Kai Li, Bingming Tang, Haiyang Li, Yunpeng Jin, Jieteng Jiang, L. Chunmei
The evaluation of grassland degradation is an important part of ecological conservation research, and rodent infestation is a significant factor in grassland degradation. The presence of a large number of mouseholes means that the environmental balance of grassland has been destroyed, so the coverage of mouseholes can be used as an evaluation method for grassland degradation levels. In this paper, the image segmentation method is used to segment the mousehole images, Upernet is used as the segmentation network, and Swin Transformer as the Backbone. FAM and FSM modules are added to the Upernet network to solve the target misalignment problem when upsampling the network. The mIoU is improved by 5.3% according to the experimental results.
{"title":"Upernet optimisation and application to mousehole segmentation","authors":"Kai Li, Bingming Tang, Haiyang Li, Yunpeng Jin, Jieteng Jiang, L. Chunmei","doi":"10.1117/12.2667353","DOIUrl":"https://doi.org/10.1117/12.2667353","url":null,"abstract":"The evaluation of grassland degradation is an important part of ecological conservation research, and rodent infestation is a significant factor in grassland degradation. The presence of a large number of mouseholes means that the environmental balance of grassland has been destroyed, so the coverage of mouseholes can be used as an evaluation method for grassland degradation levels. In this paper, the image segmentation method is used to segment the mousehole images, Upernet is used as the segmentation network, and Swin Transformer as the Backbone. FAM and FSM modules are added to the Upernet network to solve the target misalignment problem when upsampling the network. The mIoU is improved by 5.3% according to the experimental results.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128914420","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}
Aiming at the shortcomings of traditional disease recognition systems that require high shooting environment and large number of samples, this research designs a set of AR-assisted recognition schemes based on HOG-SVM. Under the premise of a small amount of material, due to the introduction of AR technology in the diagnostic system to assist shooting, this solution is better than other methods in terms of training time, recognition speed and average accuracy. Taking the Android terminal as an example, an AR-assisted HOG-SVM-based mobile vegetables disease identification system is implemented, which can quickly identify diseases and guide users to improve the quality of photographed pictures. Through the identification of disease spots in batches of images, the results of disease spot recognition are analyzed from the three aspects of disease accuracy, diseased leaf detection rate and disease spot location accuracy. Finally, AR technology and rapid identification scheme based on HOG-SVM are obtained. The combination can give faster training results and recognition results under the premise of small training samples. Its average accuracy is also higher than deep models such as YOLO v3, SSD 512, and Fast R-CNN. It is a more suitable method for disease identification on the current mobile terminal.
{"title":"AR-assisted intelligent analysis and identification system for mobile vegetables diseases based on HOG-SVM","authors":"Chao Ma, Linyi Li, Yunsheng Wang, Yong Liu, Shipu Xu","doi":"10.1117/12.2668866","DOIUrl":"https://doi.org/10.1117/12.2668866","url":null,"abstract":"Aiming at the shortcomings of traditional disease recognition systems that require high shooting environment and large number of samples, this research designs a set of AR-assisted recognition schemes based on HOG-SVM. Under the premise of a small amount of material, due to the introduction of AR technology in the diagnostic system to assist shooting, this solution is better than other methods in terms of training time, recognition speed and average accuracy. Taking the Android terminal as an example, an AR-assisted HOG-SVM-based mobile vegetables disease identification system is implemented, which can quickly identify diseases and guide users to improve the quality of photographed pictures. Through the identification of disease spots in batches of images, the results of disease spot recognition are analyzed from the three aspects of disease accuracy, diseased leaf detection rate and disease spot location accuracy. Finally, AR technology and rapid identification scheme based on HOG-SVM are obtained. The combination can give faster training results and recognition results under the premise of small training samples. Its average accuracy is also higher than deep models such as YOLO v3, SSD 512, and Fast R-CNN. It is a more suitable method for disease identification on the current mobile terminal.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127875878","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}
Aiming at the problem that it is difficult for the existing target detection algorithms to detect high-precision circuit experimental equipment in middle school, an improved YOLOX detection network model is proposed. Based on the YOLOX network model. Firstly, the ECA attention module is added to the feature extraction network to enhance the model's ability to perceive electrical experimental equipment; Secondly, the feature enhancement structure is added to enhance the semantic information of the obtained feature map and improve the detection ability of the target; Finally, EIoU is selected as the loss function to achieve high-precision positioning. The experimental results show that the improved network model mAP reaches 91.9%, which is 1.5% higher than the original network model, which proves that the improvement is effective and feasible.
{"title":"Detection of secondary school circuit experiment equipment based on improved YOLOX","authors":"Ming Liang, Lijiao Liu, Yuan Zhang","doi":"10.1117/12.2667268","DOIUrl":"https://doi.org/10.1117/12.2667268","url":null,"abstract":"Aiming at the problem that it is difficult for the existing target detection algorithms to detect high-precision circuit experimental equipment in middle school, an improved YOLOX detection network model is proposed. Based on the YOLOX network model. Firstly, the ECA attention module is added to the feature extraction network to enhance the model's ability to perceive electrical experimental equipment; Secondly, the feature enhancement structure is added to enhance the semantic information of the obtained feature map and improve the detection ability of the target; Finally, EIoU is selected as the loss function to achieve high-precision positioning. The experimental results show that the improved network model mAP reaches 91.9%, which is 1.5% higher than the original network model, which proves that the improvement is effective and feasible.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126735252","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}
Sentiment analysis is the technique of automatically evaluating and classifying emotions (often positive, negative, or neutral) from textual data, such as written comments and social media posts. Sentiment analysis is a subfield of natural language processing (NLP) that employs machine learning to classify the emotional tone of textual input. The fundamental model concentrates on positive, negative, and neutral categories, but it can also include the speaker's underlying emotions (pleasure, anger, insult, etc.) and purchase intents. Complexity is added to sentiment analysis by context. For example, consider the exclamation "Nothing!" Depending on whether or not the speaker enjoys the product, the meaning can vary significantly. In order for a machine to comprehend "I like it," it must be able to decipher the context and determine what "it" refers to. In addition, sarcasm and sarcasm can be tricky because the speaker may express a favorable sentiment while intending the opposite.
{"title":"Sentiment analysis of 2021 Canadian election tweets","authors":"Haojie Zhu","doi":"10.1117/12.2667211","DOIUrl":"https://doi.org/10.1117/12.2667211","url":null,"abstract":"Sentiment analysis is the technique of automatically evaluating and classifying emotions (often positive, negative, or neutral) from textual data, such as written comments and social media posts. Sentiment analysis is a subfield of natural language processing (NLP) that employs machine learning to classify the emotional tone of textual input. The fundamental model concentrates on positive, negative, and neutral categories, but it can also include the speaker's underlying emotions (pleasure, anger, insult, etc.) and purchase intents. Complexity is added to sentiment analysis by context. For example, consider the exclamation \"Nothing!\" Depending on whether or not the speaker enjoys the product, the meaning can vary significantly. In order for a machine to comprehend \"I like it,\" it must be able to decipher the context and determine what \"it\" refers to. In addition, sarcasm and sarcasm can be tricky because the speaker may express a favorable sentiment while intending the opposite.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121469266","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 proposes an indoor house structure mapping system, which mainly studies the SLAM positioning, laser depth binocular camera locally built figure, robot path planning, and other issues. And the three-dimensional indoor autonomous robot is built based on called VINS system. With wheel odometry and sensor fusion, this paper realizes the robot chassis indoor high-precision positioning, indoor autonomous robots scanning walls. Finally, through the integration of several modules, the chassis robot can achieve the function of indoor independent three-dimensional drawing. This system is successfully applied on a 4-wheel mecanum small car. I take a 20 square meters underground parking lot in as the experiment area. The test shows that the system of displacement drift error within 2% and the ratio of the overall path, room-built figure area error within 3%, loopback positioning error is less than 0.1 m, the robot can work independently to complete the building figure. In the end, the system is evaluated and a commercial indoor 3D drawing scheme is proposed.
{"title":"VIO-wheel-stereo mapping: an indoor room structure mapping system using monocular camera, IMU, wheel odometry and stereo","authors":"Xin Li, Kaibin Chu, Ji Zhang, Tingting Hao","doi":"10.1117/12.2667644","DOIUrl":"https://doi.org/10.1117/12.2667644","url":null,"abstract":"This paper proposes an indoor house structure mapping system, which mainly studies the SLAM positioning, laser depth binocular camera locally built figure, robot path planning, and other issues. And the three-dimensional indoor autonomous robot is built based on called VINS system. With wheel odometry and sensor fusion, this paper realizes the robot chassis indoor high-precision positioning, indoor autonomous robots scanning walls. Finally, through the integration of several modules, the chassis robot can achieve the function of indoor independent three-dimensional drawing. This system is successfully applied on a 4-wheel mecanum small car. I take a 20 square meters underground parking lot in as the experiment area. The test shows that the system of displacement drift error within 2% and the ratio of the overall path, room-built figure area error within 3%, loopback positioning error is less than 0.1 m, the robot can work independently to complete the building figure. In the end, the system is evaluated and a commercial indoor 3D drawing scheme is proposed.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133380293","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 recent years, facial landmark detection has assumed an important role in various fields. However, the current facial landmark detection algorithms are still lacking in recognition accuracy. In order to solve the above problem, this paper uses Ghost bottleneck to replace the original bottleneck on the basis of the original model of PFLD model, and adds and improves the CBAM attention mechanism. The improved PFLD model increases the ability of the model to extract facial landmark and improves the accuracy of the algorithm. The improved model has high accuracy and low parametric number and improves the accuracy of facial landmark detection. It also provides a new idea for facial landmark detection task.
{"title":"Research on facial landmark detection algorithm based on improved attention mechanism","authors":"Xinyi Cui, Tianwei Shi, Wenhua Cui, Ye Tao","doi":"10.1117/12.2667327","DOIUrl":"https://doi.org/10.1117/12.2667327","url":null,"abstract":"In recent years, facial landmark detection has assumed an important role in various fields. However, the current facial landmark detection algorithms are still lacking in recognition accuracy. In order to solve the above problem, this paper uses Ghost bottleneck to replace the original bottleneck on the basis of the original model of PFLD model, and adds and improves the CBAM attention mechanism. The improved PFLD model increases the ability of the model to extract facial landmark and improves the accuracy of the algorithm. The improved model has high accuracy and low parametric number and improves the accuracy of facial landmark detection. It also provides a new idea for facial landmark detection task.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"7 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132624341","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}
Many transformer modules, have been applied to computer vision. However, the transformer can extract the distal connections of human skeleton points and apply the attention mechanism to the problem of predicting human motion pose. We introduce a transformer module in the joint dimension. In addition, the Encoder module of the transformer is improved. Finally, our method achieves impressive results on benchmark datasets, including short- and long-term predictions of FNTU, confirming its effectiveness and efficiency.
{"title":"Skeleton-based human motion prediction via spatio and position encoding transformer network","authors":"Lingchao Mi, Rui Ding, Xiaodong Zhang","doi":"10.1117/12.2667366","DOIUrl":"https://doi.org/10.1117/12.2667366","url":null,"abstract":"Many transformer modules, have been applied to computer vision. However, the transformer can extract the distal connections of human skeleton points and apply the attention mechanism to the problem of predicting human motion pose. We introduce a transformer module in the joint dimension. In addition, the Encoder module of the transformer is improved. Finally, our method achieves impressive results on benchmark datasets, including short- and long-term predictions of FNTU, confirming its effectiveness and efficiency.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114824124","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}
Lujia Li, Shenglin Geng, Mingquan Zhou, Suxin Song
Zhaling Lake is the first lake at the source of the Yellow River. The morphology of Zhaling Lake has changed frequently in recent decades. We conducted a visual analysis of Landsat remote sensing images and weather station observations in Maduo County from 1976-2021. We derived the space-time evolution of Zhaling Lake shoreline and area change trends. This paper obtained the basic characteristics such as the area and shoreline of Zhaling Lake by using water index and visual interpretation. Based on these basic characteristics, we calculated other shoreline characteristics. We analyze the morphological, meteorological, and other data characteristics of Zhaling Lake by visualization method. Research results show that the area of Zhaling Lake is highly variable. Its overall change shows a cyclic trend of recession-expansion-recession. A significant shrinkage of Zhaling Lake occurred from 1976 to 1986, from 544.177 km2 to 537.32 km2. Between 1986 and 1993, Zhaling Lake gradually expanded, adding 9.541 km2. During 1993-2019, the area of Zhaling Lake showed a continuous trend of increase. In 2019, Zhaling Lake reached the largest area of 555.368 km2 in nearly 45 years. During 2019-2021, Zhaling Lake begins to shrink slightly again. In 2021, Zhaling Lake's area shrinks to 549.41km2.
{"title":"Visualization of the dynamic changes of Zhaling Lake in the past 45 years","authors":"Lujia Li, Shenglin Geng, Mingquan Zhou, Suxin Song","doi":"10.1117/12.2667243","DOIUrl":"https://doi.org/10.1117/12.2667243","url":null,"abstract":"Zhaling Lake is the first lake at the source of the Yellow River. The morphology of Zhaling Lake has changed frequently in recent decades. We conducted a visual analysis of Landsat remote sensing images and weather station observations in Maduo County from 1976-2021. We derived the space-time evolution of Zhaling Lake shoreline and area change trends. This paper obtained the basic characteristics such as the area and shoreline of Zhaling Lake by using water index and visual interpretation. Based on these basic characteristics, we calculated other shoreline characteristics. We analyze the morphological, meteorological, and other data characteristics of Zhaling Lake by visualization method. Research results show that the area of Zhaling Lake is highly variable. Its overall change shows a cyclic trend of recession-expansion-recession. A significant shrinkage of Zhaling Lake occurred from 1976 to 1986, from 544.177 km2 to 537.32 km2. Between 1986 and 1993, Zhaling Lake gradually expanded, adding 9.541 km2. During 1993-2019, the area of Zhaling Lake showed a continuous trend of increase. In 2019, Zhaling Lake reached the largest area of 555.368 km2 in nearly 45 years. During 2019-2021, Zhaling Lake begins to shrink slightly again. In 2021, Zhaling Lake's area shrinks to 549.41km2.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127213674","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}
Changle Yu, Suofei Zhang, Yongfei Yi, H Zhao, Yanchen Ji
The current mobile office data transmission management involves the problems of image traffic and delay, which leads to the low efficiency of mobile office applications. Therefore, this paper proposes a new energy micro-training and mobile office application based on 5G. After standard processing of mobile office images, a communication model is built to transmit information by using the characteristics of the continuous and rapid decline of 5G peak bandwidth. The experimental data shows that the time consumed by the new method is controlled below 1s, which proves that the new energy micro-training and mobile office under 5G can handle higher computing and processing needs. Even if more files are superimposed, they can be transmitted quickly in a shorter time. The method in this paper can ensure that the data transmission has a good quality of network experience, which meets the needs of mobile offices.
{"title":"New energy micro training and mobile office application based on 5G","authors":"Changle Yu, Suofei Zhang, Yongfei Yi, H Zhao, Yanchen Ji","doi":"10.1117/12.2667347","DOIUrl":"https://doi.org/10.1117/12.2667347","url":null,"abstract":"The current mobile office data transmission management involves the problems of image traffic and delay, which leads to the low efficiency of mobile office applications. Therefore, this paper proposes a new energy micro-training and mobile office application based on 5G. After standard processing of mobile office images, a communication model is built to transmit information by using the characteristics of the continuous and rapid decline of 5G peak bandwidth. The experimental data shows that the time consumed by the new method is controlled below 1s, which proves that the new energy micro-training and mobile office under 5G can handle higher computing and processing needs. Even if more files are superimposed, they can be transmitted quickly in a shorter time. The method in this paper can ensure that the data transmission has a good quality of network experience, which meets the needs of mobile offices.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128801866","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}
Social media is a platform for people to share their lives and interact with others. Image sharing is an integral component. Privacy information will inevitably be compromised during the process of sharing whole photos. Sometimes, it is not even caused by the person who shares the image but the third party who forwards the image. In the majority of cases, we do not attempt to protect the entirety of the media; rather, we seek to protect the most crucial portion, whether it is the background or specific objects, which decreasing superfluous cryptography procedures. Unfortunately, current research on such field is extremely rare. In this research, we established a novel pixel-level image encryption technique relying on Panoptic FCN panoramic segmentation and chaos for client-intended images on social media sites. Our suggested technology is capable of automated picture encryption on either the whole images or user-selected areas, whether they are rectangular or irregular, which is suited for all region of interest (ROI) encryption. Relying on a novel coupled chaotic map, this universal new encryption method flattens the array of the image ROI into a series of pixels. The module of Panoptic FCN can be replaced by any other panoptic segmentation models which are stronger in performance. Statistical and cryptographic evaluations demonstrate that our technique preserves the high efficiency for practical applications.
{"title":"Region of interest image encryption method based on panoramic segmentation and a novel coupled chaotic map","authors":"Hangming Zhang, Hanping Hu","doi":"10.1117/12.2667339","DOIUrl":"https://doi.org/10.1117/12.2667339","url":null,"abstract":"Social media is a platform for people to share their lives and interact with others. Image sharing is an integral component. Privacy information will inevitably be compromised during the process of sharing whole photos. Sometimes, it is not even caused by the person who shares the image but the third party who forwards the image. In the majority of cases, we do not attempt to protect the entirety of the media; rather, we seek to protect the most crucial portion, whether it is the background or specific objects, which decreasing superfluous cryptography procedures. Unfortunately, current research on such field is extremely rare. In this research, we established a novel pixel-level image encryption technique relying on Panoptic FCN panoramic segmentation and chaos for client-intended images on social media sites. Our suggested technology is capable of automated picture encryption on either the whole images or user-selected areas, whether they are rectangular or irregular, which is suited for all region of interest (ROI) encryption. Relying on a novel coupled chaotic map, this universal new encryption method flattens the array of the image ROI into a series of pixels. The module of Panoptic FCN can be replaced by any other panoptic segmentation models which are stronger in performance. Statistical and cryptographic evaluations demonstrate that our technique preserves the high efficiency for practical applications.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127112351","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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