Pub Date : 2024-07-25DOI: 10.54254/2755-2721/79/20241297
Ailien Liu
This paper delves into the realm of Artificial Intelligence (AI) and its transformative impact on board games, with a particular focus on chess and Go. In the domain of Go, it traces the evolution from AlphaGos historic victory over Lee Sedol to the groundbreaking AlphaGo Zero and Alpha Zero models. This survey explores the fundamental neural network architectures and reinforcement learning techniques employed in board games, ushering AI to new heights in mastering these intricate games. Furthermore, it introduces the chess AI developed by DeepMind, shedding light on the cutting-edge advancements in AI-driven board game strategies. This comprehensive examination highlights the profound influence of AI in reshaping the landscape of board games and sets the stage for further research and innovation in this exciting field.
本文深入探讨了人工智能（AI）领域及其对棋类游戏的变革性影响，尤其关注国际象棋和围棋。在围棋领域，本文追溯了从 AlphaGos 历史性地战胜李世石到开创性的 AlphaGo Zero 和 Alpha Zero 模型的演变过程。本研究探讨了棋类游戏中采用的基本神经网络架构和强化学习技术，将人工智能在掌握这些复杂游戏方面推向了新的高度。此外，它还介绍了 DeepMind 开发的国际象棋人工智能，揭示了人工智能驱动的棋类游戏策略的前沿进展。这一全面的研究凸显了人工智能在重塑棋类游戏格局方面的深远影响，并为这一激动人心的领域的进一步研究和创新奠定了基础。
{"title":"AI techniques in board game: A survey","authors":"Ailien Liu","doi":"10.54254/2755-2721/79/20241297","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241297","url":null,"abstract":"This paper delves into the realm of Artificial Intelligence (AI) and its transformative impact on board games, with a particular focus on chess and Go. In the domain of Go, it traces the evolution from AlphaGos historic victory over Lee Sedol to the groundbreaking AlphaGo Zero and Alpha Zero models. This survey explores the fundamental neural network architectures and reinforcement learning techniques employed in board games, ushering AI to new heights in mastering these intricate games. Furthermore, it introduces the chess AI developed by DeepMind, shedding light on the cutting-edge advancements in AI-driven board game strategies. This comprehensive examination highlights the profound influence of AI in reshaping the landscape of board games and sets the stage for further research and innovation in this exciting field.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"58 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804976","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: 10.54254/2755-2721/79/20241329
Hongbo Liu, Siyi Li, Zheng Yu
This research centers on predicting drug-drug interactions (DDIs) using a novel approach involving graph neural networks (GNNs) with integrated attention mechanisms. In this method, drugs and proteins are depicted as nodes within a heterogeneous graph. This graph is characterized by different types of edges symbolizing not only DDIs but also drug-protein interactions (DPIs) and protein-protein interactions (PPIs). To analyze the chemical structures of drugs, we employ a pretrained model named ChemBERTa, which utilizes simplified molecular input line entry system (SMILES) strings. The similarity between drug structures based on their SMILES strings is determined using the RDkit tool. Our model is designed to establish and link heterogeneous graph neural networks, taking into account the DPIs and PPIs as key input data. For the final prediction of interaction types between various drugs, we use the Multi-Layer Perception (MLP) technique. This model's primary objective is to enhance the accuracy of DDI predictions by factoring in additional data on both drug-protein and protein-protein interactions. The forecasted DDIs are presented with associated probabilities, offering valuable insights to healthcare professionals. These insights are crucial for assessing the potential risks and advantages of combining different drugs, particularly for patients with diseases at different stages of progression.
{"title":"Predicting drug-drug interactions using heterogeneous graph neural networks: HGNN-DDI","authors":"Hongbo Liu, Siyi Li, Zheng Yu","doi":"10.54254/2755-2721/79/20241329","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241329","url":null,"abstract":"This research centers on predicting drug-drug interactions (DDIs) using a novel approach involving graph neural networks (GNNs) with integrated attention mechanisms. In this method, drugs and proteins are depicted as nodes within a heterogeneous graph. This graph is characterized by different types of edges symbolizing not only DDIs but also drug-protein interactions (DPIs) and protein-protein interactions (PPIs). To analyze the chemical structures of drugs, we employ a pretrained model named ChemBERTa, which utilizes simplified molecular input line entry system (SMILES) strings. The similarity between drug structures based on their SMILES strings is determined using the RDkit tool. Our model is designed to establish and link heterogeneous graph neural networks, taking into account the DPIs and PPIs as key input data. For the final prediction of interaction types between various drugs, we use the Multi-Layer Perception (MLP) technique. This model's primary objective is to enhance the accuracy of DDI predictions by factoring in additional data on both drug-protein and protein-protein interactions. The forecasted DDIs are presented with associated probabilities, offering valuable insights to healthcare professionals. These insights are crucial for assessing the potential risks and advantages of combining different drugs, particularly for patients with diseases at different stages of progression.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"53 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805658","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 : 2024-07-23DOI: 10.54254/2755-2721/78/20240379
Xiudong Ren
The concept of renewable materials has received extensive advocacy and promotion in the ongoing development of the construction industry. Furthermore, emerging renewable building materials, such as bio-based composite materials, are continually evolving and gradually being incorporated into civil engineering applications. This paper focuses on the application and performance assessment of recycled building materials in civil engineering. As a crucial component of environmental protection and sustainable development, recycled building materials possess vast potential for application. In this article, the characteristics of common materials such as recycled concrete, recycled steel, and recycled glass are introduced, and their mechanical performance, durability, and other vital attributes are evaluated. Finally, the feasibility and challenges of recycled building materials are analyzed, along with discussions on sustainable development strategies and policy support. The comprehensive research results demonstrate the significant role of recycled building materials in promoting sustainable development in civil engineering, warranting increased support and promotion at the policy and societal levels.
{"title":"Application and performance evaluation of recycled building materials in civil engineering","authors":"Xiudong Ren","doi":"10.54254/2755-2721/78/20240379","DOIUrl":"https://doi.org/10.54254/2755-2721/78/20240379","url":null,"abstract":"The concept of renewable materials has received extensive advocacy and promotion in the ongoing development of the construction industry. Furthermore, emerging renewable building materials, such as bio-based composite materials, are continually evolving and gradually being incorporated into civil engineering applications. This paper focuses on the application and performance assessment of recycled building materials in civil engineering. As a crucial component of environmental protection and sustainable development, recycled building materials possess vast potential for application. In this article, the characteristics of common materials such as recycled concrete, recycled steel, and recycled glass are introduced, and their mechanical performance, durability, and other vital attributes are evaluated. Finally, the feasibility and challenges of recycled building materials are analyzed, along with discussions on sustainable development strategies and policy support. The comprehensive research results demonstrate the significant role of recycled building materials in promoting sustainable development in civil engineering, warranting increased support and promotion at the policy and societal levels.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"107 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812428","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 : 2024-07-23DOI: 10.54254/2755-2721/68/20241506
Runyu He, Anyu Ying, Xiaoyu Hu
OpenDevin, a code generation AI tool, has emerged as a powerful assistant for both technical and non-technical users, offering a practical approach to coding challenges. Unlike traditional code generators that merely output code, OpenDevin excels by executing code directly in a console, allowing for immediate testing and verification. This functionality not only streamlines the coding process but also enhances learning and troubleshooting, making it accessible to a broader audience. In this project, we address several key challenges to improve OpenDevins effectiveness, especially in handling multi-round conversations and contextually relevant code generation. Our team identified and tackled two main challenges faced by OpenDevin: variety of input, and multi-step conversations. Through incorporating a series of functions to parse, summarize, and organize LLM agents memory logs, we significantly improved OpenDevin agents capabilities among a variety of tasks. The integration of efficient memory management led to a notable increase in accuracyfrom 44.4% to 88.9% in multi-round conversations, highlighting the importance of effective memory management in AI-powered coding tools. This report details our methodology, the challenges we faced, and the solutions we implemented, showcasing OpenDevins potential to revolutionize the way users from various backgrounds engage with coding tasks.
{"title":"Improving OpenDevin: Boosting code generation LLM through advanced memory management","authors":"Runyu He, Anyu Ying, Xiaoyu Hu","doi":"10.54254/2755-2721/68/20241506","DOIUrl":"https://doi.org/10.54254/2755-2721/68/20241506","url":null,"abstract":"OpenDevin, a code generation AI tool, has emerged as a powerful assistant for both technical and non-technical users, offering a practical approach to coding challenges. Unlike traditional code generators that merely output code, OpenDevin excels by executing code directly in a console, allowing for immediate testing and verification. This functionality not only streamlines the coding process but also enhances learning and troubleshooting, making it accessible to a broader audience. In this project, we address several key challenges to improve OpenDevins effectiveness, especially in handling multi-round conversations and contextually relevant code generation. Our team identified and tackled two main challenges faced by OpenDevin: variety of input, and multi-step conversations. Through incorporating a series of functions to parse, summarize, and organize LLM agents memory logs, we significantly improved OpenDevin agents capabilities among a variety of tasks. The integration of efficient memory management led to a notable increase in accuracyfrom 44.4% to 88.9% in multi-round conversations, highlighting the importance of effective memory management in AI-powered coding tools. This report details our methodology, the challenges we faced, and the solutions we implemented, showcasing OpenDevins potential to revolutionize the way users from various backgrounds engage with coding tasks.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"84 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812884","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 : 2024-07-23DOI: 10.54254/2755-2721/69/20241478
Zihao Jiang
With continuous artificial intelligence and computer graphics technology, virtual cities are receiving widespread attention as an essential digital twin technology, . The core issue of this study is how to choose appropriate neural networks and algorithms to build models to construct virtual cities. The research methods include literature search, research and improvement of deep learning algorithms, and exploration of multi-model combinations. The research conclusion shows that choosing appropriate neural networks and algorithms is the key to building high-quality virtual cities, and targeted improvement and optimization of deep learning algorithms can further improve the accuracy and efficiency of virtual city construction. The strategy of multi-model combination also shows its unique advantages. By integrating different neural networks and algorithms, people can fully utilize their advantages and compensate for each other's deficiencies. With the advancement of technology, more innovative methods and technologies will be applied to this, which will help to build a more realistic virtual world and promote the development and application of virtual cities.
{"title":"Exploration of virtual city construction and optimization based on deep learning","authors":"Zihao Jiang","doi":"10.54254/2755-2721/69/20241478","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241478","url":null,"abstract":"With continuous artificial intelligence and computer graphics technology, virtual cities are receiving widespread attention as an essential digital twin technology, . The core issue of this study is how to choose appropriate neural networks and algorithms to build models to construct virtual cities. The research methods include literature search, research and improvement of deep learning algorithms, and exploration of multi-model combinations. The research conclusion shows that choosing appropriate neural networks and algorithms is the key to building high-quality virtual cities, and targeted improvement and optimization of deep learning algorithms can further improve the accuracy and efficiency of virtual city construction. The strategy of multi-model combination also shows its unique advantages. By integrating different neural networks and algorithms, people can fully utilize their advantages and compensate for each other's deficiencies. With the advancement of technology, more innovative methods and technologies will be applied to this, which will help to build a more realistic virtual world and promote the development and application of virtual cities.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"134 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810828","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 : 2024-07-23DOI: 10.54254/2755-2721/69/20241489
Hao Wang, Zhengyu Li, Jianwei Li
In this paper, target detection of car images in roads is performed based on the YOLOv8 model of YOLO family of models, which improves the accuracy and generalisation of the target detection task by combining multi-scale prediction, CSPNet structure and optimisation techniques such as BoF and BoS. The input images contain five types of vehicles such as Ambulance, Bus, Car, Motorcycle and Truck, which are analysed and learnt to have a classification accuracy of 75.4% on Ambulance, 53.5% on Bus, 55.1% on Car, 51.1% on Motorcycle and 42.5% on Truck. Despite the gap in specific classification accuracy, the YOLOv8 model can detect 100% of vehicles on the road, demonstrating good target detection capability. This research is of great significance for improving road traffic safety, intelligent traffic management, and the development of future autonomous driving technology. By optimising the deep learning model to achieve more accurate and efficient vehicle target detection, it can help to improve road safety and traffic efficiency, and promote the progress of intelligent transportation systems.
{"title":"Road car image target detection and recognition based on YOLOv8 deep learning algorithm","authors":"Hao Wang, Zhengyu Li, Jianwei Li","doi":"10.54254/2755-2721/69/20241489","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241489","url":null,"abstract":"In this paper, target detection of car images in roads is performed based on the YOLOv8 model of YOLO family of models, which improves the accuracy and generalisation of the target detection task by combining multi-scale prediction, CSPNet structure and optimisation techniques such as BoF and BoS. The input images contain five types of vehicles such as Ambulance, Bus, Car, Motorcycle and Truck, which are analysed and learnt to have a classification accuracy of 75.4% on Ambulance, 53.5% on Bus, 55.1% on Car, 51.1% on Motorcycle and 42.5% on Truck. Despite the gap in specific classification accuracy, the YOLOv8 model can detect 100% of vehicles on the road, demonstrating good target detection capability. This research is of great significance for improving road traffic safety, intelligent traffic management, and the development of future autonomous driving technology. By optimising the deep learning model to achieve more accurate and efficient vehicle target detection, it can help to improve road safety and traffic efficiency, and promote the progress of intelligent transportation systems.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"17 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813801","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 : 2024-07-23DOI: 10.54254/2755-2721/78/20240537
Zhiling Xu, Zhuoqi Zhang, Rui Ren, Wenhao Wang
The single-phase on-line interactive uninterruptible power supply (UPS) designed in this paper consists of three circuits, namely, a single-phase power factor correction circuit, a single-phase inverter circuit, and a bi-directional DC/DC circuit, each of which contains a main circuit as well as a control circuit. The principle, parameter design and control circuit design of each circuit are briefly described. The simulation model of the UPS is constructed, and the simulation results in normal and abnormal utility power are given to verify the correctness of the design.
{"title":"Single-phase online interactive uninterruptible power supply design","authors":"Zhiling Xu, Zhuoqi Zhang, Rui Ren, Wenhao Wang","doi":"10.54254/2755-2721/78/20240537","DOIUrl":"https://doi.org/10.54254/2755-2721/78/20240537","url":null,"abstract":"The single-phase on-line interactive uninterruptible power supply (UPS) designed in this paper consists of three circuits, namely, a single-phase power factor correction circuit, a single-phase inverter circuit, and a bi-directional DC/DC circuit, each of which contains a main circuit as well as a control circuit. The principle, parameter design and control circuit design of each circuit are briefly described. The simulation model of the UPS is constructed, and the simulation results in normal and abnormal utility power are given to verify the correctness of the design.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"34 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141814116","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 : 2024-07-23DOI: 10.54254/2755-2721/69/20241458
Chengyuan Tang
This paper examines the application of artificial intelligence (AI) and big data in economic regulation within China and the United States, highlighting the differing approaches and outcomes. In China, the centralized governance structure allows for the swift and uniform implementation of AI-driven strategies, optimizing government strategies, and balancing economic growth with social equity. The National Development and Reform Commission (NDRC) and the People's Bank of China (PBOC) are key players in utilizing AI to forecast economic trends and stabilize the economy. Conversely, the U.S. employs a decentralized approach, with AI applications driven primarily by the private sector and academia. The Federal Reserve leverages AI for policy decisions, while private firms use predictive models to enhance market strategies. Big data analysis supports decision-making in both nations, but differing governance structures lead to unique challenges and benefits. This study compares the centralized and decentralized systems, assessing their impact on economic performance and policy flexibility. The findings provide insights into how AI and big data can be optimized for economic regulation, offering lessons for other countries in adopting these technologies.
{"title":"AI and big data in economic regulation: A comparative analysis of China and the United States","authors":"Chengyuan Tang","doi":"10.54254/2755-2721/69/20241458","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241458","url":null,"abstract":"This paper examines the application of artificial intelligence (AI) and big data in economic regulation within China and the United States, highlighting the differing approaches and outcomes. In China, the centralized governance structure allows for the swift and uniform implementation of AI-driven strategies, optimizing government strategies, and balancing economic growth with social equity. The National Development and Reform Commission (NDRC) and the People's Bank of China (PBOC) are key players in utilizing AI to forecast economic trends and stabilize the economy. Conversely, the U.S. employs a decentralized approach, with AI applications driven primarily by the private sector and academia. The Federal Reserve leverages AI for policy decisions, while private firms use predictive models to enhance market strategies. Big data analysis supports decision-making in both nations, but differing governance structures lead to unique challenges and benefits. This study compares the centralized and decentralized systems, assessing their impact on economic performance and policy flexibility. The findings provide insights into how AI and big data can be optimized for economic regulation, offering lessons for other countries in adopting these technologies.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"78 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812673","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 : 2024-07-23DOI: 10.54254/2755-2721/78/20240439
Yipeng Liu, Shengyuan Xiao, Yun Yang
Given the swift expansion of the worlds population and economy, the decline in environmental quality caused by global warming, frequent climate disasters, ecosystem damage, sea level rise and other problems makes the world energy structure is changing, which means many countries start to develop their new energy industry and technology in order to meet their growing demand for energy. Therefore, promoting the energy transformation and innovation has become a new focus, and photovoltaic (PV) power generation as a green energy source is the focus of attention. PV inverters are essential components of photovoltaic array systems since they are the principal equipment capable of converting the fluctuating DC voltage produced by solar panels into mains frequency alternating current. However, the leakage current problem appeared during its operation has become one of the most important focuses of electrical engineers in recent years. This paper takes three aspects which is topology, filter and modulation mode to discuss how to suppress common mode leakage current in inverters. This paper reviews the existing research results and looks forward to their future development trends, which provides a reference for the following research.
{"title":"Leakage current suppression methods for single-phase photovoltaic inverters","authors":"Yipeng Liu, Shengyuan Xiao, Yun Yang","doi":"10.54254/2755-2721/78/20240439","DOIUrl":"https://doi.org/10.54254/2755-2721/78/20240439","url":null,"abstract":"Given the swift expansion of the worlds population and economy, the decline in environmental quality caused by global warming, frequent climate disasters, ecosystem damage, sea level rise and other problems makes the world energy structure is changing, which means many countries start to develop their new energy industry and technology in order to meet their growing demand for energy. Therefore, promoting the energy transformation and innovation has become a new focus, and photovoltaic (PV) power generation as a green energy source is the focus of attention. PV inverters are essential components of photovoltaic array systems since they are the principal equipment capable of converting the fluctuating DC voltage produced by solar panels into mains frequency alternating current. However, the leakage current problem appeared during its operation has become one of the most important focuses of electrical engineers in recent years. This paper takes three aspects which is topology, filter and modulation mode to discuss how to suppress common mode leakage current in inverters. This paper reviews the existing research results and looks forward to their future development trends, which provides a reference for the following research.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"8 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810774","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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