In the intricate regulatory landscape of today, financial institutions encounter formidable hurdles in meeting reporting mandates while upholding operational efficacy. This study delves into the transformative capacity of Artificial Intelligence (AI) and Machine Learning (ML) technologies in refining regulatory reporting procedures. Through harnessing AI/ML, entities can streamline data aggregation, analysis, and submission, thus fostering enhanced compliance and operational efficiency. Key strategies for integrating AI/ML into regulatory reporting frameworks are discussed, encompassing data standardization, predictive analytics, anomaly detection, and automation. Furthermore, the paper explores the advantages, obstacles, and optimal approaches associated with deploying AI/ML solutions in regulatory reporting. Drawing on real-world illustrations and case studies, this study offers insights into how AI/ML technologies can redefine regulatory reporting practices, empowering financial institutions to adeptly navigate regulatory intricacies while optimizing resource allocation and decision-making processes.
{"title":"Revolutionizing Regulatory Reporting through AI/ML: Approaches for Enhanced Compliance and Efficiency","authors":"Harish Padmanaban","doi":"10.60087/jaigs.v2i1.p69","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p69","url":null,"abstract":"In the intricate regulatory landscape of today, financial institutions encounter formidable hurdles in meeting reporting mandates while upholding operational efficacy. This study delves into the transformative capacity of Artificial Intelligence (AI) and Machine Learning (ML) technologies in refining regulatory reporting procedures. Through harnessing AI/ML, entities can streamline data aggregation, analysis, and submission, thus fostering enhanced compliance and operational efficiency. Key strategies for integrating AI/ML into regulatory reporting frameworks are discussed, encompassing data standardization, predictive analytics, anomaly detection, and automation. Furthermore, the paper explores the advantages, obstacles, and optimal approaches associated with deploying AI/ML solutions in regulatory reporting. Drawing on real-world illustrations and case studies, this study offers insights into how AI/ML technologies can redefine regulatory reporting practices, empowering financial institutions to adeptly navigate regulatory intricacies while optimizing resource allocation and decision-making processes.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"5 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424967","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}
This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.
{"title":"Interdisciplinary Perspectives: Fusing Artificial Intelligence with Environmental Science for Sustainable Solutions","authors":"Jeff Shuford","doi":"10.60087/jaigs.v1i1.p12","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.p12","url":null,"abstract":"This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"55 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140431112","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}
This article explores the transformative impact of Artificial Intelligence (AI) in healthcare, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by identifying patterns and risk factors, contributing to improved patient outcomes and cost-effective healthcare. Machine learning facilitates personalized treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and minimize adverse effects. AI-driven algorithms in medical imaging enhance diagnostic accuracy, providing rapid and precise assessments. Decision support systems, powered by AI, streamline healthcare workflows by offering real-time insights based on patient data and clinical guidelines, facilitating evidence-based decision-making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare interventions by tracking vital signs and identifying potential health issues in real time. The article also discusses challenges and ethical considerations associated with AI integration in healthcare, emphasizing the importance of responsible deployment and regulatory frameworks. The comprehensive exploration underscores how AI is not only transforming patient care but also shaping the future of healthcare delivery.
{"title":"AI in Healthcare: Revolutionizing Patient Care with Predictive Analytics and Decision Support Systems","authors":"José Gabriel Carrasco Ramírez","doi":"10.60087/jaigs.v1i1.p37","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.p37","url":null,"abstract":"This article explores the transformative impact of Artificial Intelligence (AI) in healthcare, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by identifying patterns and risk factors, contributing to improved patient outcomes and cost-effective healthcare. Machine learning facilitates personalized treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and minimize adverse effects. AI-driven algorithms in medical imaging enhance diagnostic accuracy, providing rapid and precise assessments. Decision support systems, powered by AI, streamline healthcare workflows by offering real-time insights based on patient data and clinical guidelines, facilitating evidence-based decision-making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare interventions by tracking vital signs and identifying potential health issues in real time. The article also discusses challenges and ethical considerations associated with AI integration in healthcare, emphasizing the importance of responsible deployment and regulatory frameworks. The comprehensive exploration underscores how AI is not only transforming patient care but also shaping the future of healthcare delivery.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"49 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140431210","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}
This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.
{"title":"Advances in Architectures for Deep Learning: A Thorough Examination of Present Trends","authors":"Md. Rashed Khan","doi":"10.60087/jaigs.v1i1.p30","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.p30","url":null,"abstract":"This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"3 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140430341","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}
This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.
{"title":"Interdisciplinary Outlook: Integrating Artificial Intelligence with Environmental Science for Sustainable Solutions","authors":"Most. Sohana Akter","doi":"10.60087/jaigs.v1i1.p23","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.p23","url":null,"abstract":"This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"38 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140431855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial Intelligence (AI) has emerged as a transformative force across numerous domains, from healthcare to finance and beyond. However, as AI systems become increasingly integrated into daily life, the ethical implications of their development and deployment are garnering significant attention. This article conducts a comprehensive survey of the ethical considerations in AI, with a specific focus on navigating the complex landscape of bias and fairness.
{"title":"Exploring Ethical Dimensions in AI: Navigating Bias and Fairness in the Field","authors":"Md.mafiqul Islam","doi":"10.60087/jaigs.v1i1.p18","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.p18","url":null,"abstract":"Artificial Intelligence (AI) has emerged as a transformative force across numerous domains, from healthcare to finance and beyond. However, as AI systems become increasingly integrated into daily life, the ethical implications of their development and deployment are garnering significant attention. This article conducts a comprehensive survey of the ethical considerations in AI, with a specific focus on navigating the complex landscape of bias and fairness.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"148 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140428780","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 integration of artificial intelligence (AI) technologies holds significant promise in addressing pressing environmental and social challenges, thus contributing to sustainable development efforts worldwide. This article provides a comprehensive overview of the role of AI in tackling various aspects of sustainability, including environmental conservation, resource management, climate change mitigation, and social equity. By leveraging AI techniques such as machine learning, optimization, and data analytics, innovative solutions are being developed to monitor ecosystems, optimize energy consumption, enhance agricultural practices, and promote social inclusion. However, alongside these opportunities, there are also ethical, regulatory, and socio-economic considerations that must be carefully addressed to ensure that AI interventions contribute positively to sustainable development goals. This paper highlights recent advancements, challenges, and future directions in utilizing AI for sustainable development, emphasizing the importance of interdisciplinary collaboration and stakeholder engagement in realizing the full potential of AI-enabled solutions.
{"title":"AI for Sustainable Development: Addressing Environmental and Social Challenges","authors":"Md.Safikul Isalm","doi":"10.60087/jaigs.v3i1.70","DOIUrl":"https://doi.org/10.60087/jaigs.v3i1.70","url":null,"abstract":"The integration of artificial intelligence (AI) technologies holds significant promise in addressing pressing environmental and social challenges, thus contributing to sustainable development efforts worldwide. This article provides a comprehensive overview of the role of AI in tackling various aspects of sustainability, including environmental conservation, resource management, climate change mitigation, and social equity. By leveraging AI techniques such as machine learning, optimization, and data analytics, innovative solutions are being developed to monitor ecosystems, optimize energy consumption, enhance agricultural practices, and promote social inclusion. However, alongside these opportunities, there are also ethical, regulatory, and socio-economic considerations that must be carefully addressed to ensure that AI interventions contribute positively to sustainable development goals. This paper highlights recent advancements, challenges, and future directions in utilizing AI for sustainable development, emphasizing the importance of interdisciplinary collaboration and stakeholder engagement in realizing the full potential of AI-enabled solutions.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"50 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140433081","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 gut microbiome's impact on physiological processes, influenced by diet and lifestyle, is profound. Dysbiosis, an imbalance in microbiota composition, is associated with diseases like obesity. This review explores the gut microbiome's role in metabolism and calorie intake, alongside recent AI advancements impacting personalized nutrition. AI has revolutionized microbiome research, especially in multi-omics data analysis. AI-driven approaches enable the integration and interpretation of diverse omics datasets, including genomics, metabolomics, and proteomics, providing comprehensive insights into the gut microbiome's functional dynamics and its impact on host metabolism. These facilitate the identification of microbial biomarkers associated with disease states and dietary interventions, paving the way for personalized nutrition strategies tailored to individual gut microbiome profiles. AI platforms can also offer tailored dietary recommendations based on microbiome composition and health objectives. Healthcare professionals leverage AI to optimize dietary interventions, promoting gut microbiome modulation and preventing chronic diseases. Challenges like data standardization and privacy persist, yet addressing them is vital for maximizing AI's benefits in health outcomes and precision medicine. Ongoing AI and microbiome research promise to revolutionize personalized nutrition and metabolic health worldwide.
{"title":"Harnessing AI and Gut Microbiome Research for Precision Health","authors":"Ritcha Saxena, Vikas Sharma, Ananya Saxena, Aakash Patel","doi":"10.60087/jaigs.v3i1.68","DOIUrl":"https://doi.org/10.60087/jaigs.v3i1.68","url":null,"abstract":"The gut microbiome's impact on physiological processes, influenced by diet and lifestyle, is profound. Dysbiosis, an imbalance in microbiota composition, is associated with diseases like obesity. This review explores the gut microbiome's role in metabolism and calorie intake, alongside recent AI advancements impacting personalized nutrition. AI has revolutionized microbiome research, especially in multi-omics data analysis. AI-driven approaches enable the integration and interpretation of diverse omics datasets, including genomics, metabolomics, and proteomics, providing comprehensive insights into the gut microbiome's functional dynamics and its impact on host metabolism. These facilitate the identification of microbial biomarkers associated with disease states and dietary interventions, paving the way for personalized nutrition strategies tailored to individual gut microbiome profiles. AI platforms can also offer tailored dietary recommendations based on microbiome composition and health objectives. Healthcare professionals leverage AI to optimize dietary interventions, promoting gut microbiome modulation and preventing chronic diseases. Challenges like data standardization and privacy persist, yet addressing them is vital for maximizing AI's benefits in health outcomes and precision medicine. Ongoing AI and microbiome research promise to revolutionize personalized nutrition and metabolic health worldwide.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"17 S1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140438719","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}
This paper delves into the incorporation of artificial intelligence and machine learning (AI/ML) technologies to optimize regulatory reporting processes. It explores how AI/ML algorithms streamline data analysis, interpretation, and compliance within regulatory frameworks. Through the utilization of advanced algorithms, organizations can bolster the efficiency and accuracy of regulatory reporting, resulting in enhanced compliance outcomes. The paper outlines key applications of AI/ML in regulatory reporting and addresses challenges and considerations linked to their implementation. Additionally, it underscores the potential benefits of adopting AI/ML-driven approaches for regulatory reporting processes across diverse industries.
{"title":"Transforming Data into Compliance: Harnessing AI/ML to Enhance Regulatory Reporting Processes","authors":"Dr. Sreeram Mullankandy","doi":"10.60087/jaigs.v3i1.66","DOIUrl":"https://doi.org/10.60087/jaigs.v3i1.66","url":null,"abstract":"This paper delves into the incorporation of artificial intelligence and machine learning (AI/ML) technologies to optimize regulatory reporting processes. It explores how AI/ML algorithms streamline data analysis, interpretation, and compliance within regulatory frameworks. Through the utilization of advanced algorithms, organizations can bolster the efficiency and accuracy of regulatory reporting, resulting in enhanced compliance outcomes. The paper outlines key applications of AI/ML in regulatory reporting and addresses challenges and considerations linked to their implementation. Additionally, it underscores the potential benefits of adopting AI/ML-driven approaches for regulatory reporting processes across diverse industries.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"126 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140446705","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}
In the ever-evolving regulatory environment, adhering to reporting standards poses a significant hurdle for organizations spanning diverse sectors. Negotiating the intricacies of regulatory obligations necessitates innovative approaches. This document delves into the utilization of Artificial Intelligence (AI) and Machine Learning (ML) methodologies to bolster the precision and efficacy of reporting procedures. Through the integration of AI/ML, entities can streamline data analysis, detect patterns, and uphold compliance with regulatory frameworks. This research probes into the potential advantages, obstacles, and optimal strategies linked with the incorporation of AI/ML technologies into reporting infrastructures. Drawing upon a thorough examination of pertinent literature and case studies, valuable insights are offered to aid organizations in proficiently leveraging AI/ML to navigate regulatory intricacies and attain accurate reporting results.
{"title":"Navigating the Complexity of Regulations: Harnessing AI/ML for Precise Reporting","authors":"Dr. Sreeram Mullankandy","doi":"10.60087/jaigs.v3i1.65","DOIUrl":"https://doi.org/10.60087/jaigs.v3i1.65","url":null,"abstract":"In the ever-evolving regulatory environment, adhering to reporting standards poses a significant hurdle for organizations spanning diverse sectors. Negotiating the intricacies of regulatory obligations necessitates innovative approaches. This document delves into the utilization of Artificial Intelligence (AI) and Machine Learning (ML) methodologies to bolster the precision and efficacy of reporting procedures. Through the integration of AI/ML, entities can streamline data analysis, detect patterns, and uphold compliance with regulatory frameworks. This research probes into the potential advantages, obstacles, and optimal strategies linked with the incorporation of AI/ML technologies into reporting infrastructures. Drawing upon a thorough examination of pertinent literature and case studies, valuable insights are offered to aid organizations in proficiently leveraging AI/ML to navigate regulatory intricacies and attain accurate reporting results.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"9 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140448840","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：