Procrastination is a widespread phenomenon among people of all ages. With the advent of the epidemic era, home-based lifestyles have exacerbated procrastination of procrastinators. Long-term procrastination can have a very negative impact on the life and psychology of procrastinators, and in serious cases, it will lead to psychological disorders such as depression and anxiety. Procrastination is mainly caused by psychological and environmental factors. At present, most procrastination designs are based on environmental restrictions, such as restrictions on the use of mobile phones in time management applications. This type of design only restricts the procrastination behavior of users, but does not change the user’s procrastination psychology, leading to rejection of products and lack of sustainability. The purpose of this study is to solve the problem of procrastination by designing from the psychological level, guide users to conduct autonomous time management by expressing different time experiences, and provide more possibilities for users’ time management. Based on regression analysis on survey data, this study analysed the correlation between the degree of delay and the overall time perception intensity and different types of time perception and refer to the perception of time that is less prone to procrastination. Furthermore, we consider how to change the user’s time perception through design means to guide the user to be in the time perception that is not easy to delay. Finally, the design elements that can effectively change the user’s time perception are extracted through reference, and applied to the subsequent interactive product design. This study innovatively applies the theories of procrastination and time perception to interactive product design, providing new ideas for the future research and design on procrastination.
{"title":"Research on interactive product design of procrastination patients","authors":"Jiayuan Lyu, Taiwen Zhang","doi":"10.1117/12.2668227","DOIUrl":"https://doi.org/10.1117/12.2668227","url":null,"abstract":"Procrastination is a widespread phenomenon among people of all ages. With the advent of the epidemic era, home-based lifestyles have exacerbated procrastination of procrastinators. Long-term procrastination can have a very negative impact on the life and psychology of procrastinators, and in serious cases, it will lead to psychological disorders such as depression and anxiety. Procrastination is mainly caused by psychological and environmental factors. At present, most procrastination designs are based on environmental restrictions, such as restrictions on the use of mobile phones in time management applications. This type of design only restricts the procrastination behavior of users, but does not change the user’s procrastination psychology, leading to rejection of products and lack of sustainability. The purpose of this study is to solve the problem of procrastination by designing from the psychological level, guide users to conduct autonomous time management by expressing different time experiences, and provide more possibilities for users’ time management. Based on regression analysis on survey data, this study analysed the correlation between the degree of delay and the overall time perception intensity and different types of time perception and refer to the perception of time that is less prone to procrastination. Furthermore, we consider how to change the user’s time perception through design means to guide the user to be in the time perception that is not easy to delay. Finally, the design elements that can effectively change the user’s time perception are extracted through reference, and applied to the subsequent interactive product design. This study innovatively applies the theories of procrastination and time perception to interactive product design, providing new ideas for the future research and design on procrastination.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134215093","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}
At present, gesture has become an important channel of human-computer interaction, and gesture recognition has been widely used in various fields. In this paper, the dynamic gesture recognition technology is studied from algorithm and system implementation for portable devices which require high real-time performance. The algorithm mainly uses the region of interest extraction based on face recognition, skin color detection based on HCrCg color space and gesture motion track marking based on scanline seed filling algorithm. The system is implemented by Xilinx ZYNQ, and a SOPC system architecture based on ARM Cortex-A9 hard core and ARM Cortex-M3 soft core and FPGA is proposed. The scanline seed filling algorithm with long running time is designed as a hardware accelerator to improve the running speed. Through the test of the prototype, the recognition accuracy can reach 95.75% in a simple background and 90.83% in a complex background. The average running time of the system is only 0.68 seconds, which is more than 30% faster than using pure software method. The system has good performance in recognition accuracy and running speed.
{"title":"Research and implementation of dynamic gesture recognition system based on ZYNQ","authors":"J. Li, Qing-qiang Liu, Zengzhen Li, Wei Chen","doi":"10.1117/12.2667718","DOIUrl":"https://doi.org/10.1117/12.2667718","url":null,"abstract":"At present, gesture has become an important channel of human-computer interaction, and gesture recognition has been widely used in various fields. In this paper, the dynamic gesture recognition technology is studied from algorithm and system implementation for portable devices which require high real-time performance. The algorithm mainly uses the region of interest extraction based on face recognition, skin color detection based on HCrCg color space and gesture motion track marking based on scanline seed filling algorithm. The system is implemented by Xilinx ZYNQ, and a SOPC system architecture based on ARM Cortex-A9 hard core and ARM Cortex-M3 soft core and FPGA is proposed. The scanline seed filling algorithm with long running time is designed as a hardware accelerator to improve the running speed. Through the test of the prototype, the recognition accuracy can reach 95.75% in a simple background and 90.83% in a complex background. The average running time of the system is only 0.68 seconds, which is more than 30% faster than using pure software method. The system has good performance in recognition accuracy and running speed.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131533233","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}
Smart industrial parks are an important part of smart cities and building an information platform using the Internet of Things and cloud computing has become the development direction of smart industrial parks. According to the current needs of related users in the smart industrial park, the architecture of the information system of the smart industrial park was proposed. The design scheme realized the functions of intelligent analysis, interconnection and optimal decision-making of the smart industrial park. And based on the Internet of Things, the application in the smart industrial park was introduced to provide guidance for the improvement of the intelligence level of the smart industrial park.
{"title":"Design and application of smart industrial park based on Internet of Things technology","authors":"Yixin Yang","doi":"10.1117/12.2667469","DOIUrl":"https://doi.org/10.1117/12.2667469","url":null,"abstract":"Smart industrial parks are an important part of smart cities and building an information platform using the Internet of Things and cloud computing has become the development direction of smart industrial parks. According to the current needs of related users in the smart industrial park, the architecture of the information system of the smart industrial park was proposed. The design scheme realized the functions of intelligent analysis, interconnection and optimal decision-making of the smart industrial park. And based on the Internet of Things, the application in the smart industrial park was introduced to provide guidance for the improvement of the intelligence level of the smart industrial park.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"2018 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132977895","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 the process of autonomous driving, there will be missed detections and false detections caused by dense crowds and occlusions during pedestrian target detection. This paper proposes a pedestrian object detection network model that combines Swin Transformer and YOLOv3. First use the lightweight Swin Transformer Tiny to replace the original Darknet53 as the backbone network of YOLOv3. The multi-scale detection is realized through the self-attention hierarchical network, which optimizes the detection effect in the case of dense pedestrians. Secondly, to deal with the occlusion in the crowd, Focal-EIoU Loss is used as a new loss function. I Introduce edge length loss and Focal L1 loss to increase the loss and gradient of IoU, thereby improving the regression accuracy. Finally, experiments are performed on the Caltech dataset. The experimental results show that the precision on the Caltech dataset reaches 95.23% and the recall rate reaches 89.57%. Compared with the original YOLOv3 algorithm, the precision is increased by 3.22%, and the recall rate is increased by 4.35%. The effectiveness of the algorithm is verified, and the performance of pedestrian detection is greatly improved.
{"title":"Research on pedestrian targe detection based on deep learning","authors":"Hansong Wang, Quan Liang","doi":"10.1117/12.2667364","DOIUrl":"https://doi.org/10.1117/12.2667364","url":null,"abstract":"In the process of autonomous driving, there will be missed detections and false detections caused by dense crowds and occlusions during pedestrian target detection. This paper proposes a pedestrian object detection network model that combines Swin Transformer and YOLOv3. First use the lightweight Swin Transformer Tiny to replace the original Darknet53 as the backbone network of YOLOv3. The multi-scale detection is realized through the self-attention hierarchical network, which optimizes the detection effect in the case of dense pedestrians. Secondly, to deal with the occlusion in the crowd, Focal-EIoU Loss is used as a new loss function. I Introduce edge length loss and Focal L1 loss to increase the loss and gradient of IoU, thereby improving the regression accuracy. Finally, experiments are performed on the Caltech dataset. The experimental results show that the precision on the Caltech dataset reaches 95.23% and the recall rate reaches 89.57%. Compared with the original YOLOv3 algorithm, the precision is increased by 3.22%, and the recall rate is increased by 4.35%. The effectiveness of the algorithm is verified, and the performance of pedestrian detection is greatly improved.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133704785","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}
Human activities are closely related to geographic location. It is proposed to combine spatial location and mobile terminal pose sensor data with meeting the characteristics of real-time accuracy and flexibility in outdoor mobile augmented reality and to realize the virtual-real superposition through the transformation relationship between 3D model coordinate system, world coordinate system, camera coordinate system, image coordinate system, and pixel coordinate system. For the limitations of the vision-based registration method in outdoor scenes, this paper derives the transformation from spatial location data to screen coordinates in detail. It gives the solution and optimization of the transformation matrix and parameters.
{"title":"Spatial location-based outdoor mobile augmented reality 3D registration technology","authors":"Qian Zhou, Qing Wang, Qiang Zhong, Mao Han","doi":"10.1117/12.2667317","DOIUrl":"https://doi.org/10.1117/12.2667317","url":null,"abstract":"Human activities are closely related to geographic location. It is proposed to combine spatial location and mobile terminal pose sensor data with meeting the characteristics of real-time accuracy and flexibility in outdoor mobile augmented reality and to realize the virtual-real superposition through the transformation relationship between 3D model coordinate system, world coordinate system, camera coordinate system, image coordinate system, and pixel coordinate system. For the limitations of the vision-based registration method in outdoor scenes, this paper derives the transformation from spatial location data to screen coordinates in detail. It gives the solution and optimization of the transformation matrix and parameters.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133475376","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}
Powered exoskeletons are a kind of wearable robotics system attached outside the limbs, providing additional force for the limbs, which plays an important role in limb rehabilitation and patient assistance. As the patient gradually recovers, the patient’s gait will change over time. Therefore, the author hopes to get real-time gait data from patients in order to provide medical guidance and help the patients recover better. Inspired by the real-time monitoring of industrial robots, the author puts forward a method of medical monitoring using digital twin onto powered exoskeletons. For cost reasons, the author uses commercially available sensors to build this system. Further, the author fabricates a demonstration system for the exoskeleton to achieve this goal together with collecting and analyzing the data.
{"title":"A method for patient gait real-time monitoring based on powered exoskeleton and digital twin","authors":"Wei Huanxia","doi":"10.1117/12.2667794","DOIUrl":"https://doi.org/10.1117/12.2667794","url":null,"abstract":"Powered exoskeletons are a kind of wearable robotics system attached outside the limbs, providing additional force for the limbs, which plays an important role in limb rehabilitation and patient assistance. As the patient gradually recovers, the patient’s gait will change over time. Therefore, the author hopes to get real-time gait data from patients in order to provide medical guidance and help the patients recover better. Inspired by the real-time monitoring of industrial robots, the author puts forward a method of medical monitoring using digital twin onto powered exoskeletons. For cost reasons, the author uses commercially available sensors to build this system. Further, the author fabricates a demonstration system for the exoskeleton to achieve this goal together with collecting and analyzing the data.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"31 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132089669","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 study, we provide an approach named TRFit for unstructured 3D point cloud normal estimation. It handles noise and uneven densities point clouds well. Recently, learning-based normal estimation methods have significantly outperformed traditional methods on benchmark normal estimation datasets. In order to estimate normals, they frequently employed neural networks to learn point-wise weights for weighted least squares polynomial surfaces fitting. However, existing methods often ignore local geometric relationships, which will make the fitted surface significantly different from the real. To this end, we propose to use graph convolutional to learn local structural information. Meanwhile, we suggest the Geometric Relation Transformer (GRT), a transformer-based scale aggregation module, to fully utilize points from various neighborhood sizes. It can adaptively capture the relations between different regions. We achieve state-of-the-art results on the baseline normal estimation dataset, and experimental results show that TRFit obviously improves the accuracy of normal estimates, preserves their details. Moreover, it exhibits robustness to noise, density variations, and outliers. Besides, we demonstrate its application to surface reconstruction and denoising.
{"title":"TRFit: learning 3D point cloud normal estimation with transformer","authors":"Hongwen Liu, Yufeng Wang, Z. Ma","doi":"10.1117/12.2667307","DOIUrl":"https://doi.org/10.1117/12.2667307","url":null,"abstract":"In this study, we provide an approach named TRFit for unstructured 3D point cloud normal estimation. It handles noise and uneven densities point clouds well. Recently, learning-based normal estimation methods have significantly outperformed traditional methods on benchmark normal estimation datasets. In order to estimate normals, they frequently employed neural networks to learn point-wise weights for weighted least squares polynomial surfaces fitting. However, existing methods often ignore local geometric relationships, which will make the fitted surface significantly different from the real. To this end, we propose to use graph convolutional to learn local structural information. Meanwhile, we suggest the Geometric Relation Transformer (GRT), a transformer-based scale aggregation module, to fully utilize points from various neighborhood sizes. It can adaptively capture the relations between different regions. We achieve state-of-the-art results on the baseline normal estimation dataset, and experimental results show that TRFit obviously improves the accuracy of normal estimates, preserves their details. Moreover, it exhibits robustness to noise, density variations, and outliers. Besides, we demonstrate its application to surface reconstruction and denoising.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134646231","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}
This paper designs an intelligent blind people guide cane based on the analysis of basic functions of conventional guide canes. The hardware components of the cane design includes environmental monitoring modules, acoustic distance measurement module, vibration alert module and GPS positioning module etc. The system can provide real-time alerting of obstacles ahead and route selection through analyzing road conditions by means of GPS positioning module. The cane’s hardware system is designed with monitoring and analysis software to enhance the user’s using experience, while ensuring the functions of user security.
{"title":"An intelligent blind people guide cane design based on Arduino","authors":"Chang Liu, Hui Mao","doi":"10.1117/12.2667423","DOIUrl":"https://doi.org/10.1117/12.2667423","url":null,"abstract":"This paper designs an intelligent blind people guide cane based on the analysis of basic functions of conventional guide canes. The hardware components of the cane design includes environmental monitoring modules, acoustic distance measurement module, vibration alert module and GPS positioning module etc. The system can provide real-time alerting of obstacles ahead and route selection through analyzing road conditions by means of GPS positioning module. The cane’s hardware system is designed with monitoring and analysis software to enhance the user’s using experience, while ensuring the functions of user security.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115624553","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}
Yi Huang, Yuhang Zhao, Yulong Han, Bin Zhong, YangFan Lao, Di Wu
Exploration and development in the far-reaching sea of the South China Sea faces many challenges, such as great difficulty in well control. Once an oil spill accident occurs, it is necessary to deal with the oil spill from submarine wells in time to avoid major environmental pollution. In order to study the spreading range of submarine blowout oil and its influencing factors, this paper uses the Fluent platform, combining with the standard 𝑘െ𝜀 turbulence model and the multiphase flow VOF model, to preliminarily establish a submarine blowout oil spill model. This paper also analyzes the influence of ocean current velocity, blowout velocity, oil spill density on underwater migration trajectory and oil spill diffusion range. Numerical simulation results show that under the conditions of higher ocean current velocity, lower blowout velocity and greater oil spill density, the oil has a long underwater migration time, and the position where the oil first floats to the sea surface is far away from the wellhead level and will cause a greater range of oil spill pollution. This research is of practical significance for the rescue and rescue of blowouts and the emergency prediction and disposal of oil spills on deep water offshore platforms.
{"title":"Research on numerical simulation of deep seabed blowout and oil spill range","authors":"Yi Huang, Yuhang Zhao, Yulong Han, Bin Zhong, YangFan Lao, Di Wu","doi":"10.1117/12.2667433","DOIUrl":"https://doi.org/10.1117/12.2667433","url":null,"abstract":"Exploration and development in the far-reaching sea of the South China Sea faces many challenges, such as great difficulty in well control. Once an oil spill accident occurs, it is necessary to deal with the oil spill from submarine wells in time to avoid major environmental pollution. In order to study the spreading range of submarine blowout oil and its influencing factors, this paper uses the Fluent platform, combining with the standard 𝑘െ𝜀 turbulence model and the multiphase flow VOF model, to preliminarily establish a submarine blowout oil spill model. This paper also analyzes the influence of ocean current velocity, blowout velocity, oil spill density on underwater migration trajectory and oil spill diffusion range. Numerical simulation results show that under the conditions of higher ocean current velocity, lower blowout velocity and greater oil spill density, the oil has a long underwater migration time, and the position where the oil first floats to the sea surface is far away from the wellhead level and will cause a greater range of oil spill pollution. This research is of practical significance for the rescue and rescue of blowouts and the emergency prediction and disposal of oil spills on deep water offshore platforms.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114306864","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}
Finding contours of interest from medical images is an important task in the field of medical image analysis. The current deep learning-based image segmentation approaches have obtained promising results. However, most of these models do not take into account the anisotropy and asymmetric features which play an important role in describing the target contours. In order to address this issue, we propose new loss-function applied to the deep learning model with dense distance regression, which can benefit the edge-based features, thus able to improve the stability of the segmentation procedure and to reduce the probability of outliers in the segmentation results. The introduced loss function is embedded into the deep learning model, which can perform an end-to-end image segmentation procedure for medical images. Ablation experiments were done with other loss functions and three datasets were used to verify whether this loss function is effective. SOTA results were obtained for the proposed loss function in this paper compared to the recently designed method for reducing the boundary error.
{"title":"Learning anisotropy and asymmetry geometric features for medical image segmentation","authors":"Ankun Li, Li Liu","doi":"10.1117/12.2667319","DOIUrl":"https://doi.org/10.1117/12.2667319","url":null,"abstract":"Finding contours of interest from medical images is an important task in the field of medical image analysis. The current deep learning-based image segmentation approaches have obtained promising results. However, most of these models do not take into account the anisotropy and asymmetric features which play an important role in describing the target contours. In order to address this issue, we propose new loss-function applied to the deep learning model with dense distance regression, which can benefit the edge-based features, thus able to improve the stability of the segmentation procedure and to reduce the probability of outliers in the segmentation results. The introduced loss function is embedded into the deep learning model, which can perform an end-to-end image segmentation procedure for medical images. Ablation experiments were done with other loss functions and three datasets were used to verify whether this loss function is effective. SOTA results were obtained for the proposed loss function in this paper compared to the recently designed method for reducing the boundary error.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114357025","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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