Industry 4.0 and Smart Factory are associated with major changes in the industrial world. The innovative forms of networking, communication and collaboration between people, machines and products have led to a new type of production system in which information and knowledge are exchanged more quickly and efficiently. As a result, among other things, the role of cognitive assistance systems and their supporting function for employees within production processes has gained in importance, both in the training phase and in the active work phase. Especially for on-the-job learning, these assistance systems open up a wide range of opportunities. This paper focuses on the opportunities and challenges associated with the use of cognitive assistance systems for workplace learning. We discuss current research on the potential uses of these assistance systems, particularly for process accomplishment as well as for supporting specific groups of employees. Furthermore, we address the limitations for the use of these systems. At the end of the paper, we identify three focus areas for the future development of AI in industrial processes and for research on this.
{"title":"AI-supported assistance systems in enterprise learning processes - prospects and limitations","authors":"N. Gronau, Gergana Vladova","doi":"10.30844/wgab_2021_5","DOIUrl":"https://doi.org/10.30844/wgab_2021_5","url":null,"abstract":"Industry 4.0 and Smart Factory are associated with major changes in the industrial world. The innovative forms of networking, communication and collaboration between people, machines and products have led to a new type of production system in which information and knowledge are exchanged more quickly and efficiently. As a result, among other things, the role of cognitive assistance systems and their supporting function for employees within production processes has gained in importance, both in the training phase and in the active work phase. Especially for on-the-job learning, these assistance systems open up a wide range of opportunities. This paper focuses on the opportunities and challenges associated with the use of cognitive assistance systems for workplace learning. We discuss current research on the potential uses of these assistance systems, particularly for process accomplishment as well as for supporting specific groups of employees. Furthermore, we address the limitations for the use of these systems. At the end of the paper, we identify three focus areas for the future development of AI in industrial processes and for research on this.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122861362","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}
Artificial Intelligence (AI) plays an increasingly important role for the implementation and failure-free operation of Cyber-Physical Production Systems (CPPS). Recent market studies show that investment in AI-enhanced maintenance is increasing as one of the most important use cases of Industry 4.0. AI systems enable the improvement of various Key Performance Indicators (KPI), ultimately leading to a reduction in costs and optimizing plant management in smart factories. At the same time, manufacturing enterprises in diverse sectors have very high expectations from any kind of AI solution comparing to conventional solutions. Today manufacturing enterprises use only a quarter of their data and therefore leave an enormous, untapped potential. The use of Text Mining (TM) realizes the untapped value of existing unstructured or semi-structured textual data. This paper presents a transferable and scalable architecture for a cognitive maintenance system of a human-centered assistance system that enables holistic sensing of the environment by using physical and virtual sensors. By focusing on generalizability, scalability, adaptability, reliability, and user acceptance, a novel architecture for cognitive maintenance system is proposed. The so called ARCHIE, Architecture for a Cognitive Maintenance System, addresses common challenges in the application of AI systems in the industrial environment. Human-centered cognitive systems aim to automate manufacturing processes and assist workers in their cognitive tasks. This can be achieved by using the untapped potential of combining unstructured and structured data in order to extract hidden knowledge. ARCHIE aims at realizing an AI-enhanced approach for a human-centered assistance system. ARCHIE incorporates physical and virtual sensors that capture machine states, parameters, human knowledge, and skills to optimize relevant KPIs. This includes a reduction in documentation time, Mean Time Between Failures (MTBF) and Mean Failure Detection Time (MFDT), as well as an increase in uptime, leading ultimately to an improved Overall Equipment Efficiency (OEE). These improvements are enabled by the combined use of AI in the form of TM, Federated Learning and Knowledge Graphs. In the presented use-case from the automotive industry, a reduction in MFDT below 60min by 97.3% and an increase in OEE by 5.3% was achieved. In the Semiconductor industry, the partial application of ARCHIE allows the querying of competence distributions based on a given maintenance task, enabling automated allocation of maintenance technicians and trend analyses. Generalizability, scalability, adaptability, reliability, and user acceptance were also evaluated in the use cases presented.
{"title":"A Modular Federated Learning Architecture for Integration of AI-enhanced Assistance in Industrial Maintenance - A novel architecture for enhancing industrial maintenance management systems in the automotive and semiconductor industry.","authors":"Linus Kohl, Fazel Ansari, W. Sihn","doi":"10.30844/wgab_2021_14","DOIUrl":"https://doi.org/10.30844/wgab_2021_14","url":null,"abstract":"Artificial Intelligence (AI) plays an increasingly important role for the implementation and failure-free operation of Cyber-Physical Production Systems (CPPS). Recent market studies show that investment in AI-enhanced maintenance is increasing as one of the most important use cases of Industry 4.0. AI systems enable the improvement of various Key Performance Indicators (KPI), ultimately leading to a reduction in costs and optimizing plant management in smart factories. At the same time, manufacturing enterprises in diverse sectors have very high expectations from any kind of AI solution comparing to conventional solutions. Today manufacturing enterprises use only a quarter of their data and therefore leave an enormous, untapped potential. The use of Text Mining (TM) realizes the untapped value of existing unstructured or semi-structured textual data. This paper presents a transferable and scalable architecture for a cognitive maintenance system of a human-centered assistance system that enables holistic sensing of the environment by using physical and virtual sensors. By focusing on generalizability, scalability, adaptability, reliability, and user acceptance, a novel architecture for cognitive maintenance system is proposed. The so called ARCHIE, Architecture for a Cognitive Maintenance System, addresses common challenges in the application of AI systems in the industrial environment. Human-centered cognitive systems aim to automate manufacturing processes and assist workers in their cognitive tasks. This can be achieved by using the untapped potential of combining unstructured and structured data in order to extract hidden knowledge. ARCHIE aims at realizing an AI-enhanced approach for a human-centered assistance system. ARCHIE incorporates physical and virtual sensors that capture machine states, parameters, human knowledge, and skills to optimize relevant KPIs. This includes a reduction in documentation time, Mean Time Between Failures (MTBF) and Mean Failure Detection Time (MFDT), as well as an increase in uptime, leading ultimately to an improved Overall Equipment Efficiency (OEE). These improvements are enabled by the combined use of AI in the form of TM, Federated Learning and Knowledge Graphs. In the presented use-case from the automotive industry, a reduction in MFDT below 60min by 97.3% and an increase in OEE by 5.3% was achieved. In the Semiconductor industry, the partial application of ARCHIE allows the querying of competence distributions based on a given maintenance task, enabling automated allocation of maintenance technicians and trend analyses. Generalizability, scalability, adaptability, reliability, and user acceptance were also evaluated in the use cases presented.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125296499","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}
Digitalization and the increasing technical possibilities of networking machines and products as well as the use of large amount of data in the hole production process offer companies the opportunity to establish new, so-called hybrid business models. This enables them to provide customers data-driven, smart services in addition to their physical products, create more value and strengthen their competitiveness. The hybridization of value creation is accompanied by numerous changes and new competence requirements in companies, which need to be shaped socio-technically. In the AnGeWaNt project, such hybrid business models were developed and implemented in three companies. The article describes the approach to analyzing and shaping changes and competence requirements that arise in companies as a result of digitalization and hybridization.
{"title":"Competence Development within Hybrid Value Creation - Need-based Competence Development for the Successful Implementation of Hybrid, Data-Driven Business Models","authors":"S. Stowasser, Nicole Ottersböck","doi":"10.30844/wgab_2021_9","DOIUrl":"https://doi.org/10.30844/wgab_2021_9","url":null,"abstract":"Digitalization and the increasing technical possibilities of networking machines and products as well as the use of large amount of data in the hole production process offer companies the opportunity to establish new, so-called hybrid business models. This enables them to provide customers data-driven, smart services in addition to their physical products, create more value and strengthen their competitiveness. The hybridization of value creation is accompanied by numerous changes and new competence requirements in companies, which need to be shaped socio-technically. In the AnGeWaNt project, such hybrid business models were developed and implemented in three companies. The article describes the approach to analyzing and shaping changes and competence requirements that arise in companies as a result of digitalization and hybridization.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124685980","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 use of LNG propulsion in ships has significant environmental benefits but also creates challenges in handling the LNG. Due to the safety regulations, the maintenance of the LNG ship systems is essential and requires a high degree of reliability and accuracy. Here, digital assistance systems, e.g., based on Augment-ed Reality (AR) technology, can support the maintenance and service purposes of LNG ship systems by displaying additional information directly on the objects to be serviced. Thus, this paper deals with the development and evaluation of a digital assistance system using AR technology. The developed assistance system based on an Android smartphone enables users to access maintenance instruc-tions and manuals, simplifies spare parts' ordering, and supports the work pro-cess step by step through context-sensitive virtualizations. Its evaluation was conducted as a combined quantitative and qualitative user study. Overall, the assistance system offers promising potentials for reducing workload and improv-ing processes.
{"title":"Human-Centered Development and Evaluation of an AR-Assistance System to Support Maintenance and Service Operations at LNG Ship Valves","authors":"Hendrik Stern, Rieke Leder, M. Lütjen","doi":"10.30844/wgab_2021_17","DOIUrl":"https://doi.org/10.30844/wgab_2021_17","url":null,"abstract":"The use of LNG propulsion in ships has significant environmental benefits but also creates challenges in handling the LNG. Due to the safety regulations, the maintenance of the LNG ship systems is essential and requires a high degree of reliability and accuracy. Here, digital assistance systems, e.g., based on Augment-ed Reality (AR) technology, can support the maintenance and service purposes of LNG ship systems by displaying additional information directly on the objects to be serviced. Thus, this paper deals with the development and evaluation of a digital assistance system using AR technology. The developed assistance system based on an Android smartphone enables users to access maintenance instruc-tions and manuals, simplifies spare parts' ordering, and supports the work pro-cess step by step through context-sensitive virtualizations. Its evaluation was conducted as a combined quantitative and qualitative user study. Overall, the assistance system offers promising potentials for reducing workload and improv-ing processes.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131615236","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}
Marc Schwarzkopf, Susann Zeiner-Fink, Angelika C. Bullinger-Hoffmann
Digitalization is forcing small and medium-sized enterprises (SMEs) to rethink their work and production processes. Initiated by this process, the organization of production and employees are subject to change. As a result, the job profiles of employees are changing and expanding, as well as the way how knowledge is imparted. Innovative and digitized formats should be integrated into existing training programs and presented in a way that is suitable for use on mobile de-vices. Therefore, suitable and target group-specific teaching/learning formats are needed that support participative methods and digital collaboration. For this purpose, a digital teaching and learning format for the application area of automotive engineering in SMEs was designed. This prototypical teach-ing/learning format was created and evaluated in an iterative process through the participation of the potential users and taking into account existing usability criteria. The two methods used to evaluate the format were Think-Aloud and focus group, the results of this evaluations are presented in this paper. The results show that when evaluating the teaching/learning format, the test subjects mainly refer to the usability criteria of DIN ISO 9241-110, the structure of the course and the information content of the course. Recommendations for the creation of future digital teaching and learning formats for SMEs are derived from these findings.
数字化正迫使中小企业(SMEs)重新思考其工作和生产流程。由这一过程发起，生产组织和雇员都可能发生变化。因此，员工的工作概况正在变化和扩展，知识的传授方式也在变化和扩展。应将创新和数字化格式整合到现有的培训计划中，并以适合移动设备使用的方式呈现。因此，需要合适的、针对特定目标群体的教学/学习格式，以支持参与式方法和数字协作。为此，设计了中小企业汽车工程应用领域的数字化教学模式。这种原型的教学/学习格式是在一个迭代过程中通过潜在用户的参与和考虑到现有的可用性标准来创建和评估的。本文采用了有声思维和焦点小组两种评估方法，并给出了评估结果。结果表明，在评价教学格式时，测试对象主要参考DIN ISO 9241-110的可用性标准、课程结构和课程信息内容。这些发现为中小企业创建未来数字化教学和学习格式提出了建议。
{"title":"This is how we learn A Best Practice Case of Qualification in SMEs for Work 4.0 - A Best Practice Case of Qualification in SMEs for Work 4.0","authors":"Marc Schwarzkopf, Susann Zeiner-Fink, Angelika C. Bullinger-Hoffmann","doi":"10.30844/wgab_2021_2","DOIUrl":"https://doi.org/10.30844/wgab_2021_2","url":null,"abstract":"Digitalization is forcing small and medium-sized enterprises (SMEs) to rethink their work and production processes. Initiated by this process, the organization of production and employees are subject to change. As a result, the job profiles of employees are changing and expanding, as well as the way how knowledge is imparted. Innovative and digitized formats should be integrated into existing training programs and presented in a way that is suitable for use on mobile de-vices. Therefore, suitable and target group-specific teaching/learning formats are needed that support participative methods and digital collaboration. For this purpose, a digital teaching and learning format for the application area of automotive engineering in SMEs was designed. This prototypical teach-ing/learning format was created and evaluated in an iterative process through the participation of the potential users and taking into account existing usability criteria. The two methods used to evaluate the format were Think-Aloud and focus group, the results of this evaluations are presented in this paper. The results show that when evaluating the teaching/learning format, the test subjects mainly refer to the usability criteria of DIN ISO 9241-110, the structure of the course and the information content of the course. Recommendations for the creation of future digital teaching and learning formats for SMEs are derived from these findings.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130652165","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}
T. Haase, W. Termath, Michael Dick, Michael Schenk
In this paper, the authors present a methodological approach for designing assistance systems conducive to learning. The theoretical framework is based on the activity system and the concept of expansive learning. From this, the authors develop the learning activity system. The application and further development of this theoretical framework is presented on the basis of an industrial application scenario of mechatronic reprocessing in the automotive industry. It includes a systematic approach to technology selection and design that serves as a practical action guide for companies designing assistance systems. In addition, dimensions conducive to learning are developed and linked to the activity system approach. This integrated model provides requirements for the design of an assistance system conducive to learning. The paper also describes concrete requirements and measures of a participatory design and implementation process.
{"title":"Workplace-integrated assistance systems conducive to learning designed for production","authors":"T. Haase, W. Termath, Michael Dick, Michael Schenk","doi":"10.30844/wgab_2021_13","DOIUrl":"https://doi.org/10.30844/wgab_2021_13","url":null,"abstract":"In this paper, the authors present a methodological approach for designing assistance systems conducive to learning. The theoretical framework is based on the activity system and the concept of expansive learning. From this, the authors develop the learning activity system. The application and further development of this theoretical framework is presented on the basis of an industrial application scenario of mechatronic reprocessing in the automotive industry. It includes a systematic approach to technology selection and design that serves as a practical action guide for companies designing assistance systems. In addition, dimensions conducive to learning are developed and linked to the activity system approach. This integrated model provides requirements for the design of an assistance system conducive to learning. The paper also describes concrete requirements and measures of a participatory design and implementation process.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130961825","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}
Monitoring learners’ mental states via a passive Brain-Computer Interface (BCI) allows to continuously estimate current abilities, available cognitive resources, and motivation. It bears the great potential to adapt educational contents, learning speed, and format to the learner’s needs via an intelligent tutoring system. We present a neurophysiological-based approach to continuously monitor learners’ current affective-emotional and cognitive states by measuring and decoding brain activity via a passive BCI. In two studies (N = 8 and N = 7), we investigate whether we can a) predict learners’ affective and cognitive states during a learning or training session, b) provide continuous feedback of recognized states to the learner and, thereby, c) increase performance and intrinsic motivation. Oscillatory power measures in the alpha (8 – 12 Hz) and theta (4 – 7 Hz) frequency band served as features for the prediction and visualization. Our results reveal that machine learning algorithms can distinguish different states of cognitive workload and affect. The approach contributes to the development of closed-loop neuro-adaptive tutoring systems which allow to monitor learners’ states, provide feedback, and adapt their parameters for an optimal learner-training fit and effective and positive learning experience.
{"title":"Neuro-adaptive tutoring systems - Neurophysiological-based recognition of affective-emotional and cognitive states of learners for intelligent neuro-adaptive tutoring systems","authors":"Katharina Lingelbach, Sabrina Gado, Wilhelm Bauer","doi":"10.30844/wgab_2021_15","DOIUrl":"https://doi.org/10.30844/wgab_2021_15","url":null,"abstract":"Monitoring learners’ mental states via a passive Brain-Computer Interface (BCI) allows to continuously estimate current abilities, available cognitive resources, and motivation. It bears the great potential to adapt educational contents, learning speed, and format to the learner’s needs via an intelligent tutoring system. We present a neurophysiological-based approach to continuously monitor learners’ current affective-emotional and cognitive states by measuring and decoding brain activity via a passive BCI. In two studies (N = 8 and N = 7), we investigate whether we can a) predict learners’ affective and cognitive states during a learning or training session, b) provide continuous feedback of recognized states to the learner and, thereby, c) increase performance and intrinsic motivation. Oscillatory power measures in the alpha (8 – 12 Hz) and theta (4 – 7 Hz) frequency band served as features for the prediction and visualization. Our results reveal that machine learning algorithms can distinguish different states of cognitive workload and affect. The approach contributes to the development of closed-loop neuro-adaptive tutoring systems which allow to monitor learners’ states, provide feedback, and adapt their parameters for an optimal learner-training fit and effective and positive learning experience.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"5 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114108559","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}
Uta Wilkens, Valentin Langholf, Greta Ontrup, A. Kluge
In our paper we present first performance measurement results of a digital simulation laboratory, which is applied in the context of industrial front-end team training. The design of the simulation laboratory is oriented towards an Escape Room. First, we situate the presented approach within existing competency understandings and accompanying training approaches in the context of Industry 4.0 Performance measurement for front-end training has been a challenge in this context so far, since performance, unlike in the back-end, is not attributable to specific production results, but becomes visible on a superior process level. Building on the competency facets of complexity management, self-reflection, creative problem solving, and cooperation (Wilkens et al., 2017) as well as action implementation (Heyse & Erpenbeck, 2009), the performance measurement presented addresses the question which individual competencies have an impact on team performance in the simulation scenario. Preliminary results show that the individual competencies among team members have a lower impact on performance than moderating factors such as heterogeneity and cohesion within the team. In order to increase the performance of front-end teams, it therefore appears to be reasonable to focus more on developing team structures rather than only on individual competence development.
{"title":"Towards a maturity model of human-centered AI – A reference for AI implementation at the workplace","authors":"Uta Wilkens, Valentin Langholf, Greta Ontrup, A. Kluge","doi":"10.30844/wgab_2021_11","DOIUrl":"https://doi.org/10.30844/wgab_2021_11","url":null,"abstract":"In our paper we present first performance measurement results of a digital simulation laboratory, which is applied in the context of industrial front-end team training. The design of the simulation laboratory is oriented towards an Escape Room. First, we situate the presented approach within existing competency understandings and accompanying training approaches in the context of Industry 4.0 Performance measurement for front-end training has been a challenge in this context so far, since performance, unlike in the back-end, is not attributable to specific production results, but becomes visible on a superior process level. Building on the competency facets of complexity management, self-reflection, creative problem solving, and cooperation (Wilkens et al., 2017) as well as action implementation (Heyse & Erpenbeck, 2009), the performance measurement presented addresses the question which individual competencies have an impact on team performance in the simulation scenario. Preliminary results show that the individual competencies among team members have a lower impact on performance than moderating factors such as heterogeneity and cohesion within the team. In order to increase the performance of front-end teams, it therefore appears to be reasonable to focus more on developing team structures rather than only on individual competence development.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115439780","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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