Magnesium-ion batteries hold promise as future energy storage solutions,yet current Mg cathodes are challenged by low voltage and specific capacity.Herein,we present an AI-driven workflow for discovering high-performa...Magnesium-ion batteries hold promise as future energy storage solutions,yet current Mg cathodes are challenged by low voltage and specific capacity.Herein,we present an AI-driven workflow for discovering high-performance Mg cathode materials.Utilizing the common characteristics of various ionic intercalation-type electrodes,we design and train a Crystal Graph Convolutional Neural Network model that can accurately predict electrode voltages for various ions with mean absolute errors(MAE)between0.25 and 0.33 V.By deploying the trained model to stable Mg compounds from Materials Project and GNoME AI dataset,we identify 160 high voltage structures out of 15,308 candidates with voltages above3.0 V and volumetric capacity over 800 mA h/cm^(3).We further train a precise NequIP model to facilitate accurate and rapid simulations of Mg ionic conductivity.From the 160 high voltage structures,the machine learning molecular dynamics simulations have selected 23 cathode materials with both high energy density and high ionic conductivity.This Al-driven workflow dramatically boosts the efficiency and precision of material discovery for multivalent ion batteries,paving the way for advanced Mg battery development.展开更多
With the growing adoption of Artifical Intelligence(AI),AI-driven autonomous techniques and automation systems have seen widespread applications,become pivotal in enhancing operational efficiency and task automation a...With the growing adoption of Artifical Intelligence(AI),AI-driven autonomous techniques and automation systems have seen widespread applications,become pivotal in enhancing operational efficiency and task automation across various aspects of human living.Over the past decade,AI-driven automation has advanced from simple rule-based systems to sophisticated multi-agent hybrid architectures.These technologies not only increase productivity but also enable more scalable and adaptable solutions,proving particularly beneficial in industries such as healthcare,finance,and customer service.However,the absence of a unified review for categorization,benchmarking,and ethical risk assessment hinders the AI-driven automation progress.To bridge this gap,in this survey,we present a comprehensive taxonomy of AI-driven automation methods and analyze recent advancements.We present a comparative analysis of performance metrics between production environments and industrial applications,along with an examination of cutting-edge developments.Specifically,we present a comparative analysis of the performance across various aspects in different industries,offering valuable insights for researchers to select the most suitable approaches for specific applications.Additionally,we also review multiple existing mainstream AI-driven automation applications in detail,highlighting their strengths and limitations.Finally,we outline open research challenges and suggest future directions to address the challenges of AI adoption while maximizing its potential in real-world AI-driven automation applications.展开更多
Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transformi...Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transforming the way we interpret and analyze imaging data,simplifying complex tasks and enabling innovative experimental methods previously thought impossible.In smart manufacturing,these improvements are especially impactful,increasing precision and efficiency in production processes.This review examines the convergence of AI with particle image analysis,an area we refer to as“particle vision analysis(PVA).”We offer a detailed overview of how this technology integrates into and impacts various fields within the physical sciences and materials sectors,where it plays a crucial role in both innovation and operational improvements.We explore four key areas of advancement-namely,particle classification,detection,segmentation,and object tracking-along with a look into the emerging field of augmented microscopy.This paper also underscores the vital role of the existing datasets and implementations that support these applications,which provide essential insights and resources that drive continuous research and development in this fast-evolving field.Our thorough analysis aims to outline the transformative potential of AI-driven PVA in improving precision in future manufacturing at the microscopic scale and thereby preparing the ground for significant technological progress and broad industrial applications in nanomanufacturing,biomanufacturing,and pharmaceutical manufacturing.This exploration not only highlights the advantages of integrating AI into conventional manufacturing processes but also anticipates the rise of next-generation smart manufacturing,which is set to revolutionize industry standards and operational practices.展开更多
Artificial intelligence(AI)is transforming the tourism industry and affecting on natural ecology,making it more environmentally friendly,efficient and personalized.In 2025,AI technologies are being actively implemente...Artificial intelligence(AI)is transforming the tourism industry and affecting on natural ecology,making it more environmentally friendly,efficient and personalized.In 2025,AI technologies are being actively implemented to reduce the carbon footprint,optimize resources,and improve the travel experience.Here are the key applications of AI in environmentally sustainable smart tourism:AI in smart tourism is not just a technological trend,but a necessity for the sustainable development of the industry.Paper analyses personalized and green travel experience and smart tourism.AI-based applications(Google ARCore)allow tourists to get information about attractions without paper booklets.Virtual tours reduce the need for physical travel by reducing the carbon footprint.Platforms offer routes with minimal impact on nature(for example,hiking trails instead of car tours).Tourists can offset their carbon footprint through AI tools by financing tree planting.The introduction of AI solutions allows combining economic benefits with environmental responsibility,creating a future where travel becomes safer for the planet.Paper confirms idea about sustainable tourism development in developing countries and focus on premium ecotourism.Instead of mass tourism,AI helps promote unique destinations(safaris,diving,ethnographic tours),which increases income with less environmental damage.Smart cities with AI-driven transport and energy-saving solutions make tourism more sustainable.展开更多
IoT has emerged as a game-changing technology that connects numerous gadgets to networks for communication,processing,and real-time monitoring across diverse applications.Due to their heterogeneous nature and constrai...IoT has emerged as a game-changing technology that connects numerous gadgets to networks for communication,processing,and real-time monitoring across diverse applications.Due to their heterogeneous nature and constrained resources,as well as the growing trend of using smart gadgets,there are privacy and security issues that are not adequately managed by conventional securitymeasures.This review offers a thorough analysis of contemporary AI solutions designed to enhance security within IoT ecosystems.The intersection of AI technologies,including ML,and blockchain,with IoT privacy and security is systematically examined,focusing on their efficacy in addressing core security issues.The methodology involves a detailed exploration of existing literature and research on AI-driven privacy-preserving security mechanisms in IoT.The reviewed solutions are categorized based on their ability to tackle specific security challenges.The review highlights key advancements,evaluates their practical applications,and identifies prevailing research gaps and challenges.The findings indicate that AI solutions,particularly those leveraging ML and blockchain,offerpromising enhancements to IoT privacy and security by improving threat detection capabilities and ensuring data integrity.This paper highlights how AI technologies might strengthen IoT privacy and security and offer suggestions for upcoming studies intended to address enduring problems and improve the robustness of IoT networks.展开更多
In this paper,we explore the ever-changing field ofDigital Twins(DT)in the Industrial Internet of Things(IIoT)context,emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0.The ...In this paper,we explore the ever-changing field ofDigital Twins(DT)in the Industrial Internet of Things(IIoT)context,emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0.The article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT,focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real integration.It discusses the emergence of DT,contextualizing its evolution within the framework of IIoT.The study categorizes the different types of DT,including prototypes and instances,and provides an in-depth analysis of the enabling technologies such as IoT,Artificial Intelligence(AI),Extended Reality(XR),cloud computing,and the Application Programming Interface(API).The paper demonstrates theDT advantages through the practical integration of real-world case studies,which highlights the technology’s exceptional capacity to improve traceability and fault detection within the context of the IIoT.This paper offers a focused,application-driven perspective on DTs in IIoT,specifically highlighting their role in key production phases such as designing,intelligent manufacturing,maintenance,resource management,automation,security,and safety.By emphasizing their potential to support human-centric,sustainable advancements in Industry 5.0,this study distinguishes itself from existing literature.It provides valuable insights that connect theoretical advancements with practical implementation,making it a crucial resource for researchers,practitioners,and industry professionals.展开更多
Biometric template protection is essential for finger-based authentication systems,as template tampering and adversarial attacks threaten the security.This paper proposes a DCT-based fragile watermarking scheme incorp...Biometric template protection is essential for finger-based authentication systems,as template tampering and adversarial attacks threaten the security.This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication.The system was tested against NIST SD4 and Anguli fingerprint datasets,wherein 10,000 watermarked fingerprints were employed for training.The designed approach recorded a tamper detection rate of 98.3%,performing 3–6%better than current DCT,SVD,and DWT-based watermarking approaches.The false positive rate(≤1.2%)and false negative rate(≤1.5%)were much lower compared to previous research,which maintained high reliability for template change detection.The system showed real-time performance,averaging 12–18 ms processing time per template,and is thus suitable for real-world biometric authentication scenarios.Quality analysis of fingerprints indicated that NFIQ scores were enhanced from 2.07 to 1.81,reflecting improved minutiae clarity and ridge structure preservation.The approach also exhibited strong resistance to compression and noise distortions,with the improvements in PSNR being 2 dB(JPEG compression Q=80)and the SSIM values rising by 3%–5%under noise attacks.Comparative assessment demonstrated that training with NIST SD4 data greatly improved the ridge continuity and quality of fingerprints,resulting in better match scores(260–295)when tested against Bozorth3.Smaller batch sizes(batch=2)also resulted in improved ridge clarity,whereas larger batch sizes(batch=8)resulted in distortions.The DCNN-based tamper detection model supported real-time classification,which greatly minimized template exposure to adversarial attacks and synthetic fingerprint forgeries.Results demonstrate that fragile watermarking with AI indeed greatly enhances fingerprint security,providing privacy-preserving biometric authentication with high robustness,accuracy,and computational efficiency.展开更多
[目的/意义]科技文献蕴含丰富的领域知识与科学数据,可为人工智能驱动的科学研究(AI for Science,AI4S)提供高质量数据支撑。本文系统梳理大语言模型(Large Language Models,LLMs)在科技文献数据挖掘中的方法技术、软件工具及应用场景,...[目的/意义]科技文献蕴含丰富的领域知识与科学数据,可为人工智能驱动的科学研究(AI for Science,AI4S)提供高质量数据支撑。本文系统梳理大语言模型(Large Language Models,LLMs)在科技文献数据挖掘中的方法技术、软件工具及应用场景,探讨其研究方向与发展趋势。[方法/过程]本文基于文献调研与归纳总结,在方法技术层面,从文本知识、科学数据与图表信息分析了LLMs驱动的科技文献细粒度数据挖掘关键技术以及综合性知识生成的方法;在软件工具层面,归纳了主流LLMs科技文献数据挖掘与知识生成工具的方法技术、核心功能和适用场景;在应用场景层面,分析了科技文献数据挖掘应用于LLMs的实践价值。[结果/结论]在方法技术方面,通过动态提示学习框架与领域适配微调等技术,LLMs极大提升科技文献数据挖掘精度与效度;在软件工具方面,已初步形成从数据标注、数据挖掘、合成数据到知识生成的全流程LLMs科技文献数据挖掘工具链;在应用方面,科技文献数据可为LLMs提供专业化语料和高质量数据,LLMs推动科技文献从单维数据服务向多模态知识生成服务的范式演进。然而,当前仍面临领域知识表征深度不足、跨模态推理效率较低、知识生成可解释性欠缺等挑战。未来应着重研发具有可解释性与跨领域适应性的LLMs科技文献数据挖掘工具,集成“人在回路”的协同机制,促进科技文献数据挖掘从效率优化向知识创造转变。展开更多
基金supported by the National Key R&D Program of China(2022YFA1203400)the National Natural Science Foundation of China(W2441009)。
文摘Magnesium-ion batteries hold promise as future energy storage solutions,yet current Mg cathodes are challenged by low voltage and specific capacity.Herein,we present an AI-driven workflow for discovering high-performance Mg cathode materials.Utilizing the common characteristics of various ionic intercalation-type electrodes,we design and train a Crystal Graph Convolutional Neural Network model that can accurately predict electrode voltages for various ions with mean absolute errors(MAE)between0.25 and 0.33 V.By deploying the trained model to stable Mg compounds from Materials Project and GNoME AI dataset,we identify 160 high voltage structures out of 15,308 candidates with voltages above3.0 V and volumetric capacity over 800 mA h/cm^(3).We further train a precise NequIP model to facilitate accurate and rapid simulations of Mg ionic conductivity.From the 160 high voltage structures,the machine learning molecular dynamics simulations have selected 23 cathode materials with both high energy density and high ionic conductivity.This Al-driven workflow dramatically boosts the efficiency and precision of material discovery for multivalent ion batteries,paving the way for advanced Mg battery development.
文摘With the growing adoption of Artifical Intelligence(AI),AI-driven autonomous techniques and automation systems have seen widespread applications,become pivotal in enhancing operational efficiency and task automation across various aspects of human living.Over the past decade,AI-driven automation has advanced from simple rule-based systems to sophisticated multi-agent hybrid architectures.These technologies not only increase productivity but also enable more scalable and adaptable solutions,proving particularly beneficial in industries such as healthcare,finance,and customer service.However,the absence of a unified review for categorization,benchmarking,and ethical risk assessment hinders the AI-driven automation progress.To bridge this gap,in this survey,we present a comprehensive taxonomy of AI-driven automation methods and analyze recent advancements.We present a comparative analysis of performance metrics between production environments and industrial applications,along with an examination of cutting-edge developments.Specifically,we present a comparative analysis of the performance across various aspects in different industries,offering valuable insights for researchers to select the most suitable approaches for specific applications.Additionally,we also review multiple existing mainstream AI-driven automation applications in detail,highlighting their strengths and limitations.Finally,we outline open research challenges and suggest future directions to address the challenges of AI adoption while maximizing its potential in real-world AI-driven automation applications.
基金funding support from the US National Science Foundation(2229092)supported by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship,a program of Schmidt Sciences,LLC.
文摘Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transforming the way we interpret and analyze imaging data,simplifying complex tasks and enabling innovative experimental methods previously thought impossible.In smart manufacturing,these improvements are especially impactful,increasing precision and efficiency in production processes.This review examines the convergence of AI with particle image analysis,an area we refer to as“particle vision analysis(PVA).”We offer a detailed overview of how this technology integrates into and impacts various fields within the physical sciences and materials sectors,where it plays a crucial role in both innovation and operational improvements.We explore four key areas of advancement-namely,particle classification,detection,segmentation,and object tracking-along with a look into the emerging field of augmented microscopy.This paper also underscores the vital role of the existing datasets and implementations that support these applications,which provide essential insights and resources that drive continuous research and development in this fast-evolving field.Our thorough analysis aims to outline the transformative potential of AI-driven PVA in improving precision in future manufacturing at the microscopic scale and thereby preparing the ground for significant technological progress and broad industrial applications in nanomanufacturing,biomanufacturing,and pharmaceutical manufacturing.This exploration not only highlights the advantages of integrating AI into conventional manufacturing processes but also anticipates the rise of next-generation smart manufacturing,which is set to revolutionize industry standards and operational practices.
基金financed as part of the project“Development of a methodology for instrumental base formation for analysis and modeling of the spatial socio-economic development of systems based on internal reserves in the context of digitalization”(FSEG-2023-0008).
文摘Artificial intelligence(AI)is transforming the tourism industry and affecting on natural ecology,making it more environmentally friendly,efficient and personalized.In 2025,AI technologies are being actively implemented to reduce the carbon footprint,optimize resources,and improve the travel experience.Here are the key applications of AI in environmentally sustainable smart tourism:AI in smart tourism is not just a technological trend,but a necessity for the sustainable development of the industry.Paper analyses personalized and green travel experience and smart tourism.AI-based applications(Google ARCore)allow tourists to get information about attractions without paper booklets.Virtual tours reduce the need for physical travel by reducing the carbon footprint.Platforms offer routes with minimal impact on nature(for example,hiking trails instead of car tours).Tourists can offset their carbon footprint through AI tools by financing tree planting.The introduction of AI solutions allows combining economic benefits with environmental responsibility,creating a future where travel becomes safer for the planet.Paper confirms idea about sustainable tourism development in developing countries and focus on premium ecotourism.Instead of mass tourism,AI helps promote unique destinations(safaris,diving,ethnographic tours),which increases income with less environmental damage.Smart cities with AI-driven transport and energy-saving solutions make tourism more sustainable.
基金The author Dr.Arshiya Sajid Ansari extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1706).
文摘IoT has emerged as a game-changing technology that connects numerous gadgets to networks for communication,processing,and real-time monitoring across diverse applications.Due to their heterogeneous nature and constrained resources,as well as the growing trend of using smart gadgets,there are privacy and security issues that are not adequately managed by conventional securitymeasures.This review offers a thorough analysis of contemporary AI solutions designed to enhance security within IoT ecosystems.The intersection of AI technologies,including ML,and blockchain,with IoT privacy and security is systematically examined,focusing on their efficacy in addressing core security issues.The methodology involves a detailed exploration of existing literature and research on AI-driven privacy-preserving security mechanisms in IoT.The reviewed solutions are categorized based on their ability to tackle specific security challenges.The review highlights key advancements,evaluates their practical applications,and identifies prevailing research gaps and challenges.The findings indicate that AI solutions,particularly those leveraging ML and blockchain,offerpromising enhancements to IoT privacy and security by improving threat detection capabilities and ensuring data integrity.This paper highlights how AI technologies might strengthen IoT privacy and security and offer suggestions for upcoming studies intended to address enduring problems and improve the robustness of IoT networks.
基金funded by Big Data Analytics Centre(BIDAC)of United Arab Emirates University under the grant numbers G00003679 and G00004526。
文摘In this paper,we explore the ever-changing field ofDigital Twins(DT)in the Industrial Internet of Things(IIoT)context,emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0.The article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT,focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real integration.It discusses the emergence of DT,contextualizing its evolution within the framework of IIoT.The study categorizes the different types of DT,including prototypes and instances,and provides an in-depth analysis of the enabling technologies such as IoT,Artificial Intelligence(AI),Extended Reality(XR),cloud computing,and the Application Programming Interface(API).The paper demonstrates theDT advantages through the practical integration of real-world case studies,which highlights the technology’s exceptional capacity to improve traceability and fault detection within the context of the IIoT.This paper offers a focused,application-driven perspective on DTs in IIoT,specifically highlighting their role in key production phases such as designing,intelligent manufacturing,maintenance,resource management,automation,security,and safety.By emphasizing their potential to support human-centric,sustainable advancements in Industry 5.0,this study distinguishes itself from existing literature.It provides valuable insights that connect theoretical advancements with practical implementation,making it a crucial resource for researchers,practitioners,and industry professionals.
文摘Biometric template protection is essential for finger-based authentication systems,as template tampering and adversarial attacks threaten the security.This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication.The system was tested against NIST SD4 and Anguli fingerprint datasets,wherein 10,000 watermarked fingerprints were employed for training.The designed approach recorded a tamper detection rate of 98.3%,performing 3–6%better than current DCT,SVD,and DWT-based watermarking approaches.The false positive rate(≤1.2%)and false negative rate(≤1.5%)were much lower compared to previous research,which maintained high reliability for template change detection.The system showed real-time performance,averaging 12–18 ms processing time per template,and is thus suitable for real-world biometric authentication scenarios.Quality analysis of fingerprints indicated that NFIQ scores were enhanced from 2.07 to 1.81,reflecting improved minutiae clarity and ridge structure preservation.The approach also exhibited strong resistance to compression and noise distortions,with the improvements in PSNR being 2 dB(JPEG compression Q=80)and the SSIM values rising by 3%–5%under noise attacks.Comparative assessment demonstrated that training with NIST SD4 data greatly improved the ridge continuity and quality of fingerprints,resulting in better match scores(260–295)when tested against Bozorth3.Smaller batch sizes(batch=2)also resulted in improved ridge clarity,whereas larger batch sizes(batch=8)resulted in distortions.The DCNN-based tamper detection model supported real-time classification,which greatly minimized template exposure to adversarial attacks and synthetic fingerprint forgeries.Results demonstrate that fragile watermarking with AI indeed greatly enhances fingerprint security,providing privacy-preserving biometric authentication with high robustness,accuracy,and computational efficiency.
文摘[目的/意义]科技文献蕴含丰富的领域知识与科学数据,可为人工智能驱动的科学研究(AI for Science,AI4S)提供高质量数据支撑。本文系统梳理大语言模型(Large Language Models,LLMs)在科技文献数据挖掘中的方法技术、软件工具及应用场景,探讨其研究方向与发展趋势。[方法/过程]本文基于文献调研与归纳总结,在方法技术层面,从文本知识、科学数据与图表信息分析了LLMs驱动的科技文献细粒度数据挖掘关键技术以及综合性知识生成的方法;在软件工具层面,归纳了主流LLMs科技文献数据挖掘与知识生成工具的方法技术、核心功能和适用场景;在应用场景层面,分析了科技文献数据挖掘应用于LLMs的实践价值。[结果/结论]在方法技术方面,通过动态提示学习框架与领域适配微调等技术,LLMs极大提升科技文献数据挖掘精度与效度;在软件工具方面,已初步形成从数据标注、数据挖掘、合成数据到知识生成的全流程LLMs科技文献数据挖掘工具链;在应用方面,科技文献数据可为LLMs提供专业化语料和高质量数据,LLMs推动科技文献从单维数据服务向多模态知识生成服务的范式演进。然而,当前仍面临领域知识表征深度不足、跨模态推理效率较低、知识生成可解释性欠缺等挑战。未来应着重研发具有可解释性与跨领域适应性的LLMs科技文献数据挖掘工具,集成“人在回路”的协同机制,促进科技文献数据挖掘从效率优化向知识创造转变。
