THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabi...THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].展开更多
Underwater Wireless Sensor Networks(UWSNs)are gaining popularity because of their potential uses in oceanography,seismic activity monitoring,environmental preservation,and underwater mapping.Yet,these networks are fac...Underwater Wireless Sensor Networks(UWSNs)are gaining popularity because of their potential uses in oceanography,seismic activity monitoring,environmental preservation,and underwater mapping.Yet,these networks are faced with challenges such as self-interference,long propagation delays,limited bandwidth,and changing network topologies.These challenges are coped with by designing advanced routing protocols.In this work,we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks(UWF-RPL),an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network nodes.Our method extends RPL with the aid of fuzzy logic to optimize depth,energy,Received Signal Strength Indicator(RSSI)to Expected Transmission Count(ETX)ratio,and latency.Theproposed protocol outperforms other techniques in that it offersmore energy efficiency,better packet delivery,lowdelay,and no queue overflow.It also exhibits better scalability and reliability in dynamic underwater networks,which is of very high importance in maintaining the network operations efficiency and the lifetime of UWSNs optimized.Compared to other recent methods,it offers improved network convergence time(10%–23%),energy efficiency(15%),packet delivery(17%),and delay(24%).展开更多
The ability to predict multiaxial fatigue life of Al-Alloy 7075-T6 under complex loading conditions is critical to assessing its durability under complex loading conditions,particularly in aerospace,automotive,and str...The ability to predict multiaxial fatigue life of Al-Alloy 7075-T6 under complex loading conditions is critical to assessing its durability under complex loading conditions,particularly in aerospace,automotive,and structural applications.This paper presents a physical-informed neural network(PINN)model to predict the fatigue life of Al-Alloy 7075-T6 over a variety of multiaxial stresses.The model integrates the principles of the Geometric Multiaxial Fatigue Life(GMFL)approach,which is a novel fatigue life prediction approach to estimating fatigue life by combining multiple fatigue criteria.The proposed model aims to estimate fatigue damage accumulation by the GMFL method.The proposed GMFL-PINN combines this physics-based approach with data-driven neural networks.Experimental validation demonstrates that GMFL-PINN outperforms FS,Smith–Watson–Topper(SWT)and Li–Zhang(LZH)fatigue life prediction methods which provides a reliable and scalable solution for structural health assessment and fatigue analysis.展开更多
As Artificial Intelligence(AI)is moving fast from Large Language Models(LLMs)to AI Agents and Agentic Intelligence,the need to incorporate new AI into Decision Intelligence(DI)is becoming more and more urgent for both...As Artificial Intelligence(AI)is moving fast from Large Language Models(LLMs)to AI Agents and Agentic Intelligence,the need to incorporate new AI into Decision Intelligence(DI)is becoming more and more urgent for both practical and theoretic reasons:both decision and process complexities would be significantly increased due to the use of advanced AI tools and agents,and both traditional and recent thinking must be rethought and reconstructed accordingly.Our perspective would like to address this important issue based on some historical milestone developments in Computer-Aided Software Engineering(CASE)and recent efforts in digital theatrical technology[1].展开更多
The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network...The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and discretion.Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities.An intrusion detection system controls the flow of network traffic with the help of computer systems.Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic.For this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network intrusion.The Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks.The proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.展开更多
Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the inside.There are many algorithms designed to protect the cloud serv...Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the inside.There are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server attacks.Still,the attackers have designed even better mechanisms to break these security algorithms.Cloud cryptography is the best data protection algorithm that exchanges data between authentic users.In this article,one symmetric cryptography algorithm will be designed to secure cloud server data,used to send and receive cloud server data securely.A double encryption algorithm will be implemented to send data in a secure format.First,the XOR function will be applied to plain text,and then salt technique will be used.Finally,a reversing mechanism will be implemented on that data to provide more data security.To decrypt data,the cipher text will be reversed,salt will be removed,andXORwill be implemented.At the end of the paper,the proposed algorithm will be compared with other algorithms,and it will conclude how much better the existing algorithm is than other algorithms.展开更多
Cloud computing is one of the most attractive and cost-saving models,which provides online services to end-users.Cloud computing allows the user to access data directly from any node.But nowadays,cloud security is one...Cloud computing is one of the most attractive and cost-saving models,which provides online services to end-users.Cloud computing allows the user to access data directly from any node.But nowadays,cloud security is one of the biggest issues that arise.Different types of malware are wreaking havoc on the clouds.Attacks on the cloud server are happening from both internal and external sides.This paper has developed a tool to prevent the cloud server from spamming attacks.When an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)classification.Cloudflare will block those IP addresses that the attacker will use and prevent spamming attacks.However,the KNN classifiers will determine which area the spammer belongs to.At the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.展开更多
File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most ...File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user files.Currently,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic modeling.However,one major drawback of current topic modeling approaches is better results.They rely on specific language types and domain similarity of the data.In this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the files.The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.展开更多
Living objects have complex internal and external interactions. The complexity is regulated and controlled by homeostasis, which is the balance of multiple opposing influences. The environmental effects finally guide ...Living objects have complex internal and external interactions. The complexity is regulated and controlled by homeostasis, which is the balance of multiple opposing influences. The environmental effects finally guide the self-organized structure. The living systems are open, dynamic structures performing random, stationary, stochastic, self-organizing processes. The self-organizing procedure is defined by the spatial-temporal fractal structure, which is self-similar both in space and time. The system’s complexity appears in its energetics, which tries the most efficient use of the available energies;for that, it organizes various well-connected networks. The controller of environmental relations is the Darwinian selection on a long-time scale. The energetics optimize the healthy processes tuned to the highest efficacy and minimal loss (minimalization of the entropy production). The organism is built up by morphogenetic rules and develops various networks from the genetic level to the organism. The networks have intensive crosstalk and form a balance in the Nash equilibrium, which is the homeostatic state in healthy conditions. Homeostasis may be described as a Nash equilibrium, which ensures energy distribution in a “democratic” way regarding the functions of the parts in the complete system. Cancer radically changes the network system in the organism. Cancer is a network disease. Deviation from healthy networking appears at every level, from genetic (molecular) to cells, tissues, organs, and organisms. The strong proliferation of malignant tissue is the origin of most of the life-threatening processes. The weak side of cancer development is the change of complex information networking in the system, being vulnerable to immune attacks. Cancer cells are masters of adaptation and evade immune surveillance. This hiding process can be broken by electromagnetic nonionizing radiation, for which the malignant structure has no adaptation strategy. Our objective is to review the different sides of living complexity and use the knowledge to fight against cancer.展开更多
The rise of foundation models has brought significant advances to artificial intelligence,especially in reasoning,commonsense understanding,and tool use.These capabilities,when integrated into agent systems,hold great...The rise of foundation models has brought significant advances to artificial intelligence,especially in reasoning,commonsense understanding,and tool use.These capabilities,when integrated into agent systems,hold great promise for real-world applications such as vision-language navigation(VLN)and vision-language action(VLA).However,deploying such models in practice presents ongoing challenges,particularly in adapting and optimizing them across diverse and changing environments.This letter proposes a parallel deep foundation model(PDFM)framework to support continuous model evolution in cloud-edge-device systems.The framework establishes a co-evolution process between two complementary capabilities:embodied cognition,which reflects the model’s grounded understanding and task adaptation in physical systems,and analogical imagination,which enables creative exploration and capacity expansion in virtual environments.Through three core processes,learning and training,experiment and evaluation,and management and control,the system supports iterative refinement and dynamic interaction between virtual and real spaces.This enables general-purpose models to gradually converge toward domain-specific intelligence,supporting longterm,adaptive deployment.展开更多
Large language models(LLMs)offer new opportunities in diabetes research by integrating contextual behavioral data with physiological biosignals,supporting more personalized care.This review surveys recent studies on t...Large language models(LLMs)offer new opportunities in diabetes research by integrating contextual behavioral data with physiological biosignals,supporting more personalized care.This review surveys recent studies on the application of LLMs to interpret interactions among stress,physical activity,and glucose dynamics across type 1,type 2,gestational,and monogenic diabetes(MODY).We examine methods for capturing contextual data beyond traditional patient diaries,including wearable sensors,lifestyle logs,and digital health tools,and discuss how these data are combined with continuous glucose monitoring.After retrieving 39 relevant studies and finally retaining 25 after screening,we summarize the current capabilities,limitations,and clinical implications of LLM-assisted multimodal approaches in diabetes management.The findings highlight both the promise and challenges of applying LLMs to synthesize heterogeneous data,providing insights for future research on enhancing individualized and evidence-based diabetes care.展开更多
Dear Editor,The 2024 Nobel Prize in Chemistry was awarded to David Baker,Demis Hassabis,and John Jumper,recognizing their groundbreaking contributions to protein design and the prediction of complex protein structures...Dear Editor,The 2024 Nobel Prize in Chemistry was awarded to David Baker,Demis Hassabis,and John Jumper,recognizing their groundbreaking contributions to protein design and the prediction of complex protein structures[1].This accomplishment advances the frontier of“Artificial Intelligence(AI)for Science”.It marks a milestone in studying complex systems,highlighting a shift in scientific exploration from traditional causal inference to a comprehensive approach centered on solving complex system problems.展开更多
CASE,control and automatic science engineering,cybernetics and automation science engineering,or convergence and autonomous science engineering,has been proposed to integrate and evolve traditional basic courses in ma...CASE,control and automatic science engineering,cybernetics and automation science engineering,or convergence and autonomous science engineering,has been proposed to integrate and evolve traditional basic courses in mathematics,physics,chemistry,biology,literature,and social sciences into new fundamental courses at all levels of education from kindergarten to post-graduate programs,i.e.,the new K21 educational system.Our goal is to build a future intelligent ecosystem through three major sociotechnical transformations:from AIR(automation,intelligence,and robotics)to IST(intelligent science and technology),LaSEE(low-altitude space economy and ecology)to CiSEE(circular in situ economy and ecology),and finally,SEE(sleeping well,eating well,and exercising well)to H_(3)O(happy,health,and help organized,operated,and optimized)for a new humanity with advanced AI.IJICS will play a major role in this important process.展开更多
intelligence(AI)has entered a new stage,shifting from“artificial”to“agentic”and then to“autonomous”systems that are transforming knowledge,professions,and societal structures.Generative models,autonomous robotic...intelligence(AI)has entered a new stage,shifting from“artificial”to“agentic”and then to“autonomous”systems that are transforming knowledge,professions,and societal structures.Generative models,autonomous robotics,and intelligent ecosystems are no longer confined to laboratories but are shaping economies,governance,and cultural life worldwide.IJICS will serve as a platform to examine these shifts,focusing on the redefinition of professional expertise,the emergence of new roles of academic publications for bridging humans and machines,and the urgent demand for transparent and accountable governance.From AI for science and digital intelligence to autonomous organizations and decentralized governance,IJICS will highlight the trajectories most critical to the evolution of intelligent systems.Through long-term vision,pragmatic innovation,and international cooperation,our journal is aiming to enhance discourse and practice,ensuring that AI development remains aligned with human values and the common good.展开更多
A cognitive model of“social voice”in sociolinguistic perception is proposed in this paper,which explores how language use reflects social identity and how listeners interpret these linguistic features to form social...A cognitive model of“social voice”in sociolinguistic perception is proposed in this paper,which explores how language use reflects social identity and how listeners interpret these linguistic features to form social judgments.Moreover,social meaning involves the stereotypes or judgments people attach to certain ways of speaking.The social cognition processes how speakers perceive these linguistic cues to form those judgments.Linguistic features act as indexes of social categories.Context determines how these features are interpreted.This interplay between language,identity,and cognition reveals how societal norms,biases,and cognitive processes shape communication.Social voice in sociolinguistic perception reveals how language both reflects and reinforces social hierarchies.By understanding the cognitive mechanisms behind these processes,such as automatic stereotyping and contextual flexibility,we can challenge biases and promote equitable communication.Future research should prioritize intersectionality,technological ethics,and strategies to amplify marginalized voices,fostering a society in which linguistic diversity is celebrated rather than stigmatized.展开更多
Parallel transportation serves as a holistic paradigm for achieving intelligent traffic management and control,focusing on addressing the complexity of human and social factors.Recently,the emergence and development o...Parallel transportation serves as a holistic paradigm for achieving intelligent traffic management and control,focusing on addressing the complexity of human and social factors.Recently,the emergence and development of foundational models(FMs)have ushered in a new era for the realization of parallel transportation.However,the inherent issues of“hallucinations,”outdated knowledge,and the“black-box”nature of FMs render their generated decisions unreliable and untrustworthy.To address these issues,we propose a TransRAG framework for parallel transportation based on retrieval-augmented generation and chain-of-thought(CoT)prompting.TransRAG is composed of three interacting layers,storage,management,and execution,which work together to deliver personalized and diverse traffic services to users.The external knowledge from the storage layer is incorporated to augment the FM in management layers for computational experiments.展开更多
基金partly supported by the Science and Technology Development Fund,Macao Special Administrative Region(SAR)(0093/2023/RIA2,0145/2023/RIA3)The National Natural Science Foundation of China(62103411)。
文摘THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].
文摘Underwater Wireless Sensor Networks(UWSNs)are gaining popularity because of their potential uses in oceanography,seismic activity monitoring,environmental preservation,and underwater mapping.Yet,these networks are faced with challenges such as self-interference,long propagation delays,limited bandwidth,and changing network topologies.These challenges are coped with by designing advanced routing protocols.In this work,we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks(UWF-RPL),an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network nodes.Our method extends RPL with the aid of fuzzy logic to optimize depth,energy,Received Signal Strength Indicator(RSSI)to Expected Transmission Count(ETX)ratio,and latency.Theproposed protocol outperforms other techniques in that it offersmore energy efficiency,better packet delivery,lowdelay,and no queue overflow.It also exhibits better scalability and reliability in dynamic underwater networks,which is of very high importance in maintaining the network operations efficiency and the lifetime of UWSNs optimized.Compared to other recent methods,it offers improved network convergence time(10%–23%),energy efficiency(15%),packet delivery(17%),and delay(24%).
文摘The ability to predict multiaxial fatigue life of Al-Alloy 7075-T6 under complex loading conditions is critical to assessing its durability under complex loading conditions,particularly in aerospace,automotive,and structural applications.This paper presents a physical-informed neural network(PINN)model to predict the fatigue life of Al-Alloy 7075-T6 over a variety of multiaxial stresses.The model integrates the principles of the Geometric Multiaxial Fatigue Life(GMFL)approach,which is a novel fatigue life prediction approach to estimating fatigue life by combining multiple fatigue criteria.The proposed model aims to estimate fatigue damage accumulation by the GMFL method.The proposed GMFL-PINN combines this physics-based approach with data-driven neural networks.Experimental validation demonstrates that GMFL-PINN outperforms FS,Smith–Watson–Topper(SWT)and Li–Zhang(LZH)fatigue life prediction methods which provides a reliable and scalable solution for structural health assessment and fatigue analysis.
基金supported by the Science and Technology Development Fund,Macao Special Administrative Region(SAR)(0093/2023/RIA2,0157/2024/RIA2,0145/2023/RIA3)the Distinguished Program of Obuda University and the DeSci Center for Parallel Intelligence at the Obuda University,Hungary.This article is based on the report of“Digital Theaters and Theatrical Intelligence for Decision Science”Project(DeSCII-0017-1023-2023,PI:Qinghua Ni)initiated by DeSci International.
文摘As Artificial Intelligence(AI)is moving fast from Large Language Models(LLMs)to AI Agents and Agentic Intelligence,the need to incorporate new AI into Decision Intelligence(DI)is becoming more and more urgent for both practical and theoretic reasons:both decision and process complexities would be significantly increased due to the use of advanced AI tools and agents,and both traditional and recent thinking must be rethought and reconstructed accordingly.Our perspective would like to address this important issue based on some historical milestone developments in Computer-Aided Software Engineering(CASE)and recent efforts in digital theatrical technology[1].
文摘The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and discretion.Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities.An intrusion detection system controls the flow of network traffic with the help of computer systems.Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic.For this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network intrusion.The Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks.The proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.
文摘Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the inside.There are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server attacks.Still,the attackers have designed even better mechanisms to break these security algorithms.Cloud cryptography is the best data protection algorithm that exchanges data between authentic users.In this article,one symmetric cryptography algorithm will be designed to secure cloud server data,used to send and receive cloud server data securely.A double encryption algorithm will be implemented to send data in a secure format.First,the XOR function will be applied to plain text,and then salt technique will be used.Finally,a reversing mechanism will be implemented on that data to provide more data security.To decrypt data,the cipher text will be reversed,salt will be removed,andXORwill be implemented.At the end of the paper,the proposed algorithm will be compared with other algorithms,and it will conclude how much better the existing algorithm is than other algorithms.
文摘Cloud computing is one of the most attractive and cost-saving models,which provides online services to end-users.Cloud computing allows the user to access data directly from any node.But nowadays,cloud security is one of the biggest issues that arise.Different types of malware are wreaking havoc on the clouds.Attacks on the cloud server are happening from both internal and external sides.This paper has developed a tool to prevent the cloud server from spamming attacks.When an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)classification.Cloudflare will block those IP addresses that the attacker will use and prevent spamming attacks.However,the KNN classifiers will determine which area the spammer belongs to.At the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.
文摘File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user files.Currently,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic modeling.However,one major drawback of current topic modeling approaches is better results.They rely on specific language types and domain similarity of the data.In this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the files.The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.
文摘Living objects have complex internal and external interactions. The complexity is regulated and controlled by homeostasis, which is the balance of multiple opposing influences. The environmental effects finally guide the self-organized structure. The living systems are open, dynamic structures performing random, stationary, stochastic, self-organizing processes. The self-organizing procedure is defined by the spatial-temporal fractal structure, which is self-similar both in space and time. The system’s complexity appears in its energetics, which tries the most efficient use of the available energies;for that, it organizes various well-connected networks. The controller of environmental relations is the Darwinian selection on a long-time scale. The energetics optimize the healthy processes tuned to the highest efficacy and minimal loss (minimalization of the entropy production). The organism is built up by morphogenetic rules and develops various networks from the genetic level to the organism. The networks have intensive crosstalk and form a balance in the Nash equilibrium, which is the homeostatic state in healthy conditions. Homeostasis may be described as a Nash equilibrium, which ensures energy distribution in a “democratic” way regarding the functions of the parts in the complete system. Cancer radically changes the network system in the organism. Cancer is a network disease. Deviation from healthy networking appears at every level, from genetic (molecular) to cells, tissues, organs, and organisms. The strong proliferation of malignant tissue is the origin of most of the life-threatening processes. The weak side of cancer development is the change of complex information networking in the system, being vulnerable to immune attacks. Cancer cells are masters of adaptation and evade immune surveillance. This hiding process can be broken by electromagnetic nonionizing radiation, for which the malignant structure has no adaptation strategy. Our objective is to review the different sides of living complexity and use the knowledge to fight against cancer.
基金supported by the National Natural Science Foundation of China(No.62303460)the Science and Technology Development Fund of Macao SAR(Nos.0145/2023/RIA3 and 0093/2023/RIA2)the Young Elite Scientists Sponsorship Program of China Association for Science and Technology(No.YESS20220372).
文摘The rise of foundation models has brought significant advances to artificial intelligence,especially in reasoning,commonsense understanding,and tool use.These capabilities,when integrated into agent systems,hold great promise for real-world applications such as vision-language navigation(VLN)and vision-language action(VLA).However,deploying such models in practice presents ongoing challenges,particularly in adapting and optimizing them across diverse and changing environments.This letter proposes a parallel deep foundation model(PDFM)framework to support continuous model evolution in cloud-edge-device systems.The framework establishes a co-evolution process between two complementary capabilities:embodied cognition,which reflects the model’s grounded understanding and task adaptation in physical systems,and analogical imagination,which enables creative exploration and capacity expansion in virtual environments.Through three core processes,learning and training,experiment and evaluation,and management and control,the system supports iterative refinement and dynamic interaction between virtual and real spaces.This enables general-purpose models to gradually converge toward domain-specific intelligence,supporting longterm,adaptive deployment.
基金supported by the Starting Research Grant ofÓbuda University,the National Research,Development and Innovation Agency(No.2024-1.1.1-KKV_FÓKUSZ-2024-00074)the National Research,Development and Innovation Fund of Hungary(No.TKP2021-NKTA-36)+4 种基金the National Natural Science Foundation of China(No.82372051)the Beijing Natural Science Foundation(No.L222099)the Science and Technology Development Fund,Macao SAR(Nos.0093/2023/RIA2,0145/2023/RIA3,and 0157/2024/RIA2)the Capital’s Funds for Health Improvement and Research(No.2024-2-4015)the Sichuan Key-Area Research and Development Program(No.2024YFHZ0011).
文摘Large language models(LLMs)offer new opportunities in diabetes research by integrating contextual behavioral data with physiological biosignals,supporting more personalized care.This review surveys recent studies on the application of LLMs to interpret interactions among stress,physical activity,and glucose dynamics across type 1,type 2,gestational,and monogenic diabetes(MODY).We examine methods for capturing contextual data beyond traditional patient diaries,including wearable sensors,lifestyle logs,and digital health tools,and discuss how these data are combined with continuous glucose monitoring.After retrieving 39 relevant studies and finally retaining 25 after screening,we summarize the current capabilities,limitations,and clinical implications of LLM-assisted multimodal approaches in diabetes management.The findings highlight both the promise and challenges of applying LLMs to synthesize heterogeneous data,providing insights for future research on enhancing individualized and evidence-based diabetes care.
基金supported by the Science and Technology Development Fund,Macao Special Administrative Region(SAR)(0093/2023/RIA2,0145/2023/RIA3).
文摘Dear Editor,The 2024 Nobel Prize in Chemistry was awarded to David Baker,Demis Hassabis,and John Jumper,recognizing their groundbreaking contributions to protein design and the prediction of complex protein structures[1].This accomplishment advances the frontier of“Artificial Intelligence(AI)for Science”.It marks a milestone in studying complex systems,highlighting a shift in scientific exploration from traditional causal inference to a comprehensive approach centered on solving complex system problems.
文摘CASE,control and automatic science engineering,cybernetics and automation science engineering,or convergence and autonomous science engineering,has been proposed to integrate and evolve traditional basic courses in mathematics,physics,chemistry,biology,literature,and social sciences into new fundamental courses at all levels of education from kindergarten to post-graduate programs,i.e.,the new K21 educational system.Our goal is to build a future intelligent ecosystem through three major sociotechnical transformations:from AIR(automation,intelligence,and robotics)to IST(intelligent science and technology),LaSEE(low-altitude space economy and ecology)to CiSEE(circular in situ economy and ecology),and finally,SEE(sleeping well,eating well,and exercising well)to H_(3)O(happy,health,and help organized,operated,and optimized)for a new humanity with advanced AI.IJICS will play a major role in this important process.
文摘intelligence(AI)has entered a new stage,shifting from“artificial”to“agentic”and then to“autonomous”systems that are transforming knowledge,professions,and societal structures.Generative models,autonomous robotics,and intelligent ecosystems are no longer confined to laboratories but are shaping economies,governance,and cultural life worldwide.IJICS will serve as a platform to examine these shifts,focusing on the redefinition of professional expertise,the emergence of new roles of academic publications for bridging humans and machines,and the urgent demand for transparent and accountable governance.From AI for science and digital intelligence to autonomous organizations and decentralized governance,IJICS will highlight the trajectories most critical to the evolution of intelligent systems.Through long-term vision,pragmatic innovation,and international cooperation,our journal is aiming to enhance discourse and practice,ensuring that AI development remains aligned with human values and the common good.
文摘A cognitive model of“social voice”in sociolinguistic perception is proposed in this paper,which explores how language use reflects social identity and how listeners interpret these linguistic features to form social judgments.Moreover,social meaning involves the stereotypes or judgments people attach to certain ways of speaking.The social cognition processes how speakers perceive these linguistic cues to form those judgments.Linguistic features act as indexes of social categories.Context determines how these features are interpreted.This interplay between language,identity,and cognition reveals how societal norms,biases,and cognitive processes shape communication.Social voice in sociolinguistic perception reveals how language both reflects and reinforces social hierarchies.By understanding the cognitive mechanisms behind these processes,such as automatic stereotyping and contextual flexibility,we can challenge biases and promote equitable communication.Future research should prioritize intersectionality,technological ethics,and strategies to amplify marginalized voices,fostering a society in which linguistic diversity is celebrated rather than stigmatized.
基金supported by the Science and Technology Development Fund of Macao SAR,China(No.0093/2023/RIA2)the National Natural Science Foundation of China(No.U1811463)。
文摘Parallel transportation serves as a holistic paradigm for achieving intelligent traffic management and control,focusing on addressing the complexity of human and social factors.Recently,the emergence and development of foundational models(FMs)have ushered in a new era for the realization of parallel transportation.However,the inherent issues of“hallucinations,”outdated knowledge,and the“black-box”nature of FMs render their generated decisions unreliable and untrustworthy.To address these issues,we propose a TransRAG framework for parallel transportation based on retrieval-augmented generation and chain-of-thought(CoT)prompting.TransRAG is composed of three interacting layers,storage,management,and execution,which work together to deliver personalized and diverse traffic services to users.The external knowledge from the storage layer is incorporated to augment the FM in management layers for computational experiments.
