In the contemporary era of data-driven processes, addressing the challenge of processing vast volumes of data has become a pressing concern. With the rapid advancement of computer science and information technology, data processing efficiency has significantly improved. Within this expansive domain, three prominent clustering techniquesnamely, K-Means clustering, spectral clustering, and Density-based spatial clustering of applications with noise (DBSCAN)have assumed pivotal roles due to their versatility and effectiveness. This essay embarks on a systematic examination of these three methods, deconstructing their fundamental principles and navigating through their practical applications.
{"title":"Analysis of clustering algorithms in Iris and breast cancer datasets","authors":"Jiasheng Chen, Changyou Jin, Hongyu Wang, Zixuan Huang, Jingxing Liang","doi":"10.54254/2755-2721/79/20241631","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241631","url":null,"abstract":"In the contemporary era of data-driven processes, addressing the challenge of processing vast volumes of data has become a pressing concern. With the rapid advancement of computer science and information technology, data processing efficiency has significantly improved. Within this expansive domain, three prominent clustering techniquesnamely, K-Means clustering, spectral clustering, and Density-based spatial clustering of applications with noise (DBSCAN)have assumed pivotal roles due to their versatility and effectiveness. This essay embarks on a systematic examination of these three methods, deconstructing their fundamental principles and navigating through their practical applications.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"53 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803957","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 : 2024-07-25DOI: 10.54254/2755-2721/69/20241477
Jingting Liu
Fully homomorphic encryption (FHE) in privacy-preserving machine learning (PPML) is a current area of research value, aiming to achieve the protection of users private data by applying the concept of full homomorphic encryption to machine learning privacy preservation. The integration of the two involves extensive model modifications and performance issues. The current difficulties mainly focus on how to improve encryption efficiency through hardware or software, and how to apply homomorphic encryption to neural network models such as RNN that process sequence data. This paper introduces this complex research field, outlines two machine learning service models (MLaas and AIaas) that are concerned by the industry, summarizes the most advanced research technologies based on these two models in recent years, and discusses the technical difficulties and future research directions. As a difficult problem that has never been overcome in cryptography in recent decades, homomorphic technology has received extensive attention from experts and scholars and ushered in new opportunities in the current explosive development of machine learning.
{"title":"Fully homomorphic encryption in PPMLAn review","authors":"Jingting Liu","doi":"10.54254/2755-2721/69/20241477","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241477","url":null,"abstract":"Fully homomorphic encryption (FHE) in privacy-preserving machine learning (PPML) is a current area of research value, aiming to achieve the protection of users private data by applying the concept of full homomorphic encryption to machine learning privacy preservation. The integration of the two involves extensive model modifications and performance issues. The current difficulties mainly focus on how to improve encryption efficiency through hardware or software, and how to apply homomorphic encryption to neural network models such as RNN that process sequence data. This paper introduces this complex research field, outlines two machine learning service models (MLaas and AIaas) that are concerned by the industry, summarizes the most advanced research technologies based on these two models in recent years, and discusses the technical difficulties and future research directions. As a difficult problem that has never been overcome in cryptography in recent decades, homomorphic technology has received extensive attention from experts and scholars and ushered in new opportunities in the current explosive development of machine learning.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805685","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 : 2024-07-25DOI: 10.54254/2755-2721/55/20241417
Yiwen Wang
Blockchain provides a decentralised, tamper-proof and trustworthy distributed database technology that is widely used in finance and economics, IoT and big data. Artificial intelligence (AI) provides a technology that can mimic human intelligence, learn autonomously and automate decision-making, which plays a major role in enhancing productivity, solving complex problems and improving decision-making. The two represent two of the major driving forces in technology today, and their integration is redefining our digital world. The aim of this paper is to explore the integration of these two technologies and the innovations, challenges, and future prospects they bring. First, we trace their history and evolution, introduce the basic characteristics of blockchain and AI, and explain in detail how they work. We then delve into the integration of blockchain and AI, highlighting their importance and significance in areas such as finance, supply chain and healthcare. We analyse the applications and implications of this integration for these areas, as well as the challenges and dilemmas faced, including issues of security, privacy, data leakage, and technical feasibility. Finally, we explore future trends and related work, highlighting the importance of global community collaboration and innovation to realize the potential of blockchain and AI.
{"title":"The integration of blockchain technology and artificial intelligence: Innovation, challenges, and future prospects","authors":"Yiwen Wang","doi":"10.54254/2755-2721/55/20241417","DOIUrl":"https://doi.org/10.54254/2755-2721/55/20241417","url":null,"abstract":"Blockchain provides a decentralised, tamper-proof and trustworthy distributed database technology that is widely used in finance and economics, IoT and big data. Artificial intelligence (AI) provides a technology that can mimic human intelligence, learn autonomously and automate decision-making, which plays a major role in enhancing productivity, solving complex problems and improving decision-making. The two represent two of the major driving forces in technology today, and their integration is redefining our digital world. The aim of this paper is to explore the integration of these two technologies and the innovations, challenges, and future prospects they bring. First, we trace their history and evolution, introduce the basic characteristics of blockchain and AI, and explain in detail how they work. We then delve into the integration of blockchain and AI, highlighting their importance and significance in areas such as finance, supply chain and healthcare. We analyse the applications and implications of this integration for these areas, as well as the challenges and dilemmas faced, including issues of security, privacy, data leakage, and technical feasibility. Finally, we explore future trends and related work, highlighting the importance of global community collaboration and innovation to realize the potential of blockchain and AI.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"34 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804395","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 : 2024-07-25DOI: 10.54254/2755-2721/69/20241521
Feng Yuan
The development of international trade depends to a large extent on the progress of international logistics. However, international logistics cannot exist independently of international trade. Without goods provided by international trade, international logistics loses its foundation. Therefore, in order to accurately assess the demand for international logistics, it is necessary to have a detailed understanding of the development of international trade and to predict its future trends accordingly. In this work, we utilize backpropagation neural networks to predict trends and needs in international trade logistics. Specifically, we build a multi-layer perceptron model, which selects a variety of input variables such as goods circulation, economic indicators, trade policies, and seasonal factors. By training this model, it is possible to effectively learn and capture the complex relationships that affect international trade logistics from historical data. In the experimental analysis, the model has been repeatedly trained and adjusted, and finally demonstrated high accuracy and reliability.
{"title":"Research on international trade logistics prediction based on back propagation neural network","authors":"Feng Yuan","doi":"10.54254/2755-2721/69/20241521","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241521","url":null,"abstract":"The development of international trade depends to a large extent on the progress of international logistics. However, international logistics cannot exist independently of international trade. Without goods provided by international trade, international logistics loses its foundation. Therefore, in order to accurately assess the demand for international logistics, it is necessary to have a detailed understanding of the development of international trade and to predict its future trends accordingly. In this work, we utilize backpropagation neural networks to predict trends and needs in international trade logistics. Specifically, we build a multi-layer perceptron model, which selects a variety of input variables such as goods circulation, economic indicators, trade policies, and seasonal factors. By training this model, it is possible to effectively learn and capture the complex relationships that affect international trade logistics from historical data. In the experimental analysis, the model has been repeatedly trained and adjusted, and finally demonstrated high accuracy and reliability.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"52 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805452","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 modern communication technology, people have higher and higher requirements for communication quality because the data transmission based on digital signal is better than the transmission of analog signal, so the transmission of digital signal becomes more and more important. DPSK, as an intermediate mode of digital modulation, has the advantages of high bandwidth utilization, low bit error rate and easier implementation, and has been widely concerned by people. This paper will compare DPSK with other digital modulation, analyze the advantages of DPSK and predict the future development prospects of DPSK.
{"title":"Advantages and development prospects of DPSK digital modulation","authors":"Jiaming Liang, Yuming You, Ruoheng Ma, Mingyuan Gao","doi":"10.54254/2755-2721/79/20241324","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241324","url":null,"abstract":"In modern communication technology, people have higher and higher requirements for communication quality because the data transmission based on digital signal is better than the transmission of analog signal, so the transmission of digital signal becomes more and more important. DPSK, as an intermediate mode of digital modulation, has the advantages of high bandwidth utilization, low bit error rate and easier implementation, and has been widely concerned by people. This paper will compare DPSK with other digital modulation, analyze the advantages of DPSK and predict the future development prospects of DPSK.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"52 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805771","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 : 2024-07-25DOI: 10.54254/2755-2721/69/20241509
Xianyi Chen
Conditional Generative Adversarial Networks (cGANs) have revolutionized digital art by enabling the creation of high-quality, artistically coherent images guided by conditional inputs. This paper examines key factors influencing the performance of cGANs, including the quality of training data, network architecture improvements, and loss function optimization. We introduce a mathematical model to quantify training data quality, emphasizing dataset diversity, data augmentation, and cleaning. Network architectural enhancements such as residual connections, attention mechanisms, and progressive growing are explored for their impact on image quality. Additionally, we discuss the integration of conditional inputs, such as labels and textual descriptions, for precise content control. Challenges in balancing realism with artistic expression, managing mode collapse, and interpreting conditional inputs are also addressed. This study provides a comprehensive framework for enhancing cGAN-generated artworks, offering insights into applications in personalized art generation, art restoration, and collaborative art projects.
{"title":"Leveraging Conditional Generative Adversarial Networks (cGANs) for enhanced artistic creation: Exploring quality improvement and content control through conditional inputs","authors":"Xianyi Chen","doi":"10.54254/2755-2721/69/20241509","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241509","url":null,"abstract":"Conditional Generative Adversarial Networks (cGANs) have revolutionized digital art by enabling the creation of high-quality, artistically coherent images guided by conditional inputs. This paper examines key factors influencing the performance of cGANs, including the quality of training data, network architecture improvements, and loss function optimization. We introduce a mathematical model to quantify training data quality, emphasizing dataset diversity, data augmentation, and cleaning. Network architectural enhancements such as residual connections, attention mechanisms, and progressive growing are explored for their impact on image quality. Additionally, we discuss the integration of conditional inputs, such as labels and textual descriptions, for precise content control. Challenges in balancing realism with artistic expression, managing mode collapse, and interpreting conditional inputs are also addressed. This study provides a comprehensive framework for enhancing cGAN-generated artworks, offering insights into applications in personalized art generation, art restoration, and collaborative art projects.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"50 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805815","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 : 2024-07-25DOI: 10.54254/2755-2721/79/20241398
Yilin Li, Zijie Tang, Miao Qin
This paper introduces in detail the performance comparative analysis of VGG and InceptionV3 based on CIFAR-100 data set in image classification tasks. The experimental results show that the InceptionV3 model performs best on the CIFAR-100 dataset, and its high accuracy and balanced classification effect are impressive. In contrast, the VGG model, while simple in structure, is slightly less accurate. Further analysis shows that InceptionV3 model has more advantages in feature extraction and fusion design, which makes it perform well in image classification tasks. Additionally, the paper explores the broader applications and future prospects of the studied models. By doing so, it provides valuable insights into potential research directions for model comparison. This comprehensive analysis serves as a benchmark, shedding light on the strengths and weaknesses of VGG and InceptionV3 models in image classification. It stands as a valuable reference for future developments in comparative model research.
{"title":"VGG and InceptionV3 model based on CIFAR data contrast analysis","authors":"Yilin Li, Zijie Tang, Miao Qin","doi":"10.54254/2755-2721/79/20241398","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241398","url":null,"abstract":"This paper introduces in detail the performance comparative analysis of VGG and InceptionV3 based on CIFAR-100 data set in image classification tasks. The experimental results show that the InceptionV3 model performs best on the CIFAR-100 dataset, and its high accuracy and balanced classification effect are impressive. In contrast, the VGG model, while simple in structure, is slightly less accurate. Further analysis shows that InceptionV3 model has more advantages in feature extraction and fusion design, which makes it perform well in image classification tasks. Additionally, the paper explores the broader applications and future prospects of the studied models. By doing so, it provides valuable insights into potential research directions for model comparison. This comprehensive analysis serves as a benchmark, shedding light on the strengths and weaknesses of VGG and InceptionV3 models in image classification. It stands as a valuable reference for future developments in comparative model research.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"41 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805834","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 : 2024-07-25DOI: 10.54254/2755-2721/69/20241494
Qi Shen
The integration of artificial intelligence (AI) in financial risk management systems has revolutionized traditional approaches, providing enhanced predictive capabilities and operational efficiency. This paper explores the various applications of AI in credit risk assessment, market risk analysis, operational risk management, and regulatory compliance. AI-driven systems leverage advanced machine learning algorithms to analyze vast datasets, including real-time market data and non-traditional sources, improving risk predictions and enabling proactive risk management. Scenario simulations, predictive modeling, real-time data analysis, and automated decision-making are discussed as core components of AI-driven systems. The paper also highlights the benefits of AI in automating routine tasks, enhancing data analytics, and ensuring regulatory compliance. By continuously learning and adapting to new data, AI systems offer dynamic risk management solutions that address evolving market conditions and regulatory requirements. This comprehensive analysis demonstrates how AI-driven financial risk management systems can significantly reduce the incidence of loan defaults, enhance portfolio quality, and improve the overall resilience of financial institutions.
{"title":"AI-driven financial risk management systems: Enhancing predictive capabilities and operational efficiency","authors":"Qi Shen","doi":"10.54254/2755-2721/69/20241494","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241494","url":null,"abstract":"The integration of artificial intelligence (AI) in financial risk management systems has revolutionized traditional approaches, providing enhanced predictive capabilities and operational efficiency. This paper explores the various applications of AI in credit risk assessment, market risk analysis, operational risk management, and regulatory compliance. AI-driven systems leverage advanced machine learning algorithms to analyze vast datasets, including real-time market data and non-traditional sources, improving risk predictions and enabling proactive risk management. Scenario simulations, predictive modeling, real-time data analysis, and automated decision-making are discussed as core components of AI-driven systems. The paper also highlights the benefits of AI in automating routine tasks, enhancing data analytics, and ensuring regulatory compliance. By continuously learning and adapting to new data, AI systems offer dynamic risk management solutions that address evolving market conditions and regulatory requirements. This comprehensive analysis demonstrates how AI-driven financial risk management systems can significantly reduce the incidence of loan defaults, enhance portfolio quality, and improve the overall resilience of financial institutions.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"16 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803005","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 : 2024-07-25DOI: 10.54254/2755-2721/69/20241462
Rui Qian
Target detection from the perspective of UAV has great potential in the field of urban regulation, limited by the dense small targets, severe environmental obstructions, camera shake, and changes in lighting conditions in the aerial view of drones, the existing object detection algorithms cannot effectively undertake this task. This paper introduces two lightweight feature extraction modules based on YOLOv5, which are C3-Faster with PConv and COT3 with transformer structure. Meanwhile, an extra small detection head is added to the output layer. These approaches enhance accuracy while maintaining the advantages of being lightweight and easy to deploy. The ablation experiments and comparative experiments are designed to verify the effectiveness of these modules. The algorithm presented in this paper can be deployed into embedded systems of small UAVs to assist UAVs in completing various regulatory tasks in complex urban scenarios.
{"title":"An effective object detection algorithm for UAV-based urban regulation","authors":"Rui Qian","doi":"10.54254/2755-2721/69/20241462","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241462","url":null,"abstract":"Target detection from the perspective of UAV has great potential in the field of urban regulation, limited by the dense small targets, severe environmental obstructions, camera shake, and changes in lighting conditions in the aerial view of drones, the existing object detection algorithms cannot effectively undertake this task. This paper introduces two lightweight feature extraction modules based on YOLOv5, which are C3-Faster with PConv and COT3 with transformer structure. Meanwhile, an extra small detection head is added to the output layer. These approaches enhance accuracy while maintaining the advantages of being lightweight and easy to deploy. The ablation experiments and comparative experiments are designed to verify the effectiveness of these modules. The algorithm presented in this paper can be deployed into embedded systems of small UAVs to assist UAVs in completing various regulatory tasks in complex urban scenarios.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"12 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804454","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 : 2024-07-25DOI: 10.54254/2755-2721/79/20241308
Borui Zhuang, Yuchen Zhang, Zhongyu Wang, Zixuan Liu
Due to 2023, over 200 million people worldwide are visually impaired. The needs of people with visual impairments receive scant attention in todays world. Most of them cannot walk independently on Crowded thoroughfares. There are still some challenges in developing assistive devices for the visually impaired. This paper focuses on a classification system within the earphone worn on the ear that can distinguish between different sounds and can be located by the Sharpless of the sound waves. The proposed method comprises two main modules: the first is to transfer the audio signals to Spectrograms, which is done in Python, and then a trained Convolutional Neural Network (CNN) is used in Matlab to identify different types of sounds. When tested in a real-life environment, this system proved useful and accurate in identifying dangerous signals. This innovation is intended to provide them with the optimal time to evacuate dangerous areas, ensuring their safety.
{"title":"Identify sound in raucous acoustic environment","authors":"Borui Zhuang, Yuchen Zhang, Zhongyu Wang, Zixuan Liu","doi":"10.54254/2755-2721/79/20241308","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241308","url":null,"abstract":"Due to 2023, over 200 million people worldwide are visually impaired. The needs of people with visual impairments receive scant attention in todays world. Most of them cannot walk independently on Crowded thoroughfares. There are still some challenges in developing assistive devices for the visually impaired. This paper focuses on a classification system within the earphone worn on the ear that can distinguish between different sounds and can be located by the Sharpless of the sound waves. The proposed method comprises two main modules: the first is to transfer the audio signals to Spectrograms, which is done in Python, and then a trained Convolutional Neural Network (CNN) is used in Matlab to identify different types of sounds. When tested in a real-life environment, this system proved useful and accurate in identifying dangerous signals. This innovation is intended to provide them with the optimal time to evacuate dangerous areas, ensuring their safety.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"47 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805084","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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