Advances in Multidisciplinary and scientific Research Journal Publication最新文献

{"title":"A Digital Transformative Tool in Achieving Sustainable Development Goals","authors":"Victor Moboluwarin Olasehinde","doi":"10.22624/aims/csean-smart2023p16","DOIUrl":"https://doi.org/10.22624/aims/csean-smart2023p16","url":null,"abstract":"inclusive economic growth, this abstract showcases the immense potential of digital tools in advancing the SDGs. It emphasizes the need for strategic partnerships between governments, organizations, and the private sector to leverage the power of technology for sustainable development. Furthermore, it discusses the challenges and risks associated with digital transformation, including issues of access, privacy, and cybersecurity, and the importance of addressing these concerns to ensure equitable and inclusive progress. Ultimately, this abstract demonstrates that digital transformation has emerged as a powerful force in accelerating progress toward sustainable development and calls for collective action to harness its full potential in building a better future for all. Keywords: Digital, Transformation, Tool, SDGs, Goals Proceedings Citation Format Olasehinde Victor Moboluwarinwa (2023): A Digital Transformative Tool in Achieving Sustainable Development Goals. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 135-142. https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/. dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P16","PeriodicalId":409325,"journal":{"name":"Advances in Multidisciplinary and scientific Research Journal Publication","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129041086","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}

{"title":"Addressing the Dilemma of a “Crisis within a crisis”: Exploring the Penetration Testing challenges in a Mobile Field Hospital Setting","authors":"N. B. Ahmed, N. Daclin, M. Olivaux, G. Dusserre","doi":"10.22624/aims/csean-smart2023p9","DOIUrl":"https://doi.org/10.22624/aims/csean-smart2023p9","url":null,"abstract":"This paper focuses on evaluating the security challenges faced by mobile field hospitals, which play a crucial role in emergency response and disaster management in remote and austere environments. The authors conducted a penetration test using the Open Source Security Testing Methodology Manual (OSSTMM) framework to assess the security posture of a mobile field hospital. The methodology employed in the study included a combination of automated and manual techniques such as network scanning, vulnerability assessments, social engineering, and exploitation. The penetration test revealed several security vulnerabilities in the mobile field hospital, including weak passwords, unpatched software, and inadequate network segmentation. Additionally, the study identified vulnerabilities in the hospital's medical devices and equipment, posing a risk of cyber-attacks that could disrupt operations and compromise patient safety. The results underscore the importance of implementing enhanced security measures in mobile field hospitals to mitigate cyber threats and ensure the uninterrupted functioning of medical operations during emergencies and disasters. This study provides a comprehensive analysis of the mobile field hospital's security posture using the OSSTMM framework and emphasizes the urgent need for improved security practices in such settings. Keywords: Cybersecurity, Healthcare, Penetration Testing, Mobile Field Hospitals, Data, Emergency Response, Open Source, Security Testing, Ethical Hacking, Security Frameworks, Proceedings Citation Format Ahmed, N.B., Daclin, N., Olivaux, M. & 4usserre, G. (2023): Addressing the Dilemma of a “Crisis within a crisis”: Exploring the Penetration Testing challenges in a Mobile Field Hospital Setting. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 67-80 https://www.csean.org.ng/. dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P9","PeriodicalId":409325,"journal":{"name":"Advances in Multidisciplinary and scientific Research Journal Publication","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134239946","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}

{"title":"Cybercrime On Social Media In Nigeria: Trends, Scams, Vulnerabilities and Prevention","authors":"Damilola Oni, Emmanuel Arshad, Bich-Ngoc Thi Pham","doi":"10.22624/aims/csean-smart2023p17","DOIUrl":"https://doi.org/10.22624/aims/csean-smart2023p17","url":null,"abstract":"Cybercrime on social media in Nigeria allow for an in-depth analysis of the role of social media in facilitating cybercrime activities in Nigeria, focusing on the prevalent scams, tactics used by cybercriminals, and the vulnerabilities that individuals and organizations face. It would also provide an opportunity to delve into the specific case studies, such as advance fee fraud, romance scams, and phishing, as well as exploring additional examples and their implications. By examining the impact of social media on cybercrime in Nigeria, this research contributes to a better understanding of the evolving nature of cyber threats in the digital landscape. It would also shed light on the specific challenges faced by Nigerian society, particularly with the high number of social media users in the country. Furthermore, the research proposes recommendations and strategies to enhance cybersecurity awareness, digital ethics, and protective measures for individuals, businesses, and policymakers to mitigate the risks associated with cybercrime. Keywords: Cybercrime, Social Media, Nigeria, Trends, Scams, Vulnerabilities, Prevention Proceedings Citation Format Oni Damilola, Arshad Emmanuel & Pham Bich Ngoc (2023): Cybercrime On Social Media In Nigeria: Trends, Scams, Vulnerabilities and Prevention. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 143-150. https://www.csean.org.ng/. dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P17","PeriodicalId":409325,"journal":{"name":"Advances in Multidisciplinary and scientific Research Journal Publication","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131833700","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}

{"title":"Rethinking Your Application Security Posture from Code to Cloud","authors":"O. Afolabi","doi":"10.22624/aims/csean-smart2023p5","DOIUrl":"https://doi.org/10.22624/aims/csean-smart2023p5","url":null,"abstract":"This paper explores the importance of reevaluating the application security posture in the context of modern software development practices and the shift towards cloud computing. It highlights the challenges posed by the evolving threat landscape and emphasizes the need for a comprehensive and proactive security strategy. The paper also discusses various techniques and best practices for securing applications from the code level to the cloud environment, encompassing secure coding practices, and the effective utilization of cloud security services. By adopting a holistic approach to application security, organizations can mitigate risks and protect their applications and data throughout the software development lifecycle. Keywords: Application Security, Software Development, Cloud Computing, Secure Coding, Vulnerability Assessment, Penetration Testing, Cloud Security Services Proceedings Citation Format Afolabi, O. (2023): Rethinking Your Application Security Posture from Code to Cloud. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 35-40. https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/ dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P5","PeriodicalId":409325,"journal":{"name":"Advances in Multidisciplinary and scientific Research Journal Publication","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115592290","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}

{"title":"A Machine Learning Approach to Fake News Detection Using Support Vector Machine (SVM) and Unsupervised Learning Model","authors":"I. Hosea, V. O. Waziri, I. Ismaila, J. Ojeniyi, M. Olalere, O. Adebayo","doi":"10.22624/aims/csean-smart2023p1","DOIUrl":"https://doi.org/10.22624/aims/csean-smart2023p1","url":null,"abstract":"Blogging over the years have become a lucrative business, the bloggers main aim is to attract people to his or her blog. In the quest for that, many blogs or page post fake news by using enticing captions to captivate the minds of readers. The captions are mostly displayed on social media and by clicking on the captions, the reader will be redirected to the blog where the news is been posted. The posted fake news can sometimes lead to misinformation to the public, violence, inciting conflict and extreme cases, death. Many works have been done on fake news detection with good accuracy rate in terms of detecting fake news. This paper presents an effective way of detecting fake news using Support Vector Machine (SVM) and Lagrangian Duality which yielded an accuracy of 95.74%. Keywords: Machine Learning, Fake News, Detection, Support Vector Machine (SVM), Security, Unsupervised Learning Model, Bloggers, Readers Proceedings Citation Format Gungbias H.I., Waziri, V.O., Ismaila, I., Ojeniyi, J., Olalere, M. & Adebayo, O. (2022): A Machine Learning Approach to Fake News Detection Using Support Vector Machine (SVM) and Unsupervised Learning Model. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 11-18 https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/ dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P1.","PeriodicalId":409325,"journal":{"name":"Advances in Multidisciplinary and scientific Research Journal Publication","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133083102","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}

{"title":"Internet of Things (IoT) Security and Private Concerns: An Overview","authors":"Y. O. Mosud, E. Ajulo, A. Yinusa","doi":"10.22624/aims/csean-smart2023p7","DOIUrl":"https://doi.org/10.22624/aims/csean-smart2023p7","url":null,"abstract":"The number of Internet of Things (IoT) applications has greatly increased during the last two decades. Globally, more than half a billion electronic devices are connected. Due to their constant connection to the Internet, IoT applications are frequently targeted by a wide range of conventional threats, such as Trojan horses, worms, malware, spyware, and malicious code injections. It is apparent that traditional threats offer services like accountability, authorization, authentication, and these factors are typically used to identify a subject and determine whether the person is qualified to access the object. With the large numbers of connected devices, it is likely that IoT systems may become more vulnerable to threats and attacks with high-frequency electromagnetic radiation and more advanced viruses. Therefore, there is a pressing need to address these privacy and security concerns because it is insufficient to rely on currently used conventional procedures. This study identifies the knowledge and research gaps in this field by exploring the various dangers that IOT devices are vulnerable to, how these dangers operate and create a recovery mechanism to mitigate the harms. The study concludes by suggesting cutting-edge authentication methods like one-time password (OTP) ID- and password-based, three-factor/multi-factor and blockchain. Keywords: Architecture, Authentication, Internet of Things, Privacy, Security Proceedings Citation Format Mosud, Y. O., Ajulo, E.B. & Yinusa, A.B. (2023): Internet of Things (IoT) Security and Private Concerns: An Overview. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 49-58 https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/ dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P7","PeriodicalId":409325,"journal":{"name":"Advances in Multidisciplinary and scientific Research Journal Publication","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121141481","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}

{"title":"Detection of Algorithmically Generated Domain Names using Ensemble Machine Learning Technique","authors":"S. Abdullahi, A. Mohammed, R.Y. Ibrahim, A. S. Shamsuddeen","doi":"10.22624/aims/csean-smart2023p4","DOIUrl":"https://doi.org/10.22624/aims/csean-smart2023p4","url":null,"abstract":"Prior to now, cyber attackers use malwares with hard-coded domain names stored in the malware binaries that communicate with a command and control (C&C) servers to launch cyber-attacks on their victim computers. Malware attacks such as botnets and ransomwares are some of the most prevalent forms of these attacks. As soon as a system is infected with a malware (either a botnet or a ransomware), one of the most essential components is to establish a secured communication with the botmaster (i.e., the malware author), through a C&C server. However, with a simple reverse engineering technique, cyber security experts could detect and block these domain names, hence, denying them the ability to communicate with the C&C servers and from receiving further instructions from the botmaster. This led to cyber criminals developing the Domain Generation Algorithm (DGA) technique, which algorithmically generate thousands or more candidate’s domain names for communication with the C&C server, thereby obfuscating the domain names of these malwares and making it difficult for cyber security experts to detect or block these domain names. This paper therefore proposes an ensemble machine learning technique for the detection and classification of algorithmically generated domain names (AGDNs) leveraging the combined strength of 4 different machine learning algorithms: Naïve Bayes, SVM, Random Forest and CART. The models were trained twice, first with 4 features and thereafter with 10 features. In order to effectively utilise the result of the predictions, we used a voting-based ensemble approach, where the final classification is decided by the majority vote of the algorithms. Result of the research shows that the Naïve Bayes model performed better than all the other models with an accuracy of 97.54% when trained with 10 features and 95.99% when trained with 4 features. Keywords: WSN, DDoS, Intrusion Detection System, Random Forest, Machine Learning. Proceedings Citation Format Abdullahi, S.M., Mohammed, A., Ibrahim, R.Y. & Shamsuddeen, A. (2023): Detection of Algorithmically Generated Domain Names using Ensemble Machine Learning Technique. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 27-34. https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/ dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P2.","PeriodicalId":409325,"journal":{"name":"Advances in Multidisciplinary and scientific Research Journal Publication","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114213905","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}

{"title":"Development of a Fake News Detection Model Using Decision Tree Algorithm","authors":"O. Adeola, G. Bello, Babatunde S. Oluwasola, Kafayat R. Lateef","doi":"10.22624/aims/csean-smart2023p10","DOIUrl":"https://doi.org/10.22624/aims/csean-smart2023p10","url":null,"abstract":"Over 500,000 Americans are influenced each year by fake news, which has an impact on society. Researchers are examining data on fake news identification and looking into machine learning models for a quicker and more accurate categorization as part of their investigation of false news detection techniques. This study's main objective is to examine, contrast, and evaluate how well three distinct machine learning algorithms do at spotting fake news. Decision tree, Random forest, and logistic regression are the machine learning algorithms. There are 21418 datasets available for real news, compared to 23503 for fake news. The open-source Kaggle dataset, which was selected for the project, served as the source of both datasets. The results of the construction and comparison showed that the decision tree model did the best forecasting with a maximum accuracy of 99.64% and the random forest model performed well with an accuracy of 98.89%. However, the logistic regression model did well in this assignment with an accuracy of 98.88%. Using these models has the critical goal of improving the news verification system's accuracy and dependability. Keyword: Fake news, Verification System, Machine Learning Model. Proceedings Citation Format Adeola, O.O., Bello, G.R. Oluwasola, B.S. & Lateef, K.R. (2023): Development of a Fake News Detection Model Using Decision Tree. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 81-88. https://www.csean.org.ng/. dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P10","PeriodicalId":409325,"journal":{"name":"Advances in Multidisciplinary and scientific Research Journal Publication","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122718347","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}

{"title":"Performance Evaluation of Random Forest Algorithm for DDoS Attacks Detection in Wireless Sensor Network Using Numericalization and Normalization as Feature Engineering Techniques","authors":"H. Sulaiman, I. O. Oyefolahan, S. Bashir, I.M. Kolo, J. Ndunagu","doi":"10.22624/aims/csean-smart2023p2","DOIUrl":"https://doi.org/10.22624/aims/csean-smart2023p2","url":null,"abstract":"Distributed Denial-Of-Service (DDoS) attacks in Wireless Sensor Networks (WSNs) aims to decrease the availability of a service by exhausting the network resources available for traffic, thus preventing legitimate users from accessing the services. This paper evaluates the performance of Random Forest algorithm for distributed denial of service attacks detection in Wireless Sensor Networks (WSNs) with numericalization and normalization as feature engineering techniques and exploratory data analysis to build the Distributed Denial of Service (DDoS) attacks detection model. The dataset was streamlined for optimum performance of the selected algorithms. The experimental result shows the Random Forest achieved an accuracy of 90%, F1 score of 90%, precision of 91% and recall of 90%. The outcomes revealed that the random forest model performed better than the competing models in terms of accuracy, F1 score, precision, and recall. The results of this study shed light on the efficiency of machine learning models in identifying DDoS attacks and suggest areas for future study. The study stresses the significance of data preparation and cleaning in the process and offers a framework for the application of machine learning models in network intrusion detection systems. Keywords: Wireless Sensor Networks, Distributed Denial of Service, Intrusion Detection System, Random Forest, Machine Learning. Proceedings Citation Format Sulaiman, H, Oyefolahan, I.O., Bashir, S.A., Kolo, I.M. & Ndunagu J.N. (2023): Performance Evaluation of Random Forest Algorithm for DDoS Attacks Detection in Wireless Sensor Network Using Numericalization and Normalization as Feature Engineering Techniques. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 19-26. https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/ dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P2.","PeriodicalId":409325,"journal":{"name":"Advances in Multidisciplinary and scientific Research Journal Publication","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122561952","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}

{"title":"Best Practices to Implement and Pitfalls to Avoid in Cloud Application Security","authors":"E.J. Oyitso, E.D. Ordia","doi":"10.22624/aims/csean-smart2023p6","DOIUrl":"https://doi.org/10.22624/aims/csean-smart2023p6","url":null,"abstract":"This paper explores the importance of reevaluating the application security posture in the context of modern software development practices and the shift towards cloud computing. It highlights the challenges posed by the evolving threat landscape and emphasizes the need for a comprehensive and proactive security strategy. The paper also discusses various techniques and best practices for securing applications from the code level to the cloud environment, encompassing secure coding practices, and the effective utilization of cloud security services. By adopting a holistic approach to application security, organizations can mitigate risks and protect their applications and data throughout the software development lifecycle. Keywords: Application Security, Software Development, Cloud Computing, Secure Coding, Vulnerability Assessment, Penetration Testing, Cloud Security Services Proceedings Citation Format Oyitso, E.J. & Ordia, E.D. (2023): Best Practices to Implement and Pitfalls to Avoid in Cloud Application Security. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 41-48. https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/ dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P6","PeriodicalId":409325,"journal":{"name":"Advances in Multidisciplinary and scientific Research Journal Publication","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116817580","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}