This research provides a framework for assessing risks in smart supply chains using a quantitative approach. This study identifies the risk factors in smart supply chains based on an extensive literature review and interviews with professionals. By analyzing different concepts of the previous frameworks, a new one is proposed for the smart supply chain. This new framework is applied to the data collected from a survey of Canadian supply chain professionals (n = 56). The authors conducted an exploratory factor analysis to examine the construct validity of the survey results. After evaluating and assessing risks for different smart supply chain risk factors, some constructs were developed. The survey's results point to the most important risk factors for the smart supply chain, prioritized based on their high probabilities and impacts. These include risk of complexity, web application failure, talent shortage, and high-cost risk. The results also show that the most commonly implemented smart technologies in the supply chain sector are bar codes and social media.
{"title":"A Framework for Smart Supply Chain Risk Assessment: An Empirical Study","authors":"K. Khan, A. Keramati","doi":"10.4018/ijisscm.316167","DOIUrl":"https://doi.org/10.4018/ijisscm.316167","url":null,"abstract":"This research provides a framework for assessing risks in smart supply chains using a quantitative approach. This study identifies the risk factors in smart supply chains based on an extensive literature review and interviews with professionals. By analyzing different concepts of the previous frameworks, a new one is proposed for the smart supply chain. This new framework is applied to the data collected from a survey of Canadian supply chain professionals (n = 56). The authors conducted an exploratory factor analysis to examine the construct validity of the survey results. After evaluating and assessing risks for different smart supply chain risk factors, some constructs were developed. The survey's results point to the most important risk factors for the smart supply chain, prioritized based on their high probabilities and impacts. These include risk of complexity, web application failure, talent shortage, and high-cost risk. The results also show that the most commonly implemented smart technologies in the supply chain sector are bar codes and social media.","PeriodicalId":44506,"journal":{"name":"International Journal of Information Systems and Supply Chain Management","volume":"32 6","pages":"1-17"},"PeriodicalIF":1.6,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72368165","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}
Youssef Tliche, A. Taghipour, Jomana Mahfod-Leroux, Mohammadali Vosooghidizaji
Downstream demand inference (DDI) emerged in the supply chain theory, allowing an upstream actor to infer the demand occurring at his formal downstream actor without need of information sharing. Literature showed that simultaneously minimizing the average inventory level and the bullwhip effect isn't possible. In this paper, the authors show that demand inference is not only possible between direct supply chain links, but also at any downstream level. The authors propose a bi-objective approach to reduce both performance indicators by adopting the genetic algorithm. Simulation results show that bullwhip effect can be reduced highly if specific configurations are selected from the Pareto frontier. Numerical results show that demand's time-series structure, lead-times, holding and shortage costs, don't affect the behaviour of the bullwhip effect indicator. Moreover, the sensitivity analysis show that the optimization approach is robust when faced to varied initializations. Finally, the authors conclude the paper with managerial implications in multi-level supply chains.
{"title":"Collaborative Bullwhip Effect-Oriented Bi-Objective Optimization for Inference-Based Weighted Moving Average Forecasting in Decentralized Supply Chain","authors":"Youssef Tliche, A. Taghipour, Jomana Mahfod-Leroux, Mohammadali Vosooghidizaji","doi":"10.4018/ijisscm.316168","DOIUrl":"https://doi.org/10.4018/ijisscm.316168","url":null,"abstract":"Downstream demand inference (DDI) emerged in the supply chain theory, allowing an upstream actor to infer the demand occurring at his formal downstream actor without need of information sharing. Literature showed that simultaneously minimizing the average inventory level and the bullwhip effect isn't possible. In this paper, the authors show that demand inference is not only possible between direct supply chain links, but also at any downstream level. The authors propose a bi-objective approach to reduce both performance indicators by adopting the genetic algorithm. Simulation results show that bullwhip effect can be reduced highly if specific configurations are selected from the Pareto frontier. Numerical results show that demand's time-series structure, lead-times, holding and shortage costs, don't affect the behaviour of the bullwhip effect indicator. Moreover, the sensitivity analysis show that the optimization approach is robust when faced to varied initializations. Finally, the authors conclude the paper with managerial implications in multi-level supply chains.","PeriodicalId":44506,"journal":{"name":"International Journal of Information Systems and Supply Chain Management","volume":"1 1","pages":"1-37"},"PeriodicalIF":1.6,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76599726","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}
Resource balance is one of the most critical concerns in the existing logistic domain within dynamic transport networks. Modern solutions are used to maximize demand and supply prediction in collaboration with these problems. However, the great difficulty of transportation networks, profound uncertainties of potential demand and availability, and non-convex market limits make conventional resource management main paths. Hence, this paper proposes an integrated deep reinforcement learning-based logistics management model (DELLMM) to increase and optimize the logistic distribution. An optimization approach can be used in inventors and price control applications. This research methodology gives the fundamentals of information retrieval and the scope of blockchain integration. The conceptual framework of use cases for an efficient logistic management system with blockchain has been discussed. This research designs the deep reinforcement learning system that can boost optimization and other business operations due to impressive improvements in generic self-learning algorithms for optimal management. Thus, the experimental results show that DELLMM improves logistics management and optimized distribution compared to other methods with the highest operability of 94.35%, latency reduction of 97.12%, efficiency of 98.01%, trust enhancement of 96.37%, and sustainability of 97.80%.
{"title":"Information Retrieval and Optimization in Distribution and Logistics Management Using Deep Reinforcement Learning","authors":"Li Yang, E. SathishkumarV., Adhiyaman Manickam","doi":"10.4018/ijisscm.316166","DOIUrl":"https://doi.org/10.4018/ijisscm.316166","url":null,"abstract":"Resource balance is one of the most critical concerns in the existing logistic domain within dynamic transport networks. Modern solutions are used to maximize demand and supply prediction in collaboration with these problems. However, the great difficulty of transportation networks, profound uncertainties of potential demand and availability, and non-convex market limits make conventional resource management main paths. Hence, this paper proposes an integrated deep reinforcement learning-based logistics management model (DELLMM) to increase and optimize the logistic distribution. An optimization approach can be used in inventors and price control applications. This research methodology gives the fundamentals of information retrieval and the scope of blockchain integration. The conceptual framework of use cases for an efficient logistic management system with blockchain has been discussed. This research designs the deep reinforcement learning system that can boost optimization and other business operations due to impressive improvements in generic self-learning algorithms for optimal management. Thus, the experimental results show that DELLMM improves logistics management and optimized distribution compared to other methods with the highest operability of 94.35%, latency reduction of 97.12%, efficiency of 98.01%, trust enhancement of 96.37%, and sustainability of 97.80%.","PeriodicalId":44506,"journal":{"name":"International Journal of Information Systems and Supply Chain Management","volume":"98 1","pages":"1-19"},"PeriodicalIF":1.6,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72952977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The automated deployment of the internet of things (IoT) and the human-machine interface provides the best advancement for dispersed warehouse scheduling management (WSM). In this paper, superior data systematic move toward warehouse scheduling management (WSM) has been suggested using the computational method to allow smart logistics. Furthermore, this paper introduces the human-machine interface framework (HMI) using IoT for collaborative warehouse order fulfillment. It consists of a layer of physical equipment, an ambient middleware network, a framework of multi-agents, and source planning. This approach is chosen to enhance the reaction capabilities of decentralized warehouse scheduling management in a dynamic environment. The simulation outcome has been performed, and the suggested method realizes a high product delivery ratio (96.5%), operational cost (94.9%), demand prediction ratio (96.5%), accuracy ratio (98.4%), and performance ratio (97.2%).
{"title":"Research on Logistic Warehouse Scheduling Management With IoT and Human-Machine Interface","authors":"Lanjing Wang, J. Daniel, Thanjai Vadivel","doi":"10.4018/ijisscm.305846","DOIUrl":"https://doi.org/10.4018/ijisscm.305846","url":null,"abstract":"The automated deployment of the internet of things (IoT) and the human-machine interface provides the best advancement for dispersed warehouse scheduling management (WSM). In this paper, superior data systematic move toward warehouse scheduling management (WSM) has been suggested using the computational method to allow smart logistics. Furthermore, this paper introduces the human-machine interface framework (HMI) using IoT for collaborative warehouse order fulfillment. It consists of a layer of physical equipment, an ambient middleware network, a framework of multi-agents, and source planning. This approach is chosen to enhance the reaction capabilities of decentralized warehouse scheduling management in a dynamic environment. The simulation outcome has been performed, and the suggested method realizes a high product delivery ratio (96.5%), operational cost (94.9%), demand prediction ratio (96.5%), accuracy ratio (98.4%), and performance ratio (97.2%).","PeriodicalId":44506,"journal":{"name":"International Journal of Information Systems and Supply Chain Management","volume":"7 1","pages":"1-15"},"PeriodicalIF":1.6,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90109465","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}
Logistics management is part of the supply chain management process for reliable, to meet consumer requirements. In most instances, consumers find it challenging to identify the product, as they have to start it manually due to time-consuming storage rooms.This paper has suggested the IoT-assisted human-machine interface (IoT-HCI)framework as a logistic warehouse management system. A warehouse management framework is designed to eliminate this issue and immediately release updates and inform people about the operations. The proposed methoddemonstrates the aspects and the exact methodology of the products' manufacture and distribution.This system is developed through the internet of things that can continuously enable communication between the management layers. Warehouses are the units for the transport and storing goods and items before they are shipped from the location. In most situations, there are no mixed environments in which automated systems and humans interact and the employee's implementation.
{"title":"Internet of Things-Enabled Logistic Warehouse Scheduling Management With Human Machine Assistance","authors":"Zihao Zhang","doi":"10.4018/ijisscm.305852","DOIUrl":"https://doi.org/10.4018/ijisscm.305852","url":null,"abstract":"Logistics management is part of the supply chain management process for reliable, to meet consumer requirements. In most instances, consumers find it challenging to identify the product, as they have to start it manually due to time-consuming storage rooms.This paper has suggested the IoT-assisted human-machine interface (IoT-HCI)framework as a logistic warehouse management system. A warehouse management framework is designed to eliminate this issue and immediately release updates and inform people about the operations. The proposed methoddemonstrates the aspects and the exact methodology of the products' manufacture and distribution.This system is developed through the internet of things that can continuously enable communication between the management layers. Warehouses are the units for the transport and storing goods and items before they are shipped from the location. In most situations, there are no mixed environments in which automated systems and humans interact and the employee's implementation.","PeriodicalId":44506,"journal":{"name":"International Journal of Information Systems and Supply Chain Management","volume":"904 1","pages":"1-17"},"PeriodicalIF":1.6,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77494574","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}
Supply chain management has become increasingly important as an academic subject due to globalization developments contributing to massive production-related benefits reallocation. The huge volume of data produced in the global economy means that new tools must be created to manage and evaluate the data and measure organizational performance worldwide. Smart technologies such as swarm intelligence and big data analytics can help get clear data of the location, condition, and environment of products and processes at any time, anywhere to make smart decisions and take corrective schedules that the supply chain can run more effectively. This study proposes the swarm intelligence modeling-based logistic analytics management (SIMLAM) in service supply chain market. A generalized structure for swarm intelligence implementation in supply chain management is suggested, which is advantageous to industry practitioners. Different deterministic methods practically fail due to the intrinsic computational complexity of the problem of higher dimensions.
{"title":"Swarm Intelligence Technique for Supply Chain Market in Logistic Analytics Management","authors":"Qian Tian, Qingwei Yin, Yagang Meng","doi":"10.4018/ijisscm.305845","DOIUrl":"https://doi.org/10.4018/ijisscm.305845","url":null,"abstract":"Supply chain management has become increasingly important as an academic subject due to globalization developments contributing to massive production-related benefits reallocation. The huge volume of data produced in the global economy means that new tools must be created to manage and evaluate the data and measure organizational performance worldwide. Smart technologies such as swarm intelligence and big data analytics can help get clear data of the location, condition, and environment of products and processes at any time, anywhere to make smart decisions and take corrective schedules that the supply chain can run more effectively. This study proposes the swarm intelligence modeling-based logistic analytics management (SIMLAM) in service supply chain market. A generalized structure for swarm intelligence implementation in supply chain management is suggested, which is advantageous to industry practitioners. Different deterministic methods practically fail due to the intrinsic computational complexity of the problem of higher dimensions.","PeriodicalId":44506,"journal":{"name":"International Journal of Information Systems and Supply Chain Management","volume":"35 1","pages":"1-20"},"PeriodicalIF":1.6,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89446545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The industry sustainability in today's globalization relies on cost-effective supply chain management of diverse markets and logistics. Supply chain risks typically limit profits over the overall expense of the supply chain. In the supply chain design practices, the volatility of demand and limitations of levels are essential concerns. In this paper, a swarm intelligence-assisted supply chain management framework (SISCMF) has been proposed to increase profit and improve logistics performance. Due to the simplicity of design and rapid convergence, swarm intelligence (SI) algorithms are widely used in most supply network design fields and efficiently solve large-dimensional problems. A significant increase in resolving these problems has been seen in particle swarm optimization and ant colony algorithm. The simulation result suggested the operational cost (92.7%), demand prediction ratio (95.2%), order delivery ratio (96.9%), customer feedback ratio (98.2%), and product quality ratio (97.2%).
{"title":"Logistic Analytics Management in the Service Supply Chain Market Using Swarm Intelligence Modelling","authors":"Congcong Wang","doi":"10.4018/ijisscm.305851","DOIUrl":"https://doi.org/10.4018/ijisscm.305851","url":null,"abstract":"The industry sustainability in today's globalization relies on cost-effective supply chain management of diverse markets and logistics. Supply chain risks typically limit profits over the overall expense of the supply chain. In the supply chain design practices, the volatility of demand and limitations of levels are essential concerns. In this paper, a swarm intelligence-assisted supply chain management framework (SISCMF) has been proposed to increase profit and improve logistics performance. Due to the simplicity of design and rapid convergence, swarm intelligence (SI) algorithms are widely used in most supply network design fields and efficiently solve large-dimensional problems. A significant increase in resolving these problems has been seen in particle swarm optimization and ant colony algorithm. The simulation result suggested the operational cost (92.7%), demand prediction ratio (95.2%), order delivery ratio (96.9%), customer feedback ratio (98.2%), and product quality ratio (97.2%).","PeriodicalId":44506,"journal":{"name":"International Journal of Information Systems and Supply Chain Management","volume":"105 1","pages":"1-16"},"PeriodicalIF":1.6,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80874336","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}
Electronic business relies heatedly on a predictive tool to provide consumers with the products online in a brief moment. E-commerce activities are handled by many buyers globally compared to conventional distribution, and with a broader range of products but a limited amount. This article aims to help the information review systemically manage consumer relationships in institutional and cultural aspects of the logistic management (ICA-LM) model. In preparation for the ICA-LM to be adequate to discuss static and dynamic attributes for removing precious secret information, the neural network and the class label are integrated. In this way, real-life client needs are defined and potential clients listed with limited time to generate client relationship maintenance (CRM) feedback for clients. The research in Hong Kong, a transportation management firm prototype, shows and validates CRM information gathering in the developing e-commerce logistics sector in the actual world.
{"title":"Institutional and Cultural Aspects of Logistic Management in the Chinese E-Commerce Sector","authors":"Yueben Wu, Aili Cai, R. Sabitha, A. Prathik","doi":"10.4018/ijisscm.305848","DOIUrl":"https://doi.org/10.4018/ijisscm.305848","url":null,"abstract":"Electronic business relies heatedly on a predictive tool to provide consumers with the products online in a brief moment. E-commerce activities are handled by many buyers globally compared to conventional distribution, and with a broader range of products but a limited amount. This article aims to help the information review systemically manage consumer relationships in institutional and cultural aspects of the logistic management (ICA-LM) model. In preparation for the ICA-LM to be adequate to discuss static and dynamic attributes for removing precious secret information, the neural network and the class label are integrated. In this way, real-life client needs are defined and potential clients listed with limited time to generate client relationship maintenance (CRM) feedback for clients. The research in Hong Kong, a transportation management firm prototype, shows and validates CRM information gathering in the developing e-commerce logistics sector in the actual world.","PeriodicalId":44506,"journal":{"name":"International Journal of Information Systems and Supply Chain Management","volume":"33 8 1","pages":"1-17"},"PeriodicalIF":1.6,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83717498","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}
There are many interdependent computers available in distributed networks. In such schemes, overall ownership costs comprise facilities, such as computers, controls, etc.; buying hardware; and running expenses such as wages, electrical charges, etc. Strom use is a large part of operating expenses. AI-assisted dynamic modelling for data management (AI-DM) framework is proposed. The high percentage of power use is connected explicitly to inadequate planning of energy. This research suggests creating a multi-objective method to plan the preparation of multi-criteria software solutions for distributed systems using the fuzzy TOPSIS tool as a comprehensive guide to multi-criteria management. The execution results demonstrate that this strategy could then sacrifice requirements by weight.
{"title":"AI-Assisted Dynamic Modelling for Data Management in a Distributed System","authors":"Yingjun Wang, Shaoyang He, Yiran Wang","doi":"10.4018/ijisscm.313623","DOIUrl":"https://doi.org/10.4018/ijisscm.313623","url":null,"abstract":"There are many interdependent computers available in distributed networks. In such schemes, overall ownership costs comprise facilities, such as computers, controls, etc.; buying hardware; and running expenses such as wages, electrical charges, etc. Strom use is a large part of operating expenses. AI-assisted dynamic modelling for data management (AI-DM) framework is proposed. The high percentage of power use is connected explicitly to inadequate planning of energy. This research suggests creating a multi-objective method to plan the preparation of multi-criteria software solutions for distributed systems using the fuzzy TOPSIS tool as a comprehensive guide to multi-criteria management. The execution results demonstrate that this strategy could then sacrifice requirements by weight.","PeriodicalId":44506,"journal":{"name":"International Journal of Information Systems and Supply Chain Management","volume":"17 1","pages":"1-18"},"PeriodicalIF":1.6,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91175870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The management of global supply chains that emerge from outsourcing and offshoring activities emphasizes a globally dispersed supply chain. All stakeholders and entrepreneurs worldwide have a common understanding of information technology's importance to support business activity in a rapidly changing era of customer preference. Today, many believe in a production process transition, which subsequently affects the supply chain flow in general, fearing overuse and inefficiency from upstream to downstream. Thus, this article proposes supply chain efficiency and effectiveness management using decision support systems (SCE2M-DSS). This conceptual framework uses an intelligent decision support system for the supply chain's proactive capacity planning under uncertain conditions. An intelligent decision-making support system is designed with reinforcement learning (RL) to validate the conceptual framework. The application of decision-making methods developed initially focused on product development and service production.
{"title":"Supply Chain Efficiency and Effectiveness Management Using Decision Support Systems","authors":"Guozheng Li","doi":"10.4018/ijisscm.305847","DOIUrl":"https://doi.org/10.4018/ijisscm.305847","url":null,"abstract":"The management of global supply chains that emerge from outsourcing and offshoring activities emphasizes a globally dispersed supply chain. All stakeholders and entrepreneurs worldwide have a common understanding of information technology's importance to support business activity in a rapidly changing era of customer preference. Today, many believe in a production process transition, which subsequently affects the supply chain flow in general, fearing overuse and inefficiency from upstream to downstream. Thus, this article proposes supply chain efficiency and effectiveness management using decision support systems (SCE2M-DSS). This conceptual framework uses an intelligent decision support system for the supply chain's proactive capacity planning under uncertain conditions. An intelligent decision-making support system is designed with reinforcement learning (RL) to validate the conceptual framework. The application of decision-making methods developed initially focused on product development and service production.","PeriodicalId":44506,"journal":{"name":"International Journal of Information Systems and Supply Chain Management","volume":"41 1","pages":"1-18"},"PeriodicalIF":1.6,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90542907","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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