data congestion is alleviated through data allocation as well as route maintenance. In the experiments, the control performance of the proposed control method is verified. The analysis of the experimental results shows that the proposed method is used to construct a multi-link data congestion control technique with a low data congestion rate and its control performance is high.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"234 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114207453","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}
Lung cancer is one of the most serious cancers, which has high death rate. In much research, researchers find that preventing lung cancer more effective for people to against it. In this paper, we aim to predict the possibility of lung cancer for the test individuals and exploit the main factors. We apply three machine learning models, including linear regression. Polynomial regression and bootstrap for this task. In the experiment. We find the linear regression achieves the best performance, with the lowest MSE (0.11). Furthermore, we find that the age, smoke and alcohol take important role in lung cancer. The author provides a comprehensive prediction and analysis for lung cancer precaution.
{"title":"Prediction and analysis of lung cancer using machine learning models","authors":"Yapeng Chen","doi":"10.1117/12.2672647","DOIUrl":"https://doi.org/10.1117/12.2672647","url":null,"abstract":"Lung cancer is one of the most serious cancers, which has high death rate. In much research, researchers find that preventing lung cancer more effective for people to against it. In this paper, we aim to predict the possibility of lung cancer for the test individuals and exploit the main factors. We apply three machine learning models, including linear regression. Polynomial regression and bootstrap for this task. In the experiment. We find the linear regression achieves the best performance, with the lowest MSE (0.11). Furthermore, we find that the age, smoke and alcohol take important role in lung cancer. The author provides a comprehensive prediction and analysis for lung cancer precaution.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"412 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116556210","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}
Breast cancer is common in women, ranking first in the incidence of cancer in women and occupying first place in the mortality rate of cancer in women. Because of the seriousness of breast cancer, researchers and institutions worldwide are making unremitting efforts to find the perfect diagnostic and therapeutic solutions. The increasing maturity of image processing technology has led to the growing use of computer-based pathological diagnosis in diagnosing various diseases, and researchers have done much research on this. This paper presents some studies on breast cancer histopathological images based on hematoxylin-eosin staining. Currently, the diagnosis of breast cancer is based on hematoxylin-eosinstained histopathological images. First, the surgeon will take a piece of tissue from the patient's lesion and make a histological section. Next, the pathologist will observe the histological section and diagnose the results. In this way of diagnosis, the patient's diagnosis depends more on the subjective judgment of the pathologist, which requires a high degree of professionalism and is not very efficient. Therefore, for hematoxylin-eosin-stained breast cancer histopathology images, there is a need for a computer-assisted automatic diagnosis method that can reduce the pathologist's burden and make the patient's diagnosis objective and efficient with the help of image processing technology. To this end, this paper compares the performance of three standard machine learning algorithms for comparing hematoxylin-eosin-stained breast cancer histopathology images.
{"title":"The comparison of CNN based networks on infiltrating ductal carcinoma images classification in the medical application field","authors":"Ling Zhu","doi":"10.1117/12.2672683","DOIUrl":"https://doi.org/10.1117/12.2672683","url":null,"abstract":"Breast cancer is common in women, ranking first in the incidence of cancer in women and occupying first place in the mortality rate of cancer in women. Because of the seriousness of breast cancer, researchers and institutions worldwide are making unremitting efforts to find the perfect diagnostic and therapeutic solutions. The increasing maturity of image processing technology has led to the growing use of computer-based pathological diagnosis in diagnosing various diseases, and researchers have done much research on this. This paper presents some studies on breast cancer histopathological images based on hematoxylin-eosin staining. Currently, the diagnosis of breast cancer is based on hematoxylin-eosinstained histopathological images. First, the surgeon will take a piece of tissue from the patient's lesion and make a histological section. Next, the pathologist will observe the histological section and diagnose the results. In this way of diagnosis, the patient's diagnosis depends more on the subjective judgment of the pathologist, which requires a high degree of professionalism and is not very efficient. Therefore, for hematoxylin-eosin-stained breast cancer histopathology images, there is a need for a computer-assisted automatic diagnosis method that can reduce the pathologist's burden and make the patient's diagnosis objective and efficient with the help of image processing technology. To this end, this paper compares the performance of three standard machine learning algorithms for comparing hematoxylin-eosin-stained breast cancer histopathology images.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117168186","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}
Visual ship image object detection has essential applications for near-shore ship management and military object location. In recent years, object detection technology based on a deep learning algorithm has been widely studied in object detection of visible ship images, and achieved outstanding results. However, due to the difference and overlap of nearshore ship objects, the object loss rate is high. Aiming at the above problems, this paper proposes an improved RetinaNet ship object detection algorithm. Firstly, channel attention is added after the residual network, and used to enhance the attention to low-frequency information. Secondly, the cyclical focal loss and the CIOU loss function are used to increase the training times of negative samples in the middle of training, which effectively improves object detection accuracy. The experimental results show that the improved RetinaNet algorithm improves the recognition accuracy of ship objects by 2.5%.
{"title":"A ship object detection algorithm based on improved RetinaNet","authors":"Ting Pan, Yubo Tian","doi":"10.1117/12.2672194","DOIUrl":"https://doi.org/10.1117/12.2672194","url":null,"abstract":"Visual ship image object detection has essential applications for near-shore ship management and military object location. In recent years, object detection technology based on a deep learning algorithm has been widely studied in object detection of visible ship images, and achieved outstanding results. However, due to the difference and overlap of nearshore ship objects, the object loss rate is high. Aiming at the above problems, this paper proposes an improved RetinaNet ship object detection algorithm. Firstly, channel attention is added after the residual network, and used to enhance the attention to low-frequency information. Secondly, the cyclical focal loss and the CIOU loss function are used to increase the training times of negative samples in the middle of training, which effectively improves object detection accuracy. The experimental results show that the improved RetinaNet algorithm improves the recognition accuracy of ship objects by 2.5%.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115019890","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}
The conventional single carrier frequency antenna pattern has a narrow main lobe level, but its side lobe level cannot be greatly reduced, which is not conducive to the improvement of the anti-interference effect. Based on this, this paper applies the sinusoidal weighted multi-carrier frequency scheme to the Frequency Diverse Array (FDA) and compares the performance of the antenna patterns of several FDA structures using the sinusoidal weighted multi-carrier scheme. It can be seen from the simulation results that compared with the single carrier frequency and other multi-carrier frequency schemes, the performance of the FDA regime radar such as SL-FDA is significantly improved after applying the sinusoidal weighted multi-carrier frequency scheme. The main lobe width in the range dimension is narrowed, and the side lobe level is also effectively suppressed. The sinusoidal weighted multi-carrier scheme is significantly better than other multi-carrier schemes.
{"title":"Performance analysis of sinusoidal weighted multi-carrier frequency scheme based on FDA","authors":"Yang Chen, Bo Tian, Chunyang Wang","doi":"10.1117/12.2673157","DOIUrl":"https://doi.org/10.1117/12.2673157","url":null,"abstract":"The conventional single carrier frequency antenna pattern has a narrow main lobe level, but its side lobe level cannot be greatly reduced, which is not conducive to the improvement of the anti-interference effect. Based on this, this paper applies the sinusoidal weighted multi-carrier frequency scheme to the Frequency Diverse Array (FDA) and compares the performance of the antenna patterns of several FDA structures using the sinusoidal weighted multi-carrier scheme. It can be seen from the simulation results that compared with the single carrier frequency and other multi-carrier frequency schemes, the performance of the FDA regime radar such as SL-FDA is significantly improved after applying the sinusoidal weighted multi-carrier frequency scheme. The main lobe width in the range dimension is narrowed, and the side lobe level is also effectively suppressed. The sinusoidal weighted multi-carrier scheme is significantly better than other multi-carrier schemes.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123928924","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}
Since it is difficult to accurately identify the minor fault of the early unbalanced exciting force fault of the linear vibrating screen, a fault diagnosis method based on the operating attitude of the screen box is proposed. Firstly, the dynamic analysis of the vibration system of the double axis linear shale shaker is carried out. Based on ADAMS environment, the dynamic simulation model of the double axis linear shale shaker is established, and six kinds of dynamic models of exciter failures are simulated to study the motion law of the screen box under the unbalanced excitation force failure. Further, the simulation analysis and field experiment of each fault dynamic model are carried out, and different fault data are trained and analyzed through the ELM neural network diagnosis algorithm. A set of attitude data acquisition system for the screen box of double axis linear vibrating screen is designed. The results show that the fault of the phase angle of the exciter can be diagnosed by detecting the screen box attitude.
{"title":"Fault diagnosis of vibrating screen exciter based on screen box attitude analysis","authors":"Jihua Bao, Cijia Zhang, Yi-xin Su","doi":"10.1117/12.2671808","DOIUrl":"https://doi.org/10.1117/12.2671808","url":null,"abstract":"Since it is difficult to accurately identify the minor fault of the early unbalanced exciting force fault of the linear vibrating screen, a fault diagnosis method based on the operating attitude of the screen box is proposed. Firstly, the dynamic analysis of the vibration system of the double axis linear shale shaker is carried out. Based on ADAMS environment, the dynamic simulation model of the double axis linear shale shaker is established, and six kinds of dynamic models of exciter failures are simulated to study the motion law of the screen box under the unbalanced excitation force failure. Further, the simulation analysis and field experiment of each fault dynamic model are carried out, and different fault data are trained and analyzed through the ELM neural network diagnosis algorithm. A set of attitude data acquisition system for the screen box of double axis linear vibrating screen is designed. The results show that the fault of the phase angle of the exciter can be diagnosed by detecting the screen box attitude.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116509676","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}
Xiaomei Hu, Y. Zhang, Yi Chen, Jianfei Chai, Jun Wu
Pear recognition is one of the key technologies of pear picking robot, and the pear recognition algorithm based on convolutional neural network has high computing cost and large parameters, which is difficult to be deployed on pear picking robot with low computer resources. This paper presents a lightweight pear real-time detection method based on YOLOv5. This method designs a lightweight feature extraction network based on Ghost bottom-leneck, and embeds the SE module into the designed network, which improves the ability of feature extraction while reducing the amount of network parameters. The experimental results show that compared with YOLOv5l, the parameters of the improved lightweight model are reduced by 48.17 %, mAP is increased by 0.9 %, and the recognition speed is increased by 36 %. The improved model is more suitable to be deployed on the picking robot with limited computing power and provides a solution for the vision system of pear picking robot.
{"title":"Lightweight pear detection algorithm based on improved YOLOv5","authors":"Xiaomei Hu, Y. Zhang, Yi Chen, Jianfei Chai, Jun Wu","doi":"10.1117/12.2671817","DOIUrl":"https://doi.org/10.1117/12.2671817","url":null,"abstract":"Pear recognition is one of the key technologies of pear picking robot, and the pear recognition algorithm based on convolutional neural network has high computing cost and large parameters, which is difficult to be deployed on pear picking robot with low computer resources. This paper presents a lightweight pear real-time detection method based on YOLOv5. This method designs a lightweight feature extraction network based on Ghost bottom-leneck, and embeds the SE module into the designed network, which improves the ability of feature extraction while reducing the amount of network parameters. The experimental results show that compared with YOLOv5l, the parameters of the improved lightweight model are reduced by 48.17 %, mAP is increased by 0.9 %, and the recognition speed is increased by 36 %. The improved model is more suitable to be deployed on the picking robot with limited computing power and provides a solution for the vision system of pear picking robot.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125247811","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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