Pub Date : 2023-11-14DOI: 10.1007/s13218-023-00815-8
Abel Barreto, Lasse Reifenrath, Richard Vogg, Fabian Sinz, Anne-Katrin Mahlein
Abstract In crop protection, disease quantification parameters such as disease incidence (DI) and disease severity (DS) are the principal indicators for decision making, aimed at ensuring the safety and productivity of crop yield. The quantification is standardized with leaf organs, defined as individual scoring units. This study focuses on identifying and segmenting individual leaves in agricultural fields using unmanned aerial vehicle (UAV), multispectral imagery of sugar beet fields, and deep instance segmentation networks (Mask R-CNN). Five strategies for achieving network robustness with limited labeled images are tested and compared, employing simple and copy-paste image augmentation techniques. The study also evaluates the impact of environmental conditions on network performance. Metrics of performance show that multispectral UAV images recorded under sunny conditions lead to a performance drop. Focusing on the practical application, we employ Mask R-CNN models in an image-processing pipeline to calculate leaf-based parameters including DS and DI. The pipeline was applied in time-series in an experimental trial with five varieties and two fungicide strategies to illustrate epidemiological development. Disease severity calculated with the model with highest Average Precision (AP) shows the strongest correlation with the same parameter assessed by experts. The time-series development of disease severity and disease incidence demonstrates the advantages of multispectral UAV-imagery in contrasting varieties for resistance, as well as the limits for disease control measurements. This study identifies key components for automatic leaf segmentation of diseased plants using UAV imagery, such as illumination and disease condition. It also provides a tool for delivering leaf-based parameters relevant to optimize crop production through automated disease quantification by imaging tools.
{"title":"Data Augmentation for Mask-Based Leaf Segmentation of UAV-Images as a Basis to Extract Leaf-Based Phenotyping Parameters","authors":"Abel Barreto, Lasse Reifenrath, Richard Vogg, Fabian Sinz, Anne-Katrin Mahlein","doi":"10.1007/s13218-023-00815-8","DOIUrl":"https://doi.org/10.1007/s13218-023-00815-8","url":null,"abstract":"Abstract In crop protection, disease quantification parameters such as disease incidence (DI) and disease severity (DS) are the principal indicators for decision making, aimed at ensuring the safety and productivity of crop yield. The quantification is standardized with leaf organs, defined as individual scoring units. This study focuses on identifying and segmenting individual leaves in agricultural fields using unmanned aerial vehicle (UAV), multispectral imagery of sugar beet fields, and deep instance segmentation networks (Mask R-CNN). Five strategies for achieving network robustness with limited labeled images are tested and compared, employing simple and copy-paste image augmentation techniques. The study also evaluates the impact of environmental conditions on network performance. Metrics of performance show that multispectral UAV images recorded under sunny conditions lead to a performance drop. Focusing on the practical application, we employ Mask R-CNN models in an image-processing pipeline to calculate leaf-based parameters including DS and DI. The pipeline was applied in time-series in an experimental trial with five varieties and two fungicide strategies to illustrate epidemiological development. Disease severity calculated with the model with highest Average Precision (AP) shows the strongest correlation with the same parameter assessed by experts. The time-series development of disease severity and disease incidence demonstrates the advantages of multispectral UAV-imagery in contrasting varieties for resistance, as well as the limits for disease control measurements. This study identifies key components for automatic leaf segmentation of diseased plants using UAV imagery, such as illumination and disease condition. It also provides a tool for delivering leaf-based parameters relevant to optimize crop production through automated disease quantification by imaging tools.","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"32 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954469","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 : 2023-11-14DOI: 10.1007/s13218-023-00818-5
Jana Seep
Abstract In order to understand what influences the movement of an object or person it is important to consider a variety of factors. These could be the visibility of certain landmarks, the current temperature or the presence of a crowded area to be avoided. These insights then can be used to understand movement in the public sector and improve our build environment, e.g. to reduce street traffic accidents or orientation in complex buildings. The following extended abstract is a summary of a doctoral thesis submitted to the University of Münster. The thesis was successfully defended in February 2023 [16]. The dissertation focuses on the analysis of so-called semantically enriched trajectories , which are used to describe observed movement. It proposes a new model based on an extended finite state machine, which allows for the representation and consideration of the information about the context of the trajectory. With the new model, we consider two main steps in trajectory analysis: First, we aim to infer a semantically enriched representative trajectory for a given cluster of trajectories. Second, we introduce a variation of the well-known k-means algorithm to calculate clusters based on the given context of trajectories. To show semantic feasibility of our approach, we conclude this work by evaluating the possibility to provide decision support for domain experts in two different public sector related contexts.
{"title":"Analyzing Semantically Enriched Trajectories","authors":"Jana Seep","doi":"10.1007/s13218-023-00818-5","DOIUrl":"https://doi.org/10.1007/s13218-023-00818-5","url":null,"abstract":"Abstract In order to understand what influences the movement of an object or person it is important to consider a variety of factors. These could be the visibility of certain landmarks, the current temperature or the presence of a crowded area to be avoided. These insights then can be used to understand movement in the public sector and improve our build environment, e.g. to reduce street traffic accidents or orientation in complex buildings. The following extended abstract is a summary of a doctoral thesis submitted to the University of Münster. The thesis was successfully defended in February 2023 [16]. The dissertation focuses on the analysis of so-called semantically enriched trajectories , which are used to describe observed movement. It proposes a new model based on an extended finite state machine, which allows for the representation and consideration of the information about the context of the trajectory. With the new model, we consider two main steps in trajectory analysis: First, we aim to infer a semantically enriched representative trajectory for a given cluster of trajectories. Second, we introduce a variation of the well-known k-means algorithm to calculate clusters based on the given context of trajectories. To show semantic feasibility of our approach, we conclude this work by evaluating the possibility to provide decision support for domain experts in two different public sector related contexts.","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"32 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991352","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 : 2023-11-13DOI: 10.1007/s13218-023-00817-6
Patrick Berger, Joerg von Garrel
Abstract With regard to AI as a key technology, this scientific paper deals with the identification of user drivers on the purchase decision of a cooperative AI (as explainable AI—XAI), as well as the analysis of the willingness to pay in the context of value-based pricing. Besides the economic dimension with regard to usefulness and usability of the system, the focus is mainly on the (innovative) explainable character. The analysis is carried out by a choice-based conjoint analysis (CBC) using the example of an intelligent assistance system for employees that supports internal business processes and workflows in business organizations. For this purpose, fictitious purchase offers were created under which decision-makers in manufacturing business organizations in Germany made simulated purchase decisions. The analysis shows that the target group attach great utility value to transparency in the sense of explanatory content, in addition to a high degree of interactivity and a high level of reliability.
{"title":"How to design a value-based Chatbot for the manufacturing industry: An empirical study of an internal assistance for employees","authors":"Patrick Berger, Joerg von Garrel","doi":"10.1007/s13218-023-00817-6","DOIUrl":"https://doi.org/10.1007/s13218-023-00817-6","url":null,"abstract":"Abstract With regard to AI as a key technology, this scientific paper deals with the identification of user drivers on the purchase decision of a cooperative AI (as explainable AI—XAI), as well as the analysis of the willingness to pay in the context of value-based pricing. Besides the economic dimension with regard to usefulness and usability of the system, the focus is mainly on the (innovative) explainable character. The analysis is carried out by a choice-based conjoint analysis (CBC) using the example of an intelligent assistance system for employees that supports internal business processes and workflows in business organizations. For this purpose, fictitious purchase offers were created under which decision-makers in manufacturing business organizations in Germany made simulated purchase decisions. The analysis shows that the target group attach great utility value to transparency in the sense of explanatory content, in addition to a high degree of interactivity and a high level of reliability.","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"60 40","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136283784","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 : 2023-11-10DOI: 10.1007/s13218-023-00813-w
Marcel Gehrke
Abstract Processes in our world are of a temporal probabilistic relational nature. An epidemic is an example of such a process. This dissertation abstract uses the scenario of an epidemic to illustrate the lifted dynamic junction tree algorithm (LDJT), which is a temporal probabilistic relational inference algorithm. More specifically, we argue that existing propositional temporal probabilistic inference algorithms are not suited to model an epidemic, i.e., without accounting for the relational part, and present how LDJT uses the relational aspect. Additionally, we illustrate how LDJT preserves groups of indistinguishable objects over time and have a look at LDJT from a theoretical side.
{"title":"Dissertation Abstract: Taming Exact Inference in Temporal Probabilistic Relational Models","authors":"Marcel Gehrke","doi":"10.1007/s13218-023-00813-w","DOIUrl":"https://doi.org/10.1007/s13218-023-00813-w","url":null,"abstract":"Abstract Processes in our world are of a temporal probabilistic relational nature. An epidemic is an example of such a process. This dissertation abstract uses the scenario of an epidemic to illustrate the lifted dynamic junction tree algorithm (LDJT), which is a temporal probabilistic relational inference algorithm. More specifically, we argue that existing propositional temporal probabilistic inference algorithms are not suited to model an epidemic, i.e., without accounting for the relational part, and present how LDJT uses the relational aspect. Additionally, we illustrate how LDJT preserves groups of indistinguishable objects over time and have a look at LDJT from a theoretical side.","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"117 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136578","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 : 2023-11-07DOI: 10.1007/s13218-023-00814-9
Elias Eder, Peter Riegler-Nurscher, Johann Prankl, Heinrich Prankl
{"title":"Grassland Yield Estimation Using Transfer Learning from Remote Sensing Data","authors":"Elias Eder, Peter Riegler-Nurscher, Johann Prankl, Heinrich Prankl","doi":"10.1007/s13218-023-00814-9","DOIUrl":"https://doi.org/10.1007/s13218-023-00814-9","url":null,"abstract":"","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"310 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135474690","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 : 2023-10-30DOI: 10.1007/s13218-023-00812-x
Jelto Branding, Dieter von Hörsten, Jens Karl Wegener, Elias Böckmann, Eberhard Hartung
Abstract Successful and efficient pest management is key to sustainable horticultural food production. While greenhouses already allow digital monitoring and control of their climate parameters, a lack of digital pest sensors hinders the advent of digital pest management systems. To close the control loop, digital systems need to be enabled to directly assess the state of different insect populations in a greenhouse. The presented article investigates the feasibility of acoustic sensors for insect detection in greenhouses. The study is based on an extensive dataset of acoustic insect recordings made with an array of high-quality microphones under noise-shielded conditions. By mixing these noise-free laboratory recordings with environmental sounds recorded with the same equipment in a greenhouse, different signal-to-noise ratios (SNR) are simulated. To explore the possibilities of this unique and novel dataset, two deep-learning models are trained on this simulation data. A simple spectrogram-based model represents the baseline for a comparison with a model capable of processing multi-channel raw audio data. Making use of the unique possibility of the dataset, the models are pre-trained on clean data and fine-tuned on noisy data. Under lab conditions, results show that both models can make use of not just insect flight sounds but also the much quieter sounds of insect movements. First attempts under simulated real-world conditions showed the challenging nature of this task and the potential of spatial filtering. The analysis enabled by the proposed methods for training and evaluation provided valuable insights that should be considered for future work.
{"title":"Towards noise robust acoustic insect detection: from the lab to the greenhouse","authors":"Jelto Branding, Dieter von Hörsten, Jens Karl Wegener, Elias Böckmann, Eberhard Hartung","doi":"10.1007/s13218-023-00812-x","DOIUrl":"https://doi.org/10.1007/s13218-023-00812-x","url":null,"abstract":"Abstract Successful and efficient pest management is key to sustainable horticultural food production. While greenhouses already allow digital monitoring and control of their climate parameters, a lack of digital pest sensors hinders the advent of digital pest management systems. To close the control loop, digital systems need to be enabled to directly assess the state of different insect populations in a greenhouse. The presented article investigates the feasibility of acoustic sensors for insect detection in greenhouses. The study is based on an extensive dataset of acoustic insect recordings made with an array of high-quality microphones under noise-shielded conditions. By mixing these noise-free laboratory recordings with environmental sounds recorded with the same equipment in a greenhouse, different signal-to-noise ratios (SNR) are simulated. To explore the possibilities of this unique and novel dataset, two deep-learning models are trained on this simulation data. A simple spectrogram-based model represents the baseline for a comparison with a model capable of processing multi-channel raw audio data. Making use of the unique possibility of the dataset, the models are pre-trained on clean data and fine-tuned on noisy data. Under lab conditions, results show that both models can make use of not just insect flight sounds but also the much quieter sounds of insect movements. First attempts under simulated real-world conditions showed the challenging nature of this task and the potential of spatial filtering. The analysis enabled by the proposed methods for training and evaluation provided valuable insights that should be considered for future work.","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"121 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103528","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 : 2023-10-09DOI: 10.1007/s13218-023-00809-6
Merle Behr, Rolf Burghaus, Christian Diedrich, Jörg Lippert
Abstract Real world data (RWD) has become an important tool in pharmaceutical research and development. Generated every time patients interact with the healthcare system when diagnoses are developed and medical interventions are selected, RWD are massive and in many regards typical big data. The use of artificial intelligence (AI) to analyze RWD seems an obvious choice. It promises new insights into medical need, drivers of diseases, and new opportunities for pharmacological interventions. When put into practice RWD analyses are challenging. The distributed generation of data, under sub-optimally standardized conditions in a patient-oriented but not information maximizing healthcare transaction, leads to a high level of sparseness and uncontrolled biases. We discuss why this needs to be addressed independent of the type of analysis approach. While classical statistical analysis and modeling approaches provide a rigorous framework for the handling of bias and sparseness, AI methods are not necessarily suited when applied naively. Special precautions need to be taken from choice of method until interpretation of results to prevent potentially harmful fallacies. The conscious use of prior medical subject matter expertise may also be required. Based on typical application examples we illustrate challenges and methodological considerations.
摘要真实世界数据(Real world data, RWD)已成为药物研究与开发的重要工具。RWD是在患者与医疗保健系统互动时产生的，在诊断制定和医疗干预措施选择时，RWD是巨大的，在许多方面都是典型的大数据。利用人工智能(AI)分析RWD似乎是一个显而易见的选择。它承诺对医疗需求、疾病驱动因素和药理学干预的新机会有新的见解。在实际应用中，RWD分析具有挑战性。在面向患者而非信息最大化的医疗保健交易中，在次优标准化条件下的分布式数据生成会导致高度稀疏和不受控制的偏差。我们讨论了为什么需要独立于分析方法的类型来解决这个问题。虽然经典的统计分析和建模方法为处理偏差和稀疏性提供了严格的框架，但人工智能方法并不一定适合天真地应用。从选择方法到解释结果都需要采取特别的预防措施，以防止潜在的有害谬误。也可能需要有意识地利用先前的医学主题专门知识。基于典型的应用程序示例，我们说明了挑战和方法上的考虑。
{"title":"Opportunities and Challenges for AI-Based Analysis of RWD in Pharmaceutical R&D: A Practical Perspective","authors":"Merle Behr, Rolf Burghaus, Christian Diedrich, Jörg Lippert","doi":"10.1007/s13218-023-00809-6","DOIUrl":"https://doi.org/10.1007/s13218-023-00809-6","url":null,"abstract":"Abstract Real world data (RWD) has become an important tool in pharmaceutical research and development. Generated every time patients interact with the healthcare system when diagnoses are developed and medical interventions are selected, RWD are massive and in many regards typical big data. The use of artificial intelligence (AI) to analyze RWD seems an obvious choice. It promises new insights into medical need, drivers of diseases, and new opportunities for pharmacological interventions. When put into practice RWD analyses are challenging. The distributed generation of data, under sub-optimally standardized conditions in a patient-oriented but not information maximizing healthcare transaction, leads to a high level of sparseness and uncontrolled biases. We discuss why this needs to be addressed independent of the type of analysis approach. While classical statistical analysis and modeling approaches provide a rigorous framework for the handling of bias and sparseness, AI methods are not necessarily suited when applied naively. Special precautions need to be taken from choice of method until interpretation of results to prevent potentially harmful fallacies. The conscious use of prior medical subject matter expertise may also be required. Based on typical application examples we illustrate challenges and methodological considerations.","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093193","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 : 2023-09-28DOI: 10.1007/s13218-023-00811-y
Yousef Sadegheih, Leon Weninger, Dorit Merhof
Abstract Diffusion magnetic resonance imaging (dMRI) is developing into one of the most important non-invasive tools for clinical brain research. This development is supported by a project funded by the German Research Foundation, in which four major obstacles related to dMRI data were addressed: (1) the lack of transferability of dMRI data between clinical sites, (2) the lack of training and label data, (3) the potential of complex diffusion data, and (4) the integration of spherical signals in neural networks to improve accuracy. To overcome the problem of different MRI systems producing slightly varying data, the project developed a method for harmonizing MRI signals. To address the issue of limited ground truth data, a framework was developed to synthesize individual diffusion data and complete datasets based on important diffusion characteristics and statistics. The integration of complex signals, often discarded during acquisition, to improve reconstruction was also explored. Finally, new methods were developed to preserve the spherical character of the diffusion data in the DL model. The resulting methods are intended to improve the usability of diffusion imaging data and to enable the creation of processing pipelines for dMRI data in clinical studies and clinical practice.
{"title":"Novel Deep Learning Approaches for Analyzing Diffusion Imaging Data","authors":"Yousef Sadegheih, Leon Weninger, Dorit Merhof","doi":"10.1007/s13218-023-00811-y","DOIUrl":"https://doi.org/10.1007/s13218-023-00811-y","url":null,"abstract":"Abstract Diffusion magnetic resonance imaging (dMRI) is developing into one of the most important non-invasive tools for clinical brain research. This development is supported by a project funded by the German Research Foundation, in which four major obstacles related to dMRI data were addressed: (1) the lack of transferability of dMRI data between clinical sites, (2) the lack of training and label data, (3) the potential of complex diffusion data, and (4) the integration of spherical signals in neural networks to improve accuracy. To overcome the problem of different MRI systems producing slightly varying data, the project developed a method for harmonizing MRI signals. To address the issue of limited ground truth data, a framework was developed to synthesize individual diffusion data and complete datasets based on important diffusion characteristics and statistics. The integration of complex signals, often discarded during acquisition, to improve reconstruction was also explored. Finally, new methods were developed to preserve the spherical character of the diffusion data in the DL model. The resulting methods are intended to improve the usability of diffusion imaging data and to enable the creation of processing pipelines for dMRI data in clinical studies and clinical practice.","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387363","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 : 2023-08-19DOI: 10.1007/s13218-023-00808-7
Johannes Chen, Maximilian Lowin, Domenic Kellner, O. Hinz, E. Adam, Angelo Ippolito, Katharina Wenger-Alakmeh
{"title":"Designing Expert-Augmented Clinical Decision Support Systems to Predict Mortality Risk in ICUs","authors":"Johannes Chen, Maximilian Lowin, Domenic Kellner, O. Hinz, E. Adam, Angelo Ippolito, Katharina Wenger-Alakmeh","doi":"10.1007/s13218-023-00808-7","DOIUrl":"https://doi.org/10.1007/s13218-023-00808-7","url":null,"abstract":"","PeriodicalId":192555,"journal":{"name":"KI - Künstliche Intelligenz","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126227842","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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