As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by...As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.展开更多
Although AI and quantum computing (QC) are fast emerging as key enablers of the future Internet, experts believe they pose an existential threat to humanity. Responding to the frenzied release of ChatGPT/GPT-4, thousa...Although AI and quantum computing (QC) are fast emerging as key enablers of the future Internet, experts believe they pose an existential threat to humanity. Responding to the frenzied release of ChatGPT/GPT-4, thousands of alarmed tech leaders recently signed an open letter to pause AI research to prepare for the catastrophic threats to humanity from uncontrolled AGI (Artificial General Intelligence). Perceived as an “epistemological nightmare”, AGI is believed to be on the anvil with GPT-5. Two computing rules appear responsible for these risks. 1) Mandatory third-party permissions that allow computers to run applications at the expense of introducing vulnerabilities. 2) The Halting Problem of Turing-complete AI programming languages potentially renders AGI unstoppable. The double whammy of these inherent weaknesses remains invincible under the legacy systems. A recent cybersecurity breakthrough shows that banning all permissions reduces the computer attack surface to zero, delivering a new zero vulnerability computing (ZVC) paradigm. Deploying ZVC and blockchain, this paper formulates and supports a hypothesis: “Safe, secure, ethical, controllable AGI/QC is possible by conquering the two unassailable rules of computability.” Pursued by a European consortium, testing/proving the proposed hypothesis will have a groundbreaking impact on the future digital infrastructure when AGI/QC starts powering the 75 billion internet devices by 2025.展开更多
Based on a review of 28 Horizon Europe-funded CCAM projects, this paper studies the current state of Connected, Cooperative, and Automated Mobility (CCAM) and identifies significant research gaps in taxonomy, cybersec...Based on a review of 28 Horizon Europe-funded CCAM projects, this paper studies the current state of Connected, Cooperative, and Automated Mobility (CCAM) and identifies significant research gaps in taxonomy, cybersecurity, Artificial Intelligence (AI) and 6G research, that hinder the advancement of a future-ready CCAM infrastructure. The research emphasizes the crucial role of infrastructure in achieving autonomous mobility, shifting focus from the current vehicle-centric approach. It critiques the SAE J3016 taxonomy for its lack of emphasis on infrastructure and proposes an updated framework with an automation level dedicated to infrastructure automation. The paper highlights the existential threats posed by Quantum Computers (QC) and AI, stressing the need for quantum-safe cybersecurity measures and an ethical, controllable AI framework proposing a decentralized Collective Artificial Super Intelligence (CASI) framework. Identifying the critical need for a cooperative approach involving Road and Transport Authorities (RTAs) to achieve 100% vehicle connectivity and robust digital infrastructure, the study outlines the European Commission’s Vision 2050 goals, aiming for zero fatalities, zero emissions, and sustainable mobility. The paper concludes by providing recommendations for future research directions to accelerate the development of a comprehensive, secure, and efficient CCAM ecosystem.展开更多
The objective of this work is to calculate and compare the energy eigenvalue of Hulthen Potential using the NU method and AIM method. Using these two methods the energy eigenvalue calculated from the NU method is less...The objective of this work is to calculate and compare the energy eigenvalue of Hulthen Potential using the NU method and AIM method. Using these two methods the energy eigenvalue calculated from the NU method is less than AIM method. Moreover, the energy eigenvalue calculated from both methods is charge independent and only depends upon the quantum numbers and screening parameters, while the third term of energy eigenvalue calculated using the NU method is only dependent on screening parameters.展开更多
The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital...The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital ecosystems.As massive,distributed data streams are generated across edge devices and network layers,there is a growing need for intelligent,privacy-preserving AI solutions that can operate efficiently at the network edge.Federated Learning(FL)enables decentralized model training without transferring sensitive data,addressing key challenges around privacy,bandwidth,and latency.Despite its benefits in enhancing efficiency,real-time analytics,and regulatory compliance,FL adoption faces challenges,including communication overhead,heterogeneity,security vulnerabilities,and limited edge resources.While recent studies have addressed these issues individually,the literature lacks a unified,cross-domain perspective that reflects the architectural complexity and application diversity of Convergence ICT.This systematic review offers a comprehensive,cross-domain examination of FL within converged ICT infrastructures.The central research question guiding this review is:How can FL be effectively integrated into Convergence ICT environments,and what are the main challenges in implementing FL in such environments,along with possible solutions?We begin with a foundational overview of FL concepts and classifications,followed by a detailed taxonomy of FL architectures,learning strategies,and privacy-preserving mechanisms.Through in-depth case studies,we analyse FL’s application across diverse verticals,including smart cities,healthcare,industrial automation,and autonomous systems.We further identify critical challenges—such as system and data heterogeneity,limited edge resources,and security vulnerabilities—and review state-of-the-art mitigation strategies,including edge-aware optimization,secure aggregation,and adaptive model updates.In addition,we explore emerging directions in FL research,such as energy-efficient learning,federated reinforcement learning,and integration with blockchain,quantum computing,and self-adaptive networks.This review not only synthesizes current literature but also proposes a forward-looking road map to support scalable,secure,and sustainable FL deployment in future ICT ecosystems.展开更多
Intracellular pH plays a critical role in biological functions,and abnormal pH values are related to various diseases.Here,we report on an intracellular pH sensor AgInS_(2)(AIS)/ZnS quantum dots(QDs)that show long flu...Intracellular pH plays a critical role in biological functions,and abnormal pH values are related to various diseases.Here,we report on an intracellular pH sensor AgInS_(2)(AIS)/ZnS quantum dots(QDs)that show long fluorescence lifetimes of hundreds of nanoseconds and low toxicity.Fluorescence lifetime imaging microscopy(FLIM)combined with AIS/ZnS QDs is used for the imaging of live cells in different pH buffers and different cell lines.The FLIM images of AIS/ZnS QDs in live cells demonstrate different intracellular pH values in different regions,such as in lysosomes or cytoplasm.This method can also distinguish cancer cells from normal cells,and the fluorescence lifetime difference of the AIS/ZnS QDs between the two types of cells is 100±7 ns.Most importantly,the exfoliated cervical cells from 20 patients are investigated using FLIM combined with AIS/ZnS QDs.The lifetime difference value between the normal and cervical cancer(CC)groups is 115±9 ns,and the difference between the normal and the precancerous lesion group is 64±9 ns.For the first time,the noninvasive method has been used for cervical cancer screening,and it has shown great improvement in sensitivity compared with a clinical conventional cytology examination.展开更多
文摘As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.
文摘Although AI and quantum computing (QC) are fast emerging as key enablers of the future Internet, experts believe they pose an existential threat to humanity. Responding to the frenzied release of ChatGPT/GPT-4, thousands of alarmed tech leaders recently signed an open letter to pause AI research to prepare for the catastrophic threats to humanity from uncontrolled AGI (Artificial General Intelligence). Perceived as an “epistemological nightmare”, AGI is believed to be on the anvil with GPT-5. Two computing rules appear responsible for these risks. 1) Mandatory third-party permissions that allow computers to run applications at the expense of introducing vulnerabilities. 2) The Halting Problem of Turing-complete AI programming languages potentially renders AGI unstoppable. The double whammy of these inherent weaknesses remains invincible under the legacy systems. A recent cybersecurity breakthrough shows that banning all permissions reduces the computer attack surface to zero, delivering a new zero vulnerability computing (ZVC) paradigm. Deploying ZVC and blockchain, this paper formulates and supports a hypothesis: “Safe, secure, ethical, controllable AGI/QC is possible by conquering the two unassailable rules of computability.” Pursued by a European consortium, testing/proving the proposed hypothesis will have a groundbreaking impact on the future digital infrastructure when AGI/QC starts powering the 75 billion internet devices by 2025.
文摘Based on a review of 28 Horizon Europe-funded CCAM projects, this paper studies the current state of Connected, Cooperative, and Automated Mobility (CCAM) and identifies significant research gaps in taxonomy, cybersecurity, Artificial Intelligence (AI) and 6G research, that hinder the advancement of a future-ready CCAM infrastructure. The research emphasizes the crucial role of infrastructure in achieving autonomous mobility, shifting focus from the current vehicle-centric approach. It critiques the SAE J3016 taxonomy for its lack of emphasis on infrastructure and proposes an updated framework with an automation level dedicated to infrastructure automation. The paper highlights the existential threats posed by Quantum Computers (QC) and AI, stressing the need for quantum-safe cybersecurity measures and an ethical, controllable AI framework proposing a decentralized Collective Artificial Super Intelligence (CASI) framework. Identifying the critical need for a cooperative approach involving Road and Transport Authorities (RTAs) to achieve 100% vehicle connectivity and robust digital infrastructure, the study outlines the European Commission’s Vision 2050 goals, aiming for zero fatalities, zero emissions, and sustainable mobility. The paper concludes by providing recommendations for future research directions to accelerate the development of a comprehensive, secure, and efficient CCAM ecosystem.
文摘The objective of this work is to calculate and compare the energy eigenvalue of Hulthen Potential using the NU method and AIM method. Using these two methods the energy eigenvalue calculated from the NU method is less than AIM method. Moreover, the energy eigenvalue calculated from both methods is charge independent and only depends upon the quantum numbers and screening parameters, while the third term of energy eigenvalue calculated using the NU method is only dependent on screening parameters.
文摘The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital ecosystems.As massive,distributed data streams are generated across edge devices and network layers,there is a growing need for intelligent,privacy-preserving AI solutions that can operate efficiently at the network edge.Federated Learning(FL)enables decentralized model training without transferring sensitive data,addressing key challenges around privacy,bandwidth,and latency.Despite its benefits in enhancing efficiency,real-time analytics,and regulatory compliance,FL adoption faces challenges,including communication overhead,heterogeneity,security vulnerabilities,and limited edge resources.While recent studies have addressed these issues individually,the literature lacks a unified,cross-domain perspective that reflects the architectural complexity and application diversity of Convergence ICT.This systematic review offers a comprehensive,cross-domain examination of FL within converged ICT infrastructures.The central research question guiding this review is:How can FL be effectively integrated into Convergence ICT environments,and what are the main challenges in implementing FL in such environments,along with possible solutions?We begin with a foundational overview of FL concepts and classifications,followed by a detailed taxonomy of FL architectures,learning strategies,and privacy-preserving mechanisms.Through in-depth case studies,we analyse FL’s application across diverse verticals,including smart cities,healthcare,industrial automation,and autonomous systems.We further identify critical challenges—such as system and data heterogeneity,limited edge resources,and security vulnerabilities—and review state-of-the-art mitigation strategies,including edge-aware optimization,secure aggregation,and adaptive model updates.In addition,we explore emerging directions in FL research,such as energy-efficient learning,federated reinforcement learning,and integration with blockchain,quantum computing,and self-adaptive networks.This review not only synthesizes current literature but also proposes a forward-looking road map to support scalable,secure,and sustainable FL deployment in future ICT ecosystems.
基金supported by the National Natural Science Foundation of China(NSFC,Nos.62074044,61904036,and 11804350)the Medical Engineering Fund of Fudan University(No.yg2021-022)+7 种基金Zhongshan-Fudan Joint Innovation Center and Jihua Laboratory Projects of Guangdong Province(No.X190111UZ190)Fudan University-CIOMP Joint Fund(No.FC2018-001)Pioneering Project of Academy for Engineering and Technology of Fudan University(Nos.gyy2018-001 and gyy2018-002)Shanghai Natural Science Foundation(Nos.20ZR1405100 and 20ZR1403700)Science and Technology Research Program of Shanghai(No.19DZ2282100)Shanghai key discipline construction plan(2020-2022)(No.GWV-10.1-XK01)Shanghai Hong Kong,Macao,and Taiwan Cooperation Project(No.19490760900)Shanghai Engineering Technology Research Center of Hair Medicine(No.19DZ2250500).
文摘Intracellular pH plays a critical role in biological functions,and abnormal pH values are related to various diseases.Here,we report on an intracellular pH sensor AgInS_(2)(AIS)/ZnS quantum dots(QDs)that show long fluorescence lifetimes of hundreds of nanoseconds and low toxicity.Fluorescence lifetime imaging microscopy(FLIM)combined with AIS/ZnS QDs is used for the imaging of live cells in different pH buffers and different cell lines.The FLIM images of AIS/ZnS QDs in live cells demonstrate different intracellular pH values in different regions,such as in lysosomes or cytoplasm.This method can also distinguish cancer cells from normal cells,and the fluorescence lifetime difference of the AIS/ZnS QDs between the two types of cells is 100±7 ns.Most importantly,the exfoliated cervical cells from 20 patients are investigated using FLIM combined with AIS/ZnS QDs.The lifetime difference value between the normal and cervical cancer(CC)groups is 115±9 ns,and the difference between the normal and the precancerous lesion group is 64±9 ns.For the first time,the noninvasive method has been used for cervical cancer screening,and it has shown great improvement in sensitivity compared with a clinical conventional cytology examination.
