Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.03.071
Benedikt Ohse , Jürgen Kampe , Christopher Schneider
Performance spaces represent the full range of attainable performances of an integrated analog circuit, including gain and bandwidth. Based on the knowledge of the optimal and least favorable values, simplified simulation models can be employed to optimize the system design process. We present a modified variant of the well-established Normal-Boundary Intersection method, which is used to approximate five-dimensional performance spaces in reasonable time. Based on these approximations, we develop a concept for implementing a system-level simulation that is used to design an operational transconductance amplifier for an analog-to-digital converter to improve its sampling rate. To demonstrate the usability of our concept, we perform several numerical experiments and visualize the data in parallel coordinates plots for better user comprehension.
{"title":"Performance Space-Based ADC Development","authors":"Benedikt Ohse , Jürgen Kampe , Christopher Schneider","doi":"10.1016/j.ifacol.2025.03.071","DOIUrl":"10.1016/j.ifacol.2025.03.071","url":null,"abstract":"<div><div>Performance spaces represent the full range of attainable performances of an integrated analog circuit, including gain and bandwidth. Based on the knowledge of the optimal and least favorable values, simplified simulation models can be employed to optimize the system design process. We present a modified variant of the well-established Normal-Boundary Intersection method, which is used to approximate five-dimensional performance spaces in reasonable time. Based on these approximations, we develop a concept for implementing a system-level simulation that is used to design an operational transconductance amplifier for an analog-to-digital converter to improve its sampling rate. To demonstrate the usability of our concept, we perform several numerical experiments and visualize the data in parallel coordinates plots for better user comprehension.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 415-420"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.03.068
M. Maton , A. Vande Wouwer
Metabolic modeling is a powerful tool for understanding microbial metabolism and is particularly appealing to a wide range of applications, from biotechnology and medicine to environmental science and sustainability. In that context, the elaboration of metabolic networks is essential despite the challenges underlying their reconstruction. While the development of genome-scale networks is computationally costly, small networks are often oversimplified, limiting their use in industrial applications. For this purpose, this paper suggests a method to identify metabolic networks of intermediate size by combining biological knowledge and a series of constraint-based methods in an iterative strategy allowing the refinement of the network definition. The present study focuses on the mathematical modeling of photosynthetic eukaryotic organisms and leads to a detailed network including energy aspects such as the proton motive force. The procedure is effective, yielding promising results while metabolic analyses provide consistent predictive capabilities of the network, in concordance with existing studies.
{"title":"Mathematical Modeling of Photosynthetic Eukaryotic Microorganisms Using Metabolic Networks","authors":"M. Maton , A. Vande Wouwer","doi":"10.1016/j.ifacol.2025.03.068","DOIUrl":"10.1016/j.ifacol.2025.03.068","url":null,"abstract":"<div><div>Metabolic modeling is a powerful tool for understanding microbial metabolism and is particularly appealing to a wide range of applications, from biotechnology and medicine to environmental science and sustainability. In that context, the elaboration of metabolic networks is essential despite the challenges underlying their reconstruction. While the development of genome-scale networks is computationally costly, small networks are often oversimplified, limiting their use in industrial applications. For this purpose, this paper suggests a method to identify metabolic networks of intermediate size by combining biological knowledge and a series of constraint-based methods in an iterative strategy allowing the refinement of the network definition. The present study focuses on the mathematical modeling of photosynthetic eukaryotic organisms and leads to a detailed network including energy aspects such as the proton motive force. The procedure is effective, yielding promising results while metabolic analyses provide consistent predictive capabilities of the network, in concordance with existing studies.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 397-402"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.03.061
Anargiros I. Delis , Nikolaos Bekiaris-Liberis
We demonstrate the epidemics transport effect in closed spaces, performing different numerical experiments via development of a coupled, macroscopic partial differential equation (PDE) model of crowd flow, epidemics spreading dynamics, and ventilation air flow dynamics. We present numerical approximations for the coupled model with which we perform different numerical tests. In particular, we study the effect of different ventilation rates in epidemics transport (in time and space), also quantifying the infection risk via computing the total number of exposed individuals (in time and space), predicted by the model. We then discuss how the model used and the numerical results obtained could be utilized, in certain scenarios, for design of epidemics transport control strategies via manipulation of the ventilation air-flow field.
{"title":"Macroscopic Modeling and Simulation-Based Investigation of Epidemics Transport Dynamics in the Presence of Ventilation⁎","authors":"Anargiros I. Delis , Nikolaos Bekiaris-Liberis","doi":"10.1016/j.ifacol.2025.03.061","DOIUrl":"10.1016/j.ifacol.2025.03.061","url":null,"abstract":"<div><div>We demonstrate the epidemics transport effect in closed spaces, performing different numerical experiments via development of a coupled, macroscopic partial differential equation (PDE) model of crowd flow, epidemics spreading dynamics, and ventilation air flow dynamics. We present numerical approximations for the coupled model with which we perform different numerical tests. In particular, we study the effect of different ventilation rates in epidemics transport (in time and space), also quantifying the infection risk via computing the total number of exposed individuals (in time and space), predicted by the model. We then discuss how the model used and the numerical results obtained could be utilized, in certain scenarios, for design of epidemics transport control strategies via manipulation of the ventilation air-flow field.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 355-360"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.03.079
L.G. van Willigenburg , J.D. Stigter
A sensitivity-based algorithm to establish state controllability is extended to establish output controllability being the ability to control the outputs of a nonlinear dynamical system instead of the full state. Due to the exceptional efficiency of the sensitivity-based algorithm, large-scale nonlinear dynamical systems can be handled, as demonstrated by several examples in this paper. As a final contribution, this paper starts with a simple analysis of state and output controllability properties of nonlinear dynamical systems in terms of connectivity’s and sensitivities. The latter relate directly to the sensitivity-based algorithm.
{"title":"Output controllability of large-scale nonlinear dynamical systems: analysis, computation and examples","authors":"L.G. van Willigenburg , J.D. Stigter","doi":"10.1016/j.ifacol.2025.03.079","DOIUrl":"10.1016/j.ifacol.2025.03.079","url":null,"abstract":"<div><div>A sensitivity-based algorithm to establish state controllability is extended to establish output controllability being the ability to control the outputs of a nonlinear dynamical system instead of the full state. Due to the exceptional efficiency of the sensitivity-based algorithm, large-scale nonlinear dynamical systems can be handled, as demonstrated by several examples in this paper. As a final contribution, this paper starts with a simple analysis of state and output controllability properties of nonlinear dynamical systems in terms of connectivity’s and sensitivities. The latter relate directly to the sensitivity-based algorithm.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 463-468"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.03.058
Gia Huy Mike Huynh , Niklas Fahse , Jonas Kneifl , Joachim Linn , Jörg Fehr
It has been shown that working with databases from heterogeneous sources of varying fidelity can be leveraged in multi-fidelity surrogate models to enhance the high-fidelity prediction accuracy or, equivalently, to reduce the amount of high-fidelity data and thus computational effort required while maintaining accuracy. In contrast, this contribution leverages low-fidelity data queried on a larger feature space to realize data-driven multi-fidelity surrogate models with a fallback option in regimes where high-fidelity data is unavailable. Accordingly, methodologies are introduced to fulfill this task and effectively resolve the contradictions, that inherently arise in multi-fidelity databases. In particular, the databases considered in this contribution feature two levels of fidelity with a defined hierarchy, where data from a high-fidelity source is, when available, prioritized over low-fidelity data. The proposed surrogate model architectures are illustrated first with a toy problem and further examined in the context of an engineering use case in autonomous driving, where the human-seat interaction is evaluated using a data-driven surrogate model, that is trained through an active learning approach. It is shown, that two proposed architectures achieve an improvement in accuracy on high-fidelity data while simultaneously performing well where high-fidelity data is unavailable compared to a naive approach.
{"title":"Multi-Fidelity Surrogate Model for Representing Hierarchical and Conflicting Databases to Approximate Human-Seat Interaction⁎","authors":"Gia Huy Mike Huynh , Niklas Fahse , Jonas Kneifl , Joachim Linn , Jörg Fehr","doi":"10.1016/j.ifacol.2025.03.058","DOIUrl":"10.1016/j.ifacol.2025.03.058","url":null,"abstract":"<div><div>It has been shown that working with databases from heterogeneous sources of varying fidelity can be leveraged in multi-fidelity surrogate models to enhance the high-fidelity prediction accuracy or, equivalently, to reduce the amount of high-fidelity data and thus computational effort required while maintaining accuracy. In contrast, this contribution leverages low-fidelity data queried on a larger feature space to realize data-driven multi-fidelity surrogate models with a fallback option in regimes where high-fidelity data is unavailable. Accordingly, methodologies are introduced to fulfill this task and effectively resolve the contradictions, that inherently arise in multi-fidelity databases. In particular, the databases considered in this contribution feature two levels of fidelity with a defined hierarchy, where data from a high-fidelity source is, when available, prioritized over low-fidelity data. The proposed surrogate model architectures are illustrated first with a toy problem and further examined in the context of an engineering use case in autonomous driving, where the human-seat interaction is evaluated using a data-driven surrogate model, that is trained through an active learning approach. It is shown, that two proposed architectures achieve an improvement in accuracy on high-fidelity data while simultaneously performing well where high-fidelity data is unavailable compared to a naive approach.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 337-342"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.03.026
A. Lepsien , A. Schaum
Spray drying is a common a technique in process engineering, involving analysis tasks for large-scale multiphysics mechanisms, typically addressed using Computational Fluid Dynamics (CFD) software based on Finite Element Method (FEM) simulation. To enable e.g. real-time control schemes to take into account spatio-temporal behavior, efficient Reduced Order Models (ROMs) need to be identified, which in the best case retain physical information and interpretability as much as possible. In this paper, this problem is approached for a simplified two-dimensional spray dryer CFD model. Two different versions of Dynamic Mode Decomposition with control (DMDc) are compared, namely (i) a direct application of DMDc to snapshot data for temperature and humidity profiles, including droplet populations as moving and distributed inputs, and (ii) an adaptation exploring structure preservation based on the physical insight to the underlying mechanisms. It turns out that for the presented case study the structure preserving DMDc yields improved results.
{"title":"Reduced order modeling of spray drying","authors":"A. Lepsien , A. Schaum","doi":"10.1016/j.ifacol.2025.03.026","DOIUrl":"10.1016/j.ifacol.2025.03.026","url":null,"abstract":"<div><div>Spray drying is a common a technique in process engineering, involving analysis tasks for large-scale multiphysics mechanisms, typically addressed using Computational Fluid Dynamics (CFD) software based on Finite Element Method (FEM) simulation. To enable e.g. real-time control schemes to take into account spatio-temporal behavior, efficient Reduced Order Models (ROMs) need to be identified, which in the best case retain physical information and interpretability as much as possible. In this paper, this problem is approached for a simplified two-dimensional spray dryer CFD model. Two different versions of Dynamic Mode Decomposition with control (DMDc) are compared, namely (i) a direct application of DMDc to snapshot data for temperature and humidity profiles, including droplet populations as moving and distributed inputs, and (ii) an adaptation exploring structure preservation based on the physical insight to the underlying mechanisms. It turns out that for the presented case study the structure preserving DMDc yields improved results.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 145-150"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.03.043
Thomas Benninger , Martin Horn , Markus Reichhartinger
A brief review of existing medical dosage guidelines for Graves’ disease is conducted. This motivates to derive a recommendation strategy and a performance assessment via limit cycle stability analysis. For this purpose, an existing mathematical treatment model is simplified by incorporating medical aspects. From an application point of view, this is related to suboptimal treatments which can be approximated by a proportional controller in combination with a time-discretization of the closed-loop model for incorporating the effect of regular appointments with a physician. The presented results highlight critical aspects to be considered during treatment.
{"title":"Limit Cycle Analysis of Guideline-based treated Graves’ Disease Patients","authors":"Thomas Benninger , Martin Horn , Markus Reichhartinger","doi":"10.1016/j.ifacol.2025.03.043","DOIUrl":"10.1016/j.ifacol.2025.03.043","url":null,"abstract":"<div><div>A brief review of existing medical dosage guidelines for Graves’ disease is conducted. This motivates to derive a recommendation strategy and a performance assessment via limit cycle stability analysis. For this purpose, an existing mathematical treatment model is simplified by incorporating medical aspects. From an application point of view, this is related to suboptimal treatments which can be approximated by a proportional controller in combination with a time-discretization of the closed-loop model for incorporating the effect of regular appointments with a physician. The presented results highlight critical aspects to be considered during treatment.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 247-252"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.03.044
Clara Horvath , Marie-Sophie Kohlmayer , Andreas Körner
This paper presents an extensive global sensitivity analysis of a mathematical model describing the female endocrine cycle. The model, based on a system of differential equations, captures the dynamics of Luteinizing Hormone, Follicle-Stimulating Hormone, Estrogen, and Progesterone, along with their regulatory feedback mechanisms. We employed three complementary methods – Latin Hypercube Sampling, Partial Rank Correlation Coefficient, and extended Fourier Amplitude Sensitivity Test – to analyze both linear and non-linear parameter-output relationships. The extended Fourier Amplitude Sensitivity Test method, in particular, revealed non-monotonic and non-linear interactions between input and output, highlighting the complexity of the hypothalamus-pituitary-ovary axis. Our findings offer significant insights for future model refinement and pave mathematical ways towards better understanding of the female endocrine cycle and potential clinical applications, especially in the diagnosis and treatment of reproductive disorders.
{"title":"Sensitivity Analysis of a Mathematical Model Representing the Female Endocrine Cycle","authors":"Clara Horvath , Marie-Sophie Kohlmayer , Andreas Körner","doi":"10.1016/j.ifacol.2025.03.044","DOIUrl":"10.1016/j.ifacol.2025.03.044","url":null,"abstract":"<div><div>This paper presents an extensive global sensitivity analysis of a mathematical model describing the female endocrine cycle. The model, based on a system of differential equations, captures the dynamics of Luteinizing Hormone, Follicle-Stimulating Hormone, Estrogen, and Progesterone, along with their regulatory feedback mechanisms. We employed three complementary methods – Latin Hypercube Sampling, Partial Rank Correlation Coefficient, and extended Fourier Amplitude Sensitivity Test – to analyze both linear and non-linear parameter-output relationships. The extended Fourier Amplitude Sensitivity Test method, in particular, revealed non-monotonic and non-linear interactions between input and output, highlighting the complexity of the hypothalamus-pituitary-ovary axis. Our findings offer significant insights for future model refinement and pave mathematical ways towards better understanding of the female endocrine cycle and potential clinical applications, especially in the diagnosis and treatment of reproductive disorders.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 253-258"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.03.017
Klaus Röbenack , Daniel Gerbet
The Ćuk converter is a particularly interesting converter, as it can be used to generate voltages which could be above or below the supply voltage. The chokes take up the most space when setting up such a converter. In this article, the chokes are replaced by transmission lines. This approach leads to a new converter topology and other mathematical models. The new distributed converter model can later on be discretized for a physical implementation to replace the two large inductances with numerous smaller inductances as suggested by Sander (2012).
{"title":"Nonlinear Coupled PDE-ODE Model of a Distributed Ćuk Converter","authors":"Klaus Röbenack , Daniel Gerbet","doi":"10.1016/j.ifacol.2025.03.017","DOIUrl":"10.1016/j.ifacol.2025.03.017","url":null,"abstract":"<div><div>The Ćuk converter is a particularly interesting converter, as it can be used to generate voltages which could be above or below the supply voltage. The chokes take up the most space when setting up such a converter. In this article, the chokes are replaced by transmission lines. This approach leads to a new converter topology and other mathematical models. The new distributed converter model can later on be discretized for a physical implementation to replace the two large inductances with numerous smaller inductances as suggested by Sander (2012).</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 91-96"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.03.073
Martin Steiger , Hans-Georg Brachtendorf
Accurate models of semiconductor devices are a key component in modern circuit development. Although parameters of such models are often derived from fundamental physical laws, device geometry or material properties, empirical models from extensive measurements have become increasingly popular. Sparse identification of non-linear dynamics (SINDy) is one approach that emphasizes interpretability. Although several extensions have been developed for this procedure, it still lacks the ability to accommodate function compositions that are very common amongst established semiconductor models. This work introduces an approach fusing the capabilities of neural networks with the core principles of SINDy to address this shortcoming. The results are compared to well-known bipolar transistor models such as Gummel-Poon and indicate promising approximation capabilities.
{"title":"System Identification with SINDy Neural Networks for Transistor Modeling","authors":"Martin Steiger , Hans-Georg Brachtendorf","doi":"10.1016/j.ifacol.2025.03.073","DOIUrl":"10.1016/j.ifacol.2025.03.073","url":null,"abstract":"<div><div>Accurate models of semiconductor devices are a key component in modern circuit development. Although parameters of such models are often derived from fundamental physical laws, device geometry or material properties, empirical models from extensive measurements have become increasingly popular. Sparse identification of non-linear dynamics (SINDy) is one approach that emphasizes interpretability. Although several extensions have been developed for this procedure, it still lacks the ability to accommodate function compositions that are very common amongst established semiconductor models. This work introduces an approach fusing the capabilities of neural networks with the core principles of SINDy to address this shortcoming. The results are compared to well-known bipolar transistor models such as Gummel-Poon and indicate promising approximation capabilities.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 427-432"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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