Pub Date : 2023-06-06DOI: 10.1109/MECO58584.2023.10155095
I. Enesi, A. Kuqi, Ambra Korra
Photogrammetry is a continuously growing 3D reconstruction technique, due to the simplicity and low costs of hardware and software parts. It is defined as the collection of physical information from 2D photos to generate 3D reconstructions. There are various software options available to perform this task, each with its own specifications in terms of quality, performance, and price. In 3D reconstruction, one of the most determining factors is the accuracy of dimensions, especially when it comes to the field of spare parts or prostheses in medicine. Undoubtedly there are many concurrent factors that establish the final precision, present in both hardware and software. Determining the dimensions of the reconstructed object, especially when the object in focus is small and has a complex geometric shape, is one of the main tasks. To achieve the 3D reconstruction, we will utilize open-source software such as Meshroom combined with measurements taken using MeshLab, along with proprietary software like Agisoft. Experimental results show that Agisoft performs better than Meshroom&Meshlab, offering more optimization techniques, reducing processing time and a higher visual quality in the reconstructed 3D object as well as a higher accuracy in size measurement.
{"title":"The Background Role in 3D Object Reconstruction","authors":"I. Enesi, A. Kuqi, Ambra Korra","doi":"10.1109/MECO58584.2023.10155095","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10155095","url":null,"abstract":"Photogrammetry is a continuously growing 3D reconstruction technique, due to the simplicity and low costs of hardware and software parts. It is defined as the collection of physical information from 2D photos to generate 3D reconstructions. There are various software options available to perform this task, each with its own specifications in terms of quality, performance, and price. In 3D reconstruction, one of the most determining factors is the accuracy of dimensions, especially when it comes to the field of spare parts or prostheses in medicine. Undoubtedly there are many concurrent factors that establish the final precision, present in both hardware and software. Determining the dimensions of the reconstructed object, especially when the object in focus is small and has a complex geometric shape, is one of the main tasks. To achieve the 3D reconstruction, we will utilize open-source software such as Meshroom combined with measurements taken using MeshLab, along with proprietary software like Agisoft. Experimental results show that Agisoft performs better than Meshroom&Meshlab, offering more optimization techniques, reducing processing time and a higher visual quality in the reconstructed 3D object as well as a higher accuracy in size measurement.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117014212","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10154960
E. Kang, Simon Hacks
Key to reliable manufacturing systems is ensuring the trustworthiness of the decision-making and control mechanisms that supplant human control, i.e., systems need to remain safe while being resilient against functional failures, unpredictable changes, and cyber-security threats. We present a correct-by-construction approach to identify and analyze essential requirements that ensure the safety and security of a manufacturing system using a combination of System Theoretic Process Analysis (STPA)-based verification and attack simulation. This approach utilizes formal modeling and analysis to remove ambiguities in the requirement and specify safety properties that should be satisfied in system design. Potential safety hazards are identified using STPA-based model checking and possible cyber-security threats are diagnosed through attack simulation. Additional safety and security constraints inhibiting the hazards and threats are generated to improve the system design accordingly. Our approach is demonstrated on an autonomous assembly line system case study.
{"title":"Safety & Security Analysis of a Manufacturing System using Formal Verification and Attack-Simulation","authors":"E. Kang, Simon Hacks","doi":"10.1109/MECO58584.2023.10154960","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10154960","url":null,"abstract":"Key to reliable manufacturing systems is ensuring the trustworthiness of the decision-making and control mechanisms that supplant human control, i.e., systems need to remain safe while being resilient against functional failures, unpredictable changes, and cyber-security threats. We present a correct-by-construction approach to identify and analyze essential requirements that ensure the safety and security of a manufacturing system using a combination of System Theoretic Process Analysis (STPA)-based verification and attack simulation. This approach utilizes formal modeling and analysis to remove ambiguities in the requirement and specify safety properties that should be satisfied in system design. Potential safety hazards are identified using STPA-based model checking and possible cyber-security threats are diagnosed through attack simulation. Additional safety and security constraints inhibiting the hazards and threats are generated to improve the system design accordingly. Our approach is demonstrated on an autonomous assembly line system case study.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114520651","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10154979
L. Guiducci, Giulia Palma, Marta Stentati, A. Rizzo, S. Paoletti
Optimal management of renewable energy is an important pillar of environmental sustainability, as it maximizes the use of clean and renewable resources. This article considers the optimal management of a renewable energy community that receives incentives for virtual self-consumption. This incentive scheme has been adopted in the Italian energy framework since 2020. The optimization problem maximizes the social welfare of the community, which includes the incentive together with the exploitation of renewable energy sources. A key role in such a problem is played by the battery energy storage system (BESS), which is crucial in balancing supply and demand. We propose a novel Reinforcement Learning-based BESS controller, aiming at maximizing the community social welfare by acting in real time and relying only on data available at the current time-step. Through different simulations in several scenarios, we demonstrate the effectiveness of our approach and its ability to outperform a state-of-the-art rule-based controller. Moreover, we assess the proposed approach by comparing its performance with that of the actual, though ideal, optimal control policy based on an oracle providing perfect knowledge of future data.
{"title":"A Reinforcement Learning approach to the management of Renewable Energy Communities","authors":"L. Guiducci, Giulia Palma, Marta Stentati, A. Rizzo, S. Paoletti","doi":"10.1109/MECO58584.2023.10154979","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10154979","url":null,"abstract":"Optimal management of renewable energy is an important pillar of environmental sustainability, as it maximizes the use of clean and renewable resources. This article considers the optimal management of a renewable energy community that receives incentives for virtual self-consumption. This incentive scheme has been adopted in the Italian energy framework since 2020. The optimization problem maximizes the social welfare of the community, which includes the incentive together with the exploitation of renewable energy sources. A key role in such a problem is played by the battery energy storage system (BESS), which is crucial in balancing supply and demand. We propose a novel Reinforcement Learning-based BESS controller, aiming at maximizing the community social welfare by acting in real time and relying only on data available at the current time-step. Through different simulations in several scenarios, we demonstrate the effectiveness of our approach and its ability to outperform a state-of-the-art rule-based controller. Moreover, we assess the proposed approach by comparing its performance with that of the actual, though ideal, optimal control policy based on an oracle providing perfect knowledge of future data.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124862538","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10154936
Veselin N. Ivanović, Nevena Radović
New methodology in the creation of the region of support of the optimal (Wiener) filter used in the estimation of two-dimensional (2D) nonstationary signals is considered. The methodology is based on the symmetrical spreading of the filter's region of support around the detected local frequency (LF) of the estimated 2D signal and on simultaneous inclusion (in the region of support creation) of points having equal distance from the detected LF. The proposed filter provides signal reconstruction free of distortion of the original (noiseless) signal.
{"title":"New Design Methodology of an Advanced Optimal Space/Spatial-Frequency Filter","authors":"Veselin N. Ivanović, Nevena Radović","doi":"10.1109/MECO58584.2023.10154936","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10154936","url":null,"abstract":"New methodology in the creation of the region of support of the optimal (Wiener) filter used in the estimation of two-dimensional (2D) nonstationary signals is considered. The methodology is based on the symmetrical spreading of the filter's region of support around the detected local frequency (LF) of the estimated 2D signal and on simultaneous inclusion (in the region of support creation) of points having equal distance from the detected LF. The proposed filter provides signal reconstruction free of distortion of the original (noiseless) signal.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121657804","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10155104
M. Samie, Akbar Sheikh-Akbari, K. K. Singh, E. Ofoegbu
Applications in harsh environments greatly suffer from intermittency faults in their interconnections/wirings. Due to the erratic behavior of intermittency that causes signal irregularities, it is tough to distinguish irregularities from an actual transmitted signal, particularly in the earlier stages where signal abnormalities mainly resemble noise. This paper explores step changes in the resistance of a wire caused by broken strands as a failure parameter. Thus, a test rig was designed to emulate the ageing mechanism of the wire. with results of the study highlighting that resistance step changes could effectively be used to locate intermittency faults in low power cable applications.
{"title":"Experimental Results of an Intermittency Fault Detection and Isolation Test Rig for Low Power No-Fault-Found Applications","authors":"M. Samie, Akbar Sheikh-Akbari, K. K. Singh, E. Ofoegbu","doi":"10.1109/MECO58584.2023.10155104","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10155104","url":null,"abstract":"Applications in harsh environments greatly suffer from intermittency faults in their interconnections/wirings. Due to the erratic behavior of intermittency that causes signal irregularities, it is tough to distinguish irregularities from an actual transmitted signal, particularly in the earlier stages where signal abnormalities mainly resemble noise. This paper explores step changes in the resistance of a wire caused by broken strands as a failure parameter. Thus, a test rig was designed to emulate the ageing mechanism of the wire. with results of the study highlighting that resistance step changes could effectively be used to locate intermittency faults in low power cable applications.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"522 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131803360","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10155077
C. Panagiotou, Lidia Pocero Fraile, C. Koulamas
Safety hazards in working environments introduce significant risks for the health of the people that are active in these environments. The prevention of such incidents becomes a high priority for safety officers. The prevention mechanisms regard compliance with safety standards and auditing of the conditions of the workplaces. This paper, presents an AI based solution that aims to identify safety risks and is able to operate in the edge focusing mobile devices. The presented approach trains an SSD Mobilenet v2 based model with a data set focused on the detection of conditions that might introduce risks for the people and the infrastructure. The trained model has been integrated in a mobile application to utilize the high quality video streams capture by modern smartphones.
{"title":"Detecting Health & Safety Hazards through AI and Edge Computing on Mobile Devices","authors":"C. Panagiotou, Lidia Pocero Fraile, C. Koulamas","doi":"10.1109/MECO58584.2023.10155077","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10155077","url":null,"abstract":"Safety hazards in working environments introduce significant risks for the health of the people that are active in these environments. The prevention of such incidents becomes a high priority for safety officers. The prevention mechanisms regard compliance with safety standards and auditing of the conditions of the workplaces. This paper, presents an AI based solution that aims to identify safety risks and is able to operate in the edge focusing mobile devices. The presented approach trains an SSD Mobilenet v2 based model with a data set focused on the detection of conditions that might introduce risks for the people and the infrastructure. The trained model has been integrated in a mobile application to utilize the high quality video streams capture by modern smartphones.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128205402","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10154963
Valeryi M. Bezruk, Stanislav A. Krivenko, Oleksandr O. Kyrsanov, Sergii S. Kryvenko, L. Kryvenko
Data exploration, wrangling, and interactive analysis and visualization were made in an integrated way. How to plot feature importance in Python calculated by the XGBoost model was considered. Features engineering in a dataset has been improved with Haar Transform. The area under the receiver operating characteristic curve was increased from 0.44 for the baseline model to 0.82 for Haar Transform Model.
{"title":"Training the Machine Learning Model for Clinical IoT Data and Device Interoperability","authors":"Valeryi M. Bezruk, Stanislav A. Krivenko, Oleksandr O. Kyrsanov, Sergii S. Kryvenko, L. Kryvenko","doi":"10.1109/MECO58584.2023.10154963","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10154963","url":null,"abstract":"Data exploration, wrangling, and interactive analysis and visualization were made in an integrated way. How to plot feature importance in Python calculated by the XGBoost model was considered. Features engineering in a dataset has been improved with Haar Transform. The area under the receiver operating characteristic curve was increased from 0.44 for the baseline model to 0.82 for Haar Transform Model.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130585261","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10154938
A. Tihak, D. Boskovic
The paper evaluates statistical significance of the differences in the feature values necessary to differentiate the signals corresponding to cardiac arrhythmia (AR) and atrial fibrillation (AF). The initial set of heart rate variability (HRV) features includes time and frequency domain metrics, as well as geometric metrics based on the Poincare diagram. Due to non-uniformity of the heart rate signal, frequency domain features are calculated using two approaches: the Lomb-Scargle method for spectral analysis for non-uniform signals, and Welch method for uniform signals, but after the signal interpolation and resampling. Selection of an appropriate statistical test was depending on the distribution of feature values. Normal distribution allowed use of parametric ANOVA test and otherwise non-parametric Wilcoxon–Mann–Whitney test were used. The statistical tests indicated statistically significant difference between the two observed groups of signals of interest with respect to the evaluated feature. The success of the classification depends on the well-chosen features according to their importance. In the paper, statistical tests resulted in selection of 27 features out of the initial 51. The proposed set of features could be used for the classification between the AR and AF signals to assist diagnosis of the mentioned heart diseases.
{"title":"Statistical-based HRV Feature Importance Evaluation for Arrhythmia and Atrial Fibrillation Classification","authors":"A. Tihak, D. Boskovic","doi":"10.1109/MECO58584.2023.10154938","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10154938","url":null,"abstract":"The paper evaluates statistical significance of the differences in the feature values necessary to differentiate the signals corresponding to cardiac arrhythmia (AR) and atrial fibrillation (AF). The initial set of heart rate variability (HRV) features includes time and frequency domain metrics, as well as geometric metrics based on the Poincare diagram. Due to non-uniformity of the heart rate signal, frequency domain features are calculated using two approaches: the Lomb-Scargle method for spectral analysis for non-uniform signals, and Welch method for uniform signals, but after the signal interpolation and resampling. Selection of an appropriate statistical test was depending on the distribution of feature values. Normal distribution allowed use of parametric ANOVA test and otherwise non-parametric Wilcoxon–Mann–Whitney test were used. The statistical tests indicated statistically significant difference between the two observed groups of signals of interest with respect to the evaluated feature. The success of the classification depends on the well-chosen features according to their importance. In the paper, statistical tests resulted in selection of 27 features out of the initial 51. The proposed set of features could be used for the classification between the AR and AF signals to assist diagnosis of the mentioned heart diseases.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"25 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116592171","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10154898
Natalia Podzharaya, Anastasiia Sochenkova, N. Zaric
New technologies especially in IT spawn new modern gadgets, which raising number causes the growth of energy utilization. Therefore, it is important to produce energy safely for the nature. In this paper we consider and compare energy production and usage for usual energy sources and for alternative energy sources also known as renewables. We also consider how the share of the renewable energy usage changes in time. According to the analysis we come to conclusion what sustainable development for new technologies applied for ecology should be like. We also consider SEE-concept in sustainable development and main goals of sustainability connected and applied to modern technologies.
{"title":"Analysis of Alternative Energy Systems Usage Leading to Sustainable Development Goals and Environmental Policies in Ecology","authors":"Natalia Podzharaya, Anastasiia Sochenkova, N. Zaric","doi":"10.1109/MECO58584.2023.10154898","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10154898","url":null,"abstract":"New technologies especially in IT spawn new modern gadgets, which raising number causes the growth of energy utilization. Therefore, it is important to produce energy safely for the nature. In this paper we consider and compare energy production and usage for usual energy sources and for alternative energy sources also known as renewables. We also consider how the share of the renewable energy usage changes in time. According to the analysis we come to conclusion what sustainable development for new technologies applied for ecology should be like. We also consider SEE-concept in sustainable development and main goals of sustainability connected and applied to modern technologies.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"369 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120942494","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 digitalization of educational processes has enabled the generation of large datasets that can be used to improve processes in academic environments. One particular problem is the prediction of student performances based on historical data. Efficient student performance prediction can be used not only to prevent dropouts at an early stage, but it can also help perspective students to determine the fields in which they can have high academic performance and build successful student profile. Due to large and diverse data, this process has to be conducted with high degree of automations. Therefore, in this paper we have conducted an extensive survey on the impact of AI and ML techniques in student performance prediction, with primary aim to detect opportunities, good practices, but most importantly to identify gaps and remaining research challenges with the ultimate goal to define an effective framework for a student performance prediction system.
{"title":"Student Performance Prediction Using AI and ML: State of the Art","authors":"Arber Hoti, Xhemal Zenuni, Mentor Hamiti, Jaumin Ajdari","doi":"10.1109/MECO58584.2023.10154933","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10154933","url":null,"abstract":"The digitalization of educational processes has enabled the generation of large datasets that can be used to improve processes in academic environments. One particular problem is the prediction of student performances based on historical data. Efficient student performance prediction can be used not only to prevent dropouts at an early stage, but it can also help perspective students to determine the fields in which they can have high academic performance and build successful student profile. Due to large and diverse data, this process has to be conducted with high degree of automations. Therefore, in this paper we have conducted an extensive survey on the impact of AI and ML techniques in student performance prediction, with primary aim to detect opportunities, good practices, but most importantly to identify gaps and remaining research challenges with the ultimate goal to define an effective framework for a student performance prediction system.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132410352","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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